From 5bd5dde84fec6b4bd5b087408f5a5e0e1adcf4b9 Mon Sep 17 00:00:00 2001 From: Daniel Falbel Date: Tue, 25 Aug 2020 17:14:21 -0300 Subject: [PATCH] remove docs folder as we will autogenerate and move to gh-pages --- .gitignore | 1 + docs/404.html | 191 - docs/CONTRIBUTING.html | 228 - docs/LICENSE-text.html | 193 - docs/LICENSE.html | 197 - docs/articles/examples/mnist-cnn.html | 215 - .../empty-anchor.js | 15 - .../header-attrs-2.1.1/header-attrs.js | 12 - .../header-attrs-2.3/header-attrs.js | 12 - docs/articles/examples/mnist-dcgan.html | 296 - .../empty-anchor.js | 15 - .../header-attrs-2.3/header-attrs.js | 12 - docs/articles/examples/mnist-mlp.html | 203 - .../empty-anchor.js | 15 - .../header-attrs-2.1.1/header-attrs.js | 12 - .../header-attrs-2.3/header-attrs.js | 12 - docs/articles/extending-autograd.html | 222 - .../empty-anchor.js | 15 - .../header-attrs-2.1.1/header-attrs.js | 12 - .../header-attrs-2.3/header-attrs.js | 12 - docs/articles/index.html | 204 - 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This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the development contributing guide.

-
-

-Fixing typos

-

You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the source file. This generally means you’ll need to edit roxygen2 comments in an .R, not a .Rd file. You can find the .R file that generates the .Rd by reading the comment in the first line.

-

See also the [Documentation] section.

-
-
-

-Filing bugs

-

If you find a bug in torch please open an issue here. Please, provide detailed information on how to reproduce the bug. It would be great to also provide a reprex.

-
-
-

-Feature requests

-

Feel free to open issues here and add the feature-request tag. Try searching if there’s already an open issue for your feature-request, in this case it’s better to comment or upvote it intead of opening a new one.

-
-
-

-Examples

-

We welcome contributed examples. feel free to open a PR with new examples. The should be placed in the vignettes/examples folder.

-

The examples should be an .R file and a .Rmd file with the same name that just renders the code.

-

See mnist-mlp.R and mnist-mlp.Rmd

-

One must be able to run the example without manually downloading any dataset/file. You should also add an entry to the _pkgdown.yaml file.

-
-
-

-Code contributions

-

We have many open issues in the github repo if there’s one item that you want to work on, you can comment on it an ask for directions.

-
-
-

-Documentation

-

We use roxygen2 to generate the documentation. IN order to update the docs, edit the file in the R directory. To regenerate and preview the docs, use the custom tools/document.R script, as we need to patch roxygen2 to avoid running the examples on CRAN.

-
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- - - -
- - - -
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Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - - - diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html deleted file mode 100644 index 3ec2627dd..000000000 --- a/docs/LICENSE-text.html +++ /dev/null @@ -1,193 +0,0 @@ - - - - - - - - -License • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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License

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YEAR: 2020
-COPYRIGHT HOLDER: Daniel Falbel
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Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - - - diff --git a/docs/LICENSE.html b/docs/LICENSE.html deleted file mode 100644 index 7b6b7b0fc..000000000 --- a/docs/LICENSE.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - - -MIT License • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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MIT License

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Copyright (c) 2020 Daniel Falbel

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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

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THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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Developed by Daniel Falbel, Javier Luraschi.

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Site built with pkgdown 1.5.1.

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- - - - - - - - diff --git a/docs/articles/examples/mnist-cnn.html b/docs/articles/examples/mnist-cnn.html deleted file mode 100644 index bc24e1af7..000000000 --- a/docs/articles/examples/mnist-cnn.html +++ /dev/null @@ -1,215 +0,0 @@ - - - - - - - -mnist-cnn • torch - - - - - - - - - - -
-
- - - - -
-
-
-

mnist-cnn

- - - Source: vignettes/examples/mnist-cnn.Rmd - - -
- - - -
dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$conv1 <- nn_conv2d(1, 32, 3, 1)
-    self$conv2 <- nn_conv2d(32, 64, 3, 1)
-    self$dropout1 <- nn_dropout2d(0.25)
-    self$dropout2 <- nn_dropout2d(0.5)
-    self$fc1 <- nn_linear(9216, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x <- self$conv1(x)
-    x <- nnf_relu(x)
-    x <- self$conv2(x)
-    x <- nnf_relu(x)
-    x <- nnf_max_pool2d(x, 2)
-    x <- self$dropout1(x)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$fc1(x)
-    x <- nnf_relu(x)
-    x <- self$dropout2(x)
-    x <- self$fc2(x)
-    output <- nnf_log_softmax(x, dim=1)
-    output
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/examples/mnist-cnn_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/examples/mnist-cnn_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-dcgan.html b/docs/articles/examples/mnist-dcgan.html deleted file mode 100644 index ca8f65769..000000000 --- a/docs/articles/examples/mnist-dcgan.html +++ /dev/null @@ -1,296 +0,0 @@ - - - - - - - -mnist-dcgan • torch - - - - - - - - - - -
-
- - - - -
-
-
-

mnist-dcgan

- - - Source: vignettes/examples/mnist-dcgan.Rmd - - -
- - - -
library(torch)
-
-dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x <- 2*(x - 0.5)
-    x[newaxis,..]
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-generator <- nn_module(
-  "generator",
-  initialize = function(latent_dim, out_channels) {
-    self$main <- nn_sequential(
-      nn_conv_transpose2d(latent_dim, 512, kernel_size = 4,
-                          stride = 1, padding = 0, bias = FALSE),
-      nn_batch_norm2d(512),
-      nn_relu(),
-      nn_conv_transpose2d(512, 256, kernel_size = 4,
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(256),
-      nn_relu(),
-      nn_conv_transpose2d(256, 128, kernel_size = 4,
-                          stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(128),
-      nn_relu(),
-      nn_conv_transpose2d(128, out_channels, kernel_size = 4,
-                          stride = 2, padding = 3, bias = FALSE),
-      nn_tanh()
-    )
-  },
-  forward = function(input) {
-    self$main(input)
-  }
-)
-
-discriminator <- nn_module(
-  "discriminator",
-  initialize = function(in_channels) {
-    self$main <- nn_sequential(
-      nn_conv2d(in_channels, 16, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(16, 32, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(32),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(32, 64, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_batch_norm2d(64),
-      nn_leaky_relu(0.2, inplace = TRUE),
-      nn_conv2d(64, 128, kernel_size = 4, stride = 2, padding = 1, bias = FALSE),
-      nn_leaky_relu(0.2, inplace = TRUE)
-    )
-    self$linear <- nn_linear(128, 1)
-    self$sigmoid <- nn_sigmoid()
-  },
-  forward = function(input) {
-    x <- self$main(input)
-    x <- torch_flatten(x, start_dim = 2)
-    x <- self$linear(x)
-    self$sigmoid(x)
-  }
-)
-
-plot_gen <- function(noise) {
-  img <- G(noise)
-  img <- img$cpu()
-  img <- img[1,1,,,newaxis]/2 + 0.5
-  img <- torch_stack(list(img, img, img), dim = 2)[..,1]
-  img <- as.raster(as_array(img))
-  plot(img)
-}
-
-device <- torch_device(ifelse(cuda_is_available(),  "cuda", "cpu"))
-
-G <- generator(latent_dim = 100, out_channels = 1)
-D <- discriminator(in_channels = 1)
-
-init_weights <- function(m) {
-  if (grepl("conv", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 0.0, 0.02)
-  } else if (grepl("batch_norm", m$.classes[[1]])) {
-    nn_init_normal_(m$weight$data(), 1.0, 0.02)
-    nn_init_constant_(m$bias$data(), 0)
-  }
-}
-
-G[[1]]$apply(init_weights)
-D[[1]]$apply(init_weights)
-
-G$to(device = device)
-D$to(device = device)
-
-G_optimizer <- optim_adam(G$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-D_optimizer <- optim_adam(D$parameters, lr = 2 * 1e-4, betas = c(0.5, 0.999))
-
-fixed_noise <- torch_randn(1, 100, 1, 1, device = device)
-
-loss <- nn_bce_loss()
-
-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss D: :lossd Loss G: :lossg"
-  )
-  lossg <- c()
-  lossd <- c()
-
-  for (b in enumerate(dl)) {
-
-    y_real <- torch_ones(32, device = device)
-    y_fake <- torch_zeros(32, device = device)
-
-    noise <- torch_randn(32, 100, 1, 1, device = device)
-    fake <- G(noise)
-
-    img <- b[[1]]$to(device = device)
-
-    # train the discriminator ---
-    D_loss <- loss(D(img), y_real) + loss(D(fake$detach()), y_fake)
-
-    D_optimizer$zero_grad()
-    D_loss$backward()
-    D_optimizer$step()
-
-    # train the generator ---
-
-    G_loss <- loss(D(fake), y_real)
-
-    G_optimizer$zero_grad()
-    G_loss$backward()
-    G_optimizer$step()
-
-    lossd <- c(lossd, D_loss$item())
-    lossg <- c(lossg, G_loss$item())
-    pb$tick(tokens = list(lossd = mean(lossd), lossg = mean(lossg)))
-  }
-  plot_gen(fixed_noise)
-
-  cat(sprintf("Epoch %d - Loss D: %3f Loss G: %3f\n", epoch, mean(lossd), mean(lossg)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/examples/mnist-dcgan_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-mlp.html b/docs/articles/examples/mnist-mlp.html deleted file mode 100644 index db40b8ea6..000000000 --- a/docs/articles/examples/mnist-mlp.html +++ /dev/null @@ -1,203 +0,0 @@ - - - - - - - -mnist-mlp • torch - - - - - - - - - - -
-
- - - - -
-
-
-

mnist-mlp

- - - Source: vignettes/examples/mnist-mlp.Rmd - - -
- - - -
dir <- "~/Downloads/mnist"
-
-ds <- mnist_dataset(
-  dir,
-  download = TRUE,
-  transform = function(x) {
-    x$to(dtype = torch_float())/256
-  }
-)
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-net <- nn_module(
-  "Net",
-  initialize = function() {
-    self$fc1 <- nn_linear(784, 128)
-    self$fc2 <- nn_linear(128, 10)
-  },
-  forward = function(x) {
-    x %>%
-      torch_flatten(start_dim = 2) %>%
-      self$fc1() %>%
-      nnf_relu() %>%
-      self$fc2() %>%
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-optimizer <- optim_sgd(model$parameters, lr = 0.01)
-
-epochs <- 10
-
-for (epoch in 1:10) {
-
-  pb <- progress::progress_bar$new(
-    total = length(dl),
-    format = "[:bar] :eta Loss: :loss"
-  )
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-    pb$tick(tokens = list(loss = mean(l)))
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/examples/mnist-mlp_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js b/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/examples/mnist-mlp_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/extending-autograd.html b/docs/articles/extending-autograd.html deleted file mode 100644 index 8c27bfa2e..000000000 --- a/docs/articles/extending-autograd.html +++ /dev/null @@ -1,222 +0,0 @@ - - - - - - - -Extending Autograd • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Extending Autograd

- - - Source: vignettes/extending-autograd.Rmd - - -
- - - -
library(torch)
-

Adding operations to autograd requires implementing a new autograd_function for each operation. Recall that autograd_functionss are what autograd uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:

-
    -

  • forward() - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with requires_grad=TRUE) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of Tensors if there are multiple outputs. Also, please refer to the docs of autograd_function to find descriptions of useful methods that can be called only from forward().

    • -

  • backward() - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many Tensors as there were Tensor's that required gradients in forward, with each of them containing the gradient w.r.t. its corresponding input.

    • -
-
-

-Note

-

It’s the user’s responsibility to use the special functions in the forward’s ctx properly in order to ensure that the new autograd_function works properly with the autograd engine.

-
    -

  • save_for_backward() must be used when saving input or ouput of the forward to be used later in the backward.

    • -

  • mark_dirty() must be used to mark any input that is modified inplace by the forward function.

    • -

  • mark_non_differentiable() must be used to tell the engine if an output is not differentiable.

    • -
-
-
-

-Examples

-

Below you can find code for a linear function:

-
linear <- autograd_function(
-  forward = function(ctx, input, weight, bias = NULL) {
-    ctx$save_for_backward(input = input, weight = weight, bias = bias)
-    output <- input$mm(weight$t())
-    if (!is.null(bias))
-      output <- output + bias$unsqueeze(0)$expand_as(output)
-
-    output
-  },
-  backward = function(ctx, grad_output) {
-
-    s <- ctx$saved_variables
-
-    grads <- list(
-      input = NULL,
-      weight = NULL,
-      bias = NULL
-    )
-
-    if (ctx$needs_input_grad$input)
-      grads$input <- grad_output$mm(s$weight)
-
-    if (ctx$needs_input_grad$weight)
-      grads$weight <- grad_output$t()$mm(s$input)
-
-    if (!is.null(s$bias) && ctx$needs_input_grad$bias)
-      grads$bias <- grad_output$sum(dim = 0)
-
-    grads
-  }
-)
-

Here, we give an additional example of a function that is parametrized by non-Tensor arguments:

-
mul_constant <- autograd_function(
-  forward = function(ctx, tensor, constant) {
-    ctx$save_for_backward(constant = constant)
-    tensor * constant
-  },
-  backward = function(ctx, grad_output) {
-    v <- ctx$saved_variables
-    list(
-      tensor = grad_output * v$constant
-    )
-  }
-)
-
x <- torch_tensor(1, requires_grad = TRUE)
-o <- mul_constant(x, 2)
-o$backward()
-x$grad
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/extending-autograd_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js b/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/extending-autograd_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 50afc9ac2..000000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,204 +0,0 @@ - - - - - - - - -Articles • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
- - - - -
- -
-
-
-

Articles

-
- -
-

All vignettes

-

- -
-
mnist-cnn
-
-
mnist-dcgan
-
-
mnist-mlp
-
-
Extending Autograd
-
-
Indexing tensors
-
-
Loading data
-
-
Creating tensors
-
-
Using autograd
-
-
-
-
-
- - -
-
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - - - diff --git a/docs/articles/indexing.html b/docs/articles/indexing.html deleted file mode 100644 index 0207c0554..000000000 --- a/docs/articles/indexing.html +++ /dev/null @@ -1,224 +0,0 @@ - - - - - - - -Indexing tensors • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Indexing tensors

- - - Source: vignettes/indexing.Rmd - - -
- - - -
library(torch)
-

In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.

-

Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the numpy indexing article available here.

-
-

-Single element indexing

-

Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)

-
x <- torch_tensor(1:10)
-x[1]
-x[-1]
-

You can also subset matrices and higher dimensions arrays using the same syntax:

-
x <- x$reshape(shape = c(2,5))
-x
-x[1,3]
-x[1,-1]
-

Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:

-
x[1]
-
-
-

-Slicing and striding

-

It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:

-
x <- torch_tensor(1:10)
-x
-x[2:5]
-x[1:(-7)]
-

You can also use the 1:10:2 syntax which means: In the range from 1 to 10, take every second item. For example:

-
x[1:5:2]
-

Another special syntax is the N, meaning the size of the specified dimension.

-
x[5:N]
-
-
-

-Getting the complete dimension

-

Like in R, you can take all elements in a dimension by leaving an index empty.

-

Consider a matrix:

-
x <- torch_randn(2, 3)
-x
-

The following syntax will give you the first row:

-
x[1,]
-

And this would give you the first 2 columns:

-
x[,1:2]
-
-
-

-Dropping dimensions

-

By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:

-
x <- torch_randn(2, 3)
-x[1,]$shape
-

You can optionally use the drop = FALSE argument to avoid dropping the dimension.

-
x[1,,drop = FALSE]$shape
-
-
-

-Adding a new dimension

-

It’s possible to add a new dimension to a tensor using index-like syntax:

-
x <- torch_tensor(c(10))
-x$shape
-x[, newaxis]$shape
-x[, newaxis, newaxis]$shape
-

You can also use NULL instead of newaxis:

-
x[,NULL]$shape
-
-
-

-Dealing with variable number of indices

-

Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use ..:

-
z <- torch_tensor(1:125)$reshape(c(5,5,5))
-z[1,..]
-z[..,1]
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/indexing_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/indexing_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js b/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/indexing_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/loading-data.html b/docs/articles/loading-data.html deleted file mode 100644 index 3fd2a41a3..000000000 --- a/docs/articles/loading-data.html +++ /dev/null @@ -1,274 +0,0 @@ - - - - - - - -Loading data • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Loading data

- - - Source: vignettes/loading-data.Rmd - - -
- - - -
library(torch)
-
-

-Datasets and data loaders

-

Central to data ingestion and preprocessing are datasets and data loaders.

-

torch comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., mnist_dataset()), which can be iterated over by means of dataloaders:

-
# ...
-ds <- mnist_dataset(
-  dir, 
-  download = TRUE, 
-  transform = function(x) {
-    x <- x$to(dtype = torch_float())/256
-    x[newaxis,..]
-  }
-)
-
-dl <- dataloader(ds, batch_size = 32, shuffle = TRUE)
-
-for (b in enumerate(dl)) {
-  # ...
-

Cf. vignettes/examples/mnist-cnn.R for a complete example.

-

What if you want to train on a different dataset? In these cases, you subclass Dataset, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement .getitem(), and say what should be returned when the data loader is asking for the next batch.

-

In .getitem(), you can implement whatever preprocessing you require. Additionally, you should implement .length(), so users can find out how many items there are in the dataset.

-

While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own dataset to train on Allison Horst's penguins.

-
-
-

-A custom dataset

-
library(palmerpenguins)
-library(magrittr)
-
-penguins
-

Datasets are R6 classes created using the dataset() constructor. You can pass a name and various member functions. Among those should be initialize(), to create instance variables, .getitem(), to indicate how the data should be returned, and .length(), to say how many items we have.

-

In addition, any number of helper functions can be defined.

-

Here, we assume the penguins have already been loaded, and all preprocessing consists in removing lines with NA values, transforming factors to numbers starting from 0, and converting from R data types to torch tensors.

-

In .getitem, we essentially decide how this data is going to be used: All variables besides species go into x, the predictor, and species will constitute y, the target. Predictor and target are returned in a list, to be accessed as batch[[1]] and batch[[2]] during training.

-
penguins_dataset <- dataset(
-
-  name = "penguins_dataset",
-
-  initialize = function() {
-    self$data <- self$prepare_penguin_data()
-  },
-
-  .getitem = function(index) {
-
-    x <- self$data[index, 2:-1]
-    y <- self$data[index, 1]$to(torch_long())
-
-    list(x, y)
-  },
-
-  .length = function() {
-    self$data$size()[[1]]
-  },
-
-  prepare_penguin_data = function() {
-
-    input <- na.omit(penguins)
-    # conveniently, the categorical data are already factors
-    input$species <- as.numeric(input$species)
-    input$island <- as.numeric(input$island)
-    input$sex <- as.numeric(input$sex)
-
-    input <- as.matrix(input)
-    torch_tensor(input)
-  }
-)
-

Let’s create the dataset , query for it’s length, and look at its first item:

-
tuxes <- penguins_dataset()
-tuxes$.length()
-tuxes$.getitem(1)
-

To be able to iterate over tuxes, we need a data loader (we override the default batch size of 1):

-
dl <-tuxes %>% dataloader(batch_size = 8)
-

Calling .length() on a data loader (as opposed to a dataset) will return the number of batches we have:

-
dl$.length()
-

And we can create an iterator to inspect the first batch:

-
iter <- dl$.iter()
-b <- iter$.next()
-b
-

To train a network, we can use enumerate to iterate over batches.

-
-
-

-Training with data loaders

-

Our example network is very simple. (In reality, we would want to treat island as the categorical variable it is, and either one-hot-encode or embed it.)

-
net <- nn_module(
-  "PenguinNet",
-  initialize = function() {
-    self$fc1 <- nn_linear(6, 32)
-    self$fc2 <- nn_linear(32, 3)
-  },
-  forward = function(x) {
-    x %>%
-      self$fc1() %>%
-      nnf_relu() %>%
-      self$fc2() %>%
-      nnf_log_softmax(dim = 1)
-  }
-)
-
-model <- net()
-

We still need an optimizer:

-
optimizer <- optim_sgd(model$parameters, lr = 0.01)
-

And we’re ready to train:

-
for (epoch in 1:10) {
-
-  l <- c()
-
-  for (b in enumerate(dl)) {
-    optimizer$zero_grad()
-    output <- model(b[[1]])
-    loss <- nnf_nll_loss(output, b[[2]])
-    loss$backward()
-    optimizer$step()
-    l <- c(l, loss$item())
-  }
-
-  cat(sprintf("Loss at epoch %d: %3f\n", epoch, mean(l)))
-}
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js b/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/loading-data_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/tensor-creation.html b/docs/articles/tensor-creation.html deleted file mode 100644 index 96bc72ff2..000000000 --- a/docs/articles/tensor-creation.html +++ /dev/null @@ -1,231 +0,0 @@ - - - - - - - -Creating tensors • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Creating tensors

- - - Source: vignettes/tensor-creation.Rmd - - -
- - - -
library(torch)
-

In this article we describe various ways of creating torch tensors in R.

-
-

-From R objects

-

You can create tensors from R objects using the torch_tensor function. The torch_tensor function takes an R vector, matrix or array and creates an equivalent torch_tensor.

-

You can see a few examples below:

-
torch_tensor(c(1,2,3))
-
-# conform to row-major indexing used in torch
-torch_tensor(matrix(1:10, ncol = 5, nrow = 2, byrow = TRUE))
-torch_tensor(array(runif(12), dim = c(2, 2, 3)))
-

By default, we will create tensors in the cpu device, converting their R datatype to the corresponding torch dtype.

-
-

Note currently, only numeric and boolean types are supported.

-
-

You can always modify dtype and device when converting an R object to a torch tensor. For example:

-
torch_tensor(1, dtype = torch_long())
-torch_tensor(1, device = "cpu", dtype = torch_float64())
-

Other options available when creating a tensor are:

-
    -
  • -requires_grad: boolean indicating if you want autograd to record operations on them for automatic differentiation.
    • -
  • -pin_memory: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.
    • -
-

These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.

-
-
-

-Using creation functions

-

You can also use the torch_* functions listed below to create torch tensors using some algorithm.

-

For example, the torch_randn function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the ... argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.

-
x <- torch_randn(5, 3)
-x
-

Another example is torch_ones, which creates a tensor filled with ones.

-
x <- torch_ones(2, 4, dtype = torch_int64(), device = "cpu")
-x
-

Here is the full list of functions that can be used to bulk-create tensors in torch:

-
    -
  • -torch_arange: Returns a tensor with a sequence of integers,
    • -
  • -torch_empty: Returns a tensor with uninitialized values,
    • -
  • -torch_eye: Returns an identity matrix,
    • -
  • -torch_full: Returns a tensor filled with a single value,
    • -
  • -torch_linspace: Returns a tensor with values linearly spaced in some interval,
    • -
  • -torch_logspace: Returns a tensor with values logarithmically spaced in some interval,
    • -
  • -torch_ones: Returns a tensor filled with all ones,
    • -
  • -torch_rand: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).
    • -
  • -torch_randint: Returns a tensor with integers randomly drawn from an interval,
    • -
  • -torch_randn: Returns a tensor filled with values drawn from a unit normal distribution,
    • -
  • -torch_randperm: Returns a tensor filled with a random permutation of integers in some interval,
    • -
  • -torch_zeros: Returns a tensor filled with all zeros.
    • -
-
-
-

-Conversion

-

Once a tensor exists you can convert between dtypes and move to a different device with to method. For example:

-
x <- torch_tensor(1)
-y <- x$to(dtype = torch_int32())
-x
-y
-

You can also copy a tensor to the GPU using:

-
x <- torch_tensor(1)
-y <- x$cuda())
-
-
- - - -
- - - -
-

Developed by Daniel Falbel, Javier Luraschi.

-
- -
-

Site built with pkgdown 1.5.1.

-
- -
-
- - - - - - diff --git a/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd6a..000000000 --- a/docs/articles/tensor-creation_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); diff --git a/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js b/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/tensor-creation_files/header-attrs-2.1.1/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js b/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js deleted file mode 100644 index dd57d92e0..000000000 --- a/docs/articles/tensor-creation_files/header-attrs-2.3/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/using-autograd.html b/docs/articles/using-autograd.html deleted file mode 100644 index 9cd634b0c..000000000 --- a/docs/articles/using-autograd.html +++ /dev/null @@ -1,268 +0,0 @@ - - - - - - - -Using autograd • torch - - - - - - - - - - -
-
- - - - -
-
-
-

Using autograd

- - - Source: vignettes/using-autograd.Rmd - - -
- - - -
library(torch)
-

So far, all we’ve been using from torch is tensors, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.

-

Before we see that in action, let’s get some more background.

-
-

-Automatic differentiation with autograd

-

Torch uses a module called autograd to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and propagating gradients back through the network. This is a form of reverse mode automatic differentiation.

-

As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with requires_grad = TRUE. E.g.

-
x <- torch_ones(2,2, requires_grad = TRUE)
-

To be clear, this is a tensor with respect to which gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data 1. If we now perform some operation on that tensor, assigning the result to y

-
y <- x$mean()
-

we find that y now has a non-empty grad_fn that tells torch how to compute the gradient of y with respect to x:

-
y$grad_fn
-

Actual computation of gradients is triggered by calling backward() on the output tensor.

-
y$backward()
-

That executed, x now has a non-empty field grad that stores the gradient of y with respect to x:

-
x$grad
-

With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.

-

Here is a slightly more complex example. We call retain_grad() on y and z just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.

-
x1 <- torch_ones(2,2, requires_grad = TRUE)
-x2 <- torch_tensor(1.1, requires_grad = TRUE)
-y <- x1 * (x2 + 2)
-y$retain_grad()
-z <- y$pow(2) * 3
-z$retain_grad()
-out <- z$mean()
-

Starting from out$grad_fn, we can follow the graph all back to the leaf nodes:

-
# how to compute the gradient for mean, the last operation executed
-out$grad_fn
-# how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3
-out$grad_fn$next_functions
-# how to compute the gradient for pow in z = y.pow(2) * 3
-out$grad_fn$next_functions[[1]]$next_functions
-# how to compute the gradient for the multiplication in y = x * (x + 2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions
-# how to compute the gradient for the two branches of y = x * (x + 2),
-# where the left branch is a leaf node (AccumulateGrad for x1)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions
-# here we arrive at the other leaf node (AccumulateGrad for x2)
-out$grad_fn$next_functions[[1]]$next_functions[[1]]$next_functions[[1]]$next_functions[[2]]$next_functions
-

After calling out$backward(), all tensors in the graph will have their respective gradients created. Without our calls to retain_grad above, z$grad and y$grad would be empty:

-
out$backward()
-z$grad
-y$grad
-x2$grad
-x1$grad
-

Thus acquainted with autograd, we’re ready to modify our example.

-
-
-

-The simple network, now using autograd

-

For a single new line calling loss$backward(), now a number of lines (that did manual backprop) are gone:

-
### generate training data -----------------------------------------------------
-# input dimensionality (number of input features)
-d_in <- 3
-# output dimensionality (number of predicted features)
-d_out <- 1
-# number of observations in training set
-n <- 100
-# create random data
-x <- torch_randn(n, d_in)
-y <- x[,1]*0.2 - x[..,2]*1.3 - x[..,3]*0.5 + torch_randn(n)
-y <- y$unsqueeze(dim = 1)
-### initialize weights ---------------------------------------------------------
-# dimensionality of hidden layer
-d_hidden <- 32
-# weights connecting input to hidden layer
-w1 <- torch_randn(d_in, d_hidden, requires_grad = TRUE)
-# weights connecting hidden to output layer
-w2 <- torch_randn(d_hidden, d_out, requires_grad = TRUE)
-# hidden layer bias
-b1 <- torch_zeros(1, d_hidden, requires_grad = TRUE)
-# output layer bias
-b2 <- torch_zeros(1, d_out,requires_grad = TRUE)
-### network parameters ---------------------------------------------------------
-learning_rate <- 1e-4
-### training loop --------------------------------------------------------------
-for (t in 1:200) {
-
-    ### -------- Forward pass -------- 
-    y_pred <- x$mm(w1)$add(b1)$clamp(min = 0)$mm(w2)$add(b2)
-    ### -------- compute loss -------- 
-    loss <- (y_pred - y)$pow(2)$mean()
-    if (t %% 10 == 0) cat(t, as_array(loss), "\n")
-    ### -------- Backpropagation -------- 
-    # compute the gradient of loss with respect to all tensors with requires_grad = True.
-    loss$backward()
-
-    ### -------- Update weights -------- 
-
-    # Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation
-    with_no_grad({
-
-      w1$sub_(learning_rate * w1$grad)
-      w2$sub_(learning_rate * w2$grad)
-      b1$sub_(learning_rate * b1$grad)
-      b2$sub_(learning_rate * b2$grad)
-
-      # Zero the gradients after every pass, because they'd accumulate otherwise
-      w1$grad$zero_()
-      w2$grad$zero_()
-      b1$grad$zero_()
-      b2$grad$zero_()
-
-    })
-
-}
-

We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.

-
-
-
-
    -

  1. Unless we want to change the data, as in adversarial example generation↩︎

    2. -
-
-
- - - -
- - - -
-

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-
- -
-

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-
- - - - -
- -
-
-
-

Authors

-
- -
    -
  • -

    Daniel Falbel. Author, maintainer. -

    -
    • -
  • -

    Javier Luraschi. Author. -

    -
    • -
  • -

    Dmitriy Selivanov. Contributor. -

    -
    • -
  • -

    Athos Damiani. Contributor. -

    -
    • -
  • -

    RStudio. Copyright holder. -

    -
    • -
- -
- -
- - - -
-
-

Developed by Daniel Falbel, Javier Luraschi.

-
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- margin-top: -.25rem; - font-size: 0.8em; - font-weight: 400; - line-height: 1.25 -} - -.algolia-autocomplete .algolia-docsearch-footer { - width: 110px; - height: 20px; - z-index: 3; - margin-top: 10.66667px; - float: right; - font-size: 0; - line-height: 0; -} - -.algolia-autocomplete .algolia-docsearch-footer--logo { - background-image: url("data:image/svg+xml;utf8,"); - background-repeat: no-repeat; - background-position: 50%; - background-size: 100%; - overflow: hidden; - text-indent: -9000px; - width: 100%; - height: 100%; - display: block; - transform: translate(-8px); -} - -.algolia-autocomplete .algolia-docsearch-suggestion--highlight { - color: #FF8C00; - background: rgba(232, 189, 54, 0.1) -} - - -.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { - box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) -} - -.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { - background-color: rgba(192, 192, 192, .15) -} diff --git a/docs/docsearch.js b/docs/docsearch.js deleted file mode 100644 index b35504cd3..000000000 --- a/docs/docsearch.js +++ /dev/null @@ -1,85 +0,0 @@ -$(function() { - - // register a handler to move the focus to the search bar - // upon pressing shift + "/" (i.e. "?") - $(document).on('keydown', function(e) { - if (e.shiftKey && e.keyCode == 191) { - e.preventDefault(); 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- // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index a36128e29..000000000 --- a/docs/index.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - -Tensors and Neural Networks with GPU Acceleration • torch - - - - - - - - - - -
    -
    - - - - -
    -
    -
    -

    -torch -

    - -
    -

    -Installation

    -

    Run:

    -
    remotes::install_github("mlverse/torch")
    -

    At the first package load additional software will be installed.

    -
    -
    -

    -Example

    -

    Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.

    -
    library(torch)
-x <- array(runif(8), dim = c(2, 2, 2))
-y <- torch_tensor(x, dtype = torch_float64())
-y
-#> torch_tensor 
-#> (1,.,.) = 
-#>   0.8687  0.0157
-#>   0.4237  0.8971
-#> 
-#> (2,.,.) = 
-#>   0.4021  0.5509
-#>   0.3374  0.9034
-#> [ CPUDoubleType{2,2,2} ]
-identical(x, as_array(y))
-#> [1] TRUE
    -
    -

    -Simple Autograd Example

    -

    In the following snippet we let torch, using the autograd feature, calculate the derivatives:

    -
    x <- torch_tensor(1, requires_grad = TRUE)
-w <- torch_tensor(2, requires_grad = TRUE)
-b <- torch_tensor(3, requires_grad = TRUE)
-y <- w * x + b
-y$backward()
-x$grad
-#> torch_tensor 
-#>  2
-#> [ CPUFloatType{1} ]
-w$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
-b$grad
-#> torch_tensor 
-#>  1
-#> [ CPUFloatType{1} ]
    -
    -
    -

    -Linear Regression

    -

    In the following example we are going to fit a linear regression from scratch using torch’s Autograd.

    -

    Note all methods that end with _ (eg. sub_), will modify the tensors in place.

    -
    x <- torch_randn(100, 2)
-y <- 0.1 + 0.5*x[,1] - 0.7*x[,2]
-
-w <- torch_randn(2, 1, requires_grad = TRUE)
-b <- torch_zeros(1, requires_grad = TRUE)
-
-lr <- 0.5
-for (i in 1:100) {
-  y_hat <- torch_mm(x, w) + b
-  loss <- torch_mean((y - y_hat$squeeze(1))^2)
-
-  loss$backward()
-
-  with_no_grad({
-    w$sub_(w$grad*lr)
-    b$sub_(b$grad*lr)
-
-    w$grad$zero_()
-    b$grad$zero_()
-  })
-}
-print(w)
-#> torch_tensor 
-#>  0.5000
-#> -0.7000
-#> [ CPUFloatType{2,1} ]
-print(b)
-#> torch_tensor 
-#> 0.01 *
-#> 10.0000
-#> [ CPUFloatType{1} ]
    -
    -
    -
    -

    -Contributing

    -

    No matter your current skills it’s possible to contribute to torch development. See the contributing guide for more information.

    -
    -
    -
    - - -
    - - -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - diff --git a/docs/link.svg b/docs/link.svg deleted file mode 100644 index 88ad82769..000000000 --- a/docs/link.svg +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - diff --git a/docs/pkgdown.css b/docs/pkgdown.css deleted file mode 100644 index c01e5923b..000000000 --- a/docs/pkgdown.css +++ /dev/null @@ -1,367 +0,0 @@ -/* Sticky footer */ - -/** - * Basic idea: https://philipwalton.github.io/solved-by-flexbox/demos/sticky-footer/ - * Details: https://github.com/philipwalton/solved-by-flexbox/blob/master/assets/css/components/site.css - * - * .Site -> body > .container - * .Site-content -> body > .container .row - * .footer -> footer - * - * Key idea seems to be to ensure that .container and __all its parents__ - * have height set to 100% - * - */ - -html, body { - height: 100%; -} - -body { - position: relative; -} - -body > .container { - display: flex; - height: 100%; - flex-direction: column; -} - -body > .container .row { - flex: 1 0 auto; -} - -footer { - margin-top: 45px; - padding: 35px 0 36px; - border-top: 1px solid #e5e5e5; - color: #666; - display: flex; - flex-shrink: 0; -} -footer p { - margin-bottom: 0; -} -footer div { - flex: 1; -} -footer .pkgdown { - text-align: right; -} -footer p { - margin-bottom: 0; -} - -img.icon { - float: right; -} - -img { - max-width: 100%; -} - -/* Fix bug in bootstrap (only seen in firefox) */ -summary { - display: list-item; -} - -/* Typographic tweaking ---------------------------------*/ - -.contents .page-header { - margin-top: calc(-60px + 1em); -} - -dd { - margin-left: 3em; -} - -/* Section anchors ---------------------------------*/ - -a.anchor { - margin-left: -30px; - display:inline-block; - width: 30px; - height: 30px; - visibility: hidden; - - background-image: url(./link.svg); - background-repeat: no-repeat; - background-size: 20px 20px; - background-position: center center; -} - -.hasAnchor:hover a.anchor { - visibility: visible; -} - -@media (max-width: 767px) { - .hasAnchor:hover a.anchor { - visibility: hidden; - } -} - - -/* Fixes for fixed navbar --------------------------*/ - -.contents h1, .contents h2, .contents h3, .contents h4 { - padding-top: 60px; - margin-top: -40px; -} - -/* Navbar submenu --------------------------*/ - -.dropdown-submenu { - position: relative; -} - -.dropdown-submenu>.dropdown-menu { - top: 0; - left: 100%; - margin-top: -6px; - margin-left: -1px; - border-radius: 0 6px 6px 6px; -} - -.dropdown-submenu:hover>.dropdown-menu { - display: block; -} - -.dropdown-submenu>a:after { - display: block; - content: " "; - float: right; - width: 0; - height: 0; - border-color: transparent; - border-style: solid; - border-width: 5px 0 5px 5px; - border-left-color: #cccccc; - margin-top: 5px; - margin-right: -10px; -} - -.dropdown-submenu:hover>a:after { - border-left-color: #ffffff; -} - -.dropdown-submenu.pull-left { - float: none; -} - -.dropdown-submenu.pull-left>.dropdown-menu { - left: -100%; - margin-left: 10px; - border-radius: 6px 0 6px 6px; -} - -/* Sidebar --------------------------*/ - -#pkgdown-sidebar { - margin-top: 30px; 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/* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} - -nav[data-toggle='toc'] .nav .nav > li > a { - padding-left: 16px; - font-size: 1.35rem; -} - -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 15px; -} - -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 15px; - font-weight: 500; - font-size: 1.35rem; -} - -/* orcid ------------------------------------------------------------------- */ - -.orcid { - font-size: 16px; - color: #A6CE39; - /* margins are required by official ORCID trademark and display guidelines */ - margin-left:4px; - margin-right:4px; - vertical-align: middle; -} - -/* Reference index & topics ----------------------------------------------- */ - -.ref-index th {font-weight: normal;} - -.ref-index td {vertical-align: top;} -.ref-index .icon {width: 40px;} -.ref-index .alias {width: 40%;} -.ref-index-icons .alias {width: calc(40% - 40px);} -.ref-index .title {width: 60%;} - -.ref-arguments th {text-align: right; padding-right: 10px;} -.ref-arguments th, .ref-arguments td {vertical-align: top;} -.ref-arguments .name {width: 20%;} -.ref-arguments .desc {width: 80%;} - -/* Nice scrolling for wide elements --------------------------------------- */ - -table { - display: block; - overflow: auto; -} - -/* Syntax highlighting ---------------------------------------------------- */ - -pre { - word-wrap: normal; - word-break: normal; - border: 1px solid #eee; -} - -pre, code { - background-color: #f8f8f8; - color: #333; -} - -pre code { - overflow: auto; - word-wrap: normal; - white-space: pre; -} - -pre .img { - margin: 5px 0; -} - -pre .img img { - background-color: #fff; - display: block; - height: auto; -} - -code a, pre a { - color: #375f84; -} - -a.sourceLine:hover { - text-decoration: none; -} - -.fl {color: #1514b5;} -.fu {color: #000000;} /* function */ -.ch,.st {color: #036a07;} /* string */ -.kw {color: #264D66;} /* keyword */ -.co {color: #888888;} /* comment */ - -.message { color: black; font-weight: bolder;} -.error { color: orange; font-weight: bolder;} -.warning { color: #6A0366; font-weight: bolder;} - -/* Clipboard --------------------------*/ - -.hasCopyButton { - position: relative; -} - -.btn-copy-ex { - position: absolute; - right: 0; - top: 0; - visibility: hidden; -} - -.hasCopyButton:hover button.btn-copy-ex { - visibility: visible; -} - -/* headroom.js ------------------------ */ - -.headroom { - will-change: transform; - transition: transform 200ms linear; -} -.headroom--pinned { - transform: translateY(0%); -} -.headroom--unpinned { - transform: translateY(-100%); -} - -/* mark.js ----------------------------*/ - -mark { - background-color: rgba(255, 255, 51, 0.5); - border-bottom: 2px solid rgba(255, 153, 51, 0.3); - padding: 1px; -} - -/* vertical spacing after htmlwidgets */ -.html-widget { - margin-bottom: 10px; -} - -/* fontawesome ------------------------ */ - -.fab { - font-family: "Font Awesome 5 Brands" !important; -} - -/* don't display links in code chunks when printing */ -/* source: https://stackoverflow.com/a/10781533 */ -@media print { - code a:link:after, code a:visited:after { - content: ""; - } -} diff --git a/docs/pkgdown.js b/docs/pkgdown.js deleted file mode 100644 index 7e7048fae..000000000 --- a/docs/pkgdown.js +++ /dev/null @@ -1,108 +0,0 @@ -/* http://gregfranko.com/blog/jquery-best-practices/ */ -(function($) { - $(function() { - - $('.navbar-fixed-top').headroom(); - - $('body').css('padding-top', $('.navbar').height() + 10); - $(window).resize(function(){ - $('body').css('padding-top', $('.navbar').height() + 10); - }); - - $('[data-toggle="tooltip"]').tooltip(); - - var cur_path = paths(location.pathname); - var links = $("#navbar ul li a"); - var max_length = -1; - var pos = -1; - for (var i = 0; i < links.length; i++) { - if (links[i].getAttribute("href") === "#") - continue; - // Ignore external links - if (links[i].host !== location.host) - continue; - - var nav_path = paths(links[i].pathname); - - var length = prefix_length(nav_path, cur_path); - if (length > max_length) { - max_length = length; - pos = i; - } - } - - // Add class to parent
  • , and enclosing
  • if in dropdown - if (pos >= 0) { - var menu_anchor = $(links[pos]); - menu_anchor.parent().addClass("active"); - menu_anchor.closest("li.dropdown").addClass("active"); - } - }); - - function paths(pathname) { - var pieces = pathname.split("/"); - pieces.shift(); // always starts with / - - var end = pieces[pieces.length - 1]; - if (end === "index.html" || end === "") - pieces.pop(); - return(pieces); - } - - // Returns -1 if not found - function prefix_length(needle, haystack) { - if (needle.length > haystack.length) - return(-1); - - // Special case for length-0 haystack, since for loop won't run - if (haystack.length === 0) { - return(needle.length === 0 ? 0 : -1); - } - - for (var i = 0; i < haystack.length; i++) { - if (needle[i] != haystack[i]) - return(i); - } - - return(haystack.length); - } - - /* Clipboard --------------------------*/ - - function changeTooltipMessage(element, msg) { - var tooltipOriginalTitle=element.getAttribute('data-original-title'); - element.setAttribute('data-original-title', msg); - $(element).tooltip('show'); - element.setAttribute('data-original-title', tooltipOriginalTitle); - } - - if(ClipboardJS.isSupported()) { - $(document).ready(function() { - var copyButton = ""; - - $(".examples, div.sourceCode").addClass("hasCopyButton"); - - // Insert copy buttons: - $(copyButton).prependTo(".hasCopyButton"); - - // Initialize tooltips: - $('.btn-copy-ex').tooltip({container: 'body'}); - - // Initialize clipboard: - var clipboardBtnCopies = new ClipboardJS('[data-clipboard-copy]', { - text: function(trigger) { - return trigger.parentNode.textContent; - } - }); - - clipboardBtnCopies.on('success', function(e) { - changeTooltipMessage(e.trigger, 'Copied!'); - e.clearSelection(); - }); - - clipboardBtnCopies.on('error', function() { - changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); - }); - }); - } -})(window.jQuery || window.$) diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml deleted file mode 100644 index ffc2acea3..000000000 --- a/docs/pkgdown.yml +++ /dev/null @@ -1,14 +0,0 @@ -pandoc: 2.9.2.1 -pkgdown: 1.5.1 -pkgdown_sha: ~ -articles: - mnist-cnn: examples/mnist-cnn.html - mnist-dcgan: examples/mnist-dcgan.html - mnist-mlp: examples/mnist-mlp.html - extending-autograd: extending-autograd.html - indexing: indexing.html - loading-data: loading-data.html - tensor-creation: tensor-creation.html - using-autograd: using-autograd.html -last_built: 2020-07-22T19:57Z - diff --git a/docs/reference/AutogradContext.html b/docs/reference/AutogradContext.html deleted file mode 100644 index b1fcfa13c..000000000 --- a/docs/reference/AutogradContext.html +++ /dev/null @@ -1,306 +0,0 @@ - - - - - - - - -Class representing the context. — AutogradContext • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Class representing the context.

    - Source: R/autograd.R - -
    - -
    -

    Class representing the context.

    -

    Class representing the context.

    -
    - - - -

    Public fields

    - -

    -
    ptr

    (Dev related) pointer to the context c++ object.

    - -

    -

    Active bindings

    - -

    -
    needs_input_grad

    boolean listing arguments of forward and whether they require_grad.

    - -
    saved_variables

    list of objects that were saved for backward via save_for_backward.

    - -

    -

    Methods

    - - -

    Public methods

    - - -

    -

    Method new()

    -

    (Dev related) Initializes the context. Not user related.

    Usage

    -

    AutogradContext$new(
-  ptr,
-  env,
-  argument_names = NULL,
-  argument_needs_grad = NULL
-)

    - -

    Arguments

    -

    -
    ptr

    pointer to the c++ object

    - -
    env

    environment that encloses both forward and backward

    - -
    argument_names

    names of forward arguments

    - -
    argument_needs_grad

    whether each argument in forward needs grad.

    - -

    -

    -

    Method save_for_backward()

    -

    Saves given objects for a future call to backward().

    -

    This should be called at most once, and only from inside the forward() -method.

    -

    Later, saved objects can be accessed through the saved_variables attribute. -Before returning them to the user, a check is made to ensure they weren’t used -in any in-place operation that modified their content.

    -

    Arguments can also be any kind of R object.

    Usage

    -

    AutogradContext$save_for_backward(...)

    - -

    Arguments

    -

    -
    ...

    any kind of R object that will be saved for the backward pass. -It's common to pass named arguments.

    - -

    -

    -

    Method mark_non_differentiable()

    -

    Marks outputs as non-differentiable.

    -

    This should be called at most once, only from inside the forward() method, -and all arguments should be outputs.

    -

    This will mark outputs as not requiring gradients, increasing the efficiency -of backward computation. You still need to accept a gradient for each output -in backward(), but it’s always going to be a zero tensor with the same -shape as the shape of a corresponding output.

    -

    This is used e.g. for indices returned from a max Function.

    Usage

    -

    AutogradContext$mark_non_differentiable(...)

    - -

    Arguments

    -

    -
    ...

    non-differentiable outputs.

    - -

    -

    -

    Method mark_dirty()

    -

    Marks given tensors as modified in an in-place operation.

    -

    This should be called at most once, only from inside the forward() method, -and all arguments should be inputs.

    -

    Every tensor that’s been modified in-place in a call to forward() should -be given to this function, to ensure correctness of our checks. It doesn’t -matter whether the function is called before or after modification.

    Usage

    -

    AutogradContext$mark_dirty(...)

    - -

    Arguments

    -

    -
    ...

    tensors that are modified in-place.

    - -

    -

    -

    Method clone()

    -

    The objects of this class are cloneable with this method.

    Usage

    -

    AutogradContext$clone(deep = FALSE)

    - -

    Arguments

    -

    -
    deep

    Whether to make a deep clone.

    - -

    - - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/as_array.html b/docs/reference/as_array.html deleted file mode 100644 index 6e35287d8..000000000 --- a/docs/reference/as_array.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Converts to array — as_array • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Converts to array

    - Source: R/tensor.R - -
    - -
    -

    Converts to array

    -
    - - 
    as_array(x)
    - -

    Arguments

    - - - - - - -
    x

    object to be converted into an array

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_backward.html b/docs/reference/autograd_backward.html deleted file mode 100644 index 087f1d74c..000000000 --- a/docs/reference/autograd_backward.html +++ /dev/null @@ -1,258 +0,0 @@ - - - - - - - - -Computes the sum of gradients of given tensors w.r.t. graph leaves. — autograd_backward • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Computes the sum of gradients of given tensors w.r.t. graph leaves.

    - Source: R/autograd.R - -
    - -
    -

    The graph is differentiated using the chain rule. If any of tensors are -non-scalar (i.e. their data has more than one element) and require gradient, -then the Jacobian-vector product would be computed, in this case the function -additionally requires specifying grad_tensors. It should be a sequence of -matching length, that contains the “vector” in the Jacobian-vector product, -usually the gradient of the differentiated function w.r.t. corresponding -tensors (None is an acceptable value for all tensors that don’t need gradient -tensors).

    -
    - - 
    autograd_backward(
-  tensors,
-  grad_tensors = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    tensors

    (list of Tensor) – Tensors of which the derivative will -be computed.

    grad_tensors

    (list of (Tensor or NULL)) – The “vector” in the Jacobian-vector product, usually gradients w.r.t. each element of corresponding tensors. NULLvalues can be specified for scalar Tensors or ones that don’t require grad. If aNULL` value would be acceptable for all -grad_tensors, then this argument is optional.

    retain_graph

    (bool, optional) – If FALSE, the graph used to compute -the grad will be freed. Note that in nearly all cases setting this option to -TRUE is not needed and often can be worked around in a much more efficient -way. Defaults to the value of create_graph.

    create_graph

    (bool, optional) – If TRUE, graph of the derivative will -be constructed, allowing to compute higher order derivative products. -Defaults to FALSE.

    - -

    Details

    - -

    This function accumulates gradients in the leaves - you might need to zero -them before calling it.

    - -

    Examples

    - 
    # \dontrun{
-x <- torch_tensor(1, requires_grad = TRUE)
-y <- 2 * x
-
-a <- torch_tensor(1, requires_grad = TRUE)
-b <- 3 * a
-
-autograd_backward(list(y, b))
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_function.html b/docs/reference/autograd_function.html deleted file mode 100644 index f5ca06c73..000000000 --- a/docs/reference/autograd_function.html +++ /dev/null @@ -1,246 +0,0 @@ - - - - - - - - -Records operation history and defines formulas for differentiating ops. — autograd_function • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Records operation history and defines formulas for differentiating ops.

    - Source: R/autograd.R - -
    - -
    -

    Every operation performed on Tensor's creates a new function object, that -performs the computation, and records that it happened. The history is -retained in the form of a DAG of functions, with edges denoting data -dependencies (input <- output). Then, when backward is called, the graph is -processed in the topological ordering, by calling backward() methods of each -Function object, and passing returned gradients on to next Function's.

    -
    - - 
    autograd_function(forward, backward)
    - -

    Arguments

    - - - - - - - - - - -
    forward

    Performs the operation. It must accept a context ctx as the first argument, -followed by any number of arguments (tensors or other types). The context can be -used to store tensors that can be then retrieved during the backward pass. -See AutogradContext for more information about context methods.

    backward

    Defines a formula for differentiating the operation. It must accept -a context ctx as the first argument, followed by as many outputs did forward() -return, and it should return a named list. Each argument is the gradient w.r.t -the given output, and each element in the returned list should be the gradient -w.r.t. the corresponding input. The context can be used to retrieve tensors saved -during the forward pass. It also has an attribute ctx$needs_input_grad as a -named list of booleans representing whether each input needs gradient. -E.g., backward() will have ctx$needs_input_grad$input = TRUE if the input -argument to forward() needs gradient computated w.r.t. the output. -See AutogradContext for more information about context methods.

    - - -

    Examples

    - 
    # \dontrun{
-
-exp2 <- autograd_function(
-  forward = function(ctx, i) {
-    result <- i$exp()
-    ctx$save_for_backward(result = result)
-    result
-  },
-  backward = function(ctx, grad_output) {
-    list(i = grad_output * ctx$saved_variable$result)
-  }
-)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_grad.html b/docs/reference/autograd_grad.html deleted file mode 100644 index da8949015..000000000 --- a/docs/reference/autograd_grad.html +++ /dev/null @@ -1,272 +0,0 @@ - - - - - - - - -Computes and returns the sum of gradients of outputs w.r.t. the inputs. — autograd_grad • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Computes and returns the sum of gradients of outputs w.r.t. the inputs.

    - Source: R/autograd.R - -
    - -
    -

    grad_outputs should be a list of length matching output containing the “vector” -in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of -the outputs. If an output doesn’t require_grad, then the gradient can be None).

    -
    - - 
    autograd_grad(
-  outputs,
-  inputs,
-  grad_outputs = NULL,
-  retain_graph = create_graph,
-  create_graph = FALSE,
-  allow_unused = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - -
    outputs

    (sequence of Tensor) – outputs of the differentiated function.

    inputs

    (sequence of Tensor) – Inputs w.r.t. which the gradient will be -returned (and not accumulated into .grad).

    grad_outputs

    (sequence of Tensor) – The “vector” in the Jacobian-vector -product. Usually gradients w.r.t. each output. None values can be specified for -scalar Tensors or ones that don’t require grad. If a None value would be acceptable -for all grad_tensors, then this argument is optional. Default: None.

    retain_graph

    (bool, optional) – If FALSE, the graph used to compute the -grad will be freed. Note that in nearly all cases setting this option to TRUE is -not needed and often can be worked around in a much more efficient way. -Defaults to the value of create_graph.

    create_graph

    (bool, optional) – If TRUE, graph of the derivative will be constructed, allowing to compute higher order derivative products. Default: FALSE`.

    allow_unused

    (bool, optional) – If FALSE, specifying inputs that were -not used when computing outputs (and therefore their grad is always zero) is an -error. Defaults to FALSE

    - -

    Details

    - -

    If only_inputs is TRUE, the function will only return a list of gradients w.r.t -the specified inputs. If it’s FALSE, then gradient w.r.t. all remaining leaves -will still be computed, and will be accumulated into their .grad attribute.

    - -

    Examples

    - 
    # \dontrun{
-w <- torch_tensor(0.5, requires_grad = TRUE)
-b <- torch_tensor(0.9, requires_grad = TRUE)
-x <- torch_tensor(runif(100))
-y <- 2 * x + 1
-loss <- (y - (w*x + b))^2
-loss <- loss$mean()
-
-o <- autograd_grad(loss, list(w, b))
-o
    #> [[1]]
-#> torch_tensor 
-#> -0.9935
-#> [ CPUFloatType{1} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -1.6206
-#> [ CPUFloatType{1} ]
-#> 
     
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/autograd_set_grad_mode.html b/docs/reference/autograd_set_grad_mode.html deleted file mode 100644 index f91387ad1..000000000 --- a/docs/reference/autograd_set_grad_mode.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Set grad mode — autograd_set_grad_mode • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Set grad mode

    - Source: R/autograd.R - -
    - -
    -

    Sets or disables gradient history.

    -
    - - 
    autograd_set_grad_mode(enabled)
    - -

    Arguments

    - - - - - - -
    enabled

    bool wether to enable or disable the gradient recording.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cuda_current_device.html b/docs/reference/cuda_current_device.html deleted file mode 100644 index 8a6145c05..000000000 --- a/docs/reference/cuda_current_device.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - - -Returns the index of a currently selected device. — cuda_current_device • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
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    Returns the index of a currently selected device.

    - Source: R/cuda.R - -
    - -
    -

    Returns the index of a currently selected device.

    -
    - - 
    cuda_current_device()
    - - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/cuda_device_count.html b/docs/reference/cuda_device_count.html deleted file mode 100644 index 4094cfa2d..000000000 --- a/docs/reference/cuda_device_count.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - - -Returns the number of GPUs available. — cuda_device_count • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Returns the number of GPUs available.

    - Source: R/cuda.R - -
    - -
    -

    Returns the number of GPUs available.

    -
    - - 
    cuda_device_count()
    - - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/cuda_is_available.html b/docs/reference/cuda_is_available.html deleted file mode 100644 index 0a5dec994..000000000 --- a/docs/reference/cuda_is_available.html +++ /dev/null @@ -1,197 +0,0 @@ - - - - - - - - -Returns a bool indicating if CUDA is currently available. — cuda_is_available • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    Returns a bool indicating if CUDA is currently available.

    - Source: R/cuda.R - -
    - -
    -

    Returns a bool indicating if CUDA is currently available.

    -
    - - 
    cuda_is_available()
    - - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/dataloader.html b/docs/reference/dataloader.html deleted file mode 100644 index 2e3cc18e4..000000000 --- a/docs/reference/dataloader.html +++ /dev/null @@ -1,278 +0,0 @@ - - - - - - - - -Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset. — dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset.

    - Source: R/utils-data-dataloader.R - -
    - -
    -

    Data loader. Combines a dataset and a sampler, and provides -single- or multi-process iterators over the dataset.

    -
    - - 
    dataloader(
-  dataset,
-  batch_size = 1,
-  shuffle = FALSE,
-  sampler = NULL,
-  batch_sampler = NULL,
-  num_workers = 0,
-  collate_fn = NULL,
-  pin_memory = FALSE,
-  drop_last = FALSE,
-  timeout = 0,
-  worker_init_fn = NULL
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    dataset

    (Dataset): dataset from which to load the data.

    batch_size

    (int, optional): how many samples per batch to load -(default: 1).

    shuffle

    (bool, optional): set to TRUE to have the data reshuffled -at every epoch (default: FALSE).

    sampler

    (Sampler, optional): defines the strategy to draw samples from -the dataset. If specified, shuffle must be False.

    batch_sampler

    (Sampler, optional): like sampler, but returns a batch of -indices at a time. Mutually exclusive with batch_size, -shuffle, sampler, and drop_last.

    num_workers

    (int, optional): how many subprocesses to use for data -loading. 0 means that the data will be loaded in the main process. -(default: 0)

    collate_fn

    (callable, optional): merges a list of samples to form a mini-batch.

    pin_memory

    (bool, optional): If TRUE, the data loader will copy tensors -into CUDA pinned memory before returning them. If your data elements -are a custom type, or your collate_fn returns a batch that is a custom type -see the example below.

    drop_last

    (bool, optional): set to TRUE to drop the last incomplete batch, -if the dataset size is not divisible by the batch size. If FALSE and -the size of dataset is not divisible by the batch size, then the last batch -will be smaller. (default: FALSE)

    timeout

    (numeric, optional): if positive, the timeout value for collecting a batch -from workers. Should always be non-negative. (default: 0)

    worker_init_fn

    (callable, optional): If not NULL, this will be called on each -worker subprocess with the worker id (an int in [0, num_workers - 1]) as -input, after seeding and before data loading. (default: NULL)

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/dataloader_make_iter.html b/docs/reference/dataloader_make_iter.html deleted file mode 100644 index 8e3cf682a..000000000 --- a/docs/reference/dataloader_make_iter.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Creates an iterator from a DataLoader — dataloader_make_iter • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Creates an iterator from a DataLoader

    - Source: R/utils-data-dataloader.R - -
    - -
    -

    Creates an iterator from a DataLoader

    -
    - - 
    dataloader_make_iter(dataloader)
    - -

    Arguments

    - - - - - - -
    dataloader

    a dataloader object.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/dataloader_next.html b/docs/reference/dataloader_next.html deleted file mode 100644 index 0ab5eef50..000000000 --- a/docs/reference/dataloader_next.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Get the next element of a dataloader iterator — dataloader_next • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    Get the next element of a dataloader iterator

    - Source: R/utils-data-dataloader.R - -
    - -
    -

    Get the next element of a dataloader iterator

    -
    - - 
    dataloader_next(iter)
    - -

    Arguments

    - - - - - - -
    iter

    a DataLoader iter created with dataloader_make_iter.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/dataset.html b/docs/reference/dataset.html deleted file mode 100644 index 35b359ea3..000000000 --- a/docs/reference/dataset.html +++ /dev/null @@ -1,230 +0,0 @@ - - - - - - - - -An abstract class representing a <code>Dataset</code>. — dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    An abstract class representing a Dataset.

    - Source: R/utils-data.R - -
    - -
    -

    All datasets that represent a map from keys to data samples should subclass -it. All subclasses should overwrite get_item, supporting fetching a -data sample for a given key. Subclasses could also optionally overwrite -lenght, which is expected to return the size of the dataset by many -~torch.utils.data.Sampler implementations and the default options -of ~torch.utils.data.DataLoader.

    -
    - - 
    dataset(name = NULL, inherit = Dataset, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    name

    a name for the dataset. It it's also used as the class -for it.

    inherit

    you can optionally inherit from a dataset when creating a -new dataset.

    ...

    public methods for the dataset class

    - -

    Note

    - -

    ~torch.utils.data.DataLoader by default constructs a index -sampler that yields integral indices. To make it work with a map-style -dataset with non-integral indices/keys, a custom sampler must be provided.

    - -
    - -
    - - -
    -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

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    - - - - - - - - diff --git a/docs/reference/default_dtype.html b/docs/reference/default_dtype.html deleted file mode 100644 index 5d27dca58..000000000 --- a/docs/reference/default_dtype.html +++ /dev/null @@ -1,208 +0,0 @@ - - - - - - - - -Gets and sets the default floating point dtype. — torch_set_default_dtype • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Gets and sets the default floating point dtype.

    - Source: R/dtype.R - -
    - -
    -

    Gets and sets the default floating point dtype.

    -
    - - 
    torch_set_default_dtype(d)
-
-torch_get_default_dtype()
    - -

    Arguments

    - - - - - - -
    d

    The default floating point dtype to set. Initially set to -torch_float().

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
    - -
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    - - - - - - - - diff --git a/docs/reference/enumerate.dataloader.html b/docs/reference/enumerate.dataloader.html deleted file mode 100644 index 9499c94fa..000000000 --- a/docs/reference/enumerate.dataloader.html +++ /dev/null @@ -1,214 +0,0 @@ - - - - - - - - -Enumerate an iterator — enumerate.dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Enumerate an iterator

    - Source: R/utils-data-enum.R - -
    - -
    -

    Enumerate an iterator

    -
    - - 
    # S3 method for dataloader
-enumerate(x, max_len = 1e+06, ...)
    - -

    Arguments

    - - - - - - - - - - - - - - -
    x

    the generator to enumerate.

    max_len

    maximum number of iterations.

    ...

    passed to specific methods.

    - - -
    - -
    - - -
    -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/enumerate.html b/docs/reference/enumerate.html deleted file mode 100644 index 86deb56d2..000000000 --- a/docs/reference/enumerate.html +++ /dev/null @@ -1,209 +0,0 @@ - - - - - - - - -Enumerate an iterator — enumerate • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Enumerate an iterator

    - Source: R/utils-data-enum.R - -
    - -
    -

    Enumerate an iterator

    -
    - - 
    enumerate(x, ...)
    - -

    Arguments

    - - - - - - - - - - -
    x

    the generator to enumerate.

    ...

    passed to specific methods.

    - - -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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z`QdfdSk;%$RsL>x`qbf9BPiZ-TWUd)0q(7!eh!09;+6MnL1onYiCdOEeX3)A-t*Ut zCH^JshyOx)unf;c!KDC$4L_uC(R&%cZSS z{++i(KD%01edX_vvoC*pK|@TK6%tVoSZA;VPWSkw>bW~A?%c_g>8sMKG!wzj*<%p$ z1C*8yben);scQa`*DAVO_RJB!n&`RR<5xks!JXTX4$=b`$RY^_{fjH^S2r)2t@?NI zw(CVrKfOK8%h(UUhbCQ%iIDN01A#_J(KNG|-*eYOYl*sT&@emMAPL%H%TO@IhiT1c z)w>!mUrw8D-2ZJ=mDzJaaOysA(HRnAGR%wX@;8Gvx2t~K9j^J(-n%Nk+~CetsIyWw z->HAe!oa}Lkeqep{off&qE%vVev69wlnQP&uZPw%7LgY(34=neZ=vUf(y2@86<@aN z>OZgewQ$M&$t9wY(2$9ShDM>$tMAvmewBK@p9YF08EuSMn#K$%xGc_ra+GRqOz747 zC!hSE2#TX0&Cr~38nO$JVN1%D_pvLp_TBH_x_8?27q3ArzMasJC^`a3uo)*+6n48@ z()Ye~_ft_^Pwb>R&r7P1v~xjD3!LE+_e|f(+VsH9w5^wDP~?J852Cp{74*vFuNXyA0;90MGX{Sen23Zsz@K z!;<(*AAj4foARaJ>(|0 zE9C!u&XRubU;K9u7X0^)l?Ivj;0I&~twH$4iu=_|m(=UOd>;2i^IyE@|8>?9{~o+p z3tC6WP+$WMGtY!8?_)JVweANS{-3*6S=Pqem8}Ljh(U(`48)_852) - - - - - - - -Function reference • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/docs/reference/install_torch.html b/docs/reference/install_torch.html deleted file mode 100644 index 22442f16f..000000000 --- a/docs/reference/install_torch.html +++ /dev/null @@ -1,234 +0,0 @@ - - - - - - - - -Install Torch — install_torch • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Install Torch

    - Source: R/install.R - -
    - -
    -

    Installs Torch and its dependencies.

    -
    - - 
    install_torch(
-  version = "1.5.0",
-  type = install_type(version = version),
-  reinstall = FALSE,
-  path = install_path(),
-  ...
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    version

    The Torch version to install.

    type

    The installation type for Torch. Valid values are "cpu" or the 'CUDA' version.

    reinstall

    Re-install Torch even if its already installed?

    path

    Optional path to install or check for an already existing installation.

    ...

    other optional arguments (like load for manual installation.)

    - -

    Details

    - -

    When using path to install in a specific location, make sure the TORCH_HOME environment -variable is set to this same path to reuse this installation. The TORCH_INSTALL environment -variable can be set to 0 to prevent auto-installing torch and TORCH_LOAD set to 0 -to avoid loading dependencies automatically. These environment variables are meant for advanced use -cases and troubleshootinng only.

    - -
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    - - -
    -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/is_dataloader.html b/docs/reference/is_dataloader.html deleted file mode 100644 index 4963f41a6..000000000 --- a/docs/reference/is_dataloader.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Checks if the object is a dataloader — is_dataloader • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Checks if the object is a dataloader

    - Source: R/utils-data-dataloader.R - -
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    Checks if the object is a dataloader

    -
    - - 
    is_dataloader(x)
    - -

    Arguments

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    x

    object to check

    - - -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/is_torch_dtype.html b/docs/reference/is_torch_dtype.html deleted file mode 100644 index a25b3fb7a..000000000 --- a/docs/reference/is_torch_dtype.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if object is a torch data type — is_torch_dtype • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Check if object is a torch data type

    - Source: R/dtype.R - -
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    Check if object is a torch data type

    -
    - - 
    is_torch_dtype(x)
    - -

    Arguments

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    x

    object to check.

    - - -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/is_torch_layout.html b/docs/reference/is_torch_layout.html deleted file mode 100644 index f3d02486c..000000000 --- a/docs/reference/is_torch_layout.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if an object is a torch layout. — is_torch_layout • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Check if an object is a torch layout.

    - Source: R/layout.R - -
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    Check if an object is a torch layout.

    -
    - - 
    is_torch_layout(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
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    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    -
    - - - - - - - - diff --git a/docs/reference/is_torch_memory_format.html b/docs/reference/is_torch_memory_format.html deleted file mode 100644 index 9352ef7d6..000000000 --- a/docs/reference/is_torch_memory_format.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Check if an object is a memory format — is_torch_memory_format • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Check if an object is a memory format

    - Source: R/memory_format.R - -
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    Check if an object is a memory format

    -
    - - 
    is_torch_memory_format(x)
    - -

    Arguments

    - - - - - - -
    x

    object to check

    - - -
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    - - -
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    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/is_torch_qscheme.html b/docs/reference/is_torch_qscheme.html deleted file mode 100644 index 4ab4b9943..000000000 --- a/docs/reference/is_torch_qscheme.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - -Checks if an object is a QScheme — is_torch_qscheme • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Checks if an object is a QScheme

    - Source: R/qscheme.R - -
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    Checks if an object is a QScheme

    -
    - - 
    is_torch_qscheme(x)
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    Arguments

    - - - - - - -
    x

    object to check

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/kmnist_dataset.html b/docs/reference/kmnist_dataset.html deleted file mode 100644 index e6723725c..000000000 --- a/docs/reference/kmnist_dataset.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -Kuzushiji-MNIST — kmnist_dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - - - - -
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    Kuzushiji-MNIST

    - Source: R/dataset-mnist.R - -
    - -
    -

    The Kuzushiji-MNIST dataset.

    -
    - - 
    kmnist_dataset(
-  root,
-  train = TRUE,
-  transform = NULL,
-  target_transform = NULL,
-  download = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    root

    (string): Root directory of dataset where KMNIST/processed/training.pt -and KMNIST/processed/test.pt exist.

    train

    (bool, optional): If TRUE, creates dataset from training.pt, -otherwise from test.pt.

    transform

    (callable, optional): A function/transform that takes in an PIL image -and returns a transformed version. E.g, transforms.RandomCrop

    target_transform

    (callable, optional): A function/transform that takes in the -target and transforms it.

    download

    (bool, optional): If true, downloads the dataset from the internet and -puts it in root directory. If dataset is already downloaded, it is not -downloaded again.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
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    -
    - - - - - - - - diff --git a/docs/reference/mnist_dataset.html b/docs/reference/mnist_dataset.html deleted file mode 100644 index d9eb3855e..000000000 --- a/docs/reference/mnist_dataset.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -MNIST dataset — mnist_dataset • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
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    MNIST dataset

    - Source: R/dataset-mnist.R - -
    - -
    -

    Prepares the MNIST dataset and optionally downloads it.

    -
    - - 
    mnist_dataset(
-  root,
-  train = TRUE,
-  transform = NULL,
-  target_transform = NULL,
-  download = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    root

    (string): Root directory of dataset where MNIST/processed/training.pt -and MNIST/processed/test.pt exist.

    train

    (bool, optional): If True, creates dataset from training.pt, -otherwise from test.pt.

    transform

    (callable, optional): A function/transform that takes in an PIL image -and returns a transformed version. E.g, transforms.RandomCrop

    target_transform

    (callable, optional): A function/transform that takes in the -target and transforms it.

    download

    (bool, optional): If true, downloads the dataset from the internet and -puts it in root directory. If dataset is already downloaded, it is not -downloaded again.

    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
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    Site built with pkgdown 1.5.1.

    -
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    - - - - - - - - diff --git a/docs/reference/nn_adaptive_log_softmax_with_loss.html b/docs/reference/nn_adaptive_log_softmax_with_loss.html deleted file mode 100644 index 8bb8c2fa7..000000000 --- a/docs/reference/nn_adaptive_log_softmax_with_loss.html +++ /dev/null @@ -1,303 +0,0 @@ - - - - - - - - -AdaptiveLogSoftmaxWithLoss module — nn_adaptive_log_softmax_with_loss • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    AdaptiveLogSoftmaxWithLoss module

    - Source: R/nn_adaptive.R - -
    - -
    -

    Efficient softmax approximation as described in -Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou

    -
    - - 
    nn_adaptive_log_softmax_with_loss(
-  in_features,
-  n_classes,
-  cutoffs,
-  div_value = 4,
-  head_bias = FALSE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    in_features

    (int): Number of features in the input tensor

    n_classes

    (int): Number of classes in the dataset

    cutoffs

    (Sequence): Cutoffs used to assign targets to their buckets

    div_value

    (float, optional): value used as an exponent to compute sizes -of the clusters. Default: 4.0

    head_bias

    (bool, optional): If True, adds a bias term to the 'head' of the -adaptive softmax. Default: False

    - -

    Value

    - -

    NamedTuple with output and loss fields:

      -

    • output is a Tensor of size N containing computed target -log probabilities for each example

      • -

    • loss is a Scalar representing the computed negative -log likelihood loss

      • -
    - -

    Details

    - -

    Adaptive softmax is an approximate strategy for training models with large -output spaces. It is most effective when the label distribution is highly -imbalanced, for example in natural language modelling, where the word -frequency distribution approximately follows the Zipf's law.

    -

    Adaptive softmax partitions the labels into several clusters, according to -their frequency. These clusters may contain different number of targets -each.

    -

    Additionally, clusters containing less frequent labels assign lower -dimensional embeddings to those labels, which speeds up the computation. -For each minibatch, only clusters for which at least one target is -present are evaluated.

    -

    The idea is that the clusters which are accessed frequently -(like the first one, containing most frequent labels), should also be cheap -to compute -- that is, contain a small number of assigned labels. -We highly recommend taking a look at the original paper for more details.

      -

    • cutoffs should be an ordered Sequence of integers sorted -in the increasing order. -It controls number of clusters and the partitioning of targets into -clusters. For example setting cutoffs = c(10, 100, 1000) -means that first 10 targets will be assigned -to the 'head' of the adaptive softmax, targets 11, 12, ..., 100 will be -assigned to the first cluster, and targets 101, 102, ..., 1000 will be -assigned to the second cluster, while targets -1001, 1002, ..., n_classes - 1 will be assigned -to the last, third cluster.

      • -

    • div_value is used to compute the size of each additional cluster, -which is given as -\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\), -where \(idx\) is the cluster index (with clusters -for less frequent words having larger indices, -and indices starting from \(1\)).

      • -

    • head_bias if set to True, adds a bias term to the 'head' of the -adaptive softmax. See paper for details. Set to False in the official -implementation.

      • -
    - -

    Note

    - -

    This module returns a NamedTuple with output -and loss fields. See further documentation for details.

    -

    To compute log-probabilities for all classes, the log_prob -method can be used.

    -

    Warning

    - - - -

    Labels passed as inputs to this module should be sorted according to -their frequency. This means that the most frequent label should be -represented by the index 0, and the least frequent -label should be represented by the index n_classes - 1.

    -

    Shape

    - - - -
      -

    • input: \((N, \mbox{in\_features})\)

      • -

    • target: \((N)\) where each value satisfies \(0 <= \mbox{target[i]} <= \mbox{n\_classes}\)

      • -

    • output1: \((N)\)

      • -

    • output2: Scalar

      • -
    - - -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_batch_norm1d.html b/docs/reference/nn_batch_norm1d.html deleted file mode 100644 index ca25d62f3..000000000 --- a/docs/reference/nn_batch_norm1d.html +++ /dev/null @@ -1,287 +0,0 @@ - - - - - - - - -BatchNorm1D module — nn_batch_norm1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
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    BatchNorm1D module

    - Source: R/nn-batchnorm.R - -
    - -
    -

    Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D -inputs with optional additional channel dimension) as described in the paper -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

    -
    - - 
    nn_batch_norm1d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    num_features

    \(C\) from an expected input of size -\((N, C, L)\) or \(L\) from input of size \((N, L)\)

    eps

    a value added to the denominator for numerical stability. -Default: 1e-5

    momentum

    the value used for the running_mean and running_var -computation. Can be set to NULL for cumulative moving average -(i.e. simple average). Default: 0.1

    affine

    a boolean value that when set to TRUE, this module has -learnable affine parameters. Default: TRUE

    track_running_stats

    a boolean value that when set to TRUE, this -module tracks the running mean and variance, and when set to FALSE, -this module does not track such statistics and always uses batch -statistics in both training and eval modes. Default: TRUE

    - -

    Details

    - -

    $$ -y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta -$$

    -

    The mean and standard-deviation are calculated per-dimension over -the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors -of size C (where C is the input size). By default, the elements of \(\gamma\) -are set to 1 and the elements of \(\beta\) are set to 0.

    -

    Also by default, during training this layer keeps running estimates of its -computed mean and variance, which are then used for normalization during -evaluation. The running estimates are kept with a default :attr:momentum -of 0.1. -If track_running_stats is set to FALSE, this layer then does not -keep running estimates, and batch statistics are instead used during -evaluation time as well.

    -

    Note

    - - - - -

    This momentum argument is different from one used in optimizer -classes and the conventional notion of momentum. Mathematically, the -update rule for running statistics here is -\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\), -where \(\hat{x}\) is the estimated statistic and \(x_t\) is the -new observed value.

    -

    Because the Batch Normalization is done over the C dimension, computing statistics -on (N, L) slices, it's common terminology to call this Temporal Batch Normalization.

    -

    Shape

    - - - -
      -

    • Input: \((N, C)\) or \((N, C, L)\)

      • -

    • Output: \((N, C)\) or \((N, C, L)\) (same shape as input)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With Learnable Parameters
-m <- nn_batch_norm1d(100)
-# Without Learnable Parameters
-m <- nn_batch_norm1d(100, affine = FALSE)
-input <- torch_randn(20, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_batch_norm2d.html b/docs/reference/nn_batch_norm2d.html deleted file mode 100644 index 34a8884a5..000000000 --- a/docs/reference/nn_batch_norm2d.html +++ /dev/null @@ -1,286 +0,0 @@ - - - - - - - - -BatchNorm2D — nn_batch_norm2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
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    BatchNorm2D

    - Source: R/nn-batchnorm.R - -
    - -
    -

    Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs -additional channel dimension) as described in the paper -Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

    -
    - - 
    nn_batch_norm2d(
-  num_features,
-  eps = 1e-05,
-  momentum = 0.1,
-  affine = TRUE,
-  track_running_stats = TRUE
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - -
    num_features

    \(C\) from an expected input of size -\((N, C, H, W)\)

    eps

    a value added to the denominator for numerical stability. -Default: 1e-5

    momentum

    the value used for the running_mean and running_var -computation. Can be set to None for cumulative moving average -(i.e. simple average). Default: 0.1

    affine

    a boolean value that when set to TRUE, this module has -learnable affine parameters. Default: TRUE

    track_running_stats

    a boolean value that when set to TRUE, this -module tracks the running mean and variance, and when set to FALSE, -this module does not track such statistics and uses batch statistics instead -in both training and eval modes if the running mean and variance are None. -Default: TRUE

    - -

    Details

    - -

    $$ - y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta -$$

    -

    The mean and standard-deviation are calculated per-dimension over -the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors -of size C (where C is the input size). By default, the elements of \(\gamma\) are set -to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated -via the biased estimator, equivalent to torch_var(input, unbiased=FALSE). -Also by default, during training this layer keeps running estimates of its -computed mean and variance, which are then used for normalization during -evaluation. The running estimates are kept with a default momentum -of 0.1.

    -

    If track_running_stats is set to FALSE, this layer then does not -keep running estimates, and batch statistics are instead used during -evaluation time as well.

    -

    Note

    - -

    This momentum argument is different from one used in optimizer -classes and the conventional notion of momentum. Mathematically, the -update rule for running statistics here is -\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\), -where \(\hat{x}\) is the estimated statistic and \(x_t\) is the -new observed value. -Because the Batch Normalization is done over the C dimension, computing statistics -on (N, H, W) slices, it's common terminology to call this Spatial Batch Normalization.

    -

    Shape

    - - - -
      -

    • Input: \((N, C, H, W)\)

      • -

    • Output: \((N, C, H, W)\) (same shape as input)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With Learnable Parameters
-m <- nn_batch_norm2d(100)
-# Without Learnable Parameters
-m <- nn_batch_norm2d(100, affine=FALSE)
-input <- torch_randn(20, 100, 35, 45)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_bce_loss.html b/docs/reference/nn_bce_loss.html deleted file mode 100644 index 9f6987f69..000000000 --- a/docs/reference/nn_bce_loss.html +++ /dev/null @@ -1,271 +0,0 @@ - - - - - - - - -Binary cross entropy loss — nn_bce_loss • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
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    Binary cross entropy loss

    - Source: R/nn-loss.R - -
    - -
    -

    Creates a criterion that measures the Binary Cross Entropy -between the target and the output:

    -
    - - 
    nn_bce_loss(weight = NULL, reduction = "mean")
    - -

    Arguments

    - - - - - - - - - - -
    weight

    (Tensor, optional): a manual rescaling weight given to the loss -of each batch element. If given, has to be a Tensor of size nbatch.

    reduction

    (string, optional): Specifies the reduction to apply to the output: -'none' | 'mean' | 'sum'. 'none': no reduction will be applied, -'mean': the sum of the output will be divided by the number of -elements in the output, 'sum': the output will be summed. Note: size_average -and reduce are in the process of being deprecated, and in the meantime, -specifying either of those two args will override reduction. Default: 'mean'

    - -

    Details

    - -

    The unreduced (i.e. with reduction set to 'none') loss can be described as: -$$ - \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad -l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right] -$$ -where \(N\) is the batch size. If reduction is not 'none' -(default 'mean'), then

    -

    $$ - \ell(x, y) = \left\{ \begin{array}{ll} -\mbox{mean}(L), & \mbox{if reduction} = \mbox{'mean';}\\ -\mbox{sum}(L), & \mbox{if reduction} = \mbox{'sum'.} -\end{array} -\right. -$$

    -

    This is used for measuring the error of a reconstruction in for example -an auto-encoder. Note that the targets \(y\) should be numbers -between 0 and 1.

    -

    Notice that if \(x_n\) is either 0 or 1, one of the log terms would be -mathematically undefined in the above loss equation. PyTorch chooses to set -\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).

    -

    However, an infinite term in the loss equation is not desirable for several reasons. -For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be -multiplying 0 with infinity. Secondly, if we have an infinite loss value, then -we would also have an infinite term in our gradient, since -\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).

    -

    This would make BCELoss's backward method nonlinear with respect to \(x_n\), -and using it for things like linear regression would not be straight-forward. -Our solution is that BCELoss clamps its log function outputs to be greater than -or equal to -100. This way, we can always have a finite loss value and a linear -backward method.

    -

    Shape

    - - - -
      -

    • Input: \((N, *)\) where \(*\) means, any number of additional -dimensions

      • -

    • Target: \((N, *)\), same shape as the input

      • -

    • Output: scalar. If reduction is 'none', then \((N, *)\), same -shape as input.

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_sigmoid()
-loss <- nn_bce_loss()
-input <- torch_randn(3, requires_grad=TRUE)
-target <- torch_rand(3)
-output <- loss(m(input), target)
-output$backward()
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_bilinear.html b/docs/reference/nn_bilinear.html deleted file mode 100644 index e2cd608a0..000000000 --- a/docs/reference/nn_bilinear.html +++ /dev/null @@ -1,257 +0,0 @@ - - - - - - - - -Bilinear module — nn_bilinear • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Bilinear module

    - Source: R/nn-linear.R - -
    - -
    -

    Applies a bilinear transformation to the incoming data -\(y = x_1^T A x_2 + b\)

    -
    - - 
    nn_bilinear(in1_features, in2_features, out_features, bias = TRUE)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - -
    in1_features

    size of each first input sample

    in2_features

    size of each second input sample

    out_features

    size of each output sample

    bias

    If set to FALSE, the layer will not learn an additive bias. -Default: TRUE

    - -

    Shape

    - - - -
      -

    • Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and -\(*\) means any number of additional dimensions. All but the last -dimension of the inputs should be the same.

      • -

    • Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).

      • -

    • Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\) -and all but the last dimension are the same shape as the input.

      • -
    - -

    Attributes

    - - - -
      -

    • weight: the learnable weights of the module of shape -\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\). -The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where -\(k = \frac{1}{\mbox{in1\_features}}\)

      • -

    • bias: the learnable bias of the module of shape \((\mbox{out\_features})\). -If bias is TRUE, the values are initialized from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where -\(k = \frac{1}{\mbox{in1\_features}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_bilinear(20, 30, 50)
-input1 <- torch_randn(128, 20)
-input2 <- torch_randn(128, 30)
-output = m(input1, input2)
-print(output$size())
    #> [1] 128  50
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_celu.html b/docs/reference/nn_celu.html deleted file mode 100644 index 28f07fdeb..000000000 --- a/docs/reference/nn_celu.html +++ /dev/null @@ -1,233 +0,0 @@ - - - - - - - - -CELU module — nn_celu • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    CELU module

    - Source: R/nn-activation.R - -
    - -
    -

    Applies the element-wise function:

    -
    - - 
    nn_celu(alpha = 1, inplace = FALSE)
    - -

    Arguments

    - - - - - - - - - - -
    alpha

    the \(\alpha\) value for the CELU formulation. Default: 1.0

    inplace

    can optionally do the operation in-place. Default: FALSE

    - -

    Details

    - -

    $$ - \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1)) -$$

    -

    More details can be found in the paper -Continuously Differentiable Exponential Linear Units.

    -

    Shape

    - - - -
      -

    • Input: \((N, *)\) where * means, any number of additional -dimensions

      • -

    • Output: \((N, *)\), same shape as the input

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_celu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv1d.html b/docs/reference/nn_conv1d.html deleted file mode 100644 index 5c37fbd8a..000000000 --- a/docs/reference/nn_conv1d.html +++ /dev/null @@ -1,344 +0,0 @@ - - - - - - - - -Conv1D module — nn_conv1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv1D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 1D convolution over an input signal composed of several input -planes. -In the simplest case, the output value of the layer with input size -\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be -precisely described as:

    -
    - - 
    nn_conv1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to both sides of -the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel -elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input -channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the -output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    $$ -\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) + - \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) -\star \mbox{input}(N_i, k) -$$

    -

    where \(\star\) is the valid -cross-correlation operator, -\(N\) is a batch size, \(C\) denotes a number of channels, -\(L\) is a length of signal sequence.

      -

    • stride controls the stride for the cross-correlation, a single -number or a one-element tuple.

      • -

    • padding controls the amount of implicit zero-paddings on both sides -for padding number of points.

      • -

    • dilation controls the spacing between the kernel points; also -known as the à trous algorithm. It is harder to describe, but this -link -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters, -of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

        • -
      • -
    - -

    Note

    - - - - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid -cross-correlation, and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size \((N, C_{in}, L_{in})\), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, L_{in})\)

      • -

    • Output: \((N, C_{out}, L_{out})\) where

      • -
    - -

    $$ - L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation} - \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor -$$

    -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape -(out_channels). If bias is TRUE, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_conv1d(16, 33, 3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv2d.html b/docs/reference/nn_conv2d.html deleted file mode 100644 index 3c2a0ce4b..000000000 --- a/docs/reference/nn_conv2d.html +++ /dev/null @@ -1,361 +0,0 @@ - - - - - - - - -Conv2D module — nn_conv2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv2D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 2D convolution over an input signal composed of several input -planes.

    -
    - - 
    nn_conv2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to both sides of -the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input -channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the -output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    In the simplest case, the output value of the layer with input size -\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\) -can be precisely described as:

    -

    $$ -\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) + - \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k) -$$

    -

    where \(\star\) is the valid 2D cross-correlation operator, -\(N\) is a batch size, \(C\) denotes a number of channels, -\(H\) is a height of input planes in pixels, and \(W\) is -width in pixels.

      -

    • stride controls the stride for the cross-correlation, a single -number or a tuple.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for padding number of points for each dimension.

      • -

    • dilation controls the spacing between the kernel points; also -known as the à trous algorithm. It is harder to describe, but this link_ -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters, of size: -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, dilation can either be:

      -

    • a single int -- in which case the same value is used for the height and -width dimension

      • -

    • a tuple of two ints -- in which case, the first int is used for the height dimension, -and the second int for the width dimension

      • -
    - -

    Note

    - - - - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size :math:(N, C_{in}, H_{in}, W_{in}), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting backends_cudnn_deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, H_{out}, W_{out})\) where -$$ - H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] - \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor -$$ -$$ - W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] - \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\), -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape -(out_channels). If bias is TRUE, -then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-
-# With square kernels and equal stride
-m <- nn_conv2d(16, 33, 3, stride = 2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
-# non-square kernels and unequal stride and with padding and dilation
-m <- nn_conv2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2), dilation=c(3, 1))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv3d.html b/docs/reference/nn_conv3d.html deleted file mode 100644 index b31fe1dc9..000000000 --- a/docs/reference/nn_conv3d.html +++ /dev/null @@ -1,349 +0,0 @@ - - - - - - - - -Conv3D module — nn_conv3d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    Conv3D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 3D convolution over an input signal composed of several input -planes. -In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\) -and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:

    -
    - - 
    nn_conv3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1,
-  bias = TRUE,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If TRUE, adds a learnable bias to the output. Default: TRUE

    padding_mode

    (string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    $$ - out(N_i, C_{out_j}) = bias(C_{out_j}) + - \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k) -$$

    -

    where \(\star\) is the valid 3D cross-correlation operator

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for padding number of points for each dimension.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

      • -

    • At groups=1, all inputs are convolved to all outputs.

      • -

    • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

      • -

    • At groups= in_channels, each input channel is convolved with -its own set of filters, of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).

      • -
    - -

    The parameters kernel_size, stride, padding, dilation can either be:

      -

    • a single int -- in which case the same value is used for the depth, height and width dimension

      • -

    • a tuple of three ints -- in which case, the first int is used for the depth dimension, -the second int for the height dimension and the third int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    When groups == in_channels and out_channels == K * in_channels, -where K is a positive integer, this operation is also termed in -literature as depthwise convolution. -In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\), -a depthwise convolution with a depthwise multiplier K, can be constructed by arguments -\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE. -Please see the notes on :doc:/notes/randomness for background.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where -$$ - D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] - \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor - $$ -$$ - H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] - \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor - $$ -$$ - W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] - \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor - $$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels). If bias is True, -then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With square kernels and equal stride
-m <- nn_conv3d(16, 33, 3, stride=2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv3d(16, 33, c(3, 5, 2), stride=c(2, 1, 1), padding=c(4, 2, 0))
-input <- torch_randn(20, 16, 10, 50, 100)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose1d.html b/docs/reference/nn_conv_transpose1d.html deleted file mode 100644 index f499ebd0b..000000000 --- a/docs/reference/nn_conv_transpose1d.html +++ /dev/null @@ -1,342 +0,0 @@ - - - - - - - - -ConvTranspose1D — nn_conv_transpose1d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranspose1D

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 1D transposed convolution operator over an input image -composed of several input planes.

    -
    - - 
    nn_conv_transpose1d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of the input. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: TRUE

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    This module can be seen as the gradient of Conv1d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the -à trous algorithm. It is harder to describe, but this link -has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a ~torch.nn.Conv1d and a ~torch.nn.ConvTranspose1d -are initialized with same parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -~torch.nn.Conv1d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, L_{in})\)

      • -

    • Output: \((N, C_{out}, L_{out})\) where -$$ - L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation} -\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels). -If bias is TRUE, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-m <- nn_conv_transpose1d(32, 16, 2)
-input <- torch_randn(10, 32, 2)
-output <- m(input)
-
-# }
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose2d.html b/docs/reference/nn_conv_transpose2d.html deleted file mode 100644 index fec88b2fb..000000000 --- a/docs/reference/nn_conv_transpose2d.html +++ /dev/null @@ -1,361 +0,0 @@ - - - - - - - - -ConvTranpose2D module — nn_conv_transpose2d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranpose2D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 2D transposed convolution operator over an input image -composed of several input planes.

    -
    - - 
    nn_conv_transpose2d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of each dimension in the input. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: True

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', -'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    This module can be seen as the gradient of Conv2d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, output_padding -can either be:

      -

    • a single int -- in which case the same value is used for the height and width dimensions

      • -

    • a tuple of two ints -- in which case, the first int is used for the height dimension, -and the second int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation_, -and not a full cross-correlation. It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a nn_conv2d and a nn_conv_transpose2d are initialized with same -parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -nn_conv2d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, H_{out}, W_{out})\) where -$$ - H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0] -\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1 -$$ -$$ - W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1] -\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels) -If bias is True, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    # \dontrun{
-# With square kernels and equal stride
-m <- nn_conv_transpose2d(16, 33, 3, stride=2)
-# non-square kernels and unequal stride and with padding
-m <- nn_conv_transpose2d(16, 33, c(3, 5), stride=c(2, 1), padding=c(4, 2))
-input <- torch_randn(20, 16, 50, 100)
-output <- m(input)
-# exact output size can be also specified as an argument
-input <- torch_randn(1, 16, 12, 12)
-downsample <- nn_conv2d(16, 16, 3, stride=2, padding=1)
-upsample <- nn_conv_transpose2d(16, 16, 3, stride=2, padding=1)
-h <- downsample(input)
-h$size()
    #> [1]  1 16  6  6
    output <- upsample(h, output_size=input$size())
-output$size()
    #> [1]  1 16 12 12
    
-# }
-
    -
    - -
    - - -
    -
    -

    Developed by Daniel Falbel, Javier Luraschi.

    -
    - -
    -

    Site built with pkgdown 1.5.1.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/reference/nn_conv_transpose3d.html b/docs/reference/nn_conv_transpose3d.html deleted file mode 100644 index e929e4949..000000000 --- a/docs/reference/nn_conv_transpose3d.html +++ /dev/null @@ -1,354 +0,0 @@ - - - - - - - - -ConvTranpose3D module — nn_conv_transpose3d • torch - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    -
    - - - - -
    - -
    -
    -
    -

    ConvTranpose3D module

    - Source: R/nn-conv.R - -
    - -
    -

    Applies a 3D transposed convolution operator over an input image composed of several input -planes.

    -
    - - 
    nn_conv_transpose3d(
-  in_channels,
-  out_channels,
-  kernel_size,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  bias = TRUE,
-  dilation = 1,
-  padding_mode = "zeros"
-)
    - -

    Arguments

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    in_channels

    (int): Number of channels in the input image

    out_channels

    (int): Number of channels produced by the convolution

    kernel_size

    (int or tuple): Size of the convolving kernel

    stride

    (int or tuple, optional): Stride of the convolution. Default: 1

    padding

    (int or tuple, optional): dilation * (kernel_size - 1) - padding zero-padding -will be added to both sides of each dimension in the input. Default: 0 -output_padding (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    output_padding

    (int or tuple, optional): Additional size added to one side -of each dimension in the output shape. Default: 0

    groups

    (int, optional): Number of blocked connections from input channels to output channels. Default: 1

    bias

    (bool, optional): If True, adds a learnable bias to the output. Default: True

    dilation

    (int or tuple, optional): Spacing between kernel elements. Default: 1

    padding_mode

    (string, optional): 'zeros', 'reflect', 'replicate' or 'circular'. Default: 'zeros'

    - -

    Details

    - -

    The transposed convolution operator multiplies each input value element-wise by a learnable kernel, -and sums over the outputs from all input feature planes.

    -

    This module can be seen as the gradient of Conv3d with respect to its input. -It is also known as a fractionally-strided convolution or -a deconvolution (although it is not an actual deconvolution operation).

      -

    • stride controls the stride for the cross-correlation.

      • -

    • padding controls the amount of implicit zero-paddings on both -sides for dilation * (kernel_size - 1) - padding number of points. See note -below for details.

      • -

    • output_padding controls the additional size added to one side -of the output shape. See note below for details.

      • -

    • dilation controls the spacing between the kernel points; also known as the à trous algorithm. -It is harder to describe, but this link_ has a nice visualization of what dilation does.

      • -

    • groups controls the connections between inputs and outputs. -in_channels and out_channels must both be divisible by -groups. For example,

        -

      • At groups=1, all inputs are convolved to all outputs.

        • -

      • At groups=2, the operation becomes equivalent to having two conv -layers side by side, each seeing half the input channels, -and producing half the output channels, and both subsequently -concatenated.

        • -

      • At groups= in_channels, each input channel is convolved with -its own set of filters (of size -\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).

        • -
      • -
    - -

    The parameters kernel_size, stride, padding, output_padding -can either be:

      -

    • a single int -- in which case the same value is used for the depth, height and width dimensions

      • -

    • a tuple of three ints -- in which case, the first int is used for the depth dimension, -the second int for the height dimension and the third int for the width dimension

      • -
    - -

    Note

    - -

    Depending of the size of your kernel, several (of the last) -columns of the input might be lost, because it is a valid cross-correlation, -and not a full cross-correlation. -It is up to the user to add proper padding.

    -

    The padding argument effectively adds dilation * (kernel_size - 1) - padding -amount of zero padding to both sizes of the input. This is set so that -when a ~torch.nn.Conv3d and a ~torch.nn.ConvTranspose3d -are initialized with same parameters, they are inverses of each other in -regard to the input and output shapes. However, when stride > 1, -~torch.nn.Conv3d maps multiple input shapes to the same output -shape. output_padding is provided to resolve this ambiguity by -effectively increasing the calculated output shape on one side. Note -that output_padding is only used to find output shape, but does -not actually add zero-padding to output.

    -

    In some circumstances when using the CUDA backend with CuDNN, this operator -may select a nondeterministic algorithm to increase performance. If this is -undesirable, you can try to make the operation deterministic (potentially at -a performance cost) by setting torch.backends.cudnn.deterministic = TRUE.

    -

    Shape

    - - - -
      -

    • Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)

      • -

    • Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where -$$ - D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0] -\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1 -$$ -$$ - H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1] -\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1 -$$ -$$ - W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2] -\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1 -$$

      • -
    - -

    Attributes

    - - - -
      -

    • weight (Tensor): the learnable weights of the module of shape -\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\) -\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\). -The values of these weights are sampled from -\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -

    • bias (Tensor): the learnable bias of the module of shape (out_channels) -If bias is True, then the values of these weights are -sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where -\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)

      • -
    - - -

    Examples

    - 
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
-
-  
-
-
-  
-
-
-
diff --git a/docs/reference/nn_cross_entropy_loss.html b/docs/reference/nn_cross_entropy_loss.html
deleted file mode 100644
index b986b77ca..000000000
--- a/docs/reference/nn_cross_entropy_loss.html
+++ /dev/null
@@ -1,277 +0,0 @@
-
-
-
-  
-  
-
-
-
-CrossEntropyLoss module — nn_cross_entropy_loss • torch
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
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-
-
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-  
-
-  
-    
    
-      
    
-      
-
-      
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-      
    
-
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-  
    
-    
    
-    
    CrossEntropyLoss module
    
-    Source: R/nn-loss.R
-    
-    
    
-
-    
    
-    
    This criterion combines nn_log_softmax() and nn_nll_loss() in one single class.
-It is useful when training a classification problem with C classes.
    
-    
    
-
-    
    nn_cross_entropy_loss(weight = NULL, ignore_index = -100, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    weight
    (Tensor, optional): a manual rescaling weight given to each class.
-If given, has to be a Tensor of size C
    ignore_index
    (int, optional): Specifies a target value that is ignored
-and does not contribute to the input gradient. When size_average is
-TRUE, the loss is averaged over non-ignored targets.
    reduction
    (string, optional): Specifies the reduction to apply to the output:
-'none' | 'mean' | 'sum'. 'none': no reduction will be applied,
-'mean': the sum of the output will be divided by the number of
-elements in the output, 'sum': the output will be summed. Note: size_average
-and reduce are in the process of being deprecated, and in the meantime,
-specifying either of those two args will override reduction. Default: 'mean'
    
-
-    
    Details
    
-
-    
    If provided, the optional argument weight should be a 1D Tensor
-assigning weight to each of the classes.
    
-
    This is particularly useful when you have an unbalanced training set.
-The input is expected to contain raw, unnormalized scores for each class.
-input has to be a Tensor of size either \((minibatch, C)\) or
-\((minibatch, C, d_1, d_2, ..., d_K)\)
-with \(K \geq 1\) for the K-dimensional case (described later).
    
-
    This criterion expects a class index in the range \([0, C-1]\) as the
-target for each value of a 1D tensor of size minibatch; if ignore_index
-is specified, this criterion also accepts this class index (this index may not
-necessarily be in the class range).
    
-
    The loss can be described as:
-$$
-  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
-= -x[class] + \log\left(\sum_j \exp(x[j])\right)
-$$
-or in the case of the weight argument being specified:
-$$
-  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
-$$
    
-
    The losses are averaged across observations for each minibatch.
-Can also be used for higher dimension inputs, such as 2D images, by providing
-an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
-where \(K\) is the number of dimensions, and a target of appropriate shape
-(see below).
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C)\) where C = number of classes, or
-\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
-in the case of K-dimensional loss.
      • 
-
    • Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
-K-dimensional loss.
      • 
-
    • Output: scalar.
-If reduction is 'none', then the same size as the target:
-\((N)\), or
-\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
-of K-dimensional loss.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-loss <- nn_cross_entropy_loss()
-input <- torch_randn(3, 5, requires_grad=TRUE)
-target <- torch_randint(low = 1, high = 5, size = 3, dtype = torch_long())
-output <- loss(input, target)
-output$backward()
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
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diff --git a/docs/reference/nn_dropout.html b/docs/reference/nn_dropout.html
deleted file mode 100644
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+++ /dev/null
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-
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-Dropout module — nn_dropout • torch
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-  
    
-    
    
-    
    Dropout module
    
-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    During training, randomly zeroes some of the elements of the input
-tensor with probability p using samples from a Bernoulli
-distribution. Each channel will be zeroed out independently on every forward
-call.
    
-    
    
-
-    
    nn_dropout(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    probability of an element to be zeroed. Default: 0.5
    inplace
    If set to TRUE, will do this operation in-place. Default: FALSE.
    
-
-    
    Details
    
-
-    
    This has proven to be an effective technique for regularization and
-preventing the co-adaptation of neurons as described in the paper
-Improving neural networks by preventing co-adaptation of feature detectors.
    
-
    Furthermore, the outputs are scaled by a factor of :math:\frac{1}{1-p} during
-training. This means that during evaluation the module simply computes an
-identity function.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\). Input can be of any shape
      • 
-
    • Output: \((*)\). Output is of the same shape as input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout(p = 0.2)
-input <- torch_randn(20, 16)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nn_dropout2d.html b/docs/reference/nn_dropout2d.html
deleted file mode 100644
index efcc07a6b..000000000
--- a/docs/reference/nn_dropout2d.html
+++ /dev/null
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-
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-Dropout2D module — nn_dropout2d • torch
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-    
    
-    
    Dropout2D module
    
-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(\mbox{input}[i, j]\)).
    
-    
    
-
-    
    nn_dropout2d(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    (float, optional): probability of an element to be zero-ed.
    inplace
    (bool, optional): If set to TRUE, will do this operation
-in-place
    
-
-    
    Details
    
-
-    
    Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
-Usually the input comes from nn_conv2d modules.
    
-
    As described in the paper
-Efficient Object Localization Using Convolutional Networks ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
-In this case, nn_dropout2d will help promote independence between
-feature maps and should be used instead.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, H, W)\)
      • 
-
    • Output: \((N, C, H, W)\) (same shape as input)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout2d(p = 0.2)
-input <- torch_randn(20, 16, 32, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_dropout3d.html b/docs/reference/nn_dropout3d.html
deleted file mode 100644
index 32d4545df..000000000
--- a/docs/reference/nn_dropout3d.html
+++ /dev/null
@@ -1,243 +0,0 @@
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-Dropout3D module — nn_dropout3d • torch
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-    
    Dropout3D module
    
-    Source: R/nn-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(\mbox{input}[i, j]\)).
    
-    
    
-
-    
    nn_dropout3d(p = 0.5, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    p
    (float, optional): probability of an element to be zeroed.
    inplace
    (bool, optional): If set to TRUE, will do this operation
-in-place
    
-
-    
    Details
    
-
-    
    Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
-Usually the input comes from nn_conv2d modules.
    
-
    As described in the paper
-Efficient Object Localization Using Convolutional Networks ,
-if adjacent pixels within feature maps are strongly correlated
-(as is normally the case in early convolution layers) then i.i.d. dropout
-will not regularize the activations and will otherwise just result
-in an effective learning rate decrease.
    
-
    In this case, nn_dropout3d will help promote independence between
-feature maps and should be used instead.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, D, H, W)\)
      • 
-
    • Output: \((N, C, D, H, W)\) (same shape as input)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_dropout3d(p = 0.2)
-input <- torch_randn(20, 16, 4, 32, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_elu.html b/docs/reference/nn_elu.html
deleted file mode 100644
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-ELU module — nn_elu • torch
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-    
    ELU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_elu(alpha = 1, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    alpha
    the \(\alpha\) value for the ELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_elu()
-input <- torch_randn(2)
-output <-  m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_embedding.html b/docs/reference/nn_embedding.html
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-Embedding module — nn_embedding • torch
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-    
    Embedding module
    
-    Source: R/nn-sparse.R
-    
-    
    
-
-    
    
-    
    A simple lookup table that stores embeddings of a fixed dictionary and size.
-This module is often used to store word embeddings and retrieve them using indices.
-The input to the module is a list of indices, and the output is the corresponding
-word embeddings.
    
-    
    
-
-    
    nn_embedding(
-  num_embeddings,
-  embedding_dim,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE,
-  .weight = NULL
-)
    
-
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    Arguments
    
-    
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    num_embeddings
    (int): size of the dictionary of embeddings
    embedding_dim
    (int): the size of each embedding vector
    padding_idx
    (int, optional): If given, pads the output with the embedding vector at padding_idx
-(initialized to zeros) whenever it encounters the index.
    max_norm
    (float, optional): If given, each embedding vector with norm larger than max_norm
-is renormalized to have norm max_norm.
    norm_type
    (float, optional): The p of the p-norm to compute for the max_norm option. Default 2.
    scale_grad_by_freq
    (boolean, optional): If given, this will scale gradients by the inverse of frequency of
-the words in the mini-batch. Default False.
    sparse
    (bool, optional): If True, gradient w.r.t. weight matrix will be a sparse tensor.
    .weight
    (Tensor) embeddings weights (in case you want to set it manually)
    
-
    See Notes for more details regarding sparse gradients.
    
-
-    
    Note
    
-
-    
    Keep in mind that only a limited number of optimizers support
-sparse gradients: currently it's optim.SGD (CUDA and CPU),
-optim.SparseAdam (CUDA and CPU) and optim.Adagrad (CPU)
    
-
    With padding_idx set, the embedding vector at
-padding_idx is initialized to all zeros. However, note that this
-vector can be modified afterwards, e.g., using a customized
-initialization method, and thus changing the vector used to pad the
-output. The gradient for this vector from nn_embedding
-is always zero.
    
-    
    Attributes
    
-
-    
-
-
      
-
    • weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
-initialized from \(\mathcal{N}(0, 1)\)
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract
      • 
-
    • Output: \((*, H)\), where * is the input shape and \(H=\mbox{embedding\_dim}\)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-# an Embedding module containing 10 tensors of size 3
-embedding <- nn_embedding(10, 3)
-# a batch of 2 samples of 4 indices each
-input <- torch_tensor(rbind(c(1,2,4,5),c(4,3,2,9)), dtype = torch_long())
-embedding(input)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.5531  0.2969 -1.9168
-#>  -0.7095 -0.1328 -0.7352
-#>  -1.5311 -0.6539  0.7804
-#>   1.5343  0.1139  1.1985
-#> 
-#> (2,.,.) = 
-#>  -1.5311 -0.6539  0.7804
-#>  -0.1120  0.9578  0.1195
-#>  -0.7095 -0.1328 -0.7352
-#>  -0.4247  0.6266 -0.1286
-#> [ CPUFloatType{2,4,3} ]
    # example with padding_idx
-embedding <- nn_embedding(10, 3, padding_idx=1)
-input <- torch_tensor(matrix(c(1,3,1,6), nrow = 1), dtype = torch_long())
-embedding(input)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0000  0.0000  0.0000
-#>  -1.2943 -1.0279  0.6483
-#>   0.0000  0.0000  0.0000
-#>   0.4053  0.7866 -0.3922
-#> [ CPUFloatType{1,4,3} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_gelu.html b/docs/reference/nn_gelu.html
deleted file mode 100644
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-    
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-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the Gaussian Error Linear Units function:
-$$\mbox{GELU}(x) = x * \Phi(x)$$
    
-    
    
-
-    
    nn_gelu()
    
-
-
-    
    Details
    
-
-    
    where \(\Phi(x)\) is the Cumulative Distribution Function for Gaussian Distribution.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m = nn_gelu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_glu.html b/docs/reference/nn_glu.html
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-GLU module — nn_glu • torch
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-    
    
-    
    GLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the gated linear unit function
-\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
-of the input matrices and \(b\) is the second half.
    
-    
    
-
-    
    nn_glu(dim = -1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    dim
    (int): the dimension on which to split the input. Default: -1
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((\ast_1, N, \ast_2)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_glu()
-input <- torch_randn(4, 2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_hardshrink.html b/docs/reference/nn_hardshrink.html
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-Hardshwink module — nn_hardshrink • torch
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    Hardshwink module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the hard shrinkage function element-wise:
    
-    
    
-
-    
    nn_hardshrink(lambd = 0.5)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    lambd
    the \(\lambda\) value for the Hardshrink formulation. Default: 0.5
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{HardShrink}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x > \lambda \\
-x, & \mbox{ if } x < -\lambda \\
-0, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
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      • 
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-    
    Examples
    
-    
    # \dontrun{
-m <- nn_hardshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
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diff --git a/docs/reference/nn_hardsigmoid.html b/docs/reference/nn_hardsigmoid.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/nn-activation.R
-    
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-    
    Applies the element-wise function:
    
-    
    
-
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    nn_hardsigmoid()
    
-
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    Details
    
-
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    $$
-\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
-  0 & \mbox{if~} x \le -3, \\
-  1 & \mbox{if~} x \ge +3, \\
-  x / 6 + 1 / 2 & \mbox{otherwise}
-\end{array}
-\right.
-$$
    
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      • 
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    Examples
    
-    
    # \dontrun{
-m <- nn_hardsigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
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diff --git a/docs/reference/nn_hardswish.html b/docs/reference/nn_hardswish.html
deleted file mode 100644
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-    Source: R/nn-activation.R
-    
-    
    
-
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-    
    Applies the hardswish function, element-wise, as described in the paper:
-Searching for MobileNetV3
    
-    
    
-
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    nn_hardswish()
    
-
-
-    
    Details
    
-
-    
    $$ \mbox{Hardswish}(x) = \left\{
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-  0 & \mbox{if } x \le -3, \\
-  x & \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 & \mbox{otherwise}
-  \end{array}
-  \right. $$
    
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diff --git a/docs/reference/nn_hardtanh.html b/docs/reference/nn_hardtanh.html
deleted file mode 100644
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-    Source: R/nn-activation.R
-    
-    
    
-
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-    
    Applies the HardTanh function element-wise
-HardTanh is defined as:
    
-    
    
-
-    
    nn_hardtanh(min_val = -1, max_val = 1, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-      
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    min_val
    minimum value of the linear region range. Default: -1
    max_val
    maximum value of the linear region range. Default: 1
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
-  1 & \mbox{ if } x > 1 \\
-  -1 & \mbox{ if } x < -1 \\
-  x & \mbox{ otherwise } \\
-\end{array}
-\right.
-$$
    
-
    The range of the linear region :math:[-1, 1] can be adjusted using
-min_val and max_val.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_hardtanh(-2, 2)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_identity.html b/docs/reference/nn_identity.html
deleted file mode 100644
index 8ce1f5eb9..000000000
--- a/docs/reference/nn_identity.html
+++ /dev/null
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-Identity module — nn_identity • torch
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    Identity module
    
-    Source: R/nn-linear.R
-    
-    
    
-
-    
    
-    
    A placeholder identity operator that is argument-insensitive.
    
-    
    
-
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    nn_identity(...)
    
-
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    Arguments
    
-    
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-    
-      
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-    
-    
    ...
    any arguments (unused)
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_identity(54, unused_argument1 = 0.1, unused_argument2 = FALSE)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
    #> [1] 128  20
    
-# }
-
    
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diff --git a/docs/reference/nn_init_calculate_gain.html b/docs/reference/nn_init_calculate_gain.html
deleted file mode 100644
index 33069b6fb..000000000
--- a/docs/reference/nn_init_calculate_gain.html
+++ /dev/null
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-
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    Calculate gain
    
-    Source: R/nn-init.R
-    
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-
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-    
    Return the recommended gain value for the given nonlinearity function.
    
-    
    
-
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    nn_init_calculate_gain(nonlinearity, param = NULL)
    
-
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    Arguments
    
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-      
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    nonlinearity
    the non-linear function
    param
    optional parameter for the non-linear function
    
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    Constant initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
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-    
    Fills the input Tensor with the value val.
    
-    
    
-
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    nn_init_constant_(tensor, val)
    
-
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    Arguments
    
-    
-    
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-      
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-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    val
    the value to fill the tensor with
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_constant_(w, 0.3)
    #> torch_tensor 
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#>  0.3000  0.3000  0.3000  0.3000  0.3000
-#> [ CPUFloatType{3,5} ]
    
-# }
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-Dirac initialization — nn_init_dirac_ • torch
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-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 3, 4, 5-dimensional input Tensor with the Dirac
-delta function. Preserves the identity of the inputs in Convolutional
-layers, where as many input channels are preserved as possible. In case
-of groups>1, each group of channels preserves identity.
    
-    
    
-
-    
    nn_init_dirac_(tensor, groups = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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    tensor
    a 3, 4, 5-dimensional torch.Tensor
    groups
    (optional) number of groups in the conv layer (default: 1)
    
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diff --git a/docs/reference/nn_init_eye_.html b/docs/reference/nn_init_eye_.html
deleted file mode 100644
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-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 2-dimensional input Tensor with the identity matrix.
-Preserves the identity of the inputs in Linear layers, where as
-many inputs are preserved as possible.
    
-    
    
-
-    
    nn_init_eye_(tensor)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    a 2-dimensional torch tensor.
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_eye_(w)
    #> torch_tensor 
-#>  1  0  0  0  0
-#>  0  1  0  0  0
-#>  0  0  1  0  0
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_kaiming_normal_.html b/docs/reference/nn_init_kaiming_normal_.html
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-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
-normal distribution.
    
-    
    
-
-    
    nn_init_kaiming_normal_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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-    
-      
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-    
-      
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-    
-    
    tensor
    an n-dimensional torch.Tensor
    a
    the negative slope of the rectifier used after this layer (only used
-with 'leaky_relu')
    mode
    either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.
    nonlinearity
    the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_kaiming_normal_(w, mode = "fan_in", nonlinearity = "leaky_relu")
    #> torch_tensor 
-#> -0.5594  0.2408  0.3946  0.5860 -0.4834
-#> -0.0442  0.7170 -0.3028  0.4015 -0.8906
-#> -0.5157 -0.1763  0.9366  0.4640 -0.5356
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_kaiming_uniform_.html b/docs/reference/nn_init_kaiming_uniform_.html
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-      
-
-      
-
-      
    
-
-
    
-  
    
-    
    
-    
    Kaiming uniform initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
-uniform distribution.
    
-    
    
-
-    
    nn_init_kaiming_uniform_(
-  tensor,
-  a = 0,
-  mode = "fan_in",
-  nonlinearity = "leaky_relu"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional torch.Tensor
    a
    the negative slope of the rectifier used after this layer (only used
-with 'leaky_relu')
    mode
    either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
-the magnitude of the variance of the weights in the forward pass. Choosing
-'fan_out' preserves the magnitudes in the backwards pass.
    nonlinearity
    the non-linear function. recommended to use only with 'relu'
-or 'leaky_relu' (default).
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_kaiming_uniform_(w, mode = "fan_in", nonlinearity = "leaky_relu")
    #> torch_tensor 
-#> -0.7460  0.2070 -0.1066 -0.4344 -0.4666
-#> -0.5351 -0.4524  0.0950 -1.0077 -0.2169
-#> -0.9525  0.8753  0.0070 -0.4553 -0.3445
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
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-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nn_init_normal_.html b/docs/reference/nn_init_normal_.html
deleted file mode 100644
index a7962cbc0..000000000
--- a/docs/reference/nn_init_normal_.html
+++ /dev/null
@@ -1,223 +0,0 @@
-
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-Normal initialization — nn_init_normal_ • torch
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-  
    
-    
    
-    
    Normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values drawn from the normal distribution
    
-    
    
-
-    
    nn_init_normal_(tensor, mean = 0, std = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    mean
    the mean of the normal distribution
    std
    the standard deviation of the normal distribution
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_normal_(w)
    #> torch_tensor 
-#> -1.0569 -1.0900  1.2740 -1.7728  0.0593
-#> -1.7131 -0.1353  0.8191  0.1481 -0.9940
-#> -0.7544 -1.0298  0.4237  1.4650  0.0575
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_init_ones_.html b/docs/reference/nn_init_ones_.html
deleted file mode 100644
index ebf2f5e6a..000000000
--- a/docs/reference/nn_init_ones_.html
+++ /dev/null
@@ -1,215 +0,0 @@
-
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-Ones initialization — nn_init_ones_ • torch
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-    
    
-    
    Ones initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with the scalar value 1
    
-    
    
-
-    
    nn_init_ones_(tensor)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_ones_(w)
    #> torch_tensor 
-#>  1  1  1  1  1
-#>  1  1  1  1  1
-#>  1  1  1  1  1
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_init_orthogonal_.html b/docs/reference/nn_init_orthogonal_.html
deleted file mode 100644
index 661a3085d..000000000
--- a/docs/reference/nn_init_orthogonal_.html
+++ /dev/null
@@ -1,225 +0,0 @@
-
-
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-
-Orthogonal initialization — nn_init_orthogonal_ • torch
-
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-  
    
-    
    
-    
    Orthogonal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with a (semi) orthogonal matrix, as
-described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
-at least 2 dimensions, and for tensors with more than 2 dimensions the
-trailing dimensions are flattened.
    
-    
    
-
-    
    nn_init_orthogonal_(tensor, gain = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    gain
    optional scaling factor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3,5)
-nn_init_orthogonal_(w)
    #> torch_tensor 
-#> -0.2147  0.0073 -0.0312 -0.0439  0.9752
-#> -0.8268  0.5222  0.0419  0.0979 -0.1802
-#>  0.3963  0.5329 -0.0498  0.7371  0.1148
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_init_sparse_.html b/docs/reference/nn_init_sparse_.html
deleted file mode 100644
index c9b963d67..000000000
--- a/docs/reference/nn_init_sparse_.html
+++ /dev/null
@@ -1,220 +0,0 @@
-
-
-
-  
-  
-
-
-
-Sparse initialization — nn_init_sparse_ • torch
-
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-  
    
-    
    
-    
    Sparse initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the 2D input Tensor as a sparse matrix, where the
-non-zero elements will be drawn from the normal distribution
-as described in Deep learning via Hessian-free optimization - Martens, J. (2010).
    
-    
    
-
-    
    nn_init_sparse_(tensor, sparsity, std = 0.01)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    sparsity
    The fraction of elements in each column to be set to zero
    std
    the standard deviation of the normal distribution used to generate
-the non-zero values
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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-
    
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diff --git a/docs/reference/nn_init_trunc_normal_.html b/docs/reference/nn_init_trunc_normal_.html
deleted file mode 100644
index 9ed52ecb0..000000000
--- a/docs/reference/nn_init_trunc_normal_.html
+++ /dev/null
@@ -1,233 +0,0 @@
-
-
-
-  
-  
-
-
-
-Truncated normal initialization — nn_init_trunc_normal_ • torch
-
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-  
    
-    
    
-    
    Truncated normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values drawn from a truncated
-normal distribution.
    
-    
    
-
-    
    nn_init_trunc_normal_(tensor, mean = 0, std = 1, a = -2, b = -2)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    mean
    the mean of the normal distribution
    std
    the standard deviation of the normal distribution
    a
    the minimum cutoff value
    b
    the maximum cutoff value
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_trunc_normal_(w)
    #> torch_tensor 
-#> -2 -2 -2 -2 -2
-#> -2 -2 -2 -2 -2
-#> -2 -2 -2 -2 -2
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nn_init_uniform_.html b/docs/reference/nn_init_uniform_.html
deleted file mode 100644
index 9e974bcd2..000000000
--- a/docs/reference/nn_init_uniform_.html
+++ /dev/null
@@ -1,223 +0,0 @@
-
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-Uniform initialization — nn_init_uniform_ • torch
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-    
    
-    
    Uniform initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values drawn from the uniform distribution
    
-    
    
-
-    
    nn_init_uniform_(tensor, a = 0, b = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    a
    the lower bound of the uniform distribution
    b
    the upper bound of the uniform distribution
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_uniform_(w)
    #> torch_tensor 
-#>  0.8556  0.9331  0.3515  0.8071  0.4948
-#>  0.6075  0.9042  0.7181  0.7329  0.7563
-#>  0.2584  0.5293  0.9757  0.3030  0.3341
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
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-      
    
-      
    
-  
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-
    
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diff --git a/docs/reference/nn_init_xavier_normal_.html b/docs/reference/nn_init_xavier_normal_.html
deleted file mode 100644
index bf0255588..000000000
--- a/docs/reference/nn_init_xavier_normal_.html
+++ /dev/null
@@ -1,223 +0,0 @@
-
-
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-  
-  
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-
-
-Xavier normal initialization — nn_init_xavier_normal_ • torch
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-    
    
-    
    Xavier normal initialization
    
-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a normal
-distribution.
    
-    
    
-
-    
    nn_init_xavier_normal_(tensor, gain = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    gain
    an optional scaling factor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_xavier_normal_(w)
    #> torch_tensor 
-#>  1.2535 -0.2197  0.5425 -3.0052 -4.2446
-#> -0.3570 -1.6970 -2.0154 -0.5348  2.7582
-#>  0.8714 -0.8924  0.7675  3.2553 -1.4333
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
-  
    
-  
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    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_init_xavier_uniform_.html b/docs/reference/nn_init_xavier_uniform_.html
deleted file mode 100644
index c925e4da3..000000000
--- a/docs/reference/nn_init_xavier_uniform_.html
+++ /dev/null
@@ -1,223 +0,0 @@
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-Xavier uniform initialization — nn_init_xavier_uniform_ • torch
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-    
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-    Source: R/nn-init.R
-    
-    
    
-
-    
    
-    
    Fills the input Tensor with values according to the method
-described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. & Bengio, Y. (2010), using a uniform
-distribution.
    
-    
    
-
-    
    nn_init_xavier_uniform_(tensor, gain = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional Tensor
    gain
    an optional scaling factor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_xavier_uniform_(w)
    #> torch_tensor 
-#>  1.3397  1.1040 -3.0453 -1.7935  0.9545
-#> -0.0194 -2.4483  2.9345  2.2750 -2.4048
-#> -0.4406 -2.2409  0.4155 -0.1573  1.9776
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_init_zeros_.html b/docs/reference/nn_init_zeros_.html
deleted file mode 100644
index 9e4a29058..000000000
--- a/docs/reference/nn_init_zeros_.html
+++ /dev/null
@@ -1,215 +0,0 @@
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-Zeros initialization — nn_init_zeros_ • torch
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-    Source: R/nn-init.R
-    
-    
    
-
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-    
    Fills the input Tensor with the scalar value 0
    
-    
    
-
-    
    nn_init_zeros_(tensor)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    an n-dimensional tensor
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-w <- torch_empty(3, 5)
-nn_init_zeros_(w)
    #> torch_tensor 
-#>  0  0  0  0  0
-#>  0  0  0  0  0
-#>  0  0  0  0  0
-#> [ CPUFloatType{3,5} ]
    
-# }
-
    
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diff --git a/docs/reference/nn_leaky_relu.html b/docs/reference/nn_leaky_relu.html
deleted file mode 100644
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+++ /dev/null
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-
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-LeakyReLU module — nn_leaky_relu • torch
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-    
    LeakyReLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_leaky_relu(negative_slope = 0.01, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    negative_slope
    Controls the angle of the negative slope. Default: 1e-2
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
-$$
-or
    
-
    $$
-  \mbox{LeakyRELU}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x \geq 0 \\
-\mbox{negative\_slope} \times x, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_leaky_relu(0.1)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_linear.html b/docs/reference/nn_linear.html
deleted file mode 100644
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--- a/docs/reference/nn_linear.html
+++ /dev/null
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-Linear module — nn_linear • torch
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-    
    Linear module
    
-    Source: R/nn-linear.R
-    
-    
    
-
-    
    
-    
    Applies a linear transformation to the incoming data: y = xA^T + b
    
-    
    
-
-    
    nn_linear(in_features, out_features, bias = TRUE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    in_features
    size of each input sample
    out_features
    size of each output sample
    bias
    If set to FALSE, the layer will not learn an additive bias.
-Default: TRUE
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: (N, *, H_in) where * means any number of
-additional dimensions and H_in = in_features.
      • 
-
    • Output: (N, *, H_out) where all but the last dimension
-are the same shape as the input and :math:H_out = out_features.
      • 
-
    
-
-    
    Attributes
    
-
-    
-
-
      
-
    • weight: the learnable weights of the module of shape
-(out_features, in_features). The values are
-initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
-\(k = \frac{1}{\mbox{in\_features}}\)
      • 
-
    • bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
-If bias is TRUE, the values are initialized from
-\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
-\(k = \frac{1}{\mbox{in\_features}}\)
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_linear(20, 30)
-input <- torch_randn(128, 20)
-output <- m(input)
-print(output$size())
    #> [1] 128  30
    
-# }
-
    
-  
    
-  
-
    
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-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_log_sigmoid.html b/docs/reference/nn_log_sigmoid.html
deleted file mode 100644
index b75b58183..000000000
--- a/docs/reference/nn_log_sigmoid.html
+++ /dev/null
@@ -1,220 +0,0 @@
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-LogSigmoid module — nn_log_sigmoid • torch
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-    
    LogSigmoid module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
- $$
    
-    
    
-
-    
    nn_log_sigmoid()
    
-
-
-    
    Shape
    
-
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-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
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      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_log_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nn_log_softmax.html b/docs/reference/nn_log_softmax.html
deleted file mode 100644
index e20e5a05a..000000000
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+++ /dev/null
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-LogSoftmax module — nn_log_softmax • torch
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-    
    LogSoftmax module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
-input Tensor. The LogSoftmax formulation can be simplified as:
    
-    
    
-
-    
    nn_log_softmax(dim)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    dim
    (int): A dimension along which LogSoftmax will be computed.
    
-
-    
    Value
    
-
-    
    a Tensor of the same dimension and shape as the input with
-values in the range [-inf, 0)
    
-    
    Details
    
-
-    
    $$
-  \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((*)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((*)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_log_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_max_pool1d.html b/docs/reference/nn_max_pool1d.html
deleted file mode 100644
index 7a707ed7b..000000000
--- a/docs/reference/nn_max_pool1d.html
+++ /dev/null
@@ -1,268 +0,0 @@
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-MaxPool1D module — nn_max_pool1d • torch
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-    Source: R/nn-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nn_max_pool1d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    
-
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    Arguments
    
-    
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    kernel_size
    the size of the window to take a max over
    stride
    the stride of the window. Default value is kernel_size
    padding
    implicit zero padding to be added on both sides
    dilation
    a parameter that controls the stride of elements in the window
    return_indices
    if TRUE, will return the max indices along with the outputs.
-Useful for  nn_max_unpool1d() later.
    ceil_mode
    when TRUE, will use ceil instead of floor to compute the output shape
    
-
-    
    Details
    
-
-    
    In the simplest case, the output value of the layer with input size \((N, C, L)\)
-and output \((N, C, L_{out})\) can be precisely described as:
    
-
    $$
-  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
-input(N_i, C_j, stride \times k + m)
-$$
    
-
    If padding is non-zero, then the input is implicitly zero-padded on both sides
-for padding number of points. dilation controls the spacing between the kernel points.
-It is harder to describe, but this link
-has a nice visualization of what dilation does.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, L_{in})\)
      • 
-
    • Output: \((N, C, L_{out})\), where
      • 
-
    
-
-
    $$
-  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
-    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-# pool of size=3, stride=2
-m <- nn_max_pool1d(3, stride=2)
-input <- torch_randn(20, 16, 50)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
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-      
    
-      
    
-  
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-
    
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diff --git a/docs/reference/nn_max_pool2d.html b/docs/reference/nn_max_pool2d.html
deleted file mode 100644
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--- a/docs/reference/nn_max_pool2d.html
+++ /dev/null
@@ -1,283 +0,0 @@
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-MaxPool2D module — nn_max_pool2d • torch
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-    
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-    Source: R/nn-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nn_max_pool2d(
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  return_indices = FALSE,
-  ceil_mode = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
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-      
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    kernel_size
    the size of the window to take a max over
    stride
    the stride of the window. Default value is kernel_size
    padding
    implicit zero padding to be added on both sides
    dilation
    a parameter that controls the stride of elements in the window
    return_indices
    if TRUE, will return the max indices along with the outputs.
-Useful for nn_max_unpool2d() later.
    ceil_mode
    when TRUE, will use ceil instead of floor to compute the output shape
    
-
-    
    Details
    
-
-    
    In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
-output \((N, C, H_{out}, W_{out})\) and kernel_size \((kH, kW)\)
-can be precisely described as:
    
-
    $$
-  \begin{array}{ll}
-out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
-& \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
-               \mbox{stride[1]} \times w + n)
-\end{array}
-$$
    
-
    If padding is non-zero, then the input is implicitly zero-padded on both sides
-for padding number of points. dilation controls the spacing between the kernel points.
-It is harder to describe, but this link has a nice visualization of what dilation does.
    
-
    The parameters kernel_size, stride, padding, dilation can either be:
      
-
    • a single int -- in which case the same value is used for the height and width dimension
      • 
-
    • a tuple of two ints -- in which case, the first int is used for the height dimension,
-and the second int for the width dimension
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, C, H_{in}, W_{in})\)
      • 
-
    • Output: \((N, C, H_{out}, W_{out})\), where
      • 
-
    
-
-
    $$
-  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
-    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
-$$
    
-
    $$
-  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
-    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-# pool of square window of size=3, stride=2
-m <- nn_max_pool2d(3, stride=2)
-# pool of non-square window
-m <- nn_max_pool2d(c(3, 2), stride=c(2, 1))
-input <- torch_randn(20, 16, 50, 32)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_module.html b/docs/reference/nn_module.html
deleted file mode 100644
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--- a/docs/reference/nn_module.html
+++ /dev/null
@@ -1,234 +0,0 @@
-
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-Base class for all neural network modules. — nn_module • torch
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-    
    Base class for all neural network modules.
    
-    Source: R/nn.R
-    
-    
    
-
-    
    
-    
    Your models should also subclass this class.
    
-    
    
-
-    
    nn_module(classname = NULL, inherit = nn_Module, ...)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-    
    classname
    an optional name for the module
    inherit
    an optional module to inherit from
    ...
    methods implementation
    
-
-    
    Details
    
-
-    
    Modules can also contain other Modules, allowing to nest them in a tree
-structure. You can assign the submodules as regular attributes.
    
-
-    
    Examples
    
-    
    # \dontrun{
-model <- nn_module(
- initialize = function() {
-   self$conv1 <- nn_conv2d(1, 20, 5)
-   self$conv2 <- nn_conv2d(20, 20, 5)
- },
- forward = function(input) {
-   input <- self$conv1(input)
-   input <- nnf_relu(input)
-   input <- self$conv2(input)
-   input <- nnf_relu(input)
-   input
- }
-)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_module_list.html b/docs/reference/nn_module_list.html
deleted file mode 100644
index b03a9e1bf..000000000
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+++ /dev/null
@@ -1,224 +0,0 @@
-
-
-
-  
-  
-
-
-
-Holds submodules in a list. — nn_module_list • torch
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-    
    Holds submodules in a list.
    
-    Source: R/nn.R
-    
-    
    
-
-    
    
-    
    nn_module_list can be indexed like a regular R list, but
-modules it contains are properly registered, and will be visible by all
-nn_module methods.
    
-    
    
-
-    
    nn_module_list(modules = list())
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    modules
    a list of modules to add
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-my_module <- nn_module(
- initialize = function() {
-   self$linears <- nn_module_list(lapply(1:10, function(x) nn_linear(10, 10)))
- },
- forward = function(x) {
-  for (i in 1:length(self$linears))
-    x <- self$linears[[i]](x)
-  x
- }
-)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
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-
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diff --git a/docs/reference/nn_multihead_attention.html b/docs/reference/nn_multihead_attention.html
deleted file mode 100644
index f7b9e3a96..000000000
--- a/docs/reference/nn_multihead_attention.html
+++ /dev/null
@@ -1,289 +0,0 @@
-
-
-
-  
-  
-
-
-
-MultiHead attention — nn_multihead_attention • torch
-
-
-
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-  
    
-    
    
-    
    MultiHead attention
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Allows the model to jointly attend to information
-from different representation subspaces.
-See reference: Attention Is All You Need
    
-    
    
-
-    
    nn_multihead_attention(
-  embed_dim,
-  num_heads,
-  dropout = 0,
-  bias = TRUE,
-  add_bias_kv = FALSE,
-  add_zero_attn = FALSE,
-  kdim = NULL,
-  vdim = NULL
-)
    
-
-    
    Arguments
    
-    
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-      
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-      
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-    
-      
-      
-    
-    
    embed_dim
    total dimension of the model.
    num_heads
    parallel attention heads.
    dropout
    a Dropout layer on attn_output_weights. Default: 0.0.
    bias
    add bias as module parameter. Default: True.
    add_bias_kv
    add bias to the key and value sequences at dim=0.
    add_zero_attn
    add a new batch of zeros to the key and
-value sequences at dim=1.
    kdim
    total number of features in key. Default: NULL
    vdim
    total number of features in value. Default: NULL.
-Note: if kdim and vdim are NULL, they will be set to embed_dim such that
-query, key, and value have the same number of features.
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
-\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
-$$
    
-    
    Shape
    
-
-    
-
-
-
    Inputs:
      
-
    • query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
      • 
-
    • key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of True will be ignored while the position with the value of False will be unchanged.
      • 
-
    • attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with True
-is not allowed to attend while False values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.
      • 
-
    
-
-
    Outputs:
      
-
    • attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
-E is the embedding dimension.
      • 
-
    • attn_output_weights: \((N, L, S)\) where N is the batch size,
-L is the target sequence length, S is the source sequence length.
      • 
-
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
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diff --git a/docs/reference/nn_prelu.html b/docs/reference/nn_prelu.html
deleted file mode 100644
index ec2da259c..000000000
--- a/docs/reference/nn_prelu.html
+++ /dev/null
@@ -1,270 +0,0 @@
-
-
-
-  
-  
-
-
-
-PReLU module — nn_prelu • torch
-
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-      
    
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-  
    
-    
    
-    
    PReLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
-$$
-or
-$$
-  \mbox{PReLU}(x) =
-  \left\{ \begin{array}{ll}
-x, & \mbox{ if } x \geq 0 \\
-ax, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    
-
-    
    nn_prelu(num_parameters = 1, init = 0.25)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    num_parameters
    (int): number of \(a\) to learn.
-Although it takes an int as input, there is only two values are legitimate:
-1, or the number of channels at input. Default: 1
    init
    (float): the initial value of \(a\). Default: 0.25
    
-
-    
    Details
    
-
-    
    Here \(a\) is a learnable parameter. When called without arguments, nn.prelu() uses a single
-parameter \(a\) across all input channels. If called with nn_prelu(nChannels),
-a separate \(a\) is used for each input channel.
    
-    
    Note
    
-
-    
    weight decay should not be used when learning \(a\) for good performance.
    
-
    Channel dim is the 2nd dim of input. When input has dims < 2, then there is
-no channel dim and the number of channels = 1.
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-    
    Attributes
    
-
-    
-
-
      
-
    • weight (Tensor): the learnable weights of shape (num_parameters).
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_prelu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
-   
    
-
-  
-
-
-  
-
-
-
diff --git a/docs/reference/nn_relu.html b/docs/reference/nn_relu.html
deleted file mode 100644
index 21a181a9b..000000000
--- a/docs/reference/nn_relu.html
+++ /dev/null
@@ -1,227 +0,0 @@
-
-
-
-  
-  
-
-
-
-ReLU module — nn_relu • torch
-
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-
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-  
    
-    
    
-    
    ReLU module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the rectified linear unit function element-wise
-$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$
    
-    
    
-
-    
    nn_relu(inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_relu()
-input <- torch_randn(2)
-m(input)
    #> torch_tensor 
-#>  0.2952
-#>  0.0000
-#> [ CPUFloatType{2} ]
    
-# }
-
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
-
-      
    
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diff --git a/docs/reference/nn_relu6.html b/docs/reference/nn_relu6.html
deleted file mode 100644
index 3bc8d5d38..000000000
--- a/docs/reference/nn_relu6.html
+++ /dev/null
@@ -1,227 +0,0 @@
-
-
-
-  
-  
-
-
-
-ReLu6 module — nn_relu6 • torch
-
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-      
    
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-    
    
-    
    ReLu6 module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_relu6(inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_relu6()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
-
    
-  
    Site built with pkgdown 1.5.1.
    
-
    
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diff --git a/docs/reference/nn_rnn.html b/docs/reference/nn_rnn.html
deleted file mode 100644
index 343ee10ff..000000000
--- a/docs/reference/nn_rnn.html
+++ /dev/null
@@ -1,446 +0,0 @@
-
-
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-RNN module — nn_rnn • torch
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-    
    RNN module
    
-    Source: R/nn-rnn.R
-    
-    
    
-
-    
    
-    
    Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
-to an input sequence.
    
-    
    
-
-    
    nn_rnn(
-  input_size,
-  hidden_size,
-  num_layers = 1,
-  nonlinearity = NULL,
-  bias = TRUE,
-  batch_first = FALSE,
-  dropout = 0,
-  bidirectional = FALSE,
-  ...
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-    
-      
-      
-    
-    
-      
-      
-    
-    
    input_size
    The number of expected features in the input x
    hidden_size
    The number of features in the hidden state h
    num_layers
    Number of recurrent layers. E.g., setting num_layers=2
-would mean stacking two RNNs together to form a stacked RNN,
-with the second RNN taking in outputs of the first RNN and
-computing the final results. Default: 1
    nonlinearity
    The non-linearity to use. Can be either 'tanh' or
-'relu'. Default: 'tanh'
    bias
    If FALSE, then the layer does not use bias weights b_ih and
-b_hh. Default: TRUE
    batch_first
    If TRUE, then the input and output tensors are provided
-as (batch, seq, feature). Default: FALSE
    dropout
    If non-zero, introduces a Dropout layer on the outputs of each
-RNN layer except the last layer, with dropout probability equal to
-dropout. Default: 0
    bidirectional
    If TRUE, becomes a bidirectional RNN. Default: FALSE
    ...
    other arguments that can be passed to the super class.
    
-
-    
    Details
    
-
-    
    For each element in the input sequence, each layer computes the following
-function:
    
-
    $$
-h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
-$$
    
-
    where \(h_t\) is the hidden state at time t, \(x_t\) is
-the input at time t, and \(h_{(t-1)}\) is the hidden state of the
-previous layer at time t-1 or the initial hidden state at time 0.
-If nonlinearity is 'relu', then \(\mbox{ReLU}\) is used instead of
-\(\tanh\).
    
-    
    Inputs
    
-
-    
-
-
      
-
    • input of shape (seq_len, batch, input_size): tensor containing the features
-of the input sequence. The input can also be a packed variable length
-sequence.
      • 
-
    • h_0 of shape (num_layers * num_directions, batch, hidden_size): tensor
-containing the initial hidden state for each element in the batch.
-Defaults to zero if not provided. If the RNN is bidirectional,
-num_directions should be 2, else it should be 1.
      • 
-
    
-
-    
    Outputs
    
-
-    
-
-
      
-
    • output of shape (seq_len, batch, num_directions * hidden_size): tensor
-containing the output features (h_t) from the last layer of the RNN,
-for each t.  If a :class:nn_packed_sequence has
-been given as the input, the output will also be a packed sequence.
-For the unpacked case, the directions can be separated
-using output$view(seq_len, batch, num_directions, hidden_size),
-with forward and backward being direction 0 and 1 respectively.
-Similarly, the directions can be separated in the packed case.
      • 
-
    • h_n of shape (num_layers * num_directions, batch, hidden_size): tensor
-containing the hidden state for t = seq_len.
-Like output, the layers can be separated using
-h_n$view(num_layers, num_directions, batch, hidden_size).
      • 
-
    
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input1: \((L, N, H_{in})\) tensor containing input features where
-\(H_{in}=\mbox{input\_size}\) and L represents a sequence length.
      • 
-
    • Input2: \((S, N, H_{out})\) tensor
-containing the initial hidden state for each element in the batch.
-\(H_{out}=\mbox{hidden\_size}\)
-Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
-If the RNN is bidirectional, num_directions should be 2, else it should be 1.
      • 
-
    • Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)
      • 
-
    • Output2: \((S, N, H_{out})\) tensor containing the next hidden state
-for each element in the batch
      • 
-
    
-
-    
    Attributes
    
-
-    
-
-
      
-
    • weight_ih_l[k]: the learnable input-hidden weights of the k-th layer,
-of shape (hidden_size, input_size) for k = 0. Otherwise, the shape is
-(hidden_size, num_directions * hidden_size)
      • 
-
    • weight_hh_l[k]: the learnable hidden-hidden weights of the k-th layer,
-of shape (hidden_size, hidden_size)
      • 
-
    • bias_ih_l[k]: the learnable input-hidden bias of the k-th layer,
-of shape (hidden_size)
      • 
-
    • bias_hh_l[k]: the learnable hidden-hidden bias of the k-th layer,
-of shape (hidden_size)
      • 
-
    
-
-    
    Note
    
-
-    
-
-
-
    All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
-where \(k = \frac{1}{\mbox{hidden\_size}}\)
    
-
-    
    Examples
    
-    
    # \dontrun{
-rnn <- nn_rnn(10, 20, 2)
-input <- torch_randn(5, 3, 10)
-h0 <- torch_randn(2, 3, 20)
-rnn(input, h0)
    #> [[1]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 9  0.1563  0.2797  0.4653  0.8483 -0.5044  0.5032 -0.2032  0.8317 -0.0350
-#>   0.1632 -0.1904  0.5733  0.3198 -0.3562  0.3904 -0.6873  0.7901 -0.9472
-#>  -0.6539 -0.6025  0.4797 -0.8375  0.7255 -0.4198  0.5030 -0.1419  0.2092
-#> 
-#> Columns 10 to 18  0.9481 -0.2581 -0.6185 -0.7787 -0.2685 -0.7144 -0.3700  0.4854  0.0008
-#>   0.8805 -0.2755 -0.9071 -0.5580 -0.0061 -0.8662 -0.7541  0.7855 -0.6157
-#>   0.5480  0.5395 -0.1759  0.2965  0.7805 -0.2651 -0.8080  0.2643  0.7689
-#> 
-#> Columns 19 to 20  0.3487 -0.1631
-#>   0.2535  0.5113
-#>  -0.0188  0.3766
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 9  0.1416  0.4498  0.1932  0.1637 -0.5115 -0.7125 -0.4544 -0.1721 -0.0345
-#>  -0.7202  0.1668  0.1129  0.6700 -0.6155  0.2947 -0.4558  0.1086  0.1323
-#>   0.3006 -0.0124 -0.0014  0.5341 -0.0718  0.3542 -0.1502  0.4042 -0.5156
-#> 
-#> Columns 10 to 18  0.8057  0.2908 -0.1019 -0.2748  0.2799  0.6493 -0.7338  0.1910  0.1886
-#>  -0.1154  0.1593 -0.5129 -0.6648  0.1733 -0.2496 -0.0886  0.2975  0.5137
-#>   0.8810  0.4194 -0.0103 -0.8285 -0.0348 -0.0102 -0.6628  0.1669 -0.4613
-#> 
-#> Columns 19 to 20 -0.0403  0.0080
-#>  -0.0600 -0.4162
-#>   0.0908  0.6937
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 9 -0.1978  0.2242  0.0024  0.3932 -0.4801 -0.4895 -0.5400 -0.0527 -0.4520
-#>   0.4544  0.0302  0.4917  0.2736 -0.5769 -0.1859 -0.4959  0.0229 -0.2535
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-#> 
-#> Columns 10 to 18  0.8121  0.5476 -0.5889 -0.2491  0.5971  0.3482 -0.4202  0.5075  0.0695
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-#> 
-#> Columns 19 to 20 -0.0226 -0.0955
-#>   0.6364  0.2054
-#>   0.1772 -0.2871
-#> 
-#> (4,.,.) = 
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-#>  -0.5379  0.1719  0.2126  0.1891 -0.5105  0.2180 -0.5122 -0.1882 -0.4472
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-#> 
-#> Columns 10 to 18  0.4677 -0.0194 -0.3609 -0.2897  0.3666  0.0276  0.0770  0.5985  0.5201
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-#>   0.6537 -0.2490 -0.3203 -0.3803  0.0304 -0.0077  0.1981  0.6495 -0.0583
-#> 
-#> Columns 19 to 20 -0.0329 -0.2124
-#>   0.1306  0.0613
-#>   0.0430 -0.0534
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#>   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#>  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#> 
-#> Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#>   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#>   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#> 
-#> Columns 19 to 20  0.2250  0.1283
-#>  -0.0717  0.2627
-#>   0.1909 -0.1445
-#> [ CPUFloatType{5,3,20} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 9 -0.2977 -0.3901 -0.3494 -0.6523  0.3627  0.1448 -0.3341  0.2196  0.1126
-#>   0.2888 -0.7529  0.1781 -0.0379 -0.2393  0.3807  0.1044 -0.0212 -0.5096
-#>  -0.1402 -0.3835 -0.2036  0.3084  0.1285 -0.3805  0.1103  0.0476  0.2100
-#> 
-#> Columns 10 to 18 -0.6325 -0.1108 -0.1481  0.0602  0.7081 -0.3749  0.6918 -0.4901 -0.2858
-#>   0.2888  0.4654  0.2154 -0.3173  0.4848  0.3496 -0.1522 -0.0645 -0.5102
-#>   0.2073  0.5197  0.0807  0.4554  0.0247 -0.2980 -0.3274 -0.1698 -0.0551
-#> 
-#> Columns 19 to 20  0.0842  0.0867
-#>   0.1297 -0.4188
-#>   0.6599 -0.5773
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 9 -0.3560  0.0896  0.2468  0.0908 -0.3990 -0.1175 -0.3947 -0.0834  0.1421
-#>   0.1891 -0.0772  0.2671  0.0296 -0.1929 -0.2009 -0.5507 -0.2240  0.2121
-#>  -0.1833  0.2226 -0.0158  0.5592 -0.5925  0.0255 -0.6282 -0.1562  0.0561
-#> 
-#> Columns 10 to 18  0.5649 -0.0882 -0.4652 -0.2057  0.0088  0.0349 -0.0315  0.3252  0.6167
-#>   0.5626  0.3505 -0.2768 -0.4894 -0.0599  0.4348 -0.1352  0.2022  0.2273
-#>   0.2647  0.0037 -0.3756 -0.3976 -0.0172 -0.1532 -0.4150  0.3451  0.3110
-#> 
-#> Columns 19 to 20  0.2250  0.1283
-#>  -0.0717  0.2627
-#>   0.1909 -0.1445
-#> [ CPUFloatType{2,3,20} ]
-#> 
    
-# }
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diff --git a/docs/reference/nn_rrelu.html b/docs/reference/nn_rrelu.html
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-    Source: R/nn-activation.R
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-
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-    
    Applies the randomized leaky rectified liner unit function, element-wise,
-as described in the paper:
    
-    
    
-
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    nn_rrelu(lower = 1/8, upper = 1/3, inplace = FALSE)
    
-
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    Arguments
    
-    
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    lower
    lower bound of the uniform distribution. Default: \(\frac{1}{8}\)
    upper
    upper bound of the uniform distribution. Default: \(\frac{1}{3}\)
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    Empirical Evaluation of Rectified Activations in Convolutional Network.
    
-
    The function is defined as:
    
-
    $$
-\mbox{RReLU}(x) =
-\left\{ \begin{array}{ll}
-x & \mbox{if } x \geq 0 \\
-ax & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-
    where \(a\) is randomly sampled from uniform distribution
-\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
-See: https://arxiv.org/pdf/1505.00853.pdf
    
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    Examples
    
-    
    # \dontrun{
-m <- nn_rrelu(0.1, 0.3)
-input <- torch_randn(2)
-m(input)
    #> torch_tensor 
-#> -0.0421
-#>  1.4246
-#> [ CPUFloatType{2} ]
    
-# }
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diff --git a/docs/reference/nn_selu.html b/docs/reference/nn_selu.html
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-    Source: R/nn-activation.R
-    
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-
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-    
    Applied element-wise, as:
    
-    
    
-
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    nn_selu(inplace = FALSE)
    
-
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    Arguments
    
-    
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-    
-      
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-    
    inplace
    (bool, optional): can optionally do the operation in-place. Default: FALSE
    
-
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    Details
    
-
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    $$
-  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
-$$
    
-
    with \(\alpha = 1.6732632423543772848170429916717\) and
-\(\mbox{scale} = 1.0507009873554804934193349852946\).
    
-
    More details can be found in the paper
-Self-Normalizing Neural Networks.
    
-    
    Shape
    
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    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
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    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_selu()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_sequential.html b/docs/reference/nn_sequential.html
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-A sequential container — nn_sequential • torch
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-    Source: R/nn.R
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    A sequential container.
-Modules will be added to it in the order they are passed in the constructor.
-See examples.
    
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    nn_sequential(..., name = NULL)
    
-
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    ...
    sequence of modules to be added
    name
    optional name for the generated module.
    
-
-
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    Examples
    
-    
    # \dontrun{
-
-model <- nn_sequential(
-  nn_conv2d(1, 20, 5),
-  nn_relu(),
-  nn_conv2d(20, 64, 5),
-  nn_relu()
-)
-input <- torch_randn(32, 1, 28, 28)
-output <- model(input)
-
-# }
    
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diff --git a/docs/reference/nn_sigmoid.html b/docs/reference/nn_sigmoid.html
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-    Source: R/nn-activation.R
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-    
    
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-
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-  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
-$$
    
-    
    Shape
    
-
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    • Input: \((N, *)\) where * means, any number of additional
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    Examples
    
-    
    # \dontrun{
-m <- nn_sigmoid()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
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diff --git a/docs/reference/nn_softmax.html b/docs/reference/nn_softmax.html
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-    Source: R/nn-activation.R
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-    
    Applies the Softmax function to an n-dimensional input Tensor
-rescaling them so that the elements of the n-dimensional output Tensor
-lie in the range [0,1] and sum to 1.
-Softmax is defined as:
    
-    
    
-
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    nn_softmax(dim)
    
-
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-    
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-      
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    dim
    (int): A dimension along which Softmax will be computed (so every slice
-along dim will sum to 1).
    
-
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    Value
    
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    :
-a Tensor of the same dimension and shape as the input with
-values in the range [0, 1]
    
-    
    Details
    
-
-    
    $$
-  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-$$
    
-
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-values are treated as -Inf.
    
-    
    Note
    
-
-    
    This module doesn't work directly with NLLLoss,
-which expects the Log to be computed between the Softmax and itself.
-Use LogSoftmax instead (it's faster and has better numerical properties).
    
-    
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-dimensions
      • 
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    • Output: \((*)\), same shape as the input
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-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmax(1)
-input <- torch_randn(2, 3)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_softmax2d.html b/docs/reference/nn_softmax2d.html
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-Softmax2d module — nn_softmax2d • torch
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-    Source: R/nn-activation.R
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-    
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-When given an image of Channels x Height x Width, it will
-apply Softmax to each location \((Channels, h_i, w_j)\)
    
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-
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-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmax2d()
-input <- torch_randn(2, 3, 12, 13)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_softmin.html b/docs/reference/nn_softmin.html
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-rescaling them so that the elements of the n-dimensional output Tensor
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-Softmin is defined as:
    
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-along dim will sum to 1).
    
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    a Tensor of the same dimension and shape as the input, with
-values in the range [0, 1].
    
-    
    Details
    
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-    
    $$
-  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
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-dimensions
      • 
-
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      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softmin(dim = 1)
-input <- torch_randn(2, 2)
-output <- m(input)
-
-# }
    
-  
    
-  
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diff --git a/docs/reference/nn_softplus.html b/docs/reference/nn_softplus.html
deleted file mode 100644
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--- a/docs/reference/nn_softplus.html
+++ /dev/null
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-    
    
-    
    Softplus module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
-$$
    
-    
    
-
-    
    nn_softplus(beta = 1, threshold = 20)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    beta
    the \(\beta\) value for the Softplus formulation. Default: 1
    threshold
    values above this revert to a linear function. Default: 20
    
-
-    
    Details
    
-
-    
    SoftPlus is a smooth approximation to the ReLU function and can be used
-to constrain the output of a machine to always be positive.
-For numerical stability the implementation reverts to the linear function
-when \(input \times \beta > threshold\).
    
-    
    Shape
    
-
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-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softplus()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/nn_softshrink.html b/docs/reference/nn_softshrink.html
deleted file mode 100644
index db79678e3..000000000
--- a/docs/reference/nn_softshrink.html
+++ /dev/null
@@ -1,233 +0,0 @@
-
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-Softshrink module — nn_softshrink • torch
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-    
    
-    
    Softshrink module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the soft shrinkage function elementwise:
    
-    
    
-
-    
    nn_softshrink(lambd = 0.5)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    lambd
    the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5
    
-
-    
    Details
    
-
-    
    $$
-  \mbox{SoftShrinkage}(x) =
-  \left\{ \begin{array}{ll}
-x - \lambda, & \mbox{ if } x > \lambda \\
-x + \lambda, & \mbox{ if } x < -\lambda \\
-0, & \mbox{ otherwise }
-\end{array}
-\right.
-$$
    
-    
    Shape
    
-
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-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nn_softsign.html b/docs/reference/nn_softsign.html
deleted file mode 100644
index 30815b625..000000000
--- a/docs/reference/nn_softsign.html
+++ /dev/null
@@ -1,220 +0,0 @@
-
-
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-  
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-Softsign module — nn_softsign • torch
-
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-    
    Softsign module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
-$$
-  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
-$$
    
-    
    
-
-    
    nn_softsign()
    
-
-
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_softsign()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nn_tanh.html b/docs/reference/nn_tanh.html
deleted file mode 100644
index 7a1a1f310..000000000
--- a/docs/reference/nn_tanh.html
+++ /dev/null
@@ -1,219 +0,0 @@
-
-
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-Tanh module — nn_tanh • torch
-
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-    
    
-    
    Tanh module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_tanh()
    
-
-
-    
    Details
    
-
-    
    $$
-  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_tanh()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
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-
    
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diff --git a/docs/reference/nn_tanhshrink.html b/docs/reference/nn_tanhshrink.html
deleted file mode 100644
index 0bd1f23cf..000000000
--- a/docs/reference/nn_tanhshrink.html
+++ /dev/null
@@ -1,219 +0,0 @@
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-
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-
-Tanhshrink module — nn_tanhshrink • torch
-
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-    
    
-    
    Tanhshrink module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function:
    
-    
    
-
-    
    nn_tanhshrink()
    
-
-
-    
    Details
    
-
-    
    $$
-  \mbox{Tanhshrink}(x) = x - \tanh(x)
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
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    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_tanhshrink()
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
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-
    
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diff --git a/docs/reference/nn_threshold.html b/docs/reference/nn_threshold.html
deleted file mode 100644
index b7f432796..000000000
--- a/docs/reference/nn_threshold.html
+++ /dev/null
@@ -1,241 +0,0 @@
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-    
    
-    
    Threshoold module
    
-    Source: R/nn-activation.R
-    
-    
    
-
-    
    
-    
    Thresholds each element of the input Tensor.
    
-    
    
-
-    
    nn_threshold(threshold, value, inplace = FALSE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-    
-    
    threshold
    The value to threshold at
    value
    The value to replace with
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    Threshold is defined as:
-$$
-  y = 
-  \left\{ \begin{array}{ll}
-  x, &\mbox{ if } x > \mbox{threshold} \\
-  \mbox{value}, &\mbox{ otherwise }
-  \end{array}
-  \right.
-$$
    
-    
    Shape
    
-
-    
-
-
      
-
    • Input: \((N, *)\) where * means, any number of additional
-dimensions
      • 
-
    • Output: \((N, *)\), same shape as the input
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-m <- nn_threshold(0.1, 20)
-input <- torch_randn(2)
-output <- m(input)
-
-# }
    
-  
    
-  
-
    
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-  
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diff --git a/docs/reference/nn_utils_rnn_pack_padded_sequence.html b/docs/reference/nn_utils_rnn_pack_padded_sequence.html
deleted file mode 100644
index 89df73e9f..000000000
--- a/docs/reference/nn_utils_rnn_pack_padded_sequence.html
+++ /dev/null
@@ -1,246 +0,0 @@
-
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-Packs a Tensor containing padded sequences of variable length. — nn_utils_rnn_pack_padded_sequence • torch
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-    
    Packs a Tensor containing padded sequences of variable length.
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    input can be of size T x B x * where T is the length of the
-longest sequence (equal to lengths[1]), B is the batch size, and
-* is any number of dimensions (including 0). If batch_first is
-TRUE, B x T x * input is expected.
    
-    
    
-
-    
    nn_utils_rnn_pack_padded_sequence(
-  input,
-  lengths,
-  batch_first = FALSE,
-  enforce_sorted = TRUE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
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-    
-      
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-    
-    
    input
    (Tensor): padded batch of variable length sequences.
    lengths
    (Tensor): list of sequences lengths of each batch element.
    batch_first
    (bool, optional): if TRUE, the input is expected in B x T x *
-format.
    enforce_sorted
    (bool, optional): if TRUE, the input is expected to
-contain sequences sorted by length in a decreasing order. If
-FALSE, the input will get sorted unconditionally. Default: TRUE.
    
-
-    
    Value
    
-
-    
    a PackedSequence object
    
-    
    Details
    
-
-    
    For unsorted sequences, use enforce_sorted = FALSE. If enforce_sorted is
-TRUE, the sequences should be sorted by length in a decreasing order, i.e.
-input[,1] should be the longest sequence, and input[,B] the shortest
-one. enforce_sorted = TRUE is only necessary for ONNX export.
    
-    
    Note
    
-
-    
    This function accepts any input that has at least two dimensions. You
-can apply it to pack the labels, and use the output of the RNN with
-them to compute the loss directly. A Tensor can be retrieved from
-a PackedSequence object by accessing its .data attribute.
    
-
-  
    
-  
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diff --git a/docs/reference/nn_utils_rnn_pack_sequence.html b/docs/reference/nn_utils_rnn_pack_sequence.html
deleted file mode 100644
index afddfe328..000000000
--- a/docs/reference/nn_utils_rnn_pack_sequence.html
+++ /dev/null
@@ -1,232 +0,0 @@
-
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-Packs a list of variable length Tensors — nn_utils_rnn_pack_sequence • torch
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-    
    Packs a list of variable length Tensors
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    sequences should be a list of Tensors of size L x *, where L is
-the length of a sequence and * is any number of trailing dimensions,
-including zero.
    
-    
    
-
-    
    nn_utils_rnn_pack_sequence(sequences, enforce_sorted = TRUE)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    sequences
    (list[Tensor]): A list of sequences of decreasing length.
    enforce_sorted
    (bool, optional): if TRUE, checks that the input
-contains sequences sorted by length in a decreasing order. If
-FALSE, this condition is not checked. Default: TRUE.
    
-
-    
    Value
    
-
-    
    a PackedSequence object
    
-    
    Details
    
-
-    
    For unsorted sequences, use enforce_sorted = FALSE. If enforce_sorted
-is TRUE, the sequences should be sorted in the order of decreasing length.
-enforce_sorted = TRUE is only necessary for ONNX export.
    
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_tensor(c(1,2,3), dtype = torch_long())
-y <- torch_tensor(c(4, 5), dtype = torch_long())
-z <- torch_tensor(c(6), dtype = torch_long())
-
-p <- nn_utils_rnn_pack_sequence(list(x, y, z))
-
-# }
    
-  
    
-  
-
    
-
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-      
    
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diff --git a/docs/reference/nn_utils_rnn_pad_packed_sequence.html b/docs/reference/nn_utils_rnn_pad_packed_sequence.html
deleted file mode 100644
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--- a/docs/reference/nn_utils_rnn_pad_packed_sequence.html
+++ /dev/null
@@ -1,273 +0,0 @@
-
-
-
-  
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-Pads a packed batch of variable length sequences. — nn_utils_rnn_pad_packed_sequence • torch
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-    
    Pads a packed batch of variable length sequences.
    
-    Source: R/nn-utils-rnn.R
-    
-    
    
-
-    
    
-    
    It is an inverse operation to nn_utils_rnn_pack_padded_sequence().
    
-    
    
-
-    
    nn_utils_rnn_pad_packed_sequence(
-  sequence,
-  batch_first = FALSE,
-  padding_value = 0,
-  total_length = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
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-      
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    sequence
    (PackedSequence): batch to pad
    batch_first
    (bool, optional): if True, the output will be in ``B x T x *`
-format.
    padding_value
    (float, optional): values for padded elements.
    total_length
    (int, optional): if not NULL, the output will be padded to
-have length total_length. This method will throw ValueError
-if total_length is less than the max sequence length in
-sequence.
    
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    Value
    
-
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    Tuple of Tensor containing the padded sequence, and a Tensor
-containing the list of lengths of each sequence in the batch.
-Batch elements will be re-ordered as they were ordered originally when
-the batch was passed to nn_utils_rnn_pack_padded_sequence() or
-nn_utils_rnn_pack_sequence().
    
-    
    Details
    
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    The returned Tensor's data will be of size T x B x *, where T is the length
-of the longest sequence and B is the batch size. If batch_first is TRUE,
-the data will be transposed into B x T x * format.
    
-    
    Note
    
-
-    
    total_length is useful to implement the
-pack sequence -> recurrent network -> unpack sequence pattern in a
-nn_module wrapped in ~torch.nn.DataParallel.
    
-
-    
    Examples
    
-    
    # \dontrun{
-seq <- torch_tensor(rbind(c(1,2,0), c(3,0,0), c(4,5,6)))
-lens <- c(2,1,3)
-packed <- nn_utils_rnn_pack_padded_sequence(seq, lens, batch_first = TRUE,
-                                            enforce_sorted = FALSE)
-packed
    #> <PackedSequence>
-#>   Public:
-#>     batch_sizes: active binding
-#>     clone: function (deep = FALSE) 
-#>     data: active binding
-#>     initialize: function (ptr = NULL) 
-#>     ptr: externalptr
-#>     sorted_indices: active binding
-#>     unsorted_indices: active binding
    nn_utils_rnn_pad_packed_sequence(packed, batch_first=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1  2  0
-#>  3  0  0
-#>  4  5  6
-#> [ CPUFloatType{3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  2
-#>  1
-#>  3
-#> [ CPULongType{3} ]
-#> 
    
-# }
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diff --git a/docs/reference/nn_utils_rnn_pad_sequence.html b/docs/reference/nn_utils_rnn_pad_sequence.html
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-Pad a list of variable length Tensors with <code>padding_value</code> — nn_utils_rnn_pad_sequence • torch
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-and pads them to equal length. For example, if the input is list of
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-otherwise.
    
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    sequences
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    padding_value
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-
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    Value
    
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    Tensor of size T x B x * if batch_first is FALSE.
-Tensor of size B x T x * otherwise
    
-    
    Details
    
-
-    
    B is batch size. It is equal to the number of elements in sequences.
-T is length of the longest sequence.
-L is length of the sequence.
-* is any number of trailing dimensions, including none.
    
-    
    Note
    
-
-    
    This function returns a Tensor of size T x B x * or B x T x *
-where T is the length of the longest sequence. This function assumes
-trailing dimensions and type of all the Tensors in sequences are same.
    
-
-    
    Examples
    
-    
    # \dontrun{
-a <- torch_ones(25, 300)
-b <- torch_ones(22, 300)
-c <- torch_ones(15, 300)
-nn_utils_rnn_pad_sequence(list(a, b, c))$size()
    #> [1]  25   3 300
    
-# }
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diff --git a/docs/reference/nnf_adaptive_avg_pool1d.html b/docs/reference/nnf_adaptive_avg_pool1d.html
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-    Source: R/nnf-pooling.R
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-    
    Applies a 1D adaptive average pooling over an input signal composed of
-several input planes.
    
-    
    
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    nnf_adaptive_avg_pool1d(input, output_size)
    
-
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-    
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    input
    input tensor of shape (minibatch , in_channels , iW)
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-    Source: R/nnf-pooling.R
-    
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-
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-    
    Applies a 2D adaptive average pooling over an input signal composed of
-several input planes.
    
-    
    
-
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    nnf_adaptive_avg_pool2d(input, output_size)
    
-
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    Arguments
    
-    
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-      
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    input
    input tensor (minibatch, in_channels , iH , iW)
    output_size
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-    Source: R/nnf-pooling.R
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-
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-    
    Applies a 3D adaptive average pooling over an input signal composed of
-several input planes.
    
-    
    
-
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    nnf_adaptive_avg_pool3d(input, output_size)
    
-
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    Arguments
    
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    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    output_size
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diff --git a/docs/reference/nnf_adaptive_max_pool1d.html b/docs/reference/nnf_adaptive_max_pool1d.html
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-    Source: R/nnf-pooling.R
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-    
    
-
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-    
    Applies a 1D adaptive max pooling over an input signal composed of
-several input planes.
    
-    
    
-
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    nnf_adaptive_max_pool1d(input, output_size, return_indices = FALSE)
    
-
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    Arguments
    
-    
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    input
    input tensor of shape (minibatch , in_channels , iW)
    output_size
    the target output size (single integer)
    return_indices
    whether to return pooling indices. Default: FALSE
    
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diff --git a/docs/reference/nnf_adaptive_max_pool2d.html b/docs/reference/nnf_adaptive_max_pool2d.html
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    input
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    the target output size (single integer or double-integer tuple)
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diff --git a/docs/reference/nnf_adaptive_max_pool3d.html b/docs/reference/nnf_adaptive_max_pool3d.html
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-
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-    
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    Arguments
    
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    (Tensor) input batch of affine matrices with shape
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-for 2D or \(N \times C \times D \times H \times W\) for 3D)
-Example: torch.Size((32, 3, 24, 24))
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-this option. Default: False
    
-
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    the input tensor
    p
    probability of an element to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE
    inplace
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-Default: FALSE
    
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diff --git a/docs/reference/nnf_avg_pool1d.html b/docs/reference/nnf_avg_pool1d.html
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-Avg_pool1d — nnf_avg_pool1d • torch
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-    
    
-    
    Avg_pool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D average pooling over an input signal composed of several
-input planes.
    
-    
    
-
-    
    nnf_avg_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE
-)
    
-
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    input
    input tensor of shape (minibatch , in_channels , iW)
    kernel_size
    the size of the window. Can be a single number or a
-tuple (kW,).
    stride
    the stride of the window. Can be a single number or a tuple
-(sW,). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padW,). Default: 0
    ceil_mode
    when True, will use ceil instead of floor to compute the
-output shape. Default: FALSE
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation. Default: TRUE
    
-
-
-  
    
-  
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-  
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diff --git a/docs/reference/nnf_avg_pool2d.html b/docs/reference/nnf_avg_pool2d.html
deleted file mode 100644
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+++ /dev/null
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-Avg_pool2d — nnf_avg_pool2d • torch
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-    
    
-    
    Avg_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 2D average-pooling operation in \(kH * kW\) regions by step size
-\(sH * sW\) steps. The number of output features is equal to the number of
-input planes.
    
-    
    
-
-    
    nnf_avg_pool2d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
-
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    Arguments
    
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    input
    input tensor (minibatch, in_channels , iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape. Default: FALSE
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation. Default: TRUE
    divisor_override
    if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: NULL
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_avg_pool3d.html b/docs/reference/nnf_avg_pool3d.html
deleted file mode 100644
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+++ /dev/null
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-Avg_pool3d — nnf_avg_pool3d • torch
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-    
    
-    
    Avg_pool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
-size \(sT * sH * sW\) steps. The number of output features is equal to
-\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).
    
-    
    
-
-    
    nnf_avg_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  ceil_mode = FALSE,
-  count_include_pad = TRUE,
-  divisor_override = NULL
-)
    
-
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    Arguments
    
-    
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-    
-    
    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kT, kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sT, sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW), Default: 0
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape
    count_include_pad
    when True, will include the zero-padding in the
-averaging calculation
    divisor_override
    NA if specified, it will be used as divisor, otherwise
-size of the pooling region will be used. Default: NULL
    
-
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-  
    
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-  
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diff --git a/docs/reference/nnf_batch_norm.html b/docs/reference/nnf_batch_norm.html
deleted file mode 100644
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+++ /dev/null
@@ -1,243 +0,0 @@
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-Batch_norm — nnf_batch_norm • torch
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-  
    
-    
    
-    
    Batch_norm
    
-    Source: R/nnf-batchnorm.R
-    
-    
    
-
-    
    
-    
    Applies Batch Normalization for each channel across a batch of data.
    
-    
    
-
-    
    nnf_batch_norm(
-  input,
-  running_mean,
-  running_var,
-  weight = NULL,
-  bias = NULL,
-  training = FALSE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
-
-    
    Arguments
    
-    
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-      
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    input
    input tensor
    running_mean
    the running_mean tensor
    running_var
    the running_var tensor
    weight
    the weight tensor
    bias
    the bias tensor
    training
    bool wether it's training. Default: FALSE
    momentum
    the value used for the running_mean and running_var computation.
-Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
-
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-  
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-  
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diff --git a/docs/reference/nnf_bilinear.html b/docs/reference/nnf_bilinear.html
deleted file mode 100644
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+++ /dev/null
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-
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-Bilinear — nnf_bilinear • torch
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-    
    
-    
    Bilinear
    
-    Source: R/nnf-linear.R
-    
-    
    
-
-    
    
-    
    Applies a bilinear transformation to the incoming data:
-\(y = x_1 A x_2 + b\)
    
-    
    
-
-    
    nnf_bilinear(input1, input2, weight, bias = NULL)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-      
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-      
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-    
    input1
    \((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
-and \(*\) means any number of additional dimensions.
-All but the last dimension of the inputs should be the same.
    input2
    \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)
    weight
    \((\mbox{out\_features}, \mbox{in1\_features},
-\mbox{in2\_features})\)
    bias
    \((\mbox{out\_features})\)
    
-
-    
    Value
    
-
-    
    output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
-and all but the last dimension are the same shape as the input.
    
-
-  
    
-  
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-  
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diff --git a/docs/reference/nnf_binary_cross_entropy.html b/docs/reference/nnf_binary_cross_entropy.html
deleted file mode 100644
index b8c9b524e..000000000
--- a/docs/reference/nnf_binary_cross_entropy.html
+++ /dev/null
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-
-
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-  
-  
-
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-Binary_cross_entropy — nnf_binary_cross_entropy • torch
-
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-    
    
-    
    Binary_cross_entropy
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that measures the Binary Cross Entropy
-between the target and the output.
    
-    
    
-
-    
    nnf_binary_cross_entropy(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none")
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    weight
    (tensor) weight for each value.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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-
    
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diff --git a/docs/reference/nnf_binary_cross_entropy_with_logits.html b/docs/reference/nnf_binary_cross_entropy_with_logits.html
deleted file mode 100644
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--- a/docs/reference/nnf_binary_cross_entropy_with_logits.html
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-Binary_cross_entropy_with_logits — nnf_binary_cross_entropy_with_logits • torch
-
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-    
    
-    
    Binary_cross_entropy_with_logits
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that measures Binary Cross Entropy between target and output
-logits.
    
-    
    
-
-    
    nnf_binary_cross_entropy_with_logits(
-  input,
-  target,
-  weight = NULL,
-  reduction = c("mean", "sum", "none"),
-  pos_weight = NULL
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-    
    input
    Tensor of arbitrary shape
    target
    Tensor of the same shape as input
    weight
    (Tensor, optional) a manual rescaling weight if provided it's
-repeated to match input tensor shape.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    pos_weight
    (Tensor, optional) a weight of positive examples.
-Must be a vector with length equal to the number of classes.
    
-
-
-  
    
-  
-
    
-
-
-      
    
-      
    
-  
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-
    
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diff --git a/docs/reference/nnf_celu.html b/docs/reference/nnf_celu.html
deleted file mode 100644
index 2df5207f0..000000000
--- a/docs/reference/nnf_celu.html
+++ /dev/null
@@ -1,216 +0,0 @@
-
-
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-  
-  
-
-
-
-Celu — nnf_celu • torch
-
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-    
    
-    
    Celu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).
    
-    
    
-
-    
    nnf_celu(input, alpha = 1, inplace = FALSE)
-
-nnf_celu_(input, alpha = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    alpha
    the alpha value for the CELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
-  
    
-  
-
    
-
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-
    
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diff --git a/docs/reference/nnf_conv1d.html b/docs/reference/nnf_conv1d.html
deleted file mode 100644
index 3f8a34f33..000000000
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+++ /dev/null
@@ -1,243 +0,0 @@
-
-
-
-  
-  
-
-
-
-Conv1d — nnf_conv1d • torch
-
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-    
    
-    
    Conv1d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1D convolution over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-    
    input
    input tensor of shape (minibatch, in_channels , iW)
    weight
    filters of shape (out_channels, in_channels/groups , kW)
    bias
    optional bias of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or
-a one-element tuple (sW,). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple (padW,). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a one-element tuple (dW,). Default: 1
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    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    
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deleted file mode 100644
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+++ /dev/null
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-Conv2d — nnf_conv2d • torch
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-    
    
-    
    Conv2d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 2D convolution over an input image composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
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    Arguments
    
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    input
    input tensor of shape (minibatch, in_channels, iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dH, dW). Default: 1
    groups
    split input into groups, in_channels should be divisible by the
-number of groups. Default: 1
    
-
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diff --git a/docs/reference/nnf_conv3d.html b/docs/reference/nnf_conv3d.html
deleted file mode 100644
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+++ /dev/null
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-Conv3d — nnf_conv3d • torch
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-    
    
-    
    Conv3d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 3D convolution over an input image composed of several input
-planes.
    
-    
    
-
-    
    nnf_conv3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  dilation = 1,
-  groups = 1
-)
    
-
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    Arguments
    
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    input
    input tensor of shape (minibatch, in_channels , iT , iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kT , kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sT, sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW). Default: 0
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dT, dH, dW). Default: 1
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    
-
-
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-  
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diff --git a/docs/reference/nnf_conv_tbc.html b/docs/reference/nnf_conv_tbc.html
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-Conv_tbc — nnf_conv_tbc • torch
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-    
    
-    
    Conv_tbc
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1-dimensional sequence convolution over an input sequence.
-Input and output dimensions are (Time, Batch, Channels) - hence TBC.
    
-    
    
-
-    
    nnf_conv_tbc(input, weight, bias, pad = 0)
    
-
-    
    Arguments
    
-    
-    
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-      
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-    
-    
    input
    input tensor of shape \((\mbox{sequence length} \times 
-batch \times \mbox{in\_channels})\)
    weight
    filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
-\times \mbox{out\_channels}\))
    bias
    bias of shape (\(\mbox{out\_channels}\))
    pad
    number of timesteps to pad. Default: 0
    
-
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diff --git a/docs/reference/nnf_conv_transpose1d.html b/docs/reference/nnf_conv_transpose1d.html
deleted file mode 100644
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--- a/docs/reference/nnf_conv_transpose1d.html
+++ /dev/null
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-Conv_transpose1d — nnf_conv_transpose1d • torch
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-    
    Conv_transpose1d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 1D transposed convolution operator over an input signal
-composed of several input planes, sometimes also called "deconvolution".
    
-    
    
-
-    
    nnf_conv_transpose1d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
-
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    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels , iW)
    weight
    filters of shape (out_channels, in_channels/groups , kW)
    bias
    optional bias of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or
-a one-element tuple (sW,). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a one-element tuple (padW,). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a one-element tuple (dW,). Default: 1
    
-
-
-  
    
-  
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-      
    
-  
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-
    
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diff --git a/docs/reference/nnf_conv_transpose2d.html b/docs/reference/nnf_conv_transpose2d.html
deleted file mode 100644
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--- a/docs/reference/nnf_conv_transpose2d.html
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-Conv_transpose2d — nnf_conv_transpose2d • torch
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-    
    
-    
    Conv_transpose2d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 2D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution".
    
-    
    
-
-    
    nnf_conv_transpose2d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
-
-    
    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels, iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by the
-number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dH, dW). Default: 1
    
-
-
-  
    
-  
-
    
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-      
    
-      
    
-  
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-
    
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-  
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diff --git a/docs/reference/nnf_conv_transpose3d.html b/docs/reference/nnf_conv_transpose3d.html
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-Conv_transpose3d — nnf_conv_transpose3d • torch
-
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-  
    
-    
    
-    
    Conv_transpose3d
    
-    Source: R/nnf-conv.R
-    
-    
    
-
-    
    
-    
    Applies a 3D transposed convolution operator over an input image
-composed of several input planes, sometimes also called "deconvolution"
    
-    
    
-
-    
    nnf_conv_transpose3d(
-  input,
-  weight,
-  bias = NULL,
-  stride = 1,
-  padding = 0,
-  output_padding = 0,
-  groups = 1,
-  dilation = 1
-)
    
-
-    
    Arguments
    
-    
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    input
    input tensor of shape (minibatch, in_channels , iT , iH , iW)
    weight
    filters of shape (out_channels , in_channels/groups, kT , kH , kW)
    bias
    optional bias tensor of shape (out_channels). Default: NULL
    stride
    the stride of the convolving kernel. Can be a single number or a
-tuple (sT, sH, sW). Default: 1
    padding
    implicit paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW). Default: 0
    output_padding
    padding applied to the output
    groups
    split input into groups, in_channels should be divisible by
-the number of groups. Default: 1
    dilation
    the spacing between kernel elements. Can be a single number or
-a tuple (dT, dH, dW). Default: 1
    
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diff --git a/docs/reference/nnf_cosine_embedding_loss.html b/docs/reference/nnf_cosine_embedding_loss.html
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index a8e21e8c0..000000000
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-Cosine_embedding_loss — nnf_cosine_embedding_loss • torch
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-    
    Cosine_embedding_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the loss given input tensors x_1, x_2 and a
-Tensor label y with values 1 or -1. This is used for measuring whether two inputs
-are similar or dissimilar, using the cosine distance, and is typically used
-for learning nonlinear embeddings or semi-supervised learning.
    
-    
    
-
-    
    nnf_cosine_embedding_loss(
-  input1,
-  input2,
-  target,
-  margin = 0,
-  reduction = c("mean", "sum", "none")
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    input1
    the input x_1 tensor
    input2
    the input x_2 tensor
    target
    the target tensor
    margin
    Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
-is missing, the default value is 0.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
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diff --git a/docs/reference/nnf_cosine_similarity.html b/docs/reference/nnf_cosine_similarity.html
deleted file mode 100644
index bc86e3d15..000000000
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-Cosine_similarity — nnf_cosine_similarity • torch
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-  
    
-    
    
-    
    Cosine_similarity
    
-    Source: R/nnf-distance.R
-    
-    
    
-
-    
    
-    
    Returns cosine similarity between x1 and x2, computed along dim.
    
-    
    
-
-    
    nnf_cosine_similarity(x1, x2, dim = 1, eps = 1e-08)
    
-
-    
    Arguments
    
-    
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-      
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-    
    x1
    (Tensor) First input.
    x2
    (Tensor) Second input (of size matching x1).
    dim
    (int, optional) Dimension of vectors. Default: 1
    eps
    (float, optional) Small value to avoid division by zero.
-Default: 1e-8
    
-
-    
    Details
    
-
-    
    $$
-    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
-$$
    
-
-  
    
-  
-
    
-
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-      
    
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-  
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-
    
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diff --git a/docs/reference/nnf_cross_entropy.html b/docs/reference/nnf_cross_entropy.html
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-
-
-
-  
-  
-
-
-
-Cross_entropy — nnf_cross_entropy • torch
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-    Source: R/nnf-loss.R
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-    
    
-
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-    
    This criterion combines log_softmax and nll_loss in a single
-function.
    
-    
    
-
-    
    nnf_cross_entropy(
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-  target,
-  weight = NULL,
-  ignore_index = -100,
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    input
    (Tensor) \((N, C)\) where C = number of classes or \((N, C, H, W)\)
-in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
-in the case of K-dimensional loss.
    target
    (Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.
    weight
    (Tensor, optional) a manual rescaling weight given to each class. If
-given, has to be a Tensor of size C
    ignore_index
    (int, optional) Specifies a target value that is ignored
-and does not contribute to the input gradient.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_ctc_loss.html b/docs/reference/nnf_ctc_loss.html
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-Ctc_loss — nnf_ctc_loss • torch
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    Ctc_loss
    
-    Source: R/nnf-loss.R
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    The Connectionist Temporal Classification loss.
    
-    
    
-
-    
    nnf_ctc_loss(
-  log_probs,
-  targets,
-  input_lengths,
-  target_lengths,
-  blank = 0,
-  reduction = c("mean", "sum", "none"),
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    \((T, N, C)\) where C = number of characters in alphabet including blank,
-T = input length, and N = batch size. The logarithmized probabilities of
-the outputs (e.g. obtained with nnf_log_softmax).
    targets
    \((N, S)\) or (sum(target_lengths)). Targets cannot be blank.
-In the second form, the targets are assumed to be concatenated.
    input_lengths
    \((N)\). Lengths of the inputs (must each be \(\leq T\))
    target_lengths
    \((N)\). Lengths of the targets
    blank
    (int, optional) Blank label. Default \(0\).
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    zero_infinity
    (bool, optional) Whether to zero infinite losses and the
-associated gradients. Default: FALSE Infinite losses mainly occur when the
-inputs are too short to be aligned to the targets.
    
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diff --git a/docs/reference/nnf_dropout.html b/docs/reference/nnf_dropout.html
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-Dropout — nnf_dropout • torch
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-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    During training, randomly zeroes some of the elements of the input
-tensor with probability p using samples from a Bernoulli
-distribution.
    
-    
    
-
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    nnf_dropout(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
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    input
    the input tensor
    p
    probability of an element to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
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diff --git a/docs/reference/nnf_dropout2d.html b/docs/reference/nnf_dropout2d.html
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-Dropout2d — nnf_dropout2d • torch
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    Dropout2d
    
-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 2D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
    
-    
    
-
-    
    nnf_dropout2d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
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    input
    the input tensor
    p
    probability of a channel to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE.
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
-
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diff --git a/docs/reference/nnf_dropout3d.html b/docs/reference/nnf_dropout3d.html
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-Dropout3d — nnf_dropout3d • torch
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    Dropout3d
    
-    Source: R/nnf-dropout.R
-    
-    
    
-
-    
    
-    
    Randomly zero out entire channels (a channel is a 3D feature map,
-e.g., the \(j\)-th channel of the \(i\)-th sample in the
-batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
-Each channel will be zeroed out independently on every forward call with
-probability p using samples from a Bernoulli distribution.
    
-    
    
-
-    
    nnf_dropout3d(input, p = 0.5, training = TRUE, inplace = FALSE)
    
-
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    input
    the input tensor
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    probability of a channel to be zeroed. Default: 0.5
    training
    apply dropout if is TRUE. Default: TRUE.
    inplace
    If set to TRUE, will do this operation in-place.
-Default: FALSE
    
-
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diff --git a/docs/reference/nnf_elu.html b/docs/reference/nnf_elu.html
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-Elu — nnf_elu • torch
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    Elu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise,
-$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.
    
-    
    
-
-    
    nnf_elu(input, alpha = 1, inplace = FALSE)
-
-nnf_elu_(input, alpha = 1)
    
-
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    Arguments
    
-    
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-      
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    alpha
    the alpha value for the ELU formulation. Default: 1.0
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_randn(2, 2)
-y <- nnf_elu(x, alpha = 1)
-nnf_elu_(x, alpha = 1)
    #> torch_tensor 
-#> -0.7520  0.2844
-#>  1.3381  0.9215
-#> [ CPUFloatType{2,2} ]
    torch_equal(x, y)
    #> [1] TRUE
    
-# }
-
    
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diff --git a/docs/reference/nnf_embedding.html b/docs/reference/nnf_embedding.html
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-Embedding — nnf_embedding • torch
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    Embedding
    
-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    A simple lookup table that looks up embeddings in a fixed dictionary and size.
    
-    
    
-
-    
    nnf_embedding(
-  input,
-  weight,
-  padding_idx = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  sparse = FALSE
-)
    
-
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    Arguments
    
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    input
    (LongTensor) Tensor containing indices into the embedding matrix
    weight
    (Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size
    padding_idx
    (int, optional) If given, pads the output with the embedding
-vector at padding_idx (initialized to zeros) whenever it encounters the index.
    max_norm
    (float, optional) If given, each embedding vector with norm larger
-than max_norm is renormalized to have norm max_norm. Note: this will modify
-weight in-place.
    norm_type
    (float, optional) The p of the p-norm to compute for the max_norm
-option. Default 2.
    scale_grad_by_freq
    (boolean, optional) If given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default FALSE.
    sparse
    (bool, optional) If TRUE, gradient w.r.t. weight will be a
-sparse tensor. See Notes under nn_embedding for more details regarding
-sparse gradients.
    
-
-    
    Details
    
-
-    
    This module is often used to retrieve word embeddings using indices.
-The input to the module is a list of indices, and the embedding matrix,
-and the output is the corresponding word embeddings.
    
-
-  
    
-  
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-
    
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diff --git a/docs/reference/nnf_embedding_bag.html b/docs/reference/nnf_embedding_bag.html
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-Embedding_bag — nnf_embedding_bag • torch
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-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    Computes sums, means or maxes of bags of embeddings, without instantiating the
-intermediate embeddings.
    
-    
    
-
-    
    nnf_embedding_bag(
-  input,
-  weight,
-  offsets = NULL,
-  max_norm = NULL,
-  norm_type = 2,
-  scale_grad_by_freq = FALSE,
-  mode = "mean",
-  sparse = FALSE,
-  per_sample_weights = NULL,
-  include_last_offset = FALSE
-)
    
-
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    input
    (LongTensor) Tensor containing bags of indices into the embedding matrix
    weight
    (Tensor) The embedding matrix with number of rows equal to the
-maximum possible index + 1, and number of columns equal to the embedding size
    offsets
    (LongTensor, optional) Only used when input is 1D. offsets
-determines the starting index position of each bag (sequence) in input.
    max_norm
    (float, optional) If given, each embedding vector with norm
-larger than max_norm is renormalized to have norm max_norm.
-Note: this will modify weight in-place.
    norm_type
    (float, optional) The p in the p-norm to compute for the
-max_norm option. Default 2.
    scale_grad_by_freq
    (boolean, optional) if given, this will scale gradients
-by the inverse of frequency of the words in the mini-batch. Default FALSE.                                            Note: this option is not supported when mode="max".
    mode
    (string, optional) "sum", "mean" or "max". Specifies
-the way to reduce the bag. Default: 'mean'
    sparse
    (bool, optional) if TRUE, gradient w.r.t. weight will be a
-sparse tensor. See Notes under nn_embedding for more details regarding
-sparse gradients. Note: this option is not supported when mode="max".
    per_sample_weights
    (Tensor, optional) a tensor of float / double weights,
-or NULL to indicate all weights should be taken to be 1. If specified,
-per_sample_weights must have exactly the same shape as input and is treated
-as having the same offsets, if those are not NULL.
    include_last_offset
    (bool, optional) if TRUE, the size of offsets is
-equal to the number of bags + 1.
    
-
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diff --git a/docs/reference/nnf_fold.html b/docs/reference/nnf_fold.html
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    Fold
    
-    Source: R/nnf-fold.R
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-    
    Combines an array of sliding local blocks into a large containing
-tensor.
    
-    
    
-
-    
    nnf_fold(
-  input,
-  output_size,
-  kernel_size,
-  dilation = 1,
-  padding = 0,
-  stride = 1
-)
    
-
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    Arguments
    
-    
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    input
    the input tensor
    output_size
    the shape of the spatial dimensions of the output (i.e.,
-output$sizes()[-c(1,2)])
    kernel_size
    the size of the sliding blocks
    dilation
    a parameter that controls the stride of elements within the
-neighborhood. Default: 1
    padding
    implicit zero padding to be added on both sides of input.
-Default: 0
    stride
    the stride of the sliding blocks in the input spatial dimensions.
-Default: 1
    
-
-    
    Warning
    
-
-    
-
-
-
    Currently, only 4-D output tensors (batched image-like tensors) are
-supported.
    
-
-  
    
-  
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diff --git a/docs/reference/nnf_fractional_max_pool2d.html b/docs/reference/nnf_fractional_max_pool2d.html
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    Fractional_max_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 2D fractional max pooling over an input signal composed of several input planes.
    
-    
    
-
-    
    nnf_fractional_max_pool2d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
-
-    
    Arguments
    
-    
-    
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-      
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    input
    the input tensor
    kernel_size
    the size of the window to take a max over. Can be a
-single number \(k\) (for a square kernel of \(k * k\)) or
-a tuple (kH, kW)
    output_size
    the target output size of the image of the form \(oH * oW\).
-Can be a tuple (oH, oW) or a single number \(oH\) for a square image \(oH * oH\)
    output_ratio
    If one wants to have an output size as a ratio of the input size,
-this option can be given. This has to be a number or tuple in the range (0, 1)
    return_indices
    if True, will return the indices along with the outputs.
    random_samples
    optional random samples.
    
-
-    
    Details
    
-
-    
    Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham
    
-
    The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.
    
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-  
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diff --git a/docs/reference/nnf_fractional_max_pool3d.html b/docs/reference/nnf_fractional_max_pool3d.html
deleted file mode 100644
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-Fractional_max_pool3d — nnf_fractional_max_pool3d • torch
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-    
    Fractional_max_pool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies 3D fractional max pooling over an input signal composed of several input planes.
    
-    
    
-
-    
    nnf_fractional_max_pool3d(
-  input,
-  kernel_size,
-  output_size = NULL,
-  output_ratio = NULL,
-  return_indices = FALSE,
-  random_samples = NULL
-)
    
-
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    Arguments
    
-    
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    input
    the input tensor
    kernel_size
    the size of the window to take a max over. Can be a single number \(k\)
-(for a square kernel of \(k * k * k\)) or a tuple (kT, kH, kW)
    output_size
    the target output size of the form \(oT * oH * oW\).
-Can be a tuple (oT, oH, oW) or a single number \(oH\) for a cubic output
-\(oH * oH * oH\)
    output_ratio
    If one wants to have an output size as a ratio of the
-input size, this option can be given. This has to be a number or tuple in the
-range (0, 1)
    return_indices
    if True, will return the indices along with the outputs.
    random_samples
    undocumented argument.
    
-
-    
    Details
    
-
-    
    Fractional MaxPooling is described in detail in the paper Fractional MaxPooling_ by Ben Graham
    
-
    The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
-step size determined by the target output size.
-The number of output features is equal to the number of input planes.
    
-
-  
    
-  
-
    
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-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
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diff --git a/docs/reference/nnf_gelu.html b/docs/reference/nnf_gelu.html
deleted file mode 100644
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-    
    
-    
    Gelu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Gelu
    
-    
    
-
-    
    nnf_gelu(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
-    
    gelu(input) -> Tensor 
    
-
-    
-
-
-
    Applies element-wise the function
-\(GELU(x) = x * \Phi(x)\)
    
-
    where \(\Phi(x)\) is the Cumulative Distribution Function for
-Gaussian Distribution.
    
-
    See Gaussian Error Linear Units (GELUs).
    
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diff --git a/docs/reference/nnf_glu.html b/docs/reference/nnf_glu.html
deleted file mode 100644
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-Glu — nnf_glu • torch
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    Glu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    The gated linear unit. Computes:
    
-    
    
-
-    
    nnf_glu(input, dim = -1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-      
-      
-    
-    
    input
    (Tensor) input tensor
    dim
    (int) dimension on which to split the input. Default: -1
    
-
-    
    Details
    
-
-    
    $$GLU(a, b) = a \otimes \sigma(b)$$
    
-
    where input is split in half along dim to form a and b, \(\sigma\)
-is the sigmoid function and \(\otimes\) is the element-wise product
-between matrices.
    
-
    See Language Modeling with Gated Convolutional Networks.
    
-
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diff --git a/docs/reference/nnf_grid_sample.html b/docs/reference/nnf_grid_sample.html
deleted file mode 100644
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-Grid_sample — nnf_grid_sample • torch
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-    
    Grid_sample
    
-    Source: R/nnf-vision.R
-    
-    
    
-
-    
    
-    
    Given an input and a flow-field grid, computes the
-output using input values and pixel locations from grid.
    
-    
    
-
-    
    nnf_grid_sample(
-  input,
-  grid,
-  mode = c("bilinear", "nearest"),
-  padding_mode = c("zeros", "border", "reflection"),
-  align_corners = FALSE
-)
    
-
-    
    Arguments
    
-    
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    input
    (Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)
    grid
    (Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)
    mode
    (str) interpolation mode to calculate output values 'bilinear' | 'nearest'.
-Default: 'bilinear'
    padding_mode
    (str) padding mode for outside grid values 'zeros' | 'border'
-| 'reflection'. Default: 'zeros'
    align_corners
    (bool, optional) Geometrically, we consider the pixels of the
-input  as squares rather than points. If set to True, the extrema (-1 and
-1) are considered as referring to the center points of the input's corner pixels.
-If set to False, they are instead considered as referring to the corner
-points of the input's corner pixels, making the sampling more resolution
-agnostic. This option parallels the align_corners option in  nnf_interpolate(), and
-so whichever option is used here should also be used there to resize the input
-image before grid sampling. Default: False
    
-
-    
    Details
    
-
-    
    Currently, only spatial (4-D) and volumetric (5-D) input are
-supported.
    
-
    In the spatial (4-D) case, for input with shape
-\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and grid with shape
-\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
-\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).
    
-
    For each output location output[n, :, h, w], the size-2 vector
-grid[n, h, w] specifies input pixel locations x and y,
-which are used to interpolate the output value output[n, :, h, w].
-In the case of 5D inputs, grid[n, d, h, w] specifies the
-x, y, z pixel locations for interpolating
-output[n, :, d, h, w]. mode argument specifies nearest or
-bilinear interpolation method to sample the input pixels.
    
-
    grid specifies the sampling pixel locations normalized by the
-input spatial dimensions. Therefore, it should have most values in
-the range of [-1, 1]. For example, values x = -1, y = -1 is the
-left-top pixel of input, and values  x = 1, y = 1 is the
-right-bottom pixel of input.
    
-
    If grid has values outside the range of [-1, 1], the corresponding
-outputs are handled as defined by padding_mode. Options are
      
-
    • padding_mode="zeros": use 0 for out-of-bound grid locations,
      • 
-
    • padding_mode="border": use border values for out-of-bound grid locations,
      • 
-
    • padding_mode="reflection": use values at locations reflected by
-the border for out-of-bound grid locations. For location far away
-from the border, it will keep being reflected until becoming in bound,
-e.g., (normalized) pixel location x = -3.5 reflects by border -1
-and becomes x' = 1.5, then reflects by border 1 and becomes
-x'' = -0.5.
      • 
-
    
-
-    
    Note
    
-
-    
-
-
-
    This function is often used in conjunction with nnf_affine_grid()
-to build Spatial Transformer Networks_ .
    
-
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diff --git a/docs/reference/nnf_group_norm.html b/docs/reference/nnf_group_norm.html
deleted file mode 100644
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-Group_norm — nnf_group_norm • torch
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-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Applies Group Normalization for last certain number of dimensions.
    
-    
    
-
-    
    nnf_group_norm(input, num_groups, weight = NULL, bias = NULL, eps = 1e-05)
    
-
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    Arguments
    
-    
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-      
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    input
    the input tensor
    num_groups
    number of groups to separate the channels into
    weight
    the weight tensor
    bias
    the bias tensor
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
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diff --git a/docs/reference/nnf_gumbel_softmax.html b/docs/reference/nnf_gumbel_softmax.html
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-Gumbel_softmax — nnf_gumbel_softmax • torch
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    Gumbel_softmax
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Samples from the Gumbel-Softmax distribution and
-optionally discretizes.
    
-    
    
-
-    
    nnf_gumbel_softmax(logits, tau = 1, hard = FALSE, dim = -1)
    
-
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    Arguments
    
-    
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    logits
    [..., num_features] unnormalized log probabilities
    tau
    non-negative scalar temperature
    hard
    if True, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd
    dim
    (int) A dimension along which softmax will be computed. Default: -1.
    
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diff --git a/docs/reference/nnf_hardshrink.html b/docs/reference/nnf_hardshrink.html
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-    Source: R/nnf-activation.R
-    
-    
    
-
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-    
    Applies the hard shrinkage function element-wise
    
-    
    
-
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    nnf_hardshrink(input, lambd = 0.5)
    
-
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    Arguments
    
-    
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-      
-      
-    
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-      
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    lambd
    the lambda value for the Hardshrink formulation. Default: 0.5
    
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diff --git a/docs/reference/nnf_hardsigmoid.html b/docs/reference/nnf_hardsigmoid.html
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-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies the element-wise function \(\mbox{Hardsigmoid}(x) = \frac{ReLU6(x + 3)}{6}\)
    
-    
    
-
-    
    nnf_hardsigmoid(input, inplace = FALSE)
    
-
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    Arguments
    
-    
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-      
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-      
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
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-Hardswish — nnf_hardswish • torch
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-    
    Hardswish
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies the hardswish function, element-wise, as described in the paper:
-Searching for MobileNetV3.
    
-    
    
-
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    nnf_hardswish(input, inplace = FALSE)
    
-
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    Arguments
    
-    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-    
    Details
    
-
-    
    $$ \mbox{Hardswish}(x) = \left\{
-  \begin{array}{ll}
-  0 & \mbox{if } x \le -3, \\
-  x & \mbox{if } x \ge +3, \\
-  x \cdot (x + 3)/6 & \mbox{otherwise}
-  \end{array}
-  \right. $$
    
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-Hardtanh — nnf_hardtanh • torch
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    Hardtanh
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies the HardTanh function element-wise.
    
-    
    
-
-    
    nnf_hardtanh(input, min_val = -1, max_val = 1, inplace = FALSE)
-
-nnf_hardtanh_(input, min_val = -1, max_val = 1)
    
-
-    
    Arguments
    
-    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    min_val
    minimum value of the linear region range. Default: -1
    max_val
    maximum value of the linear region range. Default: 1
    inplace
    can optionally do the operation in-place. Default: FALSE
    
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diff --git a/docs/reference/nnf_hinge_embedding_loss.html b/docs/reference/nnf_hinge_embedding_loss.html
deleted file mode 100644
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-Hinge_embedding_loss — nnf_hinge_embedding_loss • torch
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-    
    
-    
    Hinge_embedding_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
-This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
-using the L1 pairwise distance as xx , and is typically used for learning nonlinear
-embeddings or semi-supervised learning.
    
-    
    
-
-    
    nnf_hinge_embedding_loss(input, target, margin = 1, reduction = "mean")
    
-
-    
    Arguments
    
-    
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-      
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    margin
    Has a default value of 1.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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diff --git a/docs/reference/nnf_instance_norm.html b/docs/reference/nnf_instance_norm.html
deleted file mode 100644
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-  
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-Instance_norm — nnf_instance_norm • torch
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-    
    Instance_norm
    
-    Source: R/nnf-instancenorm.R
-    
-    
    
-
-    
    
-    
    Applies Instance Normalization for each channel in each data sample in a
-batch.
    
-    
    
-
-    
    nnf_instance_norm(
-  input,
-  running_mean = NULL,
-  running_var = NULL,
-  weight = NULL,
-  bias = NULL,
-  use_input_stats = TRUE,
-  momentum = 0.1,
-  eps = 1e-05
-)
    
-
-    
    Arguments
    
-    
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    input
    the input tensor
    running_mean
    the running_mean tensor
    running_var
    the running var tensor
    weight
    the weight tensor
    bias
    the bias tensor
    use_input_stats
    whether to use input stats
    momentum
    a double for the momentum
    eps
    an eps double for numerical stability
    
-
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-  
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diff --git a/docs/reference/nnf_interpolate.html b/docs/reference/nnf_interpolate.html
deleted file mode 100644
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+++ /dev/null
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-Interpolate — nnf_interpolate • torch
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-    
    Interpolate
    
-    Source: R/nnf-upsampling.R
-    
-    
    
-
-    
    
-    
    Down/up samples the input to either the given size or the given
-scale_factor
    
-    
    
-
-    
    nnf_interpolate(
-  input,
-  size = NULL,
-  scale_factor = NULL,
-  mode = "nearest",
-  align_corners = FALSE,
-  recompute_scale_factor = NULL
-)
    
-
-    
    Arguments
    
-    
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-      
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    input
    (Tensor) the input tensor
    size
    (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int])
-output spatial size.
    scale_factor
    (float or Tuple[float]) multiplier for spatial size.
-Has to match input size if it is a tuple.
    mode
    (str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
-| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'
    align_corners
    (bool, optional) Geometrically, we consider the pixels
-of the input and output as squares rather than points. If set to TRUE,
-the input and output tensors are aligned by the center points of their corner
-pixels, preserving the values at the corner pixels. If set to False, the
-input and output tensors are aligned by the corner points of their corner pixels,
-and the interpolation uses edge value padding for out-of-boundary values,
-making this operation independent of input size when scale_factor is kept
-the same. This only has an effect when mode  is 'linear', 'bilinear',
-'bicubic' or 'trilinear'.  Default: False
    recompute_scale_factor
    (bool, optional) recompute the scale_factor
-for use in the interpolation calculation.  When scale_factor is passed
-as a parameter, it is used to compute the output_size.  If recompute_scale_factor
-is ```True`` or not specified, a new scale_factor will be computed based on
-the output and input sizes for use in the interpolation computation (i.e. the
-computation will be identical to if the computed `output_size` were passed-in
-explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
-interpolation computation.  Note that when `scale_factor` is floating-point,
-the recomputed scale_factor may differ from the one passed in due to rounding
-and precision issues.
    
-
-    
    Details
    
-
-    
    The algorithm used for interpolation is determined by mode.
    
-
    Currently temporal, spatial and volumetric sampling are supported, i.e.
-expected inputs are 3-D, 4-D or 5-D in shape.
    
-
    The input dimensions are interpreted in the form:
-mini-batch x channels x [optional depth] x [optional height] x width.
    
-
    The modes available for resizing are: nearest, linear (3D-only),
-bilinear, bicubic (4D-only), trilinear (5D-only), area
    
-
-  
    
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diff --git a/docs/reference/nnf_kl_div.html b/docs/reference/nnf_kl_div.html
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-
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-Kl_div — nnf_kl_div • torch
-
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-    
    
-    
    Kl_div
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    The Kullback-Leibler divergence Loss.
    
-    
    
-
-    
    nnf_kl_div(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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-  
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-
    
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diff --git a/docs/reference/nnf_l1_loss.html b/docs/reference/nnf_l1_loss.html
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-
-
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-  
-  
-
-
-
-L1_loss — nnf_l1_loss • torch
-
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-    
    
-    
    L1_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Function that takes the mean element-wise absolute value difference.
    
-    
    
-
-    
    nnf_l1_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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-  
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diff --git a/docs/reference/nnf_layer_norm.html b/docs/reference/nnf_layer_norm.html
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-  
-  
-
-
-
-Layer_norm — nnf_layer_norm • torch
-
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-
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-  
    
-    
    
-    
    Layer_norm
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Applies Layer Normalization for last certain number of dimensions.
    
-    
    
-
-    
    nnf_layer_norm(
-  input,
-  normalized_shape,
-  weight = NULL,
-  bias = NULL,
-  eps = 1e-05
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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-    
-    
-      
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-    
-    
-      
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-    
-      
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-    
-    
    input
    the input tensor
    normalized_shape
    input shape from an expected input of size. If a single
-integer is used, it is treated as a singleton list, and this module will normalize
-over the last dimension which is expected to be of that specific size.
    weight
    the weight tensor
    bias
    the bias tensor
    eps
    a value added to the denominator for numerical stability. Default: 1e-5
    
-
-
-  
    
-  
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diff --git a/docs/reference/nnf_leaky_relu.html b/docs/reference/nnf_leaky_relu.html
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+++ /dev/null
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-
-
-
-  
-  
-
-
-
-Leaky_relu — nnf_leaky_relu • torch
-
-
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-    Source: R/nnf-activation.R
-    
-    
    
-
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-    
    Applies element-wise,
-\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)
    
-    
    
-
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    nnf_leaky_relu(input, negative_slope = 0.01, inplace = FALSE)
    
-
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    negative_slope
    Controls the angle of the negative slope. Default: 1e-2
    inplace
    can optionally do the operation in-place. Default: FALSE
    
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diff --git a/docs/reference/nnf_linear.html b/docs/reference/nnf_linear.html
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-Linear — nnf_linear • torch
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    Linear
    
-    Source: R/nnf-linear.R
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-
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-    
    Applies a linear transformation to the incoming data: \(y = xA^T + b\).
    
-    
    
-
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    nnf_linear(input, weight, bias = NULL)
    
-
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    input
    \((N, *, in\_features)\) where * means any number of
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    optional tensor \((out\_features)\)
    
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diff --git a/docs/reference/nnf_local_response_norm.html b/docs/reference/nnf_local_response_norm.html
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-Local_response_norm — nnf_local_response_norm • torch
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-    
    Local_response_norm
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Applies local response normalization over an input signal composed of
-several input planes, where channels occupy the second dimension.
-Applies normalization across channels.
    
-    
    
-
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    nnf_local_response_norm(input, size, alpha = 1e-04, beta = 0.75, k = 1)
    
-
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    Arguments
    
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    input
    the input tensor
    size
    amount of neighbouring channels used for normalization
    alpha
    multiplicative factor. Default: 0.0001
    beta
    exponent. Default: 0.75
    k
    additive factor. Default: 1
    
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diff --git a/docs/reference/nnf_log_softmax.html b/docs/reference/nnf_log_softmax.html
deleted file mode 100644
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-Log_softmax — nnf_log_softmax • torch
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    Log_softmax
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies a softmax followed by a logarithm.
    
-    
    
-
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    nnf_log_softmax(input, dim = NULL, dtype = NULL)
    
-
-    
    Arguments
    
-    
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    input
    (Tensor) input
    dim
    (int) A dimension along which log_softmax will be computed.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.
-If specified, the input tensor is casted to dtype before the operation
-is performed. This is useful for preventing data type overflows.
-Default: NULL.
    
-
-    
    Details
    
-
-    
    While mathematically equivalent to log(softmax(x)), doing these two
-operations separately is slower, and numerically unstable. This function
-uses an alternative formulation to compute the output and gradient correctly.
    
-
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diff --git a/docs/reference/nnf_logsigmoid.html b/docs/reference/nnf_logsigmoid.html
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-Logsigmoid — nnf_logsigmoid • torch
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-    
    
-    
    Logsigmoid
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise \(LogSigmoid(x_i) = log(\frac{1}{1 + exp(-x_i)})\)
    
-    
    
-
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    nnf_logsigmoid(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
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-Lp_pool1d — nnf_lp_pool1d • torch
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-    
    Lp_pool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of p is
-zero, the gradient is set to zero as well.
    
-    
    
-
-    
    nnf_lp_pool1d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
-
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    Arguments
    
-    
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    input
    the input tensor
    norm_type
    if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)
    kernel_size
    a single int, the size of the window
    stride
    a single int, the stride of the window. Default value is kernel_size
    ceil_mode
    when True, will use ceil instead of floor to compute the output shape
    
-
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-Lp_pool2d — nnf_lp_pool2d • torch
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-    
    Lp_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D power-average pooling over an input signal composed of
-several input planes. If the sum of all inputs to the power of p is
-zero, the gradient is set to zero as well.
    
-    
    
-
-    
    nnf_lp_pool2d(input, norm_type, kernel_size, stride = NULL, ceil_mode = FALSE)
    
-
-    
    Arguments
    
-    
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-      
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    input
    the input tensor
    norm_type
    if inf than one gets max pooling if 0 you get sum pooling (
-proportional to the avg pooling)
    kernel_size
    a single int, the size of the window
    stride
    a single int, the stride of the window. Default value is kernel_size
    ceil_mode
    when True, will use ceil instead of floor to compute the output shape
    
-
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diff --git a/docs/reference/nnf_margin_ranking_loss.html b/docs/reference/nnf_margin_ranking_loss.html
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-Margin_ranking_loss — nnf_margin_ranking_loss • torch
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-    
    Margin_ranking_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
-mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).
    
-    
    
-
-    
    nnf_margin_ranking_loss(input1, input2, target, margin = 0, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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    input1
    the first tensor
    input2
    the second input tensor
    target
    the target tensor
    margin
    Has a default value of 00 .
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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diff --git a/docs/reference/nnf_max_pool1d.html b/docs/reference/nnf_max_pool1d.html
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-
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-  
-
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-Max_pool1d — nnf_max_pool1d • torch
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    Max_pool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 1D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool1d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
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    input
    input tensor of shape (minibatch , in_channels , iW)
    kernel_size
    the size of the window. Can be a single number or a
-tuple (kW,).
    stride
    the stride of the window. Can be a single number or a tuple
-(sW,). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padW,). Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor to compute the
-output shape. Default: FALSE
    return_indices
    whether to return the indices where the max occurs.
    
-
-
-  
    
-  
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-
    
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diff --git a/docs/reference/nnf_max_pool2d.html b/docs/reference/nnf_max_pool2d.html
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+++ /dev/null
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-  
-
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-
-Max_pool2d — nnf_max_pool2d • torch
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-    
    Max_pool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 2D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool2d(
-  input,
-  kernel_size,
-  stride = kernel_size,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
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-      
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-    
-    
    input
    input tensor (minibatch, in_channels , iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padH, padW). Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape. Default: FALSE
    return_indices
    whether to return the indices where the max occurs.
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
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diff --git a/docs/reference/nnf_max_pool3d.html b/docs/reference/nnf_max_pool3d.html
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-Max_pool3d — nnf_max_pool3d • torch
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-    
    
-    
    Max_pool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Applies a 3D max pooling over an input signal composed of several input
-planes.
    
-    
    
-
-    
    nnf_max_pool3d(
-  input,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  dilation = 1,
-  ceil_mode = FALSE,
-  return_indices = FALSE
-)
    
-
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    Arguments
    
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    input
    input tensor (minibatch, in_channels , iT * iH , iW)
    kernel_size
    size of the pooling region. Can be a single number or a
-tuple (kT, kH, kW)
    stride
    stride of the pooling operation. Can be a single number or a
-tuple (sT, sH, sW). Default: kernel_size
    padding
    implicit zero paddings on both sides of the input. Can be a
-single number or a tuple (padT, padH, padW), Default: 0
    dilation
    controls the spacing between the kernel points; also known as
-the à trous algorithm.
    ceil_mode
    when True, will use ceil instead of floor in the formula
-to compute the output shape
    return_indices
    whether to return the indices where the max occurs.
    
-
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diff --git a/docs/reference/nnf_max_unpool1d.html b/docs/reference/nnf_max_unpool1d.html
deleted file mode 100644
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-Max_unpool1d — nnf_max_unpool1d • torch
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-    
    Max_unpool1d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool1d.
    
-    
    
-
-    
    nnf_max_unpool1d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
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    Arguments
    
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    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
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-  
    
-  
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-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_max_unpool2d.html b/docs/reference/nnf_max_unpool2d.html
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+++ /dev/null
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-Max_unpool2d — nnf_max_unpool2d • torch
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-    
    Max_unpool2d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool2d.
    
-    
    
-
-    
    nnf_max_unpool2d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
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    Arguments
    
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    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
-
-  
    
-  
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/nnf_max_unpool3d.html b/docs/reference/nnf_max_unpool3d.html
deleted file mode 100644
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+++ /dev/null
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-
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-  
-  
-
-
-
-Max_unpool3d — nnf_max_unpool3d • torch
-
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-  
    
-    
    
-    
    Max_unpool3d
    
-    Source: R/nnf-pooling.R
-    
-    
    
-
-    
    
-    
    Computes a partial inverse of MaxPool3d.
    
-    
    
-
-    
    nnf_max_unpool3d(
-  input,
-  indices,
-  kernel_size,
-  stride = NULL,
-  padding = 0,
-  output_size = NULL
-)
    
-
-    
    Arguments
    
-    
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-      
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-    
-    
    input
    the input Tensor to invert
    indices
    the indices given out by max pool
    kernel_size
    Size of the max pooling window.
    stride
    Stride of the max pooling window. It is set to kernel_size by default.
    padding
    Padding that was added to the input
    output_size
    the targeted output size
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
-
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-  
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diff --git a/docs/reference/nnf_mse_loss.html b/docs/reference/nnf_mse_loss.html
deleted file mode 100644
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-
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-Mse_loss — nnf_mse_loss • torch
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-    
    
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    Mse_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Measures the element-wise mean squared error.
    
-    
    
-
-    
    nnf_mse_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/nnf_multi_head_attention_forward.html b/docs/reference/nnf_multi_head_attention_forward.html
deleted file mode 100644
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+++ /dev/null
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-Multi head attention forward — nnf_multi_head_attention_forward • torch
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-    
    Multi head attention forward
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Allows the model to jointly attend to information from different representation
-subspaces. See reference: Attention Is All You Need
    
-    
    
-
-    
    nnf_multi_head_attention_forward(
-  query,
-  key,
-  value,
-  embed_dim_to_check,
-  num_heads,
-  in_proj_weight,
-  in_proj_bias,
-  bias_k,
-  bias_v,
-  add_zero_attn,
-  dropout_p,
-  out_proj_weight,
-  out_proj_bias,
-  training = TRUE,
-  key_padding_mask = NULL,
-  need_weights = TRUE,
-  attn_mask = NULL,
-  use_separate_proj_weight = FALSE,
-  q_proj_weight = NULL,
-  k_proj_weight = NULL,
-  v_proj_weight = NULL,
-  static_k = NULL,
-  static_v = NULL
-)
    
-
-    
    Arguments
    
-    
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-    
-    
    query
    \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
-the embedding dimension.
    key
    \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
    value
    \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
-the embedding dimension.
    embed_dim_to_check
    total dimension of the model.
    num_heads
    parallel attention heads.
    in_proj_weight
    input projection weight and bias.
    in_proj_bias
    currently undocumented.
    bias_k
    bias of the key and value sequences to be added at dim=0.
    bias_v
    currently undocumented.
    add_zero_attn
    add a new batch of zeros to the key and
-value sequences at dim=1.
    dropout_p
    probability of an element to be zeroed.
    out_proj_weight
    the output projection weight and bias.
    out_proj_bias
    currently undocumented.
    training
    apply dropout if is TRUE.
    key_padding_mask
    \((N, S)\) where N is the batch size, S is the source sequence length.
-If a ByteTensor is provided, the non-zero positions will be ignored while the position
-with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
-value of True will be ignored while the position with the value of False will be unchanged.
    need_weights
    output attn_output_weights.
    attn_mask
    2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
-3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
-S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-while the zero positions will be unchanged. If a BoolTensor is provided, positions with True
-is not allowed to attend while False values will be unchanged. If a FloatTensor
-is provided, it will be added to the attention weight.
    use_separate_proj_weight
    the function accept the proj. weights for
-query, key, and value in different forms. If false, in_proj_weight will be used,
-which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.
    q_proj_weight
    input projection weight and bias.
    k_proj_weight
    currently undocumented.
    v_proj_weight
    currently undocumented.
    static_k
    static key and value used for attention operators.
    static_v
    currently undocumented.
    
-
-
-  
    
-  
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diff --git a/docs/reference/nnf_multi_margin_loss.html b/docs/reference/nnf_multi_margin_loss.html
deleted file mode 100644
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+++ /dev/null
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-Multi_margin_loss — nnf_multi_margin_loss • torch
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-    
    Multi_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-class classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
-(which is a 1D tensor of target class indices, 0 <= y <= x$size(2) - 1 ).
    
-    
    
-
-    
    nnf_multi_margin_loss(
-  input,
-  target,
-  p = 1,
-  margin = 1,
-  weight = NULL,
-  reduction = "mean"
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
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-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    p
    Has a default value of 1. 1 and 2 are the only supported values.
    margin
    Has a default value of 1.
    weight
    a manual rescaling weight given to each class. If given, it has to
-be a Tensor of size C. Otherwise, it is treated as if having all ones.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
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-  
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    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/nnf_multilabel_margin_loss.html b/docs/reference/nnf_multilabel_margin_loss.html
deleted file mode 100644
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--- a/docs/reference/nnf_multilabel_margin_loss.html
+++ /dev/null
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-
-
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-  
-  
-
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-Multilabel_margin_loss — nnf_multilabel_margin_loss • torch
-
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-  
    
-    
    
-    
    Multilabel_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-class multi-classification hinge loss
-(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
-is a 2D Tensor of target class indices).
    
-    
    
-
-    
    nnf_multilabel_margin_loss(input, target, reduction = "mean")
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
-
-  
    
-  
-
    
-
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-      
    
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-  
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-
    
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diff --git a/docs/reference/nnf_multilabel_soft_margin_loss.html b/docs/reference/nnf_multilabel_soft_margin_loss.html
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-Multilabel_soft_margin_loss — nnf_multilabel_soft_margin_loss • torch
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-    
    Multilabel_soft_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that optimizes a multi-label one-versus-all loss based on
-max-entropy, between input x and target y of size (N, C).
    
-    
    
-
-    
    nnf_multilabel_soft_margin_loss(input, target, weight, reduction = "mean")
    
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    weight
    weight tensor to apply on the loss.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_nll_loss.html b/docs/reference/nnf_nll_loss.html
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-Nll_loss — nnf_nll_loss • torch
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-    Source: R/nnf-loss.R
-    
-    
    
-
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-    
    The negative log likelihood loss.
    
-    
    
-
-    
    nnf_nll_loss(
-  input,
-  target,
-  weight = NULL,
-  ignore_index = -100,
-  reduction = "mean"
-)
    
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    Arguments
    
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    input
    \((N, C)\) where C = number of classes or \((N, C, H, W)\) in
-case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
-the case of K-dimensional loss.
    target
    \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
-or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.
    weight
    (Tensor, optional) a manual rescaling weight given to each class.
-If given, has to be a Tensor of size C
    ignore_index
    (int, optional) Specifies a target value that is ignored and
-does not contribute to the input gradient.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_normalize.html b/docs/reference/nnf_normalize.html
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-Normalize — nnf_normalize • torch
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-    
    Normalize
    
-    Source: R/nnf-normalization.R
-    
-    
    
-
-    
    
-    
    Performs \(L_p\) normalization of inputs over specified dimension.
    
-    
    
-
-    
    nnf_normalize(input, p = 2, dim = 1, eps = 1e-12, out = NULL)
    
-
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    Arguments
    
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    input
    input tensor of any shape
    p
    (float) the exponent value in the norm formulation. Default: 2
    dim
    (int) the dimension to reduce. Default: 1
    eps
    (float) small value to avoid division by zero. Default: 1e-12
    out
    (Tensor, optional) the output tensor. If out is used, this                            operation won't be differentiable.
    
-
-    
    Details
    
-
-    
    For a tensor input of sizes \((n_0, ..., n_{dim}, ..., n_k)\), each
-\(n_{dim}\) -element vector \(v\) along dimension dim is transformed as
    
-
    $$
-        v = \frac{v}{\max(\Vert v \Vert_p, \epsilon)}.
-$$
    
-
    With the default arguments it uses the Euclidean norm over vectors along
-dimension \(1\) for normalization.
    
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diff --git a/docs/reference/nnf_one_hot.html b/docs/reference/nnf_one_hot.html
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-One_hot — nnf_one_hot • torch
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    One_hot
    
-    Source: R/nnf-embedding.R
-    
-    
    
-
-    
    
-    
    Takes LongTensor with index values of shape (*) and returns a tensor
-of shape (*, num_classes) that have zeros everywhere except where the
-index of last dimension matches the corresponding value of the input tensor,
-in which case it will be 1.
    
-    
    
-
-    
    nnf_one_hot(tensor, num_classes = -1)
    
-
-    
    Arguments
    
-    
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-      
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-      
-      
-    
-    
    tensor
    (LongTensor) class values of any shape.
    num_classes
    (int) Total number of classes. If set to -1, the number
-of classes will be inferred as one greater than the largest class value in
-the input tensor.
    
-
-    
    Details
    
-
-    
    One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot
    
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diff --git a/docs/reference/nnf_pad.html b/docs/reference/nnf_pad.html
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-Pad — nnf_pad • torch
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    Pad
    
-    Source: R/nnf-padding.R
-    
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-
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-    
    Pads tensor.
    
-    
    
-
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    nnf_pad(input, pad, mode = "constant", value = 0)
    
-
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    Arguments
    
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    input
    (Tensor) N-dimensional tensor
    pad
    (tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
-and \(m\) is even.
    mode
    'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
    value
    fill value for 'constant' padding. Default: 0.
    
-
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    Padding size
    
-
-    
-
-
-
    The padding size by which to pad some dimensions of input
-are described starting from the last dimension and moving forward.
-\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
-of input will be padded.
-For example, to pad only the last dimension of the input tensor, then
-pad has the form
-\((\mbox{padding\_left}, \mbox{padding\_right})\);
-to pad the last 2 dimensions of the input tensor, then use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
-to pad the last 3 dimensions, use
-\((\mbox{padding\_left}, \mbox{padding\_right},\)
-\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
-\(\mbox{padding\_front}, \mbox{padding\_back})\).
    
-    
    Padding mode
    
-
-    
-
-
-
    See nn_constant_pad_2d, nn_reflection_pad_2d, and
-nn_replication_pad_2d for concrete examples on how each of the
-padding modes works. Constant padding is implemented for arbitrary dimensions.
-tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
-3D input tensor. Reflect padding is only implemented for padding the last 2
-dimensions of 4D input tensor, or the last dimension of 3D input tensor.
    
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diff --git a/docs/reference/nnf_pairwise_distance.html b/docs/reference/nnf_pairwise_distance.html
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-Pairwise_distance — nnf_pairwise_distance • torch
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-    Source: R/nnf-distance.R
-    
-    
    
-
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-    
    Computes the batchwise pairwise distance between vectors using the p-norm.
    
-    
    
-
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    nnf_pairwise_distance(x1, x2, p = 2, eps = 1e-06, keepdim = FALSE)
    
-
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    Arguments
    
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    x1
    (Tensor) First input.
    x2
    (Tensor) Second input (of size matching x1).
    p
    the norm degree. Default: 2
    eps
    (float, optional) Small value to avoid division by zero.
-Default: 1e-8
    keepdim
    Determines whether or not to keep the vector dimension. Default: False
    
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diff --git a/docs/reference/nnf_pdist.html b/docs/reference/nnf_pdist.html
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-Pdist — nnf_pdist • torch
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    Pdist
    
-    Source: R/nnf-distance.R
-    
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-
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-    
    Computes the p-norm distance between every pair of row vectors in the input.
-This is identical to the upper triangular portion, excluding the diagonal, of
-torch_norm(input[:, None] - input, dim=2, p=p). This function will be faster
-if the rows are contiguous.
    
-    
    
-
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    nnf_pdist(input, p = 2)
    
-
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    Arguments
    
-    
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-      
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    input
    input tensor of shape \(N \times M\).
    p
    p value for the p-norm distance to calculate between each vector pair
-\(\in [0, \infty]\).
    
-
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    Details
    
-
-    
    If input has shape \(N \times M\) then the output will have shape
-\(\frac{1}{2} N (N - 1)\).
    
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diff --git a/docs/reference/nnf_pixel_shuffle.html b/docs/reference/nnf_pixel_shuffle.html
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-Pixel_shuffle — nnf_pixel_shuffle • torch
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-    Source: R/nnf-pixelshuffle.R
-    
-    
    
-
-    
    
-    
    Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
-tensor of shape \((*, C, H \times r, W \times r)\).
    
-    
    
-
-    
    nnf_pixel_shuffle(input, upscale_factor)
    
-
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    Arguments
    
-    
-    
-    
-      
-      
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-      
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    input
    (Tensor) the input tensor
    upscale_factor
    (int) factor to increase spatial resolution by
    
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diff --git a/docs/reference/nnf_poisson_nll_loss.html b/docs/reference/nnf_poisson_nll_loss.html
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    Poisson_nll_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Poisson negative log likelihood loss.
    
-    
    
-
-    
    nnf_poisson_nll_loss(
-  input,
-  target,
-  log_input = TRUE,
-  full = FALSE,
-  eps = 1e-08,
-  reduction = "mean"
-)
    
-
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    Arguments
    
-    
-    
-    
-      
-      
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    input
    tensor (N,*) where ** means, any number of additional dimensions
    target
    tensor (N,*) , same shape as the input
    log_input
    if TRUE the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
-if FALSE then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
-Default: TRUE.
    full
    whether to compute full loss, i. e. to add the Stirling approximation
-term. Default: FALSE.
    eps
    (float, optional) Small value to avoid evaluation of \(\log(0)\) when
-log_input=FALSE. Default: 1e-8
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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diff --git a/docs/reference/nnf_prelu.html b/docs/reference/nnf_prelu.html
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-\(PReLU(x) = max(0,x) + weight * min(0,x)\)
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-    
    
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    nnf_relu(input, inplace = FALSE)
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-    Source: R/nnf-activation.R
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-    
    
-
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-    
    Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).
    
-    
    
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    nnf_relu6(input, inplace = FALSE)
    
-
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    Arguments
    
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    input
    (N,*) tensor, where * means, any number of additional
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    Rrelu
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Randomized leaky ReLU.
    
-    
    
-
-    
    nnf_rrelu(input, lower = 1/8, upper = 1/3, training = FALSE, inplace = FALSE)
-
-nnf_rrelu_(input, lower = 1/8, upper = 1/3, training = FALSE)
    
-
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    Arguments
    
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    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    lower
    lower bound of the uniform distribution. Default: 1/8
    upper
    upper bound of the uniform distribution. Default: 1/3
    training
    bool wether it's a training pass. DEfault: FALSE
    inplace
    can optionally do the operation in-place. Default: FALSE
    
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-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise,
-$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
-with \(\alpha=1.6732632423543772848170429916717\) and
-\(scale=1.0507009873554804934193349852946\).
    
-    
    
-
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    nnf_selu(input, inplace = FALSE)
-
-nnf_selu_(input)
    
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    input
    (N,*) tensor, where * means, any number of additional
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    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x <- torch_randn(2, 2)
-y <- nnf_selu(x)
-nnf_selu_(x)
    #> torch_tensor 
-#>  0.3549  0.1844
-#> -1.4839 -0.8035
-#> [ CPUFloatType{2,2} ]
    torch_equal(x, y)
    #> [1] TRUE
    
-# }
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diff --git a/docs/reference/nnf_smooth_l1_loss.html b/docs/reference/nnf_smooth_l1_loss.html
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-Smooth_l1_loss — nnf_smooth_l1_loss • torch
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-    Source: R/nnf-loss.R
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-
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-    
    Function that uses a squared term if the absolute
-element-wise error falls below 1 and an L1 term otherwise.
    
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    input
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    target
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    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_soft_margin_loss.html b/docs/reference/nnf_soft_margin_loss.html
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-    Source: R/nnf-loss.R
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-
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-    
    Creates a criterion that optimizes a two-class classification logistic loss
-between input tensor x and target tensor y (containing 1 or -1).
    
-    
    
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    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
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diff --git a/docs/reference/nnf_softmax.html b/docs/reference/nnf_softmax.html
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    Softmax
    
-    Source: R/nnf-activation.R
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-
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-    
    Applies a softmax function.
    
-    
    
-
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    dtype
    (torch.dtype, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to dtype before the operation      is performed. This is useful for preventing data type overflows.
-Default: NULL.
    
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    Details
    
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    Softmax is defined as:
    
-
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-
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diff --git a/docs/reference/nnf_softmin.html b/docs/reference/nnf_softmin.html
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-    Source: R/nnf-activation.R
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-    
    Applies a softmin function.
    
-    
    
-
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    input
    (Tensor) input
    dim
    (int) A dimension along which softmin will be computed
-(so every slice        along dim will sum to 1).
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to dtype before the operation      is performed.
-This is useful for preventing data type overflows. Default: NULL.
    
-
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    Details
    
-
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    Note that
    
-
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-
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-    Source: R/nnf-activation.R
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-
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-    
    Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).
    
-    
    
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-
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    input
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-dimensions
    beta
    the beta value for the Softplus formulation. Default: 1
    threshold
    values above this revert to a linear function. Default: 20
    
-
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    Details
    
-
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-when \(input * \beta > threshold\).
    
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diff --git a/docs/reference/nnf_softshrink.html b/docs/reference/nnf_softshrink.html
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    Softshrink
    
-    Source: R/nnf-activation.R
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-
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-    
    Applies the soft shrinkage function elementwise
    
-    
    
-
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    nnf_softshrink(input, lambd = 0.5)
    
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    input
    (N,*) tensor, where * means, any number of additional
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    lambd
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-formulation. Default: 0.5
    
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diff --git a/docs/reference/nnf_softsign.html b/docs/reference/nnf_softsign.html
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-    
    
-    
    Softsign
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)
    
-    
    
-
-    
    nnf_softsign(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
-
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deleted file mode 100644
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-Tanhshrink — nnf_tanhshrink • torch
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    Tanhshrink
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Applies element-wise, \(Tanhshrink(x) = x - Tanh(x)\)
    
-    
    
-
-    
    nnf_tanhshrink(input)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    
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diff --git a/docs/reference/nnf_threshold.html b/docs/reference/nnf_threshold.html
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-Threshold — nnf_threshold • torch
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-    
    Threshold
    
-    Source: R/nnf-activation.R
-    
-    
    
-
-    
    
-    
    Thresholds each element of the input Tensor.
    
-    
    
-
-    
    nnf_threshold(input, threshold, value, inplace = FALSE)
-
-nnf_threshold_(input, threshold, value)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
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-    
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-    
-    
    input
    (N,*) tensor, where * means, any number of additional
-dimensions
    threshold
    The value to threshold at
    value
    The value to replace with
    inplace
    can optionally do the operation in-place. Default: FALSE
    
-
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-  
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diff --git a/docs/reference/nnf_triplet_margin_loss.html b/docs/reference/nnf_triplet_margin_loss.html
deleted file mode 100644
index ba202431a..000000000
--- a/docs/reference/nnf_triplet_margin_loss.html
+++ /dev/null
@@ -1,255 +0,0 @@
-
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-  
-
-
-
-Triplet_margin_loss — nnf_triplet_margin_loss • torch
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-    
    
-    
    Triplet_margin_loss
    
-    Source: R/nnf-loss.R
-    
-    
    
-
-    
    
-    
    Creates a criterion that measures the triplet loss given an input tensors x1 ,
-x2 , x3 and a margin with a value greater than 0 . This is used for measuring
-a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
-anchor, positive examples and negative examples respectively). The shapes of all
-input tensors should be (N, D).
    
-    
    
-
-    
    nnf_triplet_margin_loss(
-  anchor,
-  positive,
-  negative,
-  margin = 1,
-  p = 2,
-  eps = 1e-06,
-  swap = FALSE,
-  reduction = "mean"
-)
    
-
-    
    Arguments
    
-    
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    anchor
    the anchor input tensor
    positive
    the positive input tensor
    negative
    the negative input tensor
    margin
    Default: 1.
    p
    The norm degree for pairwise distance. Default: 2.
    eps
    (float, optional) Small value to avoid division by zero.
    swap
    The distance swap is described in detail in the paper Learning shallow
-convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
-Default: FALSE.
    reduction
    (string, optional) – Specifies the reduction to apply to the
-output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
-the sum of the output will be divided by the number of elements in the output,
-'sum': the output will be summed. Default: 'mean'
    
-
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diff --git a/docs/reference/nnf_unfold.html b/docs/reference/nnf_unfold.html
deleted file mode 100644
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-Unfold — nnf_unfold • torch
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-  
    
-    
    
-    
    Unfold
    
-    Source: R/nnf-fold.R
-    
-    
    
-
-    
    
-    
    Extracts sliding local blocks from an batched input tensor.
    
-    
    
-
-    
    nnf_unfold(input, kernel_size, dilation = 1, padding = 0, stride = 1)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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-    
-    
    input
    the input tensor
    kernel_size
    the size of the sliding blocks
    dilation
    a parameter that controls the stride of elements within the
-neighborhood. Default: 1
    padding
    implicit zero padding to be added on both sides of input.
-Default: 0
    stride
    the stride of the sliding blocks in the input spatial dimensions.
-Default: 1
    
-
-    
    Warning
    
-
-    
-
-
-
    Currently, only 4-D input tensors (batched image-like tensors) are
-supported.
    
-
-
-
    More than one element of the unfolded tensor may refer to a single
-memory location. As a result, in-place operations (especially ones that
-are vectorized) may result in incorrect behavior. If you need to write
-to the tensor, please clone it first.
    
-
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/optim_adam.html b/docs/reference/optim_adam.html
deleted file mode 100644
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--- a/docs/reference/optim_adam.html
+++ /dev/null
@@ -1,239 +0,0 @@
-
-
-
-  
-  
-
-
-
-Implements Adam algorithm. — optim_adam • torch
-
-
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-    
    
-    
    Implements Adam algorithm.
    
-    Source: R/optim-adam.R
-    
-    
    
-
-    
    
-    
    It has been proposed in Adam: A Method for Stochastic Optimization.
    
-    
    
-
-    
    optim_adam(
-  params,
-  lr = 0.001,
-  betas = c(0.9, 0.999),
-  eps = 1e-08,
-  weight_decay = 0,
-  amsgrad = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
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    params
    (iterable): iterable of parameters to optimize or dicts defining
-parameter groups
    lr
    (float, optional): learning rate (default: 1e-3)
    betas
    (Tuple[float, float], optional): coefficients used for computing
-running averages of gradient and its square (default: (0.9, 0.999))
    eps
    (float, optional): term added to the denominator to improve
-numerical stability (default: 1e-8)
    weight_decay
    (float, optional): weight decay (L2 penalty) (default: 0)
    amsgrad
    (boolean, optional): whether to use the AMSGrad variant of this
-algorithm from the paper On the Convergence of Adam and Beyond
-(default: FALSE)
    
-
-
-    
    Examples
    
-    
    
-  
    
-  
-
    
-
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-      
    
-      
    
-  
    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/optim_required.html b/docs/reference/optim_required.html
deleted file mode 100644
index 8316f053e..000000000
--- a/docs/reference/optim_required.html
+++ /dev/null
@@ -1,197 +0,0 @@
-
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-  
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-Dummy value indicating a required value. — optim_required • torch
-
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-    
    Dummy value indicating a required value.
    
-    Source: R/optim.R
-    
-    
    
-
-    
    
-    
    export
    
-    
    
-
-    
    optim_required()
    
-
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diff --git a/docs/reference/optim_sgd.html b/docs/reference/optim_sgd.html
deleted file mode 100644
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+++ /dev/null
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-
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-  
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-SGD optimizer — optim_sgd • torch
-
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-  
    
-    
    
-    
    SGD optimizer
    
-    Source: R/optim-sgd.R
-    
-    
    
-
-    
    
-    
    Implements stochastic gradient descent (optionally with momentum).
-Nesterov momentum is based on the formula from
-On the importance of initialization and momentum in deep learning.
    
-    
    
-
-    
    optim_sgd(
-  params,
-  lr = optim_required(),
-  momentum = 0,
-  dampening = 0,
-  weight_decay = 0,
-  nesterov = FALSE
-)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
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-      
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-      
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-    
-    
    params
    (iterable): iterable of parameters to optimize or dicts defining
-parameter groups
    lr
    (float): learning rate
    momentum
    (float, optional): momentum factor (default: 0)
    dampening
    (float, optional): dampening for momentum (default: 0)
    weight_decay
    (float, optional): weight decay (L2 penalty) (default: 0)
    nesterov
    (bool, optional): enables Nesterov momentum (default: FALSE)
    
-
-    
    Note
    
-
-    
-
-
-
    The implementation of SGD with Momentum-Nesterov subtly differs from
-Sutskever et. al. and implementations in some other frameworks.
    
-
    Considering the specific case of Momentum, the update can be written as
-$$
-  \begin{array}{ll}
-v_{t+1} & = \mu * v_{t} + g_{t+1}, \\
-p_{t+1} & = p_{t} - \mbox{lr} * v_{t+1},
-\end{array}
-$$
    
-
    where \(p\), \(g\), \(v\) and \(\mu\) denote the
-parameters, gradient, velocity, and momentum respectively.
    
-
    This is in contrast to Sutskever et. al. and
-other frameworks which employ an update of the form
    
-
    $$
-  \begin{array}{ll}
-v_{t+1} & = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
-p_{t+1} & = p_{t} - v_{t+1}.
-\end{array}
-$$
-The Nesterov version is analogously modified.
    
-
-    
    Examples
    
-    
    
-  
    
-  
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diff --git a/docs/reference/tensor_dataset.html b/docs/reference/tensor_dataset.html
deleted file mode 100644
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--- a/docs/reference/tensor_dataset.html
+++ /dev/null
@@ -1,205 +0,0 @@
-
-
-
-  
-  
-
-
-
-Dataset wrapping tensors. — tensor_dataset • torch
-
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-    
    
-    
    Dataset wrapping tensors.
    
-    Source: R/utils-data.R
-    
-    
    
-
-    
    
-    
    Each sample will be retrieved by indexing tensors along the first dimension.
    
-    
    
-
-    
    tensor_dataset(...)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    ...
    tensors that have the same size of the first dimension.
    
-
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-  
    
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diff --git a/docs/reference/torch_abs.html b/docs/reference/torch_abs.html
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-Abs — torch_abs • torch
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-    
    Abs
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Abs
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    abs(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the element-wise absolute value of the given input tensor.
    
-
    $$
-    \mbox{out}_{i} = |\mbox{input}_{i}|
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_abs(torch_tensor(c(-1, -2, 3)))
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    # }
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
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    #> torch_tensor 
-#> -0.6137
-#>  1.1598
-#>  0.0958
-#> -0.2733
-#> [ CPUFloatType{4} ]
    torch_acos(a)
    #> torch_tensor 
-#>  2.2315
-#>     nan
-#>  1.4748
-#>  1.8476
-#> [ CPUFloatType{4} ]
    # }
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-$$
-If input is of type FloatTensor or DoubleTensor, other must be
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    add(input, other, *, alpha=1, out=None) 
    
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-alpha and added to each element of the tensor input.
-The resulting tensor is returned.
    
-
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-broadcastable .
    
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-    \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
-$$
-If other is of type FloatTensor or DoubleTensor, alpha must be
-a real number, otherwise it should be an integer.
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.2160
-#>  0.1973
-#> -0.1795
-#> -0.9024
-#> [ CPUFloatType{4} ]
    torch_add(a, 20)
    #> torch_tensor 
-#>  20.2160
-#>  20.1973
-#>  19.8204
-#>  19.0976
-#> [ CPUFloatType{4} ]
    
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -1.6998
-#> -0.1848
-#> -0.4348
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-#> [ CPUFloatType{4} ]
    b = torch_randn(c(4, 1))
-b
    #> torch_tensor 
-#>  0.9213
-#>  0.5193
-#>  0.3855
-#> -1.5317
-#> [ CPUFloatType{4,1} ]
    torch_add(a, b)
    #> torch_tensor 
-#> -0.7785  0.7364  0.4865  0.1738
-#> -1.1805  0.3345  0.0845 -0.2282
-#> -1.3142  0.2007 -0.0492 -0.3619
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diff --git a/docs/reference/torch_addbmm.html b/docs/reference/torch_addbmm.html
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    input
    (Tensor) matrix to be added
    alpha
    (Number, optional) multiplier for batch1 @ batch2 (\(\alpha\))
    out
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-along the first dimension).
-input is added to the final result.
    
-
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-same number of matrices.
    
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-\((b \times m \times p)\) tensor, input must be
-broadcastable  with a \((n \times p)\) tensor
-and out will be a \((n \times p)\) tensor.
    
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-    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha
-must be real numbers, otherwise they should be integers.
    
-
-    
    Examples
    
-    
    # \dontrun{
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-M = torch_randn(c(3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_addbmm(M, batch1, batch2)
    #> torch_tensor 
-#>   5.7025   7.7808   5.3946   0.1290   2.4487
-#>  -1.9730  -3.0379  -5.4090   0.6009  -3.1469
-#>   4.6785   6.4997 -11.4732   6.3957  10.2272
-#> [ CPUFloatType{3,5} ]
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diff --git a/docs/reference/torch_addcdiv.html b/docs/reference/torch_addcdiv.html
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    tensor1
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    tensor2
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    value
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    out
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-multiply the result by the scalar value and add it to input.
    
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-
-
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-addcdiv will perform a true division of tensor1 and tensor2.
-The current addcdiv behavior can be replicated using torch_floor_divide()
-for integral inputs
-(input + value * tensor1 // tensor2)
-and torch_div() for float inputs
-(input + value * tensor1 / tensor2).
-The new addcdiv behavior can be implemented with torch_true_divide()
-(input + value * torch.true_divide(tensor1,
-tensor2).
    
-
    $$
-    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
-$$
    
-
    The shapes of input, tensor1, and tensor2 must be
-broadcastable .
    
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-a real number, otherwise an integer.
    
-
-    
    Examples
    
-    
    # \dontrun{
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-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcdiv(t, t1, t2, 0.1)
    #> torch_tensor 
-#> -0.3050 -0.1424  0.5617
-#> -0.3424  0.0045  1.0519
-#> -0.2932 -0.1885  0.4079
-#> [ CPUFloatType{3,3} ]
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-The shapes of tensor, tensor1, and tensor2 must be
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-a real number, otherwise an integer.
    
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    Examples
    
-    
    # \dontrun{
-
-t = torch_randn(c(1, 3))
-t1 = torch_randn(c(3, 1))
-t2 = torch_randn(c(1, 3))
-torch_addcmul(t, t1, t2, 0.1)
    #> torch_tensor 
-#> -0.2008 -0.8560  0.9351
-#> -0.1664 -0.8433  0.9042
-#> -0.5670 -0.9908  1.2630
-#> [ CPUFloatType{3,3} ]
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diff --git a/docs/reference/torch_addmm.html b/docs/reference/torch_addmm.html
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-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_addmm(M, mat1, mat2)
    #> torch_tensor 
-#> -1.4411  0.9520  5.5685
-#>  2.0314  0.6255  2.2542
-#> [ CPUFloatType{2,3} ]
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diff --git a/docs/reference/torch_addmv.html b/docs/reference/torch_addmv.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Addmv
    
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    input
    (Tensor) vector to be added
    mat
    (Tensor) matrix to be multiplied
    vec
    (Tensor) vector to be multiplied
    beta
    (Number, optional) multiplier for input (\(\beta\))
    alpha
    (Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))
    out
    (Tensor, optional) the output tensor.
    
-
-    
    addmv(input, mat, vec, *, beta=1, alpha=1, out=None) -> Tensor 
    
-
-    
-
-
-
    Performs a matrix-vector product of the matrix mat and
-the vector vec.
-The vector input is added to the final result.
    
-
    If mat is a \((n \times m)\) tensor, vec is a 1-D tensor of
-size m, then input must be
-broadcastable  with a 1-D tensor of size n and
-out will be 1-D tensor of size n.
    
-
    alpha and beta are scaling factors on matrix-vector product between
-mat and vec and the added tensor input respectively.
    
-
    $$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-M = torch_randn(c(2))
-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_addmv(M, mat, vec)
    #> torch_tensor 
-#>  1.9265
-#>  1.5524
-#> [ CPUFloatType{2} ]
    # }
-
    
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diff --git a/docs/reference/torch_addr.html b/docs/reference/torch_addr.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Addr
    
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    input
    (Tensor) matrix to be added
    vec1
    (Tensor) the first vector of the outer product
    vec2
    (Tensor) the second vector of the outer product
    beta
    (Number, optional) multiplier for input (\(\beta\))
    alpha
    (Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))
    out
    (Tensor, optional) the output tensor.
    
-
-    
    addr(input, vec1, vec2, *, beta=1, alpha=1, out=None) -> Tensor 
    
-
-    
-
-
-
    Performs the outer-product of vectors vec1 and vec2
-and adds it to the matrix input.
    
-
    Optional values beta and alpha are scaling factors on the
-outer product between vec1 and vec2 and the added matrix
-input respectively.
    
-
    $$
-    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
-$$
-If vec1 is a vector of size n and vec2 is a vector
-of size m, then input must be
-broadcastable  with a matrix of size
-\((n \times m)\) and out will be a matrix of size
-\((n \times m)\).
    
-
    For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-vec1 = torch_arange(1., 4.)
-vec2 = torch_arange(1., 3.)
-M = torch_zeros(c(3, 2))
-torch_addr(M, vec1, vec2)
    #> torch_tensor 
-#>  1  2
-#>  2  4
-#>  3  6
-#> [ CPUFloatType{3,2} ]
    # }
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diff --git a/docs/reference/torch_allclose.html b/docs/reference/torch_allclose.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Allclose
    
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    input
    (Tensor) first tensor to compare
    other
    (Tensor) second tensor to compare
    atol
    (float, optional) absolute tolerance. Default: 1e-08
    rtol
    (float, optional) relative tolerance. Default: 1e-05
    equal_nan
    (bool, optional) if True, then two NaN s will be compared as equal. Default: False
    
-
-    
    allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -> bool 
    
-
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    This function checks if all input and other satisfy the condition:
    
-
    $$
-    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
-$$
-elementwise, for all elements of input and other. The behaviour of this function is analogous to
-numpy.allclose <https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html>_
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_allclose(torch_tensor(c(10000., 1e-07)), torch_tensor(c(10000.1, 1e-08)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(10000., 1e-08)), torch_tensor(c(10000.1, 1e-09)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)))
    #> [1] FALSE
    torch_allclose(torch_tensor(c(1.0, NaN)), torch_tensor(c(1.0, NaN)), equal_nan=TRUE)
    #> [1] TRUE
    # }
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diff --git a/docs/reference/torch_angle.html b/docs/reference/torch_angle.html
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-Angle — torch_angle • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Angle
    
-    
    
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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    angle(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the element-wise angle (in radians) of the given input tensor.
    
-
    $$
-    \mbox{out}_{i} = angle(\mbox{input}_{i})
-$$
    
-
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    Examples
    
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diff --git a/docs/reference/torch_arange.html b/docs/reference/torch_arange.html
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-Arange — torch_arange • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Number) the starting value for the set of points. Default: 0.
    end
    (Number) the ending value for the set of points
    step
    (Number) the gap between each pair of adjacent points. Default: 1.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input        arguments. If any of start, end, or stop are floating-point, the        dtype is inferred to be the default dtype, see        ~torch.get_default_dtype. Otherwise, the dtype is inferred to        be torch.int64.
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
-with values from the interval [start, end) taken with common difference
-step beginning from start.
    
-
    Note that non-integer step is subject to floating point rounding errors when
-comparing against end; to avoid inconsistency, we advise adding a small epsilon to end
-in such cases.
    
-
    $$
-    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_arange(start = 0, end = 5)
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{5} ]
    torch_arange(1, 4)
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    torch_arange(1, 2.5, 0.5)
    #> torch_tensor 
-#>  1.0000
-#>  1.5000
-#>  2.0000
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_argmax.html b/docs/reference/torch_argmax.html
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    Argmax
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Argmax
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    dim
    (int) the dimension to reduce. If None, the argmax of the flattened input is returned.
    keepdim
    (bool) whether the output tensor has dim retained or not. Ignored if dim=None.
    
-
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    argmax(input) -> LongTensor 
    
-
-    
-
-
-
    Returns the indices of the maximum value of all elements in the input tensor.
    
-
    This is the second value returned by torch_max. See its
-documentation for the exact semantics of this method.
    
-    
    argmax(input, dim, keepdim=False) -> LongTensor 
    
-
-    
-
-
-
    Returns the indices of the maximum values of a tensor across a dimension.
    
-
    This is the second value returned by torch_max. See its
-documentation for the exact semantics of this method.
    
-
-    
    Examples
    
-    
    
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diff --git a/docs/reference/torch_argmin.html b/docs/reference/torch_argmin.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Argmin
    
-    
    
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-    
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    input
    (Tensor) the input tensor.
    dim
    (int) the dimension to reduce. If None, the argmin of the flattened input is returned.
    keepdim
    (bool) whether the output tensor has dim retained or not. Ignored if dim=None.
    
-
-    
    argmin(input) -> LongTensor 
    
-
-    
-
-
-
    Returns the indices of the minimum value of all elements in the input tensor.
    
-
    This is the second value returned by torch_min. See its
-documentation for the exact semantics of this method.
    
-    
    argmin(input, dim, keepdim=False, out=None) -> LongTensor 
    
-
-    
-
-
-
    Returns the indices of the minimum values of a tensor across a dimension.
    
-
    This is the second value returned by torch_min. See its
-documentation for the exact semantics of this method.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  1.6530 -1.9398 -0.7858 -0.6979
-#>  1.3467  2.4378  2.4695 -0.0903
-#>  0.5428 -0.8464 -0.8918 -0.2703
-#>  1.0460 -0.3144  0.2131 -0.1355
-#> [ CPUFloatType{4,4} ]
    torch_argmin(a)
    #> torch_tensor 
-#> 1
-#> [ CPULongType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.3917 -1.7360  2.1245 -0.4908
-#> -1.2249 -0.1974  0.3145 -1.2540
-#>  2.5169  0.8670  1.2077  0.5393
-#>  0.2843 -0.6558 -0.7945  1.3721
-#> [ CPUFloatType{4,4} ]
    torch_argmin(a, dim=1)
    #> torch_tensor 
-#>  1
-#>  0
-#>  3
-#>  1
-#> [ CPULongType{4} ]
    # }
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diff --git a/docs/reference/torch_argsort.html b/docs/reference/torch_argsort.html
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-
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    argsort(input, dim=-1, descending=False) -> LongTensor 
    
-
-    
-
-
-
    Returns the indices that sort a tensor along a given dimension in ascending
-order by value.
    
-
    This is the second value returned by torch_sort.  See its documentation
-for the exact semantics of this method.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.8250 -0.5984 -1.2454  0.4598
-#> -0.9256  0.0695 -1.6829  1.5544
-#>  2.1622  0.7200  0.7667 -0.4872
-#>  1.1699  0.8607  2.5965  0.0434
-#> [ CPUFloatType{4,4} ]
    torch_argsort(a, dim=1)
    #> torch_tensor 
-#>  1  0  1  2
-#>  0  1  0  3
-#>  3  2  2  0
-#>  2  3  3  1
-#> [ CPULongType{4,4} ]
    # }
-
    
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diff --git a/docs/reference/torch_as_strided.html b/docs/reference/torch_as_strided.html
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+++ /dev/null
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    (tuple or ints) the stride of the output tensor
    storage_offset
    (int, optional) the offset in the underlying storage of the output tensor
    
-
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-
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-
-
-
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-size, stride and storage_offset.
    
-    
    Warning
    
-
-    
-
-
    More than one element of a created tensor may refer to a single memory
-location. As a result, in-place operations (especially ones that are
-vectorized) may result in incorrect behavior. If you need to write to
-the tensors, please clone them first.
    Many PyTorch functions, which return a view of a tensor, are internally
-implemented with this function. Those functions, like
-`torch_Tensor.expand`, are easier to read and are therefore more
-advisable to use.
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 3))
-x
    #> torch_tensor 
-#> -1.5576  0.5216 -0.6254
-#>  0.5108 -0.2964  0.5801
-#> -0.7827  0.2806  0.0976
-#> [ CPUFloatType{3,3} ]
    t = torch_as_strided(x, list(2, 2), list(1, 2))
-t
    #> torch_tensor 
-#> -1.5576 -0.6254
-#>  0.5216  0.5108
-#> [ CPUFloatType{2,2} ]
    t = torch_as_strided(x, list(2, 2), list(1, 2), 1)
-t
    #> torch_tensor 
-#>  0.5216  0.5108
-#> -0.6254 -0.2964
-#> [ CPUFloatType{2,2} ]
    # }
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diff --git a/docs/reference/torch_asin.html b/docs/reference/torch_asin.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Asin
    
-    
    
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    Arguments
    
-    
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    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    asin(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor with the arcsine  of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.3857
-#>  1.8551
-#>  0.4113
-#>  0.7013
-#> [ CPUFloatType{4} ]
    torch_asin(a)
    #> torch_tensor 
-#> -0.3959
-#>     nan
-#>  0.4239
-#>  0.7773
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_atan.html b/docs/reference/torch_atan.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    
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    Atan
    
-    
    
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    Arguments
    
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    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    atan(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor with the arctangent  of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.7742
-#>  0.3914
-#> -0.0984
-#>  0.7190
-#> [ CPUFloatType{4} ]
    torch_atan(a)
    #> torch_tensor 
-#> -0.6588
-#>  0.3730
-#> -0.0980
-#>  0.6234
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_atan2.html b/docs/reference/torch_atan2.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Atan2
    
-    
    
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    input
    (Tensor) the first input tensor
    other
    (Tensor) the second input tensor
    out
    (Tensor, optional) the output tensor.
    
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    atan2(input, other, out=None) -> Tensor 
    
-
-    
-
-
-
    Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
-with consideration of the quadrant. Returns a new tensor with the signed angles
-in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
-and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
-parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
-parameter, is the y-coordinate.)
    
-
    The shapes of input and other must be
-broadcastable .
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.7384
-#>  1.5533
-#>  0.0480
-#>  0.5090
-#> [ CPUFloatType{4} ]
    torch_atan2(a, torch_randn(c(4)))
    #> torch_tensor 
-#>  0.3252
-#>  1.4989
-#>  3.0686
-#>  1.3146
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_avg_pool1d.html b/docs/reference/torch_avg_pool1d.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)
    kernel_size
    NA the size of the window. Can be a single number or a      tuple (kW,)
    stride
    NA the stride of the window. Can be a single number or a tuple      (sW,). Default: kernel_size
    padding
    NA implicit zero paddings on both sides of the input. Can be a      single number or a tuple (padW,). Default: 0
    ceil_mode
    NA when True, will use ceil instead of floor to compute the        output shape. Default: False
    count_include_pad
    NA when True, will include the zero-padding in the        averaging calculation. Default: True
    
-
-    
    avg_pool1d(input, kernel_size, stride=None, padding=0, ceil_mode=False, count_include_pad=True) -> Tensor 
    
-
-    
-
-
-
    Applies a 1D average pooling over an input signal composed of several
-input planes.
    
-
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diff --git a/docs/reference/torch_baddbmm.html b/docs/reference/torch_baddbmm.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the tensor to be added
    batch1
    (Tensor) the first batch of matrices to be multiplied
    batch2
    (Tensor) the second batch of matrices to be multiplied
    beta
    (Number, optional) multiplier for input (\(\beta\))
    alpha
    (Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))
    out
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-
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-and batch2.
-input is added to the final result.
    
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-number of matrices.
    
-
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-\((b \times m \times p)\) tensor, then input must be
-broadcastable  with a
-\((b \times n \times p)\) tensor and out will be a
-\((b \times n \times p)\) tensor. Both alpha and beta mean the
-same as the scaling factors used in torch_addbmm.
    
-
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-    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
-$$
-For inputs of type FloatTensor or DoubleTensor, arguments beta and
-alpha must be real numbers, otherwise they should be integers.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-M = torch_randn(c(10, 3, 5))
-batch1 = torch_randn(c(10, 3, 4))
-batch2 = torch_randn(c(10, 4, 5))
-torch_baddbmm(M, batch1, batch2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   3.2697 -5.0643  0.0743 -0.2398 -2.5402
-#>  -0.3596 -0.1524 -2.2537 -0.6132  0.4815
-#>   1.1825 -2.2500  0.8243  1.5010 -2.4894
-#> 
-#> (2,.,.) = 
-#>   0.4770 -0.8900  3.0012  2.0244  2.9934
-#>  -2.0624 -0.7371 -0.6249 -1.4119 -1.0305
-#>   0.4525  1.1938  1.2075  2.4423  0.5840
-#> 
-#> (3,.,.) = 
-#>  -1.5483  0.0002 -0.7736 -0.1712  2.3502
-#>   1.3820  1.9069 -1.1504  2.8244 -0.5037
-#>  -0.7816  0.0485  3.1307 -0.7125  2.3957
-#> 
-#> (4,.,.) = 
-#>   3.2263  1.9973 -2.7929 -0.6880 -1.8358
-#>   3.9498  0.1835 -3.6300 -0.7907 -2.9265
-#>   1.5720 -1.5571 -0.5235  0.2169 -0.7204
-#> 
-#> (5,.,.) = 
-#>  -1.5198 -1.4044  0.6454  1.6571  1.6412
-#>   0.6481 -0.1620  0.7348 -2.5747 -1.5232
-#>  -3.9663  0.6486 -0.1782 -0.2130 -0.2005
-#> 
-#> (6,.,.) = 
-#>  -0.7923 -0.1696 -0.0210 -1.4651  0.1979
-#>  -0.2874  2.4903 -2.5324  0.1213  4.3363
-#>   0.8367  0.5843  2.6930 -0.5081 -0.7514
-#> 
-#> (7,.,.) = 
-#>   0.9376 -5.8062 -2.4161 -2.2368  1.7258
-#>  -0.5255  2.0584  1.1016 -2.1323 -1.1418
-#>  -1.8125  0.8110 -0.2142  1.9131  2.4363
-#> 
-#> (8,.,.) = 
-#>  -1.3094  0.0064 -0.5161  4.1986 -0.5380
-#>  -4.8329 -0.6216  0.9426 -3.9339 -1.2310
-#>   7.8403  0.3146 -1.0314  3.4608  2.3111
-#> 
-#> (9,.,.) = 
-#>  -0.9910 -4.0243  4.6838 -4.8655  0.7247
-#>   1.0314  0.6343  0.5493 -0.0418 -0.5915
-#>   0.1801  0.7773 -1.0913  0.2247 -2.3853
-#> 
-#> (10,.,.) = 
-#>  -1.1792  0.4361 -0.6693 -0.4414  0.9327
-#>  -4.9029 -6.8475 -4.1729 -2.2513  0.6501
-#>   1.3470  1.4167 -0.9282  0.5063 -2.4436
-#> [ CPUFloatType{10,3,5} ]
    # }
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    window_length
    (int) the size of returned window
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    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-    
    bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
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    Bartlett window function.
    
-
    $$
-    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
-        \frac{2n}{N - 1} & \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
-        2 - \frac{2n}{N - 1} & \mbox{if } \frac{N - 1}{2} < n < N \\
-    \end{array}
-    \right. ,
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_bartlett_window(L, periodic=True) equal to
-torch_bartlett_window(L + 1, periodic=False)[:-1]).
    
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diff --git a/docs/reference/torch_bernoulli.html b/docs/reference/torch_bernoulli.html
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    (Tensor) the input tensor of probability values for the Bernoulli distribution
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    (torch.Generator, optional) a pseudorandom number generator for sampling
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    (Tensor, optional) the output tensor.
    
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-
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-to be used for drawing the binary random number.
-Hence, all values in input have to be in the range:
-\(0 \leq \mbox{input}_i \leq 1\).
    
-
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-value \(1\) according to the \(\mbox{i}^{th}\) probability value given
-in input.
    
-
    $$
-    \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
-$$
-The returned out tensor only has values 0 or 1 and is of the same
-shape as input.
    
-
    out can have integral dtype, but input must have floating
-point dtype.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_empty(c(3, 3))$uniform_(0, 1)  # generate a uniform random matrix with range c(0, 1)
-a
    #> torch_tensor 
-#>  0.8765  0.8092  0.3962
-#>  0.4623  0.3192  0.0298
-#>  0.7755  0.1732  0.0310
-#> [ CPUFloatType{3,3} ]
    torch_bernoulli(a)
    #> torch_tensor 
-#>  0  1  1
-#>  0  1  0
-#>  1  0  0
-#> [ CPUFloatType{3,3} ]
    a = torch_ones(c(3, 3)) # probability of drawing "1" is 1
-torch_bernoulli(a)
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{3,3} ]
    a = torch_zeros(c(3, 3)) # probability of drawing "1" is 0
-torch_bernoulli(a)
    #> torch_tensor 
-#>  0  0  0
-#>  0  0  0
-#>  0  0  0
-#> [ CPUFloatType{3,3} ]
    # }
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diff --git a/docs/reference/torch_bincount.html b/docs/reference/torch_bincount.html
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    (Tensor) 1-d int tensor
    weights
    (Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.
    minlength
    (int) optional, minimum number of bins. Should be non-negative.
    
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-
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-input unless input is empty, in which case the result is a
-tensor of size 0. If minlength is specified, the number of bins is at least
-minlength and if input is empty, then the result is tensor of size
-minlength filled with zeros. If n is the value at position i,
-out[n] += weights[i] if weights is specified else
-out[n] += 1.
    
-
    .. include:: cuda_deterministic.rst
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-input = torch_randint(0, 8, list(5), dtype=torch_int64())
-weights = torch_linspace(0, 1, steps=5)
-input
    #> torch_tensor 
-#>  2
-#>  7
-#>  5
-#>  3
-#>  6
-#> [ CPULongType{5} ]
    weights
    #> torch_tensor 
-#>  0.0000
-#>  0.2500
-#>  0.5000
-#>  0.7500
-#>  1.0000
-#> [ CPUFloatType{5} ]
    torch_bincount(input, weights)
    #> torch_tensor 
-#>  0.0000
-#>  0.0000
-#>  0.0000
-#>  0.7500
-#>  0.0000
-#>  0.5000
-#>  1.0000
-#>  0.2500
-#> [ CPUFloatType{8} ]
    input$bincount(weights)
    #> torch_tensor 
-#>  0.0000
-#>  0.0000
-#>  0.0000
-#>  0.7500
-#>  0.0000
-#>  0.5000
-#>  1.0000
-#>  0.2500
-#> [ CPUFloatType{8} ]
    # }
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diff --git a/docs/reference/torch_bitwise_and.html b/docs/reference/torch_bitwise_and.html
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    out
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    bitwise_or(input, other, out=None) -> Tensor 
    
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-integral or Boolean types. For bool tensors, it computes the logical OR.
    
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diff --git a/docs/reference/torch_blackman_window.html b/docs/reference/torch_blackman_window.html
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    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
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-
    
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-    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
-$$
-where \(N\) is the full window size.
    
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    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_blackman_window(L, periodic=True) equal to
-torch_blackman_window(L + 1, periodic=False)[:-1]).
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Bmm
    
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    Arguments
    
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    input
    (Tensor) the first batch of matrices to be multiplied
    mat2
    (Tensor) the second batch of matrices to be multiplied
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
    This function does not broadcast .
-For broadcasting matrix products, see torch_matmul.
    
-    
    bmm(input, mat2, out=None) -> Tensor 
    
-
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-
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-
    Performs a batch matrix-matrix product of matrices stored in input
-and mat2.
    
-
    input and mat2 must be 3-D tensors each containing
-the same number of matrices.
    
-
    If input is a \((b \times n \times m)\) tensor, mat2 is a
-\((b \times m \times p)\) tensor, out will be a
-\((b \times n \times p)\) tensor.
    
-
    $$
-    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-input = torch_randn(c(10, 3, 4))
-mat2 = torch_randn(c(10, 4, 5))
-res = torch_bmm(input, mat2)
-res
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -1.1937  1.0490 -1.3460 -1.3636  0.2908
-#>  -0.7399 -0.3916 -0.0894  1.5547 -0.5792
-#>   1.6370 -1.8825  0.6914  0.4735  0.2958
-#> 
-#> (2,.,.) = 
-#>   3.0209 -2.4298  0.3410  0.0615 -2.5501
-#>  -3.8228  0.7082  0.9869  0.1536  1.1400
-#>   2.5718 -0.3476  1.3377  0.6290 -0.2315
-#> 
-#> (3,.,.) = 
-#>  -0.2813 -0.3510 -0.6811 -0.8482  1.3861
-#>   3.3843 -1.2077 -1.9622 -1.1351 -1.8477
-#>   2.6732 -2.4184  0.7855  2.8759 -1.4808
-#> 
-#> (4,.,.) = 
-#>  -6.8546  1.0791  2.1027 -2.8185  0.7520
-#>  -0.9041  0.8896  2.4743  0.6284  0.2519
-#>   2.6052 -1.4564 -1.6375  1.3288  0.3487
-#> 
-#> (5,.,.) = 
-#>   2.5222 -1.6164 -2.2116 -1.0754  0.7719
-#>  -3.6324  2.5302  0.9988 -2.1378  0.6788
-#>   5.6221  0.7932  2.1447  4.9035 -5.1887
-#> 
-#> (6,.,.) = 
-#>   0.2683 -1.0509  2.6643 -0.2398  0.4529
-#>  -2.3240 -3.0188  2.6981  1.3544  0.8555
-#>  -0.4469  0.3477  1.0020  4.7555  1.9801
-#> 
-#> (7,.,.) = 
-#>  -1.5234  0.5375  0.0234  2.3384 -3.3980
-#>   1.3228  3.1686  1.4053 -2.2938  7.3319
-#>   1.9968 -5.2192 -0.6723 -1.0900 -3.2833
-#> 
-#> (8,.,.) = 
-#>  -2.3741  2.0837 -0.4425 -1.5224  2.2040
-#>  -0.7937  1.1621  6.6647  0.5726  1.9161
-#>  -1.2275 -0.9221 -1.9841 -0.4629  0.8082
-#> 
-#> (9,.,.) = 
-#>  -1.4034 -0.0159 -0.7663  0.1020  0.4187
-#>  -1.1376  2.4816  1.0544  1.9942 -1.1878
-#>  -0.8727  0.0230 -0.5804  0.0939  0.1387
-#> 
-#> (10,.,.) = 
-#>  -0.4053 -0.3109 -2.1835 -0.1594  1.8523
-#>  -1.8857  1.2782  3.0087 -2.7136 -0.9552
-#>  -1.9781  1.0313  2.1664 -2.4844 -0.7711
-#> [ CPUFloatType{10,3,5} ]
    # }
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-Broadcast_tensors — torch_broadcast_tensors • torch
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    Broadcast_tensors
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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-    
    Broadcast_tensors
    
-    
    
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    Arguments
    
-    
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-      
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    *tensors
    NA any number of tensors of the same type
    
-
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    broadcast_tensors(*tensors) -> List of Tensors 
    
-
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-
-
-
    Broadcasts the given tensors according to broadcasting-semantics.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_arange(0, 3)$view(c(1, 3))
-y = torch_arange(0, 2)$view(c(2, 1))
-out = torch_broadcast_tensors(list(x, y))
-out[[1]]
    #> torch_tensor 
-#>  0  1  2
-#>  0  1  2
-#> [ CPUFloatType{2,3} ]
    # }
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-Can_cast — torch_can_cast • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Can_cast
    
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    Arguments
    
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    from
    (dtype) The original torch_dtype.
    to
    (dtype) The target torch_dtype.
    
-
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    can_cast(from, to) -> bool 
    
-
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-
    Determines if a type conversion is allowed under PyTorch casting rules
-described in the type promotion documentation .
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_can_cast(torch_double(), torch_float())
    #> [1] TRUE
    torch_can_cast(torch_float(), torch_int())
    #> [1] FALSE
    # }
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-Cartesian_prod — torch_cartesian_prod • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    
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    Cartesian_prod
    
-    
    
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    Arguments
    
-    
-    
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-      
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-    
    *tensors
    NA any number of 1 dimensional tensors.
    
-
-    
    TEST 
    
-
-    
-
-
-
    Do cartesian product of the given sequence of tensors. The behavior is similar to
-python's itertools.product.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = c(1, 2, 3)
-b = c(4, 5)
-tensor_a = torch_tensor(a)
-tensor_b = torch_tensor(b)
-torch_cartesian_prod(list(tensor_a, tensor_b))
    #> torch_tensor 
-#>  1  4
-#>  1  5
-#>  2  4
-#>  2  5
-#>  3  4
-#>  3  5
-#> [ CPUFloatType{6,2} ]
    # }
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diff --git a/docs/reference/torch_cat.html b/docs/reference/torch_cat.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Cat
    
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    Arguments
    
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-    
    tensors
    (sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.
    dim
    (int, optional) the dimension over which the tensors are concatenated
    out
    (Tensor, optional) the output tensor.
    
-
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    cat(tensors, dim=0, out=None) -> Tensor 
    
-
-    
-
-
-
    Concatenates the given sequence of seq tensors in the given dimension.
-All tensors must either have the same shape (except in the concatenating
-dimension) or be empty.
    
-
    torch_cat can be seen as an inverse operation for torch_split()
-and torch_chunk.
    
-
    torch_cat can be best understood via examples.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(2, 3))
-x
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#> [ CPUFloatType{2,3} ]
    torch_cat(list(x, x, x), 1)
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#>  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703
-#> [ CPUFloatType{6,3} ]
    torch_cat(list(x, x, x), 2)
    #> torch_tensor 
-#>  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774  1.5078  0.8533  1.7774
-#> -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703 -0.7864  1.4110  0.6703
-#> [ CPUFloatType{2,9} ]
    # }
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    Cdist
    
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    x1
    (Tensor) input tensor of shape \(B \times P \times M\).
    x2
    (Tensor) input tensor of shape \(B \times R \times M\).
    p
    NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).
    compute_mode
    NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P > 25 or R > 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.
    
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    Ceil
    
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    (Tensor) the input tensor.
    out
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-the smallest integer greater than or equal to each element.
    
-
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-$$
    
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-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.9465
-#>  1.5480
-#> -0.6969
-#> -0.4820
-#> [ CPUFloatType{4} ]
    torch_ceil(a)
    #> torch_tensor 
-#>  1
-#>  2
-#> -0
-#> -0
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    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Chain_matmul
    
-    
    
-
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    Arguments
    
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    matrices
    (Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.
    
-
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    TEST 
    
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-using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
-of arithmetic operations ([CLRS]_). Note that since this is a function to compute the product, \(N\)
-needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
-If \(N\) is 1, then this is a no-op - the original matrix is returned as is.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 4))
-b = torch_randn(c(4, 5))
-c = torch_randn(c(5, 6))
-d = torch_randn(c(6, 7))
-torch_chain_matmul(list(a, b, c, d))
    #> torch_tensor 
-#>   2.2025   6.9263 -12.0433  -1.8318   6.1157  -1.9091  -2.5474
-#>  -9.2675   3.6580 -10.9555   3.7499  -0.9984  -2.1468  18.3629
-#>  -4.3318 -10.0159  20.3315   2.5116  -9.5372   3.4920   7.3516
-#> [ CPUFloatType{3,7} ]
    # }
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diff --git a/docs/reference/torch_cholesky.html b/docs/reference/torch_cholesky.html
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    Cholesky
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Cholesky
    
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    input
    (Tensor) the input tensor \(A\) of size \((*, n, n)\) where * is zero or more                batch dimensions consisting of symmetric positive-definite matrices.
    upper
    (bool, optional) flag that indicates whether to return a                            upper or lower triangular matrix. Default: False
    out
    (Tensor, optional) the output matrix
    
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    cholesky(input, upper=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the Cholesky decomposition of a symmetric positive-definite
-matrix \(A\) or for batches of symmetric positive-definite matrices.
    
-
    If upper is True, the returned matrix U is upper-triangular, and
-the decomposition has the form:
    
-
    $$
-  A = U^TU
-$$
-If upper is False, the returned matrix L is lower-triangular, and
-the decomposition has the form:
    
-
    $$
-    A = LL^T
-$$
-If upper is True, and \(A\) is a batch of symmetric positive-definite
-matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
-of each of the individual matrices. Similarly, when upper is False, the returned
-tensor will be composed of lower-triangular Cholesky factors of each of the individual
-matrices.
    
-
-    
    Examples
    
-    
    
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diff --git a/docs/reference/torch_cholesky_inverse.html b/docs/reference/torch_cholesky_inverse.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Cholesky_inverse
    
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    input
    (Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor
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    out
    (Tensor, optional) the output tensor for inv
    
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-
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-
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    Computes the inverse of a symmetric positive-definite matrix \(A\) using its
-Cholesky factor \(u\): returns matrix inv. The inverse is computed using
-LAPACK routines dpotri and spotri (and the corresponding MAGMA routines).
    
-
    If upper is False, \(u\) is lower triangular
-such that the returned tensor is
    
-
    $$
-    inv = (uu^{{T}})^{{-1}}
-$$
-If upper is True or not provided, \(u\) is upper
-triangular such that the returned tensor is
    
-
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-    inv = (u^T u)^{{-1}}
-$$
    
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    Examples
    
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diff --git a/docs/reference/torch_cholesky_solve.html b/docs/reference/torch_cholesky_solve.html
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    Cholesky_solve
    
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    input
    (Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions
    input2
    (Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor
    upper
    (bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: False.
    out
    (Tensor, optional) the output tensor for c
    
-
-    
    cholesky_solve(input, input2, upper=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Solves a linear system of equations with a positive semidefinite
-matrix to be inverted given its Cholesky factor matrix \(u\).
    
-
    If upper is False, \(u\) is and lower triangular and c is
-returned such that:
    
-
    $$
-    c = (u u^T)^{{-1}} b
-$$
-If upper is True or not provided, \(u\) is upper triangular
-and c is returned such that:
    
-
    $$
-    c = (u^T u)^{{-1}} b
-$$
-torch_cholesky_solve(b, u) can take in 2D inputs b, u or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs c
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a = torch_mm(a, a$t()) # make symmetric positive definite
-u = torch_cholesky(a)
-a
    #> torch_tensor 
-#>  4.8833 -0.7896 -0.4785
-#> -0.7896  1.0348 -0.2048
-#> -0.4785 -0.2048  0.8552
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(3, 2))
-b
    #> torch_tensor 
-#>  0.5712 -0.1153
-#> -1.2014  0.0291
-#>  1.1547  0.9237
-#> [ CPUFloatType{3,2} ]
    torch_cholesky_solve(b, u)
    #> torch_tensor 
-#>  0.0975  0.1667
-#> -0.8489  0.4068
-#>  1.2015  1.2708
-#> [ CPUFloatType{3,2} ]
    torch_mm(a$inverse(), b)
    #> torch_tensor 
-#>  0.0975  0.1667
-#> -0.8489  0.4068
-#>  1.2015  1.2708
-#> [ CPUFloatType{3,2} ]
    # }
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    Chunk
    
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    input
    (Tensor) the tensor to split
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    dim
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor.
    min
    (Number) lower-bound of the range to be clamped to
    max
    (Number) upper-bound of the range to be clamped to
    out
    (Tensor, optional) the output tensor.
    value
    (Number) minimal value of each element in the output
    
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-
-    
-
-
-
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-a resulting tensor:
    
-
    $$
-    y_i = \left\{ \begin{array}{ll}
-        \mbox{min} & \mbox{if } x_i < \mbox{min} \\
-        x_i & \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
-        \mbox{max} & \mbox{if } x_i > \mbox{max}
-    \end{array}
-    \right.
-$$
-If input is of type FloatTensor or DoubleTensor, args min
-and max must be real numbers, otherwise they should be integers.
    
-    
    clamp(input, *, min, out=None) -> Tensor 
    
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-
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-should be a real number, otherwise it should be an integer.
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.9506
-#>  2.2284
-#> -0.7040
-#> -0.4355
-#> [ CPUFloatType{4} ]
    torch_clamp(a, min=-0.5, max=0.5)
    #> torch_tensor 
-#> -0.5000
-#>  0.5000
-#> -0.5000
-#> -0.4355
-#> [ CPUFloatType{4} ]
    
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-a
    #> torch_tensor 
-#>  0.9982
-#> -1.4524
-#> -1.4201
-#>  0.5077
-#> [ CPUFloatType{4} ]
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-#>  0.9982
-#>  0.5000
-#>  0.5000
-#>  0.5077
-#> [ CPUFloatType{4} ]
    
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  1.9805
-#> -1.3783
-#>  0.7469
-#> -0.5865
-#> [ CPUFloatType{4} ]
    torch_clamp(a, max=0.5)
    #> torch_tensor 
-#>  0.5000
-#> -1.3783
-#>  0.5000
-#> -0.5865
-#> [ CPUFloatType{4} ]
    # }
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    r
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    # \dontrun{
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-a = c(1, 2, 3)
-tensor_a = torch_tensor(a)
-torch_combinations(tensor_a)
    #> torch_tensor 
-#>  1  2
-#>  1  3
-#>  2  3
-#> [ CPUFloatType{3,2} ]
    torch_combinations(tensor_a, r=3)
    #> torch_tensor 
-#>  1  2  3
-#> [ CPUFloatType{1,3} ]
    torch_combinations(tensor_a, with_replacement=TRUE)
    #> torch_tensor 
-#>  1  1
-#>  1  2
-#>  1  3
-#>  2  2
-#>  2  3
-#>  3  3
-#> [ CPUFloatType{6,2} ]
    # }
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-filters = torch_randn(c(33, 16, 3))
-inputs = torch_randn(c(20, 16, 50))
-nnf_conv1d(inputs, filters)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 8   8.7199   1.0094 -13.1435   0.0848  10.3420   0.6087   4.9930  -1.3194
-#>    4.9928  -0.9228  -0.9986   6.1375  -2.7563   6.2688  -4.3247   1.3281
-#>   -9.2853   2.2857   5.2282  -3.3548  -1.1207   0.3963   0.6191   9.8601
-#>   -2.9242  10.2787   0.3779   3.2870  -0.1333   5.6178   6.8783   0.1472
-#>   -3.6903  -1.2163  11.9645  -7.1134  -7.8627   8.8960  -5.3701   6.3564
-#>    6.0586 -13.3361   8.7976   5.4943  -0.7770  16.2518  -1.2318  -3.5980
-#>   -2.6689   7.9637  -3.4276  -8.6586 -11.7605   7.9358   3.0729  -3.9882
-#>    4.5930  -2.0741  -3.8087   2.1398   0.5135  -4.2471  -6.8810  -0.7988
-#>    8.2812   1.7671   0.9025  -1.8145  -4.3674  -2.3088   0.6176   3.8401
-#>    9.8749   2.9536   2.1752   0.1801  -4.7035  -4.8083  -2.0966   7.7104
-#>   -4.8894  -5.0632  10.0122 -17.8657  14.6597  -3.3046 -14.7129   8.5806
-#>   -8.5251 -10.4482  13.8896   8.8005   1.1426  10.7473  -8.9987  -1.4510
-#>   -0.4190   6.8991  -5.3740   4.5935 -17.9217  -4.3224  -7.6038   5.2284
-#>    4.8968   7.2210  -3.0931   1.1218  -3.9569  -6.7213   7.2451   0.0891
-#>    2.0933   2.8404  -0.0990   2.7687  -4.0503  12.9643   9.7891   6.1938
-#>   -0.1327   4.4571  -4.0763   0.9068  -4.0663 -10.0672  -0.0225   1.3389
-#>   -9.7440  14.3030  -2.2208   2.3737   4.2655  -4.8818  -6.3157 -14.4964
-#>   -3.8432 -18.8804   5.3663   1.4313  -1.3751  14.9081  -9.7717  -4.8676
-#>   -3.7739   0.8794   1.7302 -10.1387   2.1366 -12.9728  -9.7771  22.9757
-#>    2.7529   9.6604  -0.1306  -3.2509  -3.3979  -0.0270   9.5813   1.2052
-#>   -1.1247  -1.7801   1.5065  -2.9020   6.6147   0.8074  -6.8228  -8.4092
-#>   10.0025   6.9134  -9.1085   8.0784 -11.5030  -9.0789  10.6144  -9.2909
-#>   -7.9290   6.5677   6.5561  -1.8320  11.5627  -5.7025   6.3629   4.9404
-#>  -11.5437   8.3430 -13.9013  -4.2953   6.8770 -10.3218   6.7874   2.5812
-#>   -1.8688  -0.4796  -2.9052   1.1717  10.4609   3.9477   2.4715   1.7198
-#>   -2.8262   7.9209  -3.2288  -6.6791  -5.2385  -4.3199   2.6352   2.9522
-#>    6.2078  -0.4560   6.5245  -3.6146  -1.6950  14.1103   5.0830  -5.4658
-#>  -11.5664   6.8556   2.3652   4.2702   1.0913  -5.0231   1.3486 -16.3077
-#>    8.8823  -0.4775  -1.9547  10.6024   1.7667  -2.6328  12.5331   9.6971
-#>  -11.2919  12.3110   6.9462  -8.8800  -1.5003 -14.4211  -7.1624   7.2424
-#>    2.9990  -6.5829  10.0200   5.3001   2.1273  -3.6609 -12.9631   7.9098
-#>   -3.6909  11.1726   7.8032   2.4063   7.1658   4.1017   4.1518   0.2259
-#>    1.0179   6.3694  -1.5287   5.6457  -8.5666  -2.8587   5.0281   7.5907
-#> 
-#> Columns 9 to 16   2.7100  12.4777  -0.7324  -7.0967  -8.4348   0.8085   6.8330   0.4832
-#>   -8.0836  -5.8170   3.4022  -9.3509  10.5116  -4.9803   6.4073   0.3220
-#>    0.1177  -4.8427  -9.6902  -4.1170   4.2751   4.1760   7.5186  -2.3325
-#>   -0.2762 -10.6201  -1.7163   0.3247  21.2705  -3.6339   2.3409  -1.9524
-#>   -5.4666   1.8799   7.4607   6.1014  -5.0309  -0.7846  -7.0460  -4.5347
-#>  -14.8343   1.0224   2.7581   1.0455  -8.7523   6.5568   2.4569   0.8886
-#>    1.1306   8.4311   6.7053   7.2054   4.8025   0.7849  12.7978  10.1318
-#>    1.7782   0.2690  -2.7999   2.6726  -4.2082  -5.3613  13.9660  -4.6798
-#>    2.3140  -8.0598  12.3022   4.7510   0.3521   0.5352  -5.9258   3.5649
-#>   -3.9661  -1.8614   1.3968  -5.5604   3.0089  -9.1651  -4.2411   1.4253
-#>   -6.1841  17.0519   3.7948   8.4733 -14.7091   8.7843  -6.6218   4.4110
-#>    1.9766   7.1204 -14.9206 -10.7534  -8.4386  -3.9708   4.7609 -10.6990
-#>   15.3699  -7.0543   5.9875   2.9363   9.7296  -1.8657   3.1239  -3.5793
-#>   -3.2610   3.2806   7.3320   9.7861  -1.4700   4.6751  -1.0152   5.4633
-#>    4.6915  -7.1968   1.9738   9.5179  -6.9344   6.6227  -1.7615  -4.0994
-#>   12.9562  -5.4254  -4.4734  -3.8013   1.8657   8.0770 -13.3441   3.3371
-#>   -8.7960   1.4661   7.6683 -12.3310  11.6751 -13.0036  -1.9325  -2.9310
-#>   -8.7788  10.2001   7.3673  -9.2273  -6.8593  -1.2400   0.5811  -6.0189
-#>    2.1427   6.4209   0.6752   9.6093   6.0686   2.9096   9.4132   6.9639
-#>   -6.5691   5.4340  -2.0061 -11.7639  -5.7487 -12.4004  12.9011   3.9542
-#>   -7.6302   3.3180  -3.8970 -15.9881  -3.6270  -1.2098   1.5831  -4.1935
-#>   11.1702  -0.4212   1.5514   3.8853   4.7498  13.8274  -3.8186   8.3544
-#>   -9.0928   2.6163  -5.1443   9.0578   6.0795  -3.2900   0.8558   5.4144
-#>   -3.7635  14.0854  -0.3645  -2.8304  11.9087   6.8293   0.3143   4.1031
-#>   -3.6399   3.6001   0.3297 -10.6195 -22.8159  -4.6401  -6.4364  -2.2262
-#>    4.5382   0.7714  -5.7943  15.3865  -6.2597   2.9687   7.7204  -7.6626
-#>   -1.1954   1.3419   3.5047   6.7636   0.5723  -3.9505   2.3417   0.7854
-#>   -3.7300   4.1058  -8.3983   2.9162 -12.2176  10.6979 -12.7928  -4.0314
-#>   -6.2145  -8.7189 -11.9263  -8.0741 -15.0889 -15.3207   3.2689  -9.3045
-#>   -3.6201  -1.4019  -4.1480   8.8221   3.6578  -1.4179 -12.9980  -2.9236
-#>    3.1550  -3.8547  -9.1263   5.0425   6.9819  -7.1466  -8.7011 -15.4836
-#>    3.3937 -11.3399  -8.4114  -9.2620   2.7354 -16.1142  -0.4008  -9.2902
-#>   14.1278 -11.2060   0.8862  -2.8045  -7.4519   9.9396  -3.1323   0.3984
-#> 
-#> Columns 17 to 24   3.1652   2.7114   3.4294   3.0415  -4.4903   7.3292  -7.9222   5.6389
-#>    1.4022  -2.9618   4.4276   5.1812  -3.8627  -7.9557   1.3882  -5.7022
-#>   -0.7707   0.4761   1.1342 -13.3373   0.1454  -9.6590 -10.9706  -8.7114
-#>    2.3547   3.2183  11.7203  -4.3086 -10.5229   7.3772  -9.7069 -10.1956
-#>    6.7410 -11.6240  -0.2382   4.6611   7.6330  -1.6570   3.7988  14.0638
-#>    6.2723   5.4443   5.0403 -11.4443   9.5750   9.6919  -5.5292   4.9020
-#>    3.5478   9.5459  -7.2635   0.9142   7.1907   0.9225  -0.5947   9.1271
-#>   -7.7508  13.0199   0.7921  -3.7426  -1.0259  -3.3604  -5.9647   5.9305
-#>    9.3377   0.7645  -0.9862  -2.8337  -1.9219  -2.1858  -2.1245  -5.5627
-#>   -0.3122  -6.7192   1.9777   6.8633  12.8850  -9.8542   2.4686  -7.5124
-#>   11.0900 -14.9795   3.5174  -3.2619  16.1453  -6.4522  12.5797  12.6404
-#>   -5.9324  -2.8032   5.1964  -1.0824  -1.9832  11.7757  -1.7279 -12.9944
-#>   -0.9892   1.0205   2.8183  -7.3203   2.3917  -2.5111   1.4563  -3.8504
-#>    7.3162   1.6155   5.1525 -12.5349  11.1861  -7.4035  -3.2327  -2.9030
-#>    7.1859   5.1134  -0.2741  -4.0530  -1.2997  -3.2875  -5.5183  15.7646
-#>   -8.1788   2.5854   5.3686  -2.9653   8.9160  -4.7501  -5.7845  -0.0098
-#>  -12.0708  -1.6919  -3.5657   2.9483  10.7586   0.2813   2.4652  -9.4908
-#>    0.5983  -5.5621  -1.6855  12.0777   7.8527   6.7515  -3.6835  -2.2488
-#>    4.0004  13.8937  17.0219 -16.7366   1.2763  -9.2189  12.1063   6.0094
-#>   -3.6126  -3.3000  -1.6039  10.1845  -2.7015 -11.2980   8.8211  -1.4717
-#>   -4.4065  -2.8283   5.3857   7.3212 -15.1150  -6.8182  -3.2339 -11.4930
-#>    5.9961  14.4878  -7.1496   3.3353  -8.7106   1.7533  -4.1133  -3.2559
-#>    7.3600  19.3142   8.8778  -5.9481   8.3435   0.9410  11.6001   3.9670
-#>   10.8095  -7.0179  13.0225  -2.6706   6.3683  -3.4559  24.2514 -12.8641
-#>    3.2058  -7.5246   7.2564   2.0544  -1.8178  -7.5897   7.9397  -2.8945
-#>   -5.4792  13.4542   5.1535  -3.1458   4.5727   7.0280  -3.7451  19.4841
-#>    4.7460  -2.8855 -13.2574  -4.7093  -8.3299  -5.1462  -5.0900   8.6444
-#>  -10.8531  -6.6294  -9.2172  -6.9031  14.4248  -4.8154  -2.9415   1.8398
-#>   -6.2820  -4.4764  -2.3139   4.8700   4.6623 -12.3261  -3.9658  -0.6185
-#>    1.0061  -5.7484   3.3581   0.1569  19.8596   7.6068  -0.7496   2.7470
-#>   -2.2016   6.4260  -2.1709 -11.6076  -5.6707   0.9438  -6.6296   9.2304
-#>  -13.8891  -0.0952   2.0144  -1.3961  -1.1307   0.9833  -5.6071  -2.3964
-#>    7.1253   3.4768  -1.7254   3.0641   2.3469   4.7614   0.0285   5.2199
-#> 
-#> Columns 25 to 32  -5.7640   1.9080   1.2179  -7.9288  12.6428  -4.0817  -2.0562  11.2799
-#>   -2.2999   3.3650  -3.3909   4.6736   2.4997  -0.5393  -8.2946  11.0434
-#>    0.4672   3.7375  -6.1090   5.6665   0.1840  -1.2466   3.4214   2.1810
-#>   16.8931   1.4244  -2.1984  -6.3534   9.9076   5.2015  -8.9448   2.1251
-#>    3.4824   6.7484  -2.2112   3.6076   0.1031   6.6900   7.2862  -4.4783
-#>    1.2703  -7.0325 -10.5537   2.3520 -14.0131  -8.6101   2.5236   4.7246
-#>    6.5031   2.1143  -7.2054  -3.5723   1.5327  -2.3223  13.5636   1.2073
-#>    2.6270 -10.5701   0.3349   7.4437   1.2394 -11.9173   2.2026   1.5314
-#>    3.6801 -10.6600   7.3837  -3.9585   1.3472   3.7613 -11.5850  -5.6722
-#>   -0.3205   0.1430  -8.6536   5.9411   0.8691  -1.4013   0.8575   9.8608
-#>    6.6989   4.2341   4.1919   9.8073 -13.1589  -0.6517  10.5697  -4.4361
-#>   10.4385   1.2819 -12.3164   7.7010  -3.0824  -7.2327   3.1755   0.4660
-#>   -0.6344  11.4283   1.1485   3.0558   0.5925  -0.7444  -0.7708   4.2235
-#>   13.7591  -4.8287   2.2579  -7.3116  -9.8958  10.0413  -1.5291  -8.6872
-#>    2.3379  15.0105   6.5214   8.6823  -7.9255  -2.5331   6.9940  -9.1324
-#>   17.5126  -5.5142   8.5011  -5.0201   5.6609   9.2456  -6.1677   3.4066
-#>    2.7979  -5.5795  -8.3789  -8.7183   2.8860   1.6986   1.9887   8.5494
-#>    1.4318  -1.6808  -7.4402  -0.1204   7.6516   5.0772  13.7477   3.2697
-#>   12.2700  15.0287  -0.2295  15.0833   0.3382 -10.1724  -0.2717 -12.9545
-#>   -2.9782  -0.0518  -3.3041   0.6671  -6.4112  -0.1517  10.1593  -1.5037
-#>   -8.0853 -10.9545   3.6043  -9.2561   1.8679   5.7242 -10.7073  -2.8222
-#>    9.8356  -9.7044  16.5548  -3.7865  -4.3693   8.5636  -6.1174  -5.1056
-#>    7.9162   3.0483  -3.3383   4.8383  -7.7680  -6.6377   3.4518  -2.8278
-#>   -7.1266   7.2581  -1.7810  -8.9348   4.6869  -5.0878  -7.0578   6.4613
-#>  -14.1705  -1.9353  -5.3650   2.5194   0.2712   1.1505  -7.4139 -11.8516
-#>    6.0882   2.5719  -1.0495  12.0958   1.7734  -4.2679  16.0315  -4.3643
-#>    5.2256  -1.9650   0.6906  -2.4795  -8.6869  -5.6020 -11.8749  -6.4854
-#>    0.8150  -3.5122  -8.1720  -7.3898 -15.8716  -2.2908  14.4807  -9.1374
-#>   -5.1087  -3.3034   0.2836   4.4421  -9.0222   7.1890  -0.6107 -11.7751
-#>   -0.4007   4.7543  -6.2101  -4.1952  -6.2441  10.7278  12.6808   6.7488
-#>   12.5466   1.6176  -6.0263   5.8591  -2.0679 -16.3913  -3.3983  -2.6660
-#>   -5.7903   3.3609  -9.2619   3.9730  -6.3071  -0.9969  -5.8838  -4.9410
-#>    1.3117   1.3398  10.9463  -3.0687  -8.4949   7.5141  -6.1267   0.4574
-#> 
-#> Columns 33 to 40   5.2150  -0.8799   8.5244   8.7060   7.5206   5.6204   3.4728  -2.8515
-#>   -7.1945  -2.2905  -5.6205  -9.4417   2.0720  -7.6105   6.4689  -3.4050
-#>   -3.1793   3.3743   1.3336 -13.1295  -2.0912  -1.7854   4.5045  -2.4045
-#>  -12.8614   5.7727  -6.9273  -9.1353   4.9521  -4.8614   3.4216   3.9188
-#>   -3.9518  -1.9401   0.1467   6.3261   1.4010   8.1285  11.6837  -2.6874
-#>    3.4330  -5.8609  -5.7545   0.2943   2.2022  -3.8233   4.7208  -8.1877
-#>   -4.1200  -3.3579  -1.6158  -2.2032  -4.3891   2.5318  11.8702  -1.2812
-#>   -4.3724  -2.4815  -7.6249  -0.3565  -6.4236  -6.1582   3.4271  -2.6058
-#>  -10.7067  -2.7378  -0.1568   0.4353  -2.4969  13.3673  -4.5670   1.2707
-#>  -15.9498   4.6342  -3.5398  -2.3704   2.9344  -3.9178   7.6102   3.9654
-#>    5.2910  -6.9972  -1.8721   4.3733   1.1638   4.6027   5.1656   7.6044
-#>   -6.5072   0.3133  -9.6089  -2.3442  -2.4470  -1.7199   5.8528  -6.2555
-#>   -4.4756   2.8104   8.7608  -6.9807  -1.2307   2.2073  -9.4628  14.5121
-#>   -0.8752   0.9772  -0.6482  -1.2494   4.9640   4.4118   1.1982  -2.9184
-#>   -1.4969  -2.8102   8.4710  -4.1947  13.4710  15.7840   8.0034   6.4894
-#>   -0.2018  -2.0787   3.6000  -5.0466  -6.3977  -0.3397  -2.1683   5.1826
-#>  -10.4754  -0.3989  -2.1770   7.0205   3.4139  -9.8878  -0.6335   6.0262
-#>   -2.2778   7.0196  -4.0415  -0.7292  -0.8091  -2.1652   5.3104 -10.2415
-#>   -4.7832   6.1902  -6.4281 -16.2450  -0.6198  -1.2905   7.3274  13.9358
-#>    0.0250   0.0059   2.8493   7.5505   7.3472   3.1271   3.9622  14.2142
-#>   -7.1615  -4.8397  -3.9963  -2.8790 -11.1888  -3.5380  -8.0007 -17.4729
-#>    6.1410  -7.7925   3.5944  11.6301   6.0241   5.4524  -3.4412   1.0908
-#>    9.0182  -4.6797  -7.1141   7.6652   1.9939  -8.9450   4.6569   2.0778
-#>    8.6951  -0.7674   8.8390  -2.0409  -1.4114  -5.7077 -10.2438  -6.6126
-#>   -0.7592  -7.9487   6.5387 -19.3390   0.3024  -2.0058   3.5009  -6.7498
-#>    6.0412  -5.7854   6.8616   3.3522   2.0884   2.2317  12.6853   5.7927
-#>   -1.7431 -10.2967  -2.6837  13.5075   1.5764  -0.3518   1.4739   1.8059
-#>   11.0760 -16.0045   0.1230  -9.1960   4.6352  -1.8363   3.4795  -4.1980
-#>   -6.6729   3.4266  -1.6780 -10.0093   6.0663   0.8971  13.7264  -5.1570
-#>    2.7899  -3.6841   7.6861  -0.9843  -1.9141  -4.7011  -3.1319   7.7179
-#>   -9.1538  -4.4516  -8.9869  -2.2559   1.1936   0.6045   0.1807  -3.2672
-#>   -8.1173   5.0338  -8.0610   1.1754  -1.3898   1.5216  -1.7338   3.0696
-#>    4.5485  -4.0602  14.4818   1.3694  -0.8208   3.5496   6.0590  -6.4393
-#> 
-#> Columns 41 to 48   9.4318   8.2171   4.8322   5.6457  -2.4645  -3.5706  -0.8075   4.8571
-#>   -3.2900   0.4726  -2.3633   1.8494  -2.6713  -3.3732  -5.7716  -4.3053
-#>   -0.2045 -11.8373 -13.0920  -5.4249   2.9510  -3.4678 -15.6115  -8.6613
-#>   -1.0374  -2.1204  -9.4928  -0.0420   5.7950   3.1081  -1.5467   1.4160
-#>    2.2251  -6.1046  -3.2651   2.6999   2.5576  10.5800   0.0160  -3.9492
-#>    9.1404   4.3808   6.6542  -0.1789   0.4081   8.8088   0.6046  -0.0137
-#>    1.1672  -9.2990   3.7059   5.6774   2.8614  -2.1775  -7.2604   0.4836
-#>   -2.4184  -0.9147  -0.6182   1.0150   3.7945  -2.1332   1.4857  -7.8693
-#>   -6.0545  -8.6096   1.3435   0.2288   4.7465  -0.0030  -7.0416  -1.5885
-#>  -11.9655   0.3257  -4.6491   2.0680  -3.0774  -0.2861   8.1254  -4.1858
-#>    0.5986  12.9359  -1.3932   3.2026   4.7596   4.9578   0.6192  -0.7744
-#>    0.2748   4.1774  -1.7001  -4.7775   0.3853   0.3061  -3.2432   8.5125
-#>  -11.3921 -10.8563  -7.5818  -1.9139  -3.8404   3.6539   5.9986  -0.6856
-#>    7.9886  -7.6804  -0.7950   2.8417   4.4662   9.4306   0.1284  11.4431
-#>   -1.4610  -1.3248   0.5573  -4.3987  -2.9653  -3.4342  -0.6057  -0.4561
-#>   -1.3849  -9.5639  -2.3433   1.1506   7.7221   1.7906   3.4410   1.6867
-#>   -1.6394   5.4429  -8.0806  -0.0369   0.6217  -3.6089   7.8226   6.9275
-#>   13.5281  -1.9440   4.2859  -6.6300  -2.5808  10.4666   6.6420   5.9351
-#>   -7.1382   0.0638  -2.3715   7.4408   9.4569   0.4644  -4.9758  -6.1014
-#>   -3.6463  10.5673   2.6788   2.2272   7.1652  -0.5320 -10.1751 -13.5356
-#>    6.0480  -6.9958 -12.2500  -4.8509   2.3519  -1.9778 -14.9620   3.8193
-#>    4.9883  -8.4463  10.3489   3.0552   0.4646  -7.8505   1.3567   7.2660
-#>   13.4826  10.6962   4.5866   4.7238   8.7162   5.6177   5.2107   6.1307
-#>   -3.1070   0.8094  -0.3173   4.1823  -7.6181  -4.1219   2.5528  20.6973
-#>    4.1262   0.0812  10.5087   6.9784   5.2514   3.9877 -13.4908  -0.9355
-#>   -6.2435  -0.0535  -5.1731  -1.5473   4.3737  -7.7088  -2.1305  -1.1972
-#>   -4.0223   3.8423  -0.1538   2.1298   3.1662   4.6899  -4.0121  -8.6478
-#>    1.9742   0.8946  -9.8064  -1.7442  -1.2579  -0.8046  14.5273   9.2468
-#>   -5.3971   1.5371  -2.6447   2.3005  -2.7475  -1.9236 -11.5593  -9.2965
-#>    5.1362   3.7165  -5.0594   1.5171   6.5340   4.1253  13.0958   8.7482
-#>   -2.3735  -2.9054   4.5685   1.3250   8.4590  16.7741   3.8116   1.4827
-#>  -24.0291   1.0120  -9.4348  -3.3122   4.4437  -0.8187   1.9867  -5.7997
-#>   11.1663  -8.7557   4.2210   2.3879  -1.6097   0.6809  -6.8775  -2.3145
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 6 -7.1509e-01 -4.9349e-01  2.9598e+00  7.5520e+00  4.0061e+00 -1.3150e+00
-#>   6.1158e-01 -6.8831e+00  4.3357e+00  2.3711e+00  5.0670e+00  4.3449e+00
-#>  -5.1513e+00 -1.9952e+00  3.0471e-01 -1.4798e+00 -2.9636e+00  1.1523e+00
-#>   7.4474e-02  1.0948e+00 -6.2086e+00 -5.7473e+00  3.9698e+00 -3.6172e+00
-#>  -1.0056e+01  1.9371e+00 -1.2662e+00 -1.7345e+00 -1.1842e+01  3.6392e+00
-#>   3.7177e+00 -8.3396e-01 -6.0881e+00  4.9506e+00 -5.1576e+00 -4.8864e+00
-#>   2.7897e+00  3.9768e+00 -1.1350e+01 -5.2308e+00 -2.3766e+00  1.1203e+01
-#>   5.9575e+00  3.1428e+00 -5.9637e+00  5.7530e+00  7.2610e-01  5.6093e+00
-#>   1.1167e+00  4.4847e+00  7.6154e+00 -1.3624e+00  8.2594e+00 -9.0724e+00
-#>  -4.8284e+00 -1.0704e+01  2.0032e+00  2.9894e+00 -6.4011e-01  8.4435e+00
-#>   5.1141e+00 -1.6564e+00  4.4674e+00  6.0807e+00 -6.4299e+00  5.6468e-01
-#>   2.3772e-01  6.5366e+00  8.5935e+00  3.8245e+00 -1.4917e+01 -9.2021e-01
-#>  -5.6431e+00 -1.2715e+00 -2.1304e+00  6.5598e+00 -5.9600e+00 -3.6707e+00
-#>  -4.7215e+00 -1.9434e-01  2.5668e+00  1.0392e+01 -3.2271e+00 -6.6734e+00
-#>  -3.0612e+00 -2.3705e+00 -7.3181e-01  9.4870e+00 -7.3197e-01 -2.5297e+00
-#>  -5.7609e+00 -8.1802e+00 -7.4856e+00 -1.3887e+01  2.7613e+00 -5.9915e+00
-#>   7.5561e+00  6.5578e+00  5.6733e+00  9.0824e+00  1.1058e+01  1.6211e+01
-#>  -2.8835e+00 -4.0863e+00  2.6817e+00  6.0484e+00 -3.5239e+00  3.4253e+00
-#>   8.4178e+00 -1.9318e+00 -8.1999e+00  5.6324e+00 -4.5409e+00  6.2325e+00
-#>  -4.9633e+00  1.1019e+01  3.7780e+00  1.7794e+00 -4.6708e+00 -5.6795e-01
-#>   5.0768e+00  5.8880e+00 -2.3387e+00 -2.4749e+00  4.1631e+00  2.0668e-01
-#>   3.3728e+00  1.9753e+00  1.2435e+01 -9.1491e+00  6.8930e+00 -1.4042e+01
-#>  -2.3147e+00  1.1081e+00 -4.7888e+00  3.3629e+00 -5.7519e+00  7.9487e+00
-#>  -4.1190e+00 -6.3947e-01 -1.4461e+01 -6.3951e+00 -6.1554e+00  3.6205e+00
-#>   1.3226e+00  5.5740e+00 -4.9470e+00 -3.4072e+00  5.9575e-01 -7.4203e+00
-#>  -2.0237e+00  6.2413e+00 -1.3620e+01 -4.2507e+00  7.2215e+00  6.4567e+00
-#>   4.3053e+00  7.2772e+00  8.3178e+00  3.6260e+00 -4.1942e+00 -1.9330e+00
-#>  -1.4517e+00  7.2922e-01  4.5117e+00 -2.4268e+00  7.4411e+00  6.0721e+00
-#>  -1.0672e+01 -5.0188e+00  1.7881e-01  6.3910e+00 -4.9103e+00 -1.8536e+00
-#>  -6.9459e+00 -5.0136e+00 -2.8328e+00  4.1931e+00 -1.0879e+00  8.8608e+00
-#>  -1.3535e+00 -6.3363e-01  6.6245e+00  8.1844e+00 -4.6843e+00 -3.0662e+00
-#>  -1.7060e+00 -5.8109e+00 -2.4222e+00 -1.0309e+01 -8.6567e+00  3.7424e-01
-#>  -9.0191e+00 -5.1409e+00 -2.7915e+00 -1.0410e+01 -5.4462e+00 -1.1519e+01
-#> 
-#> Columns 7 to 12 -5.9516e+00  7.0156e+00  5.0552e+00  8.3654e-01  4.9012e+00  3.2226e+00
-#>  -2.9401e-03 -8.3668e+00 -3.8962e+00  1.0038e+01 -3.0033e+00 -8.2599e-01
-#>   3.8983e+00 -8.2420e+00 -2.4075e+00  1.6270e+00 -1.6823e+01  1.3287e+00
-#>  -1.0277e+00  1.8767e-01 -1.2692e+01  1.2294e+00 -1.2903e+01  1.2650e+01
-#>  -4.3204e+00 -8.9256e+00 -5.2164e-01 -1.7049e+00 -2.2844e+00 -8.9570e+00
-#>   5.9251e+00  2.8970e+00 -2.0009e-01 -3.1560e+00  5.2960e+00 -5.3078e+00
-#>   1.7958e+00  4.7085e-02 -7.6300e+00  8.0213e+00 -1.4238e+01 -3.1199e+00
-#>   4.0685e-01  8.8669e-01 -1.9305e+00  9.5970e+00  4.0827e+00 -5.9016e+00
-#>   3.5766e+00  3.4390e+00 -5.6902e+00 -1.2657e+01 -5.3466e+00 -3.8703e+00
-#>   1.9449e+00 -9.8307e+00  3.2450e+00  2.4425e+00 -3.7293e+00  8.7565e-01
-#>  -1.5291e+00 -8.5767e+00  6.2224e+00  4.7074e+00 -1.7078e+00  3.0609e+00
-#>  -3.9321e+00  6.1785e+00  5.0283e+00  4.1094e+00 -2.6448e+00  5.3437e-01
-#>   1.5058e+00 -1.2001e+00 -9.9301e+00  7.9571e+00 -4.7609e+00 -1.0210e+01
-#>   4.2221e+00 -3.1459e+00 -2.5754e+00  3.1745e+00 -1.0673e+01 -4.2940e+00
-#>  -1.5951e+00 -2.1660e+01 -6.0590e-01  1.2761e+00 -5.5640e+00 -4.4963e+00
-#>   3.5914e+00 -7.2424e+00 -4.2301e-01 -2.1719e+00 -5.9560e+00 -6.6360e+00
-#>   6.0457e+00  9.1813e-01  9.8854e+00  1.5404e+01  8.7826e+00 -1.0195e+01
-#>   7.1532e+00  1.1020e+01 -7.9245e-01  1.8356e-01 -1.0282e+01 -2.5896e-01
-#>   4.4396e+00 -6.3894e+00 -3.1537e+00 -1.0250e+01 -9.5840e+00 -3.5273e+00
-#>  -1.0148e+01 -6.4415e+00  7.3811e+00  1.6038e+01 -1.2954e+00 -7.3381e-01
-#>  -1.1356e+01  1.0164e+01  3.7982e+00  1.1379e+01 -1.3215e+01 -4.7850e+00
-#>  -7.0512e+00  3.6091e+00 -1.1422e+00 -7.8434e+00  8.2024e-01  3.2626e+00
-#>  -5.2845e+00 -3.2287e+00  1.0512e+01  3.9971e+00 -5.6834e+00 -9.3933e+00
-#>  -1.4016e+00  9.6409e-01  8.2726e+00  1.9828e+00  4.4276e+00  3.6313e+00
-#>   1.9920e+00 -9.7004e-01  5.1981e+00 -9.5342e+00 -5.0760e+00  3.3793e+00
-#>   2.1394e+00 -1.4262e+01  3.0598e+00  7.2985e+00  1.9970e+00 -9.2220e+00
-#>  -1.0893e+01 -2.5566e+00 -6.1387e+00  1.0846e+01  6.5455e+00  1.6814e+00
-#>   3.2075e+00 -1.5686e+01  1.4660e+01  6.6920e+00  2.2215e+00 -1.4393e+01
-#>   3.2561e+00 -3.0157e+00 -2.8714e+00 -8.9107e-02 -8.5308e-01  9.6009e-01
-#>   6.7504e+00  9.9947e+00  1.3110e+01 -1.0746e+01  8.8372e+00 -1.4285e+01
-#>   9.1524e-01  2.6967e+00  1.3832e+00  7.8529e+00  4.4973e+00  4.9536e+00
-#>  -2.6921e+00 -1.4808e+01 -2.9850e+00  1.8306e+00  2.6230e-01  4.2523e-01
-#>  -1.2366e+01 -7.5979e-01 -2.1096e+00 -6.5637e-02  2.7424e+00 -8.5872e+00
-#> 
-#> Columns 13 to 18  2.7000e+00  8.5381e+00 -8.1091e+00 -3.7162e+00  2.0213e+00 -6.0458e+00
-#>  -4.4232e+00 -1.0786e+01  1.4304e+01  7.0479e-01 -3.7465e+00 -1.9488e+00
-#>   2.8797e+00  4.6008e-01  7.6718e+00 -2.5439e+00  5.3121e+00  1.3722e+00
-#>   1.9172e+00  3.2784e+00  7.7748e+00 -1.2554e+01  6.0663e+00  3.9239e+00
-#>   1.9770e+00  1.4078e+01 -6.5417e-01  9.7094e+00 -2.4134e+00  5.5477e+00
-#>  -3.2059e+00  6.4861e-02  4.7822e+00 -6.6546e+00 -4.4499e+00 -1.5853e+00
-#>  -2.4581e+00 -1.5303e+00  4.5577e+00 -3.0146e+00  1.0402e+01  9.5618e+00
-#>  -6.2991e+00 -5.5900e+00 -5.3919e+00 -1.0916e+00  9.6959e+00 -5.6915e+00
-#>   1.0740e+01  9.5805e-01 -4.2546e+00  8.1128e+00 -7.7091e+00 -2.4287e+00
-#>  -1.1507e+00  5.1993e+00  8.7242e+00  1.1963e+01 -6.3006e-01 -3.7769e-01
-#>  -6.9138e+00 -1.0091e+01 -1.2608e+01  8.1199e+00  1.6238e+00  3.1721e+00
-#>  -1.6155e+01  7.7175e-01  1.0307e+01 -2.4856e+00  1.0481e+01 -6.2984e+00
-#>   3.8670e+00 -2.1379e+00 -1.2605e+00  2.7508e+00  7.6017e-01 -8.3812e+00
-#>   5.5454e+00  6.7137e+00 -1.7055e+00 -7.7823e+00 -3.4515e+00  4.7732e+00
-#>   7.6535e+00  9.3814e+00 -8.8797e+00 -1.1818e+01 -8.5267e+00 -1.4578e+00
-#>   2.7738e+00  1.0396e+01  3.6270e+00  3.0116e+00  2.7857e-01  1.8120e+01
-#>   3.4237e+00 -1.0749e+01  5.3512e+00  2.4723e-01 -7.9703e+00  1.9826e+01
-#>   6.5276e+00  9.1472e+00  2.5897e+00  5.6845e-01  2.4023e+00  9.8481e-01
-#>  -8.0553e+00 -1.1198e+01 -1.0056e+01  1.4838e+00  6.0982e+00  1.8073e+00
-#>   7.9459e+00 -8.7685e+00  2.4101e+00  4.0183e+00 -2.7991e-01  8.6546e+00
-#>  -1.1821e+01  4.5998e+00  6.9386e-01 -4.8410e+00  3.9323e+00  3.4028e-02
-#>   6.9009e+00  6.5700e+00 -2.3530e+00 -2.3630e+00 -3.5682e-01 -3.1213e+00
-#>  -6.3269e+00  3.8343e+00 -7.5480e+00 -1.1199e+01 -8.8526e+00  1.1089e+01
-#>  -7.9600e+00 -2.6078e-01  7.3612e+00  3.3860e+00 -5.8622e+00 -5.7458e+00
-#>   1.6788e+00 -4.5105e+00  4.8601e+00  2.6536e+00  6.3036e-01  1.6862e+00
-#>  -8.7453e+00  1.9969e+00 -2.7020e+00  1.2625e+00  8.6836e+00  4.4179e+00
-#>  -3.8124e+00 -5.3169e+00 -1.0316e+01 -6.2008e+00 -1.0265e+01 -4.3392e+00
-#>  -1.1196e+01  1.0953e+00  1.2303e+01 -3.2832e+00 -2.6740e+00 -8.8153e-02
-#>  -4.3849e+00  9.3394e-02  1.0517e+01  6.6309e+00 -1.0079e-01 -2.0375e+00
-#>   2.8490e+00  1.0117e+01 -5.2329e+00  9.0272e-01  4.3376e+00  7.2262e+00
-#>   1.2064e+00  2.9931e+00 -1.0497e+01 -1.0496e+01  5.1515e+00 -1.6065e+00
-#>   1.3753e+01 -3.8291e+00  1.2658e+01 -2.3645e+00 -1.1169e+01  4.3685e+00
-#>  -2.9462e+00  3.2668e+00  4.3312e+00  4.1991e+00  6.5311e+00 -3.7727e+00
-#> 
-#> Columns 19 to 24 -2.6109e-01  4.8078e+00 -1.0124e+01  7.8367e+00 -4.4960e+00 -9.2124e+00
-#>   3.4842e+00 -8.3740e-01  1.0619e+01  2.2888e-01 -9.4949e+00  4.2166e-01
-#>   7.1282e-02  5.1503e+00  8.8166e+00 -3.2567e+00 -3.4595e+00  2.0536e+00
-#>   7.7086e+00 -1.0302e+01  2.3211e+00 -1.3219e+00 -6.5876e-01 -9.8129e+00
-#>  -3.6886e+00  1.1903e+01 -2.9881e+00 -7.3948e+00 -1.5627e+00  8.1673e+00
-#>   1.1306e+01 -6.6918e+00 -3.6204e+00 -4.8952e+00 -9.2597e+00 -6.2369e+00
-#>  -6.9676e+00 -1.6389e+00 -2.3276e+00  3.5074e+00 -2.0643e+00 -2.0283e-02
-#>  -4.5878e+00 -2.5588e+00  8.6070e+00  3.8829e+00 -3.9444e+00 -3.7486e+00
-#>  -5.5344e+00  8.6413e+00  8.5727e-02  9.4498e-01  8.3532e+00 -1.1366e+01
-#>  -5.5513e+00  2.5488e+00  7.4590e+00 -3.5227e+00 -5.4778e+00  7.7005e+00
-#>   6.5902e+00 -6.7006e+00 -1.9534e+00 -4.8660e+00 -7.5983e+00  1.9447e+01
-#>  -1.2766e+01  6.2877e+00  7.3611e+00 -7.5964e+00  9.8168e+00  3.7307e+00
-#>  -1.1571e+00  3.2945e+00  3.3334e+00 -6.9614e-01  1.3975e+00  8.6417e+00
-#>  -2.3076e+00 -3.3192e-01  3.1283e+00 -1.2837e+01  5.1154e+00 -5.8279e+00
-#>   5.5291e+00  7.0720e+00 -6.0436e-02 -5.0244e+00 -1.6342e+01  5.6834e+00
-#>   4.0753e+00 -8.6386e-01 -1.2163e+00 -3.3384e-01  8.5434e+00 -6.3283e+00
-#>  -8.1850e+00  7.5152e-02 -5.8536e-01  1.3266e+00 -7.6313e+00  1.1692e+01
-#>   1.2035e+01  2.2418e+00  1.5214e+00  2.7325e+00 -4.7900e+00 -8.3553e+00
-#>   1.3710e+00 -5.7864e+00  3.9161e+00  4.7640e+00  9.8295e-01 -8.4390e-01
-#>  -5.9023e+00 -7.4171e+00 -5.0537e+00 -4.7018e+00 -8.9029e-01  9.8228e+00
-#>  -9.1306e+00 -1.9524e+00  8.9289e+00  3.8039e+00  1.5887e+01 -5.0845e+00
-#>   7.2528e+00 -3.6954e+00 -3.4254e+00 -4.3860e-02  5.0786e+00 -8.6250e+00
-#>  -1.7334e+00  3.8103e+00 -2.5993e-01 -1.1297e+01 -1.1125e+01  6.7070e-01
-#>  -1.3782e+01  5.9581e+00 -3.7445e+00  7.2762e+00  8.2591e+00  1.3241e+00
-#>   7.8407e+00 -3.1490e+00 -2.0267e+00  2.5580e+00  1.0221e+01 -6.0490e+00
-#>  -4.8752e-01 -9.7989e+00  3.6149e+00  6.3880e-01 -6.0927e+00  6.9155e+00
-#>  -5.2781e+00  2.3281e+00 -2.9401e+00 -5.2770e+00 -4.5600e+00  4.0959e-02
-#>  -1.6510e+00 -4.4628e+00 -4.4317e-01 -3.7385e+00 -7.0257e-01  1.2179e+01
-#>  -1.4263e+00  2.4627e-02  1.5154e+01 -8.5295e+00  1.1547e+01 -1.7261e+00
-#>   3.3542e-01  3.9312e+00 -1.0944e+01  2.7019e+00  6.4122e+00  2.6792e+00
-#>   4.4669e-01  1.9376e+00  7.9560e+00 -3.4105e+00 -3.0819e+00  2.3268e+00
-#>  -9.2093e+00 -2.2787e+00  6.0683e-01 -5.0750e+00 -4.6972e+00  2.2780e+01
-#>   3.7401e+00  2.3183e-01 -5.3432e+00 -7.5792e+00  1.1843e+01  2.6668e+00
-#> 
-#> Columns 25 to 30  1.3633e+01  1.1635e+01 -1.4768e+00 -1.0947e+00 -1.4935e+00  7.3367e+00
-#>   1.8347e+00 -5.9848e+00 -1.5196e+01  8.0386e+00 -8.6412e-01  5.7371e+00
-#>   3.3975e-01 -8.7520e-01 -2.1590e+00  6.9778e+00  2.7767e+00  4.6087e+00
-#>   1.4119e+01 -3.1869e+00 -7.2740e+00  9.6347e+00  8.3509e+00 -9.9526e+00
-#>   1.7852e+00 -6.6034e+00  1.3139e+00  5.3587e+00  6.7300e+00  1.1495e+01
-#>   1.0884e+01 -3.6163e+00 -6.1070e-01  9.3564e+00  1.5856e+00 -1.4446e+01
-#>   4.5013e+00 -3.0506e+00 -1.3709e+00 -6.1256e+00 -1.4894e+00  6.7897e+00
-#>   7.1776e+00 -3.4181e+00 -5.2673e+00 -8.4402e+00 -1.3334e+01  9.0230e+00
-#>  -4.5190e+00 -8.1797e+00  1.2852e+00 -1.2342e+00  1.4442e+01  3.5929e+00
-#>  -4.9083e+00 -8.2237e+00 -7.7480e+00  1.4833e+00 -3.7700e+00  8.9328e+00
-#>   1.3677e+01 -1.1703e+01 -3.4264e+00  8.4007e+00  3.0622e+00 -3.9277e+00
-#>  -1.2444e+00 -7.7115e+00 -1.1126e+00 -4.7788e+00 -9.1684e+00  1.7096e+00
-#>  -7.9805e+00  1.9402e+00  5.8282e+00 -1.0984e+01  5.7442e-01  4.8096e+00
-#>   3.5863e+00 -6.9901e+00  2.0761e+01 -6.0529e+00  1.0542e+01 -5.5653e+00
-#>   4.7324e-01  1.0031e+01  1.2682e+01  5.8382e+00  2.4513e+01  1.7300e+01
-#>   4.3019e+00 -3.9498e+00  4.4878e+00 -2.3613e+00  8.3003e+00 -1.0475e+01
-#>   6.1743e+00 -6.1079e+00 -1.0839e+01 -4.8769e+00 -6.2840e+00  7.5247e+00
-#>   1.1283e+01 -1.8325e+00 -7.0253e+00  5.0291e+00 -2.7680e+00 -7.1598e+00
-#>   2.6188e-01 -6.6821e+00 -1.2407e+00  7.5910e+00 -1.4712e+01  4.1615e+00
-#>   1.2098e+00  7.7760e+00  8.4132e+00 -1.8960e+00  9.5166e-02  1.1113e+01
-#>  -8.5597e-01 -1.1365e+01 -6.1514e-01 -7.5014e+00 -4.4777e+00  7.6538e-01
-#>  -5.3641e+00  8.4976e+00  7.7201e+00 -9.8481e+00  1.3061e+01  2.3271e+00
-#>  -5.7804e-01 -2.1340e+00  6.5594e+00  1.0442e+01  2.3031e+00  5.4915e+00
-#>  -2.4073e+00 -7.2948e+00 -7.1055e+00 -1.4424e+01 -1.8628e+01  6.5668e+00
-#>  -1.7887e+00 -1.0561e+01 -3.0666e+00  1.3548e+01  1.8072e+00 -8.3541e+00
-#>   1.4815e+01  2.1536e+00 -2.1050e+00 -2.4299e+00 -2.1874e+00  7.0530e+00
-#>   1.2392e+01  1.9511e+00 -5.1508e+00  4.0476e-01  1.3191e+01  1.0705e+01
-#>  -3.2279e+00  1.7266e+00 -4.3829e+00 -7.9983e+00  2.0262e+00  6.8517e+00
-#>  -8.2418e+00 -8.7575e+00  1.0860e+01  1.2190e+01 -1.1073e+00  7.0043e+00
-#>  -4.0054e+00  7.2396e-01 -1.3193e+00  4.3663e+00 -1.4141e+01  6.7344e+00
-#>   8.4796e+00 -7.0270e+00 -5.3384e+00 -9.5140e+00 -5.6786e-01  3.8349e+00
-#>  -1.6548e+01  7.2795e-01  7.1365e+00  8.1874e+00  2.1921e+00 -9.6058e+00
-#>  -3.1823e+00 -8.5109e-01  4.5027e+00  4.7013e+00  8.7304e+00 -3.1466e+00
-#> 
-#> Columns 31 to 36 -3.0502e+00 -5.4226e+00 -1.1866e+00 -7.7360e-04  1.0046e+01 -1.4339e+01
-#>   5.1785e+00  8.5924e+00  7.3401e+00 -9.1732e+00  6.5585e+00  4.2616e+00
-#>   4.7791e+00  1.9069e+00  1.2301e+00 -3.1632e+00  1.0141e+01 -7.9537e+00
-#>  -3.2250e+00  1.1265e+01  8.3408e+00 -1.5957e+01  1.1140e+01  3.0385e+00
-#>  -5.7449e+00  3.2804e+00 -3.5045e+00  3.9281e+00 -4.7560e+00  5.1381e+00
-#>  -3.3620e+00  3.1483e+00  6.3762e+00 -3.0654e+00 -5.8521e+00 -2.6027e+00
-#>  -6.8715e-01 -1.3580e+01  2.9583e+00 -2.8945e+00 -2.2106e+00 -1.0597e+00
-#>   2.3841e+00 -2.2713e+00  5.1123e-01  1.3546e+00  2.8196e+00 -4.8460e+00
-#>   4.1300e+00 -8.9717e-01 -2.0092e+00  8.0245e+00  8.9510e+00 -5.7317e+00
-#>   4.9242e+00  5.6605e+00  4.8302e-01 -1.6416e+00 -5.6424e+00 -3.1269e+00
-#>  -1.7472e+01  9.0286e+00 -1.1657e+00  1.5655e+01 -1.8243e+01  1.5363e+01
-#>  -1.8474e+00  4.0897e+00  7.5385e+00  6.4631e+00  1.1732e+01 -1.5875e+01
-#>  -2.9430e+00  1.0304e-02 -6.3027e+00  4.3613e+00  7.5438e+00 -7.5815e+00
-#>  -6.3004e+00 -2.0406e+00 -2.1198e+00 -1.7317e+00  5.3290e+00  1.1506e+00
-#>   4.4363e+00 -7.1078e+00 -2.2689e+00  9.1593e+00 -1.0209e+00  1.4142e+01
-#>  -1.0158e+00  3.4533e-01  1.0040e+01 -5.2964e+00 -3.8510e+00 -9.8925e+00
-#>   7.0940e+00 -1.9105e+00 -2.6435e+00 -6.7222e+00  4.9688e+00 -9.4728e+00
-#>  -1.4549e+01  5.3767e-01  2.3201e+00 -1.9899e+00 -5.1085e+00 -2.1957e+00
-#>   9.1778e+00  1.1308e+01 -4.9178e+00 -2.6880e+00  3.7381e+00 -4.8390e-02
-#>  -1.4211e+00 -4.6613e+00 -5.0277e+00  7.5841e+00 -5.6116e+00  1.0896e+01
-#>  -1.3916e+00  1.0658e+00  6.3661e+00 -3.2695e+00  8.0748e+00  1.1913e+01
-#>  -5.4840e-01 -7.0615e+00 -2.1461e-01  5.8277e+00  4.4998e+00  6.1279e-01
-#>  -7.2273e+00 -3.3353e+00 -2.2451e-01 -4.2120e+00  1.4607e+00  2.1736e+00
-#>   2.3888e+00  7.9467e+00 -1.4923e+01 -2.8997e+00 -1.4515e+00 -1.0238e+01
-#>   1.9038e+00 -2.1767e+00  1.7820e+00  5.0317e+00  9.6143e+00  3.5390e+00
-#>  -2.2836e+00 -6.7059e-01 -3.3277e+00  2.3968e+00 -6.2656e+00  7.1541e-02
-#>   1.8024e+00 -4.6275e+00  3.8931e+00  2.2845e+00  9.2879e+00  2.6737e+00
-#>  -2.7062e+00 -6.9413e+00 -2.2579e+00  9.6534e+00 -2.0443e+01  1.0024e+01
-#>   7.2623e+00  3.7980e+00  6.2083e+00 -5.2031e-01  6.6343e+00  2.8769e+00
-#>   4.6087e-01 -1.1342e+00 -1.0071e+01 -1.0014e+01 -1.0012e+00 -1.6799e+01
-#>   1.3046e+01  1.2031e+01 -5.9934e+00  2.7704e+00  5.1693e+00 -9.1025e+00
-#>   2.3357e+00  3.6832e+00  1.0729e+01  2.8181e+00  4.0101e+00  2.8323e+00
-#>  -3.7210e+00  9.5575e-01  9.1658e+00 -3.9453e+00  6.6812e+00 -9.8705e+00
-#> 
-#> Columns 37 to 42  8.0274e+00  1.3473e+00 -7.9683e+00 -2.5021e+00 -1.4562e+00  6.9043e+00
-#>   6.4800e+00 -6.4930e+00  6.5987e+00  1.6992e+00  3.3423e+00  2.9007e+00
-#>   4.1236e+00  2.8806e+00  3.4910e+00  9.0047e+00  1.3609e+00 -4.1131e+00
-#>   3.9045e+00 -2.7661e+00  8.0995e+00 -3.2099e+00 -6.3028e+00  3.6100e+00
-#>   2.8595e+00  8.5890e+00  6.9759e+00 -3.6375e+00  1.2716e+01 -5.1055e+00
-#>  -3.9744e+00 -3.7970e+00  4.5694e+00 -4.4010e+00  4.6595e+00  1.0435e+01
-#>   8.7296e+00  2.9286e+00 -1.9703e+00  2.7617e+00  1.0797e+00  2.7736e+00
-#>   4.3076e+00 -1.6706e+00 -7.8943e+00  7.5354e+00 -6.1941e+00  3.9286e+00
-#>   1.2360e+01 -5.4219e+00  1.6949e+01 -6.1366e+00  1.3807e+01 -6.2802e+00
-#>   1.7763e+00  6.6658e+00 -1.9258e+00  7.6167e+00  4.0521e+00 -4.5945e+00
-#>  -8.7879e+00  2.0683e+00 -1.3782e+00 -1.0502e+00  4.1240e+00 -9.3366e+00
-#>  -2.4919e+00  4.6335e+00  5.6993e-01  5.8056e+00  1.3667e+00 -3.8362e+00
-#>  -7.9760e+00 -9.7185e+00  6.3919e+00  5.8327e+00 -5.5591e+00 -1.0145e+01
-#>  -2.4361e+00 -1.3595e+00  5.3784e+00 -1.0564e+01  4.4731e+00 -7.7008e+00
-#>  -1.2498e+00  1.1055e+01  1.4361e+01  4.4318e+00 -9.1977e-01 -1.0886e+01
-#>   7.8499e+00 -4.4737e-01  4.0138e+00 -1.6863e+01  3.4583e+00 -8.8853e+00
-#>   5.6519e+00  7.7085e+00  1.0316e+00 -5.9979e+00  1.2462e+00 -5.8032e-02
-#>  -6.5523e+00 -4.4294e+00  1.6930e+00 -8.1491e+00 -1.3399e+00 -7.3746e-01
-#>  -2.9941e+00 -7.9544e+00 -9.6285e+00  2.6299e+00  3.5760e+00 -3.2629e+00
-#>   5.6632e+00  1.0023e+00 -3.3555e+00  8.9834e+00 -2.5112e-01 -3.7210e+00
-#>  -1.3401e+00  1.2614e+00 -1.3780e+00 -1.5681e+00  3.4846e+00 -2.9290e+00
-#>   7.9777e+00 -9.5111e+00  9.9007e+00  7.6525e-01 -7.4262e+00 -2.2465e+00
-#>  -1.0764e+01  1.0823e+01 -1.8126e+01 -8.9734e+00 -3.3309e+00  7.3897e+00
-#>  -2.4367e+00 -9.3883e+00  5.1017e+00  1.9732e+00  1.5514e+01 -9.8860e+00
-#>   1.1193e+01 -1.3586e+01  1.2199e+01 -6.2468e+00  4.8108e+00 -4.9684e+00
-#>   5.5543e+00 -7.2836e+00 -1.2312e+01  1.6544e+00 -7.8411e+00 -8.5071e-01
-#>   7.2632e+00  7.6805e+00  1.0372e+01  4.6467e+00  3.2371e+00  1.8547e+01
-#>   1.0000e+01  4.6672e+00  8.9041e+00  6.0691e+00  2.6341e+00 -1.5674e+00
-#>   1.2061e+01  3.0764e+00  5.6641e+00  4.6102e+00 -1.0707e+01 -2.9058e-01
-#>   9.3883e+00  4.1780e+00 -1.0755e+01 -3.1317e+00  1.2084e+00 -6.2495e+00
-#>   1.5567e+00  1.9683e+01  1.0854e+01  2.5242e+00  3.8949e+00  4.5147e+00
-#>  -8.4709e+00  2.0079e+01 -4.0331e+00  3.4815e+00 -1.0787e+01 -2.4349e+00
-#>   8.2260e+00 -1.2772e+01  5.0200e+00 -5.5445e+00 -2.8086e+00 -3.2149e+00
-#> 
-#> Columns 43 to 48 -3.8896e+00  6.5787e-01 -1.2978e+00 -6.1221e+00  1.3124e+00 -6.3121e-01
-#>   3.2791e+00 -2.4867e+00  1.8503e+00 -2.1419e+00 -2.8203e+00  6.7540e+00
-#>  -3.9395e+00  8.9447e-01  4.1483e+00  2.9906e+00 -1.8324e+00 -1.3498e+01
-#>   9.0459e+00  2.9160e+00  1.3991e+01 -3.4683e+00 -7.5850e+00  9.4991e-01
-#>  -4.8166e+00  4.3716e+00 -4.0342e+00  1.5501e+01  6.5063e+00  1.4848e+01
-#>   4.3978e+00 -1.7978e+00 -1.5569e+01  9.5838e-01 -4.2077e+00  1.1983e+01
-#>  -2.2132e+00  5.1870e+00 -2.2836e+00  7.6397e+00 -3.4778e-01  6.4687e+00
-#>  -3.0285e+00 -6.3110e+00 -7.4775e-01 -4.4857e+00 -6.5898e+00  1.2399e+00
-#>   8.5546e+00  9.6174e+00  5.5361e+00  1.3995e+00  7.3978e+00  6.0620e+00
-#>   9.5426e-01 -4.6008e-01  2.7788e+00 -1.6533e+00  7.6717e+00 -6.0897e+00
-#>  -2.3392e+00 -1.4206e+00  9.6478e-01  1.2578e+00  4.4325e+00  3.5684e+00
-#>  -1.2031e+01  3.2264e-01 -1.2314e+00  5.9877e+00 -1.8689e-01 -6.1992e+00
-#>   3.6786e+00  4.4235e+00  7.3856e+00 -3.5121e+00  1.8254e+00 -2.7950e+00
-#>   1.2859e+01  3.8752e+00  3.0138e+00  2.1199e+00 -2.3312e+00  8.4692e+00
-#>   3.6537e+00 -3.2655e+00  6.4011e+00  9.5566e+00  1.0377e+00  1.9657e+00
-#>   9.0634e+00 -6.8571e+00  4.1418e+00 -2.2360e+00 -4.1616e+00 -2.5369e+00
-#>  -1.2561e+01 -7.0413e+00  9.6420e-01 -1.5377e+00  7.4733e+00 -6.1888e+00
-#>  -1.2041e+01 -4.9432e+00 -6.8733e+00  7.2480e+00  4.7230e+00  1.5316e+00
-#>   4.2213e-01  2.5705e+00 -4.9289e+00  8.1845e+00 -1.2568e+01 -6.1808e+00
-#>   3.1592e+00 -2.7227e+00 -1.4107e+00  6.9731e+00  2.3468e+00 -2.5774e+00
-#>   4.4478e+00 -1.9829e+00  2.7573e+00 -2.3223e+00 -1.0544e+01  2.5697e-01
-#>   1.6394e+01  4.0235e+00  1.8288e+01 -1.6814e+00  2.0892e+00  1.2139e+01
-#>   3.9782e+00 -2.6290e+00 -4.9761e+00  1.6872e-01 -6.4128e+00  4.5709e+00
-#>   6.5372e+00  7.4668e+00 -6.9902e+00  1.1342e+01 -9.0094e+00  3.0885e+00
-#>  -2.4901e+00  1.1389e+01 -6.9001e+00  8.2024e+00 -1.1017e+01 -4.0060e+00
-#>  -4.9509e+00 -5.8122e+00 -1.0876e+00  7.2729e+00 -7.2931e+00  6.0226e+00
-#>  -9.5747e-01  1.3897e+00 -9.5474e-01 -5.0532e+00  3.3397e+00  1.9763e+00
-#>   2.7213e+00 -1.0482e+01  1.0440e-01  5.5811e+00 -1.7081e-01  3.2723e+00
-#>  -4.6305e+00  2.3510e+00 -2.4568e+00 -6.0458e+00 -6.0839e+00 -7.8381e+00
-#>  -1.0843e+01  6.2862e-01 -1.2367e+00  5.5577e+00 -3.5569e+00 -8.7925e-02
-#>  -2.7422e+00  1.8663e+00 -5.8871e-01 -4.5779e+00 -2.7187e+00 -1.7621e+00
-#>   3.8242e+00  4.7493e+00  8.6695e+00 -5.1289e+00 -1.1561e+00 -1.6367e+01
-#>  -2.5585e+00  4.8995e+00  5.4630e+00 -1.4587e+00 -6.8408e+00  1.0571e+01
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 6  1.4080e+00 -1.1899e+01 -4.1353e+00 -1.4412e+00  1.1742e+01  6.9489e+00
-#>  -6.5218e+00  1.3255e+01  9.3847e+00 -7.3147e+00  1.4185e+00  2.9938e+00
-#>  -9.4088e+00  9.8435e+00  7.5546e+00 -1.8330e+00  5.6999e-01  2.4329e+00
-#>  -2.1506e+00 -1.1168e+00  1.6671e-02  7.6816e+00  7.6185e+00 -1.3157e+01
-#>  -1.4543e+00 -7.0075e+00  3.7290e+00 -6.4805e+00 -4.2380e+00 -1.1300e+01
-#>  -6.9435e+00 -3.1541e+00  1.6184e+01  2.3992e+00  2.1464e+00 -2.0194e+01
-#>  -2.1261e+00 -6.1874e+00 -3.1801e+00 -9.9955e+00  4.5779e+00 -3.3643e+00
-#>  -1.3131e+00  6.6850e+00 -6.6825e-01 -3.8057e+00 -2.1247e-01 -1.0433e+01
-#>  -5.0201e+00 -5.4811e+00  1.9074e-02 -9.6455e-01 -6.4131e+00  6.0250e+00
-#>  -5.4076e+00  1.1422e+01  5.0324e+00 -2.3835e+00 -5.3782e+00  3.2404e+00
-#>  -8.0323e+00  2.0626e+00 -1.0257e+01  2.4690e+00 -4.1333e+00 -1.6341e+01
-#>   8.1692e-01 -4.8066e+00  6.5308e+00 -2.0152e+00  1.1058e+01 -1.1816e+01
-#>  -1.1623e+00  4.4053e+00  4.9868e+00 -1.4910e+00 -8.6254e+00  8.2777e+00
-#>  -8.3532e+00  2.3937e+00 -1.1702e+01  5.5676e+00 -4.8145e+00 -6.8998e+00
-#>  -8.3821e+00 -2.0785e+00 -8.5879e+00 -1.1150e+01  4.8695e+00 -3.8867e+00
-#>  -1.2349e+00  7.4407e+00  3.5352e+00 -2.9630e+00 -4.7452e+00  4.3327e+00
-#>  -7.6731e+00  9.8579e+00 -5.3611e+00 -2.5492e-01 -8.7314e+00  4.6041e+00
-#>  -5.3861e+00 -9.1160e-01  7.6677e+00  1.7004e+00  7.4107e+00 -6.2495e+00
-#>  -2.6204e+00  3.1218e+00 -5.8771e+00 -9.5579e+00 -9.5801e+00 -6.1502e+00
-#>   2.1210e-01  4.4249e+00 -1.3583e+01 -5.0736e+00 -1.4737e+00  9.9051e+00
-#>   1.1629e+01  5.7636e+00  4.6826e+00 -2.0306e+00  3.1641e+00 -3.3694e+00
-#>   4.8854e+00 -7.1501e+00 -1.1292e+01 -2.5348e+00  5.3458e+00  1.3115e+01
-#>   8.2982e-01 -1.4568e+00 -8.7658e+00  6.9908e+00  1.3195e+00 -7.1090e+00
-#>   1.1800e+00  1.6513e+00  6.2211e+00  2.4865e+00 -1.1569e+01  1.5910e+01
-#>  -1.0067e+01 -2.4248e+00  3.5204e+00 -7.3729e+00 -2.9552e+00  9.0173e+00
-#>  -3.5460e-01 -1.3474e+00  3.3893e+00  1.2785e+00  4.2084e+00 -1.0641e+01
-#>  -1.9705e+00 -1.6608e+01 -3.1503e+00 -4.9205e+00 -5.1441e+00 -7.2497e+00
-#>  -4.0333e+00  1.4794e+01 -1.0558e+01 -5.8430e+00 -1.0755e+01 -1.7470e-01
-#>  -1.1416e+01  1.0601e+01 -3.5518e+00 -1.1902e+00 -4.6416e+00  5.6309e+00
-#>  -6.7081e+00  3.0187e-01 -8.6661e-01 -5.8872e+00  2.0574e+00 -3.1048e+00
-#>  -7.0169e+00 -5.9670e+00 -8.7838e+00 -1.1772e+01 -4.3665e+00 -1.3870e+01
-#>   6.1087e-02  9.9251e+00  2.8213e+00  1.0690e+01 -1.8414e+00  2.7020e+00
-#>   7.8980e-01 -3.7714e+00  1.1692e+01 -1.0431e+00  3.6993e-01 -3.0131e+00
-#> 
-#> Columns 7 to 12  4.9066e+00 -4.1308e+00  6.7264e+00 -2.3971e+00  2.5494e+00  3.4488e+00
-#>   6.5412e-01  6.1340e-01  3.3838e+00 -8.8767e-01 -5.3286e-01  1.1592e+01
-#>   8.5030e+00  4.7017e+00  2.5309e+00  1.8400e+00 -2.3989e-01  5.0525e+00
-#>  -2.8190e+00  1.1768e+00  2.9686e+00  9.4875e-01 -2.0928e+00  2.8620e+00
-#>   8.0581e+00  2.0097e+00  7.7453e-01 -2.4825e+00  5.6598e+00 -5.6388e+00
-#>  -2.9977e+00  5.2568e+00 -8.4047e+00  4.8200e+00  9.3105e+00 -1.2028e+00
-#>  -1.1490e+01  4.0066e+00  9.3038e+00 -1.0326e+00  8.8266e-01  8.2433e+00
-#>  -1.9693e+00  9.3338e-02  6.7674e+00 -7.5838e+00 -1.2921e+00  4.9577e+00
-#>   1.2455e+01  1.2788e+01 -1.2791e+00  6.4981e-01 -3.5957e+00 -1.3183e+00
-#>   9.4231e+00 -2.2993e+00  4.4472e+00 -7.1652e+00 -2.1302e-02  3.9951e+00
-#>  -2.4195e+00 -2.5335e+00 -4.7614e+00 -2.5586e+00  2.0540e+00  2.6202e+00
-#>  -1.3300e+01  8.5292e+00  4.0447e+00 -6.2759e+00  7.4482e+00  7.2431e-01
-#>   4.9680e+00 -3.3235e+00 -4.6155e-01 -1.5015e+00 -1.1129e+01  7.0367e+00
-#>   1.1640e+01 -2.5281e+00 -6.0034e+00  1.1838e+01  8.8062e-01 -1.9703e+00
-#>   5.5088e+00  3.4637e+00 -8.0601e+00  3.6713e+00  9.8656e+00 -4.1082e+00
-#>   9.0013e-01 -7.9194e+00 -1.2030e+01  6.5047e+00 -4.0442e+00  3.4924e+00
-#>   1.9017e+00  2.0474e+00  7.3447e-01  4.4541e+00 -8.6128e+00 -1.1041e+01
-#>   3.9773e+00  5.6824e+00 -3.7343e+00 -6.4544e+00  5.0678e+00 -1.2625e+01
-#>  -1.3919e+01 -7.6696e+00 -8.0680e+00 -7.8409e-01  1.7832e-01  1.4216e+01
-#>  -8.4426e+00 -6.3279e-01  9.9402e+00  7.9904e+00  2.1366e+00  9.8434e+00
-#>   1.0340e+01  2.9289e+00  7.5236e+00 -3.0736e+00  5.6299e+00  2.1383e+00
-#>   4.8554e+00  2.0273e+00 -3.4944e+00  9.8330e+00 -1.5486e+00 -7.0196e+00
-#>  -1.3181e+01 -6.1692e+00 -4.3927e+00 -6.9112e-01  7.3283e+00 -4.3668e+00
-#>  -1.0650e+01 -1.0113e+01 -1.0493e+00 -4.0480e+00 -5.0376e+00  1.5352e+01
-#>  -1.2744e+00  1.3469e+01 -8.7806e+00  4.8216e+00  4.3525e+00  7.1390e+00
-#>  -7.6099e+00  8.2266e+00 -1.6163e+00 -5.2979e+00 -9.2686e-01  4.1946e+00
-#>  -6.8688e+00  6.8039e+00  4.5720e+00  2.1791e+00  3.2972e+00  2.7397e+00
-#>  -1.3702e+01  5.0406e+00 -2.5053e+00  1.6890e+01  2.2438e+00  7.1325e+00
-#>   1.4510e+01  3.9684e+00 -2.3852e-01  4.7563e+00  3.9079e+00  1.0018e+01
-#>   3.8940e+00 -7.0659e+00 -5.8890e+00  1.1028e+01 -8.5083e+00 -7.1714e+00
-#>  -7.7616e+00 -1.2060e+00  1.5313e+00 -1.0961e+00  4.9758e+00 -2.1184e+00
-#>   6.0980e+00 -1.2272e+00  5.8412e+00  1.2258e+00 -2.6513e+00  5.4603e+00
-#>   6.5693e+00 -2.6360e+00 -1.1088e+01  3.7412e+00  4.6298e+00  2.0834e+00
-#> 
-#> Columns 13 to 18  4.6010e+00  4.5067e+00 -2.9038e+00  3.2502e+00 -2.4335e+00 -6.4512e+00
-#>  -2.3157e+00 -6.3810e+00  1.3949e+01  9.6802e-01 -3.6449e-01  2.2528e+00
-#>  -3.6608e+00 -8.8017e+00  5.9599e+00 -3.7491e+00 -7.6035e+00  6.1058e+00
-#>  -4.8018e+00  2.0252e+00  4.9238e-01 -9.8625e+00  4.6778e+00 -2.1808e-01
-#>   3.1025e+00  8.5312e+00 -3.7468e+00  6.2848e+00 -5.9987e-01  4.7689e+00
-#>   3.6285e+00  1.5422e+01 -3.4136e+00  1.2394e+00 -6.1809e+00 -3.1180e+00
-#>   2.5461e+00 -5.0396e-01  3.0065e+00 -7.7716e+00 -1.0346e+01  4.0124e+00
-#>  -7.0728e-02  3.7410e+00  1.0579e+01 -4.4932e+00  1.1285e+00 -6.4617e+00
-#>  -7.7204e-01 -9.0712e+00 -1.7770e+00 -9.2219e+00 -8.7108e+00 -4.0555e-01
-#>   9.1434e+00  5.0025e+00  8.2765e+00  2.4627e-01 -1.8783e+00 -4.6973e+00
-#>  -3.2545e+00  5.0161e+00 -3.9063e+00 -1.1353e+01  5.4595e+00 -6.0954e+00
-#>  -4.9873e+00  1.4954e+01  5.8090e+00 -6.2336e-01 -4.7916e+00  3.4780e-01
-#>  -4.5335e+00 -3.1057e+00  1.1861e+00  2.7490e+00  2.5365e+00  1.1029e+01
-#>  -1.5909e+00  1.3848e+00 -5.3683e+00 -5.9884e+00 -3.4089e+00  6.0434e+00
-#>  -1.3089e+01  6.8337e+00 -8.6937e+00 -5.4593e+00 -2.6766e+00  1.0728e+01
-#>   2.8165e+00 -4.1133e+00  5.6637e+00 -4.4917e+00 -1.4141e-01 -1.4887e+00
-#>   9.4678e+00  4.1728e+00  1.1035e+01  9.4205e+00 -2.1007e+00 -7.6966e-01
-#>  -1.0080e+00  2.3142e+00  3.3514e+00  1.3970e+00 -8.1402e-01  8.2785e+00
-#>  -3.4290e+00 -3.6558e+00 -1.9663e+00 -1.4807e+01 -5.8848e+00 -3.5223e+00
-#>   9.5822e+00  5.5018e+00  1.1599e+01 -1.9601e+00 -2.5891e+00  5.5822e-01
-#>   5.1442e-01  4.4396e+00 -6.9159e+00 -6.6224e+00 -7.4509e+00 -5.4584e+00
-#>   3.9008e+00 -7.7933e+00 -3.8777e+00 -9.7138e+00  8.1011e+00 -1.8866e+00
-#>  -6.4980e-01  1.0553e+01  1.3711e+00  3.4205e+00  6.2344e+00 -6.0786e+00
-#>   1.8012e+00 -2.5126e+00 -6.1118e+00 -4.8493e+00 -2.3870e+00 -8.4272e-01
-#>  -8.4451e-01  3.0856e+00 -4.3986e+00 -5.1790e+00 -1.4728e+01 -3.6166e+00
-#>  -2.4438e+00  1.3056e+01  1.2298e+01 -5.6175e+00  8.8998e+00 -9.7530e-01
-#>   1.0519e+00  8.9182e-01 -3.7485e+00  1.4638e+00 -2.2603e+00 -1.1745e+01
-#>  -6.2835e-01 -3.9113e+00 -5.2103e-01 -1.3101e+01 -5.4891e+00 -3.1151e+00
-#>   3.8050e-01 -3.9765e+00  3.3705e+00 -5.4369e+00 -7.1983e+00 -4.7236e+00
-#>   5.7476e+00  4.3498e+00  1.7391e+00  9.4122e+00 -1.8259e+00  4.8131e+00
-#>  -5.2306e+00  1.1402e+01  2.8967e+00  5.5618e+00 -7.5690e-01 -2.9042e+00
-#>   2.3695e+00 -8.2757e+00 -4.8955e+00 -7.9524e-01 -2.1519e+00  2.2029e+00
-#>   5.9470e+00  5.0308e+00 -6.3217e+00  4.2271e+00 -5.8797e+00 -1.2538e+00
-#> 
-#> Columns 19 to 24  4.0035e+00 -5.2533e+00 -6.4376e+00 -6.5796e+00  1.4593e+01  3.5199e+00
-#>   5.3346e+00 -1.0014e+00  8.6347e-01  2.0348e+00 -5.6212e+00 -4.0300e+00
-#>  -2.2729e+00 -1.5539e+00  2.4310e+00  1.7948e+00  1.5205e+00  4.4759e+00
-#>  -1.0538e+01 -2.0725e+00  4.6372e+00 -3.4391e+00 -2.4950e-01 -2.3709e-01
-#>   4.2838e-01  2.8630e+00  3.5305e+00  1.2936e+00 -2.5255e+00 -4.0722e+00
-#>  -2.2387e+00  3.0365e+00  2.7049e+00 -1.3206e+01 -4.8693e+00  1.7665e+01
-#>  -1.0460e+00  3.7885e+00 -2.7064e+00  7.5554e+00 -7.9784e+00 -1.5450e+00
-#>   3.3267e+00 -2.1692e+00  5.9835e+00 -6.4481e+00 -7.5086e+00  3.9535e+00
-#>  -2.4311e+00 -3.8278e+00 -5.4211e-01 -8.2869e-02  3.1314e+00 -1.1352e+01
-#>   4.5032e+00 -1.3754e+00 -1.0011e+00  3.0971e+00 -6.2162e+00 -2.0818e+00
-#>   1.2915e+00 -6.8684e+00  6.6977e+00 -2.4609e+00  1.3056e+00  6.7367e+00
-#>  -8.9935e+00 -9.3973e+00  9.1078e+00  1.0718e+00 -1.8343e+01  2.3391e+00
-#>   6.7598e-01  1.4605e+01 -1.1669e+01  1.4837e+01 -4.9263e+00 -8.4995e+00
-#>  -8.4471e+00  3.9360e+00 -5.0993e+00  1.2821e+00 -5.0476e-01 -5.0610e-01
-#>  -3.1034e+00 -1.9458e+00 -1.6512e+00  4.0102e+00 -1.5717e+00  1.5329e+01
-#>  -9.0065e-01 -3.1436e-01 -1.4189e+00 -2.0881e+00  2.7689e+00 -9.9803e+00
-#>  -1.0392e+01  6.7957e+00 -8.4666e+00  1.5654e+01  8.5171e+00 -2.7840e-02
-#>   5.7474e+00 -7.4794e+00  2.5581e+00 -8.2204e+00 -2.3948e-02  4.2277e+00
-#>  -8.4238e+00  6.8594e+00 -7.1296e+00  1.5019e+00 -7.6827e+00  8.1506e+00
-#>  -7.1825e+00 -2.6377e+00 -4.2360e+00  1.5290e+01 -8.4564e+00 -1.9189e+00
-#>  -1.6717e+00 -6.7846e+00  1.0160e+01 -1.1922e+01  4.1626e-01 -7.3792e+00
-#>   5.8180e+00 -7.7029e+00  3.5438e+00 -5.4828e+00  1.0892e+01 -1.7366e+01
-#>  -4.1492e+00  5.8430e+00 -8.5405e-01  9.1512e-01 -9.1028e+00  8.8594e+00
-#>  -2.9390e+00  1.2791e+01 -1.4800e+01  8.0902e+00  9.1215e-02 -7.9433e+00
-#>  -1.1290e+01 -4.3278e+00 -6.7345e+00 -7.3926e+00 -7.8704e+00 -7.1902e+00
-#>   8.8550e-04  6.6792e+00  6.1034e+00  9.4450e-01 -8.4841e+00  9.1573e+00
-#>  -5.3408e+00 -4.3279e+00 -2.6200e+00  7.4204e+00  2.9669e+00  4.2542e+00
-#>  -4.2130e+00  3.1756e+00 -9.8007e-01 -3.4946e+00  3.6135e+00  4.5236e+00
-#>  -3.6319e+00 -1.5918e+00 -4.6717e+00  1.3942e+00 -4.0734e+00  1.0169e-02
-#>  -5.5155e-01  8.8678e+00 -2.8303e+00  4.7980e+00  1.5487e+01 -2.1578e+00
-#>  -1.0643e+01 -9.3310e+00 -5.1527e+00  5.4748e+00 -2.3128e+00  1.1993e+01
-#>  -4.4540e+00  8.2229e+00 -7.8324e+00  5.7167e+00 -2.1282e+01  5.6597e+00
-#>   1.6077e+00  5.2613e-01  6.0516e+00 -8.0079e+00  1.0206e+01 -9.7924e+00
-#> 
-#> Columns 25 to 30  1.1147e+01  3.6391e+00  1.1187e+01  6.0805e+00 -8.1869e+00 -2.5905e+00
-#>   5.2926e+00 -1.6961e+01 -5.4181e+00 -3.6479e+00 -4.9755e+00 -2.2046e+00
-#>   4.7799e+00 -2.3298e+00 -3.9841e+00  3.9436e+00 -2.6750e+00 -2.1315e-01
-#>   6.8501e+00 -8.3940e+00  8.5949e+00 -7.8190e+00 -1.7399e+00 -5.6473e+00
-#>  -1.1091e+01  1.1763e+00 -3.5075e+00 -3.0827e+00  4.8371e+00  1.9554e+00
-#>  -1.1648e+01 -7.4380e+00  2.7608e+00 -8.0752e-01  5.0588e+00  2.9817e-01
-#>  -7.2856e+00  9.5683e+00 -1.2037e+01  4.4913e+00  2.2812e+00 -5.4674e+00
-#>   3.0411e+00 -2.4650e+00 -6.0758e+00  6.5565e+00  3.3859e+00 -1.2842e+01
-#>   1.2164e+01 -1.1720e+01  4.6296e+00 -4.5861e+00 -1.8319e+00 -3.1555e+00
-#>   1.0095e+00  2.8889e+00 -3.4291e+00  1.0095e+00 -4.2277e+00  7.2100e+00
-#>  -8.5960e+00 -2.2444e+00 -4.6157e+00  2.9012e+00  3.8361e-01 -6.3091e+00
-#>  -5.5840e+00 -1.8335e+00  6.8453e-01  7.2422e+00  5.8593e+00 -6.2295e+00
-#>   2.0426e+00 -3.1267e+00 -3.0833e+00  1.8640e+00 -3.1724e+00  2.6324e+00
-#>   1.8683e+00 -4.9115e+00  5.0103e+00  2.4655e+00 -5.9479e+00  5.0536e+00
-#>   7.5598e-02 -7.1535e+00 -4.4489e-01  4.7277e+00 -5.1581e+00  3.8179e+00
-#>   1.1880e+01 -2.5464e+00  1.2672e+01 -5.9125e-02 -4.3747e+00  1.1664e+01
-#>  -1.0828e+01  3.0879e+00 -1.4164e+00  2.4949e+00 -8.1771e+00 -1.1576e+00
-#>  -6.1593e+00  3.6951e+00  1.3597e+01  6.8700e+00  3.7079e+00 -5.8486e+00
-#>   2.5296e+00  1.7619e+00 -4.1247e+00 -7.1495e+00 -9.2967e+00 -2.0392e+00
-#>   1.4032e+00  2.5314e+00 -1.4011e+01  4.0010e+00  1.7084e+01  4.9672e+00
-#>  -6.2591e-01 -4.5542e+00  5.4845e+00  8.8774e+00  6.3592e+00 -5.5222e+00
-#>   6.6866e+00 -6.4466e+00  3.6383e+00 -5.8592e+00 -2.6308e+00  2.5872e+00
-#>  -8.4094e+00  3.9811e+00 -9.8913e+00 -2.0240e+00 -8.8618e-01  5.7595e+00
-#>   8.8510e+00  4.8119e-01  5.9794e+00 -2.5156e+00 -7.9376e+00  6.2095e-03
-#>   5.2374e+00 -3.6271e+00  1.6991e+01  1.2048e+00  4.1467e+00  4.2666e+00
-#>  -9.1448e-01 -5.3934e+00 -2.2018e+00  4.8980e+00  3.2072e+00 -1.5705e+00
-#>  -1.4598e-01 -3.9422e+00 -1.3923e+01 -2.8437e+00  4.3032e+00 -6.9459e+00
-#>  -9.6621e+00 -4.9686e+00  6.9225e+00 -2.1751e+00 -1.7244e-01  1.2816e+01
-#>   9.4945e+00 -1.7000e+00  5.5441e+00  9.0715e+00  2.7920e+00  1.0386e+01
-#>  -7.8475e+00  5.6721e+00  2.7749e+00 -1.0268e+00  7.0137e-01  7.0984e+00
-#>   3.0558e+00 -1.8220e+00  5.1956e+00 -7.3876e+00 -9.1895e+00 -3.0264e+00
-#>  -3.3453e+00  9.7614e+00 -7.0201e+00  9.7704e+00  3.7403e+00  7.6579e+00
-#>   1.2433e+00 -8.8167e+00  7.2628e+00 -3.4548e+00  3.0066e+00  4.6408e+00
-#> 
-#> Columns 31 to 36  1.6280e+00  3.6802e+00 -1.1593e+01 -8.4596e-01 -1.7400e+00  6.1596e+00
-#>  -4.5575e+00  4.7161e+00  1.3676e+01  1.6125e+00  1.5714e+01  1.5754e+01
-#>  -1.8076e+00  2.8713e+00  3.8487e+00 -1.0413e+00  4.4413e+00  6.8242e+00
-#>   3.1493e+00  4.0503e+00 -1.4582e+01 -1.5412e+01  1.5087e+01 -2.7381e+00
-#>   3.5763e+00 -6.4330e+00 -2.3951e+00  1.6168e+01 -1.4622e+01  6.6627e+00
-#>  -2.4222e+00 -8.8587e+00 -3.3878e+00  1.8356e+00  2.1590e+00  6.4357e+00
-#>  -5.4561e+00 -4.7786e+00 -5.5594e+00  8.7320e+00 -5.4292e+00  6.9659e+00
-#>  -2.4925e+00  5.7615e+00  3.0381e+00  5.1190e+00  6.9788e+00 -3.4237e+00
-#>   4.2632e+00 -1.4803e+01  4.0556e+00  6.8307e+00 -3.7379e-01  2.1806e+00
-#>  -2.8327e+00 -3.2625e+00  9.2088e+00 -5.7277e+00  6.3689e+00  9.3465e+00
-#>   1.1018e+01  8.0445e+00  9.3478e+00 -5.5861e+00  1.5302e-01  3.1772e+00
-#>   8.2271e+00  1.5902e+01  1.8643e+01 -1.6779e+00  1.8905e+00  1.0224e+01
-#>   6.3268e+00 -6.0640e+00  1.0101e+01 -5.8077e+00 -4.8274e+00  1.0012e+01
-#>   5.0458e+00 -7.7735e+00 -9.2099e+00 -5.3822e+00 -2.3859e+00 -3.3018e+00
-#>  -4.3210e+00  1.1542e+01 -1.9341e-01  1.0544e+00 -5.4314e+00  7.5066e+00
-#>   5.5280e+00 -1.2632e+00 -4.0221e+00 -6.5578e+00 -6.1401e+00 -4.2676e+00
-#>   6.8111e+00 -6.7595e-01  1.1877e+01 -4.4416e+00  7.3254e+00  9.2634e+00
-#>  -9.3897e-01 -7.3323e+00 -5.6501e+00 -4.2338e+00 -1.6136e+01 -7.9817e+00
-#>   5.1431e+00  7.9962e+00  1.9244e+00 -1.3078e+01  9.7229e+00  1.4527e+01
-#>  -6.6730e+00  9.3903e-01  1.7861e+01  4.9748e+00  6.6223e+00  4.5511e+00
-#>   5.7490e+00  4.0505e+00  1.2850e+00  1.7379e+01  6.4363e+00 -3.4994e+00
-#>  -4.3550e+00 -1.3790e+00 -4.9837e-01  1.6309e+00  7.2332e+00 -9.8394e+00
-#>   5.2478e-01  6.5799e-01 -9.2388e+00 -3.2802e-01 -1.0465e+01 -7.3347e-01
-#>   1.2429e+00 -7.9775e+00  6.9857e+00 -4.2260e+00 -4.6722e+00  1.1488e+01
-#>   1.9202e+00 -3.3812e+00 -3.2940e-01 -1.7986e-01 -4.8731e+00  3.0378e+00
-#>  -1.2325e+00  1.7000e+01 -1.5116e+01  2.6105e+00  6.9141e+00  3.5265e+00
-#>  -4.5038e-02 -1.0016e+00  1.0130e+01  1.7839e+01 -2.6167e+00  7.1525e+00
-#>   3.8105e+00  1.5240e+01 -6.2467e+00  6.8072e+00  1.5233e+01 -1.2775e+01
-#>   4.2361e+00  1.1523e+01 -4.7938e+00  2.2597e+00  9.2862e+00 -5.7732e+00
-#>   8.5660e+00  1.7525e+00 -1.1605e+01 -1.7779e+01 -1.0388e+01  5.5783e+00
-#>   1.7085e+00  9.5260e+00 -3.2841e-01 -8.4488e+00  9.9827e+00  2.2759e+00
-#>  -8.8871e-01  1.1511e+01  7.0996e-01 -1.5810e+01  3.7052e+00  1.9111e+00
-#>   1.7095e+01  6.1128e+00 -3.5600e+00  7.4983e+00 -1.8792e+00  3.0595e+00
-#> 
-#> Columns 37 to 42  4.0209e+00  2.8236e+00  3.2228e+00  4.9068e+00 -1.0765e+01 -1.8393e+00
-#>   1.7493e+00 -1.6852e-01 -8.9894e+00 -7.7516e+00 -5.7265e+00  1.5782e+01
-#>  -7.5828e-01  5.2742e+00 -6.7648e+00 -7.0245e-01 -2.9208e+00  8.0485e+00
-#>   2.4751e+00  2.0166e+00 -2.7255e+00 -1.6698e+01  2.6687e+00  2.0290e+00
-#>   5.5240e-01 -2.9833e+00 -1.0485e+01 -4.2613e+00 -3.7978e+00 -4.5257e+00
-#>  -5.4438e+00 -4.8938e+00 -1.2284e+01  2.8564e+00  8.7059e+00 -2.1239e+00
-#>   3.2328e+00 -4.8299e+00 -8.4738e+00  5.2165e+00 -6.1057e+00 -2.3035e+00
-#>   9.0472e+00 -1.7072e+00  1.4483e+00  1.0006e+01  1.4309e+00  3.3362e+00
-#>  -3.5516e+00 -2.4131e+00 -7.4484e+00 -9.3446e+00 -2.2438e+00 -5.8856e+00
-#>   4.3916e+00  1.3265e+00 -9.2051e+00 -6.4683e+00 -1.0181e+01  1.0631e+01
-#>   2.4456e+00 -6.5465e+00  6.4855e+00 -2.8299e+00 -1.2262e+00  1.0795e+01
-#>  -4.5987e+00 -5.7546e+00 -3.0917e+00  1.2375e+01  8.1699e+00  4.7183e+00
-#>  -9.2398e+00  1.1088e+01  1.1573e+01 -1.9905e+00 -1.2787e+01  6.5136e+00
-#>  -7.4235e+00 -2.3060e-01  9.7640e+00 -1.0802e+01 -2.6236e+00  1.8933e-01
-#>  -1.0108e+01 -2.2754e+00  3.0799e+00 -8.2495e+00 -6.9606e+00  9.3663e+00
-#>  -7.5995e+00  7.7299e+00  3.7903e+00 -1.0401e+01  5.5817e+00  4.9853e+00
-#>  -5.1197e-01  1.0524e+01 -7.7887e+00  7.9674e+00 -1.1456e+01 -1.8496e+00
-#>   3.4752e+00 -7.9746e+00  1.3573e+00 -3.7112e+00 -7.2776e+00 -3.8574e+00
-#>  -1.8658e+00  3.9683e+00 -1.6261e+01  3.5811e+00  3.0788e+00  1.8561e+01
-#>   1.0342e+01 -2.8909e+00 -2.8083e+00  7.6817e+00 -4.0105e+00  4.0795e+00
-#>   3.1097e+00 -1.7027e+01  5.6430e+00  8.0713e+00  3.7843e+00 -2.7920e+00
-#>  -5.6453e+00 -8.9694e+00  1.8651e+01 -1.3044e+01 -4.0992e+00 -5.1164e+00
-#>  -4.7247e+00  2.1068e+00 -6.8930e+00 -3.1100e+00 -4.0142e+00  2.4032e+00
-#>  -8.0732e+00 -1.9362e+00 -1.1289e+00  1.5146e+01 -1.2932e+01  3.7773e-02
-#>  -3.3798e+00 -1.0183e+01 -2.1320e+01  4.0629e+00  7.0527e+00  5.3845e+00
-#>   1.3099e+01  2.9590e+00 -5.3575e+00 -1.3653e+00  1.5970e+00 -1.6306e+00
-#>   6.9089e-01 -4.2401e-02  6.2614e-01  8.6173e-02  5.2735e+00  5.8539e+00
-#>  -6.8782e+00 -8.2778e+00  8.0748e+00  7.1342e+00  5.1705e+00  2.6406e+00
-#>   6.2406e+00 -6.6000e+00 -6.3177e+00  1.2859e+00  9.0834e+00  1.1542e+01
-#>  -1.0031e+01  1.7066e+01 -4.2424e+00  1.1230e+01 -1.5471e+01 -1.2751e+01
-#>  -3.2882e+00  9.8471e+00  6.1404e+00 -4.0524e+00  8.4977e+00  5.9648e+00
-#>  -1.6528e+01  8.3521e+00 -9.0226e+00 -1.2466e+00  8.3971e+00  8.7195e+00
-#>  -3.5397e+00  5.0278e+00  3.5069e+00  3.1022e-01  1.0142e+01 -2.0094e+00
-#> 
-#> Columns 43 to 48  1.9703e+00 -1.1875e+01 -8.7695e+00  1.5051e+00 -5.7029e+00 -9.7498e+00
-#>  -1.3301e+00  3.2313e+00  8.9848e+00 -1.0432e+01 -4.4717e+00 -7.2384e+00
-#>   7.5183e+00  1.2554e+01  7.1470e+00 -3.7589e-01 -2.4162e+00  2.9486e+00
-#>   3.3490e-01  4.8699e+00  3.7211e+00 -1.0333e+01  9.6389e-01 -1.0573e+01
-#>   9.7084e+00 -4.1592e+00  5.4356e+00 -6.3258e+00  3.1986e-01  1.1790e+01
-#>  -9.9423e+00 -1.5987e+01 -6.0567e+00  8.4694e+00 -1.5238e+00 -1.0579e+00
-#>   8.9344e+00  5.8672e+00 -7.3183e+00  4.8573e+00 -1.2020e+01  2.9273e+00
-#>   1.1320e+01 -7.6255e+00 -3.5554e-01  1.0035e+00 -8.6486e+00 -6.9284e+00
-#>   1.2469e+01  3.7959e+00 -6.8073e+00  1.0302e+00  1.1334e+01  3.4938e+00
-#>   4.6032e+00 -2.1861e+00 -2.2557e+00 -8.5205e+00 -9.0304e+00  5.3963e+00
-#>   1.2137e+00 -7.5389e+00  1.7085e+01 -6.2938e+00  2.4187e+00  8.8205e-01
-#>   8.5736e+00  8.6609e+00 -7.3895e+00 -5.0710e-01 -1.4946e-01 -2.8082e+00
-#>   5.7984e+00  1.0131e+01  5.3564e+00 -7.1073e+00  2.3574e+00  8.9853e-01
-#>   1.5896e+00  1.8233e+00 -2.4928e+00 -2.7644e+00 -2.4445e+00  3.6724e+00
-#>   3.1770e+00 -1.2907e+01 -3.4434e+00 -7.8347e+00 -9.8390e+00  7.6037e+00
-#>   7.2079e+00  8.7224e+00 -6.1744e+00  6.8033e+00 -1.0701e+00 -3.7301e+00
-#>  -6.1776e+00  8.1282e+00 -1.6816e+01  4.8213e-01 -1.7013e+01 -6.6154e+00
-#>  -1.8096e+00 -8.0818e+00  7.6369e+00  4.6508e-01  1.3007e+00 -2.2696e+00
-#>   5.6270e+00  5.2678e+00 -1.9481e+00 -5.9225e+00 -1.8582e+01  1.7727e+00
-#>   7.5104e+00 -3.5144e+00  1.7004e+00 -6.3550e+00 -5.8357e+00  3.4854e-01
-#>   1.6668e+01 -1.5817e+00  1.3947e+01 -2.3133e+00  8.9867e+00 -5.4141e+00
-#>   5.5480e+00  4.2205e+00  2.1355e+00 -5.3126e+00  1.0447e+01 -3.6338e+00
-#>  -9.6799e+00  3.6726e-01 -1.1408e+01 -5.9977e-01 -1.0750e+01  1.0392e+00
-#>  -4.2397e-01 -3.0862e-01  5.3942e-01 -1.0992e+00  7.5872e+00  7.4528e+00
-#>   6.6743e-01 -3.1385e+00 -1.0539e+01  1.2112e+00 -1.9948e+00  2.6654e-01
-#>   1.2855e+01 -4.1853e+00 -4.4663e+00 -3.0913e+00 -2.1435e+01 -1.0684e+00
-#>  -4.6279e-03  1.4366e+00 -1.3314e+01 -4.3680e+00  2.9159e+00  5.4549e-01
-#>  -1.4838e+01  1.6832e+00  1.4140e+01  1.0610e+01  7.4335e+00  1.5544e+01
-#>  -5.9024e+00 -5.5648e+00 -8.7734e+00 -7.4661e+00 -1.0869e+01  1.4613e+00
-#>  -3.0699e+00  4.7420e+00 -4.3134e+00  5.4277e+00 -6.0626e+00  2.3346e+00
-#>   3.9549e+00 -4.3669e+00 -1.1736e+01 -6.6775e+00 -7.9589e+00 -2.7425e-01
-#>  -1.5342e+01  5.3356e+00 -1.0145e+01  3.7000e-01 -4.6578e+00  5.8591e+00
-#>   3.7243e+00  2.5135e+00  4.8050e-01 -6.2709e+00  5.8081e+00 -4.3069e+00
-#> 
-#> (4,.,.) = 
-#>  Columns 1 to 6 -2.4260e+00 -1.6090e+01 -7.1397e+00  1.0959e+01  1.3156e+01 -3.1173e+00
-#>  -8.2181e+00 -1.3502e+01  3.9274e+00 -2.2057e+00 -1.2749e+01  4.7140e+00
-#>  -1.4480e+01 -2.6388e+00  1.6621e+01  2.2499e+00 -1.5909e+01  9.4288e-02
-#>  -4.2047e+00  4.9874e+00  1.2744e+01 -2.2830e+00 -6.1745e+00 -3.4464e+00
-#>  -1.0277e+01 -1.6134e+00 -8.3036e-02 -1.7949e+00 -1.4716e+00 -1.4627e+00
-#>  -2.1850e+00 -6.9099e+00  3.0435e+00  8.3562e+00  2.9605e+00 -5.9025e+00
-#>  -1.0164e+00 -4.8070e-01  2.9863e+00 -1.1825e+00 -1.4028e+01  4.0764e+00
-#>  -4.2190e-02 -5.4516e+00  6.1648e+00 -5.6370e+00  4.4230e-01  5.5009e+00
-#>   3.7369e+00  6.2467e+00  6.4830e-01  3.9464e+00  4.0394e+00 -1.3070e+01
-#>  -3.4354e+00 -1.3006e+01  1.2709e+00  2.2926e+00 -7.9340e+00  7.8264e-01
-#>  -2.8094e+00 -6.3679e-01  3.3087e+00 -4.7293e+00  1.1885e+00  8.2875e+00
-#>  -9.3574e+00 -1.2533e+01  5.0777e+00  6.9207e+00  9.7479e+00  1.9588e+01
-#>  -4.3638e-01  1.3491e+01 -6.4486e+00 -7.1892e+00  7.7435e-01 -9.9856e+00
-#>  -1.7874e+00  9.6274e+00  1.0126e+01 -1.0308e+01  8.8940e+00 -8.6108e+00
-#>  -1.1105e+01  8.8923e+00  6.5329e+00 -8.6803e+00 -7.5524e+00 -8.0187e+00
-#>   4.4636e-01  4.6858e+00  1.1691e+01  1.0431e+00 -7.0344e-01 -8.0854e+00
-#>  -5.0888e+00 -1.2481e+01 -4.9545e+00  5.0573e+00 -1.8414e-01  3.1990e+00
-#>  -8.5629e-01 -9.0537e+00  4.4783e+00  1.1009e+01  4.8588e+00 -4.0643e+00
-#>  -3.3022e+00  2.5722e+00  1.6664e+01 -2.9806e+00 -1.4120e+01 -4.9962e+00
-#>  -5.7592e+00 -1.0260e+01 -8.1434e-01  4.3060e-01 -4.0868e+00  9.3831e+00
-#>  -1.3060e+01 -3.0198e+00  1.7335e+01  5.5972e+00  7.4274e+00  5.9574e+00
-#>   3.5454e-01  1.7126e+01 -2.0991e+00 -9.9543e+00  3.3188e-01 -6.5695e+00
-#>  -3.2862e+00  3.9254e+00  1.3965e+01 -1.2239e+00 -1.8622e-01  3.0077e+00
-#>   8.3303e+00 -9.2190e+00  3.1816e+00  1.9540e+00  8.2113e-01 -6.3173e+00
-#>   6.4037e+00 -6.7632e+00  7.7021e+00  1.1969e+01  5.9819e+00  4.0527e+00
-#>  -1.1441e+00 -7.0357e+00  5.4759e+00 -4.3076e+00 -8.1542e+00  2.4943e+00
-#>  -3.9106e+00 -7.4675e-01 -6.9691e+00 -2.8430e+00 -6.4367e-01  9.7454e+00
-#>  -9.9073e+00 -4.1061e+00  1.1572e+01 -1.0203e+00  2.5985e+00  1.9412e+01
-#>   3.7332e+00 -7.6847e+00  9.5770e+00 -3.4412e+00  5.8689e+00  1.1729e+01
-#>  -6.3888e+00  3.3612e+00  5.4892e+00  2.0501e+00  8.9452e+00  1.8355e+00
-#>  -1.4714e+01 -4.1082e+00 -1.1987e+00  4.5001e-01  3.4602e+00  2.8930e+00
-#>   8.4743e+00  1.4654e+01 -2.0721e+00  6.1471e+00 -3.8771e+00  3.2316e+00
-#>  -2.5601e+00  1.2247e+01 -3.2863e+00 -6.9482e+00  7.3815e+00  5.2209e-01
-#> 
-#> Columns 7 to 12  2.7039e+00  1.7928e+01  4.7722e+00 -9.0033e+00 -9.2337e+00  4.3650e+00
-#>   5.6106e-01 -3.7155e-01 -9.4022e-01  6.6389e-01 -1.0687e+00 -3.6676e+00
-#>  -1.5103e+00 -4.8982e+00  2.8042e+00  3.2289e+00  1.7656e+00  1.3508e+00
-#>  -1.7560e+00 -1.2062e+00 -4.4098e+00 -8.6041e+00 -9.1194e-01 -2.4839e+00
-#>  -7.7607e-02 -1.4647e+00 -1.4750e+00  2.2109e-01 -9.2688e+00 -1.6978e+00
-#>  -6.4080e+00 -1.4036e+01 -3.3036e+00  7.0697e+00 -2.8365e+00 -1.5730e+00
-#>  -8.7269e-02  2.7889e+00  6.2123e+00  3.2134e+00 -4.1613e+00 -4.4587e+00
-#>  -2.1674e+00 -1.1504e+00  5.9939e+00  1.1103e+01  1.1826e+01  5.6860e-01
-#>  -6.9300e+00 -6.7035e+00 -7.3591e+00 -7.8506e+00 -2.8402e+00 -7.3483e+00
-#>  -1.1257e+00  2.5082e+00  2.6712e+00 -3.2684e+00 -4.4637e+00  8.8603e-01
-#>  -7.6516e-02 -1.4251e-01 -2.3874e+00  1.1411e+00 -2.4513e+00 -1.6663e+01
-#>   4.6774e+00 -1.4641e+00  8.1328e-01 -1.1810e+01 -1.5955e+01  1.2982e+01
-#>  -8.5431e+00  3.9702e+00  9.8489e+00  4.6478e+00  1.2068e+01  4.8349e+00
-#>  -5.8179e+00 -8.1493e+00  1.6114e+00 -4.0638e+00  6.5257e+00 -5.3299e+00
-#>  -7.2035e-01 -4.5514e+00 -1.9175e+00  1.3576e+00  9.6702e+00 -6.4111e+00
-#>   3.2116e+00 -2.5445e+00 -4.9105e+00 -8.8980e-01  1.1377e+01 -3.6236e+00
-#>   1.0368e+01  2.7730e+00  8.0303e+00 -6.5360e+00 -1.2322e+01  2.1166e+01
-#>  -4.8024e+00  4.8266e+00  8.0571e-01  8.9774e-01 -8.2069e+00 -9.2629e+00
-#>   2.2174e-01 -6.4808e+00 -5.2895e+00  3.6308e-01 -1.2931e+00  2.7188e+00
-#>   9.5745e+00  1.2801e+00  7.4423e+00  1.8955e+00 -4.1224e+00 -4.7624e+00
-#>  -5.2236e+00 -1.0175e+00 -5.3479e+00  1.6259e-02  1.3361e+00  7.1376e+00
-#>  -7.6194e+00  6.1334e+00  1.1134e+00 -9.8276e+00  7.6407e+00 -1.5565e+01
-#>  -1.8772e+00 -1.9989e+01 -1.6736e+01  2.7651e+00 -3.2357e+00  6.1813e+00
-#>   8.6555e+00  1.2206e+01  7.3953e+00 -1.3803e+01 -9.0671e+00  1.8454e+01
-#>   6.6007e+00 -5.4982e+00 -7.9111e+00 -1.0212e+01 -1.3995e+01 -2.5820e+00
-#>   3.3626e+00 -2.6789e+00  7.5463e+00  8.0132e+00  7.1050e+00 -4.7084e+00
-#>   3.1330e+00  1.9449e+00 -7.4689e+00  1.5632e+00 -5.9301e+00  6.4092e+00
-#>   1.0197e+01 -7.0770e+00 -4.1250e+00 -8.2758e+00  1.3048e+01 -7.7559e+00
-#>  -2.1499e+00 -6.4168e+00 -2.3384e+00 -1.8222e+00  1.1717e-01  3.8442e+00
-#>  -6.0073e+00 -8.4099e+00  3.9294e+00 -6.3322e+00 -6.4771e+00  2.1426e+01
-#>   1.1076e+01  5.8835e+00 -4.9446e+00 -4.7285e+00  2.9562e+00  1.0060e+01
-#>   3.8793e+00 -1.3364e+01 -5.9460e+00 -5.2039e-01  4.9388e+00  9.8313e+00
-#>  -7.5788e+00 -4.2444e+00  2.7747e+00  2.6616e+00  4.5422e+00  3.9646e-01
-#> 
-#> Columns 13 to 18 -6.5245e+00  2.3812e+00 -2.7277e+00 -2.9581e+00 -1.6089e+01 -1.4964e+01
-#>  -5.1098e-01 -1.6415e+00 -1.0149e+01  5.5287e+00  4.1055e+00  1.2482e+00
-#>  -6.7670e-01 -1.7101e+00 -4.9976e+00  1.0970e+01 -3.4336e+00 -5.6223e+00
-#>  -4.6039e+00 -1.9907e+00 -2.7878e+00 -1.1976e-01  5.3809e-01  2.7278e-01
-#>  -4.1515e+00  6.1336e+00 -2.5220e+00  6.6164e+00  9.2006e+00  3.1525e+00
-#>  -3.0194e+00 -2.8319e+00 -2.1512e+01  2.1852e+00  9.3475e+00 -8.1556e-02
-#>  -2.9080e+01  2.7905e+00  7.8276e-01  2.3943e+00  1.5815e+00 -3.1924e+00
-#>  -4.5630e+00  4.8802e+00 -6.8771e+00 -8.7013e-01 -1.4001e+00  1.2690e-01
-#>   5.6586e+00  8.6122e+00  1.9926e+00 -2.5550e-01  1.9946e+00  3.0214e+00
-#>   5.9062e+00 -3.4057e+00  3.9138e+00 -3.8029e-01 -7.9910e+00  3.1446e+00
-#>   1.0425e+01 -7.9493e+00  3.6418e-01  1.0828e+01 -8.4890e+00  2.3251e+00
-#>   2.3559e+00  3.4553e+00  3.1563e-01  1.0489e+01 -6.5857e+00 -1.4040e+01
-#>  -7.9710e-01  4.0536e+00 -3.0672e+00  1.1442e+00  3.5079e+00  1.6066e+00
-#>  -7.5029e+00  1.2243e+00 -3.7898e-01 -1.5373e+00 -1.1446e+00  3.8205e-01
-#>   4.1850e+00  2.2051e+00 -2.0903e-01  2.1436e+00  1.8623e+00 -3.8784e+00
-#>  -4.1479e+00 -7.1423e+00 -3.1687e+00 -3.1377e+00  5.9941e+00  5.1574e+00
-#>   8.0884e+00  5.1589e-01  4.0641e+00 -1.9410e+00 -1.3332e+01 -7.8220e+00
-#>   8.4895e-01  8.6939e+00 -8.5540e+00  7.4821e+00  4.9653e+00 -1.1706e+01
-#>  -1.3710e+01 -9.1676e+00 -2.5768e+00  9.1837e+00 -1.1243e+01  2.3341e-01
-#>  -4.1771e+00 -2.4854e+00  2.1148e+00 -4.6356e+00 -2.6337e+00  9.0549e-01
-#>   5.2531e+00 -3.7544e+00 -5.2746e+00  4.3894e-01  6.3111e+00 -1.2990e+00
-#>  -3.5439e+00 -6.1807e-01  9.6415e+00 -8.0851e+00  4.2410e+00  7.7130e+00
-#>  -6.7433e+00 -1.0340e+01 -8.5594e+00  2.3174e+00 -2.7162e+00 -8.6086e-01
-#>  -2.7265e+00 -7.5501e+00 -3.1762e+00  1.0278e+01 -8.0296e+00  3.2575e-01
-#>  -4.1769e-01 -2.5689e+00 -9.3748e+00  2.6912e-01 -4.4217e+00  2.3118e-01
-#>  -4.7241e+00  3.1261e+00 -3.9767e+00 -3.7091e+00 -2.0489e+00  2.0303e+00
-#>   5.1013e+00  1.4020e+01 -4.9716e+00 -9.3442e+00  1.9353e+00 -4.5956e+00
-#>  -4.8233e+00 -8.7770e+00  1.9722e+01  9.5561e-01  3.9089e+00  1.3546e+01
-#>   9.2608e-01  3.2052e+00 -1.4554e-01 -1.4802e+01 -5.1632e+00  6.9924e+00
-#>  -5.7905e+00 -5.4141e+00  4.2075e+00  6.7678e+00 -1.7884e+01 -3.1346e+00
-#>   1.0392e+01  2.7993e+00 -2.3305e+00  7.8083e+00 -1.1962e+01 -1.3522e+01
-#>   5.6182e+00 -2.6997e+00  3.7665e+00  1.6071e+00 -3.7697e-01  1.3203e+00
-#>  -4.3505e+00 -1.3015e+00 -1.3011e+01 -7.3237e+00  4.3552e+00  6.8349e+00
-#> 
-#> Columns 19 to 24 -4.3832e+00  1.1854e+00  3.7849e+00 -8.7515e-01 -7.2142e+00  2.8712e+00
-#>   4.3580e+00  1.0377e+01  7.7775e+00  5.6452e+00 -5.5160e+00 -4.8094e+00
-#>   1.2200e+00  2.5590e+00  7.4564e-01  8.7451e+00  8.5624e+00  4.9214e-01
-#>  -4.0737e+00  1.1564e+01  3.7737e+00  5.8098e+00 -9.9780e+00  6.1329e+00
-#>   8.3322e+00 -8.3092e+00 -6.6719e+00 -1.1653e+00  9.0612e+00 -6.9816e-01
-#>   4.1872e+00  5.6975e+00  3.3598e+00  1.7068e+00 -1.0912e+01  1.0734e-01
-#>   6.7030e+00 -6.7618e+00  1.2650e+00 -1.8857e+00  1.7936e+00  1.0692e+01
-#>   1.0883e+00  4.4177e+00 -2.1538e+00  1.1302e+00 -3.1243e+00  1.4657e+00
-#>   2.5484e+00  2.1354e+00 -5.3882e+00  2.5681e+00  9.9810e+00 -5.4780e+00
-#>   7.7740e+00  1.0476e+00 -5.1584e+00  8.7806e-01  9.8774e-01 -1.1787e+00
-#>   2.5723e+00 -1.0043e+01 -2.5171e+00  1.2171e+00  6.1552e+00  1.3662e+00
-#>   4.5510e+00 -1.4972e+00  1.1797e+00  9.6993e+00  1.1976e+00 -4.3668e+00
-#>   2.2118e+00 -2.7551e+00 -8.4045e-01  3.4012e-01  9.2944e+00  3.2099e+00
-#>  -9.9009e+00  1.7620e+00 -3.0801e+00  7.2096e+00  8.1377e+00  8.4507e+00
-#>   1.2373e+00 -2.8196e+00 -2.6450e+00 -7.0677e+00  9.0234e+00  6.5156e+00
-#>  -4.0219e+00  8.0798e-01 -3.0108e+00 -1.4071e+00  6.3210e+00  6.3155e+00
-#>   4.1826e+00  1.0785e+01 -7.9641e-02 -8.0781e+00 -2.8322e+00  5.8314e+00
-#>  -1.6334e+00 -8.2829e-01 -5.2327e-01  9.1560e-01  2.7875e-01  1.0949e+00
-#>   1.1560e+01  7.1775e+00  2.4676e+00  4.3689e+00  1.2348e+00  1.5355e+00
-#>  -7.2999e+00 -8.7090e+00 -1.0109e+00  8.0552e+00  5.2843e+00 -5.5434e+00
-#>  -5.6621e+00  2.0950e+00  5.6926e+00  8.5302e+00 -1.8420e+00  4.9828e+00
-#>  -6.2905e+00 -3.2019e+00  3.9112e+00 -2.8016e+00 -2.4425e+00 -3.9356e+00
-#>  -1.2319e+00  9.3284e+00  7.1342e+00 -2.9960e-01  3.5464e+00  9.6238e+00
-#>  -2.5003e+00  3.7505e-01 -1.8017e+00 -2.4822e+00 -1.3950e+01  4.2405e+00
-#>   6.2448e-01  5.0793e+00 -2.1631e+00  4.3120e+00 -6.7088e+00 -6.2485e+00
-#>   1.0608e+00  8.4343e-01  1.4154e+00 -8.8833e-01 -4.9350e-02  5.2178e+00
-#>   5.0803e-01 -2.5242e-02  5.2353e+00 -4.7372e+00  1.6922e+00  2.6130e+00
-#>  -8.1313e-01 -1.4678e+01 -8.0687e+00 -7.8823e+00  5.9716e+00  9.7268e+00
-#>  -4.1983e+00  1.7083e+00 -4.8165e+00  1.0524e+01  2.4571e+00 -5.8272e-01
-#>   9.0055e+00  1.6781e+00 -3.9620e+00 -1.9208e-01  7.7751e+00  4.8966e+00
-#>   5.6446e+00  1.0289e+01 -2.6237e+00 -9.7269e+00 -1.8522e-01 -1.3218e+00
-#>  -3.3620e+00 -1.8233e+00 -3.1495e-01  1.1295e+00  5.1915e+00 -6.8499e+00
-#>  -1.2493e+00 -3.7749e+00 -4.4679e+00  4.6988e+00  6.2839e+00  1.0712e+00
-#> 
-#> Columns 25 to 30 -3.7678e+00 -5.7242e+00 -6.5666e+00 -6.8208e+00 -5.8472e+00 -4.0306e-01
-#>  -3.7068e+00 -1.8190e+00 -3.1648e+00  6.9638e+00  5.9712e+00  4.9328e+00
-#>  -1.0388e+01 -5.5448e+00 -1.4448e+00  2.6815e+00  3.7214e+00 -3.2213e-01
-#>  -3.6283e+00 -2.1454e+00  3.2638e-01  6.5090e+00  3.4894e+00  5.7031e+00
-#>  -5.3661e-02  4.1527e+00  2.4702e+00 -1.5813e-01 -1.6505e+00  1.4394e+00
-#>   1.6428e+01 -9.1191e+00  1.1358e+00 -3.5391e+00  8.9095e+00 -2.4697e+00
-#>  -4.4852e+00  1.1873e-01 -3.0818e+00  2.8583e+00  2.7108e+00  5.9310e-01
-#>   3.1718e-01  5.4803e+00 -7.4583e+00 -3.3241e+00  1.9538e+00 -9.9190e+00
-#>  -5.7366e+00  9.0325e+00 -3.6218e-01  1.2094e+01 -5.1567e+00  2.4442e+00
-#>  -1.0432e+01  4.0087e+00 -5.2226e+00 -3.0613e+00 -2.6841e+00  4.3142e+00
-#>   1.5683e+01 -4.8294e+00 -3.7603e+00 -3.9085e+00  6.4568e+00 -9.4047e-01
-#>  -8.2675e+00 -1.2175e+01 -2.7009e-01  3.1945e+00  7.9773e+00 -9.3286e+00
-#>  -1.1884e+00  6.6128e-01  5.1019e+00  5.5271e+00 -1.3284e+01 -6.6113e+00
-#>  -1.3139e+01  4.7815e+00  1.3458e+00  7.1244e+00 -8.1554e+00  5.0782e-01
-#>   4.6021e+00  5.5465e+00  1.0623e+01 -6.7340e+00 -1.0824e+01  9.9315e+00
-#>  -1.2730e+01  1.4776e+01 -2.8510e+00  1.9954e+00 -6.1477e+00  6.8560e+00
-#>  -8.4356e+00  3.8520e-01 -9.1840e+00  1.9059e+00 -8.1112e+00 -5.6377e+00
-#>  -7.3018e+00 -2.0424e+00  2.1029e+00 -1.0376e+00  2.2140e+00 -6.5560e+00
-#>   5.9514e+00  2.8834e+00  8.2532e+00 -1.2182e-01  1.4292e+01  6.1602e+00
-#>   2.3185e-01 -5.6710e+00 -1.6720e+01 -6.4373e+00 -8.8326e-01  1.2590e+00
-#>  -8.3497e+00 -1.0511e+01 -9.8981e+00  1.9591e+01  7.4912e+00  4.6404e+00
-#>  -6.4806e+00  9.1930e+00  3.6603e+00  6.1261e+00 -4.8635e+00 -1.2633e+01
-#>   1.6575e+00  1.2219e+01  1.8400e+00 -5.0897e-01  4.8157e-01 -1.5647e+00
-#>   1.0697e+01 -1.0563e+01 -1.9335e+00  5.2464e+00 -4.9309e+00  5.6560e+00
-#>   2.7579e+00 -1.0974e+01  3.7751e+00  2.4941e+00  1.2820e+01  1.2087e+01
-#>   1.0323e+01  9.6038e-01 -8.0909e+00 -1.2246e+01  5.8800e+00 -8.1793e+00
-#>  -3.2346e-01  5.5698e+00 -4.4115e+00  9.1758e-02 -5.0301e+00  3.8046e-02
-#>   5.3821e+00 -8.0975e+00  3.9839e+00 -1.6710e+00  1.3352e+01 -6.0166e+00
-#>  -1.8801e+01 -7.5684e-01 -6.5811e+00  2.7727e+00  8.5961e+00  8.7461e+00
-#>   5.9716e+00 -2.9117e-01  6.6215e+00 -6.3897e+00  3.0783e+00 -7.0034e+00
-#>   3.3100e+00  5.9482e+00  7.1441e+00 -7.2146e+00 -1.1453e+01 -2.1975e-01
-#>  -2.5174e+00  6.1783e-04  3.0374e+00  9.1811e-01  1.3462e+00  1.2635e+01
-#>  -1.1931e+00 -5.5093e+00 -6.1567e+00 -2.6138e+00  2.6214e+00  1.4476e+00
-#> 
-#> Columns 31 to 36  7.4223e-01  7.0131e+00  1.1629e+01 -5.2268e+00 -1.1719e+01  3.1106e+00
-#>   2.6765e+00  7.4811e-01  2.4381e+00 -6.1843e+00  8.3653e+00  1.0713e+01
-#>   5.0892e+00  1.7012e+01  5.5125e+00 -1.1288e+01  4.5462e+00  5.6505e+00
-#>   6.4937e+00  5.3456e+00  7.8426e+00 -1.6293e+01 -2.3455e+00 -5.8268e+00
-#>  -1.0484e+00  3.4260e+00 -8.2399e+00  4.6738e+00 -5.2327e+00  7.4706e+00
-#>   5.3485e+00 -1.8475e+00 -3.6222e+00 -1.6178e+01 -3.4572e+00  3.2129e+00
-#>   6.6275e+00 -2.6183e+00  1.6539e+00  4.8539e+00 -5.4083e+00  5.7612e+00
-#>  -2.7518e+00  5.2740e+00  1.5759e+01 -9.9001e+00  2.3535e+00  7.7896e+00
-#>   3.7825e+00 -3.4907e-01 -3.4481e+00 -2.5707e+00 -9.7787e+00  1.5958e+01
-#>  -3.8520e-01  1.4803e+01  3.4473e+00 -6.6064e+00  4.9638e+00  7.6164e+00
-#>  -2.5500e+00  4.3077e+00 -1.0892e+01 -4.6185e+00  3.9399e+00  9.3895e+00
-#>  -8.5954e+00  5.5902e+00  9.9969e+00  9.0837e+00 -7.0463e+00  1.6710e+00
-#>   2.7788e+00  7.4384e+00  8.5017e+00 -2.4923e+00  1.0762e+01  5.9373e+00
-#>   4.4875e+00  1.5368e+00 -1.6538e+00 -1.0821e+01  4.6451e+00 -1.2634e+00
-#>   1.2521e+00  1.4079e+01  2.2609e+00 -1.0919e+01  8.3839e+00 -3.6383e+00
-#>   1.1817e+01  2.9383e+00 -4.2285e+00  2.2827e+00 -1.1505e+01  3.6831e+00
-#>   5.6361e-01  1.7811e+00 -2.0413e-01  8.8456e-01 -2.2689e+00 -3.1501e+00
-#>  -9.8936e+00  3.8421e-02  1.3489e+00 -7.7164e+00  1.4918e+00  1.5608e+00
-#>   3.9265e+00  1.5115e+01 -4.5694e+00 -7.6374e+00  3.5958e+00  2.6907e+00
-#>  -2.7697e+00 -1.5410e-01  5.8248e+00  3.0651e+00  9.2409e+00 -4.3196e-01
-#>  -3.9996e+00  3.8280e+00  7.0900e+00 -9.7899e+00 -1.5215e+00  1.3456e+01
-#>   1.0321e+01 -9.7283e+00 -4.8336e+00  1.3471e+01  4.9134e+00  3.0929e+00
-#>   8.3774e-01  4.2876e+00 -1.7407e+01  2.7746e+00 -6.0241e+00 -9.6043e+00
-#>   3.1691e+00  8.8499e+00 -9.8104e-01  5.2230e+00 -8.6498e-01  1.2157e+01
-#>   9.0028e-01 -7.4834e+00 -3.0958e+00 -4.7875e+00 -1.0619e+01  1.0254e+00
-#>   5.0236e+00  1.2426e+01  1.5218e+01 -4.1620e+00 -5.4289e+00  7.4496e+00
-#>   1.5874e+01 -1.8470e+00 -5.5536e+00  1.0463e+01 -1.4672e+01 -4.7601e+00
-#>  -9.4839e-01  1.9456e-02 -2.4531e+01 -1.4408e+00  4.2728e+00 -7.0230e+00
-#>   9.5121e+00  1.6671e+00  8.0547e-01 -7.9168e+00  4.5085e+00 -9.3673e+00
-#>  -1.4331e+00 -3.4387e+00 -8.9806e+00  3.4347e-01 -1.6345e+01 -8.6711e-01
-#>   3.4529e+00  7.9604e+00  7.3007e+00 -1.6455e+01 -9.1844e+00 -7.5894e+00
-#>   4.7911e+00  6.6002e+00 -3.2026e+00 -9.1167e-01  3.9497e+00 -1.9500e+01
-#>   1.6120e+01 -1.0085e+00  2.9139e+00  9.0391e+00 -4.1158e+00  1.2126e+01
-#> 
-#> Columns 37 to 42 -4.2708e+00 -6.2193e+00  4.0405e+00 -2.4626e+00  2.6114e+00 -6.6725e+00
-#>  -2.6733e+00  5.4875e+00 -9.1574e-01  1.0587e+01  1.4299e+00  7.1512e+00
-#>  -1.1510e+01 -1.1695e+01 -6.2071e+00 -8.2175e+00  1.9661e+00  1.0048e+01
-#>  -2.6016e+00 -7.4609e+00 -5.6747e+00  9.3854e+00  9.3149e+00  5.0030e+00
-#>  -1.1570e+00  8.6847e-01  4.5684e+00  1.1916e+01  4.5815e+00  3.9475e+00
-#>   4.1254e+00  5.6743e+00 -2.8369e+00  8.3233e+00 -6.2624e+00 -6.4872e+00
-#>   6.2880e+00 -1.0629e+01  4.8851e+00  4.4807e+00  7.8539e+00  2.9114e+00
-#>   5.6523e+00 -9.5010e+00  1.1585e+01 -7.8069e+00 -1.9712e+00 -1.4402e+00
-#>  -9.8762e+00  1.1291e+01  3.9450e+00  7.5963e+00  1.1955e+00  1.0262e+01
-#>   6.1205e-01 -3.9418e-01  6.0771e-01  1.7916e+00  4.0193e+00  1.2516e+01
-#>  -5.3330e+00  5.4140e+00  1.0709e+01  5.9039e+00 -2.1101e+00  5.9015e+00
-#>   4.4010e+00 -5.8462e+00  2.4776e+00 -2.7191e-01  2.4018e+00 -5.4125e+00
-#>  -1.6926e+00  3.5603e+00  6.8201e-01 -1.0361e+01 -1.1898e+00  1.4208e+01
-#>  -8.8385e+00  9.4390e-01 -1.0417e-01  7.5702e-01  5.1242e+00  6.3443e+00
-#>  -1.0421e+01  3.4307e+00  1.0905e+00 -3.4570e-01 -4.2475e+00  3.7947e+00
-#>  -8.2029e+00 -7.5056e-02 -1.0317e+01  9.6688e-01  5.8398e+00  6.9069e+00
-#>   9.2056e+00  2.6418e+00 -6.2371e+00  3.8774e-01  1.1345e+00 -3.7623e+00
-#>  -4.0129e+00 -4.6145e+00 -4.4591e+00 -1.5093e-01  3.8242e+00 -2.6517e+00
-#>  -7.3270e-01  3.5610e+00  9.5007e+00  5.1863e+00  5.7790e+00 -9.9534e+00
-#>  -1.9771e+00 -4.4519e+00  2.3197e+00 -5.2061e+00 -4.5916e-01 -1.5046e-01
-#>  -6.4935e+00 -5.2702e+00  4.9656e+00 -7.4239e+00  9.6984e-01  5.0983e+00
-#>  -3.6458e+00  1.0499e+01 -3.4077e-02  2.9104e+00 -4.5600e+00  9.1534e+00
-#>   9.5156e+00 -1.5982e+00  4.5251e+00  3.1438e+00  4.4952e+00 -1.8068e+01
-#>  -2.3347e+00  8.9090e+00 -3.1034e+00 -5.2686e+00  1.9010e+00  3.1109e+00
-#>  -1.6672e+01  7.5201e+00 -1.2067e+00  3.2212e+00 -9.8372e-02 -1.1015e+01
-#>   1.0232e+01 -6.7055e+00  1.3532e+01 -2.2495e+00 -3.1926e+00 -3.1087e+00
-#>  -5.6343e-01 -2.0494e+00  3.1293e+00  8.3727e+00  6.9515e+00 -7.7970e+00
-#>  -3.8374e-01  4.6507e+00 -1.0472e+01 -1.6164e+00  9.6006e+00  6.4874e+00
-#>  -6.7306e+00 -5.4101e+00  5.1182e+00 -7.9834e+00  9.7989e+00 -5.6655e+00
-#>   3.6640e+00 -1.7696e+00 -4.1505e+00  1.7499e+00  8.3987e-01  4.7474e-01
-#>  -4.6932e+00 -3.6107e+00  6.9508e+00  8.5656e+00  6.0091e+00 -3.3539e+00
-#>   8.8946e+00 -6.8784e+00 -5.4160e-01  8.3567e+00 -8.4307e+00  1.9961e+00
-#>  -3.1567e-01  6.1672e+00 -1.2873e+00 -3.1027e+00 -7.7037e+00  4.4967e+00
-#> 
-#> Columns 43 to 48  7.6586e+00 -1.1518e-01 -3.6124e+00 -6.9019e-01  5.0078e+00 -7.1079e+00
-#>  -1.1350e+01  5.9555e+00  6.9061e+00  7.6141e+00  4.6341e-03 -9.8206e+00
-#>  -8.3167e+00 -4.8917e+00  6.2786e-01  3.4504e+00  3.6465e+00 -8.0655e+00
-#>  -9.8720e+00 -1.3697e+00  1.8162e+00 -1.2960e+00  5.4253e+00 -8.4163e+00
-#>  -4.6093e+00  3.1744e-01  1.3845e+01  5.9917e+00 -6.8247e+00  1.5222e+00
-#>   2.9278e+00  7.6591e+00  8.8834e+00 -7.3206e+00  3.5923e+00  1.1554e+00
-#>   1.7499e+01 -7.5501e+00  1.0134e+01 -2.2161e+00  2.7105e-01 -1.2830e+01
-#>   4.5848e+00 -6.8604e+00 -7.4688e-01  2.7601e+00  1.7959e+00 -4.4080e+00
-#>  -6.2159e+00  1.0061e+01  8.0681e+00  4.7329e+00 -1.4557e+01 -5.9783e+00
-#>  -1.4388e+01  6.6257e+00  5.5512e+00  1.4502e+01  2.5892e-01  1.5548e+00
-#>  -5.8538e+00  1.7142e+00  3.0457e-01  5.8071e+00 -1.3670e+01  1.0826e+01
-#>  -2.2469e+00 -8.5539e+00  2.7219e+00 -3.7882e+00  6.9489e-01  4.0771e-01
-#>  -1.2400e+00 -6.6279e-01 -5.0088e+00  1.0212e+00  4.5081e+00  2.4033e+00
-#>   6.0568e-01  1.0213e+00  4.9064e+00 -1.2058e+01 -5.7127e+00  1.8151e+00
-#>   7.8649e-01  3.0735e+00  9.0810e+00 -1.4522e+00 -2.7879e+00 -1.1300e+01
-#>  -6.7457e+00 -3.5366e+00 -8.0993e+00  2.4395e+00  3.8280e+00  2.8154e-01
-#>   3.8816e-01  4.0043e+00 -1.2529e-02  2.9227e+00  1.1307e+00 -2.1515e+00
-#>  -1.3813e+00 -7.7966e+00  6.1247e-01 -6.6544e+00  6.6106e-01  6.3762e+00
-#>  -2.4359e+00 -4.7479e+00 -1.0501e+01  3.6424e+00 -3.3815e+00 -7.2181e+00
-#>   3.5248e+00  4.5957e+00  1.1000e+01  2.9740e-01 -5.6136e+00  8.5737e+00
-#>   3.5406e+00  6.4048e+00  1.1231e+01 -8.7433e+00 -7.5803e+00  1.0159e+00
-#>   8.7339e+00  1.2499e+01  3.4785e-01 -1.0078e+01 -6.6816e+00 -5.8729e+00
-#>   6.4248e+00 -5.9144e-01  5.8935e+00 -6.0223e+00 -8.3323e+00  5.0163e-02
-#>   9.3207e+00  1.0922e+01 -2.6929e+00 -1.4812e-01  2.8792e+00  1.0079e+01
-#>  -1.1273e-01 -3.4145e+00  7.7179e+00 -3.9226e+00 -3.9568e+00 -3.7776e+00
-#>   6.0739e+00 -1.0924e+01  6.6230e+00  8.0777e+00  4.8874e-01 -8.7335e+00
-#>   6.0373e+00 -2.6559e+00  6.3964e+00 -3.3931e+00 -4.7059e+00 -4.6125e+00
-#>   1.0763e+01  3.2660e+00  1.2023e+00  1.4326e+01  2.6127e+00  5.9575e+00
-#>  -5.4855e+00 -7.0947e+00  9.2428e+00  6.1904e+00 -4.3986e+00 -5.3961e+00
-#>   6.7367e+00  2.6316e+00 -3.6548e+00  9.4662e+00  7.7264e+00  5.8823e+00
-#>  -6.9419e-01 -7.8668e+00  8.8550e-01  1.0576e+01  6.2078e+00 -4.0643e+00
-#>  -8.6446e+00  4.2446e+00 -2.4906e+00  5.2612e+00  9.2999e+00  7.0488e-01
-#>  -5.8305e-01 -7.3184e+00  2.9134e+00 -4.2438e+00  1.0961e+00 -5.6040e+00
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 6 -2.3830e+00  1.5045e+01  1.0755e+01  1.9093e+00  3.4693e+00  6.0668e+00
-#>  -9.1152e-01 -3.0114e+00 -2.5333e+00  5.3111e+00  7.9649e-01 -1.6620e-01
-#>   3.2936e+00 -8.9781e+00 -3.2925e+00  4.2987e+00  8.5330e+00 -2.5871e+00
-#>  -6.7483e+00 -1.5892e+00 -8.2601e-01  8.3264e+00  2.0445e+00  8.9829e-02
-#>   7.6095e-01 -3.3332e+00  2.8401e+00 -6.2223e+00  3.0014e+00  5.3401e+00
-#>   4.4991e+00  6.8539e+00 -7.9476e-01 -1.1602e+01 -1.2376e+01  5.7659e+00
-#>  -3.9050e-01 -6.8403e+00  3.2067e+00  2.6319e+00  2.6960e+00  5.4775e+00
-#>   2.5490e+00 -2.2545e+00  1.9085e+00  1.9497e+00 -5.6462e+00 -5.9350e+00
-#>  -7.9047e+00  2.6436e+00  2.0883e+00 -4.8206e+00  1.5255e-01  9.9853e-01
-#>  -6.8098e+00 -4.4973e+00 -9.4591e-01  4.4095e+00  6.2232e+00  4.2034e+00
-#>   3.6400e+00  4.5510e+00 -1.4693e+00 -1.9791e+00  1.4600e+01 -1.1596e+01
-#>   2.0117e+00 -7.4495e+00  1.9576e+00  1.4078e+01 -1.7327e+00 -7.9045e+00
-#>  -5.4675e+00 -8.1726e+00 -2.1059e+00  6.7968e+00 -4.1634e+00  7.1000e-01
-#>   3.3354e-01 -2.4415e-02  1.1163e+00  5.2675e+00  4.8768e+00 -1.1194e+01
-#>   5.9835e+00  8.5104e+00  4.7339e+00  1.0464e+01  4.9154e+00  2.1113e+00
-#>  -1.3915e+01 -1.9716e+00 -2.9983e+00 -1.9405e+00  2.1733e+00 -9.8436e-02
-#>   7.1030e+00 -2.7523e+00  3.8541e+00 -6.1061e+00  6.9082e-01  4.5209e+00
-#>   9.2541e+00 -1.7739e+00 -4.2750e-01 -9.3434e+00 -6.3315e-01  1.9368e+01
-#>  -3.5566e+00 -1.6541e+00  4.1912e+00  4.9319e+00  9.8950e+00 -1.2191e+01
-#>   1.1695e+01 -3.1335e+00 -3.6334e-01  5.4959e+00  1.1974e+01 -3.9265e+00
-#>   5.5305e+00 -2.7223e-01 -8.4738e+00  3.1758e+00 -1.2866e+00 -6.1654e+00
-#>  -5.3902e-01 -3.0807e+00 -9.3326e-01  7.4006e+00 -3.2220e+00  7.1673e+00
-#>  -5.6331e-01  4.7113e-01  1.0444e+00 -5.5636e+00  7.4316e-01 -1.0495e+01
-#>  -5.2943e+00 -3.6060e+00 -2.2940e+00 -2.6019e+00  1.6932e+01 -9.5823e+00
-#>   1.2445e+01  1.6621e+00  1.4975e+00 -4.6527e+00  3.0234e+00 -3.0627e-01
-#>   3.7138e+00 -7.4142e-01  6.6150e+00 -2.3159e-01  2.8262e+00  1.0662e+00
-#>   7.6823e+00  8.5604e+00  9.3044e+00  5.1059e+00 -6.4475e+00 -5.8448e+00
-#>  -3.1239e+00 -6.4697e+00  2.3946e+00  3.6733e-01 -7.1365e+00 -9.1770e+00
-#>   1.0079e+01  1.0610e+00  7.7564e+00  9.0786e+00  3.1420e+00 -1.1080e+01
-#>   3.1758e-02 -6.1169e+00 -2.4867e+00 -7.9838e+00  1.3619e+00 -9.4099e+00
-#>   2.5039e+00  9.3481e+00  6.4029e+00  3.7382e+00 -3.6803e+00 -1.0931e+01
-#>  -7.1225e+00 -5.6279e-03  5.9118e+00  1.0263e+01  2.1115e+00 -7.7262e+00
-#>   2.5962e+00 -4.0675e+00  1.1823e-01  4.6199e+00 -8.2061e+00  7.5710e+00
-#> 
-#> Columns 7 to 12  1.8408e+01  1.5652e+00 -1.9543e+00 -2.8085e+00 -6.8231e-01  4.1349e+00
-#>  -6.5164e+00  1.4820e+01 -1.1288e+00  4.5189e+00  8.7818e+00 -4.2647e+00
-#>  -7.7523e+00  7.2089e+00  1.1656e+00 -2.2606e+00  2.2263e+00 -8.8486e+00
-#>  -6.4213e+00 -7.8116e-01 -1.3705e+01 -1.4288e+00  7.0685e-01 -9.6210e+00
-#>  -5.2904e+00 -4.3724e+00 -4.5616e+00 -2.1462e+00 -1.6026e+00  2.4151e+00
-#>   7.6076e+00  1.4017e-01  7.1753e+00  1.6275e+00 -1.8538e+01 -6.3133e+00
-#>  -3.0048e-01  1.1206e+00 -2.7181e+00 -8.8819e+00  1.1768e+01 -2.3927e-01
-#>   3.9040e+00  1.5906e+00 -4.6165e+00  5.6143e+00 -2.3985e+00  3.8737e+00
-#>  -1.8581e+00  4.4859e+00 -8.5661e-01  9.1416e+00 -7.1321e+00  1.5359e+00
-#>   2.5870e+00  6.4013e+00 -3.5112e+00  3.0193e+00 -9.9813e-01  4.9914e-01
-#>  -5.6749e-01 -2.5634e+00 -2.0049e+01  7.6160e+00 -5.6121e+00  1.2002e+01
-#>  -5.1016e+00  4.5954e+00 -6.6194e+00  1.0558e+01  8.4648e+00  4.0876e+00
-#>   1.9074e+00 -4.4138e+00  1.6856e+01 -2.3015e+00  9.8041e+00  5.1201e+00
-#>   7.9012e+00 -7.8831e+00  6.1398e+00  3.3781e+00 -1.0034e+01  1.1867e+00
-#>   3.8663e+00 -8.9898e+00  8.8156e+00 -1.6206e+01  1.1986e-01 -2.6682e-02
-#>  -3.2041e+00 -1.4711e+01 -1.0443e+00  5.8869e-01 -9.3162e+00 -3.5679e+00
-#>   8.5277e-01 -6.4199e-01 -6.1192e+00 -1.5057e+01  7.4858e+00  1.3985e+00
-#>   2.5524e+00  1.4245e+01 -5.8290e+00 -2.5738e+00 -9.5414e+00 -2.3828e+00
-#>  -1.0419e+01 -1.1870e+01 -3.9730e+00  1.5484e+00  6.0266e+00  8.5218e+00
-#>  -3.8284e+00  1.5833e+00 -6.3376e-01 -1.2201e+01  5.7151e+00  1.7104e+00
-#>  -7.7268e+00  4.3423e+00 -1.8297e+00  1.2690e+01  6.7800e-02  8.8895e+00
-#>   2.4643e+00  2.1095e+00  2.8859e+00  4.3288e+00  6.2184e-01  4.8834e+00
-#>   1.5286e+00 -5.3391e-01  6.7303e+00  2.7903e+00 -2.8789e-01 -4.8311e+00
-#>   5.6804e+00 -1.5724e+00  4.5631e+00  1.4864e+00  1.0664e+01  7.5194e+00
-#>  -5.1199e+00 -6.2939e+00 -1.4950e+00 -2.8765e+00 -3.7088e-01 -3.6455e+00
-#>   9.5212e+00 -1.0249e+01  4.2535e+00 -1.1122e+00  1.0850e+01  5.5438e+00
-#>  -9.8939e+00 -7.7922e+00 -6.7318e+00 -3.9111e+00 -5.4714e-02 -1.3669e+00
-#>   4.4817e-01 -1.5928e+01  7.6097e-01 -9.1389e+00  8.9798e+00 -6.3461e+00
-#>   1.1088e+00 -5.1600e+00  1.5623e+00  4.1705e+00 -3.6206e+00 -2.3324e+00
-#>   2.9760e+00 -4.8346e-01  2.1871e+00 -7.7891e+00 -8.0293e+00 -6.5323e+00
-#>  -1.4006e+01 -1.1111e+01 -9.3578e+00 -1.0229e+01 -9.0496e+00 -5.3950e+00
-#>  -6.5811e+00 -4.4025e+00  7.0217e+00 -1.9153e+00  1.4510e+01 -6.2039e+00
-#>  -8.7036e+00 -6.6167e+00  7.2552e+00  8.6312e+00  3.9875e+00  7.1604e-01
-#> 
-#> Columns 13 to 18 -4.0327e+00  1.2063e-01  3.8963e-01  1.2082e+01  4.4529e+00 -4.0982e+00
-#>   7.4974e+00  1.8638e+00 -8.1714e+00 -2.7034e+00  5.9391e-01 -1.8888e+00
-#>  -1.9006e+00 -4.8901e+00 -5.2321e+00 -1.3496e+01  1.1133e+00 -1.3292e+00
-#>  -1.7714e+00 -6.2307e-01 -1.4108e+01  7.9459e+00  4.0040e+00  4.6520e+00
-#>  -5.9653e+00 -6.6982e+00  2.3026e+00 -4.1065e+00 -9.7532e-01 -4.1826e+00
-#>   9.5455e+00 -1.2973e+01  9.3023e+00  8.9305e+00 -2.2556e+00 -8.2025e+00
-#>   6.3662e+00  2.3125e+00  6.1421e+00 -2.8569e+00 -6.1378e+00  3.1209e+00
-#>   1.7458e+01  1.5194e+00  1.1149e+00 -1.0232e+00 -5.4569e+00  4.5391e+00
-#>  -8.9644e+00  5.6622e+00 -9.7496e+00  6.8962e+00  2.9958e+00 -5.5083e+00
-#>  -3.4634e+00 -4.3375e+00 -3.3078e+00 -1.6976e+00 -1.8537e+00 -8.7774e-01
-#>   6.9377e+00  3.8605e+00  6.0268e+00  2.3274e+00 -8.8593e+00 -2.4879e+00
-#>   6.8199e+00 -5.9747e+00 -7.2865e+00 -1.4146e+00  7.5257e+00 -2.5840e+00
-#>   5.0461e+00 -6.7386e-01 -8.0948e+00 -6.2207e+00 -4.3244e+00 -6.1133e+00
-#>  -2.8252e+00 -2.5870e+00  1.8028e+00 -3.6711e-01  2.0952e+00  3.6568e+00
-#>   2.3060e+00 -6.2086e+00  3.9923e+00 -1.4150e+00 -8.5676e-01  5.8699e+00
-#>  -1.3242e+01  6.3704e+00 -1.0038e+01  2.3710e+00 -1.4868e+01  6.5075e+00
-#>  -2.8410e+00 -6.0744e+00  9.4370e+00  1.7102e+00  5.2457e+00 -7.8648e+00
-#>   8.1169e+00 -9.1841e+00  8.2035e-01  4.0471e+00 -2.7658e-01 -3.2713e+00
-#>   7.9194e+00 -5.5048e-02 -3.5546e+00  2.4541e+00 -9.2249e+00 -5.8647e+00
-#>  -1.7885e+00  5.6227e+00  9.0222e+00 -1.3317e+01 -3.9801e-01  1.6702e+01
-#>  -3.9880e+00  8.6878e+00 -2.2664e-02 -7.4790e+00 -1.3203e+00  1.8476e+01
-#>  -4.4812e+00  5.0602e+00 -3.5708e+00  1.2414e+00 -2.9848e+00  1.2678e+01
-#>  -4.5570e+00 -2.6359e+00  1.4333e+01  9.2020e-01  4.0976e+00  4.4490e+00
-#>  -1.1185e+01  3.1190e+00 -5.5076e+00  4.0855e+00  6.6168e+00  1.6649e+00
-#>  -6.8375e-01 -2.9971e+00  3.8258e+00  5.9877e+00  6.4271e+00 -5.3766e+00
-#>   1.2921e+01 -1.8491e+00  1.1426e+00 -2.6788e+00 -1.8496e+01  7.4915e+00
-#>  -7.3414e+00  3.5322e+00  2.5229e+00  5.7388e+00  1.1160e+01 -3.6650e+00
-#>  -3.9620e+00 -4.8352e-02  7.4024e+00 -7.2069e-01 -9.0369e+00  1.8158e+01
-#>   1.4811e+00 -2.8406e+00  2.1601e+00 -6.3998e+00  8.4908e+00  7.1127e+00
-#>  -1.0033e+01 -8.8568e-01  4.8732e+00  1.6056e+00  6.6930e+00 -8.4509e+00
-#>  -3.1729e+00 -1.4420e+01  2.9933e+00  9.6449e+00  6.0507e+00  3.3586e+00
-#>  -5.9448e+00 -4.6698e+00  2.9528e+00 -4.8673e+00  3.8821e+00  2.8102e+00
-#>   2.5439e+00  5.1470e-01 -2.2484e+00 -7.2786e+00 -2.3403e+00 -1.0638e+00
-#> 
-#> Columns 19 to 24  9.0982e+00  5.1736e+00 -2.4757e+00  1.5844e-01 -6.7332e-01  5.6665e+00
-#>  -4.8000e+00 -8.6078e-01  9.5131e-01 -2.8858e+00  1.2703e+00  4.7499e-01
-#>  -7.5245e+00  3.5997e+00  5.3406e+00 -3.8828e+00  5.1932e-01  7.5683e-02
-#>  -8.4505e+00  1.1701e+01 -2.4831e+00 -1.4745e+01  5.4654e+00 -1.7338e+01
-#>   1.0567e+01  6.2607e+00 -3.6660e+00 -8.7202e+00 -1.5690e+01 -2.2930e+00
-#>   9.8490e+00  8.0237e+00 -3.6052e+00  3.3250e+00 -7.4190e+00  4.9683e+00
-#>   7.4106e+00 -6.2680e+00  5.1907e-02 -1.2005e+01 -6.8563e+00  4.7717e+00
-#>  -9.6498e+00 -3.7699e+00  1.3658e+00  4.0453e+00  6.3397e+00  2.1112e+00
-#>   3.6101e-01  2.1361e+00 -5.9951e-01 -7.0478e+00  3.9619e+00 -1.2897e+01
-#>   1.7499e+01  1.4941e+00 -1.6669e+00 -1.9513e+00 -1.1608e+01  6.2823e+00
-#>   5.6085e+00  3.7327e+00 -1.0657e+01  6.8685e+00 -2.5067e+00 -2.8697e+00
-#>   4.8413e+00  1.7147e+00  3.9855e+00 -2.3646e+00 -1.2719e+00  5.9345e-01
-#>  -1.1901e+01  2.1154e-01  1.2861e+01  6.5933e+00  5.5342e+00 -1.1736e+01
-#>  -4.1619e+00 -4.0502e+00  4.5874e+00 -5.9631e+00  6.2003e+00 -1.5919e+01
-#>   3.2160e+00  6.1556e+00  5.7512e+00 -2.4275e+00  1.1169e+00 -7.0630e+00
-#>   3.8809e+00 -5.3835e+00  5.9786e+00 -1.2241e+01 -1.6439e+00 -1.5627e+01
-#>   1.7291e+01  2.2233e+00 -5.2146e+00  6.4464e-01 -5.5866e+00  1.7714e+01
-#>   5.3964e+00  1.3962e+01  1.9331e+00 -1.3314e+00 -5.3025e+00 -2.3913e-01
-#>  -7.8175e+00 -7.1026e+00 -6.0704e+00 -8.0185e-02 -2.7631e-01 -3.9497e+00
-#>   2.5051e+00 -6.0993e+00 -5.4260e+00 -5.7393e+00 -3.3067e+00  9.7217e+00
-#>  -1.2314e+01 -1.6606e+01  1.5940e+00 -6.1850e+00  1.7046e+01 -4.5048e-01
-#>  -8.8948e+00 -5.6061e+00  5.1760e-01 -4.5070e+00  2.5624e+00 -1.1760e+01
-#>  -8.3118e+00 -4.8301e-01  2.4386e+00  1.1577e+00 -5.4625e+00  9.3602e+00
-#>   1.0364e+01 -1.7660e+00 -7.1697e+00  4.0364e+00  6.2778e+00  8.9278e+00
-#>   9.0088e+00  3.4713e+00 -1.4689e+01 -3.7752e+00 -2.6339e-01 -5.4922e+00
-#>  -6.4503e+00  7.0307e+00 -2.5707e+00 -4.4928e+00 -3.8815e+00  2.7975e+00
-#>  -4.7806e+00 -1.3645e+00 -2.0122e+00 -2.8309e+00  3.8261e-01  9.4296e+00
-#>   1.8947e+00 -7.1179e+00  2.2631e-02  3.1296e+00 -5.7349e-01  6.5689e+00
-#>   2.1592e+00 -2.2114e+00  1.0351e+00 -1.8950e+00 -2.5478e+00 -7.2731e+00
-#>   9.4142e+00  3.9147e+00 -3.7307e+00  6.2922e-01 -2.9651e+00  7.1303e+00
-#>  -2.8823e-01  7.4542e+00 -4.3850e-01 -7.6315e+00 -3.2843e+00 -6.0237e+00
-#>   9.5521e+00 -1.9974e+00  2.2915e+00  2.4075e+00 -7.1246e+00  3.7398e+00
-#>  -4.8636e+00  1.7748e+00  9.8526e-01 -3.3251e+00  3.6598e+00 -1.3092e+01
-#> 
-#> Columns 25 to 30  3.1280e+00  1.2788e+00 -5.9366e+00 -4.2194e+00 -1.0509e+01  1.7713e+00
-#>  -5.3974e+00 -4.3479e-01  4.8893e+00  4.0898e+00  5.8615e+00 -5.5451e+00
-#>  -7.2648e+00  1.4969e+00 -6.0060e+00 -3.7986e+00  4.8079e+00  6.1958e+00
-#>   7.6044e+00 -5.9375e+00 -1.1993e+01  3.3922e+00  5.0549e+00  9.1746e+00
-#>   5.0515e+00 -5.0973e+00  4.8219e+00  1.0619e+00  4.5165e+00 -8.5143e+00
-#>   2.8204e+00 -3.9292e+00  9.8225e-01 -6.2513e+00  3.4724e+00  5.7119e+00
-#>  -5.8743e+00 -4.5231e-01 -8.1410e+00 -4.4791e+00  1.2651e+01  1.2361e+00
-#>  -7.2702e+00 -2.2595e+00  6.5974e+00  3.6935e+00 -1.9022e+00  7.6575e+00
-#>   9.8234e+00  2.3628e-01  2.1644e+00  1.7129e+00 -6.3852e+00 -1.3977e-03
-#>   7.5595e+00 -2.3019e+00  9.2416e+00  2.9271e+00  4.4853e-01 -4.5047e+00
-#>  -2.7705e+00 -7.2757e+00 -2.2790e+00 -2.7667e+00 -2.1357e+00 -3.7666e+00
-#>  -1.5240e+00 -2.3198e+00 -1.0283e+01 -3.7943e+00 -9.4341e-01  6.0867e+00
-#>   2.9003e-01  1.1639e+01  5.3857e+00 -3.1820e+00 -1.7843e+00  1.6590e+00
-#>   1.1793e+01 -5.1962e-01 -1.2450e+01 -7.9430e+00 -1.1940e+00  2.2993e-01
-#>   3.3375e+00  1.0644e+01  5.7075e+00 -6.2523e+00  1.4935e+00 -8.8530e+00
-#>   7.4632e+00 -1.0253e+01 -5.3447e+00  2.2636e+00 -3.4865e+00 -2.5092e+00
-#>   6.9494e+00  6.0295e+00  2.3967e+00 -4.1855e+00 -8.0001e-01 -6.9067e+00
-#>   6.3739e+00  3.9918e-02 -5.2436e+00 -6.9196e+00  4.8604e+00  1.7641e+00
-#>  -8.2620e+00 -6.7295e+00 -1.7506e+00  4.9274e-01 -5.8497e+00  4.8315e+00
-#>  -8.1358e+00 -8.7697e+00 -1.4823e+00 -1.1388e+01 -4.1414e+00 -4.4856e-01
-#>  -2.1427e+00 -8.0484e+00  1.3658e+00 -4.8648e+00 -1.7214e+00  2.5082e+00
-#>   4.4764e-01 -1.6163e-01 -2.3081e+00 -3.4154e+00 -3.8190e+00 -2.5088e+00
-#>   6.5203e-01 -6.3120e+00 -2.4546e+00 -4.3488e+00 -4.5031e+00 -4.1094e+00
-#>   8.2619e+00  2.1131e-01  7.1995e+00  3.4129e+00 -4.1694e+00  6.2864e+00
-#>  -1.2033e+01 -1.0293e+01 -8.4255e+00 -1.1377e+00 -7.0015e+00  1.5763e+00
-#>   1.9668e+00  8.1584e-01  1.2777e+01  3.1784e+00 -6.6701e+00 -3.4124e-01
-#>  -3.6915e+00 -5.8549e+00 -4.4516e+00  4.3056e+00  4.6994e+00 -2.7244e+00
-#>  -1.6473e+00  1.9608e+00 -5.7647e+00 -7.5848e+00  4.9922e+00 -4.6011e+00
-#>  -8.8363e+00  3.1260e+00 -2.4390e+00  1.9239e+00 -1.3892e+00  3.3748e-01
-#>  -1.2075e+00  7.8508e+00 -3.4638e+00 -5.1220e+00 -1.4918e+01 -1.8710e+00
-#>   1.6721e+00 -1.1214e+00 -1.0962e+00  2.9899e+00 -4.2842e+00 -1.8938e-01
-#>  -2.8022e+00  4.7344e+00  4.4270e+00  6.5299e+00  1.0474e+01 -4.4890e+00
-#>  -5.0150e+00  2.8116e+00 -1.5807e+00 -4.7746e-01 -7.7342e+00  1.1266e+00
-#> 
-#> Columns 31 to 36  3.5927e-01 -5.8005e-04 -1.1270e+01  9.0515e+00 -8.1627e+00  6.4540e+00
-#>   3.4068e+00 -1.2006e+01  5.3405e+00  8.2016e-01  6.0324e+00  7.1262e+00
-#>   3.7822e+00 -2.6076e+00  2.8620e+00 -5.2815e+00 -2.5095e+00 -1.0144e+00
-#>  -8.2290e-02 -1.3884e+01  1.2595e+00 -3.4814e-02 -9.7531e+00  5.2547e-01
-#>  -1.4943e+01  5.3147e+00  5.4917e+00 -1.0074e+00  9.0445e-01 -1.3210e+01
-#>  -9.3631e+00 -3.5228e+00  4.4674e+00 -1.0704e+01  7.5457e+00 -6.0601e-01
-#>  -1.3818e+01  5.2119e+00 -1.1345e+01  3.5436e+00  1.3895e+00  1.4796e+00
-#>   2.4844e+00 -8.3415e+00  6.8261e+00  4.8637e+00  5.8557e+00  1.5788e+01
-#>  -6.6571e+00 -1.9480e-01 -5.0670e+00  5.2520e-02 -9.8149e+00  1.1303e+01
-#>   5.5721e+00 -6.1646e+00  1.1623e+01 -1.4157e+00 -5.3222e-01 -5.4600e+00
-#>  -6.5203e+00 -6.7455e+00  7.7968e+00 -9.9367e+00  1.3339e+01 -1.4284e+01
-#>  -1.4378e+00  8.9679e+00  9.4038e+00 -8.1249e-01  6.5240e-02  3.7578e+00
-#>   9.4793e+00 -4.9221e+00 -1.6958e+00  2.6819e+00  2.1806e+00 -3.3611e+00
-#>  -1.5861e+00  2.8447e-01 -1.2740e+01 -8.7952e+00  5.9439e+00 -3.9432e+00
-#>   4.3443e+00 -6.5303e+00 -7.6826e+00 -4.1726e+00  7.0762e+00 -1.4766e+01
-#>   1.7967e+00  1.0627e+01 -8.8136e+00  3.6239e+00 -9.1490e+00 -2.4560e+00
-#>  -1.2485e+01  8.5619e+00  2.9441e+00  1.5589e+01 -4.7064e+00  1.8462e+00
-#>  -4.0600e+00 -1.4710e+00  4.7904e+00 -1.8557e+00  8.3307e+00 -7.1752e+00
-#>  -3.9976e+00 -6.5843e+00  2.6554e+00 -3.1964e+00  1.0752e+00 -4.8778e-01
-#>   3.3116e+00 -1.7098e+00  3.2721e+00 -4.4763e+00 -2.2594e+00 -7.9010e+00
-#>   5.7161e+00  6.5650e+00 -1.6615e+00 -1.1369e+01  4.7180e+00  5.7694e+00
-#>   8.5136e+00 -5.9878e-01 -1.5651e+01  8.4650e-01 -2.4974e+00  6.7638e+00
-#>  -5.8394e+00  5.7140e+00 -1.1210e+01 -2.2551e+00  1.6443e+00 -6.3403e+00
-#>   4.9703e+00  1.8214e+01 -9.8342e+00 -3.6146e+00 -5.6450e-01  7.4347e-01
-#>  -7.1601e+00  8.6449e+00  1.8380e+00 -6.6168e+00 -9.3282e+00  4.5143e+00
-#>  -7.4665e+00 -4.2576e+00  1.4170e+01  3.6051e+00 -4.7992e+00  3.7910e-01
-#>  -1.4914e+01  3.7157e+00 -8.1508e+00  7.3911e+00  1.0948e+00  4.0697e+00
-#>  -1.0161e+01  3.9304e+00  3.7996e-01 -1.3344e+01  9.3577e+00 -1.5207e+01
-#>  -1.7293e+00 -2.0529e+00  1.0785e+01 -9.5662e+00 -1.9826e+00  5.8388e+00
-#>   2.3011e+00  4.4566e+00 -1.1964e+01 -7.5399e-01 -9.7352e+00 -6.9948e+00
-#>   2.0763e+00 -1.1806e+01 -4.8473e-01  5.3601e+00  2.5147e+00  8.4158e+00
-#>   7.8955e+00 -2.6015e+00  6.0200e+00 -2.1845e+00 -1.3762e+01 -6.8676e+00
-#>   4.4075e+00  1.2753e+01  1.1472e+00  2.4227e+00 -1.0693e+01  4.7613e+00
-#> 
-#> Columns 37 to 42 -1.1020e+01  6.6465e+00 -1.9290e+00 -4.8945e+00  1.8490e+00  1.5339e+00
-#>  -4.5310e+00 -1.0638e+00 -4.4853e+00  7.1065e+00 -8.2337e-01  6.9402e+00
-#>   1.7499e+00 -3.6581e+00  1.2350e+00  2.8491e+00  1.7408e+00 -1.7779e-01
-#>  -1.4229e-01  5.2913e-02 -2.4037e+00  8.6961e+00 -1.7492e+00  5.9939e+00
-#>  -5.8223e+00  1.6047e+00  6.7784e+00  2.6981e+00 -1.2013e+01 -1.1739e-01
-#>  -1.6281e+01 -1.5035e+00 -2.1397e+00 -6.4374e+00  6.2523e+00 -9.6294e+00
-#>   2.9919e+00  4.3445e+00  1.8871e+00  8.1929e+00 -1.2139e+01 -1.4848e+00
-#>   2.7343e+00  1.6345e+00 -3.4733e+00  3.3497e+00  6.7508e+00 -1.8764e+00
-#>  -1.9815e+00  4.4974e+00 -1.5160e-02 -2.7958e+00 -4.8445e-01 -1.2368e+00
-#>  -2.6236e+00 -1.4458e+01  5.1467e+00  4.9547e-01 -3.0636e+00 -5.2946e-02
-#>   1.2763e+01 -6.6685e+00  2.1419e+00 -2.5938e-01  1.0607e+01  9.4662e-02
-#>  -2.5186e-01  1.7765e+00  1.8338e+01  5.5510e+00 -1.2427e+00  3.7455e+00
-#>   1.2176e+01 -1.7097e+01 -1.3656e+00  5.5532e+00 -7.6164e-01 -8.6892e-01
-#>  -6.6048e-01 -4.9274e+00  1.1193e+01 -3.6997e+00 -6.0456e-01 -2.1174e+00
-#>  -2.6654e+00 -6.7153e+00  4.3796e-01  8.9276e-01  3.0882e-01  6.6746e+00
-#>  -3.7119e+00 -4.4686e+00  3.3810e+00  1.2998e+00  5.5979e+00  1.9030e+00
-#>  -2.1550e-01 -7.2725e+00  3.9841e+00  1.9504e+00 -1.3238e+00 -9.6834e+00
-#>  -1.7437e+01  7.4582e+00 -5.9427e+00  5.0374e-01 -2.0163e+00  6.8246e+00
-#>   1.0537e+01 -6.8816e+00  3.1621e+00  4.3694e+00  1.6787e+01  4.0453e+00
-#>   7.4132e+00  2.5091e+00  5.2410e+00  6.6752e+00 -7.1528e+00 -4.8371e+00
-#>   6.0238e+00  1.1466e+01  2.0985e-01  1.3284e+01 -6.6727e+00  9.7171e+00
-#>   1.6460e+00  1.2047e+01  1.4384e+00 -9.1026e-01 -3.0676e+00 -3.0724e-01
-#>  -2.9586e+00 -8.5117e+00  2.8998e+00 -7.8197e+00 -9.8610e+00  4.7346e+00
-#>   3.0021e+00 -6.0272e+00  2.6469e+00 -4.5248e+00 -1.1962e+01 -5.6412e-01
-#>  -2.9277e+00  1.6119e+01 -4.9798e-01  5.0180e+00  1.2726e+01  6.9309e+00
-#>  -1.4897e+00 -8.5421e+00 -9.3005e+00  6.3357e+00  2.9906e+00 -3.1236e+00
-#>   3.2158e+00  1.0030e+01  7.0613e+00 -3.3065e+00 -7.8101e+00  1.4496e+00
-#>  -3.6055e-01  2.4626e+00  1.1457e+01  8.1908e+00  9.5694e-01 -1.1264e+01
-#>  -6.4157e+00 -2.8063e+00  6.9518e+00  9.7039e+00  1.0435e+01  2.9756e+00
-#>  -2.5457e+00 -2.9342e+00 -1.4990e+00 -1.3760e+01  5.3558e+00 -1.0135e+01
-#>   2.4038e+00  8.8341e+00  1.1209e+01  1.9843e+00  3.2433e+00 -2.7260e+00
-#>   5.9133e+00 -8.8468e+00  3.7391e+00  3.1379e+00  7.2138e-01  4.3503e+00
-#>  -9.0470e+00  2.9551e+00 -7.0717e+00  4.9854e+00  4.3781e+00  4.0203e-01
-#> 
-#> Columns 43 to 48 -8.8934e+00 -5.8731e+00  5.3382e+00  3.8283e+00 -7.9933e+00 -6.6487e+00
-#>  -2.6201e+00  6.9511e+00 -1.7930e+00  1.0116e+01  3.7670e+00  9.1283e+00
-#>   1.2525e+00  7.8141e+00 -2.4204e+00 -5.5882e+00  6.7728e+00  1.0990e+01
-#>  -9.0517e+00  5.1123e+00  2.6250e+00 -3.6983e+00 -1.6602e+00  3.9326e+00
-#>  -1.8962e+00  2.7367e+00 -1.8428e+00 -2.0395e+00 -5.9638e+00  1.1027e+01
-#>  -4.4575e+00  1.3333e+01  3.1724e+00  7.7592e-01 -5.7508e+00  1.6969e+01
-#>   8.7714e+00  1.4718e+00 -6.6618e+00 -7.8995e+00  9.0566e+00  9.4957e+00
-#>   4.8238e+00  6.6510e+00 -8.4335e+00  1.5356e+00  2.9097e+00  5.2194e-02
-#>   8.9002e-01  3.0509e-01  3.3963e-01 -4.1054e+00 -4.2206e+00 -3.0510e+00
-#>  -1.1020e+00 -4.3733e-01 -3.6850e+00  7.9086e+00  4.6608e+00  6.0527e+00
-#>   7.2235e+00  2.0171e+00 -1.2581e+01 -1.5761e+01 -6.6077e-01  2.3741e+00
-#>   3.0645e+00  6.5877e+00  5.2796e+00 -7.6957e+00 -5.3312e+00  9.6305e+00
-#>   2.5498e+00 -3.3503e+00 -4.4238e+00 -1.2314e+01  7.3962e+00  1.7007e+00
-#>   3.7125e+00 -4.4944e+00 -3.4737e+00 -9.5028e+00 -3.9384e+00 -1.1472e+00
-#>   2.9937e+00  1.5265e+00  3.5508e+00  2.3970e+00  9.5820e+00  1.5473e+00
-#>  -1.3902e+00 -8.5131e+00 -1.1774e+01 -4.6225e+00  7.6096e+00 -7.9541e+00
-#>   6.0351e+00  5.2485e+00  7.6588e+00  4.2709e+00  4.7346e+00  6.0520e+00
-#>  -3.9830e+00  7.0588e+00  6.1215e+00  4.3871e+00 -1.1102e+01  8.4514e+00
-#>  -2.8928e+00 -7.0470e+00 -9.8818e+00  1.0352e+00  4.5459e+00 -8.3517e-01
-#>   7.1982e+00 -3.6810e+00 -1.4942e+00 -9.4427e+00  1.7525e+00  4.1326e+00
-#>   9.4196e+00  3.0754e+00  1.7681e-01 -1.2273e+00 -7.9381e+00 -1.5840e+00
-#>   9.9200e-01 -3.7385e+00 -4.5607e+00 -9.1025e+00 -1.7741e+00 -5.9780e+00
-#>   3.4331e+00  6.9655e+00  2.2767e-01  5.4729e+00 -1.9004e+00  1.0142e+01
-#>  -2.0826e+00 -6.2107e+00  3.6577e+00  2.0191e+00 -1.2671e+00  2.2451e+00
-#>   3.1745e+00 -1.8666e+00  9.5549e-01  1.1877e+00 -5.7058e+00 -1.1480e+01
-#>  -3.5149e+00  1.5049e+00 -5.9846e+00  5.1825e-01  1.1530e+01  7.2957e+00
-#>   1.3030e+00  2.6066e+00  1.7635e+00 -9.6033e+00 -4.6952e-01  6.4410e-01
-#>   1.0071e+01  1.2886e+00  9.9078e-02 -1.1346e+01  2.7033e+00 -1.6566e+01
-#>   3.3214e+00 -8.9271e+00 -1.5329e+00  1.2822e+01  7.3061e+00 -1.0610e+01
-#>  -5.5313e+00 -1.9182e+00  5.2385e-01  3.3351e+00 -2.3038e+00 -2.5890e+00
-#>  -9.6380e+00 -2.2434e+00  4.5545e-01 -2.4699e+00 -8.2808e+00 -7.3125e+00
-#>   5.7026e+00  2.7890e+00 -1.1418e+01 -2.5375e+00  6.9807e+00  4.0363e+00
-#>  -4.7068e+00 -5.1611e+00 -9.9217e+00  2.1581e-01  5.1793e-01  1.5048e+00
-#> 
-#> (6,.,.) = 
-#>  Columns 1 to 8  -0.1104   0.9378  -5.7425  -1.3886  -6.7944  -1.2199   2.5925  -9.2208
-#>   -9.2785   1.3463   2.8895   6.0180  -2.2107  -3.1544   9.8532  -1.1469
-#>   -2.0518 -13.8740  -2.3745   6.4815  -3.4840  -8.7533   2.0470  -1.2614
-#>    2.8974   1.6251  -6.4410  11.9340  -4.1402   5.6601   2.2437   1.0130
-#>    2.1063  -0.8892   6.5428   0.7713   1.8376   0.2168  -1.7869  -3.1655
-#>    5.9192  -6.5299   3.9429  -4.7417  -2.0345  -2.1108   1.4094  -1.7040
-#>   -3.1214  -9.4695   7.8207  -5.2906   3.8265  -3.1306   0.2918   8.0468
-#>   -2.8765  -5.4424   5.3099   5.6219  -2.0720  -3.4595   6.4237   9.0764
-#>   -6.5373   2.7311   5.2222   3.8423   3.1656   0.4355   8.0006  -2.0814
-#>    1.2809   2.4835   1.5944   4.1153  -2.1983  -3.9559  -3.0186   0.6905
-#>  -12.6721   7.8146  11.8049  -1.7205 -10.0410   8.1629   3.5770 -11.8054
-#>    5.8688  -5.7280   6.1080   3.1844   7.2086  -9.3712  -2.7592   6.2454
-#>   -5.4817   8.3479   0.4329  -4.7231  -3.7640  -0.2761  -4.6210   0.6497
-#>   -2.6736  -0.6957   4.6696   0.0148  -5.5836   0.8055   0.7237  -2.8764
-#>    5.1874   2.4803  -2.1401   4.0009   0.3659   0.4488   8.4825   6.3955
-#>    8.2776  -6.1439  -6.7428   0.6655   4.5106  -7.2390  -1.9144  -5.6041
-#>    2.6683   0.6469  -7.0735  -0.0103   9.7698  -1.6489  -1.4062   0.4159
-#>   -3.5536 -11.1788   8.0697  -5.3634   0.5343   5.5538   7.8198  -7.0964
-#>    7.5448   3.3799  -3.9270   0.0477   7.1459  -2.2052   0.3239   2.4638
-#>   -8.3648  11.6064  -1.8365  -2.8855  -3.7194  -9.8075 -13.9457   2.6624
-#>   -0.8695  -7.6880   2.9305   1.6563  -4.7491  -5.6331   7.2559   7.0108
-#>   -4.5930   8.6262  10.1692  -1.9337  -1.3028   1.9578   4.1001  -1.4597
-#>    5.8933   4.6195   0.6673  -3.3505   4.2870  -3.4126  -1.0499  -1.0670
-#>    3.0959  11.1196 -11.3394  -2.6640  -3.7993  -3.4703  -4.4409   4.4927
-#>    7.0263   1.0446  -5.2210  -0.0563   7.9781   3.2414   4.7680   0.0977
-#>    8.4570   2.5366 -12.1487   8.7612  -3.6473  -6.9408  -9.0835   3.5213
-#>    1.4681  12.0926   4.3367  -7.4203   5.1335  -1.2544   1.0279   2.6569
-#>   -1.6785 -21.8863  -1.3839   9.7845  -9.6162  -2.6612   3.2954  -1.9311
-#>    0.9169  -1.5492  -3.9720   3.8593  -0.8790   1.1036   1.5049   6.8214
-#>    0.2175 -10.2392  -8.1670  -1.4911  -2.5520 -10.8965  -1.0136  -5.1678
-#>    1.3053   0.3541  -2.9467   7.1714   4.1810   2.3582   4.0887   4.2044
-#>    5.4859  11.4342 -12.2243   6.8399   2.0426  -0.6062  -4.4571   6.7048
-#>   14.3603  -3.2200   3.9387   1.3679  -3.8398  -8.6160  -2.1590  -8.0406
-#> 
-#> Columns 9 to 16  -6.3904   5.2458  -4.8878   0.4777   9.6190  -4.0347   6.2520   0.1535
-#>    5.3624  -4.1868  -9.0603   6.5989  -2.7368  -0.5048  -8.6676   5.6638
-#>    5.2559   3.3481  -7.0247  -1.2221  11.6998   5.6450  -0.9032   6.0717
-#>    0.3988   8.7230   1.8028  14.9125   0.2944  -0.6893   1.6754  -1.2014
-#>    5.9161  -2.8328  11.3242  -0.0847   1.8416   2.2938  -4.5816   2.5918
-#>   -1.9162  -1.6693  12.8858  -7.4261  -4.7459  21.0233  -0.7926 -13.2537
-#>    0.4529  -1.3662  16.3296  -6.9726  11.5936  -2.3820 -12.0824  -0.4903
-#>   -3.9553   2.5013  -4.9634 -13.3355  -3.9944  -0.6187   6.8524  -4.1107
-#>    4.6945   4.5045  -8.6546  10.3891  -2.7178  -5.6847   4.6090   1.6181
-#>    9.8551  -1.0476  -7.6735   9.0922   5.5801   2.0157  -3.8528  14.7383
-#>   -1.8485   2.3732   5.4091  -0.7128   9.2158  -4.9523 -13.8347 -14.1544
-#>    1.9425  -4.4296  -0.9140 -20.1580   3.1129  -4.2815  -1.1541  -4.7674
-#>   -6.3371  -1.7931  -2.5522   3.0294  -5.5946   0.2522   2.1318   6.1523
-#>   -6.9545   4.2920   3.5247  12.1743  -8.3309   1.5324  -4.0184  -2.4786
-#>   -7.9861   0.6720   8.6008   8.4768  -5.7279   6.4261  -0.0801  -1.1222
-#>    2.8641  11.7266  -2.5167  11.6780  -3.7370   0.2652   7.3983   3.9098
-#>    9.1588  -3.0015  -3.0287   7.1194  -4.4060   0.8970  -6.1486   5.8799
-#>    1.1350   4.9105   5.6519  -7.8276   5.2783   3.6180  -5.6088  -4.1530
-#>   11.4123   0.2572  -2.4716  -1.5631  -4.3751  -8.8379  -9.4847 -10.3646
-#>   -2.2827  -5.8110   4.0167   5.5697  -1.9213  -6.4974  -6.3152   3.8903
-#>    0.8398   3.9075  -4.3331  -8.2620  -0.2703 -12.2609  -1.9502  -0.2135
-#>  -12.2079  -1.3780  -1.2939  10.9357  -1.0795 -15.5522   3.0758  10.4788
-#>   -1.6880  -4.5991  12.9073  -0.1889  -3.7366   3.0858  -3.7235  -6.3634
-#>   14.6158 -10.9498  -2.9490  14.3120  11.5878   8.4827  10.1251   7.4441
-#>   12.6866  -0.0641  -3.6279   3.0155  -9.3858 -11.3412 -11.1634  -9.6604
-#>   -1.7954   3.4595   3.4319  -3.6742  -8.5487  -3.0773   3.5025   1.9246
-#>   -2.6488   0.0993   6.1076  -3.1861   1.9818  -3.6518   6.4687  -0.0724
-#>   -1.3800  -9.8271   2.9700   5.3824  -0.3110   7.3517  -8.2606 -11.3658
-#>    7.2609   4.6560 -10.1995   8.5112  -9.4829 -10.5180  -6.1653  -0.7339
-#>    6.4446   3.1358   1.7575  11.6717  15.9693  -1.8292  -1.2986  -5.1633
-#>    5.2860   6.0090  -0.7773  -4.0574   2.2034   8.1937  10.7889   3.1794
-#>   -2.7170   0.8778   7.5877   0.2280 -13.0934  12.6423 -10.7424   2.5844
-#>   -3.7412   1.8666  -0.7106  -0.6447   2.4800  -1.3900   2.4915  -3.1562
-#> 
-#> Columns 17 to 24   5.4172  -8.8975   4.1241   4.2239  -9.0144   0.6235  -4.9852  -5.5359
-#>   -6.1860  14.5444  12.7085  -4.9095  -1.4238  -1.5653  -4.4326  -0.0120
-#>   -6.4332  15.0012   7.7659  -0.6413   3.6668   7.6917  -0.7941  -3.8065
-#>   -4.8550   0.6845  -5.9768   1.8772  -7.2331   7.2820   2.7222  -3.4189
-#>   -1.8828  -5.6188   3.9142 -15.6999   1.0825   3.6080   0.5859   0.2142
-#>  -10.4254  -7.3322   7.6011  12.1679   2.1166  -1.3719  -0.8404  -5.9731
-#>    1.6563  -8.6208   0.9193  -1.9948   4.1875   9.4993  -1.0747  -8.5283
-#>    3.6731   1.8565  -0.7910   9.2543   4.0691   0.0453  -3.9086  -7.4447
-#>    1.2494   1.7534   0.7568  -3.5535   5.5141   0.9877   0.9665   3.8092
-#>   -7.3750   5.0936   5.8954  -5.7369  -3.3362  -4.8037  -4.0630  -7.6349
-#>  -15.0747   6.5869  10.2412  -8.1852  12.6544  -6.4481 -17.7733   9.8440
-#>    2.3890   5.0567   2.5480  11.3571   6.9356   1.6431  -4.5357  -2.4146
-#>   11.5287   3.0665 -12.9552  10.2215 -10.5193  -0.5883   4.4301  -2.9194
-#>   -4.5341   4.6156 -11.1645   5.2721   4.7755  -5.4269   8.8919  -4.1058
-#>   -2.5422  -4.8962  -4.2533  -3.4713   0.4350  -0.3875   5.2587   6.7581
-#>   -2.2825  -1.3043  -5.7796   0.6123  -1.6119  -9.6504   6.3327  -4.8924
-#>   11.4267   3.9958  -5.8304  -0.8209  -9.3814  -1.0441   7.5002 -10.5179
-#>   -7.2332  -2.1754  21.7108  -6.8779   0.6091   2.9913  -5.5717  -9.2769
-#>   -8.4591   6.1532  -3.9296   0.5712  -5.5937   2.2535 -12.5900  -2.0844
-#>    4.4383   5.7097  -7.7355  -5.1943   5.3091   4.7351  -3.1911  -7.3453
-#>    2.7565  17.1407   7.5476   2.4632  18.5448  -0.6328   1.3705  -5.2688
-#>   21.0880  -6.0673 -15.0749  -6.9460  -0.4218 -10.5325   7.6761   7.2181
-#>  -13.9597  -5.9834 -13.9455   3.4727  13.5404   2.1127   6.0688   3.8601
-#>   -9.5446  -6.9907 -11.8360  -0.7782  -3.4148   0.4085  -7.5634   9.1145
-#>   -7.9267   4.0797  13.3777   0.9393   7.7659   7.1630  -9.1600  -2.5893
-#>    6.0249  -5.8716  -5.5973   0.0046  -3.0231   1.1144  -5.0819  -5.3707
-#>   -4.0046 -13.7106  -4.8585   4.0724   6.5528   8.7553   3.9177   6.3200
-#>   -0.9138   6.5573   8.3198   2.7524   4.8368  -0.0342   4.3763   0.9721
-#>   -4.3229  19.7568  11.2584   5.4611  -1.0099  -5.6638  -2.6794  -7.9756
-#>    3.7537   1.4670  -7.9778   1.0656 -12.9156   2.0157   6.9521   0.2425
-#>   -5.8054  -7.1049  -2.3444  -1.4620  -7.0817   3.2761   2.1241   0.6728
-#>   -4.7399   8.6810  -0.3137  12.5848  -2.7645  -4.2000  -7.7583  -4.0197
-#>    6.5134  -6.5004  -1.0067   3.1483  -7.6219  -9.4831   2.1260  -2.9960
-#> 
-#> Columns 25 to 32   0.9164   6.6364  -2.1096   7.6167   5.2051  -3.5309   5.7607  -3.6761
-#>    5.7481  -6.2620   5.5865  -7.7751   6.8103   4.0659  -5.4968   3.3758
-#>    8.8301   0.1947   1.3354 -10.1364   2.6818  -6.0935  -2.1317   5.1515
-#>    3.2282  10.2814  -1.9183  -5.0089   0.9938   0.0229 -14.7824  11.1888
-#>   -1.7901   0.0106   0.7380   3.1113   0.0016   3.8805   8.3862   7.7820
-#>   -5.4735   9.7038   0.6526   1.3334   2.2150 -16.1218  -2.6737  12.9847
-#>    7.7840  -3.3068  -1.8455   2.6265   0.7157  -3.4875   8.9484   1.1720
-#>   -5.0259  12.4743  -4.9562   4.6295   0.6454 -10.6554  -1.6643  11.3980
-#>    5.6431  -4.1585  -3.5017  -4.5217  -6.4074   2.7111  -0.2705   2.2997
-#>   -4.3305  -5.4521  13.0141  -1.0215  -2.0584   3.4156   4.8674  12.2027
-#>   -2.6645  -3.5395 -15.7281   3.9482  -7.8991   0.9436  -1.4279  -2.3085
-#>  -21.3975  -6.5419  -0.5706  10.4837   9.4136 -14.3615 -14.6530   9.4419
-#>   10.6879  -8.3461  -4.0221  -4.7406  -8.1651   0.4021   6.4712  -5.6879
-#>    3.2240  -2.9612   0.0767  -8.3518  -6.7247   3.3232  -3.6010  -3.9666
-#>   10.8131   6.4351   4.1059  -3.3593  -8.6990  -0.5441   8.8988  -9.9437
-#>  -11.7232  -2.4128   4.6083  -4.7411 -12.0841   8.8367  -2.9589   0.9392
-#>    6.9387 -11.6099  13.2340   0.9739   8.6156 -11.7220   7.7174   1.2421
-#>   -3.5701  -0.7452  -2.5017   6.0676   1.6873  -1.5639  -3.9503   9.2895
-#>   -1.7005   5.3688  -5.0744 -16.4163 -10.0591 -11.3082  -6.0288  -1.3911
-#>    3.5844  -5.8183   4.4966  -0.8732  11.8576   4.8436   1.3823  -1.7042
-#>   -6.3087   5.5004 -10.4076  -2.8330  -7.5559   0.0676 -10.2145  -0.1479
-#>    4.1106  -1.4924  -9.4611   4.5932  -3.8251  16.5333   1.7181   0.8679
-#>   -6.7011   4.7048   7.9716  -4.6916   3.7326  -8.1865 -15.0386  -4.2498
-#>    2.4939  -2.1954   7.4570  -0.5020   4.1974   1.0839  11.5044  -6.8840
-#>   -6.2699 -12.1483   2.7677  -7.6698   3.2706  -6.9778   1.7840 -10.8523
-#>   -8.1987   9.3442  -1.6294   1.6090  -3.3236 -11.7136  -0.4056  11.0062
-#>   10.5299  -1.8471   0.9151  -0.6181   4.6984  -2.4208  -5.0834  -1.4955
-#>   -3.2630   2.9769   0.9128  -4.3088 -10.9615   1.7562  10.8109  -7.8176
-#>   -2.5736 -10.3437  11.3399 -11.4009  -1.4814   0.4352  -0.8255  -4.6240
-#>   -9.0465  11.5223   0.9622  -0.6233   3.3622  -9.5351   8.7453   3.0839
-#>    0.4572   3.4590   7.2394   1.7829   4.6249  -0.4822  -1.4148  10.4267
-#>    2.5418  -7.5339   9.3177  -9.9249  -0.6547  -6.0251   2.0060  -3.2288
-#>   -9.2620   1.0335  -8.6599  -0.5788  -5.1153   1.5086   2.8250  -4.3265
-#> 
-#> Columns 33 to 40 -11.8986   2.4147  -4.4610  -0.6107   3.3662  -8.5069  -2.2273  -9.2873
-#>    8.6256  -1.8284   9.2563  -3.4672  -0.0532   1.5168  -5.4890   3.4163
-#>    4.2299  -1.9203   1.9084  -2.1578 -10.2429   7.3712   0.1574  -2.3494
-#>   -1.5205  -7.9155   7.4539 -18.9782  19.1242  -7.3024  -4.1140   5.9851
-#>   11.3259  -2.9690  -0.6773   6.6274  -1.2912   3.8586   5.5946   4.4924
-#>   -1.0683   9.5736 -10.4562  27.6726 -11.6011 -16.5427  14.6381   5.0497
-#>    7.0892  -9.6719  -9.7624   1.9728 -13.5009   7.0491   4.0457  -5.3395
-#>   -7.4001   3.7149  -4.0817  -8.0094  -7.2760  -3.4291   3.6974  -1.5636
-#>   -2.8440  -3.4581  -5.7945 -11.4628   6.9835  -9.2022   0.4153   0.5739
-#>   12.5440  -7.6764   0.5098   1.7730   9.1909   8.1671   2.5764  -7.0154
-#>  -15.5586   7.9803   6.0739 -10.7840 -12.0563  12.1422   7.7948  -3.1358
-#>    5.9472  -2.5652  -0.9360   5.3704   1.3411   0.7240  11.4107 -11.6393
-#>    8.1663  -5.4020   8.3506 -17.4386  -0.8662   2.8202   6.6108  -3.2091
-#>    1.4430  -6.1873   0.6227  -7.8972  -9.4365   0.6525  -3.3555  -0.8793
-#>    6.1050 -16.9247   6.6229  -5.1295 -18.5341   2.2928  -2.2324  -5.7622
-#>    9.0676  -5.8671   5.1432  -8.3789   8.0141   8.3037   7.0876   6.2444
-#>    9.7063   1.2566 -10.7937  -0.6233  -1.8329  -8.1201   1.5637   1.3777
-#>    2.8862   2.2535  -0.1103   8.3647  -7.7593   2.0481   5.5800   3.2890
-#>   -5.4787  -6.0615 -15.2475  -3.8781  -7.9978   9.5445  -4.6079  -0.9424
-#>    8.5528  -0.5881   4.5892   2.4927  -3.2778   8.8345  -3.8547   1.1981
-#>    0.0472  -3.2210  11.5313 -10.6498  -2.6684  -0.0521  -8.7467   0.4957
-#>   -3.0700  -6.1665   6.6928 -15.0433   3.9053   1.0258  -1.8824   0.8121
-#>    1.9634   3.3713  -0.2318   5.9410  -6.4410  -0.9862 -14.5837  18.1802
-#>    0.7374  -9.0851  -4.4673  12.4889   7.7687   6.9073  -7.1165  -0.6474
-#>   -1.6195   1.2593 -19.5939  21.1321  -5.2771   5.0347  -4.9280   4.1432
-#>    3.3800   2.1993 -10.3884  -0.5506  -3.8106   6.3740   1.3545   2.2678
-#>  -12.3134  -3.8762  -1.9338  -3.3150  -4.5496 -18.2740  -7.0708   5.3476
-#>    6.4340  -3.6236  11.0587  11.1597 -18.1282  13.3269 -13.9402  -2.0978
-#>    1.6286  -1.0577  -4.4468   7.5563   0.7859  11.6447 -11.9411  -3.2172
-#>    1.3110   6.0144   1.8548   4.5404  -7.4083   5.3819   3.6763  -4.2610
-#>   -7.4247  -1.7670  -4.6530  -8.1285 -10.1503  -3.5548   0.2605  -7.1283
-#>    0.1491  -3.1582   8.1519   0.3861   5.1820   2.5664   3.2590  -3.3111
-#>   -0.4734   2.3086   2.7129   1.1263   9.1693   9.1878  10.2963   6.6774
-#> 
-#> Columns 41 to 48   1.2782   3.0907   1.9709   2.3417  -4.3050   7.1534  11.8273   0.9176
-#>    2.4463  10.8948   1.6810  -8.5839  -0.7853  -4.7783 -14.4342 -11.8971
-#>    1.7757   5.7692   5.3072  -4.5191  -3.9519   6.7591  -0.8277  -3.8367
-#>    2.7664  12.2129 -13.4740  -5.2795  16.2868  -3.6188   4.1092  -1.3353
-#>    9.7374  -6.3342 -17.3494  -0.5017  -9.1257   4.6217   3.1513   3.2992
-#>    4.9675   4.2960 -21.1272  20.9616  -4.4320   1.8313   6.9209   4.1419
-#>   -0.6570   6.6272  -0.8664  10.3004  -7.1146  10.9799  -2.9945  -2.8342
-#>   -6.3293   3.1892   2.5886   5.4950   3.4890  -8.0250  -2.8254  -7.9372
-#>   -0.5784   3.8178   4.4441  -3.1310   7.4993   3.6207  -0.3728   2.2499
-#>    8.4700  -1.6866  -3.0571 -16.1363   5.3445  -0.7077  -9.9704  -6.9214
-#>    2.5221 -11.8837   8.1810   1.1480  -4.3493   8.9619  -4.5526   1.7608
-#>  -12.1878  21.2674  -6.3019  -5.0895 -14.0484  -0.5744   2.9402  -5.4836
-#>   -7.0631   5.2959   7.0089  -3.0844   3.8373  -6.6631   2.1493  -0.9894
-#>    2.0537  -0.0306  -1.7520  -3.4060  11.9724   1.3590   4.3287  12.5580
-#>   12.4126  -4.2870  -4.4063  -1.9916   7.4882   3.6379  -0.4937  10.1923
-#>    1.7027  -6.4505  -2.7387 -16.2455  12.9007   0.3193  -9.1811   9.0046
-#>    2.1747   0.5451 -11.6460 -13.3928   0.0783  -4.0526   3.3838 -17.6555
-#>    4.6698   2.2650 -13.3213   6.2734 -15.1414   3.1142   4.6771  -1.2094
-#>   -2.7839   0.2344   8.5908   4.0064   3.6063   3.4178  -3.9916  -4.0202
-#>  -10.4147 -11.0731   1.7277  -4.9056   6.9644  -3.1199   2.0394  -2.1820
-#>   -5.5745  -2.2343   5.6448   6.1782   7.3012  -7.1318 -10.1754  -5.7040
-#>   -4.8389   4.2046   7.1973  -1.9157   8.2896  -0.3394 -12.3002  10.6009
-#>    4.0387  -2.4189  -3.1629  -2.7025   5.4084   1.8913   2.7038   9.0425
-#>   -8.2393  12.7998  14.6245   0.2574   3.2928   4.0283  -3.1100 -17.5313
-#>   -9.2878  -1.9203  -6.5422   7.5714   3.6611   7.3589  -3.7316  -9.5962
-#>   -1.1382  -0.0783 -10.7222  -6.2943   9.0036   2.3743  -2.7191  -0.3087
-#>    0.0976   1.9307  -4.1910   5.6042   0.3211   4.8762   9.9717  11.4200
-#>    5.8096  -6.6802  -3.5446  -9.9357   3.0509  -1.8566  -7.1458   5.7246
-#>   -1.5739  -1.6134   0.8694 -13.7485   8.9703  -8.6950  -7.3309  -1.9867
-#>   -1.3322  -1.9676  -8.1164 -11.3705  -4.6452  10.2379   7.2234  -2.2219
-#>    0.1392   5.5978  -9.3507  -3.7589  -5.3242   2.3767   8.0046   2.3059
-#>   -1.6345 -10.6635   2.6171  -2.6985   4.7007  -0.0966   3.1772   1.3217
-#>   -9.4622   6.9922  -7.3082   4.2840   0.9404   0.6788  -3.7414   0.1027
-#> 
-#> (7,.,.) = 
-#>  Columns 1 to 8  -8.3868 -11.6401   7.4697 -10.9227  -1.2344  -0.8900  -8.8770  -5.4839
-#>   -9.1322   4.3897   0.3318  -1.2337   5.0459   2.4164  -1.0493   0.3225
-#>    0.1665   5.2499  -0.4661  -2.1035  -2.2735   1.1229   1.1841   1.3380
-#>   -2.3096   2.9836  -0.8848 -14.8167   6.2941  -7.0506  -2.1243  12.7500
-#>   10.8477  -7.4534  -3.2469   1.2711  -1.6800   2.5467   1.2383   5.5295
-#>    5.1895  -2.6360  -9.6760   9.0771 -11.6362   3.2688  -2.5761  -4.0012
-#>   -6.1945  -9.9513   2.7478 -14.0589  -5.7439  -4.8767  -6.6987  16.0320
-#>   -1.7465   2.0612   5.0697  -4.8202  -0.9129  -1.0709   5.3457  -3.9324
-#>    3.5950  -0.1859  -6.9557  -6.4294   1.4565  -4.4959  14.1353   2.4524
-#>  -12.4705   0.6877  -3.4191   6.5834   5.5183  -1.0149   1.1827   5.6798
-#>   -8.8835   2.0619  -3.5445   8.5120  -7.9080   6.9911   8.6304  -8.9595
-#>   -0.2742  -3.0854  -1.6339   9.4392  -2.5067   4.5785   0.2035  -5.0767
-#>   -1.1434  15.9596  -7.8063  -9.7865  10.5698  -4.6604   8.0900  -5.7061
-#>    4.6076   3.9945 -11.6087  -5.4445  -6.3514  -2.9826  -1.0471   3.7196
-#>   12.9841   5.3269  -6.8384   0.2756  -5.1947  -6.7814   2.5640  -7.7269
-#>    2.2016  -1.5726  -2.6084  -0.5966   8.7939   6.1295  -0.2765   0.4567
-#>    1.7501   2.1831   2.6268   4.4627   9.1659  -1.7995  -2.6477   0.7981
-#>    0.1504  -7.4290  -3.9983  -5.3382  -1.8106   0.6926  -6.7663  12.2016
-#>   -8.6294  10.0757  -7.5659   6.8896  -6.0096   6.9586  -4.1332   3.6935
-#>   -1.4324   4.3200   6.0882   6.4074  -4.5371 -13.4335   3.0922   2.4905
-#>    6.0976  -1.1818  10.5174  -4.9773  -5.7739   6.8962   0.0486   3.7851
-#>   -4.0463  -1.4940   1.7253 -16.4487   2.5028  -6.1684   4.3875   7.6539
-#>    7.5972  -0.5269  -2.3114   6.6299  -8.5260  -3.5701 -16.3837   0.6222
-#>   -8.7690  -5.4894  -4.1165   5.5410   4.2613   7.8740 -11.9896  -5.6817
-#>   -2.9779  -3.2116 -11.2902   7.1702  -9.1442   5.8912  -4.4738   3.3884
-#>    6.4202   2.3732  -0.1645  -4.8198  -0.4243  -1.2116   0.7092  -4.4674
-#>    5.3454   6.0369   2.3749  -5.2803  -0.7246  -2.2842   6.0546  -0.1393
-#>    1.7873   1.5501   9.4551  11.0980  -7.7700  14.2810  -6.7579  -0.5943
-#>   -3.9084   9.7229  -3.9989   3.4151  -2.5499   3.7245  -2.2462   1.4619
-#>   -3.2414   0.1511  12.3477   9.7800  -3.8685   3.8956 -13.8782  -1.4243
-#>    5.0264   3.0037  -7.3815   9.3482  10.0306   2.9168   0.5762  -5.2767
-#>    3.6499  -5.1904  -9.1577   5.1506   5.4749  -2.8899  16.6732  -6.3274
-#>    3.2116  -2.8429   3.4621  -5.2504   5.2777   8.3226  -5.6161 -11.3883
-#> 
-#> Columns 9 to 16   0.1044   0.9257  -1.7411 -12.9353  -1.1161  -2.5187   6.6271  -4.6543
-#>    6.4146   6.4546  -0.6852  -0.9331   3.4367  10.3163   1.5969   1.0194
-#>    9.9608   6.6149   4.0068   1.5514   4.0437   0.5026  -0.8534   5.0826
-#>    6.3817  -2.3573   1.7044   0.2486   8.7588   7.6900  -7.8460  -1.7487
-#>    6.5979   5.1956  -0.4842   7.8987   2.7213   0.9117  -0.3715  -2.7825
-#>    9.8294  -3.1727   8.8739  -1.6314  -3.7998   0.8782 -10.0484   8.0967
-#>   -6.6380   6.3176 -10.2232   2.1496   8.0068   6.6707  -4.9896 -10.9107
-#>  -19.6346   4.5560   4.6483 -10.3726  -4.4241  -0.3028  -3.2161  10.4890
-#>   10.0427   9.7625   0.8205  10.1058  -0.2017   2.8767   2.2373   2.5954
-#>    6.4168   2.5372   4.0594   2.1162   8.3249  14.2594   1.9229   2.2237
-#>   -1.2217 -11.7178  -4.6981  -7.9874  -4.3094  12.9896  -2.1611  -1.9043
-#>   -2.7255 -11.3130 -13.3637  -3.7082  -2.8801   4.3433 -12.1371  13.4229
-#>   -4.5561   3.4566   4.1829  10.1662   5.0826  -6.1936  -6.7400  -4.1677
-#>    4.8390  -1.7285  -2.7652  11.2970   4.4895  -6.7624  -8.2958  -6.0062
-#>    4.2228   2.8737   1.6859  15.0594  -4.6857  -5.1865   7.1145 -14.9673
-#>    3.8024   2.3836  -2.7731  14.1042  -3.3697   2.4403  -8.3130   2.2377
-#>    2.1825   9.9036  -8.2547 -19.5848  16.7161   6.8862  11.2340  -0.6450
-#>    1.9039   1.6240  -5.4858  -6.3526  -6.1712   3.0947   0.7988  12.7938
-#>   -3.8852 -13.2789  -1.2641  -3.7055   4.4524   7.2319  -9.9409  -2.3498
-#>  -15.0849   5.1506  -0.8828  -9.4088   8.0829  -0.0959   3.6027 -13.6473
-#>   -1.5873  12.1405  -3.7498  -0.2601   1.4899   6.2359  -9.1811  -1.0767
-#>   -5.0776  -0.6590  -1.7319   9.0051   4.7089   2.1270  -2.0193  -4.1000
-#>   -8.3452   5.3808  -5.5685  -2.0289  -0.4346  -1.4210  -2.9761   0.2022
-#>    6.6507  -1.0421  -5.2787   0.5100   9.9340  -4.4988   7.4089  -7.7939
-#>   11.7048 -10.1678  -6.2539  -5.6852   1.4961   0.2818   9.4267  -2.1771
-#>  -13.4982  10.8678   3.8737  -5.1592   6.8410   0.9065  -1.2240  -8.9981
-#>  -13.6598   8.9172   0.6429   0.0794  -3.5587  -6.8124  -9.3900  -2.4623
-#>   12.8951  -6.3400  -1.7720   6.4668   7.2949  -1.0787  -0.7375 -11.4979
-#>   -4.4058   3.9306   2.9124   0.6470  10.7702  -6.2025   3.5430  -2.4585
-#>   14.9334  -7.0435   9.1913  -5.1234   6.4600   6.1955   4.8577  -0.3465
-#>   -4.0835  -2.4654   6.2225  -0.5559  -9.6441 -11.2900   4.6662  17.0918
-#>   -4.6378   1.4535   4.4265  13.6624   9.1190  -7.6378  -0.3699  -2.8796
-#>    0.9298  -4.5409   0.4691   5.7132   2.0187  -0.3962  -6.2617  -4.4942
-#> 
-#> Columns 17 to 24  -7.5082   0.1230  -2.7170  -0.1290   1.2378   3.9310  -6.8861  -2.4757
-#>   -0.0840  -4.9533   6.8870  -7.8175  -6.2897  -1.5212  12.2053   1.7018
-#>   -6.1950  -5.3380   8.6784  -1.1482 -13.9410 -14.6168   7.9137   3.0913
-#>    2.9134  -8.0645   1.5240  -5.6552  -3.8008   4.0944  -0.5454  -8.5642
-#>   -2.7359  -4.8647  -4.7398   3.6420   4.1403  -0.2886  -4.6023  -1.1916
-#>   -4.4233   1.7021  -9.9913  12.8842   7.4480  -4.9905   0.0126   1.7723
-#>    0.5143  -6.9647   8.9175   2.7165  -0.2401   2.0856   1.3035  -3.8266
-#>   -1.7071  -3.9459   4.0166   1.7316   2.3844  -5.6508   0.4939   6.4315
-#>   -1.7874   2.9283  -1.7397  -0.0795   3.3628  -3.7478  -3.3556   2.6575
-#>    3.8874  -5.4597   1.9906  -2.7408  -3.4235   0.8202  16.5486   1.5903
-#>  -13.6912   7.5320   3.8591  -2.7932   2.2483  -5.1478   9.2281  11.0863
-#>   14.2475  11.0989  -1.1390  -4.1648 -10.9931  -2.8091  14.1621   8.3148
-#>    4.0171 -24.1954   7.6173   6.6532  -1.9288  -2.6925   2.4648   8.5419
-#>   -1.1056  -3.7104  -8.6558   7.8332   3.5572  -4.8852   5.6026  -7.8859
-#>  -17.2301 -14.5154  -2.0305   0.4276  -9.1197  -0.3856  -9.3708   3.1188
-#>    0.1942 -10.6688   1.5796  11.6256  -2.3149  -0.3170  -1.3611  -3.2544
-#>    8.5874   3.6442   1.8841  -5.7845   3.7858   3.3507   9.5682   5.7652
-#>   11.5020   7.4198   3.1556   7.7805   4.3205  -6.7041   3.4949  -0.9564
-#>  -20.2656  -5.5543   8.3369   6.5278  -4.8215  -6.9655   8.4572  -8.7750
-#>   -2.4090  -6.5655   0.6052  -7.9955  -2.2705   1.3170   5.0470  -6.7168
-#>    2.3647   9.7501   0.1719   1.5766   3.1177   1.5264   5.3542  -6.0455
-#>    5.8564  -6.1934  -3.3479  -0.8891  -4.3840  13.8145  -6.9400  -5.6010
-#>  -12.5772  -2.0369  -4.0395  -4.4490   6.7978   5.7791  -2.5945  -7.7926
-#>   -2.7811  12.2068   8.0313   8.7803  -3.9315   2.2293  16.3355  -9.4390
-#>   -4.9157  21.3081  -4.7641  -0.2722   0.8232  -7.7434   7.1377 -10.3087
-#>  -16.9968 -18.4087   6.2145   7.1472   1.6623   4.9139  -7.8575   2.1788
-#>   -4.1799 -10.8815  -1.5725 -13.0412   4.1585   2.2265 -13.8721   0.6302
-#>    9.3972   5.2940   2.4569   5.7679  -4.9076  12.6545   9.4319  -5.0808
-#>    1.1354   0.0392  -5.1914  -9.7269   0.4172  -2.1484  14.0104 -10.9378
-#>   -8.9589   5.7229  -7.1192   5.9937   2.2431  11.6666   5.2665   2.3037
-#>   -9.6188  -2.3606   8.3819  -5.6156  -7.0418  -2.6749   2.5972   0.8289
-#>   -0.9066 -14.1249   0.7172  -3.3330  -2.2977   3.1394  -2.7918   8.9389
-#>   -7.4333  -3.1721  -8.1599   4.1023  -1.1054  -1.7108  -0.0041   4.4734
-#> 
-#> Columns 25 to 32   6.2032   6.7606  -4.1842   4.8856   4.8326  -4.3465  12.9020  -1.9198
-#>   -4.9915   7.2119 -13.3811  12.4029   4.2098  -8.6987  -7.5963 -10.2318
-#>    3.2876   0.8499 -10.0451  10.1758   9.1603   4.5728   2.2774  -5.3308
-#>    6.9995  -2.8632 -17.0167   4.8331  13.0833  10.4276  -6.8767   9.1409
-#>  -12.4603  -7.5971   0.9850   3.2682  -1.8272  -9.1431  11.7883 -14.3354
-#>   -5.0676   0.1846  -2.6840   0.9372 -13.7782   6.9465 -10.2780  -4.0720
-#>   -2.2802  -7.5383   3.5250   2.1399  11.5853  -7.8419  -4.0121  -3.3568
-#>    0.4598  -6.3054  -5.5410  13.9453  -2.0206  -7.8521  -1.9166   4.1174
-#>   -4.2545  -2.3393   7.3020  12.9533   1.3055   3.4879   6.2869  -5.5537
-#>  -12.6416  -3.1648 -10.2565  -0.2521  -1.3447 -10.9668  -0.7903 -17.2056
-#>   -3.9141  -9.5369   3.0149  21.3584  -2.6780   1.7901   5.7324 -17.4045
-#>   10.1366   4.0803  -5.2090  22.8942  -9.7008  -9.1265  -3.6588  -1.8557
-#>   -4.3599 -13.6794   5.6573 -10.4187  -1.9024   0.6204   1.9504  -1.9618
-#>    0.9198  -9.5261   5.4805  -1.3540   2.4746   9.3641   7.0959  -0.9933
-#>   -8.8033 -11.0557  13.0802  -6.0954  12.3576   8.5847  16.7252   1.8883
-#>   -5.7972  -4.5999  -3.2640 -12.8288   3.2058   4.2547  -2.3899  -2.1646
-#>    7.3529  -7.6689   1.1825 -13.8196   8.5992  -0.3931  -5.4558   3.0841
-#>    3.8626   0.6250 -15.1615   5.8399  -9.6220   0.8828   1.3470  -3.3132
-#>   -8.2905  -6.0210  -6.0656  17.1575   8.8731   1.6172   1.9336  -2.0237
-#>    4.8286   1.5069  11.3829  -1.7471   5.2813   4.3474  -6.8825  -5.2249
-#>    8.4692  11.5821  -8.9529  11.3588  -1.6938  -5.6908  -3.0364  -2.1104
-#>    6.3942   4.8667  15.7654  -2.2110  -7.7429  18.1713  -8.2298   4.1817
-#>    5.3835  -2.9582  -5.8010  -4.3772   0.1724  -4.1213  14.8204  -4.8059
-#>    9.6845   0.8019 -10.6839 -13.3050 -10.2778  -5.5199   0.4439 -10.2108
-#>   -7.2615   6.5667   9.5359  18.8324  -0.5216   8.7538  -9.2066  -5.9030
-#>   -0.3073 -15.2456   2.1572  -3.2268  -5.6128  -1.0913  -2.9713  -4.1415
-#>    5.9096  -4.1621   1.9846   9.2107  14.1094   3.7912   8.5795  -0.3664
-#>    3.1452  -3.1311  11.1002  -6.0726  -3.6456   6.7602  -9.3967   9.3666
-#>  -10.4010   2.7401   8.1540  12.5843   3.1052 -10.2762  -4.5395   6.4634
-#>   -1.8610  -6.9201 -18.4132 -11.7961  -3.1464  -0.3100   4.7452   1.2053
-#>    0.6977 -13.1071 -13.5327  13.9274   8.3351   3.5292   7.3812   5.6499
-#>  -13.9419  -2.8361   9.5517 -16.0587   8.9735 -15.3313   1.8182   3.1891
-#>   -8.8529   4.9793   5.3233   2.8270 -13.7933   5.3210  -6.3498  -4.2234
-#> 
-#> Columns 33 to 40  -4.6260   8.8620   3.4329  -8.1481  -8.1702  -0.7852   2.0633  -2.5541
-#>    9.5929   2.5161  -5.7370   7.1015   2.3320  -6.9793  -8.9219   5.1713
-#>    6.1647   1.8937   4.1104  19.2877   1.8289 -11.5295  -3.7615   7.3228
-#>    2.2258  -5.6179   0.3055   9.3003 -11.9471  -4.2971  -6.7805  14.6903
-#>   -6.3973  -2.0035  -2.7854   0.8171   7.2534  -9.8019   3.8554  -7.0032
-#>    1.0929  -7.6663 -11.2568   4.7428  -4.0117  12.4513   5.8052   5.8733
-#>  -17.8153  -1.4327   5.2808   1.4281   0.3969 -17.7094   3.8365 -10.8317
-#>   -7.7704   4.2667  -4.6862   6.3226   0.9491  -1.5931  -0.6221   7.8181
-#>   -3.7231   5.6704   4.0881  -1.1873  -4.5026  -0.5250   2.7140   0.5003
-#>   12.6789   3.0519  -6.3219  -0.5452   1.5616  -9.3017 -15.5234   1.3124
-#>    5.1097   6.3707  -4.6902  -3.8234  -8.2346   6.2555   7.4859 -11.0259
-#>   10.0714   7.5715  -2.5473  13.3906  -2.7765  -7.9680   2.9459   6.5335
-#>   -1.5787 -12.5808  -0.2462   9.8006   0.4333   0.8598  -2.3843  -9.3571
-#>   -6.2396  -2.2068 -10.0107   7.9180  -2.8846  -2.9688   1.4483   2.2000
-#>   -2.5841   0.5950  -0.6526  -0.3271  -2.4743  -6.3197   9.3940  -4.1160
-#>    2.2653   3.4443  -8.9261  -0.0572   5.1695  -6.9099  -6.3007  -0.9485
-#>    2.9831   2.9335   1.6797 -10.6045   4.4246  -4.5346 -11.1331  -8.0911
-#>   -2.4504   7.1525   4.0060   9.7077   1.3683  -5.5369  -0.4538  -3.3585
-#>   -0.3564  -4.4546   4.0470  -3.3142  -4.0203 -10.6082  -5.8961  10.2849
-#>    0.5000  -3.4236  -2.3786  -2.6622   8.2442  -9.2194  -2.3547   1.3446
-#>    2.0190   0.3457  -8.0455  11.9630   5.4565  -7.7393   3.8689   6.4768
-#>    1.7497  -0.7774  -1.2803   3.1482  -8.3563   2.5110   0.5142  -6.7214
-#>   -7.3846  -3.3771  -8.7545  -6.7862   8.2003  -4.0938  15.2718   3.2306
-#>    8.8394  -4.8474  -1.2207  -3.2168   7.5263  -7.6474  -2.2813  -6.1993
-#>    4.1752   8.8815   5.9743  -9.0465  -5.3511  -6.1333  -7.7893   7.6450
-#>   -9.8636  -2.4300  -3.4376  -5.1739   8.7395  -9.5285  -2.1348  -0.2848
-#>   -6.7656   0.5918  -0.9239 -10.9065  -5.3489   3.6691  15.7931  -3.7872
-#>    5.8799 -10.8813  -7.1825   1.8036  10.5524  -7.3634  12.1371   2.8828
-#>    5.1392   5.2809  -7.0235  -8.9886   1.4891  -8.4972  -8.1288  17.9264
-#>    1.7187 -11.7695  -8.5988  -4.9949  -1.4106   0.4465  -3.4039  -0.1012
-#>    1.4442  -4.1300  -1.3585  -4.7286 -16.1419  -2.8185   0.3761   9.5014
-#>    5.2136  -3.7293   4.7115  -7.1455  -1.6220  -1.0989  -2.3889   5.3699
-#>    3.4604  -1.1786 -13.8256   4.2584   1.8335   6.6330  -3.3588   1.7650
-#> 
-#> Columns 41 to 48 -16.4893   5.8363   5.3839   6.3958  -1.4048   4.1891   5.3464   2.6790
-#>   -2.8933   2.2947   7.0668   1.9631   0.7939   2.5534   3.7270   0.8783
-#>   -3.1012   4.6196   1.3724  -5.0885   2.5820   3.7266   3.9187  -7.3649
-#>   -3.5867   4.6952   7.6381   4.0358  -9.3693  -0.2167  -3.8898   2.1728
-#>    3.5989   5.3581   0.7363  -6.6457  -6.0644  -3.5028  -3.2641   4.5286
-#>  -14.7376   2.8292   6.0339  -4.5955   0.7311  14.3921   0.4553  -9.5141
-#>    7.2329  11.5357  -6.6458 -10.2309  12.7271  -7.7142  -5.5970   2.3869
-#>   -9.5121  -4.3462   0.2946   5.5319  -0.1472   7.0475   4.4480   2.4542
-#>    1.9695  -4.8489  -0.9918   1.9768  -8.7223   0.4888   0.2278  -7.7381
-#>    7.7019 -10.5364   3.4470   1.3466  -1.5905 -14.6580   7.5356  -2.0517
-#>    5.6417  -0.9466  -9.8949   1.3275   3.2663  -1.9140  -2.8904   5.5347
-#>    3.7832  -2.9117  -5.5314   0.2180  14.6717   4.5803   9.6055  13.1461
-#>    7.8353 -18.9302   3.6607   1.4060   3.8057  -9.1981   0.9597   1.2560
-#>   -2.9988  -7.1067  -6.3133   7.0839   2.7961  -6.5958   9.6824  -2.1668
-#>   12.1370  -7.3829  -1.9034  -2.0261   7.5014  -1.3085   0.9310   4.8365
-#>   15.2301  -2.5347   1.4293   4.3834 -11.1265  -9.4680   8.2599   0.5567
-#>   -6.9354  -0.8273  12.9513 -16.3247   8.7733 -10.5149  -8.3016  -3.5463
-#>  -14.8789   6.2359   0.9929   5.7673  -0.0762   0.3267  -2.3356  -5.3080
-#>    4.9092   5.9725  10.4473  -7.8813  -2.5593  11.1572   8.9717  -0.6405
-#>    2.2686  -2.9832  -0.0737  -2.9151   3.4551   8.8344   8.4443   1.2575
-#>   -5.6279  -1.6709   0.0603   4.5958  -2.0800   9.7000   7.3730   1.3542
-#>    3.8505   2.0401  -9.5092  18.2622  -3.4618  -5.5624  -1.9886  -0.1771
-#>   -1.8987   5.4359   3.5942  -6.1446   0.4830   9.7549  10.8543   2.9409
-#>   -3.1502  11.0579  -4.4697 -10.3236  -4.4525  -3.1020 -20.5302  -8.3858
-#>    2.7279  21.5577  -0.4661   5.0657   2.4091  18.8487   3.7039   4.6378
-#>   -5.8267   4.8931  -3.0858   2.2141 -10.2913  12.2682   2.7239   7.4378
-#>    1.9541  -1.8613   0.1603  -3.9407  -2.8795   6.0980   3.9917  -1.0544
-#>    3.9225   4.6235 -17.5093  -8.6676   0.0359  -8.2765  -9.6504  -2.3546
-#>   -3.0591  -0.6578  -2.2810  14.9146   1.5137   4.6708  18.2630  -1.8310
-#>   -2.0140  -2.5775  -2.4864  -8.7478  -2.2091  -5.5142 -10.5906  -5.6048
-#>   -5.8611  -1.2948   3.3547   0.3666 -10.2013  -2.2231   5.5278  -1.7056
-#>    8.8016 -10.1636   6.4712  -8.4551  13.4827  -1.8375   6.0630   9.2747
-#>    1.3997   1.9604  -6.4808   8.4185  -0.9404   6.2133   0.5617  11.8576
-#> 
-#> (8,.,.) = 
-#>  Columns 1 to 8   9.7505  21.5211  -0.7218   3.5807   9.2035  -5.7990  -5.3427  -6.5380
-#>   -9.3461  -4.3623  -9.2135   0.1155 -17.1370  11.1844  -8.7337  -8.3448
-#>    4.5790  -3.9913  -6.5917   6.4134 -14.5408  -5.1644   1.6960  -7.1082
-#>   -0.1047  -7.5503  -1.7047   1.2399  -2.9983   4.1608  -5.1501   2.1459
-#>  -12.8589  -4.4785  -3.2548   0.9505   5.6102   1.1982  10.0071   6.0460
-#>  -11.4783   5.1726  -4.2252   4.2747   1.2827 -10.9742   0.3886   2.7719
-#>    4.9565  -1.0287  -9.1887  -7.2761  13.3099 -11.3184  -5.4476 -12.9089
-#>    0.8144   4.3780  -7.6663  -5.0387   8.2941  -7.1678  -3.1985  -3.6510
-#>    2.9263  -6.0486   2.0268   2.1189  -5.3773   0.8080  -6.3592   8.9286
-#>   -8.7668   2.5708  -1.3839   7.6679  -9.7390  10.7981  10.0152   3.3428
-#>    1.9171  -0.7832  -3.3217   9.5469   0.1392 -13.4776   6.0295  -2.9709
-#>    0.3317   2.2112  -6.3795  -2.9447   2.0840 -22.5732  -2.1504   6.8618
-#>   -6.5304  -0.3599  -4.6970   1.2913   2.6933   7.7431   1.0711   7.2043
-#>   -0.4512  -4.5796   7.8038   6.7893  -3.4706   0.2633  -1.9958  -5.2133
-#>    5.8139  -2.7947   0.3844   9.0265   6.0386   0.1709  -2.3162  -5.0494
-#>   -9.7443  -8.1242  10.9562  -1.5658  -3.0498  11.7880   6.9681   6.9782
-#>    2.8668   5.6868   7.3995   0.1941  -8.7889   8.4973  -0.6561   0.4459
-#>    4.1562   5.1469   0.3420   3.6547   5.1419  -9.6600   3.7330   3.8443
-#>   -2.3604   1.4771  -4.2629   0.4718   3.2103  -9.8736  -0.7177 -12.7919
-#>   12.6961   3.1390  -5.6916   0.6333  -0.3446   1.4370  -2.3489 -14.2977
-#>    0.3057  -4.6486  -2.6362  -7.3607  -1.4229  -4.7620  -1.7483  -6.2187
-#>    6.4337  -9.8663  -2.6658  -0.2098  -3.9033   6.9333 -10.1242  -6.0069
-#>  -14.3827  -8.0791   2.1936  -2.1635   7.4595  -9.9999  -4.5255 -15.1139
-#>   -6.5837   0.5889   9.7503   1.1785   2.1524  11.7576   8.8130  -2.7063
-#>   12.3878  -0.1523   1.3679   2.5950 -13.6549  -3.5532  -3.3741  -9.7953
-#>   -0.7170  -6.3100  -8.0999   9.8903  14.4547  -6.2736   9.8533  -5.8607
-#>    1.1360   1.0335  -0.0059   3.1830  -3.2109  -7.0617  -5.7204  -3.7372
-#>    5.6506  -5.1117  11.7300  -3.5025  -7.5426   7.8535   7.1664  -9.1174
-#>    5.7702  -0.2175   1.7750   6.0243 -21.9186   8.0276   9.3998  -4.4018
-#>  -10.6650  12.4759   5.2718  -1.5027  -2.9467   7.5737  15.0786   2.4842
-#>    6.5021   8.8091  -0.6611   4.7909   5.5022  -8.2502   3.1660   7.5191
-#>   -8.0627  -1.6115   0.2394   6.6535   2.8433  13.6172   2.0723  19.1200
-#>   -6.4064  -6.9318   2.5021   0.6963  -5.9183   5.2332   4.6153  12.9524
-#> 
-#> Columns 9 to 16  -7.4489  -0.8042   2.7854   8.4652  -6.6809  15.0909  19.8745   7.1479
-#>   -9.3199   9.4770  -3.4972   7.5095  -0.6291  -4.8227   2.6740  -6.6423
-#>   -2.7731   0.6078  -0.3694  -3.5848   4.8989   4.5288  -0.2155   2.2294
-#>    0.0430   7.7739   0.5964   5.9654  -5.0387  -8.3233   6.6414  -0.9961
-#>    4.0313  -1.3787   6.4081  -6.2994   2.1910  -5.4143  -3.6866  -1.5317
-#>   -0.4060   3.4386  -5.3304  -1.7402  -5.4494   8.4392  -2.5724  -2.6365
-#>    2.4761  -6.6946  -2.1128   2.0522   7.0601   7.0882  -7.5598  16.4945
-#>    1.7845  -5.1161  -4.2109   0.0422   2.5480   2.2563 -13.5602   6.9806
-#>  -10.9724   2.8955  -0.9919  -4.6993   3.9657   5.9168   2.8627  -3.5043
-#>   -2.7526   3.3810   0.4070   4.7812   2.5217 -14.3968  -4.0289   3.0694
-#>   11.0097  -4.5242  -2.8502  -7.9287  15.9853 -11.4852   8.3789 -10.0767
-#>    1.5480  -3.0409  -0.6347   7.7452   6.0101   2.0608  -2.8751   7.2602
-#>    0.0410  -5.5461   7.5252 -11.0345  12.0098 -14.8030  -7.9794   0.9225
-#>    4.2580 -11.4530   3.3565  -3.9571   5.1758  -4.5969   5.6352   9.2885
-#>   12.7585  -1.5618  -3.5641 -13.9890   5.7753  -1.2041  -3.7336   6.4951
-#>    1.9021   3.1302  -0.2939   3.6689  -5.5221  -9.6258  -4.1210   5.6672
-#>   -0.9260  -2.8063 -10.6438   5.8411   4.1513  -2.4985 -12.3546   3.7584
-#>   -2.3836   2.6476  -0.4543   4.2394   5.1216   6.6579   1.9311  -3.5211
-#>   -2.4128   7.1206  -3.8751 -13.5594   4.8275  -8.7876  -0.1497  -2.2992
-#>    0.6666  -1.5999  -1.9034  -4.0682  11.3346  -6.7277   5.9325  15.2344
-#>   -6.1739  -7.7141   1.4850   1.0927   4.3074  -3.4131  11.6982   5.0749
-#>   -2.6437   2.8348  -0.0386  16.8818  -1.2101  -2.0663   8.1696   7.1242
-#>    6.6301  -1.2115  -2.6788  -3.4775  -9.8312  -4.7657   6.6134  -4.9456
-#>   -3.8740  10.2621  12.2994  -6.8315   2.0823 -15.4459  13.4714 -16.5075
-#>   -6.1147   7.5286  -2.9784  -3.6033   4.8837  12.5250   1.7987  -0.8655
-#>    9.8514  -3.1028  -6.3037   1.3988  -3.0110   1.9040 -11.5565   9.3783
-#>    5.2401  -1.0755  -3.6053  -5.0613 -11.9937   5.4612   9.3271  -4.1031
-#>   13.2205  -8.3787  -7.4333   1.0424   3.7193  -8.2087   1.5639   6.6348
-#>    5.1053  -8.5397  -4.6884   5.3031  -1.3494   4.1826   0.5436  14.7424
-#>   -0.3656  -4.0147  -4.7661  -7.8632  -4.5603   1.3236  -5.4402  -3.0846
-#>    4.5304   0.9833  -2.5083  -9.7851 -14.0094  -0.4676   3.3590  -8.4788
-#>   10.5364   4.5215  -1.5188   5.0515  -7.1089  -6.8000  -7.6796   2.9325
-#>   -0.4371  -3.9301   3.3137   3.9294  -1.5344  -0.7612  -7.9348   8.6682
-#> 
-#> Columns 17 to 24  -0.1389   2.7833  -7.6417   1.0127   4.3739  15.8968  13.7240  -2.0858
-#>    1.2018  -1.3251  11.9279   4.4186  -5.6396  -6.8106   1.4250  -3.8938
-#>    7.1025   7.5194  15.7088  10.0171 -14.3076 -12.7381   3.2395   0.8369
-#>   -4.8720  -8.5710  12.2525   3.2695  -5.5304  -5.0498  15.3786  -3.1256
-#>    5.6980   6.7997  -3.1759   0.1066   2.2419   3.0356  -4.8155  -1.3508
-#>  -15.3042  -7.0760   4.9612 -14.5196   6.9446  11.3609   1.7151  -9.4168
-#>    2.4581  11.1489  -2.4932 -12.1783   8.0708  -0.7791   8.1304  -3.2390
-#>  -11.4518   5.1643   1.6748  -0.9467  -7.1317  -7.2802   3.5551  -1.4782
-#>    8.5132   6.0701   2.0084   2.9306  -7.6543   4.9825   2.7986  -1.4610
-#>    7.6029  -2.1964  -4.7003   4.6741  -2.4956   4.5839  -3.3663  -0.7649
-#>   16.4216   1.3188  -0.2868  -7.9091  -4.4317  11.5826  -4.2421   3.3804
-#>   -0.3521   1.5658  -3.3197  -8.8169  -8.6169  -7.3379   0.2026  -4.2922
-#>    4.6503   0.3193   4.8151   1.6662  -5.3378  -8.9674  -5.4164  10.2136
-#>    0.3513  -0.3407   2.1979  -3.5283  -2.5753   8.7386   1.5805   5.4534
-#>   10.4485  13.4950   1.1411  -0.7244  -8.2095  -4.7985  -5.4840  17.3438
-#>    5.4441  -7.1625   3.4126   6.5226  -3.5294  -6.1442  -2.3265   1.3708
-#>   -2.3677   4.9284 -11.7406   5.4634  -2.0317   7.2705   9.2657   1.4044
-#>  -11.1751   5.8955  -3.9999  -6.3525   7.5790   1.7701   2.9943  -8.7027
-#>    5.8696   2.9949  17.6991  -1.4768 -12.4445  -6.9235  -3.2380  -6.8725
-#>   12.3087   3.4907  -5.1019   8.4455   7.1078  -1.3708  -3.1946  10.9005
-#>   -7.0519  -0.9545   2.6750   5.3558   0.6328 -18.4314  -3.3503  -8.6130
-#>    8.1444   1.3283  -1.1925  -3.6192   8.7250   0.5334   0.0338  12.2328
-#>  -13.1485  -2.2758  -0.7310  -1.1975  -0.2313  -1.8796   8.2425  -5.9023
-#>   -0.4631 -21.9035  -2.7409   1.5576   2.6490   8.8296 -12.3949  -3.2970
-#>   16.1738  -3.6051   5.2913  -6.8463  -0.1715   3.8577  -4.4052  -7.1396
-#>    8.6622  -2.5606   4.2791   0.3756  -2.5184  -2.4997   1.7716   2.0096
-#>   -0.6146   6.1604   0.3380  -3.5578   5.9450  -6.0484   8.9132   2.9027
-#>   11.9040 -15.3711   4.8235  -6.3088  -3.0654   5.7914 -16.9265  10.7671
-#>    9.6258   0.8721   2.1063  -1.4997  -7.8480   0.8220  -1.0926  -5.7129
-#>   -0.8224  -4.4742  -5.6826   3.2623  -3.7203  13.1767   5.8564   0.3523
-#>   -8.5367   4.8810   4.6889  -6.7867 -10.1217  -1.6946  14.1697  10.3410
-#>   -3.1274  -7.0905 -12.2435   2.7620 -10.2159  -6.9943  -0.7448  -2.0076
-#>    6.4558  -4.3491   1.4251  -2.4677   2.4405  -4.2120   0.5065  -1.7806
-#> 
-#> Columns 25 to 32  17.1026   2.5812  -7.0203   9.4465   6.9103  -0.1044  -2.8706   1.0071
-#>   -0.1017  -4.5751  -2.9160 -13.9657  -7.6557  -3.4798  -5.2746   0.7955
-#>    2.6565  -3.7401   3.4362   1.4084  -8.9534   3.8798   0.9005  -5.1114
-#>  -13.6695   5.2867  12.6991   4.7499  -5.6753  -3.7951  -0.4789   4.1006
-#>   -3.4392  -0.5906  -1.6030   4.2723  -7.6491   0.5247  -3.4139  -9.0802
-#>   -8.7874  12.8729  -2.4180  -4.9332  -6.5361   0.6753  -5.9675   1.7622
-#>   -0.2850  -1.0839   6.2215   9.3789  -1.1318  -3.4875  -1.5527  -1.2908
-#>    3.6416  -1.7346   6.5360  -5.6006   3.7726   3.3410   6.3362  13.5934
-#>   -0.0879  -0.4969   1.7138  -9.7765  -9.0325   1.5765  -4.2677  -6.9798
-#>    0.4966 -10.6000  -5.1651  -9.7292  -4.8165   6.4431  -7.8703 -12.7564
-#>  -12.0424  -2.4013   8.9028  -3.3145   7.1346   3.8412   1.4704  -9.6413
-#>   -3.1423  -5.1933  -0.0150   1.9071  -0.2029  -0.1701   0.1602   1.7525
-#>   -1.8765   1.9230   1.4065   0.4553  -0.2087   8.1157   8.0254  -4.5533
-#>   -4.3565  10.7568   7.1821   9.0265   2.6125   0.1118  -1.6544  -0.7527
-#>   -0.7168  -2.0606  -8.3614  11.2642   3.2440   1.1178   4.9414  -2.3209
-#>   -4.8396  -6.3713   5.5836   5.3154  10.2439   3.4682  -1.7568  -1.7856
-#>    1.6700  -9.3760 -19.3531  -6.9326  -2.3024  -1.7312   2.0125   2.4404
-#>    1.9675  -0.2319  -0.9638  -6.1850   0.7932   3.1152   1.3602  -7.4149
-#>    3.1073  -2.1754  13.7664   0.9557  12.6638   4.6034   1.6815  -3.3305
-#>    0.4340  -7.9141   5.9485   2.6441   6.5036 -10.2815  -8.1310  -2.6895
-#>   -5.8532   1.4265   1.8267  -6.1927   3.4157  -7.9354   2.2704  10.0210
-#>    1.6743  -1.2345   0.3427   1.3384   1.5342  -8.3515  -8.5344   5.3275
-#>   -2.2453  -5.9218   2.3372   2.9031   9.8127 -13.8977   2.0153   9.5521
-#>   12.5302  -2.4257  -7.4401  -5.6927   5.5491   1.6091  -0.7882  -6.3543
-#>    5.6356  -1.7172   3.7151  -2.9179   3.5514   4.6399  -0.7576 -12.3475
-#>   -3.5529  -8.4877  11.6536   3.9503   0.1079  -2.8133   9.7080   0.9127
-#>    7.0334   5.4122   2.6707   0.3616   4.5236  -6.2855   5.1213  14.4611
-#>  -11.7232 -12.7253   4.7953   3.4767   5.8241  -8.2993   0.8080 -11.5194
-#>    1.3343  -6.4987   7.4096  -5.8047   7.1858   3.4141  -5.2520  -4.4160
-#>   -0.1721 -14.0198  -8.1386   7.9760  -0.4833   1.1973  -5.2975  -2.7722
-#>   10.6461   1.8665  -7.1903   5.6824   6.1101   2.6576   6.4654   1.1838
-#>   -6.6105   5.0036   2.2007  10.3305  -6.5373   0.0834  -0.2109   2.9936
-#>   -8.1512  -1.1948   3.9096  11.5425  -2.3290   4.5866   1.5263  -2.7111
-#> 
-#> Columns 33 to 40 -13.6757   3.2050  -1.7102   3.0423   3.4864  -6.0581  -7.0705   2.3436
-#>    2.8154   6.7909  11.3422  10.5838  -1.1585   0.3751  -6.7020   3.0085
-#>   -2.3170   5.7280   8.5315   5.9173  -2.9746  -7.2888   9.4413   7.2076
-#>   -5.7367   7.5299  -1.4259  -1.6888 -14.4085   4.8950   5.3013   8.0799
-#>    0.9883   0.7480  -2.2983  -8.1308  -4.4850  -8.3801  -8.7969   7.5343
-#>   -0.8695  -1.1404  -3.4372  -2.3562  -0.8389   6.6296  -5.0157   3.7476
-#>   -0.6605   1.1048  -7.0557  -4.1054   4.1530  -0.1704   9.4408  -5.8617
-#>   -0.5488   4.6426  -2.6103   6.9008   6.1783   6.9762   2.5179 -14.8938
-#>   -4.0633   8.2448  -3.4058  -0.3540  -8.4536  -6.8800   1.3375  -0.1555
-#>    1.3970  -0.2304   0.0156  -5.2144   3.5280  -1.1255 -12.1279  -0.2955
-#>   -2.0486 -10.7888   4.8500   2.6335   2.3910   5.6371  -9.2602  -6.2757
-#>   -2.1683  11.4737  14.6329  13.6383   3.6652  -0.8922   1.0977 -11.2232
-#>   -6.7400   3.4379  -0.9003   4.8029 -10.6919   0.7908   6.7403 -16.2157
-#>   -4.8868  -2.2131   0.3401   0.4005  -6.0264  -4.2342  10.5046  -2.7749
-#>   12.2537  -3.7335  -1.7123  -8.3265 -19.5502  -2.4142   1.6112   1.2166
-#>   -6.8644  -1.0912  -2.1300  -4.5600  -3.7915  -4.2678   6.2871  -1.8575
-#>   -1.6467  -3.3780  -2.6142  -6.3843   8.7063  -4.1567  14.1473  -0.9055
-#>   -4.3967  -1.3374   5.1231   2.8412  -0.3606   1.1262  -8.2442   9.5898
-#>   -1.0255   1.8714   5.5460  -3.0920   2.2492  -1.4507   3.2481  -4.1082
-#>   15.2278  -1.4644  -1.6724   7.7990   5.4575  -9.2883   1.3260  -0.2767
-#>    2.4734   1.0298   3.4018  19.7773   6.4603   0.6927  -2.3733  10.8856
-#>    7.5390   5.9991  -1.6665  -1.1839 -15.8801  -4.4179   0.7634  -3.5811
-#>    7.5150  -7.2280   4.7583  -7.6827   7.1725  -8.0871   6.3701   6.3065
-#>   -4.4429 -10.0434 -13.7058  -4.5672   2.3839  -7.5390   7.9516   3.9969
-#>    8.9826   8.9293   7.4789   6.6279  10.4749   2.4352  -2.6923   0.4486
-#>    0.9305  -0.8680  -7.8173  -8.4552  -0.5972  -2.2555   3.4722  -5.0586
-#>    0.5122  -4.1389  -4.9615   0.4793   3.6580  -3.4449   4.5336   3.1636
-#>    2.0379 -19.8241   2.8024   2.6805  10.5936   2.1072   1.4258 -11.4136
-#>    4.7889   6.2284  10.5768   4.5156   9.1442  -8.2460  -6.0661  -2.1584
-#>  -15.9749  -3.6706   0.0012  -7.2619  -0.1787  -9.4787  11.7275   4.4441
-#>   -3.1463   4.3559  -0.9214  -5.9368  -7.9699   3.1736  -0.3443  -1.0745
-#>    2.4498   1.2488   6.3804  -3.6066  -0.9166  -0.3392  -0.6077  -6.2136
-#>  -11.3278   5.6230  -1.1469   5.0627  -6.2094  -1.0932  -3.9888  -2.4802
-#> 
-#> Columns 41 to 48   7.0135   6.3294  -8.8584 -14.5510  10.0676  -0.6149  -4.1354   4.4232
-#>  -10.7895  -2.7615   2.1639  20.1588   2.0257   0.0967  -6.4809   2.2671
-#>   -4.5688  -2.0050   4.8668   6.7397 -11.2983   5.5228  -5.6060 -14.5368
-#>   -7.8524  -5.3228  12.4785  -5.4817   0.3444  -3.9921  -2.4454  -5.6591
-#>    2.3190  15.2425  -2.7544   8.7666 -13.2045  -6.8948  -1.2019  -8.1382
-#>    7.4866  11.4021  -1.9954   1.7636   0.1764   4.6639   5.1616  11.7051
-#>    5.7654   4.7622   5.9318   5.4125  -3.8339   5.5030   1.5205  -8.6833
-#>   -3.0645  -1.9771   6.8651  -6.9690   1.3674   6.4900   2.4330   0.6932
-#>    4.0131  14.1373   0.8940   3.5991  -1.6315   1.8919  -5.1338  -4.8808
-#>    0.9488  10.4619  -3.4536   5.8847  -6.6616   3.8535   3.3347  -8.1186
-#>   -1.3349  -1.6230  -8.1500   4.8047  16.3066   0.6186  -6.0828  -4.2537
-#>  -17.0976  -8.4809  10.3442  -6.5025  -4.5308   6.4224  -4.3393  -4.1076
-#>    1.9242   0.5845  19.1147   0.7265  -2.8166   2.5570  -3.9082  -3.4749
-#>    3.3098   7.0949   7.2359  12.9875  -7.0863  -0.1894   5.2748 -13.0611
-#>   12.1284  16.8619   8.8710   5.6396 -11.2881  -9.3478   1.6231  -0.1281
-#>    4.4235   0.4590 -10.8071   9.9598 -10.6359  -3.5016   4.7264   0.5766
-#>   -1.2458  -2.6800  -3.8881  -0.7222  -1.0022   1.7662   3.4330   0.8911
-#>    0.1136   5.5672   0.2567  -0.0802  -3.1427   5.4188   4.5744  -6.9384
-#>   -2.1668   1.9430  18.8833   9.1453   9.9037  11.1195   0.9507  -8.9267
-#>    1.1640 -10.1598   3.5957  -4.8968  -5.7853   0.4013  -3.8952  -8.5320
-#>  -14.0711 -19.9621   0.9685  11.0321  -2.2575   3.2575  10.8891  -0.8897
-#>    0.3868   7.2838   3.2818  -0.9068  -1.4344  -2.2705 -17.2004   6.3904
-#>   -1.4807   6.5627  12.2064  17.1711   0.4854   8.3901  14.3149   2.6802
-#>   -6.3829 -12.7508  -9.0151   0.1801   2.1498   3.4304   5.9517  10.1215
-#>   -1.7794   0.6457  -5.0495   2.3017  -2.0233  -6.5212   4.6659   5.2673
-#>    4.9225  10.4069   4.0690   2.3700   1.0188  -5.1772   4.6426   8.9672
-#>    3.3292   1.8687  -3.6283  -8.1369   5.5408  -5.3452 -12.8441  -0.9602
-#>    3.5477 -10.2707 -13.2258  10.0899  -0.9471   0.1878   5.2863  -0.4901
-#>    3.1693   2.1068  -3.6108   3.8335  -0.3474  -9.2534   2.9291  -8.8937
-#>   -1.5092   0.1917   3.0646   0.2466   6.9229  11.0598   3.4176   4.7661
-#>   -1.5282   1.3181   2.3549 -15.1336  -7.1468  -5.1242  -1.5188   4.6585
-#>   -1.7846   4.1737  -5.0727  -4.0856  -6.7048  -6.8882   0.4729   6.3599
-#>   -1.1021  -4.5408  -5.4687   5.1239  -2.5769  -4.7390  -4.1566   8.3170
-#> 
-#> (9,.,.) = 
-#>  Columns 1 to 6 -5.9772e-01 -9.8983e+00  1.0404e+01 -6.1356e+00  3.5001e+00 -8.9590e+00
-#>  -6.7052e+00 -2.4473e+00 -5.9757e+00 -6.9124e+00 -7.5695e+00  1.5501e+01
-#>  -5.1638e+00 -3.0836e+00 -8.2642e+00 -1.0065e+01 -2.8839e-01  1.8010e+01
-#>  -3.9167e-01 -4.5961e+00  2.2247e+00 -1.1997e+01  1.2477e+01 -5.6814e+00
-#>   6.0788e+00  1.2863e+01  3.9910e+00  2.3731e+00  7.4136e-01  4.5087e+00
-#>  -3.1761e+00  1.3578e+00 -4.6626e+00  2.6179e+00 -3.8575e+00  9.9712e+00
-#>  -1.1396e+01  3.3664e+00  1.0453e+00  6.6824e+00 -3.1272e+00  7.3237e+00
-#>   3.7370e-01 -8.0273e+00  6.5533e+00 -1.0999e+01 -9.4513e+00 -8.1806e-01
-#>  -1.0576e+01 -1.6506e+00  1.9676e+00 -8.0581e+00  7.0890e+00 -4.9045e+00
-#>  -4.7729e+00  1.8349e+00 -1.5519e+00  8.6583e+00 -7.6534e+00  9.1328e+00
-#>   8.7335e+00  1.4546e+01 -5.9599e+00 -5.1928e+00  2.0621e+00  9.8500e+00
-#>   1.0081e+01 -1.6942e+00 -6.2237e+00 -5.0624e+00  7.8544e+00  1.6970e+01
-#>   1.7356e+00 -9.4270e+00 -5.2761e+00 -6.2843e+00  2.3019e+00  1.8504e+00
-#>  -5.1167e+00  1.2433e+00 -1.0857e+01 -5.2793e+00  1.4291e+01 -7.3475e+00
-#>   1.5092e+00  5.9874e+00 -1.4614e+01 -1.3576e+01 -3.3374e+00  1.4458e+00
-#>  -5.6186e+00  3.3894e+00  2.7115e+00  8.4038e+00  1.0308e+01 -7.5663e+00
-#>  -2.6028e+00 -3.0251e+00 -1.3113e+01  8.9866e+00  2.3850e+00 -2.6730e+00
-#>  -8.7767e+00  1.3823e+01  4.7101e+00  6.0249e-02  1.1898e+00  8.5789e+00
-#>  -6.3649e+00 -7.8938e-01 -1.2153e+01 -5.6260e+00 -6.6550e+00  1.4229e+01
-#>   1.1580e+01  8.7932e+00 -2.5971e+00 -2.3929e+00 -6.2260e+00  1.0944e+00
-#>  -4.3348e-01 -4.3489e+00 -5.5791e+00 -1.3491e+01  8.0694e+00  1.1862e+00
-#>   1.6337e+00 -8.1348e+00 -3.0525e+00  4.3178e+00  1.4512e+01 -7.3191e+00
-#>  -4.4990e+00  1.0172e+00 -2.2629e+01  4.2001e+00 -7.2138e+00  1.2539e-01
-#>  -1.9345e+00 -2.1191e+00 -4.6567e+00  7.5494e+00  1.1774e+01 -6.5196e+00
-#>  -7.8636e+00  1.3880e+01  1.7210e+00 -3.0380e+00  3.4229e+00 -4.0000e-01
-#>   6.5259e+00 -4.9772e+00  1.7033e-01 -1.4080e+01 -6.3298e+00  3.6542e+00
-#>   6.0277e-01  1.8627e+00 -5.3594e+00 -4.0737e+00 -5.3710e+00 -3.8900e+00
-#>   6.8195e+00  1.4984e+01  5.6257e+00  1.4304e+01  7.8092e+00 -5.7403e-01
-#>  -4.1558e+00 -5.3392e-01  3.6684e+00 -9.8107e+00 -2.4841e+00  1.7410e+00
-#>   2.3968e+00 -1.2837e+01  7.0652e+00  1.3442e+01  5.5320e+00 -5.8472e+00
-#>   1.0226e+00 -3.8321e+00  7.9918e-01  1.0291e+00  6.0297e+00 -4.5766e+00
-#>   1.9976e+01 -9.2697e+00 -1.4449e+01  4.4617e+00 -1.1223e+01  4.3968e+00
-#>   4.3422e+00 -8.1300e+00  2.7784e+00 -8.8056e+00  9.0905e+00 -3.6160e+00
-#> 
-#> Columns 7 to 12 -7.9886e+00 -4.1789e-01  3.4889e+00 -6.5670e+00  1.7025e+00 -6.1554e+00
-#>   1.2431e+00 -3.3220e+00 -8.6634e+00  3.4699e+00  9.3183e+00  1.4846e+01
-#>   1.4251e+01 -3.7282e-01 -6.0053e+00 -1.1380e+00  3.1382e+00  7.6234e+00
-#>   2.7875e+00 -1.0526e+01  1.5817e+01  5.0829e+00 -1.1793e+01  3.4515e+00
-#>   3.1888e-01  4.2712e+00  5.5459e+00  1.3774e+00  4.5625e+00 -2.3585e+00
-#>  -3.6468e+00  9.7521e+00 -3.9350e+00 -2.4761e+00  2.9528e+00  1.9369e+00
-#>   8.7279e+00 -3.0858e+00 -4.8988e+00  1.0578e+01 -2.2710e+00 -8.5113e-01
-#>   6.3638e+00 -3.3849e+00 -1.4784e+01 -5.1161e+00 -2.4338e-01  5.7786e+00
-#>   1.1709e+01  7.0229e+00  1.0275e+01  2.0638e+00 -8.0311e-01  2.2046e+00
-#>  -1.9236e+00 -5.5674e+00  3.4492e+00  4.4238e+00  1.4859e+01  9.4732e-01
-#>   7.5186e+00 -7.2396e+00 -1.1684e+01 -8.1177e+00  1.1721e+01  9.8822e+00
-#>  -1.7116e+01 -2.7047e+01 -1.5828e+01  1.8199e+00  3.7760e+00  1.2151e+01
-#>   1.5202e+01 -1.6115e+00  4.3183e+00 -2.2170e-01 -1.3697e+00  6.1547e+00
-#>   1.0838e+01 -2.1337e+00  1.5393e+01 -3.0158e+00 -2.5116e+00 -1.3567e+00
-#>   9.8020e+00  3.9353e+00  8.0539e+00  1.0331e+00  8.9065e+00  4.4400e+00
-#>   1.1014e+01 -5.1721e-01  1.0056e+01 -1.8658e+00 -1.0587e-01 -1.0652e+00
-#>  -9.9327e+00 -7.8304e+00  6.8668e+00  5.4561e+00  1.7317e+01 -8.1515e+00
-#>  -3.6940e-02 -2.0545e-01 -4.2434e+00  2.7756e+00 -1.0222e+00  3.4477e+00
-#>   7.5637e+00  5.2881e+00  5.0657e+00  8.2493e-01  2.7979e+00  4.6146e+00
-#>   6.9088e+00 -1.3395e+01 -1.2399e+01  3.8654e+00  1.9648e+00  3.5629e+00
-#>   6.7725e+00 -7.7367e+00  4.9227e-01  8.8902e+00 -9.8244e+00  1.1019e+01
-#>  -5.6635e+00 -1.9914e+00  7.1919e+00  9.3059e+00 -3.8622e+00 -3.8176e-01
-#>  -9.7490e+00  1.1049e+01  1.6366e+01 -2.3207e+00  6.3493e+00 -1.8590e+00
-#>  -9.0104e+00 -1.3411e+01  1.2738e+01 -5.6306e+00 -3.5250e+00 -8.7249e+00
-#>   4.4496e-03 -2.9238e+00 -6.2142e+00 -1.1927e+00 -1.8179e+00  5.8272e-01
-#>   8.8736e+00  3.5496e+00  1.7175e+00  9.9175e+00  4.7218e+00  4.1414e+00
-#>  -6.9808e+00  5.4581e+00  1.1534e+00 -1.2803e+01 -9.3482e+00 -5.8145e-01
-#>   2.9285e+00 -5.5349e+00 -3.5046e+00  7.2675e+00  5.2552e+00 -3.8660e+00
-#>  -1.4056e+00 -7.3416e+00 -1.8322e+00  6.9944e+00  7.7574e+00  8.1254e+00
-#>  -1.3827e+01  1.4488e+01  1.4742e+01  9.4897e+00  1.2354e+01 -9.0142e+00
-#>  -1.9852e+01 -3.4075e+00 -1.7569e+00 -9.4174e+00 -3.2770e+00 -6.5168e-01
-#>  -1.2156e+01 -7.0697e-01 -7.5206e+00  1.0838e+00  9.1425e+00 -4.8583e+00
-#>   8.3659e-01  4.9569e-01 -8.0905e+00  3.2208e+00  2.9091e-01  1.9936e+00
-#> 
-#> Columns 13 to 18  1.4268e+00 -6.4643e-01 -1.7622e+01 -9.2782e-01 -6.9855e+00 -5.9646e+00
-#>   5.6406e+00  6.4896e+00  3.9732e+00  1.1957e+01  5.9071e+00 -3.4283e+00
-#>   1.4863e+00 -3.2103e+00  6.5280e+00  3.0830e+00  7.4875e+00  3.9739e+00
-#>  -1.3436e+01  1.1493e+01 -5.9182e+00  2.1436e+01 -3.7133e+00 -4.1458e-01
-#>   7.0292e+00  4.3810e+00  1.8445e+01  9.7100e-01 -1.0219e+00  9.3279e+00
-#>   1.1690e+01  9.6098e-01 -6.6718e-01 -8.3299e+00  1.1058e+01 -4.3912e+00
-#>  -8.2923e-01 -3.1380e+00  5.5529e-01 -4.2088e+00 -5.2010e-01  4.0494e+00
-#>  -4.9005e+00  9.1976e-01 -6.6567e+00  4.6711e+00  1.0360e-01  2.0231e+00
-#>   1.0765e+00  6.4593e+00  3.4552e+00 -9.2336e+00  4.4749e+00 -3.8992e+00
-#>   2.2649e+00 -1.8091e-01  1.3098e+01  2.2921e+00  6.5336e+00  6.9644e+00
-#>  -5.2512e+00 -9.9104e+00  6.5997e+00  2.8382e+00 -5.9939e+00  5.7500e+00
-#>  -7.6590e+00 -4.8979e+00  4.1078e+00 -9.0289e+00  2.2878e+00  6.8816e+00
-#>  -1.0736e+01  6.7032e-01  6.6919e+00  6.4399e+00 -2.3449e+00  9.0975e+00
-#>  -9.6398e+00 -2.0480e+00  4.3620e+00 -2.9848e+00 -2.7863e+00  3.0141e-02
-#>   7.9273e+00  4.9398e+00  4.2805e+00  1.4641e+01 -5.9008e+00 -8.4110e+00
-#>  -9.7235e+00  4.5998e+00  1.4957e-01  1.4007e+01  1.1557e+01  3.9815e+00
-#>   1.1669e+01 -1.4081e+01  7.0342e+00 -3.7801e+00 -3.2983e+00  4.9881e+00
-#>  -4.2155e-01 -8.0756e+00 -1.6138e+01 -3.7417e+00 -5.7215e+00 -6.8203e+00
-#>  -6.5037e+00  5.9370e+00  8.5020e+00  1.4392e+01  1.2422e+01  6.3810e+00
-#>   3.2395e+00  9.6114e+00  4.6214e+00 -2.9670e+00  6.5430e+00  8.2656e+00
-#>  -3.6188e+00  1.2400e+01 -1.9658e+01  1.3046e+00 -2.5884e-01 -4.3679e+00
-#>  -8.8569e+00  5.8866e+00 -1.1162e+01 -1.3357e+00 -2.4089e-02 -7.8688e+00
-#>   8.4758e+00 -4.6751e-01  6.0961e+00  3.6116e+00 -9.4077e+00  1.3468e+01
-#>  -5.6394e+00 -2.3949e+00 -1.3362e+01 -1.1423e+01  5.0500e+00 -6.3947e+00
-#>   5.6710e+00  9.3222e+00 -4.5345e+00 -1.2849e+01  1.2083e+01 -8.1289e+00
-#>  -1.7648e+00  1.4789e+01  3.0152e+00  1.3705e+01 -1.6920e+00  9.6765e+00
-#>   2.4847e+00  6.5077e+00  5.2258e+00  4.6694e+00 -1.9813e+00  4.2211e+00
-#>  -4.8027e+00 -8.0172e+00 -6.2700e+00  1.1320e+00 -9.2289e+00 -8.3512e+00
-#>  -2.1190e+00  1.2911e+01  1.1840e+01  6.4975e+00  4.7221e+00 -1.3022e+00
-#>  -2.8746e+00 -8.6969e+00  9.1544e+00  1.7319e+00  6.2875e+00 -3.9191e-02
-#>  -7.6621e+00 -4.1807e+00  9.5632e+00  1.3037e+01 -7.3126e-01  3.5519e+00
-#>   1.0035e+01 -9.1915e+00  2.2972e+01  8.9583e-02 -5.2422e+00  9.7728e+00
-#>  -4.1256e+00  9.7167e+00  1.2161e+01  5.9556e+00  9.9613e+00  6.0163e+00
-#> 
-#> Columns 19 to 24 -1.4788e+00  6.5384e-01  3.4991e+00 -5.0203e+00  1.1833e+01  6.5428e+00
-#>   1.0071e+01  2.6512e+00 -1.8944e+00 -8.1566e+00  3.5798e+00 -1.0335e+01
-#>   3.6254e+00 -6.5613e+00  3.1483e+00  6.8949e+00 -7.4044e+00 -1.5177e+01
-#>   9.3763e+00  8.3278e-01  2.8163e+00 -1.2261e+01  2.4854e+00 -6.6130e+00
-#>  -6.1210e-01 -5.5153e+00 -9.4884e+00 -8.2171e+00 -3.7525e+00 -5.8954e+00
-#>   8.1481e+00  5.1251e+00 -2.1194e+01  9.2614e+00  3.1435e+00 -6.1527e+00
-#>  -2.6929e+00  4.8587e+00  2.8694e+00  4.9911e-04 -1.0671e+01  1.7866e+01
-#>   9.1479e+00  2.6889e+00 -4.1593e+00  5.9204e+00  2.1084e+00  4.5870e+00
-#>  -6.2372e+00 -8.9761e+00 -4.5223e-01 -1.2422e+01  8.3073e+00 -1.0398e+01
-#>  -3.1561e+00 -7.1218e+00 -6.7626e+00 -7.2100e+00 -1.9478e+00 -1.4644e+01
-#>   1.2066e+01 -2.1823e+00  5.2041e-01  4.1326e+00 -2.5256e+00  9.1427e+00
-#>   5.0821e+00  5.6678e+00  3.3381e+00  1.1488e+01 -7.2279e+00  6.3522e+00
-#>  -7.3032e-01 -2.6469e+00  3.1833e+00  8.2736e+00 -5.3655e+00 -6.5288e+00
-#>   4.6056e+00 -2.7989e+00 -1.0808e+00  4.8777e+00  7.8457e+00 -8.4777e+00
-#>  -4.3655e+00 -8.9310e+00 -1.7927e+01 -5.9276e+00 -5.2484e+00 -6.5302e+00
-#>   7.2124e+00 -3.5134e+00  5.8129e+00 -8.7390e+00  1.4915e+01 -8.4247e+00
-#>  -2.9340e+00  1.6258e+00 -4.3462e-01 -7.5343e+00  3.9852e+00  4.6746e+00
-#>   6.7741e+00  1.7857e+00 -4.5787e+00  1.1741e+01  5.7908e+00 -5.6829e+00
-#>   1.2928e+01  5.4310e-02  3.4659e+00  1.9901e+01  2.8366e+00  2.5718e+00
-#>  -4.6991e+00 -3.8227e+00  3.3682e+00 -3.3209e+00 -1.0747e+01  9.3639e+00
-#>   4.2007e+00 -7.2732e+00  2.2640e+00  1.5743e+00  6.7326e+00  7.5268e+00
-#>  -4.4983e+00 -4.3638e+00  1.5736e+01 -1.3776e+01  4.9824e+00  3.0897e+00
-#>   8.9306e+00  3.4701e+00 -2.1963e+00  1.0783e+00  2.4437e+00  1.6603e+01
-#>  -1.1078e+01 -1.6464e+00  1.2810e+01 -6.5072e+00  2.1953e+00 -1.2031e+01
-#>   1.7704e+00  2.0420e+00 -2.6357e+00 -3.8983e+00  1.6743e+01 -1.0175e-01
-#>   5.4945e+00 -3.4841e+00 -5.5210e+00 -6.9099e+00  1.0233e+01  1.2634e+01
-#>  -5.5126e+00  4.0176e+00 -1.0951e+00 -1.7160e+01 -8.5082e+00  1.4385e+01
-#>   2.0308e+00 -8.7291e+00  5.6261e+00 -3.4511e+00 -9.2142e+00 -3.6996e+00
-#>  -5.1241e+00 -9.3991e+00 -7.1547e+00 -3.7527e+00  5.2667e+00 -7.4117e+00
-#>   2.3922e+00 -2.5907e+00  3.1423e+00  5.9142e+00 -2.4298e+00 -5.1382e+00
-#>   8.3085e+00  4.0955e+00 -1.1548e+01  2.3639e+00 -7.7381e+00 -8.7379e+00
-#>  -8.2212e+00 -5.8799e+00 -1.5753e+01  8.4185e-01 -1.7796e+01  2.3051e+00
-#>   4.0001e+00 -4.4698e+00  1.2708e+00 -1.2979e+00  1.1418e+01 -7.5451e+00
-#> 
-#> Columns 25 to 30  7.9131e+00  2.7154e+00  1.4872e+01 -3.7386e+00  1.0172e+01  5.7043e+00
-#>   1.2539e+01 -5.7977e+00  1.4523e+00 -9.3483e+00 -3.8128e+00  8.8916e+00
-#>   5.1914e+00  7.2358e+00 -1.7755e+00 -4.7946e+00 -7.9701e+00 -4.5566e+00
-#>   5.7365e+00 -4.7697e+00  3.2019e+00 -1.5064e+01 -3.9985e+00  9.4707e-01
-#>   1.3723e+00  6.2656e+00 -6.1616e+00  7.3707e+00  7.5894e+00 -2.7101e+00
-#>   3.7877e+00 -1.2307e+00 -1.3621e+00 -4.5134e+00 -1.5887e-01  5.9096e+00
-#>  -2.1808e+00  9.1400e+00 -1.8969e+01  1.0006e+01  4.8628e+00 -4.7325e+00
-#>   2.6560e+00 -7.3123e+00 -1.2357e+00 -7.4553e+00  1.0254e+01 -8.7145e+00
-#>   2.8662e+00  3.2610e+00 -6.6772e+00  7.8181e-01 -1.5148e+00 -2.0216e+00
-#>   4.2846e-01 -2.5294e+00  3.9553e+00 -7.1783e+00  4.7985e+00  1.1505e+01
-#>   9.3605e+00 -7.1987e+00 -8.8640e+00  4.7875e+00 -2.3975e+00 -7.0165e-01
-#>  -1.1350e+01  3.1453e+00 -1.0100e+00  1.1050e+00 -8.0596e+00 -5.7571e+00
-#>  -9.8132e+00  1.3579e+01 -2.7146e+00  9.5315e+00 -1.0039e+01  6.6940e+00
-#>   6.9763e+00  6.9958e+00  8.9224e-01  5.1560e+00  4.1708e+00 -9.1118e+00
-#>   8.2931e+00  2.2381e+00 -8.5325e-01  9.0331e+00  3.3555e+00 -4.0119e-01
-#>  -3.5723e+00  4.3296e-01 -9.3968e-01 -9.5574e+00  1.9474e+00 -2.2715e+00
-#>  -4.5663e+00  1.2040e+00 -7.5696e+00  6.6384e+00  4.2499e+00  8.5393e+00
-#>   5.1188e+00  5.2276e+00 -4.3429e-01 -7.5187e+00  4.4111e+00 -6.0097e+00
-#>  -3.9696e+00 -5.2015e+00 -1.2500e+01 -2.1139e+00 -1.9498e+00  3.8756e+00
-#>   1.5824e+01  1.9293e+00 -8.2049e+00  2.0788e+01  6.3813e+00 -5.7230e+00
-#>   2.1565e+00 -5.5108e+00 -1.5508e+00  1.4097e+00  2.1417e+00 -1.3263e+01
-#>   8.3929e+00 -6.8624e+00  6.7333e+00  2.3216e-01  6.8439e-02 -9.4227e+00
-#>  -2.0929e+00  2.5265e-01 -7.1508e+00  4.1465e+00  3.3456e+00 -7.6238e+00
-#>  -8.5652e+00 -7.6295e+00  3.7898e+00  7.8058e+00 -1.4628e+01  9.4347e+00
-#>   1.1126e+01 -2.1903e-01 -3.5481e+00 -1.7516e+00 -9.3764e+00  5.1781e+00
-#>   4.5579e-01 -9.3047e+00  1.5870e+00  2.4300e+00  1.7651e+01 -4.7332e+00
-#>  -7.6143e+00  1.1278e+01 -7.4274e+00  1.3487e+01 -6.3099e-01 -2.0147e-01
-#>  -1.1968e+00  8.2476e-02  5.4258e+00  1.4257e+00  9.6109e+00 -3.0527e+00
-#>   5.3096e+00  8.0046e-02  7.3033e+00 -9.9037e+00  1.3863e+01  1.9194e-02
-#>  -1.4189e+00  6.8598e+00  5.9213e+00 -2.9910e+00  4.5579e+00  8.9131e+00
-#>   3.3798e-02  8.6234e+00 -3.4579e+00 -6.6073e+00  2.8995e+00  8.0238e+00
-#>  -1.2174e+00  1.7211e-01 -7.2720e-02  5.1705e+00 -7.9263e+00  6.3274e+00
-#>  -1.0282e+01  5.6483e-01  5.9698e+00 -1.0672e+00 -4.2042e+00 -1.7796e-02
-#> 
-#> Columns 31 to 36  1.1844e+00 -7.2629e-01 -1.8941e+00 -7.6083e+00 -4.2147e+00  2.5961e+00
-#>  -4.1939e+00 -2.9817e+00 -4.4593e+00 -5.4455e+00 -5.9977e+00  1.0099e+01
-#>  -4.0233e+00  6.7645e+00  2.6826e+00 -5.2496e+00  3.4417e+00  2.0870e-01
-#>   4.8918e+00  1.9567e+00 -1.6991e+01  1.1052e+01 -2.3925e+00  1.4149e+01
-#>   3.3314e+00 -6.8083e+00  1.0376e+01 -3.6049e+00  9.1836e+00 -1.4545e+00
-#>   1.6061e-01 -1.3304e+01  9.7826e+00  8.3859e+00  8.1357e+00  9.6278e+00
-#>   1.5257e+00  2.2147e+00  8.2917e+00 -5.8503e+00  8.1073e+00 -6.5636e-02
-#>   4.5712e+00  1.0947e+00  2.4104e+00 -1.5979e+00 -1.6107e+01  1.4548e+00
-#>  -1.9434e-01 -2.3166e+00 -2.6052e+00  9.9696e+00  4.3325e+00  1.0414e+00
-#>  -1.3384e+00 -2.7895e+00  6.8319e-01  2.3454e+00 -1.4055e+00  2.0878e-01
-#>   5.9838e+00  1.0921e+00  1.9156e+00 -4.6902e+00  7.7425e+00 -5.7468e+00
-#>  -7.3102e+00  5.0646e+00 -3.5681e+00 -9.5191e+00 -6.9702e-01 -2.1993e+00
-#>   4.7431e-01  4.3941e+00  6.1900e+00  5.5946e-01  2.3139e+00  1.3793e+00
-#>   9.5830e+00 -2.6180e+00  2.3090e+00  1.0488e+01  3.5078e+00  7.5632e+00
-#>   2.1537e+00  7.1923e+00  7.6483e+00 -3.7928e+00  6.7952e+00 -3.0645e+00
-#>   5.4097e+00  2.5142e+00  1.7182e-01  6.4000e+00 -4.7737e-01 -1.1004e+00
-#>  -4.6148e+00  4.4991e-01 -6.9146e+00 -6.9988e+00 -3.4297e+00 -1.2070e+00
-#>   1.4174e+00 -4.9231e+00  6.5810e+00 -1.2322e+00 -3.7342e+00 -1.9426e+00
-#>  -2.0832e+00  1.0677e+00  3.1704e+00 -4.3507e+00  1.0039e+01 -2.5501e-02
-#>  -3.6884e+00  4.0975e+00  3.6172e+00 -4.0332e+00 -3.9415e+00  4.6806e+00
-#>  -6.6195e-01  1.4217e+00  3.0961e+00  2.9925e+00 -7.7697e+00  6.8150e+00
-#>   1.5677e+00  8.5009e-01 -1.0536e+01  5.7060e+00 -4.8438e+00  8.2975e+00
-#>   1.4551e+00 -4.0907e+00  3.4321e+00 -6.6505e+00  1.5629e+01  1.1222e+01
-#>  -4.7277e+00 -3.8640e+00  5.8621e+00 -3.9335e+00  1.0030e+01 -2.7991e+00
-#>  -5.8399e+00 -2.7366e+00  2.3711e-01  3.0909e+00  3.6587e+00 -8.1478e+00
-#>   1.2677e+01 -1.5665e-01 -1.6311e+00 -4.9779e+00  3.6424e+00  3.0476e+00
-#>   2.8489e-01 -4.8924e-02  7.1833e+00 -2.2264e+00  4.6149e+00  6.1171e+00
-#>  -3.6819e+00  5.9067e+00  3.2096e+00  1.0361e+00  7.6217e+00 -3.1803e+00
-#>   7.7033e+00  3.5124e-02  1.5344e-01  6.7063e+00 -1.2995e+01 -2.4705e+00
-#>  -7.7243e-01  5.8335e+00 -9.0640e-01  7.5684e-01  1.5670e+01 -1.5860e+00
-#>   2.1432e+00  6.0483e+00  4.0227e-01 -7.1577e+00 -2.8218e+00 -5.0067e+00
-#>   2.5484e-01  1.0097e+01  2.4836e+00 -1.1225e+00  5.1515e+00 -2.9331e+00
-#>   2.1140e+00  1.2718e+00 -6.7375e+00  3.4060e+00  1.5925e+00  6.5458e+00
-#> 
-#> Columns 37 to 42 -9.0413e+00  4.2641e+00  1.5076e+01 -2.2102e+00  5.7407e-03 -2.3147e+00
-#>  -2.5366e+00  9.2487e+00  5.6971e+00 -7.5793e-01  3.1941e+00  6.2136e+00
-#>  -1.0855e-01  5.7570e+00 -2.9134e+00  3.9281e+00  7.0567e+00  3.6867e+00
-#>  -5.7815e+00  6.1053e+00  2.5409e+00 -4.6347e+00  4.2671e+00  1.3293e+01
-#>   8.7166e+00 -1.9208e+00 -2.0736e+00  1.6139e+00  2.7751e+00  4.2373e+00
-#>  -7.0585e+00  8.1723e+00 -9.2192e+00 -5.7954e+00  1.1677e+01  1.4162e-02
-#>   9.3435e+00  1.7496e+00 -6.9971e+00  1.2072e+01  4.4855e-02  1.0169e+01
-#>  -5.4095e+00 -5.3696e+00 -1.0430e+01  5.7073e-01  1.6181e+00  3.0055e+00
-#>   3.1037e+00  2.6920e+00  1.0490e+01  9.2666e+00  2.1840e+00  5.4630e+00
-#>   4.2201e+00 -1.5488e+00  9.5045e+00  6.7580e+00  2.3942e+00  5.4924e+00
-#>   1.5608e+01 -1.2334e+01 -2.4745e+00  8.0635e+00  4.5486e+00  3.4762e+00
-#>  -5.2987e+00  3.9534e+00 -4.9090e+00 -5.6588e+00 -1.2606e+01 -2.5031e+00
-#>  -9.3156e-01  6.0539e-01 -2.3783e+00  2.9253e+00  8.3484e+00  9.2735e-01
-#>   7.2469e+00 -6.1220e+00 -1.6000e+00  5.0525e+00  3.5506e+00  9.3286e+00
-#>   1.0778e+01 -2.7546e+00 -5.9711e+00 -6.2599e+00  1.7342e+01 -1.2956e-01
-#>   8.0654e+00 -7.5368e+00  4.2084e+00 -2.8554e+00  5.2685e+00  5.8940e+00
-#>  -4.1574e+00  3.0891e+00 -1.7797e-02  2.6921e+00 -6.5087e+00  3.0199e+00
-#>  -1.3272e+01 -3.6118e-01 -1.0552e+01 -3.4381e-01 -5.9032e+00  2.5379e+00
-#>   7.1396e+00 -2.4519e-01  1.4729e+00  2.1580e+00  4.3612e+00  6.5780e+00
-#>   6.7803e+00 -6.1947e+00  5.5043e+00  1.2180e+01 -8.9490e+00 -3.8433e+00
-#>  -1.3721e+00 -9.1113e+00  5.5899e+00  3.0196e+00 -9.7791e+00  6.4865e+00
-#>   1.7351e+00  3.5879e+00  1.5976e+01  4.0530e+00  2.1135e+00 -5.7476e+00
-#>   1.5130e+01 -1.0406e+01 -6.5211e+00 -7.1649e+00 -3.4955e+00  1.1013e+00
-#>   3.6414e+00  5.7045e+00  2.1475e+01 -7.1721e+00 -7.4133e+00  8.6888e+00
-#>  -1.0062e+01  2.4678e+00 -4.0043e+00 -7.9186e-01 -3.4969e+00 -3.5037e+00
-#>   3.4416e+00 -3.2598e+00 -3.1747e+00 -4.2460e+00  1.4092e+01  4.3662e+00
-#>   1.3994e+00  1.1805e+00  7.6355e-01  8.1165e-01 -7.8432e+00  4.3089e+00
-#>   1.7457e+01 -3.9223e-01 -1.0589e+01 -5.4922e+00  1.0360e+01 -4.8000e+00
-#>  -6.0066e+00 -5.0413e-01 -3.5014e+00  7.0190e+00 -6.6348e+00 -4.3041e-01
-#>   5.7813e+00  9.0441e-01  8.9422e-01 -4.0966e+00  9.7021e-01 -6.7914e+00
-#>  -8.4177e+00  7.5051e+00 -8.2865e+00 -5.5693e+00  3.7791e+00  4.6787e+00
-#>   9.4031e-01 -5.7692e+00 -9.4047e+00 -2.7730e+00  1.0719e+01 -5.6055e+00
-#>  -2.0762e+00  7.6085e+00  4.4843e-01  4.3434e+00  8.3749e+00 -4.6087e+00
-#> 
-#> Columns 43 to 48  4.3737e+00 -2.5087e+00  6.3889e+00 -8.0037e+00  1.2070e+01  7.6759e+00
-#>  -2.0968e+00 -2.8082e+00 -1.3237e+00  1.1395e+01 -8.8094e+00 -2.2518e-01
-#>   4.4428e+00 -1.5217e+00  7.1631e+00  1.0258e+01  7.0296e+00 -6.6016e+00
-#>   9.3830e-01  1.0754e+00  6.3390e+00  6.4996e+00  1.1364e+00  6.5365e+00
-#>  -7.3669e+00  1.2879e+00 -3.6561e+00  6.0226e+00  1.0079e+00 -5.1102e+00
-#>  -3.1869e+00  6.5918e+00  2.0732e-01 -4.5766e+00 -6.3377e+00  2.8138e+00
-#>   2.0437e+00 -1.0918e+01  1.2007e+01 -9.5038e+00 -3.3561e+00 -5.4644e+00
-#>  -2.2200e-02 -2.5758e+00  2.5701e+00 -1.5186e+00 -1.5802e+00 -6.7054e+00
-#>  -1.1495e+00  7.3191e+00 -8.1472e-01  6.5960e+00 -4.5626e+00 -6.9297e+00
-#>  -3.1921e+00  1.1575e+00  1.1334e+00  1.0130e+01 -3.0875e+00  1.0981e+00
-#>  -1.3860e+01 -7.7124e+00 -8.0822e+00  4.3692e+00 -1.8890e+00 -1.1706e+01
-#>  -1.0236e+01 -1.4173e+01  6.1403e+00  1.3038e+00 -6.5673e+00 -4.4560e+00
-#>   1.6044e+00 -2.1522e+00 -3.1384e+00  4.5684e+00 -1.2475e-01  8.4231e-01
-#>  -9.8657e+00  6.5702e+00  4.0053e+00  8.1363e-01 -3.0906e+00 -8.5054e-01
-#>  -6.7798e-01  6.9365e+00  6.7617e+00  7.0552e+00  5.7285e+00 -7.3362e-01
-#>  -1.9092e+00 -1.2977e+00  1.3451e+00  2.6043e+00  7.1558e+00  2.2102e+00
-#>   1.3258e+01 -4.5233e+00  4.2634e+00  2.9977e+00  1.4320e+00  6.0799e+00
-#>  -9.4925e+00 -2.7318e-01 -5.4690e+00 -6.3278e+00  1.4818e+00  1.2148e+01
-#>  -1.5550e+00 -4.9477e+00 -3.7344e+00  6.9157e+00  5.1898e+00 -2.2928e+01
-#>  -4.9061e+00  2.7184e+00  2.0085e+00  1.0371e+01 -5.9143e+00  4.4246e-01
-#>  -1.1608e+01  6.4426e+00  2.3071e+00 -2.4539e-01 -1.0232e+01  4.0275e+00
-#>  -1.8309e+00  8.3980e+00  4.2186e+00 -3.7221e+00 -1.2211e+01  9.1400e+00
-#>  -4.2552e+00  2.3704e+00  1.4545e+00 -3.9296e+00  1.6062e+00 -1.3370e+01
-#>  -2.1842e+00  1.5637e+00 -5.2733e+00  1.9259e+00 -6.5270e+00  9.1398e-01
-#>  -5.7691e+00 -2.3866e+00 -3.4253e+00  3.6446e+00 -7.2987e-01 -9.9818e+00
-#>  -5.3362e-01 -4.7567e+00  2.5631e+00  5.8880e+00  3.7545e+00 -9.2315e+00
-#>   8.1852e+00  5.2404e+00  4.6403e+00 -5.1693e+00  5.6024e+00 -1.9305e+00
-#>  -1.9630e+00 -1.4028e+01  1.1234e+00 -4.9099e+00 -8.5770e+00  9.8347e+00
-#>  -2.3442e+00  8.7382e-01  6.1165e+00  9.1952e+00  3.3971e+00 -4.7489e+00
-#>   1.4446e+01 -5.3570e+00 -9.9253e-01  1.6825e+00  1.0498e+01 -7.8251e+00
-#>  -1.2283e+00  8.3363e+00  3.4820e+00  8.8028e+00  1.2035e+01 -5.4122e+00
-#>   3.3773e+00 -5.1518e+00  3.0077e+00  1.2355e+01  5.0393e+00  1.6614e+00
-#>   4.3089e-01 -4.3456e+00  1.1466e+00  5.3134e+00 -2.3118e+00  8.6921e-02
-#> 
-#> (10,.,.) = 
-#>  Columns 1 to 6 -9.5958e+00 -7.3723e+00  1.2047e+01  6.5237e-01 -4.7717e+00 -1.2016e+01
-#>   4.8334e+00  5.0307e+00  8.7751e+00 -4.1145e+00 -2.9787e+00 -7.0777e+00
-#>   5.3799e+00  2.5190e+00 -1.0327e+00 -3.6891e+00 -3.6897e+00 -2.2001e+00
-#>   3.0633e+00 -7.7809e+00  9.6613e+00 -4.0909e+00 -3.6797e+00 -5.8602e+00
-#>  -8.7687e+00  7.7281e+00  2.8332e+00 -9.3733e+00 -4.9154e+00  4.3284e+00
-#>   2.6050e+00  7.4308e+00  2.4147e+00 -1.8745e+00  5.2642e+00  7.2496e+00
-#>   6.1107e-01  3.7037e+00 -3.6763e-01 -3.7137e+00 -6.8335e+00 -2.2041e-02
-#>   6.3727e+00 -4.0470e+00  9.8372e+00 -9.1580e-02  1.2816e+01 -7.3477e+00
-#>   4.6817e+00  1.1897e+00 -4.7628e+00 -3.2549e+00 -5.2942e+00 -4.7634e+00
-#>   1.2118e+00  3.4150e+00  2.0200e+01  4.0394e+00 -6.6602e+00 -4.1004e+00
-#>  -1.8507e+01  1.0203e+01 -6.0837e+00  4.8159e+00 -1.6472e+00  2.0550e+00
-#>  -1.1630e+00  3.0891e+00  7.1104e+00 -2.4322e+00 -2.3919e+00  2.3690e+00
-#>   2.2192e+00  5.9340e+00 -4.8951e+00  1.4631e+01  3.8242e+00 -3.2868e+00
-#>  -3.2127e+00 -3.8830e+00 -6.4114e+00 -5.0048e-01  8.1078e+00 -5.9243e+00
-#>  -4.0585e+00 -2.5125e-01 -1.9762e+01 -5.1743e+00 -7.5492e+00  1.3552e+00
-#>   6.4482e+00 -4.5000e+00 -5.4551e+00  1.3725e+01 -3.2925e+00 -9.4615e-01
-#>   1.8685e-01 -7.3666e+00  1.3488e+01  1.1538e+01  2.1585e+00 -3.5440e+00
-#>  -8.2571e+00 -7.2357e+00  2.6185e+00 -5.7009e+00  1.0855e+01 -6.0345e+00
-#>  -1.0681e+01  7.8015e-01  1.1207e+01  2.2349e-01 -1.9994e+01 -4.4592e+00
-#>  -2.9950e+00  7.2179e+00  3.6860e+00 -3.2118e+00  3.0814e+00 -8.9734e+00
-#>   1.2245e+01  4.6379e+00  8.6471e-01 -1.1092e+01  1.4047e+01 -2.6863e+00
-#>   3.6473e+00  9.6788e-01 -1.5797e+01 -7.5575e-01  1.0347e+00 -1.4668e+01
-#>  -8.9120e+00 -7.8948e+00 -2.6262e+00 -2.0313e+00  4.8857e+00  5.1242e+00
-#>   5.4599e+00  2.0633e+01  1.3699e+01 -6.1316e+00 -7.5593e+00  6.0918e+00
-#>  -2.3579e+00  3.9159e+00 -2.1032e+00 -1.1156e+01 -1.1244e+01 -1.0482e+00
-#>  -1.1059e+01  3.7168e+00  1.0351e+01  3.6616e+00 -2.1056e+00 -4.0807e+00
-#>  -3.3395e+00  4.6382e+00 -5.7943e+00 -4.8561e-01 -4.6544e+00  2.2636e+00
-#>  -4.9313e+00  1.4905e+01 -1.6279e+01  1.7440e+00  1.5006e-01  1.0771e+01
-#>   1.8941e+00 -2.4229e+00  6.7721e+00 -1.3173e+01 -3.8212e+00 -9.3271e+00
-#>  -9.6208e+00 -3.8180e+00  9.4949e+00  1.4090e+01  2.8480e-01  8.8882e+00
-#>  -1.6255e-01 -3.3643e+00  1.2077e+01  3.0009e+00 -2.8539e-01 -2.1709e+00
-#>   1.0549e+01  4.7026e+00 -8.8984e-01  2.2968e+00 -1.7090e+00  1.1730e+01
-#>   5.7700e+00  5.7668e+00  8.5146e-01  1.1141e+00 -3.9964e-01 -4.2993e+00
-#> 
-#> Columns 7 to 12 -5.5264e+00  5.2635e+00 -4.5586e+00 -7.7724e+00 -6.7307e+00 -8.4080e+00
-#>   4.2303e+00 -3.7358e-01  7.6817e-01  6.5046e+00 -3.3298e+00  1.6328e+01
-#>   4.7472e+00 -1.0522e+00  1.4437e+01  6.2140e+00  3.2500e+00  7.5858e+00
-#>   1.5293e+01  5.4497e+00  1.0482e-01  8.9316e-01 -2.1978e-01  5.0263e+00
-#>   2.2517e+00  1.9998e+00 -2.8958e+00  8.6876e+00  5.9590e-01 -2.5562e+00
-#>   1.5858e+01  2.7522e+00 -4.7113e+00  6.5628e+00 -3.8360e+00 -6.7326e+00
-#>  -2.1941e+00 -6.8718e+00  3.7223e+00 -1.0419e+01 -1.1370e+00 -4.4845e-01
-#>  -1.1046e+01  3.7126e-01 -4.4773e+00 -3.0009e+00  4.1116e+00  3.8538e+00
-#>  -4.7099e+00 -1.8970e+00  1.4741e+00  7.1659e-01  3.7944e+00  6.2840e+00
-#>  -7.3374e+00  7.9661e+00  8.4182e+00  3.5170e+00 -6.3520e+00 -1.7288e-02
-#>  -1.7208e+01 -3.3698e+00  4.1292e+00 -2.0220e+00 -1.0347e+01  1.5438e+01
-#>  -1.0082e+01 -6.4995e+00  5.5515e-01  5.9078e+00  7.1912e+00  3.6817e+00
-#>  -2.3130e+00  8.0063e-01  1.1618e+01  9.1944e+00 -7.0368e-01  1.0089e-01
-#>   1.9452e+00  4.3857e-01  9.5861e+00 -5.9947e+00  3.2025e+00 -4.1489e+00
-#>   2.3898e+00  5.1869e+00  7.7287e+00  3.4755e+00  5.7937e+00  5.4252e-01
-#>  -5.9263e-01  9.0569e+00  1.5395e+01 -2.7766e+00  1.0821e+01 -1.7293e+00
-#>   4.5239e-01 -1.0123e+01 -3.0120e+00 -4.2275e+00 -5.5924e+00 -1.9935e+00
-#>   1.8789e+00 -5.4162e+00 -1.0534e+01  3.1018e+00 -6.8032e+00 -4.4391e+00
-#>  -1.0391e+00  7.1574e+00 -2.2047e+00  6.4773e+00  2.0316e+01  2.3733e+00
-#>  -3.7556e+00  6.4101e+00  4.5364e+00 -4.5714e+00 -1.0283e+01  3.7152e+00
-#>  -1.0023e+01  4.5924e+00 -7.0045e+00 -3.7151e+00  7.6043e+00  1.0022e+01
-#>  -4.4115e+00  4.4164e-02  3.7332e+00 -1.2023e+00 -6.7839e+00  3.3401e+00
-#>   4.4598e+00  7.0718e+00  2.9898e+00  3.5532e+00  1.2489e+01 -4.6912e+00
-#>   7.4195e+00 -3.9552e+00 -3.8079e+00 -6.0742e+00 -6.6667e+00 -1.4535e+01
-#>   3.6264e+00 -1.3222e+01 -6.8985e+00 -2.4302e+00  1.0476e+01  3.2049e+00
-#>   6.4970e+00  5.5345e+00  2.7960e+00  2.5680e+00  1.0286e+01  5.2805e-01
-#>   2.8223e+00  4.0463e-01 -3.0897e+00 -2.8653e+00 -4.7640e+00  3.1136e+00
-#>   1.0542e+00 -1.2056e+01  2.3570e+00 -1.0653e+01 -2.1960e+00 -5.1746e+00
-#>   2.5563e+00  4.3190e+00  6.0527e+00 -1.5861e+00  1.3050e+01  3.7657e+00
-#>  -2.7193e+00  8.2389e+00  4.5454e+00  1.9444e+00 -6.1626e+00 -1.5945e+01
-#>  -1.5180e+00 -3.8249e+00 -4.1468e+00 -2.9882e+00 -4.2244e+00 -2.6074e+00
-#>   1.0457e+01  1.1844e+00  1.2966e+01  7.0765e+00 -3.6259e+00  7.7815e+00
-#>   4.6665e+00  5.6338e+00  1.1487e+01  1.3988e+01  6.3386e+00 -8.3386e-01
-#> 
-#> Columns 13 to 18  2.0886e+00 -7.3745e-01  3.8596e+00  1.1250e+01  7.6722e+00  5.0116e+00
-#>   2.1444e+00 -1.6645e+00  6.8044e-01 -4.4825e+00 -2.3838e+00 -8.9263e+00
-#>   1.7066e+00  8.2733e-01  1.3864e+00 -5.2769e+00  2.1373e+00  2.8794e+00
-#>  -9.2881e-01  5.1343e+00 -4.6063e-01 -6.8650e+00  5.7070e+00 -3.9509e+00
-#>   8.9417e+00 -3.2170e+00 -7.5011e+00 -1.3436e+00  2.3125e+00 -1.0677e+01
-#>   2.6758e+00  2.1154e+00 -1.4393e+01  1.9364e+00 -1.8340e+00 -1.4569e+00
-#>   1.2783e+01 -2.6929e+00  3.6496e+00 -6.7124e+00 -3.2855e+00 -7.3697e+00
-#>   3.5376e+00 -1.2442e+00  6.6557e-01 -9.2502e+00 -4.3490e+00  5.2186e+00
-#>  -1.0287e+00  1.7489e+00 -6.3890e+00 -3.3085e+00  4.4065e+00  4.9332e+00
-#>  -6.0900e+00 -6.4055e+00 -2.4614e+00 -3.4166e+00  3.4514e-01 -1.0389e+01
-#>   1.3470e+01 -1.1791e+01 -2.7705e+00  8.8360e+00  8.9835e+00  4.3432e-02
-#>  -2.2199e+00  3.6928e+00  7.5354e+00  1.7304e+00 -6.0398e-02  5.4968e+00
-#>  -1.4242e+00 -8.0811e-01 -3.7795e+00 -1.4466e+01  1.3720e+01  3.5974e-01
-#>  -1.2734e+00  1.6379e+00 -1.8523e+00 -1.4098e+01  1.9150e+01  4.3712e+00
-#>   3.7641e+00  2.0794e+00 -4.8163e+00 -1.0482e+01  6.0374e+00 -6.2612e+00
-#>  -5.7567e+00 -3.5951e-01  3.5209e+00 -9.5553e+00  7.4145e+00 -1.5226e+00
-#>  -4.3966e+00 -8.0719e+00 -7.3956e-01  8.1789e-01 -5.3823e+00 -4.8919e+00
-#>   5.3892e+00 -5.2176e+00 -1.4142e+01  7.2664e+00  1.4347e+01  2.4555e-01
-#>   7.7653e+00 -5.8794e+00  1.2585e+01  2.1114e+00 -1.4813e+00 -7.0001e+00
-#>   3.9473e+00 -5.0185e+00  1.1463e+01  7.1674e+00 -1.6456e-01  5.4728e-03
-#>   2.4814e+00 -1.7935e-01  6.9283e+00 -1.1585e+01  1.4784e+00  2.3654e+00
-#>  -6.4205e+00  1.1133e+01  2.9250e+00  1.7329e+00  7.0688e-01  5.7559e+00
-#>   1.0456e+01 -6.8576e+00  4.8140e+00 -7.6468e+00 -2.7327e+00 -6.2993e+00
-#>   6.8432e-01 -7.0138e+00  1.2649e+01 -9.6007e+00 -7.8085e+00 -2.0163e+01
-#>   5.6327e-01 -9.2424e-01  7.8005e+00  1.6437e+01  4.0998e+00  1.4246e+00
-#>  -3.8699e-01 -3.5711e+00  4.4379e+00 -9.7836e+00 -6.2413e+00 -2.5627e+00
-#>   1.2638e+01  8.7734e-01 -3.8200e+00 -2.4395e-01 -3.4485e+00  1.2097e+00
-#>  -5.7193e-01  1.1901e+01  2.9845e+00 -1.4858e+01 -6.7063e-01  2.3071e+00
-#>  -9.6732e+00 -5.1042e-01  8.5809e+00 -3.6766e+00  4.3520e+00  6.9453e+00
-#>   1.8797e+00 -2.1659e+00  4.6082e-01 -3.9050e+00 -7.2183e+00  1.1659e+01
-#>   1.8875e-01  3.7510e+00 -4.7080e+00  2.4531e-01 -2.2246e+00 -9.3851e+00
-#>  -7.1921e+00 -8.4018e+00  1.4588e+00  2.4293e+00 -7.9774e+00 -1.2957e+00
-#>  -7.1650e+00  6.5995e+00 -8.2609e-01 -4.5824e+00  1.8581e+00  1.0594e+01
-#> 
-#> Columns 19 to 24 -1.6547e+01 -1.7455e-01 -1.3965e+01  1.1901e+01  1.1083e+01  7.0531e-01
-#>  -9.7378e+00  4.9298e+00 -3.4363e+00 -8.7591e-01 -2.4670e+00 -5.0297e+00
-#>   4.3643e+00 -1.5493e+00 -5.8622e+00 -7.1102e+00 -1.2048e+01  2.1534e+00
-#>  -1.5390e+01  2.9438e+00  3.5965e+00 -4.0425e+00  4.1766e+00 -2.8972e-01
-#>   2.0704e-01 -8.7539e-01 -6.3799e+00 -4.0346e+00 -1.2937e+01 -2.5051e+00
-#>   1.3951e+00 -1.5320e+00 -2.3383e+00  8.2027e+00 -4.7966e+00  2.8239e+00
-#>  -8.0676e+00 -6.4718e+00  5.0619e+00 -5.0724e+00 -6.6217e+00  8.5565e+00
-#>   3.2756e+00  7.5328e+00 -5.7972e+00  9.7473e-02  3.8714e+00 -7.3040e-01
-#>  -9.6483e+00 -2.0600e+00 -9.7193e+00 -4.9492e+00 -7.4333e-01 -8.6179e+00
-#>  -1.0411e+01 -6.1163e+00 -6.2946e+00 -6.3084e+00 -8.5870e+00 -4.3519e+00
-#>   1.3475e+01 -6.2340e+00  9.6245e-02  4.0572e+00  4.9656e+00  6.8442e+00
-#>   1.2770e+01  7.3423e+00  5.9753e+00 -3.9718e+00 -5.8074e+00  1.2654e+01
-#>   6.1425e+00 -1.5625e+00 -5.7367e-01 -8.6138e+00 -5.9765e+00 -2.7418e+00
-#>   2.2703e+00 -2.2491e+00  8.4373e-01 -6.4280e+00  3.2190e+00  3.5527e+00
-#>   1.1549e+01 -7.9122e+00 -1.8938e+01 -1.5693e+01 -4.1604e+00  1.1917e+01
-#>  -5.9029e+00  9.0893e+00  7.5842e-01 -5.2862e+00  8.7295e+00 -7.4853e+00
-#>  -1.2061e+01 -6.9287e+00 -3.7148e-01  5.6541e+00  1.5193e+00  3.5425e-01
-#>  -5.0123e+00 -4.6697e+00 -1.0465e+00  1.5650e+00 -1.0680e+00  2.7245e+00
-#>   1.0073e+01  7.4417e+00  9.5893e+00  4.2450e+00  4.2740e+00  6.7323e-01
-#>   4.4783e+00 -4.2850e+00  5.4657e+00 -3.8559e+00 -6.5410e+00  3.1018e+00
-#>   2.0051e+00  2.6213e+00  3.6959e+00  1.6965e+00  5.2516e+00 -1.1366e+00
-#>  -1.3355e+01 -2.0926e+00  1.9470e+00 -9.4491e+00  7.5002e+00 -6.4533e+00
-#>   1.8658e+01  4.7384e+00  1.5558e+00  2.8851e+00  6.7274e+00 -1.1672e+00
-#>  -3.9936e+00 -6.7976e-01  3.4615e+00  1.5546e-01 -4.7202e+00  3.0028e+00
-#>  -1.1919e+00 -1.3104e+00  1.5569e+00  1.2456e+01  3.1025e+00  2.3721e+00
-#>   1.1173e+00  4.5066e+00 -9.3090e+00 -8.5264e-03  1.7803e+00 -6.3240e+00
-#>   1.9434e+00  1.0001e+01 -4.6523e+00 -7.2178e+00  1.9133e+00  4.2573e+00
-#>   1.7640e+01  2.0178e+00  1.2119e+01 -1.2353e+00 -1.2177e+00  3.3461e+00
-#>  -1.1616e+01 -2.9746e+00 -7.5183e+00 -4.7508e-01  4.7900e+00  1.1289e+00
-#>  -4.1454e+00 -8.5355e+00 -4.8618e+00  7.4444e+00 -6.7522e-01  1.5182e+00
-#>   9.5614e+00  7.6493e-01 -1.7208e+01 -4.1176e+00  5.2517e+00  3.0220e+00
-#>   7.9180e+00 -8.7570e-01 -7.2966e+00 -8.8530e+00 -1.2356e+00  9.8385e+00
-#>   8.0671e-01 -7.2320e-01 -2.2440e+00  6.0880e+00  2.4059e+00 -2.9827e+00
-#> 
-#> Columns 25 to 30 -1.7654e+00  6.1573e+00 -4.7365e+00 -8.6320e+00 -1.0574e+01 -2.4142e+00
-#>   9.6550e+00 -1.3046e+01  3.9530e+00 -7.7652e+00 -7.8142e-03 -2.3785e+00
-#>   7.7095e+00 -4.5380e-02 -2.2185e+00 -2.1431e+00 -2.8480e+00 -4.1918e+00
-#>  -1.2611e+00 -6.1082e+00  8.4187e+00 -1.1930e+01 -1.8830e+00  9.4992e-01
-#>   2.9813e+00  1.7482e+00 -3.5632e+00  1.0422e+01  7.9552e+00 -9.4522e+00
-#>   2.3804e+00 -3.7486e+00 -7.8931e+00 -8.6517e-02  3.5921e+00  5.1656e+00
-#>  -1.0735e+01  2.3283e+00 -1.0351e+01  9.9058e+00 -1.6368e+01  1.7590e+01
-#>  -3.3455e+00  6.6997e+00  6.1150e+00 -1.3539e+01  3.1509e+00  9.2606e+00
-#>   9.9220e+00  2.6841e+00 -7.4650e+00 -3.8941e+00  4.9308e+00 -9.6910e+00
-#>   1.2039e+01 -5.5931e+00  3.8058e+00 -3.7076e-02  4.8538e+00 -1.3906e+01
-#>  -1.2222e+00 -5.8971e+00  5.7628e+00 -1.3399e+00  8.7426e+00  2.8881e-01
-#>  -3.1671e-01 -1.3056e+00 -8.4503e+00  6.6954e+00  4.4994e+00  7.4874e-03
-#>   5.8190e+00  1.4675e+01 -4.8289e+00  5.9821e+00  6.1915e+00 -1.3343e-01
-#>   3.3220e-04  4.6068e+00 -4.6546e+00  5.7364e+00 -2.2543e+00  2.7028e+00
-#>   9.3891e+00 -2.0363e+00 -8.9049e+00  4.3891e+00 -8.8804e+00 -9.8594e+00
-#>  -1.0224e+00  5.4304e+00  5.4160e+00  1.0815e+01 -9.0729e+00  3.5370e-01
-#>  -1.5242e+00 -5.8958e+00 -9.4318e+00  1.0289e+01 -2.9070e+00 -4.4505e+00
-#>  -2.7731e-01 -6.3541e+00  1.7985e+00  4.7676e+00  1.1100e+01  5.3108e+00
-#>  -4.6237e+00  5.7527e+00  1.9249e+00  2.4828e+00 -6.9288e+00  4.5745e+00
-#>   1.0163e+01  5.8311e-01 -1.0615e+00 -6.9244e+00 -7.4802e+00 -1.2862e+01
-#>  -2.9575e+00  2.7526e+00  3.0471e+00 -1.4789e+01  4.2150e-01  4.5949e+00
-#>   5.3139e+00 -8.4273e-01 -1.1009e+00 -1.0658e+01  2.4117e-01 -5.2637e+00
-#>  -1.8128e+00  3.4119e+00 -9.5389e+00  4.7784e+00  1.5970e+00  8.7575e+00
-#>   7.7266e+00 -1.3421e+00 -1.6817e+01  6.9556e+00  8.4550e-01 -3.6458e+00
-#>   2.7317e+00 -5.6457e+00 -4.8476e+00  4.2472e+00 -7.1554e+00 -4.5735e+00
-#>   2.0287e+00  1.2595e+01  5.9875e+00 -6.9651e+00 -3.8292e-01  4.1640e+00
-#>   4.7382e+00  1.5736e+00 -1.6172e+01  8.5885e-01 -4.5621e+00 -4.1614e+00
-#>  -1.3447e+01 -8.9498e+00  1.1139e+01  1.4416e+01 -6.6645e+00  1.1075e+01
-#>   1.9097e+00 -3.9173e+00  1.9079e+00 -7.1014e+00 -6.5555e+00 -1.1570e+01
-#>  -6.9789e+00  1.3138e+00  2.7922e+00  1.4033e+01 -3.5511e+00 -6.7887e+00
-#>   4.4467e+00  4.0878e-01 -4.3903e+00 -4.1820e+00 -1.0072e+00 -4.2013e+00
-#>   5.4239e+00 -5.3318e+00  1.1106e+01  8.8659e+00 -6.3158e+00 -8.8431e+00
-#>   1.7667e+00  7.0973e+00  1.4381e-01 -1.7409e+00 -1.4521e+00 -8.0895e+00
-#> 
-#> Columns 31 to 36  5.7877e+00 -4.6230e+00  7.2711e-01 -5.3029e+00 -1.0070e+01  1.5749e+00
-#>  -6.4950e+00 -8.0376e+00 -4.5445e+00 -6.8741e-01  7.7429e+00 -1.0083e+01
-#>  -1.5847e+01  1.7480e-01 -2.1960e+00 -3.0769e+00  1.8834e+00 -1.6263e+01
-#>  -6.5188e+00 -4.4961e+00 -7.4398e+00  4.9623e+00 -5.0504e+00 -1.0719e+01
-#>  -6.9759e+00 -2.7550e+00  3.8908e+00 -1.4866e+00  1.4092e+00  8.6068e+00
-#>   1.1555e+00 -7.9262e+00 -1.7627e+00  1.4443e+01  5.2027e+00  1.9195e+01
-#>  -1.1875e+01  1.7406e-01  3.8870e+00 -1.6582e+01 -7.6358e-01 -9.4129e+00
-#>  -5.6199e+00  2.9915e-01 -4.2304e+00  1.3446e+00 -8.7123e+00 -8.4327e+00
-#>   1.4075e+00  2.2520e+00 -5.4539e-01  1.2540e-01  2.7837e+00 -1.9737e+00
-#>  -1.0888e+01  2.6625e+00 -2.2844e-01 -5.8239e+00  9.2506e+00 -4.6154e+00
-#>   3.2156e-01 -4.3800e+00  1.0448e+01  1.1336e+01 -5.0925e-01  1.1347e+01
-#>  -8.9026e+00  8.4311e+00 -1.1829e+00  8.5838e+00  4.1165e+00 -2.6017e+00
-#>  -1.0618e+01  4.5853e+00 -8.2806e-01  3.4717e-01 -5.1240e+00 -7.8976e+00
-#>  -1.1902e+01  1.0810e+01  3.4999e+00  3.2599e+00  9.5764e-01 -8.7301e+00
-#>  -1.3804e+01  3.4591e+00  2.7214e-01  3.7915e+00  8.5312e-03 -5.0884e-01
-#>  -8.1244e+00  1.0172e+00  1.8569e+00 -2.3522e+00  6.7773e+00 -1.2270e+01
-#>   1.8376e+00  8.6732e-01  1.6401e+01 -7.6989e+00 -2.0114e-01  2.8492e+00
-#>   1.0577e+00 -1.9100e+00  3.3745e+00  1.2003e+01  3.8370e-01 -1.6340e-01
-#>  -2.0837e+00  8.0215e-01  4.6182e+00  2.0742e+00 -5.6774e+00  1.3857e+00
-#>  -6.0330e+00  1.0794e+01  5.6998e+00 -6.3962e+00  4.2862e+00 -2.6237e+00
-#>  -3.0855e+00  7.1994e+00 -2.1118e+00  8.3192e+00  1.5085e+00 -1.3735e+01
-#>   2.6148e-01  1.1909e+01 -1.5958e+00 -8.5097e+00 -1.8810e+00 -1.4911e+01
-#>  -8.3884e+00  3.3756e+00  1.4041e+01  9.2340e-01  8.0913e+00  2.0051e+01
-#>   7.7919e+00 -1.8161e+00 -5.1652e+00 -1.1685e+01  6.0734e+00  5.4655e+00
-#>   7.2264e+00  3.7018e+00 -1.2938e+00 -3.1015e+00  7.4691e+00  3.9491e+00
-#>  -1.2834e+01  9.6482e-01  2.5271e+00 -7.3225e+00 -1.5531e+00  3.0103e+00
-#>  -3.2939e+00 -7.1995e-01 -1.5961e-01 -8.3620e+00 -3.4409e+00  2.4556e+00
-#>   8.2753e+00 -4.5557e+00  9.8521e+00  1.1992e+01  1.5305e+01 -8.5115e-01
-#>  -7.4579e+00  8.0515e+00 -3.0819e+00 -1.4713e+00  9.2651e+00 -1.5457e+01
-#>   9.7496e+00  4.9703e+00  1.0001e+01  9.2289e+00  1.9981e+00  8.2831e+00
-#>  -1.0757e+01  3.1880e+00 -2.7738e+00  1.1525e+00 -1.5764e+01  1.4619e+00
-#>  -5.5567e+00 -7.2724e+00  4.9576e+00 -2.6230e+00  5.0559e+00  9.4046e+00
-#>  -7.6573e+00  3.0302e+00 -5.1165e+00  3.8154e-01  4.2957e+00 -2.9837e+00
-#> 
-#> Columns 37 to 42  1.4781e+01  5.8403e+00 -7.9739e+00  4.3284e-01  4.0315e+00 -1.8781e+00
-#>   1.0714e+01 -3.9545e+00  5.9812e+00 -1.0373e+01  4.7588e+00  8.2855e+00
-#>  -2.0618e+00 -2.1084e+00  4.1794e+00 -1.0581e+01  4.8777e+00  8.7927e+00
-#>   1.1532e+01 -1.2932e+00 -6.2592e+00 -2.0058e+00  1.2718e+01 -1.0143e+01
-#>  -6.9627e+00 -1.5297e+00 -4.0824e+00 -1.5353e+00 -6.1356e+00  3.2539e+00
-#>   1.0944e+01 -1.2924e+00 -8.0913e+00  8.3946e-01  5.9463e+00  4.7262e+00
-#>  -8.8214e-01 -7.6500e+00 -9.0881e+00  1.2230e+01  7.8738e+00  1.2728e+00
-#>  -3.6937e-01 -7.4623e-01  2.8593e+00  6.0178e+00 -4.1878e+00 -6.2481e+00
-#>   4.6512e+00 -3.8977e+00 -8.6991e+00 -7.2066e+00 -8.0469e+00  4.5942e+00
-#>  -1.1658e-01 -2.2086e+00  4.4043e+00 -8.7130e+00 -8.1418e+00  6.6675e+00
-#>   3.2866e+00 -1.4364e+01 -1.1202e+01  2.3180e+00  9.5690e-01 -2.2525e+00
-#>  -6.0650e+00  4.9470e+00 -8.6046e+00 -3.2993e+00  1.0456e+01 -8.4463e+00
-#>  -1.1800e+00  4.0888e+00  1.0231e+01 -6.0781e+00 -1.3315e+01  8.7547e+00
-#>   5.2642e+00 -5.8803e+00  2.3019e+00  7.4136e+00 -4.8173e+00  4.0485e+00
-#>  -2.2386e+00 -9.5951e+00 -1.3358e+00 -4.3168e+00 -5.1432e+00  1.0044e+01
-#>   4.5064e+00 -1.4719e+01  8.1893e+00 -3.3648e-01 -9.4532e+00  3.5265e+00
-#>  -8.1611e+00  4.5290e+00 -4.8454e+00  3.8065e+00  5.1539e-01 -3.5527e+00
-#>   5.1429e+00 -6.1683e+00 -7.7518e+00  2.2368e+00  3.3335e+00 -3.2178e+00
-#>  -7.6238e+00 -8.8446e+00  3.7186e+00 -4.0304e+00 -1.2876e+00  3.0657e+00
-#>  -9.6459e+00  7.1043e+00  1.1225e+00 -8.9580e-01  3.6940e-01  8.4879e+00
-#>   1.3031e+00 -6.8280e+00 -9.7464e-01 -1.7561e+00  4.3782e+00 -5.3408e+00
-#>   6.9679e+00 -5.8045e+00  1.6076e+00 -1.3174e+00 -2.8242e+00  3.5411e+00
-#>   4.8554e+00  5.4867e-01  4.6937e+00 -8.3519e-01  2.8432e+00  1.4558e+00
-#>  -4.0727e+00  5.3502e+00 -6.1120e+00 -1.2640e+01  6.2638e+00  9.2251e+00
-#>  -4.0730e+00 -4.3857e+00  4.1525e-03 -5.7744e+00  5.5562e+00  1.8900e+00
-#>  -1.4340e+00 -1.0864e+00  6.5575e+00  2.4515e+00 -8.7689e+00  1.5012e-01
-#>   7.8476e+00  3.2083e-01 -1.9992e+00  1.2308e+00  1.4226e+00  4.5515e+00
-#>   1.4021e+00 -6.5290e+00  3.1490e+00  1.2606e+00  1.7082e-01 -2.3939e+00
-#>  -6.8526e+00 -6.5598e+00  7.8596e+00 -2.0899e+00 -4.9527e+00  3.3671e+00
-#>  -7.9504e+00  1.3332e+01 -5.1713e+00 -4.8172e+00  3.8252e+00 -9.7357e+00
-#>   4.2879e-01  1.0015e-02 -3.9781e+00 -9.1234e+00  1.0227e+00 -1.5022e+00
-#>  -9.6010e+00  7.0668e+00  9.2475e-01  3.4143e+00 -3.9469e+00  2.5812e+00
-#>  -7.8083e+00  9.0833e-01  7.7153e+00 -7.5587e+00 -5.6300e-01  4.6345e+00
-#> 
-#> Columns 43 to 48  1.2810e+01 -1.3189e+01 -7.7055e+00 -6.3976e+00  1.5547e+01  8.8330e+00
-#>  -1.5065e+01  9.7159e+00 -6.3723e+00  3.3489e-01  2.0888e+00  3.7348e+00
-#>  -4.2313e+00  6.3053e+00  1.9464e+00 -8.2544e-01  8.5206e+00  5.7207e+00
-#>   2.0316e-01  1.0027e+01 -5.5466e+00  9.0533e+00  1.8833e+00  5.5861e+00
-#>   3.3048e+00  3.1319e+00  6.2988e+00  3.1838e+00 -2.9775e-01 -9.9813e+00
-#>   1.6776e+00 -1.1691e+00 -5.3201e+00  3.5656e+00 -4.7045e+00 -8.3086e+00
-#>  -4.5635e+00  2.0188e+00  9.2125e+00 -3.9032e+00 -3.8079e+00 -6.3385e+00
-#>   5.3327e+00  7.2008e+00  1.8978e+00 -5.8745e+00 -3.4445e+00  5.1775e+00
-#>   5.9848e+00  2.5756e+00  2.4851e+00 -4.3141e+00 -7.1245e+00 -4.7120e+00
-#>  -4.2353e+00  4.1360e+00  2.4754e+00 -3.3324e+00  6.6476e+00 -2.6378e+00
-#>  -3.8737e+00 -2.7430e+00 -4.2719e+00  6.1796e+00  3.7464e+00  3.1966e+00
-#>  -8.9031e+00  5.3667e-02 -2.0811e+00  4.1157e+00  7.7390e+00  4.8121e+00
-#>   2.6389e+00  4.2188e+00 -3.3962e+00  4.2881e+00 -2.4315e+00  9.4363e+00
-#>   8.9556e+00  1.2537e+00 -2.8011e+00 -7.3613e+00  1.4005e+00 -3.8574e+00
-#>   1.2019e+01  1.4726e+01  6.9024e+00 -2.1664e+00  5.1618e+00 -1.1799e+00
-#>  -3.3054e+00  3.7320e-01  1.0245e+01 -1.4051e+00  1.6680e+00 -8.1630e-02
-#>   2.8422e-01 -7.4191e+00  2.8573e+00  1.6843e+00  1.4833e+01 -2.8418e+00
-#>  -4.4291e+00 -2.8135e+00 -1.5023e+01 -3.1354e+00  2.7697e+00 -1.9877e+00
-#>  -1.1016e+00  7.1654e+00  1.2580e+00  7.7710e+00  4.1162e+00  5.1481e+00
-#>  -6.9081e+00  5.2128e+00  2.9860e+00 -1.4196e+00 -1.7435e+00  8.4676e+00
-#>  -5.3983e+00  1.0983e+01 -3.1357e+00 -1.0623e+01 -4.6322e+00  7.8922e-01
-#>   8.3450e-01 -4.0142e-01 -7.6967e+00 -1.4488e+00 -8.4348e+00  2.2065e+00
-#>   5.7479e+00  3.1151e+00  1.8005e+00 -5.7872e+00  3.0122e+00 -9.6315e+00
-#>  -7.8079e+00 -7.6238e+00 -1.7575e+00  1.8205e+00  7.2539e+00  3.8008e+00
-#>  -1.0109e+01 -3.5757e+00  8.2105e-01 -4.4458e+00 -5.1365e+00 -1.5818e+01
-#>   4.6419e+00  8.5954e+00  8.1159e+00 -1.8950e+00  1.0888e+00  7.3860e+00
-#>   1.0279e+01  4.0486e+00  3.8668e+00  6.8263e+00  4.4360e+00  1.7308e+00
-#>  -1.8266e+01  3.0640e+00  4.1436e+00  8.5667e-01  6.0407e-01 -1.2738e+01
-#>   7.3158e-01  8.1287e+00  8.3764e+00 -5.1014e+00  2.8116e-01 -7.9374e+00
-#>   3.0224e-01 -5.2574e+00  4.2073e+00  6.6237e+00  5.8265e+00  1.7484e+00
-#>   5.1284e+00  5.3158e+00  4.4600e+00  1.2923e+01  1.3637e+01 -3.8000e+00
-#>   2.7022e-01 -4.0573e+00  9.2956e+00  1.5066e+00 -9.9581e+00 -8.9386e+00
-#>   5.2094e+00  1.3030e+00  2.6377e+00  3.6572e+00 -1.2780e+00  1.9778e+00
-#> 
-#> (11,.,.) = 
-#>  Columns 1 to 8   2.8781   2.2097  -1.0166   8.1775  -0.7718   8.1902  -0.9899 -11.8470
-#>   -6.2581   5.5743   7.7125 -12.8765  -3.1623   0.1986  -5.9956  -9.2363
-#>   -0.2172  -0.8420   9.4055 -11.2229  -1.8000  -1.2664  -1.7218  -1.0896
-#>   11.4138   1.5981   0.7542   1.7948  -1.1702  -1.7719  -9.2642  -9.8508
-#>   -3.8479   8.2731   3.5893  -6.5526   3.9244  -7.8203  -9.8070   3.4631
-#>    9.6756   1.5901 -10.3462 -10.4983   5.3770  -0.4591  -2.7411   3.5073
-#>    5.7282  -6.6532   7.0050  -3.8995  11.4720  -1.5143  -3.0646   7.9134
-#>  -10.4324   2.0450   1.2011   5.9723  -2.9955   5.4474   7.2957  -3.6325
-#>    8.0859   1.0551   0.5931   0.0603  -6.0898  -3.1183   5.7426  -6.1302
-#>  -12.6554  11.6160   9.1281  -8.6554  -0.7018   3.1528  -6.0132  -5.0604
-#>  -14.8811   9.6235  -2.2084   0.9378   5.9259   2.6533  -0.6158  -0.3892
-#>   -7.6016  11.6173  -4.0455  -9.1039   7.3522   1.6756   1.2683   7.6569
-#>   -4.3699  -5.2974   5.9473   7.1043  -3.2537   5.4743  -1.0449  -5.1495
-#>    5.2138  -2.3339  -1.0099   0.8368   2.8119   2.7774   3.6714   0.0031
-#>    8.2720  -6.8858   7.2203  -5.7084 -10.2222  -5.5913  -9.6192   0.9030
-#>   -2.1772  -4.7607  14.7243   3.4797  -0.6596  -1.4458   2.4896  -5.4278
-#>   -7.1641  -0.1249  -7.9006  -3.6037  -3.3484   8.6855  -5.7696   2.6918
-#>   -2.5526  -4.4526  -2.4355  -3.2067  14.2889   3.3216   1.6071   0.7929
-#>  -15.2051  14.2628  12.3325   3.1186  -1.8251  -7.4071  -2.9547  -1.7397
-#>   -5.2387   9.3243  -7.0639  -0.8562  -4.1239   3.8398   1.4057  -1.6789
-#>    0.5528  -0.8265 -10.4466  -4.1306   6.3734   1.5282  12.3649   4.0504
-#>   11.1108  -3.0633  -3.6177   5.7682   5.1008  -2.4357   7.9087  -9.6357
-#>  -10.3575  15.5950  -6.8540 -12.3639  -0.0412  -8.0590  -2.7639   6.2915
-#>   -7.8598  14.0037  -4.7523   2.2997   6.1635   1.7026  -6.2875  -1.1226
-#>    3.5824  -5.8863   4.7592  -3.0903   2.6901  -1.9886   2.6103   5.2841
-#>   -1.5312   4.2905   8.7163   1.5517  -6.8520   1.7236  -6.1933  -7.1499
-#>    9.4862   2.0688  -4.7118  -1.9646  -3.7517  -5.6849  -2.6916  -0.4818
-#>   -6.3272  -8.3648   1.5437 -12.2026   4.5188  11.4448  -0.7743  21.8454
-#>   -4.0406   3.5181   4.3892  -3.6517 -10.6121   1.8866   2.7786  -0.3579
-#>  -14.2391   7.8265 -13.1909   0.1867  -0.9296   1.5074   5.8571  -1.2301
-#>   -0.2945   6.9921   6.2890   1.7168  -1.7118  -6.4731  -5.1135  -1.5401
-#>    2.0167  -0.0208   2.3058   4.0023  -9.0107   0.2120  -7.0342   6.9787
-#>    7.1531   0.8605   9.0946  -3.1487   0.0899  -3.9209   2.6381  -3.0970
-#> 
-#> Columns 9 to 16  -6.1915  -8.2626  -9.0006  -4.0418  -5.2003  -5.3085  -4.1699  16.7122
-#>   19.5822   3.5655  -0.5455   2.5924   2.8166  -7.1293   2.1717   0.6939
-#>   -0.0666  -1.4415   2.4082   7.5363   0.5220  -1.4166   4.4682  -3.4278
-#>    4.9616   0.3099  -4.0076  11.1014  -5.7296   2.0147  -7.3795   9.0969
-#>   -8.2887  -3.4343   9.5883  -2.1146   0.5618  -2.2804   5.8889  -7.3778
-#>   14.1456   5.9190  -2.0328  -2.6687   2.2124   0.8063   6.1982   2.2195
-#>   -1.5928   0.4058   8.9747   1.6188   3.1345  -9.7028  -7.3274   6.0963
-#>   10.4677   2.9256   0.9422  11.0137  -2.2644 -11.5415   7.8916   2.9541
-#>   -6.0708   4.4333   3.1589  -5.0676  -7.9851   2.3739  -6.0588  -2.1832
-#>   10.8193   4.0739   0.8660  -2.5729   0.8408  -8.6158   4.2908  -6.9327
-#>   -6.8986   5.9385   4.4483 -11.7290 -14.5286   0.4827  17.4183  -2.5515
-#>    5.1375   4.0198   8.6159  -4.3389 -13.8087   0.7683   8.3413   2.9647
-#>   -6.5967  -1.5645  -1.2306  12.1490  -6.1894  14.5564   8.7421  -4.9975
-#>  -11.2655   0.8737  -6.1851   0.2204  -5.8681   4.7180   5.9456   1.9183
-#>  -12.2213 -15.1589  -2.7198   2.5953  -6.5069 -10.0845   9.1320 -10.0574
-#>   -0.7626   1.8518  -2.9269   1.0017  12.3075   5.7738  -5.0093   9.1991
-#>    3.9567  -2.3733   5.1063   4.6667  -0.3354 -10.5436 -11.7094   8.5977
-#>    2.9944  -6.6322   4.3978  -6.5010  -0.0759 -10.2965   4.9730   2.9660
-#>    8.9919   2.6644  -4.4370   7.2588   4.9745   1.3050  19.2494  10.3855
-#>   -1.0392  -2.9545  -5.6787   3.8888  -1.4883  -5.5207   6.1828  -3.4936
-#>   10.7327   4.6214  -0.5641   0.3687  -1.4311  -4.9733   3.9833   0.7176
-#>   -8.3585  -5.7510  -4.0912  -2.6170  -1.7727   7.6623  -6.1395  -9.1816
-#>    2.2889  -0.2266   3.1189  -2.6195   4.2082  -5.6330   9.8348   7.9776
-#>    8.7933  12.3731  -3.5023   2.7876  -0.1834   2.7520 -10.9274  -2.5809
-#>   11.7923   6.6071  -4.3929 -12.0979  -4.8559  -3.5806  -1.5844  13.4595
-#>    0.7568  -5.8900   2.0718  14.9415   2.2399  -6.6502  14.8196   8.2244
-#>   -3.6494  -1.7169   6.9579  -0.4423  -3.3180  -1.1368 -11.8920   6.4290
-#>    6.7029 -10.1809  -1.9819  -5.6661  -2.7719  -2.5286  -3.1997  -9.4372
-#>    8.7309  -4.0389 -11.5737  -8.1458  -3.5622  -0.5749   8.0254  -1.8079
-#>  -10.1502  -2.2120  -3.8260   6.5190   6.1603   0.6438   0.1826   1.3515
-#>   -3.4241  -1.1157   6.5079  11.2266  -4.7174  -7.4841   2.2192  -3.6866
-#>   -1.7339  -2.9707  -5.3129  -6.4849  -7.4874   8.1912   9.1298  -6.6721
-#>   -3.9521   1.4523   0.2940  -1.9947  -3.2829  16.9990   4.8489   6.0814
-#> 
-#> Columns 17 to 24   5.6664  11.0845 -10.2942  -4.7752  -6.0080   8.1106  -3.5690   0.9122
-#>   -7.6126 -19.9690   4.6329  -8.9806  -0.4967   2.6903   7.8598  -4.5414
-#>  -11.8706  -5.2885  -0.0619  -1.4086   3.6219   0.9005   1.2591  -2.8981
-#>   -5.7637 -11.8687  -0.4377  -7.7378   4.4512  -7.9747  -8.7335   0.4574
-#>    3.1455  -1.0820   2.9134   0.2344   3.9357 -11.2709   0.1974  -3.3359
-#>  -12.8174  -2.0394  -9.5841   2.4451   7.2319  -0.3387   0.5652  -0.1930
-#>  -13.7959   8.0847   3.8370  -1.3418 -10.4121   5.0823  -8.9951  -1.7491
-#>   -6.6635   3.5142   1.4026 -11.0846  -2.7440   9.2802   3.5806   5.7885
-#>   -1.1767  -0.9026  -8.0627  -2.0746  12.1456  -3.9612  -5.6939   3.9885
-#>   -1.7274 -16.3384   7.3582  -6.4908  -0.4155  -1.4168  10.5900  -8.3264
-#>   22.9371  -2.6684   7.5032  -5.1639  -0.7731 -15.6504   2.5496 -25.6584
-#>   -9.9856  13.7973   0.9767 -14.3910  -3.7744   0.1570  -6.9156  -0.4604
-#>   -1.7880   3.5345   1.4397   1.9685   8.2998  -7.1436   2.2520  -1.8548
-#>    2.6198   6.9447   1.9977  -4.7944   7.8972  -9.6881 -14.5384   2.0402
-#>    2.4234   3.0974  -5.9777  12.3375   2.4437  -5.6257   0.3055   3.5800
-#>   -2.0321  -4.9167   1.8321  -1.6269  -7.0493  -7.4840  -6.6248   8.4155
-#>   -6.3536  -5.2497  -0.8554  11.0275   0.3792   2.4790   6.4480  -4.4525
-#>    0.3469   6.9205   4.5552  -4.9644   9.5360  -1.3360  -9.3090  -4.5213
-#>   -6.4502 -11.7928  -6.1335  -8.0252   0.8029  -9.4381   5.9685  -4.3368
-#>    8.8216   0.8329  -0.2735  -7.1837  -8.0822  -9.6586   1.2252   0.6287
-#>   -1.3171  -6.6742   6.8636 -18.5117   5.8795   5.2577   0.1714   3.6908
-#>    1.8756   8.8235  -7.2492   1.8759  -0.0843  -4.4299 -16.8106   9.7002
-#>    5.3941  -1.8122  -2.9792 -10.6169   3.2304  -1.0800  -0.9455  -3.4328
-#>   -7.0644  -8.4130   4.7494   5.3553   7.0956  -8.0271  20.0530  -7.7643
-#>   -2.8851  -2.7643  -9.6940   0.6988 -13.5548 -11.2170  -6.3717   6.5294
-#>    1.4785  -1.5019 -13.8826   2.5866  -6.0112   1.4736   5.9116  13.5663
-#>    4.4456   3.7312  -3.7568  -0.6211   3.6518   0.4688  -5.2054   4.7016
-#>    4.1419   7.2961  22.8483  18.7562 -13.6192  10.4419   5.3509 -19.8762
-#>   -1.3481 -10.2438  -1.7149 -11.4341 -10.5016   4.5516  -6.6300  10.4442
-#>    4.8445   8.3227  -5.4541   7.5567   0.8922  -0.4789   0.3967  -5.8435
-#>   -4.6249  13.5624  -6.0266  -7.0758   0.9136  -2.8116   0.4097   5.0527
-#>    2.1420  -5.8441   1.0169   3.2376 -15.4424 -10.0194   7.6189   0.4871
-#>   -6.5184  -1.2838 -10.3528  -3.5135  -7.1717  -4.0199 -10.5834   9.9731
-#> 
-#> Columns 25 to 32 -10.3515   3.4975  -3.6517   7.7534   0.3283   0.2941  -7.0501  -6.8274
-#>    1.4270  12.2531   9.8292  -0.3401   3.0283   1.5113   2.4921   3.2593
-#>   -7.5005   7.8531   8.1607   1.5169   3.4709 -12.8539  -1.9708  12.2384
-#>    2.4944   3.9962   0.7539   3.6741  -9.7667  -4.9758   3.8036   2.6912
-#>    5.5318   2.7070   1.6485  -5.0410  -0.7996  -4.4982   4.1551  15.4612
-#>  -10.5261  -5.4848   4.7405  -1.1069   3.9326   5.6820  -5.2473  -8.3052
-#>   -1.1329  -1.5255  12.7872  -3.1154  -0.9708   2.8787   1.7427  14.3937
-#>  -12.2924  -4.7862   7.1931   0.5332   7.4366   7.9451  -9.8216  -8.7823
-#>   -0.7814   4.8600   0.4755  11.8892  -3.6985  -3.3411   5.5341   0.2644
-#>    3.4088   5.4109   8.5450  -2.0580  -0.5300  -3.7219   7.3510   7.8470
-#>    9.0335   9.4278  11.0499  -3.8728  -1.4293 -12.1467   1.2945  -5.4632
-#>    6.8743  11.7608   1.6648  -4.8751  13.1060  -2.2418   4.4712   3.7576
-#>    6.7995   3.0815   1.0554  -3.7866   1.1449 -10.6269  -4.1921  -1.4030
-#>   -0.4489   3.0557   1.8424   2.0436  -6.4865  -3.8906   0.9938  -2.0567
-#>  -10.4877  -4.4549   1.0167   0.4971  -7.5357  -6.5673 -10.2117   4.3548
-#>   -0.8417   2.0043  -0.5281  -1.6514 -12.2684  -1.2485  -2.1550   2.2551
-#>  -12.7666  -6.6186   0.8067   5.8773  -3.6131  -2.2668  11.4308   7.8232
-#>   -9.4452  -1.5626   6.0157  -1.6627   4.5507  -0.6084  -3.8642   1.6012
-#>    2.9794   0.1325   3.8421  -8.8259  -0.4939 -18.2918 -11.1140  -4.3855
-#>    6.2471   4.9542   9.4580   0.6201  -2.4679   0.5176   9.5204   6.5565
-#>   -0.3907   4.6630  12.7648  -0.0060   7.7318   9.3784  -0.8921   5.6945
-#>   12.5322  13.0636  -1.7965   4.8947  -6.6470   2.9905   9.2692  -3.8457
-#>   -0.9241 -11.4957  -1.6680  -5.9663   9.7777  -4.1905  -8.1391  -2.7973
-#>    5.2600  -4.3981  -7.8513  -1.1895   4.0581  -2.9813  10.5569   5.8662
-#>   -6.3160   9.7658   2.4030  10.7301  -2.1206   0.5316   2.3882   3.0174
-#>   -9.0679  -6.4728   0.3893   0.7980  -4.6750  -4.5644 -10.7221   4.0892
-#>    1.4679  -5.2258  -3.4626  -0.5695   2.0237  -1.4349   2.7530  -4.8818
-#>   -7.9846   6.8764   4.2227   2.3961   0.2227  10.2062   9.4042   9.6672
-#>    0.3163  12.4046  -0.8354   0.3742  -5.6021  -1.8028  -0.4147   4.0197
-#>   -7.1034  -3.1581 -15.2460   3.4164   1.7271 -13.3808   8.1067   5.0651
-#>   -5.3282  -7.1169 -14.3244  -2.9758   9.1079  -3.9552  -3.5461  -5.6140
-#>   15.6531   4.8156   1.1635  -6.2082  -6.7749   4.0234  -1.0711  -5.8332
-#>    4.3762  14.6048  -1.9513   1.7074  -6.0273  -8.7757  -5.1166   8.4456
-#> 
-#> Columns 33 to 40 -10.1023  -3.9219  -9.1799   9.5749   8.4975 -11.1930   9.7151   7.1257
-#>   -4.6959  -0.7548  12.8207  10.9396   0.5109  -2.8755  13.5778  -6.5760
-#>   -5.4996  -7.8202   4.7448   6.0284  -1.3823   8.4791  13.4582   9.2748
-#>   -6.7925   3.9432   4.9907   6.8099   3.3344   2.6087   3.8572 -13.1837
-#>    4.0631   2.9278  -2.1702   7.0277  -5.9275  -6.6336 -14.0380  -0.8810
-#>   13.7508   7.2422  10.8456   3.6631  -9.4903  -0.0949  -9.4799 -13.1683
-#>   -6.4126  -4.4764  -3.1920   3.6200   9.2765  -0.6455  -2.4736  -3.5236
-#>    5.1701  -2.2355  -4.0393  -2.0168   9.7074  -5.0182   5.5302   0.5364
-#>   -4.7535   2.4163  -7.0061  14.8368  -6.1606   2.4834  -2.1907   0.2419
-#>    1.8371  -6.5680   2.0259   6.1676   1.0617  -9.2078   1.2528   6.3787
-#>    9.4472  -6.5630   1.2068  10.8190  -6.5610   3.5445  -2.8692   2.9642
-#>   -7.2891   6.0978  -3.3751   4.1682   2.6300   1.0304   6.1595 -13.8636
-#>    0.6786  -0.7477  -7.2634  -1.8376   3.1016   5.0136   3.9515   1.5174
-#>  -12.6435   4.9984   1.0216   3.7301 -11.5680   9.5201  -1.4775  -8.6870
-#>   -3.6625   4.2057   5.5981   4.0501  -3.0935   9.0510  -4.4381  -1.4277
-#>   -7.8689  12.5444  -3.1417  10.3896  -9.9695   3.2747   2.8322   1.7519
-#>    1.8620 -12.7264   2.2756  -1.6458   1.6316 -13.8687  -4.1662  16.7878
-#>    0.3420   3.7973  -1.1359  -1.4965  -4.0083  -4.1914   1.7148   2.4032
-#>    6.3714   2.4896   7.8242   6.8193   7.0321   3.2046   6.8605  -4.6502
-#>    2.8786 -16.2526   2.4352  -3.8882   2.2542  -2.4454  -1.5951   4.7389
-#>  -23.1875  -4.1333   6.3962   6.6887  -6.1362   0.8058  15.6956   7.2013
-#>   -8.7329   7.1801  -5.0613   1.1381  -2.0916  10.0014  -3.2677 -12.7174
-#>   -4.5880  11.6920  14.5654   1.7693   4.4473  -4.4503  -6.5495  -8.7196
-#>  -12.2589  -7.2170   7.9125   3.5010  -9.2617  -7.6637   7.5840   8.4875
-#>   -5.3479  -5.0334   5.0921   6.9245  -8.3232   7.1854   4.1576  -1.5226
-#>    8.1612 -11.9883  -3.6214  13.7768   3.6118  -6.4589  -4.7312  -0.0460
-#>   -4.4050   2.7748  -5.1623   7.4096   5.4151  -9.0273  -6.8535  -1.7368
-#>   -2.0037  -5.8586   4.3548  -7.4482 -17.8355   7.5988  -5.9774   3.9989
-#>   -4.5390  -2.9569   6.4785   2.0471   4.1070  -0.4610  10.3860  -6.3458
-#>    2.7225   0.7216  -7.2652  -4.7884   1.4376  -6.6834  -0.9750   8.7055
-#>    6.1952   6.8069  -4.0349   3.0092  -4.8960   1.1997  -1.7488  -7.0332
-#>    3.4292  -9.9456   6.0187  -7.4573   6.3376  -2.4621  -2.0052   0.2439
-#>   -2.6957   6.6021  -1.6683   9.6269  -5.0222   3.0561  -0.4602  -3.9711
-#> 
-#> Columns 41 to 48  -2.3662  -4.1178   6.0542 -17.2736   1.1544 -14.6532  -4.2327  -1.6723
-#>   -9.6362  -4.0162   8.9831   0.0999   0.5063  -5.7267  -0.4100  -3.6152
-#>    4.8958 -15.0266  -0.8540  -2.6382   2.8027   3.7457   6.4793  -3.2880
-#>    0.2428 -20.9852  -2.8938  -5.1603  11.2823  -5.0218   3.5444   2.0790
-#>   -4.0056   2.6767 -14.2301  -2.8351  -1.5785   9.4275  -3.2266   0.2698
-#>   13.7052  -6.2582 -17.5901  13.6814   2.5738   7.9126  -6.7576   3.7483
-#>   -0.4129  -9.1739  -1.6229   1.1630   0.6235   4.9927   0.3573  -0.4206
-#>    9.1233   3.1910  10.6905   9.6390   2.7398  -2.4940   0.5708  -2.8679
-#>   -7.8422  -9.4731  -9.4229  -5.5931   0.8138   8.7929  -2.3753  -1.6864
-#>   -3.2461   3.6471   0.5257 -10.9632   3.6299  -0.0675   3.1798 -15.2807
-#>  -14.9918  -3.9584  -9.7075  14.5392  -5.4990  -1.5539 -12.2078   8.0035
-#>    9.5730  11.4291  -7.0751   0.0573 -15.4524   8.8957   3.7455   2.1895
-#>    7.8082  -3.8102   6.6591  -1.2311  -4.3900   0.9965   9.2195 -10.1490
-#>    3.8526 -15.6402  -5.1694  -3.2650   5.3320   0.6164   3.4690  -5.3112
-#>   -1.2557 -12.2737  -7.2549  -3.3846   7.4051   1.0831  -4.2061  -6.6609
-#>    0.8970 -14.5638   8.9470  -8.6792  -1.5913  10.7282   3.4311  -6.1475
-#>    4.0533   3.6346  22.0376 -11.0145  -3.3696 -10.2912   1.8133  -4.4615
-#>    6.9292   1.4592 -10.4186   9.6825   1.6393   7.4733  -6.5915   1.2002
-#>   -9.6357  -5.2183   6.0034   6.9522  -8.2266   2.9211  -1.4970   7.7575
-#>   -4.3216  -1.2334  -8.8963  -0.6198   2.8602  -9.6112 -12.6712  -1.2095
-#>   -0.2654  -4.1480   0.0734   8.8209   2.3408   0.8615  -4.3848   7.7182
-#>   -7.8335 -11.9261  -1.9578  -4.8017  -4.1270  -2.7510  -7.9025   3.6308
-#>   -5.1774   1.1036  -3.9699  -7.1527   4.0851   8.1275   0.2665   4.3704
-#>  -10.5100   8.6654   3.9859 -11.6839  -6.7456  -1.7912   4.1879   6.8301
-#>   -8.2875  -7.6335  -5.0641   3.4509  -0.7044   5.7884  -1.5903   5.4270
-#>   14.7860   1.6551   6.0810  -0.1190  -0.6695  -3.8393  -2.5997  -2.9030
-#>    3.0867  -8.2454  -2.4351   3.7962  -2.4292   0.2244  -0.2360   6.7647
-#>    2.3410   2.5302  -3.1083  -6.5420 -13.2832   6.7880   2.6604   2.6548
-#>   -0.9989  -4.2706   7.0629  -3.4784   6.5034  -6.2423   9.9963 -14.3233
-#>    4.2896  -4.0390  -1.4066 -14.4308  -2.8039   9.3129  -0.1018   2.7691
-#>   -2.0399   6.2666   2.7508  -5.6485  -1.2999   4.8603   2.6266  -1.8459
-#>   -3.3496  -4.6115   1.7148  -2.4124  11.2194  -6.3833  11.5255  -9.6916
-#>    3.6975  -7.3336  -0.8093  -4.4650  -9.5633   5.6365   0.4674  -2.7346
-#> 
-#> (12,.,.) = 
-#>  Columns 1 to 8  -1.6277   1.2656  -1.4825  11.3328  10.6316   3.1802   2.7966   7.3602
-#>    0.7563  11.4658   3.0252  -5.1515  -9.5619  -2.4433  -5.7716   3.6334
-#>  -10.5882  12.0208  10.2176   0.2408  -6.5390  -8.0264  -0.8759  -1.1588
-#>   -0.7622   4.3897  -1.1896  -1.7668  -8.9001   1.3332  -7.5461   8.9010
-#>   -3.4753  -0.1087   8.2465  -6.7310  -5.2670  -7.9490  10.5989   5.9618
-#>   -5.2578  -9.5628   3.8548  -2.9971  -7.3184  -3.6002  -3.0816 -13.1817
-#>   -1.4256   1.5171  -0.5876 -11.1181  -4.1151  -5.4291   6.7526   8.3762
-#>   11.6134  -3.6160   3.2140  -0.5094   0.0399  -2.3758  -3.6816   5.9869
-#>   -3.0449  -2.3651  -3.8286   4.9898  -7.6807   3.3386   0.9428  -0.6329
-#>   -8.3678   1.8662   0.6236  -3.0013  -9.7194  -8.1617  -1.3421   4.1308
-#>   15.7022   3.1097  -0.6749 -15.2644  -1.2098  -6.0772   6.4032  -5.7096
-#>    1.1139  12.4873   7.5104  -6.8317   3.5047   0.8986   8.7020   5.2628
-#>   10.1008   0.3333   1.9349  -2.2046 -10.1011  -1.9751  -6.3534   0.1331
-#>   -6.7504   4.4257  -8.3930   4.4600  -6.5332   1.4475   0.3728  -2.6905
-#>    2.4878   5.3327  -1.4330  -2.6156  -6.1966   0.1642   9.1931   0.4174
-#>   -7.9441  -2.8109 -10.7725   1.8952 -18.0593   0.0408  -9.0493  -2.1951
-#>  -15.9260   9.6935   2.5494  -2.1085  -0.7622  -8.1082   2.7542  10.5612
-#>  -15.6643  -9.0980   9.7987  -4.7460  -1.0128  -5.6632   4.0524  -1.4456
-#>   -0.5765  -0.7434  -8.1518 -23.3519 -10.5594  -6.9221  -8.8655   1.9328
-#>   -1.7641   8.7819  11.6229   2.5808   6.2782  -1.7949   8.3206  13.0015
-#>    7.5008   1.6181   3.6726  -4.3403   0.8462  -0.1154  -2.3327   4.6810
-#>   14.0324   2.9959 -11.2916   8.4420  -1.3429  10.1206   6.9614  -1.7276
-#>   -9.8757  -3.8125  -9.9773  -9.4471   5.8427  -8.9117  -6.0019  -4.9530
-#>   -7.5694  13.4387 -14.0810 -13.3506  -0.1395  -1.3370   4.0942   3.1384
-#>  -15.3091   1.5466  -7.9623  -5.0435   0.9694   9.4680   9.3944  -0.9201
-#>   10.9647  -3.0660  -0.2466  -3.3298 -10.6204 -13.0028  -1.8922  10.1039
-#>    1.9402   5.1355  11.9231   5.3982   4.5491   2.5113   4.7216   5.9898
-#>    3.4196   5.3555  -3.5012   5.1005   2.0839   0.7908  10.2228  -5.0219
-#>  -12.1527   0.5536  -4.1541   5.5112  -1.1533   6.0899  -1.1417   5.2220
-#>   -5.5688  -0.8625  -9.4234  -4.4074  -1.7161  -8.4068  -7.6863  -3.9451
-#>    7.5764   6.6458  -1.9726  -7.0190   4.9921   6.4549   1.7532   6.0018
-#>    7.9313  12.2998  -4.0938  -7.3600  -3.4463   5.2630  -3.4504  -4.2298
-#>    5.3973  -4.2705  -3.6850   1.1945  -8.5218  -1.3317  -2.4017   0.3735
-#> 
-#> Columns 9 to 16  -9.1700  -5.4425  -9.0628   2.4983   4.2815  11.4832   3.8628 -12.8614
-#>   -2.9090  -0.2501  -6.0506  14.9251   0.4494  -0.0867   5.6211   1.6150
-#>   -8.0287  -5.9114  -0.7751   4.1525   0.1630   7.7034   5.3568   3.9979
-#>  -13.7131  -4.1252   9.3736  11.7485   9.8657  -4.1714   1.9415  -9.1430
-#>   -0.1387   3.0135   0.7491  -1.8394   0.1614   2.2556   8.3285  -0.2316
-#>   -7.1668   2.9922   2.4090  15.7913  -6.2054   1.9636  -3.4500   7.7883
-#>   -4.2918  -4.8643   9.0532  -4.6586   1.6853  11.3118  -0.0763  12.0158
-#>   -3.6470   2.7414   1.4341   3.2723  -2.6892  -4.6365   0.6876   2.2663
-#>   -1.0103  11.8353   2.4095   3.1762   3.4075   2.2852  10.2877  -2.9286
-#>    0.0213  -3.4105  -5.0831  -0.0772   4.8501   1.4105   6.8952   0.7864
-#>   20.3048   2.1310  -2.1970   0.2460   3.9726  -6.4222  -8.8292  -4.2873
-#>   -9.2621  -9.3889  -2.6775   3.2402 -10.3275   6.7707  -0.3343  -5.5113
-#>   10.8026   8.6806   0.4268  -7.1872  -6.6650   8.9344   7.0174  -5.5875
-#>    1.4095   2.6411   5.9594  -5.0065   8.6666   0.3373  11.5285  -3.7100
-#>    8.7947   7.9955   5.4419  -9.5228  12.7298   8.9183   5.0018   4.0518
-#>    0.5228  -2.5511  -0.3390   1.5011   7.9529  -9.4144  -2.0756  -8.6872
-#>   -7.0986  -6.2085  -1.4724  -1.9024  -6.0415   5.1373  -8.1891  -2.0590
-#>   -8.7808  -0.9564 -12.4611  -8.2841  -4.8299  -0.9901  -2.5113  -4.2294
-#>    5.1060   3.8005  10.3105  18.3401  -9.3370  -6.2471  -4.2201  -1.0089
-#>   -4.3429 -12.8005  -3.6652  -3.7718   6.2987  10.2018   4.2139  10.7305
-#>  -17.5902  -7.8866  12.9877   4.4011   2.7936  -7.6180  13.9074   1.7445
-#>    4.2564   8.6423   2.3472  -9.2550   8.6073   9.5538   6.6635  -6.1956
-#>  -13.6064  -5.4308   6.6853   3.2149  -1.3079  -3.1043  -5.8836   0.8049
-#>   -1.6895   4.3386   8.2895   3.9100 -12.1941   5.3036   6.2939 -16.0732
-#>   -8.1796  -5.9860  -5.0674   9.5823  -5.6035   5.2585  -8.3461   4.2852
-#>   -7.3154  -9.5797   6.5789   5.3402  -3.7686   1.3971  -2.6134   1.4748
-#>    0.2336   9.6401   6.4530   8.9854   9.1470  12.0268  -3.3962   2.6446
-#>   10.4801  -7.9177   8.4949  -1.7847  -3.1570   4.6640  -3.5705  11.5037
-#>   -3.3292  -7.2115  -7.2814   5.5860   2.5205  -4.7651   2.1381  11.7788
-#>    1.8813  -1.8615  -7.5800   2.1251 -14.1831  -0.7501  -3.1613  -4.1596
-#>    7.9042   5.9622   5.9364   7.8909   1.2282   4.5489 -10.1794 -10.3223
-#>    1.7342   0.1356   1.8621   8.5985  10.3165   1.0473  -3.2761   5.2590
-#>    2.2492   2.5793  -1.9641   3.2908  -4.4731  -0.2186   0.3898   0.2026
-#> 
-#> Columns 17 to 24  -4.2233   6.4903   6.4824  -1.4196   4.8213   6.1748   6.0226   2.4244
-#>    3.0449   3.7639  -2.5759   6.9809  -0.7800   1.7511   3.8831   4.2390
-#>    1.0488  -2.6044  -4.1870   0.7029   5.0582  13.9877   5.3120   3.5888
-#>    6.0070   6.5205  -8.5075  13.3583  -0.2409   6.9363   0.4215   1.7057
-#>    4.8480   3.5007  -1.8699 -10.8386   5.1291  -4.9043  -2.1371  -5.0365
-#>   19.0496 -13.6680  -6.3532   9.1346  -4.0195  -3.4128   8.0442 -18.1827
-#>  -10.8550 -11.0827   8.8548  -1.0606  -3.7950   6.9387   1.0117   4.5115
-#>   -1.6574  -3.8386   1.0533  -3.1069  -7.0465   2.9287   9.3957   2.9272
-#>    4.9568   3.9067  -6.9688  -5.0304   6.6911  -2.0881   1.5644   4.6007
-#>    1.8367   7.9528   0.6834  -2.0801   4.4093  -1.0517   3.4551  12.8767
-#>   -2.4516   1.4194  -6.7113   2.9893  11.2839  -8.8115 -12.6606  -2.3365
-#>   -5.6027   4.1230  -0.2919 -10.3406   8.2155   1.4383  -3.7078 -15.4252
-#>    6.5895   2.5759  -1.3891   0.8086 -11.1012   9.8928 -10.9215  15.6942
-#>    1.6642  -1.2642  -0.4765   2.7864  -2.2443   6.1590   1.0146   2.5986
-#>    1.0419   1.3659   0.6039  -5.0359  -2.2402  14.3927   9.0557   5.2491
-#>    6.4229   3.5484  -1.2524  -0.4892  -3.4019   3.0809   3.7813   4.4377
-#>   -5.1540   0.9143  14.0819  -1.7899   0.6947   1.4108  -3.0455   4.5929
-#>  -10.6781  -6.3872  -4.5548  -6.6081  -0.5025   4.2739   5.1464  -5.9072
-#>    8.7503  -5.9570   2.4831  -4.3309   4.5570   6.4887   7.9486   3.6529
-#>   -6.3005   4.0479   9.9377   3.0526  -0.2363  -7.0282  -0.0508   4.9989
-#>   -6.6149   4.3750  -2.5539   0.0184  -6.1014  -5.4061   6.5405   3.4628
-#>   -9.9721   8.5501  -2.9886   9.5830  -8.8987  -0.2068   2.3084  -0.8445
-#>    7.9700  -2.0417   7.5790 -15.0848   2.8634  -5.3935   2.9578  -3.5884
-#>    3.0857  -0.8034  -2.5963  -4.5656   2.6836  -8.8144   0.8256   4.5884
-#>    1.8981  -7.6407   0.2676   2.9430   9.5955  -6.7089   7.3951  -6.9108
-#>    9.6305   4.2658   1.6670  -3.3669  -5.1492  -0.5657  11.6158   5.5527
-#>    1.0008   7.9193   9.3322  -5.0705   4.1512   6.1594  -3.7197  -6.7994
-#>   -2.6899  -4.9149  -2.1636   7.0355  -8.7861  -5.2892 -10.5869 -17.6919
-#>    0.6838   0.8199   7.2066   2.8384   4.5133   5.0984   3.4396   3.6857
-#>    4.4714   2.8224   7.5290  -7.9173   3.3588  -8.8742 -11.3356  -7.5563
-#>    8.5606   6.7477   3.0738  -9.4245   6.5681  11.2474  -3.1492  -0.1624
-#>    8.2534  -2.3553   5.9498   1.4656   7.2768  -0.2787  -7.3997   6.9294
-#>    9.2180   6.3995  -3.5352   4.6713   2.0107   1.4465  -1.4998 -13.3744
-#> 
-#> Columns 25 to 32   1.2006 -21.4737   0.9001   5.9005   6.3590  13.1644  12.8056  -2.2806
-#>    1.8015   1.9923   7.9683  -2.1466   1.5827   1.7011  -0.8395  12.8080
-#>    5.4818  -0.8662   3.4282   3.0912   0.0991 -15.9086   4.2903   9.6336
-#>   -4.9352   4.1994  -1.9150   3.4635  -0.2113  -2.8963   2.8749  -2.0883
-#>    9.9119  -2.3160   8.6780 -10.4556  -3.6645  -2.5613  -7.9138   1.2894
-#>    7.8871 -15.3103  -8.5266  -3.0464   4.2798   2.4715  -6.7049  17.0776
-#>    4.4950 -10.8351   3.4407  19.0463  -7.8943  -0.6938   4.3225  -0.0173
-#>    3.2397  -3.7939  -4.6765   8.3715   4.3350   2.6491   5.8642  -4.2448
-#>    3.7390   0.5699   1.5634   1.3752  -8.9437  -1.8978  -5.1261  -3.9542
-#>   -4.8704   5.9890  -1.4254 -10.6813   6.4396   0.1326  11.2238   8.7575
-#>  -10.8583  -4.5291  11.5939  -4.8853  -2.0942  -8.0907  -3.6970   4.8505
-#>   10.9864   5.7334 -10.6233   7.9893  -1.8921 -14.8004   7.8390  10.5799
-#>   -0.9188  11.9645   0.7103  -1.0071  -5.8439 -12.7488   4.8880  -0.4999
-#>   -9.1022   1.4576   4.3436  -4.2208  -4.4709  -6.3411  -0.1393   9.1185
-#>   -7.6898  -9.3494   9.6089 -16.1926  -1.2733   9.3045  -3.8841  -5.5536
-#>   -0.3512  -3.1724   1.7844  -1.9881 -15.5762   2.9981  -0.3080  -6.5510
-#>   -7.1989  -0.4827  -6.9958   6.3886  11.6115   4.9651  14.6168   3.8082
-#>    8.4152  -8.2617   4.9626  -1.7486  -6.7048   4.1693   2.4776  20.8345
-#>   -9.3380  -1.1969  -0.6511   2.3570  -4.3794  -1.5527  -4.2422 -17.0752
-#>  -13.8299   6.4868  -8.9810   0.4807  13.4931   4.3804  10.0231   1.8395
-#>    4.7416  -5.4565   2.1269   8.8037   2.9015  -2.4739 -10.3819   3.0256
-#>    4.5900   1.1081   7.7875   4.9756  -5.1406   9.5396   0.5284  -7.9706
-#>  -10.6671  -1.8440  -5.7507  -9.6134   0.4597   2.5786 -10.8169   6.2005
-#>   -2.9426  -5.4963   0.2590   6.3128   4.9737   0.8832  -0.7476  -6.4966
-#>    6.2490  -8.2589   2.1780  -4.8646  -2.6697  14.5472  -7.1675  -2.4236
-#>    6.2239  -4.4174  -3.0557  -0.4300  -0.0547  10.7877   2.4003 -14.0559
-#>   -6.4656  -0.9594  -4.6296  10.1206  -4.0693   0.3593  -6.3911  -5.4361
-#>   -7.3114 -12.1247   3.1014  10.2935  -7.8968 -12.6844  -6.3857  -8.1799
-#>   -5.6425   8.9205  -3.6614  -5.8458   3.4007   3.4536   7.0519  -0.3873
-#>    9.2386 -12.7774  -2.1179  -5.5670   1.9680  -9.7900  10.5361   0.4400
-#>   -7.3526  -4.2630  -2.5834  -8.1674   3.0012   9.5988  -1.7162  -3.8316
-#>   -8.0502  21.1164 -10.2557 -11.3802   5.5937 -13.6895   4.5989   6.8188
-#>   16.1440  -3.9049   5.8081  -1.8903  -4.4917   0.1119  -8.0698   3.1317
-#> 
-#> Columns 33 to 40  13.8945   5.1376  11.2892   4.7304   5.2678  -3.4176  -3.9634   6.5021
-#>   -8.8483  -1.0424  -7.2540  -1.0065  -9.8846  -5.2708  -4.0247   8.4387
-#>  -10.8568   7.6129   8.4755  -3.1647  -4.1767   1.8637  -5.9465   6.6877
-#>   -4.7889   2.6010 -11.0272  -0.9510  -7.6316   5.0427  -2.6191   1.1624
-#>   -8.7421 -19.1770  -4.6337 -12.8940  -2.2656   1.9619   0.7837  -0.8762
-#>   12.0341  -4.1220  -0.8017   8.1249   3.1926   1.6903 -11.3236   9.4815
-#>   -9.4582  -4.6722   1.3877   2.0304   5.5673   7.5023  -1.9270  13.0872
-#>   -4.0317   0.9335   8.0035   4.8700   6.0325  -2.9948  -8.9064  -1.1722
-#>    0.2323   8.4465  -1.6160  -7.1798  -0.7423   1.5307   1.2431   6.8303
-#>   -7.3312  -0.3445  -5.3169 -14.5565  -6.2619 -10.5918   4.3772   5.1601
-#>    2.6516 -13.7986  -4.7847  -6.2661  10.1320  -5.9332   4.6941  -3.3675
-#>   -7.1772  -1.6759   7.9337   0.4910   1.9311  -5.4967  -6.4506  -0.9066
-#>   -1.4451   8.3609  -1.1478  -4.2416  -2.5835   3.1141   4.8414  -4.9543
-#>    2.4346   2.4627   6.9711   2.8532  -1.3404  18.3892   5.8931   1.3370
-#>   -0.6633  -8.7064   0.6492  -5.9467  -6.5899  10.7705  14.2726  -1.0755
-#>   -8.1477  -5.0272  -2.3789  -7.4334  -8.4012   4.5336  -5.6346   1.4006
-#>   -2.9836   4.9160  -1.7135   2.6437 -10.8257  -3.6766   5.8261   1.5997
-#>    1.9477   1.5791   0.3320  -3.6651  -1.7538  -9.1123  -4.8502   5.0894
-#>  -11.1569  -1.6841  -2.3055  -0.9117  -7.9841  -3.5817  14.3205  12.3716
-#>  -10.0935  -2.0168  -5.7619   7.2712  13.5324   6.1849   6.3196 -13.0108
-#>  -10.8633   2.1813  13.8934   7.0051   9.0097  -9.0596  -5.2719   4.5808
-#>   -3.4282   5.9018  -7.4001  -6.6291  -0.2669  13.3171   4.1376  -2.6469
-#>    6.5581  -1.5709   9.2863   6.6821  -1.6363   8.3377   6.7423  -1.2998
-#>    2.4276   3.2596  -1.8186   2.4292 -10.6535 -12.4743   0.6836  12.4658
-#>   -2.0732  -0.9243  -3.3457   5.6381   0.9751   2.0973  -2.0401   7.6741
-#>   -3.1224  -5.8254   2.7578   5.8531  -4.7348   2.8611  -9.0410  -2.6366
-#>    2.3521  -2.6690   1.4861   8.4215  12.6360   9.4422   4.0272  -4.4723
-#>   -4.9759  -4.6801   3.2078   6.0508  -0.4729  12.5741   7.1624   0.3861
-#>   -3.6352  -1.5695  -2.0715   1.1457  -3.7931   1.3690   1.7022  -1.2421
-#>    3.2337   3.5467   6.6668  -9.6162  -6.5442   1.8203   8.3872   8.9253
-#>   -6.3699  -3.4739   0.2831  -2.6326  -3.4428  -0.5782   3.6256  -2.2742
-#>   -2.2679  -5.6235  -3.2640   5.4520  -7.1208   5.1629  -5.4996 -18.7610
-#>   -4.0078  -3.6185   1.1123 -10.8744  -4.2643  10.0603 -11.6839  -1.0615
-#> 
-#> Columns 41 to 48  -5.8828   2.6631  -3.0110  -6.7809   0.0293  -9.6178  -4.4755  -2.6119
-#>    4.1385  -0.2211  -5.7847  -0.0816   4.6601  -1.8591   1.6024  -4.3605
-#>    4.1862   1.2995 -10.7519  -5.6809   1.6123  -1.3902   2.0055  -6.9491
-#>    7.3520  -4.4029  -4.7040  13.0503 -17.7398  -1.9633   4.4114   3.0867
-#>   -6.6644   3.7429   8.4155 -15.3608   9.3341   0.3042   6.8965  -2.5737
-#>    3.2898   6.2717  12.4690   8.1115  -9.0811   1.5432   2.7472  -3.1159
-#>   -3.8740  -6.8035   9.4345  -3.9589   0.1824   4.4107   5.2822   4.0166
-#>    2.5250  -2.8588   4.2052  -0.8799  -2.6479  14.9133  -5.0021  -3.4389
-#>    3.1106  -1.1804  -7.3940   4.4292  -1.1075  -1.8306   8.6412   3.1824
-#>    5.9995   8.5145   9.8750 -12.1270   4.5127   2.0419   2.5088   5.1208
-#>    2.3775   1.5137  -4.6429 -16.8772  17.8410   8.7111  -9.2237   4.7237
-#>   -6.3879 -10.6307 -10.1143  -0.3322   7.1997   4.7498 -13.2150 -12.5384
-#>   -1.1561   5.3692   5.9235  14.8841 -11.4622   7.4580  -0.6476   7.9789
-#>   -2.6277   0.0278   4.4945   5.5632  -3.9244 -11.6345  13.3383  -2.1119
-#>  -13.9300  -0.8732   8.3611 -12.4894  -7.5368   4.1963   4.3556  -8.3775
-#>    2.0822  12.0235   5.5337  -4.5093  -0.8370  -1.8867  12.7920  -2.3058
-#>    5.4194   3.3481   8.3929  -4.1561  -2.4016  -0.4821  -2.4102   5.9310
-#>   -2.7429   2.2809  13.0835 -13.3393  -4.6388  -2.4268  -4.5999  -6.9089
-#>   11.8154  -0.0086  -8.7581  -1.8602   6.0035   7.5693  -1.2043   9.0918
-#>    3.5296  -8.8532  -8.3397  -6.5962  -0.2161   4.7971  -1.8156  -0.7327
-#>    3.9167  -5.9942  -4.0317  -1.1674  15.1550  -5.2999  -0.7470  -2.4903
-#>  -13.4262  -8.0015  -4.5641  11.9610  -6.9310 -14.0014   5.2533   7.4856
-#>   -0.4048  10.0374  12.3274  -7.3601  13.5395 -12.6968  -3.3714  -0.7631
-#>    8.6631   5.0238   5.7300  13.5159   5.2352 -13.0944   8.8396   3.8043
-#>   -2.9619 -11.5246 -16.4590  -2.5229  -0.7031  -3.5418  11.5896 -11.8498
-#>    6.3225   4.4412  10.7698   5.8504  -2.5050   5.8819  -1.0052  -3.7116
-#>   -9.8836  -0.2882  -1.3196 -10.6948  -0.0785   9.0144  11.0455   5.9665
-#>   -0.6816  -3.5955   4.0003  -4.2055   6.3928   3.0317   0.4626  -1.8370
-#>    1.2728 -12.7381 -11.3945  -3.2397   9.7050  -1.7765   6.0116 -12.0875
-#>    9.3059  15.8616  -3.6912  11.0371   6.4374  -8.0325  -1.6756   7.2352
-#>   -1.4773   9.8128  -8.6768  -7.0641  -0.5934  10.5564   7.9960   4.2665
-#>    1.8995   6.3863  -1.6942   4.8941   0.6913  12.0428  -9.7984  -1.0175
-#>   -2.9466   4.8298  -9.6127  13.0258   4.2701  -3.4983   3.2971  -9.5837
-#> 
-#> (13,.,.) = 
-#>  Columns 1 to 8 -13.7181  -0.3447   5.8356   1.5973   6.2881   7.5921  -5.4251   7.5284
-#>    6.1287   1.0172   4.8493 -11.8992   0.0052   0.0865   8.0248  -0.1148
-#>   -2.7358   6.5331   9.9707  -0.5685  -7.6053  -2.8306   4.6883  -3.0714
-#>   10.5341   0.0862   2.1901   0.7360   5.3057  -1.7246  10.2474  -0.4840
-#>   -5.9171  -1.9733  -0.1388 -11.1198   6.1604   1.0356   0.4460   0.5814
-#>   -1.5607  -0.5406   7.0331   7.1030   5.0921   4.7854   3.2406  -1.8484
-#>   -5.0085   2.8199   4.1907  -1.4205  -1.0750  11.5682   3.4545  10.3755
-#>    3.5391   0.1704   0.2633   0.0874 -16.5464   4.8874   3.8234   0.3897
-#>    2.8643  -0.8036  10.4355   0.0936   3.7378  -3.9538   3.5668  -6.0208
-#>   -0.3664   0.5333   2.8271  -6.5794   7.8843   2.2596  12.7841   1.2995
-#>    1.6696  17.1502 -12.6774 -21.5333  -0.3590  -2.8230   0.7653  -5.5555
-#>    0.3097  -3.6061  -5.9365 -14.6621  -9.4706  -1.2509  -8.0828  16.4437
-#>   -0.9067  -5.4808  -5.9482   3.4010 -11.3237  -5.6148   0.5647   2.0937
-#>   -2.1402  -2.1574   1.6820   1.7839   9.6963  -5.1659  10.5187 -17.2321
-#>  -12.0609   8.6689   7.5419  -5.2172  -1.5569   1.8496   9.3615 -19.1509
-#>   -7.8590  -8.8998   3.7620  -0.8961   1.9522  -4.9404  13.7316  -8.6320
-#>   -1.3495   1.6034   4.7689   7.2803   3.5203  12.2188  -7.4717  19.8181
-#>   -5.8713   0.0045  16.6472  -7.4291   2.1585   1.1381  -5.0502  -6.4395
-#>   -8.0780  10.4833  -7.7087  -4.3378  -6.3973   7.0898  -0.6432  -5.0435
-#>    1.3963   3.7577 -13.2720   2.1819  -3.5963   3.7020   4.4230  11.6741
-#>   15.3527  -7.6608   6.6919  -3.0026   5.1196  -7.1289   2.5166  -8.8008
-#>   12.4373 -10.7005 -10.4661  -0.0378   5.0841  -9.1134   6.2372  -1.9012
-#>   -3.8107  -2.2387  -2.3170   6.6957   7.6508  11.8243  -6.0962  -4.2987
-#>   -6.0589  -6.3465  -3.5900   8.8230  10.4383   0.5384  -1.9179  11.7249
-#>   -8.0175   8.1418   4.3234  -0.7016  -1.8186   4.3396   1.3985   3.0427
-#>   -9.0953   0.0205 -16.0239   7.2141  -6.1833   5.6397  12.4842  -7.2513
-#>    1.8304  -2.2553   3.7247   4.4194  -2.4612   5.6431 -11.8009  13.6524
-#>   -1.0657  13.1455  -6.4824   2.7998  -2.0011   2.4593  -0.1380 -18.9450
-#>    2.1227   1.4994  -2.0051   1.0570  -2.4858   9.9079  11.9125  -8.3056
-#>  -18.7767   7.6160   2.8001  13.6778   9.9759  11.7203  -7.3760  14.7773
-#>   -9.2775   3.5073   5.8065  -3.2242  -0.1886   1.7389  10.1169   5.1351
-#>    2.8881   1.9169 -14.5476   0.0994  -0.6918   1.7538   6.6907  -2.9264
-#>   -1.5610 -15.5603  -9.3431  -5.6707  -2.7868  -0.6588   0.4745   7.5594
-#> 
-#> Columns 9 to 16  -0.4537  13.3681 -18.0402   5.3283   7.8392  -6.5154   2.1867  -0.0730
-#>    0.4234  -2.8830   8.3738   5.2371  -5.4255  -9.4456   7.8477  -3.3967
-#>    0.8610  -4.0427   7.0516   8.8728  -8.9026  -6.1186   0.6182  -0.7321
-#>  -12.4426  18.5727   2.2000  -3.9517   1.9837  -4.6114  12.5401  -3.7230
-#>   11.1755 -13.6765   2.2766   7.0844  -5.9924   2.1159  -2.8463  -9.8425
-#>    3.4147  -0.2241   1.2905   2.3795  -0.3784   1.7020  -1.0787   2.1012
-#>  -12.1654   3.6320   0.2117  13.0024 -15.6153   0.4192   0.7902  11.0978
-#>   -3.3314  11.4002   1.2202  11.2767  -9.8292   1.9364  -3.5906   0.8670
-#>    1.3514  -4.4492  -4.2492 -10.7704   8.9424  -7.2758   0.2141  -8.3028
-#>    3.5126 -18.3866  -2.6702   9.4522  -6.4155 -10.0933  14.7170  -5.0721
-#>    7.3894  -9.3259  19.8691  -1.6937  -5.9989   5.7458  -7.6616   4.0485
-#>   -2.3914  -0.6550  -3.2121  10.7993  -6.6451  -0.9181  -8.1194   8.8540
-#>   -5.0520  11.2931   4.6047   2.6079  -2.1932   1.5243   0.4934  -4.4654
-#>    3.8567  13.9962   9.6761  -5.5595   0.2366  -1.1271   9.6884  -5.6431
-#>    4.9715  10.0399  18.6660   6.2897  -8.3365  -3.5119   1.8689 -19.4082
-#>    1.4753  -2.3583   1.3196   4.5634   5.9071  -7.1842  11.7466   3.7223
-#>   -1.7783  -7.2138 -12.7813  -4.8437  -0.8411 -10.0547  -6.0221  -7.1953
-#>    3.2388  -6.6003  -9.2452   5.9463  -6.5550   6.6169  -1.9871  -0.7962
-#>    0.6546   8.8318  12.1956  20.1071  -6.5240   8.5515  -3.0849   4.9281
-#>    2.2310  -1.9946  14.7048   2.0993 -13.3510   1.7606   4.6692   0.4065
-#>   -7.1707   0.6846   3.2662  -1.9376  -3.8787  -3.6412   4.8520   2.4025
-#>    2.3054   7.0808  10.2049 -20.7992  -2.9452  -0.6283   9.2520   1.4946
-#>    9.4116  -2.0544  11.1145   8.0121  -7.4675  15.5080  -6.3952   9.1342
-#>   -4.9404 -15.8663  -9.9113   0.3530   3.1372  -6.2037   3.0748   9.5932
-#>    4.7657  -7.6916  -1.1407   2.0595   8.4072  -2.1366   2.2217   1.0098
-#>    4.1171   3.3204   5.5477  16.9107 -11.5887   2.8421   1.1560  -8.0653
-#>    6.5559   9.8319   0.0996  -2.4849  -0.1135  10.7154 -12.6500  -6.1913
-#>   -1.2950 -24.7869  11.4024   2.9801 -18.1488  -3.0154   3.3089   2.1303
-#>    1.2271   3.9828  -0.3498   6.1050  -2.0436  -0.7848   4.1063 -10.9277
-#>  -10.3081 -13.2193  -5.8505  -0.1730   4.0578   2.0895  -1.7765  10.1179
-#>   14.2637  11.8619  -5.1396   0.1210  -2.3210   0.0329  -7.7430 -10.3766
-#>   -6.8249   6.6138  12.0710  -1.9869   5.8455  -0.9538   2.8660   5.0700
-#>    1.6378   4.3483   8.2711  -2.6529   7.7409  -0.4693  -0.2296   3.8611
-#> 
-#> Columns 17 to 24 -18.2529  -0.7843  11.8839  -4.1371  -7.5558   7.9947  11.7894   2.7651
-#>   12.3867   4.1103  -4.3933  -6.1404  11.4483  -3.2177  -6.0363  -4.0282
-#>   13.4974   2.2192   5.8247   3.9458   6.4274   4.3778  -9.2454  -6.9374
-#>   10.8095  -0.0647   6.7780  -4.2264  10.1764  -9.3652   8.2440   5.3695
-#>    1.5547   1.4251 -10.5475   3.5811   0.9859   4.3994   2.8293  -1.6326
-#>    7.2752  -7.2462  -9.2140   1.8691 -10.8841   9.8740   4.3779 -14.9532
-#>  -10.0497  -4.1462   5.6859  -1.9422  -7.2311  -2.2039  -6.7706  -7.7515
-#>   -7.2611   0.3434  10.8226  -2.5999 -11.7364  -1.3330 -11.1642  -6.6722
-#>   -0.5093  -2.7057   1.1337   5.9100  -0.0395   8.2051   8.4493   2.2405
-#>    3.0575   8.8751  -8.7246  -5.4412   6.1884   3.0971  -1.2062  15.2766
-#>   -9.0075   6.7520 -11.1594  -3.4083  11.5826  10.3835 -13.9912   5.7969
-#>   11.1581   3.9730   1.6344   9.1667  -8.0174  -3.3163   1.8599 -14.1742
-#>    8.8165 -10.4774   5.9238  -7.9778  15.0429  -2.0832  -8.3252  -0.3596
-#>    7.0435 -16.1194   0.4444   3.5330   2.8708   7.6799  -3.6777   4.3081
-#>  -11.0742 -13.8376   2.3848  -3.8527  10.4867   1.3087 -13.1818   8.7516
-#>    9.0784   2.0644   4.0886   4.4662   0.3837   4.4890   3.2950   7.3878
-#>   -3.0825   2.8900   1.5499  -1.8036  -6.2011  -5.9667   3.2099  18.0180
-#>   -6.6086  -0.4012  -0.6056  -1.2367  -2.0511   6.0924  -0.1952  -1.4324
-#>   -5.3393   5.1967  -0.9222  -0.1756  -2.3408   2.1642  -3.2054   3.4058
-#>   -2.7763  -1.9330   5.2968   6.1661  11.7368  -1.3482 -13.2555  10.1649
-#>    7.7419  -1.5774   6.6621   4.8569  -2.8487  -1.1811  -1.2904  -3.5283
-#>   -2.1136  -3.8623  10.4048   5.8220  10.9268  -1.0691  -0.8613   6.4316
-#>   -5.7798   1.2923 -15.6228   1.4064  -8.7560  -1.9435   0.4758  -2.0679
-#>    9.5343   4.2179  -6.6049  -5.6621  -1.8436   2.4079   3.7554  -6.3271
-#>    4.9440   5.6170  -2.8385   6.9373  -4.0377   1.8346   3.6950   0.9082
-#>   -9.6127   7.4395   3.0402  -2.2246  -7.6659  -1.1280  -1.8699   9.9940
-#>  -12.7051   4.4045  -3.1659  -2.2974  -1.9191  -4.9103   1.0011 -13.5765
-#>    1.8682  -9.5825 -10.4569 -11.7557   4.8390   6.9839  -4.2444   9.4414
-#>    6.5605   1.6008  -2.1709   3.3722   1.2255  -0.9195   0.8182  14.2683
-#>   -5.5613 -12.4914  -7.0133  -0.1660  -9.0405  12.4403   5.1551   7.0832
-#>   -7.8657  -4.8879   2.4015  -0.1641  -8.1200  -4.2953  -0.9813  -2.0122
-#>   13.1090  -1.1549  -9.9288   3.4933  10.4903  -6.2657   4.1032  11.1627
-#>   22.9732   5.6470   3.7499   6.1426   6.4469   4.5686   8.4451 -10.3352
-#> 
-#> Columns 25 to 32   5.7054  -5.3441  -6.0978   6.0447 -10.4055   8.0234  -0.7850  -5.8711
-#>    4.4482   5.9026   8.5244  -6.5094  -4.0751   6.0330  -6.4294   0.0522
-#>    9.7296   3.1358   8.8016  -8.0296   5.5832  -3.2571   5.4091   4.3074
-#>   -5.8150   4.7967  -0.4531   0.9397  -2.1079   3.9001  -4.1233   0.6805
-#>    1.8793   6.4777  -0.0286  10.9774  -0.6168  -2.8688  -0.0493  11.3595
-#>   16.6737   1.7602 -10.4666 -16.6184   8.4079  11.1176  -7.8161  12.0327
-#>   13.1589 -15.4014   3.1226   7.8401  11.9626 -20.9714   6.7551   3.3441
-#>    5.5559  -8.3731 -10.7067   2.6777  -6.6518   6.9165   5.1903  -0.0387
-#>    0.6119   9.3752   1.1777  10.1055  -7.7612   2.4682  -2.6647  -3.2515
-#>    1.4933   0.8544   6.1785   1.2085   0.6575  -1.3207   0.0227   5.0657
-#>    3.8068  -3.8701   6.7254   6.9575  -7.4438   5.2901   1.0952  -7.0580
-#>   10.7262   7.8740   0.3326 -21.5471  -7.1646  -4.9007   9.6586   9.2646
-#>    0.9556  -0.0027  11.4575  -1.3609  -1.2580  -5.2391   2.3976   8.6294
-#>   -0.6346   4.0469   3.8204  -2.8324  -1.9279  -6.9823   0.8844   9.6346
-#>   10.8041  -2.5132   4.4946   3.0518  -0.3110   0.5100   2.4100  -2.7544
-#>    1.7201  -0.0440  -3.2218   2.4878   0.9819   0.4591  -1.7396   4.6635
-#>    3.1439  -8.0099  -2.2478   2.0788  19.5492 -13.2788  -6.1485  10.1090
-#>   17.1915   1.1694  -0.3155  -3.8923  -4.0340  -0.6389   0.5847  10.7342
-#>   -3.1465  -5.3648   4.2524  -2.8767   4.0003   2.2823   3.7417  -0.6386
-#>   -7.0934  -3.8575   1.9482   8.5610  -4.5604 -11.2780   8.2424  -1.5578
-#>    1.1857  -1.3874   1.4302   2.4410 -17.7996  -2.9959  -4.0117  -7.0299
-#>   -4.5234  -1.2063   1.6614   6.9609  -5.7689  -0.8851  -1.3269  -8.5309
-#>   -1.9256   3.0458  -3.4664 -10.7231   5.4695  -2.0249   1.7123   5.4240
-#>  -15.2104   4.3967  -0.5276   2.4381  -4.1284   6.0441   3.7916  -6.4473
-#>    7.1233   0.2088   0.9726  -8.5699  -3.5048  -4.0555 -10.4901  -1.2846
-#>    1.7306   0.8744  -4.3449  13.0051  -0.1197   2.0478  -3.5184   5.3571
-#>   -3.3896  -4.1441  -3.2758  -1.9248   3.6815  -1.8899   4.8495   0.0962
-#>   -0.2889  -3.1917   0.6236  -3.3692  14.2575  -8.5865 -10.0056  -3.6139
-#>   -3.2592   8.1670   2.5470  -6.6863  -1.6065  -5.4853  -9.3110  -1.9122
-#>    6.8977  -4.4494  -8.8062  11.3942  14.2317  -5.9056  -6.1580   0.3269
-#>    1.0336  -5.3697 -12.3649 -15.0611   5.9503  13.7932  10.8067   4.7749
-#>   -2.3155  12.2920   2.8133 -11.2705  -3.1521  -5.7135   4.6695  -6.1455
-#>    2.0441  -0.5978   2.9946   2.2039   1.6005   0.5476 -10.1008   5.1450
-#> 
-#> Columns 33 to 40  13.9888   1.6621  -6.2028  -5.8881 -15.0765  -7.4038  -6.6962  -5.2960
-#>   -2.1251  -0.9159   0.2313   6.4890   5.8609   1.1657  -1.4320   1.8357
-#>  -13.0969  -3.3009   4.5870   0.1656   6.5823   4.0142   0.2058   5.6121
-#>    4.0445  -7.3677  -3.3419   6.4198  -2.8480   5.5827  13.7324   2.4249
-#>  -18.3887 -10.2412  13.6661   7.2739   0.7158   3.9746  -4.5782   9.0107
-#>    1.4559 -11.6066  -6.2290  -2.9388  -5.0435  -1.1411  -1.3263  -4.6638
-#>   -9.8511  -7.0693  11.5878   9.9078  -6.7654  -3.8763   0.2616   1.9187
-#>    6.3136  -5.5531  -0.5147   2.4884   5.5770   0.8150  -1.4526  -5.0775
-#>  -13.1313   3.6136  -6.0809   1.1632   6.8753   4.5192   2.9325  -4.9329
-#>  -16.4135  -5.5086   5.1798   0.9344   4.7721  -5.9660  -6.8268  12.8024
-#>   -7.5546   8.2615   3.2973  -7.1439   6.9062  -3.2143   3.7799  -3.6713
-#>  -10.0234  -4.3302   6.5380  -3.5359  -0.6433 -12.9074   0.1519   6.9613
-#>    1.6368  -3.8118   7.6408  -0.7032   9.1176   7.9364  -6.1997   0.0139
-#>  -11.1897   8.9903   6.2937  -8.6550   0.1846   3.1229   7.4796  -9.4778
-#>  -13.8819 -10.0532  17.6174  -8.0613  -1.6298   9.8393  -9.3358  -9.0107
-#>   -1.8548   1.0385  -0.4659   6.4678  10.0948  -3.8695  14.7695   8.0527
-#>   -8.6322  -9.9610  -2.1486   5.3907  -7.8803  -8.2907 -17.0719   9.2418
-#>   -5.7645  -4.1250  -0.8757  -5.3394  -1.9818   0.1242   1.4888   1.3666
-#>    4.2515  -3.5361   3.5730   9.5375   8.4103   2.8748  11.2373   2.8970
-#>   -7.1270   7.0362   6.7197   0.2017  -9.1090  -3.3480 -11.7893  -5.8566
-#>   -0.3841   5.4366  -0.9445  -2.6105  -5.0244   3.3680  13.2543  -1.5195
-#>    1.0747  14.1734   1.1987  -1.7408   4.8632   1.5659   5.5709 -19.8471
-#>    1.6983  -1.2366   4.8798  -2.5299  -9.1657   0.9159   2.1604  -8.2412
-#>   -1.0699  -3.8091  -0.6495   0.3911 -17.1536  -1.6623   1.0929   8.2718
-#>   -1.9780   0.0814   0.5713   5.0296  -6.1497   2.7823   4.6570   2.8901
-#>    9.9274 -15.5823  11.1468  13.4251  -6.8414  -1.5312  -4.1873  -3.8815
-#>   -3.2455  -0.2084  -6.4184   2.6211  -4.3341  -1.4415  -4.0072 -10.8088
-#>   -5.4387  -5.7921   0.6411  -5.3342  -2.9096 -17.0201   8.5314   9.1331
-#>   -9.3609   2.5416  -0.6532   0.8389   3.4634  -5.3714   1.6504   6.9934
-#>    1.2955  -0.9298   0.3603  -5.5105  -4.1873  -4.4538   2.8055  14.2132
-#>  -10.8144  -7.4770  -1.9756  -0.7660   0.2557   5.3072   3.9560  -1.3456
-#>  -12.0214   2.5167   8.1731  -1.4205  -1.2927   8.0969  -3.6897  16.6704
-#>    1.7988   0.0107   8.2771   4.0196   2.4219  -0.4908   1.5162   0.4107
-#> 
-#> Columns 41 to 48   6.8223  -1.1953 -16.5700  -0.2202  -1.8059  -4.1714   2.2586   3.1392
-#>   -9.7408  -4.4622   9.2715   9.7497  -9.3702  -6.9846   1.4185   0.2367
-#>   -1.3460  -4.0343   6.5043   8.9333  -2.8347  -0.7131  -4.1512  -1.6062
-#>    0.1806   3.0887  -2.6297  -1.5396   3.7892  -3.0346  -1.3348   9.8749
-#>   -6.5430   1.0502   4.3606   7.9445   2.2091  -2.4708  -2.1457 -11.0325
-#>   -0.3296  -1.0538  19.1806  -0.6681   7.7155   1.0968  14.9470   9.6190
-#>  -13.6597   1.3308   4.1055  18.2011   5.4537   1.0127  -9.1060  -0.3762
-#>   -0.2162  -1.0438   5.0688  -8.3406   2.6659  -4.0165  -0.4390  -0.7727
-#>    0.0441  -5.9841  -4.3727  12.3835  -6.2386  11.5710  -3.6306   8.3481
-#>   -1.1854   2.9303  -2.0192   6.8946  -1.6485  -7.4601  -0.4546  -1.8196
-#>  -12.3307  -2.6553  18.9656  -0.5494   0.6465  -8.9811  -3.0752 -17.2511
-#>   -5.0318   0.9506  13.3649  -5.5659  -9.9188 -11.2742 -10.3484 -13.8310
-#>   -7.5226   8.9456  -1.2620 -12.0812   6.8642   5.7276 -10.8331   1.1360
-#>  -10.2075  10.5311   7.0963  -2.4166  -4.3044   9.0821  -1.8509   4.5489
-#>  -10.6392   2.3903   7.6245  -5.2061   1.1225   0.5899   0.8800  -0.8110
-#>   14.0755   5.1738  -7.7262   3.4819 -10.6563  11.5738 -15.0048  10.6674
-#>    4.9958   8.7628 -13.1731  -2.1885   5.2119  -3.2639 -12.4037   3.4309
-#>    6.2419  10.6203  11.5762   3.5668  -7.6028  -9.1614   4.8107  -7.8572
-#>   -0.7675 -12.4405   2.2323  -6.8869   3.5631  -2.5244  -6.3250   8.6155
-#>   -4.1113   2.0419   1.0660  -4.4972   4.0218  -6.5370  -6.7348  -5.9009
-#>   -0.9550   8.0446   7.0813   0.4395 -12.6257  -4.2030   3.9091  -0.8361
-#>   -0.6885   7.8596 -12.6022  -7.9810  -2.3373  11.8033   1.6770  -1.6105
-#>   -6.5064   4.1790   4.0999   1.1366   7.9675  11.6719   3.3407   6.3710
-#>   -1.5531 -11.0727  -1.7039  10.3451   7.2884   6.7369   0.9745  10.8481
-#>   17.5924  -8.7264  11.3075   2.8726 -14.5478 -11.4686  -3.0124  10.2257
-#>    7.9230  -6.6112   1.0433  -3.9745   6.6238  -8.8205 -12.9009   0.8202
-#>   -4.9997  -3.1634 -10.4045   6.3738   4.9820  12.5160  -2.0337  -2.6327
-#>   -5.0487  -8.2076  19.7135   2.4629  -0.8365   2.6996  -0.6732  -1.3441
-#>    7.2947  -0.0056   5.3458  -0.1922 -20.1747 -14.5637  -0.0136  -0.7484
-#>    5.6158  -6.5718   1.9386  -6.6233  14.2731   6.9216  -3.8060   3.3327
-#>    4.9116  -5.7868 -13.6616 -10.8264   3.4249   4.6447   6.1558   2.3811
-#>   -0.3448  12.0279  -1.1617  -2.9150  -6.2155 -14.8863  -4.9204  10.1638
-#>    2.2497  -1.4470   0.6458  -3.5691  -0.2670  -2.3919  -8.6552  -3.1209
-#> 
-#> (14,.,.) = 
-#>  Columns 1 to 8  -2.1444   0.6275  -5.3745   7.5895  16.7731 -13.2129 -10.5422   9.0688
-#>   -8.6767 -10.2206   9.7955   6.8703   2.7551 -14.1020  -4.8347 -11.2143
-#>   -7.6052 -18.8394   2.9669   5.0451   4.2223  -1.5257   7.6646  -7.3693
-#>  -10.0294  -4.8756  15.5625   8.1806   3.2329  -9.9985   2.3995   2.0965
-#>    4.9436   1.4775  -3.2586  -7.1490 -10.9452   7.2870   3.4860   0.2213
-#>    6.0503 -10.1194  16.5172   0.6114 -16.4727   1.2303   4.3059  -0.2589
-#>   -1.6701   3.0303   2.6328   1.9484  -4.5294   1.2428  -4.5693  -9.4682
-#>    5.1566  -4.0968  -5.4200   5.1647  -1.7696  -1.5406  -3.8415  -1.7984
-#>    3.6765   3.4669  -5.7077   0.1770  -7.3732  -7.5296  -0.5793  -2.3397
-#>   -5.7430  -8.4634  -5.5900   1.6437   6.9694 -11.7347  -6.2177  -4.6205
-#>    0.6710  -4.0882   9.8013  -1.7805   1.2103  15.6890  -3.8652  -8.9470
-#>   12.5851   4.4384  10.4783   1.7717   1.1894  14.1159 -11.6373   0.3138
-#>    0.3266  -3.3944 -12.8845  -4.9403  -1.4425  -2.2774   4.4732  -4.2001
-#>    7.7831  -0.6257  12.6476  -4.7705  -1.3084   3.2592  -4.7172   3.3935
-#>    0.2951 -13.8237  -0.6221  -3.9925  -6.7304   4.5764  -0.5526  -9.1893
-#>   -3.1852  -3.4848  -5.6748  -3.6255  12.7578   2.8069   1.2936   9.8560
-#>   -8.7286  10.1477   3.0263  -0.5387   2.2513  -3.9209  -9.0792  -0.1861
-#>    8.2871  -9.3489   6.1300   5.8867   0.3383  16.0642  13.6681   5.7864
-#>   -2.4832 -11.1671   4.5390  16.8200   1.3044  -0.3483  -5.8973 -11.7801
-#>    1.2391  -1.0048   2.2145  11.8020  -6.3125  -1.3823  -4.2399 -10.7427
-#>    6.6959  -6.8043   7.1455   3.5484  -2.3540  -6.0205  -8.9290  -1.8643
-#>   -0.3061  12.4510  10.5427  -7.4929   0.3782 -12.1543  -9.1551   6.2211
-#>    9.7317   4.5159  10.3419  -2.1879  -2.6870   7.7015  -1.9451   4.4211
-#>    1.7871  -0.0642 -10.1845   0.0613  -3.8851  -5.8144  -6.2688   1.7174
-#>   13.4599  -2.8482   8.3512  16.3706   3.3594   5.3914   1.4781 -11.9550
-#>   -4.2157 -11.1060 -12.5199   8.8629  -1.2847  -3.2273  -1.1830  -2.4803
-#>    2.6273  15.6500   5.7461 -16.2158 -15.0618  -6.3968  -4.2644  -2.5628
-#>   -4.7286   5.6862  14.9851  -0.5636  -7.2220  -2.7243  -2.4637   2.2551
-#>   -3.3462  -7.6389   1.9538   5.6908   4.4791  -4.6541  -5.4730 -10.1804
-#>    2.2027   2.9326  -3.4673  -5.2041  11.2478   4.3450  -5.5164  13.3213
-#>   13.4535   6.0531   0.5279  -0.7927  -3.0388  -6.7141  -5.2771   0.3138
-#>   -9.7670   6.7562  -5.5766  -8.8379   5.7746   2.1539   0.0484 -15.2464
-#>    0.5020  -6.4378  -6.9533  -6.7794   6.2663  -0.8423  -2.0734   5.1950
-#> 
-#> Columns 9 to 16  -0.9762   3.0234   1.0104   7.0104   6.8566 -10.5834  16.1784  -4.9472
-#>   -3.3504  -8.0560   1.4816   4.3687  -7.1903   3.3486   1.2851  -8.6248
-#>   -5.5563  -8.5923   5.3647   3.1766  -4.4003  -2.6062  -1.3784 -10.4659
-#>  -10.4741  -5.5723  -0.6460  13.2420   3.4350 -11.2556   0.8239 -15.1732
-#>   -4.9091 -10.6420  12.9322  -3.6824  -3.0656   1.6655  -5.3298   4.0569
-#>  -13.2547   6.9211   6.5194 -13.3752  -5.8592   6.8385 -10.8662   6.6610
-#>   -6.7226  -2.5321   0.3613   3.7425   0.5684  -2.5713  -4.4943  10.9557
-#>   -3.2104   1.4335  -3.7294   3.8372  -5.0969   5.9416   8.7966  -0.9288
-#>   -7.9040   5.0802   1.7175   9.3299 -16.1795  -0.6950  -4.9105  -7.8795
-#>   -1.7059 -23.6254   8.1958  13.6085  -8.8340   5.5690   3.1202  -6.7429
-#>    6.1610   0.7591  -0.4275 -16.4001  -3.7464  14.8188   2.7395  -6.1317
-#>   -4.6173 -10.0567   8.0694  -2.3891  -5.2332   2.1577  -2.2042   5.9672
-#>    5.1777  -7.5451  -6.8466  -0.0412   4.9360   5.7510   4.0687  -7.6527
-#>   -3.6797  -2.3772   2.3120  -1.6865  -4.3676   8.5093  -5.7215  -7.1319
-#>    0.6331  -0.7750   5.7840  -3.2438   2.5939  -0.9735   1.9388  -7.4345
-#>   -3.6778  -6.0983 -16.4988  10.7501  -8.7596   6.7864  -1.5069  -5.2718
-#>   12.5570 -12.5301   3.2381  10.6822  -0.4238  -1.2423   1.9058   0.9420
-#>   -8.1062  -3.3461   1.2314  -5.4680  -1.8716  14.2853   2.3487   4.5719
-#>   -7.1079 -11.9893   0.3655   4.2615   4.4804   6.5035  -8.9245  -4.1921
-#>    2.1747  -8.1742   7.0437   1.0222   2.6713  -3.7678  -6.9374   9.9402
-#>   -1.1591  -3.6203  -0.1846  -5.2856  -8.4578  -5.6804  -3.7338   7.4090
-#>   -4.8591  16.9304  -8.2518   6.5114   2.1960  -4.1031  -0.9657  -2.2481
-#>    5.8799 -13.4325   1.9374  -7.1436  -4.0939  -1.5348  -4.6633   2.8750
-#>   11.9135  -5.9931  10.6113  -0.2672   7.9714  -6.8968   5.2002  14.0565
-#>   -5.4820  -0.7658   0.9432  -0.8519  -2.5855  -5.5044 -12.3018   1.7075
-#>   -6.5375  -9.9194 -10.3241  11.1755   3.7264   0.8428   6.9324   4.6708
-#>    1.6639  13.2267  11.1546   1.4382 -10.2988 -11.9187  -4.2637  -0.9742
-#>    7.6500   3.4899   4.6013  -9.5289  -8.5190   1.7298  -7.5066   1.5496
-#>   -4.4808  -9.7392   1.8155   3.2473  -9.2740   2.8275  -5.9945 -12.2909
-#>    4.8999  -8.4155  -6.2310  -7.1038   6.0395  -7.5361   8.3684  -1.2237
-#>   -0.3172   2.4322  10.8408   4.7848  -1.3948  -2.0308  -3.4644  -3.6158
-#>    1.2032  -0.9063  -0.4967  -4.0826   1.0183  -3.8041   1.0185  -9.7255
-#>   -4.6066   0.2925 -11.5063  -3.3608   3.6180   1.6864   2.4220  -4.2550
-#> 
-#> Columns 17 to 24   3.7132   2.7026  -1.0514  -6.3857   5.8852   9.2122  -7.7985 -10.9549
-#>   -3.5542 -10.4813 -10.9183   5.2912   0.7644  -1.3522   5.8959   4.7606
-#>   -1.8724  -4.6788  -9.0386  -0.9991   8.7160   1.1418   3.4454   0.1548
-#>    0.3223  -5.1287  -7.9411   2.5696  13.8285 -13.6017 -11.8921  -1.6590
-#>   -7.4142   8.6525  -8.4901  11.6494  -4.1533   0.5475  -0.9908  -3.4346
-#>   -4.0823  -2.7602  -2.7770   7.8444  -8.3679  -1.6173  -2.7454  -4.3434
-#>   -8.7138  -5.5199   5.0951  -9.3132  10.9098  -0.3270  -5.0873 -10.6834
-#>    1.7729   0.1606   3.5235  -3.2069  -1.4126   5.3862   1.6555   6.0412
-#>   -2.3556  -3.7612  -5.9476  -0.6006   8.3621  -6.4094   0.4973   8.1502
-#>    5.8877  -8.2092 -14.1709   4.3547  -1.2038   5.5549   6.8928   1.8750
-#>    2.7522  11.6348 -15.4113  14.9461  -7.2504   3.8405  -1.0440   1.2750
-#>   -2.4977   6.1348  -1.9506 -13.9133   4.9634   8.0606  10.5353  -9.5552
-#>    3.4657   0.2648  -3.3996  -1.7110   5.0830  -2.7579   0.3588   4.0522
-#>   -2.9026  -0.4903   4.1745   1.7057  12.6265 -10.0762  -9.1472  -2.8776
-#>  -24.6963   4.9028   2.2497  12.7402   6.2606   0.9909   5.0337   3.1554
-#>    2.6343  -1.5731  -0.3098   1.5872   8.9305  -9.5032  -3.1197  -4.7093
-#>   11.4809 -10.5834   7.0071  -0.9855   0.1514  11.3191  16.7707  -4.3523
-#>   -5.0535  12.5095  -3.0928  -3.5276   3.5058  -5.5731  -6.5917  -3.1749
-#>   -4.4402  -4.3286  -2.5488   4.6506  -4.4229  -2.9584  -2.2505   0.4066
-#>   -1.5600  -3.5898   2.8236  -0.6024   4.6846   4.3762   5.7524   1.0433
-#>  -14.8293  -1.8766   0.7403  -6.9910  16.1613   1.9846  -0.6524   2.4959
-#>   -5.4296   0.8858  -2.0244   0.4708  10.3446  -7.2269 -11.1847   6.7621
-#>   -6.3072  -7.2304   5.8156   6.9135  -6.5860  -9.2921  -0.1708  -7.5872
-#>   -1.2483 -13.9717  -4.7192  -0.4497  -5.7567   0.0720  -3.2322  -5.1351
-#>   -3.1657  -4.7625   8.0419  -5.4016  -1.2495  -8.2174   9.3439  -3.0667
-#>   -1.3187   1.6499  -6.5667   4.9880   1.5150   0.8097  -3.4620  -2.6438
-#>   -2.3831  -2.7993   7.8687   7.6837   5.1708  -8.4030  16.2167   2.6972
-#>   -1.5868   2.9036   0.2707   3.7706   8.1882  14.5434   5.2464  -5.5609
-#>    5.3385 -10.0304   7.2175  -3.0437  11.3225   2.2313  12.7802   2.7522
-#>    2.7248   4.7965  -6.9295   1.4670  -5.0119  18.2201  -6.2492 -13.9647
-#>   -0.5215   6.2616   6.8941   3.0859   5.0107   0.5953   7.2855   2.0261
-#>    4.9730 -10.6661   2.0325   5.6798 -10.4148   6.9674  -0.3137  -1.7415
-#>    1.3191   1.6653  -0.9755  -6.9042   1.9775   1.5980  -2.9887  -7.3644
-#> 
-#> Columns 25 to 32  -2.2584  -3.1411  -1.7190   5.6135  -0.7605  -1.8872   8.8467   0.3317
-#>    1.8911   2.5071   4.7714   2.8800 -11.7468  10.1048  -1.3142  -1.2634
-#>   -0.3020  -6.2638   0.6670   0.2206   1.6318  -0.1777   3.8507   1.0090
-#>    3.9061  11.3403  -0.1531   2.3443  -0.2155  13.3499 -17.7884   5.8076
-#>    4.6891   4.9715   1.8122  -3.1096  15.1079  -8.8061   5.2626  -1.7620
-#>   11.8195  13.2659  -0.9978  -3.7929  10.2876 -14.6448   0.8412   5.7287
-#>    4.9830  -6.6374   0.5644  -1.2754  -0.3460  -2.1989 -15.5581   7.8374
-#>    3.7161  -4.0108  -5.7117   0.2989  -4.1622  -9.0537  -7.2892  12.5500
-#>   -4.2194  12.9871  -8.7733  -1.4598  -6.5197  -3.6219  -2.2843  -3.0821
-#>    5.8795   0.5183   3.4822   1.5533  -7.1084  -0.2438   8.3458   1.0638
-#>   -4.5325 -12.4497  -9.7003  13.0726   2.2119 -18.1307  11.2652  -3.5084
-#>  -12.6648  -0.2436   6.2022  -5.6211  12.8947  -5.3681 -11.4360   6.7527
-#>  -10.3917  -4.2899  -4.2763   3.9326  -9.5481   8.7687  -0.3994   1.4870
-#>   -1.9819  -3.9799   0.4570   0.6859   8.2083  -2.0543  -8.9140   3.7974
-#>    7.5153  -4.8186  -5.2606  -6.9110  19.4118  -2.6405   1.9254  -4.9273
-#>   -7.9555  12.0409  -8.3323  -0.7393  -3.4030  11.7891  -4.2925   2.8215
-#>   10.9420   6.2364   4.0612  -0.0166  -4.1987   6.9281  -6.5285  -7.9298
-#>    8.7943   1.2502   4.2291  -1.3830  10.3950  -2.8952  -1.2324   7.2852
-#>    5.0857  -6.5046 -11.5604   3.5139  -9.5032   1.5154  -8.9062   1.5487
-#>   -4.3588 -12.7515   4.9994   7.2508  -1.5145   3.3324  -0.5210  -0.6055
-#>    3.6695  -6.7156  -2.2916  -3.3440  -8.8127  -3.9514  -8.6936   5.4232
-#>  -17.4519   0.3753  -0.1577   0.2455  -3.8726   2.1221   3.0209  -8.5284
-#>    7.7403   3.7682  11.5437  -1.9562  11.3489   2.4882 -14.7405   5.0131
-#>   -3.4348  -6.1867  -2.1967  -0.9979 -15.9601  12.7055   7.6482  -5.3109
-#>    7.6323   3.3177   1.1548  -1.6291  -1.5787  -4.0459  -5.3410  -8.6709
-#>    1.7209   5.6033  -4.2626  -4.5748   5.7546  -4.3819  -3.5714   9.4888
-#>   -7.2977   4.9754   0.1989  -1.4958  -2.5134   2.0138  -8.3286  -1.0149
-#>   -5.3932  -8.4312  -1.7728  -1.3764  -1.8622  -2.0587   1.6540 -13.3436
-#>   -0.6438  -4.0675   9.3477  -2.1537  -6.8739   6.6785  -3.6679   2.0648
-#>    3.0280  14.1391  -0.7212   6.4096   9.0701  -3.9991  10.6811 -11.6501
-#>    7.7576   3.7023   2.2892  -3.6257  11.5897  -3.1216 -11.6945   3.9960
-#>    0.0091   1.1377   5.4606   3.3294   2.3344   0.6012  -3.5069   1.8844
-#>  -14.2707  -0.6768  -4.0255  -3.9383   3.2496  -0.0379   8.5746   0.7396
-#> 
-#> Columns 33 to 40  -7.0296 -12.8663  14.3987   3.2322   9.2232   0.7883  -1.6867   7.8755
-#>   -1.5344   4.4558   8.8551   0.2860   3.9697   4.3488   3.3198  -2.3698
-#>    1.2647   5.6437   5.3609   1.3164  -0.4757   2.6660   5.7626   0.0191
-#>   -6.5931   5.5725   7.9819  -2.8026  -7.9575  -0.1073   0.7064   6.7801
-#>   -6.1830  -5.9598  -9.5774 -15.6217  -0.8016   4.2489   1.4874   1.1143
-#>    5.8825   9.6267  -5.7148   1.8366  -3.2282   0.2424  -7.8084   9.1993
-#>   -3.0657  -5.0323   7.8977   0.8028 -12.1188  -3.9341  -4.0435   5.0439
-#>    2.6450   6.5642   4.8798   1.0387  -9.5429   0.2942   2.1092  -2.6703
-#>   -2.1335   3.0644  -8.9395  -4.4608   3.8032   9.3958  14.5904   2.0776
-#>   -4.7665   0.4154  -1.4496   1.7975   9.1528  -1.9465   6.8242   2.5062
-#>   -6.3660  -3.0849  -4.3183 -17.0690   5.4184   8.2195  -5.6109  -3.5211
-#>   -6.4113   2.3761   4.4897   7.6312  -2.9152  -0.5913  -7.5612  10.6324
-#>    7.5895  10.0746 -10.9153   0.2363  -1.3659   4.2210   4.9896  -9.9209
-#>   -5.0598  12.0518  -9.0581  -0.0027  -3.1009   6.3472  -0.5132  -1.0659
-#>    0.3511  -1.0510  -7.4713  -7.5874   8.9896  19.5600  10.1285   4.5457
-#>    7.4178   3.2575  -6.0450   1.1417  -5.1602 -17.7806  -3.1945  -1.6117
-#>   -4.3079  -1.4211   0.9433  25.7182  16.7786  -0.5493  -1.7191   3.3322
-#>   -8.1885   1.3356   1.4096  -8.2148  -2.4566  -1.6299  -9.5060  -3.4289
-#>    3.3245   5.9569   5.9231  -0.3552 -18.8013  -5.1879   2.9843  -2.1808
-#>   -8.7187  -7.0404  -4.1315  13.2902   6.9334   6.2543   1.4562  -8.1287
-#>   -6.5858  10.9018  20.6336  10.7662   0.3862  -2.1395  -6.7924  -5.6453
-#>   -2.4868  -2.3452  -6.7193  -4.8742   9.7127  -1.3753   4.8112   3.5227
-#>    0.2052   6.9031   5.0824  -2.2491  -0.1196   0.8441  -9.4147   1.7011
-#>   -8.9176  -3.0189   9.5077   4.9989  -1.1380  -5.3146  -5.5733  -6.7260
-#>   -0.4082   4.1821   5.2904   2.3013   1.2743  -2.9406  -6.0362  -3.0038
-#>   -2.0832  -1.8657   4.9447   1.4180   0.1416  -0.1899   4.1653  -3.4298
-#>   -4.8597   0.9150   0.4392  -4.8070   7.4141  14.8686   3.7364  10.1229
-#>    8.3424  -6.1335   4.3202  -2.1732 -11.5991 -13.4142   1.2317  -8.0982
-#>   -0.2153   6.0834   3.4220  10.8916   4.3969   8.2267  10.9871  -7.5181
-#>    2.3822  -8.7823   4.6057  10.1561  -1.5710 -10.0309  -2.0746  -2.2866
-#>   -5.8263  -0.6575  -6.0233  -4.0320  -7.1885   4.3454   9.4840   7.2089
-#>   13.3708   2.2496  -7.7976   8.7236   5.5935   5.6348   4.9756   5.5582
-#>    5.9179   1.3443  -3.3823  -1.2847   8.5173   0.9727  -0.8258  -1.4907
-#> 
-#> Columns 41 to 48  -4.8874   4.2648   1.4254 -11.1819  -7.5432 -11.3109  -9.2888   0.4967
-#>    9.5153  11.7349  -2.2791   0.8585  -2.8558   0.2460   1.0491   1.4689
-#>    9.3801  15.6711   2.0565   4.8049   3.5439   4.0364  15.1548  -3.6749
-#>   16.3325  -9.3679   2.0192  -3.3858  -9.8849   2.9303   1.8756   4.9476
-#>   -0.8684   4.5410  -9.3713  -3.4632  12.9519   2.7012  12.1265  -1.4925
-#>  -10.3416  -2.8101  -0.1286  -2.5805  10.9838   4.5101  -2.4895  11.1958
-#>   -2.4855  -0.2830  -1.4296  -3.1296  -0.3691  11.4104  -3.1588 -15.5533
-#>   -8.7580  -3.4773  10.9539  -2.5102   3.5234   5.4549  -5.1429   0.9529
-#>    8.7106   8.7735  -1.1035  -5.3187 -10.2647   3.4108 -11.0404  -0.5582
-#>    1.5927  12.1160  -0.9894  -0.0483  -0.4213 -11.2137   3.4887  -5.2136
-#>    8.0233  -7.5385  -1.8384  -3.2708  12.0102 -10.5743   9.0551  -0.5432
-#>    2.3194  -5.6273  -8.4390   9.7349   4.6735   9.8609  -3.3499  -5.7117
-#>    8.2240   9.1067   0.2126   0.6903  -3.8826   2.3915   8.0595  -2.0850
-#>    8.5066   5.3333  -5.2188 -13.0931  -3.6299   0.0899  -1.9622  -4.6138
-#>    8.9595   7.7200 -12.6849 -11.6411  11.2263  -0.6814  -1.3068   4.1395
-#>   -3.7121  -0.5109   7.3844   4.4809  -0.1424  12.3762   1.8442   3.6132
-#>    3.2045  -0.0386  -0.3754   5.7350  -9.9900 -16.1272   2.7240 -17.6808
-#>   -2.4684   3.9191  -5.6213  -6.9191   7.2749  -3.8292 -12.5974  10.8177
-#>    0.0444   1.4745  -0.4502  -0.5448   3.1292   4.7605  12.5336  -0.3326
-#>   15.8767   2.8549  -9.2265  -2.5690  -2.1676   1.7361   7.1797  -3.3801
-#>   -4.3841   5.1055   6.1935   1.2127   0.0729   3.1731  -5.4489   2.8111
-#>   14.3846   1.3005  -4.7567 -11.0473 -13.8530   3.5960 -16.5615  -2.5091
-#>   -2.9694  -2.3522  -5.3276  -5.7980   4.1376  -3.9642  14.9809  -3.5241
-#>    5.5171  -2.1734   0.9172   4.5474   1.9186  -9.3068   0.5903 -19.9337
-#>   -9.6287  -6.7558 -12.1460  -1.5988  -3.5005   0.7460 -12.0347   4.5429
-#>   -3.4691  -4.8915   8.4898   1.0439   6.9208   8.3621   3.7009   2.0818
-#>    2.2342  -6.7275 -13.9861  -5.2344  -2.4440   7.9633   3.3043   1.9516
-#>    3.4051  -5.0082  12.2827  14.6407   3.5717   1.9640   6.0396  -9.4632
-#>   -9.2561   4.1927  -4.7531  -2.0654  -6.8197   1.7003  -2.1707  -0.6996
-#>   -6.5002   9.6357   6.3987   5.9241  -6.2198 -14.6919   6.5156  -6.7869
-#>   -4.5410  -3.4889  -8.7651  -1.1678   3.2435  -0.3625  -6.3166   3.1699
-#>   -1.0508 -10.6551  -7.6276  12.8458   1.8464  -5.5304  10.2748 -19.5096
-#>   -7.3590   3.5982   3.9005   4.6067  -1.3096  10.7647   1.2693   2.7367
-#> 
-#> (15,.,.) = 
-#>  Columns 1 to 8   7.1115  -4.8512   6.6240  -2.8056  -3.3284  -8.8784  -1.8778   2.6504
-#>    2.0700   2.2446   3.5921  -8.6059  11.0003   4.2419  -2.6831 -13.7168
-#>   -3.0170  -4.9289  -6.9572  -6.9360   3.4179  10.0614  -1.7287  -5.8838
-#>    6.8548 -18.6215   4.7617   1.7003   5.2943 -13.6111   7.5037   7.0371
-#>    0.2961   6.8685  -6.2783   5.1014   0.8988   2.8195   3.8814   6.8040
-#>   -4.8547   5.2219   4.0477  16.4903 -12.9067   4.1274  18.0589   2.3969
-#>   -3.1119  -5.7625   2.6623   1.4139   2.1222  -0.2198  10.5954   7.2307
-#>   -7.6110   2.3029  -1.1292  -7.6671  -3.8505   7.4110   3.5311 -16.9414
-#>    1.3999  -9.8199   3.7912   5.3425   5.5118   5.7815   1.5328   8.6705
-#>    3.1219   5.7515   1.3880  -0.1989   5.4088   0.5510 -11.7477  -0.2784
-#>  -14.2673  10.1715  -8.1456   1.4596  -2.2529  16.0602   3.6823 -10.3038
-#>   11.0010  -7.4895 -21.9005   7.8930   0.6953  -2.4494  -1.8775  -1.9337
-#>    8.0189  -9.5857   0.1912  -0.3771  13.6362   1.4489  -6.2672  -7.9932
-#>   -2.5327  -3.8656  -7.2625  12.7074   0.4264   1.5368   5.6500  -3.9857
-#>    1.8156   1.5734   0.2571   1.3652   5.4486   9.7826   3.4503 -10.3677
-#>   -3.8268  -7.5919   8.7224  -7.5668  -4.8322   2.4466  -1.4175  -2.6361
-#>    8.6276  -4.3907   2.3532 -11.3650   0.8983  -8.9388 -21.6246  -0.2398
-#>    5.3831   5.5249   1.9599   0.7754 -11.9672  -3.6293   0.7607  -1.5836
-#>  -15.1287  -0.2102  -8.7870  -9.7320   3.5318   3.8834   3.2313  13.0888
-#>    2.2043   1.2443  -6.2095  -0.5036   8.3035  -2.9883 -11.5146  -0.9311
-#>   -2.2014  -4.3662  -9.8363  -0.0592   2.2272   7.1386  -2.3986  -9.8353
-#>    7.3829 -15.6095   4.8038  13.5743   8.6284  -4.1394  -8.4229  -2.4527
-#>  -13.6528   4.5778  -9.1974  10.2745   5.6171   1.3481   0.0050   2.3714
-#>    5.1346   5.7869  -3.6878   5.2730  12.8479  -8.4027  -0.2686  12.3093
-#>   -1.9531   1.3696  -5.5243  -0.3543  -4.0368   0.4145   1.2231   6.5016
-#>   -4.4855   3.5932   0.2520  -5.4919   1.7162  -2.1047   3.5553 -10.0349
-#>   -0.7338 -12.2822   3.8291   1.8916  -5.6240   4.1292  13.4338   8.6450
-#>    4.1223   8.7543  -3.3275 -10.5468   0.5172  11.4885   8.5203   4.6293
-#>    0.8531   9.3552  -4.1095  -4.7907   0.7956  -1.3426  -3.6576  -5.8040
-#>    3.3943  -0.0591  -5.4711   1.9219   1.3790  -3.3002 -14.7059   9.0316
-#>    0.1388  -3.6049 -10.5187  -4.0452  -4.5910   0.0103   2.7861  -6.4096
-#>   -6.2126  -1.2538   5.6032   1.9384   8.2004   3.6508   5.4220  -2.7547
-#>    1.4816  -4.9908  -3.5770   5.7046   5.3249   7.0368  -4.7861  -8.5862
-#> 
-#> Columns 9 to 16   7.3293   0.7758  11.2714   0.4524  10.0081  -1.8373  -6.1661  -5.1543
-#>   -5.8472  -0.0340   6.9379  -9.0681   0.1452  -5.2057 -12.2126  -1.6486
-#>    2.3437  -8.1255   4.6813   4.4023  -1.8926  -3.2216   1.2384  -9.5795
-#>   11.8843 -12.4493  13.3344  -0.9240 -10.9565   4.5401  -3.0791   7.8568
-#>   -9.3088   5.4911  -0.1078   0.6599   0.1283   2.3134   8.2952   5.1707
-#>    4.1178   8.3648  -9.3887  -6.1874  10.2946  -2.3948  -1.2944   1.9575
-#>    3.9512  -2.3895  -3.5280   6.1398   4.6451  -8.5465  -1.5480  -6.9995
-#>    2.2374   3.9606  -5.4656   4.2810   4.2215  -8.9381  -2.8852  -8.8617
-#>   -3.6895  -4.0458   6.1441 -12.8404  -8.1936   1.2354  -1.3595   1.0117
-#>   -0.8024  -6.1211   2.9179   0.7460   3.9652   0.3023   0.6151   5.0391
-#>  -11.5725  28.7443 -21.9473  12.8849  -7.2069  -3.0946 -10.9281  -3.5617
-#>   -5.4936  -0.1804  -4.5155   5.1270  -0.6444  -8.3817  -5.6771 -16.6725
-#>    2.3760  -7.0744  -3.9384  -0.7086  -7.3069   4.5342   2.1592   2.9307
-#>   18.0141  -6.5316   8.1737  -1.6791  -7.9068  -0.4408  -1.8226   5.0003
-#>    7.7288  10.7306   6.2264   3.0890   7.9790  -2.7280   5.4462  15.1681
-#>    8.5514  -4.4508   5.4930  -0.7188   3.4840   1.9186   1.2016  11.9940
-#>    4.5466  -9.5580  14.4330  -8.1170   8.1214   3.3552  -7.4451   0.8764
-#>   -6.6361  -2.7121   0.2718   2.8905   5.6385   1.1932   0.1248  -9.6453
-#>    4.6519  10.0845 -11.9125   7.3490 -12.6394  -5.6982  -3.9589  -5.8524
-#>    7.5585   4.5320   4.8395   8.3544  -6.1310   1.5636  -1.5205   3.5457
-#>    8.8557  -3.6722  -4.3582  11.1995 -13.1770  -5.3377   0.8860  -7.7558
-#>    9.9494  -9.0174   6.8326  -8.1302 -15.9986  -1.7770  -6.3664  14.5632
-#>   12.8819  16.6322   0.9788   0.6587  -9.5508  -0.4219   1.1572   1.0094
-#>  -13.2621  -4.8329  -3.3790   0.6607  -3.8674  19.4960  -2.4050   1.7530
-#>    8.2321   3.4733   4.7779  -5.3348   0.2874   1.8593  -7.6614  -6.7333
-#>    9.0961   5.4900  -4.4653   6.5680   0.2827  -4.5207   3.0002   8.9991
-#>   -4.3711   8.0910  14.0509  -7.3617  -4.7490   9.0630  -1.7344  -2.7312
-#>   -1.7179   1.9318  -9.6681  16.6352   4.7708  -6.8043  -6.1491  -0.2504
-#>   14.4281 -10.5506  18.0719   0.6034   2.2336   0.7469   4.2527   3.9240
-#>    3.4098   3.1782  -1.2625   5.2707   3.1843   3.9704   6.9703   3.3117
-#>    1.1391   6.7281   8.8927   5.2655   5.8992  -1.3787   1.0948  -1.4335
-#>   -7.3976   0.4969  -0.9509  -7.0835  14.7301  -5.6787   4.1450  15.2954
-#>   12.4407  -4.6198  -0.9390  -2.1177  -4.9047  -1.7441   7.3410  10.5900
-#> 
-#> Columns 17 to 24  -7.8632  -2.9569  -7.4812 -13.7157   8.0637   2.3197 -11.0591   6.0891
-#>    5.2080  13.6162   9.5811   9.7852   5.6479  -0.6554  -1.9055   6.7459
-#>    0.0767   2.2985  -3.4298   3.6582   3.5657   1.8876   4.9415  -0.0905
-#>   -0.8436  -8.1971   5.6436  -0.8189  -2.7185  -6.8124   1.8173   1.5997
-#>   16.2083  16.5765  -9.1467   1.2284 -12.6130  -4.5548   2.0418   1.2082
-#>   -0.3672  -0.1228  -0.0400  -4.1638   0.4942  -6.9269   7.8788  -6.5652
-#>   13.4752  -1.3951  -7.7839  -2.8148  10.6688   3.2956   5.4087  -6.8197
-#>   -5.1719   3.6068   8.4387   2.3832   9.2946   7.0327  -3.0611  -2.2567
-#>    1.8095  11.6708  -3.7466   6.6039   0.9131  -5.9405  12.7895  -1.3867
-#>   16.9034  14.6173  -1.4816  12.0900  -9.7003  -8.1873 -10.4125   3.2021
-#>   18.8224  -3.7732  -8.4614  17.2741 -12.3936  11.3341  -9.2802   9.3729
-#>    2.1984   4.4343  -2.0045  17.9349  12.9631 -10.8768   5.0915  -1.7478
-#>   -1.1283  10.2250  -0.7795   9.2527   2.0497  -6.3417   6.1346  -4.9914
-#>    3.7214 -17.4149  -2.9213  -3.1648  -9.2139  -1.9090  16.3691  -1.6833
-#>   17.7869   1.2456  -8.1943  -6.7983 -12.6561   1.0241   8.7451   1.9875
-#>    4.7919  -7.9875   5.3562  -4.6082 -12.4467  -9.7171   4.8647  -3.7000
-#>    8.2106   7.4021  -7.2296  14.3541   0.3103  -1.5755   0.0523 -11.7865
-#>    4.0221  -0.2243  -7.8152   2.0820   3.3237  -8.2121  -3.1208   0.4177
-#>    2.1557   6.9841   3.9537   3.1003  -4.4127  13.4479   6.2930   9.3346
-#>    6.3638  -7.4629 -22.5791   1.1678  -7.8419  11.4958   5.5791  -2.9846
-#>   -8.2702 -13.1500   3.4012   4.5281  11.8472   9.1611  -0.4623   4.2150
-#>    5.4411 -12.5264   6.7563  -2.7371   5.6250  -2.9614   7.0123   4.2767
-#>    5.8710  -6.3890   1.2708 -12.6806 -11.1545   2.2729   2.5553  14.9886
-#>    3.2095   8.6801  -0.5554   8.1789  -1.8506   0.1934 -27.5572  -1.5930
-#>   -6.8670  -1.6240 -12.6488  -5.6393   6.8329  -1.8703   6.1925  -2.0215
-#>    6.8801   3.9142  -2.3834  -8.1555   2.7157   7.8278   2.2930  -6.3260
-#>   -2.3847  -1.3235  -3.3893  -0.3491   4.1305  -3.4685  10.7051   2.8873
-#>    8.2645 -10.5847  -0.8847   1.2763 -10.3263   7.4525  -9.3715   3.6793
-#>   -2.3518  -0.1390   1.0705  -5.8702  -6.4483  -4.0465   8.3043   5.2560
-#>    3.2468   8.2362 -10.9262  -6.5290 -12.8279  -5.7303  -1.3841  -0.0960
-#>   -3.0723  10.6398  12.0713   0.7000  -3.5167 -14.5697   3.6254  -2.7057
-#>    0.0036  -1.4688   3.9714  -0.6494 -15.3354  -3.4704   3.0982  -7.4453
-#>    0.4233   3.8652   1.7919 -11.7475   5.7635 -13.0052   8.0559  -2.3109
-#> 
-#> Columns 25 to 32   1.6111   9.8913  -5.1667   1.7823   4.4968  11.4306   4.7141   4.1485
-#>   11.6637  -6.4701   7.3143  -4.6603   5.1235  -7.4082   8.3548  -0.5569
-#>    0.6283  -6.8813   2.5591  -1.5133   4.7381  -2.9992   9.0115  -5.5502
-#>   -5.2239  -4.3276   5.1755  -0.0235   4.7770  -7.2770  -5.2854   5.8824
-#>   -8.7289  -0.1826   4.4126  -2.3563  -3.5984  -9.1043   9.6573  -0.8086
-#>    4.8235   5.1700   1.8015   4.0285   7.1082  -7.7678   5.7203 -16.4164
-#>    4.0143   7.2377   4.8562   9.9579  -4.2431   0.6437  -3.0818  -3.3971
-#>   -1.1889   6.8866   4.1312   3.2933  -1.7267   1.8717  -1.0299  -4.4844
-#>    7.9196   5.0489  -2.6998  -6.7225  -5.8232  -7.8611  -8.9995   8.6625
-#>    6.5469  -5.5016   5.5969  -7.3770   1.7542 -13.9674   6.6170  -3.4024
-#>   -3.2858  -0.3681  14.5794  -3.3098  12.1049  -8.3324  -0.8725 -12.8950
-#>   -4.5755   8.1768  10.8769  -4.6412  -1.5418   6.8211   5.3414  -1.7760
-#>    4.8843   2.1762  -8.7745  -0.1388   2.1083   1.3516 -11.3251  10.4455
-#>    3.3188   0.7462   2.9958   3.4060   2.7944  -4.2444   4.0027  -0.6496
-#>    3.7560 -13.5172   1.9928  10.8493  -1.9686   0.5085   3.2974   7.6224
-#>    0.7598  -7.6283  -2.8274   4.6157   4.2587  -4.9954  -2.3554   0.9759
-#>   -3.5939   7.8641   0.4557   9.6153 -11.5511   0.3652   5.8262   1.3749
-#>   -1.5936  -5.9849  -0.1979  -1.2771  -2.1348 -12.1289   1.5487  -8.8455
-#>    7.6842   4.1295   8.3468  10.5882   5.1853 -11.4518   6.2370  -5.1755
-#>    1.1376  -0.9603  11.4751   7.1682   0.3050  13.5715  11.1495   2.8284
-#>    8.3730  10.5045  14.0180 -11.2211  -1.3625   2.3138   3.8882  -6.8152
-#>    3.7236  -1.7148  -7.6170 -10.5873  -3.2708  10.6142  -7.5766   8.5804
-#>   -7.0491   2.5127   8.0336   9.0789   0.4238   6.1985  12.7871 -13.7677
-#>    2.8779  10.3715 -17.5331 -10.4388  -4.2289  -2.8544   3.5366 -13.0072
-#>   14.9275   8.7207   3.2939   4.4447   3.7926  -9.0021   9.4168  -1.2667
-#>   -5.4648   7.6661  13.5957  10.1384   1.0440  -2.1800   3.8088   2.7490
-#>   -6.1931   2.9256   6.1095   2.7975   6.8424  10.0426   6.3487   9.9140
-#>    5.5017  -2.1072   6.9378   2.6380  -1.2445  -0.7814  -0.1116 -12.2251
-#>    7.5641  -1.5218  12.6226   3.0393   4.7455 -10.6471   8.8348   8.3849
-#>  -10.8081   6.4331  -0.9774   6.6743   2.1463   2.1770  -8.2339 -16.4321
-#>   -4.9075  -7.1971   2.3138   7.9496   2.5724   2.2093   3.0592  -2.0602
-#>    2.3171  -3.5195   3.2429   3.8005  -9.6169   5.8675  -6.2395   5.4772
-#>   -4.0414   7.8393  -3.1173  -0.2646   2.4630   4.9677  -1.7572   7.2229
-#> 
-#> Columns 33 to 40  -1.1876 -10.1433  -6.3296  -0.4366  -8.4276   4.5743  10.7330   9.1425
-#>    5.0169   1.3334  11.1390  -3.0555  -5.2267   2.4215   3.0188 -13.6635
-#>   -2.9091   7.9281   9.3381   8.1316  -7.7611  -3.3668   7.0043   7.1997
-#>   -3.4941  -8.8710  22.0236   2.1432 -11.5798  13.7316  10.7663  -0.7452
-#>    0.7552  17.7075  -3.7739  -8.6597   7.8503  -3.0094  -2.7635   0.9666
-#>  -10.9439  -3.6854   1.2291 -11.6378  14.9099   0.2285 -15.1566  -4.2762
-#>   -0.5783  15.5054   3.0954  -3.3504 -13.4509  -2.3075   9.3905   1.3299
-#>    2.8292  -1.6403   8.7995  -4.3770  -1.7240  -1.7656  -8.9716  -2.7895
-#>    2.3974  13.9734   1.0183  -8.1300   0.0174   6.9550   8.2570  -8.0587
-#>    3.0301  11.8596   8.8648  -3.7870  -8.1850  -2.5475  -4.2095  -0.0444
-#>   10.4876  10.5542  -4.7367   4.3377 -11.2366  -0.7845   0.3486  -2.2428
-#>   -5.8378   2.4098  -0.9600  -4.4779   0.1891  -6.8651  -9.0412  -1.2870
-#>    0.5132   3.5568   7.9932   3.6603  -3.2506  -9.6204  -3.2829  11.5460
-#>    4.3603  -4.4309   2.1633  -4.6632  -2.0197  10.3826 -10.8853  18.2003
-#>    7.5312   9.4417 -14.0978 -10.5704   7.4753   2.4065 -12.0700   8.9292
-#>   -5.9442   5.7016  10.2508   5.5452  -4.8535  -2.5900  -5.1088   4.2898
-#>  -18.8976   5.1532  -1.7340   8.3101   5.6807  -2.8783  -3.3924   0.9158
-#>   -4.6655  -2.7845   2.2621  -5.1463  11.9983   5.4542   5.0857   3.3174
-#>    4.7172   8.5207   8.1565   2.4791  -7.0028  -0.8125  -4.5575  -3.3890
-#>    4.9781   3.8925  -6.7012  -3.3485  -9.6601  -9.7996   0.6632   4.9651
-#>    6.2889  -6.5221  -0.1579   0.8559  -3.5223  -2.9480  -8.2845  -3.6279
-#>   16.2025  -4.6872   0.9619  -2.2647  -0.9014  -1.9281  10.6382   1.3064
-#>  -11.7320  -8.8520  -8.2607  -1.3817   9.6871   3.5138 -12.0845   0.8755
-#>   -1.4128  -0.8471  -8.2075   6.5583  -9.8319   0.0534  -2.4484   3.2296
-#>    0.1093   5.4801  -5.7720  -4.4883   1.0138  11.8541  -5.7959 -13.5700
-#>   -2.7510   3.9824   7.7852  -8.5101  -0.6763  -0.6188  -4.2763  -4.1366
-#>   -7.6959   1.4529  -2.4865 -10.2923   0.1622   3.2543   0.6042  -8.5631
-#>   -5.8255   3.2380  -5.6855  10.0065 -13.5559 -18.4401  -6.9620   6.1546
-#>   14.2976  -3.6410   2.4764 -14.9836  -9.5727  12.6544 -12.3706  -4.0015
-#>   -4.1990   1.5840  -2.1817  14.1879  -4.8348 -12.4974   6.0161  17.1821
-#>   -0.7287   3.5166   6.5444  -5.4296   2.1586  -1.0379  -4.0262   1.5960
-#>   -3.1356   4.7152  -3.5899  -3.5598   4.9465 -12.6657  -0.7683  -1.2444
-#>    4.7171   0.5994   0.7119  -1.2080  -0.6086 -13.2839  -5.4848   2.1458
-#> 
-#> Columns 41 to 48  12.1076   4.1011  -2.1738 -12.9176   6.0466  -1.2080   4.1916 -14.2161
-#>  -11.0032  -2.0739  -9.6891   9.1633  -6.7289  -0.9261 -11.6838  -9.1522
-#>   -2.8997  -6.1992   0.2605  -0.8266   3.4657   6.5743  -7.7402  -0.3860
-#>   -9.4210   0.0803 -11.6765   7.1376   2.6870  -8.4538  -5.3343  -1.7722
-#>   -6.3655 -12.2643   8.3479  -0.7230 -13.5265   0.0571  -4.1167  -0.2769
-#>    7.3242  -2.8557   6.1877   5.6059  -0.9670   0.8556   8.1311   3.8170
-#>    0.9440  -6.0001   5.4027   3.7200   8.2664  -4.4506  -8.2239   3.3115
-#>    1.1671   1.2033  -0.1148   4.6368   9.2238  -8.6745   9.8473   1.3444
-#>   -7.4524  -2.3227   2.5718   0.3380   4.2563  -6.7630  -0.9309 -14.5055
-#>  -10.5321  -0.5466   3.3322   8.5984 -15.1215  -3.8004  -5.8031  -4.9785
-#>    0.5278  -2.1349   0.5429   6.7788  -7.7084  -6.8593   3.6495   5.3098
-#>   -3.9173 -12.6153   6.4391  -2.6274   5.9788  -2.6989   5.6161   4.0751
-#>   -9.5523   9.0388  -3.6559   0.7355  -6.6148   2.9918  -4.4288  10.3985
-#>    0.3431  -0.1633   9.4491  -0.1662   0.1703  -8.1686   3.9250  -1.9232
-#>    1.5288  -2.3083  -5.0019  -2.2136 -13.1283  -6.2529 -12.6464   3.3033
-#>    1.8642  -1.6308   0.4104  -2.1203   6.6278  -7.2271   9.7675  -0.6935
-#>   -3.5142  -5.4236   0.4054   4.4837  -1.8545  -1.1056 -16.1740 -11.8274
-#>    7.2128   0.4841   1.1405   1.7895  -2.4622   1.0410   5.6031  -9.5526
-#>   -5.1216  -4.1475   0.7678  -7.3741   7.0402  -3.9091   5.0237   1.8940
-#>   -2.9024   6.0801  -1.3226  -1.9505  -7.1316  -1.0240  -7.6500   1.4707
-#>   -4.6808  -3.3517  -0.6546  -1.4612   6.0778   8.9817   7.7796 -10.4063
-#>   -3.9926   9.0640  -7.9229   4.0447   2.5237 -11.6426  -7.8623 -10.9089
-#>    4.1280  -9.8034   9.1217   5.1864   5.1205  -6.8890  -4.8244  -0.4584
-#>   -0.4377   6.9769   8.7374 -10.3751   9.5877  14.0208   1.6259  -4.7922
-#>    5.0793  -7.7669   5.9387 -13.6609   4.1338   1.2238  10.1254  -4.0397
-#>    4.3210  -7.1856  -3.9623   2.0145  -3.4725 -12.8630   3.5290   2.1779
-#>    0.4560  -4.6627   1.9266   2.4260  -2.8334  -6.1060   1.7071   4.3015
-#>    5.2938  -0.4437  -0.2218   4.4239   6.5981  10.4136   4.6831   3.9865
-#>    0.0827  -6.5887   1.4364  -9.5022 -10.8417  -7.3839  10.5732  -8.4682
-#>   -0.0789  -5.1351  12.8619 -10.3998  11.1001  -0.8602  -1.0153  -5.6380
-#>   -0.8525  -8.5984  -4.2362   2.2819   5.7807   3.9225   5.1029  -3.2233
-#>   -6.7520   2.5125  -7.0239   0.2492 -17.7612   7.3457  -5.1606  21.6853
-#>   -1.2298  -2.5437   6.2544  -6.8056  -5.4504  -6.6582   7.4377   4.6695
-#> 
-#> (16,.,.) = 
-#>  Columns 1 to 8  -1.3204   0.6526  10.5092  -5.6327  11.6096   5.4670   4.2140   0.0346
-#>    5.5739  -3.2615 -10.9422  15.8088  -5.3255  -9.1013  -4.6959  -0.5321
-#>   -1.4791   6.4599  -3.5330  18.5985  -7.7456  -0.7409   4.7767  -1.2939
-#>    2.1681   2.9503   8.4330  -5.2447 -17.1378   7.1880 -10.5660   0.0754
-#>   -7.0732  14.9318   3.6820  -3.9117 -10.7969  -8.7032   2.4902   0.1089
-#>   -6.4173   8.3155  -8.7507  -5.8865 -10.0633   6.2456  -5.8225  -4.2588
-#>   -0.2864   3.1815   8.6395  -0.3138   0.3553  -5.9444  -0.0749  -2.6012
-#>    1.0320  -8.3929   1.9943   1.1173   5.1423  -6.1033   0.8580   4.4847
-#>    4.8267   6.8426   0.9700  -0.1088  -1.3790   4.9978   2.2415  -0.3459
-#>    3.9409   0.6129 -12.8229  13.8631  -6.3605 -12.7444  -0.1254   3.9160
-#>    0.0052  12.2717   2.5303  -3.5619  -2.9978   1.4443   1.1008  -9.0313
-#>    2.6251  -6.9490  14.1019   9.4191 -15.9656  -6.0713  10.5775  -3.8202
-#>   -1.0193  -6.3644   1.8182  11.5701  -0.1826  -6.2748  -2.8998  16.5332
-#>   -9.2020  19.8385  -5.9341  12.3887 -10.9639   9.4341  -1.0446   8.9571
-#>  -14.0495  11.1773   9.4797   2.2405 -11.3045 -14.3312  -0.5920   8.3753
-#>    7.1392   4.5487 -10.9330  -3.0501   2.0338   0.5864  -6.0851   6.9316
-#>    2.6657   0.9035  -5.8575  -3.1847   9.1681  -1.4086   2.6142  13.2898
-#>   -2.2047  10.0648   8.4240   1.1256  -2.6297  -0.3316   4.8572  -6.3603
-#>    9.5895 -10.9603   2.9531   4.1686  -4.6578   1.8648  -3.9940  -0.8700
-#>    7.0689  -1.4863   0.9992   7.1206  -0.5830  -4.1616   6.5459   7.8172
-#>    9.3923  -5.3182  -5.8125   1.4640   1.8993   9.1349   3.1091   0.5354
-#>    5.0575   3.5568   4.8846  -3.3064   4.8651   3.0455  -4.5414   6.5744
-#>  -12.5866   3.3297   1.1292  -7.9103  -4.2261  -5.4005   4.1291   1.8921
-#>   10.1886 -21.3950 -15.0034   5.4307  -1.9230   2.3486 -17.8280  -3.1337
-#>   10.0871  13.7585  -9.1602   2.4039  -4.3966   6.6193  -0.0315  -5.2115
-#>  -10.1880   3.9860   5.6668 -11.3652   2.0414  -4.1076  -1.0125   8.7918
-#>    0.1394   5.1834   9.7742 -11.5945   1.0495  -1.1102   5.8734  -1.9149
-#>  -17.5434  -6.5157   1.6953   4.7723  -0.9014   0.5178   6.9369   2.9006
-#>   -0.3901   9.4402 -11.9338  12.7419  -8.8317  -5.6769   6.9202   0.9321
-#>   -7.3379  -7.1466  -0.9682   0.3887   4.4588   8.6968   2.9510   1.6989
-#>   -0.7674  -1.7466   3.9111   2.1979 -10.2515  -4.2773   9.5866   1.0826
-#>   -1.5783   7.1853  -6.7278  -9.1641 -15.5977 -10.6462   3.5289   5.9880
-#>   -4.7147   7.8592  -9.0302  -5.2358  -3.3409  -1.7656  -4.6556  -2.6884
-#> 
-#> Columns 9 to 16   1.0254  10.3361  11.6621   5.4113  -5.4113   1.7748  -6.8710  -9.8104
-#>    0.4625   4.9036   5.4948  -4.6719  -3.3553  -0.6379   0.6686  12.4877
-#>   -7.4537  -5.7242  -1.2048  -7.6518 -10.1702  -7.5392   1.4355  12.3038
-#>   -0.9118   1.9426 -22.7376  -2.0421  -3.2993   6.2670  -8.1239   7.8196
-#>    3.0716  -2.4960  -0.7130 -13.1407   9.6765  10.1313  14.9288  -0.7429
-#>    7.0794  -9.5038   2.5003  -5.2492  -1.7645  -1.3024  -3.8371   8.9790
-#>   -5.1030  -6.9457  -0.9513   0.6588  -1.2483  -1.0004   2.7857  -3.1608
-#>   -2.9562  -6.1328   3.7558   8.8549 -16.1291  -5.9037  -3.9408   6.5159
-#>   -1.6588   3.0108 -12.8467  -1.6978 -15.3076   9.3569   4.3810  -2.0955
-#>   -4.3906   0.5790   2.3439 -12.0252  -5.3709   9.9952  10.5764  10.3523
-#>    6.5793   1.0548  11.9372  -2.0547   8.4397  -1.5919   3.8448   2.3324
-#>   -1.5664   9.7998  22.0340   8.1030 -11.4524 -15.2741   0.8491  -3.5748
-#>  -11.9949  -1.1028  -1.6206  -2.0485  -9.2684  -1.9960   6.5735  15.1946
-#>    0.5942  -1.1728  -5.7841   3.6862  -7.8729   5.5735  -7.0033  -1.6853
-#>   -1.7875  -9.1322  -7.6958 -18.2343   4.8327   9.3955   3.9783  -3.5601
-#>  -10.6473  -8.1347 -16.5191   3.3761  -4.2651  15.5675   4.2293   0.2734
-#>    7.0049  13.8239  -7.1527   1.1260  -1.0699  -1.6748  -1.0046  -0.9413
-#>    7.5757   2.4887  13.2990  -6.5295   1.1039   4.3652  -0.2265   7.1046
-#>   -5.9953 -13.3316  -5.4457  -8.3539   4.9143  -7.1104  -1.9930   8.8147
-#>    8.4529   7.0763   6.0135   4.3569  11.1647   2.8066 -11.1874 -16.3402
-#>    2.9189  -8.3507   4.3038   3.2874  -5.8044  -2.3187 -17.9639  -7.2240
-#>    3.0913   7.9585  -5.1885   3.2803  -2.8590   9.8295  -2.5579 -16.5575
-#>    2.7880   2.8779   0.6772  -9.4118   8.2246  -8.6007  -2.6125  -6.7030
-#>  -10.5021  -5.8253   2.1096  -1.5385   2.2725  -4.3075   1.2849   1.6933
-#>   13.3302 -10.1520  -1.0219  -0.6172   9.4727   3.5504  -4.7962  -8.7593
-#>    3.4697  -6.5768  -6.3320  -1.2067   4.5735   2.2156   4.8014  -4.8593
-#>    1.3233   2.2615  -7.9351  -8.0059  -1.7954  -6.8646  -4.6570  -2.4058
-#>    0.3469   0.1460  17.7335   4.3834   9.7596  -7.1045   2.4103  -7.7036
-#>    1.1120  -6.3162  -1.7628  -1.7410  -3.0155   7.6801   0.8479  -1.0478
-#>    4.1956  10.0632   1.7495  -5.7685   3.5873  -0.3796   8.8077  -4.3659
-#>   -5.1423   1.6058   2.3845  -8.5698 -10.1795  -4.4278   7.6315   3.5999
-#>  -12.7859  -1.8685 -14.3316   3.0497   7.8189  -6.5503   9.9442   5.3623
-#>   -4.5302  -7.2908  -7.1151   4.5172  -1.1260   6.3266   7.6171  -7.9990
-#> 
-#> Columns 17 to 24   2.8025   2.8271   2.6480   3.4959   5.0904   2.8226  -0.0594  13.4359
-#>  -14.2048  -9.2178   2.9372  -0.3767  -5.0131  -6.1643   1.8080  -4.0727
-#>    1.7859  -2.5607  -1.9930   8.0479  -0.7414   3.8054   5.1039  -2.8461
-#>   10.4622  -6.9029 -11.8365  10.8772  -2.8240  -2.7649   7.2829  -5.8833
-#>  -14.1812 -10.3616  -4.2358   3.8300   1.7081   3.1369  -2.0032  -6.1442
-#>   -0.5696   2.5405 -15.5316  -5.1835  -6.0208   2.9822  -0.5974  20.8739
-#>   -5.6804  -8.5035  -1.6632  -0.6585  -2.4957   3.7135  14.2337  -4.5331
-#>    1.7501   0.6361  -5.0097  -6.8476  -5.1944  12.4870  -3.7354   0.9425
-#>    5.1651 -11.6754   1.1540   1.5580   7.1286   4.3611  -2.7560 -18.3934
-#>  -17.0134 -11.4174   0.8793  -0.6620   4.0115   1.4018  -0.8441   3.0652
-#>  -10.4883  -1.5086   8.5144   3.9381  -1.8614  13.6181  -9.9868   7.9517
-#>  -10.4059  22.3095   2.7550  -2.2265   1.2176   4.2821   5.2935  -1.3582
-#>    4.4460  -3.1476  -7.2154  -9.6695   3.5336  -1.6278  -9.2159 -10.0800
-#>    8.1050   2.0959  -3.9473  -0.7106  -2.8918   5.4461  -3.6736  -1.2730
-#>   -6.1247  -1.4076  -4.4631   1.3634   5.7326  -2.7705 -12.1562  -6.0150
-#>    6.4228  -7.8171   5.9113   3.0675   2.0606  -1.1603   4.6238  -8.2037
-#>   -1.6866  -5.6572   0.2234   4.5182   3.1158 -13.9857   6.7237   4.4937
-#>    0.1855  -2.4545  -7.3087   1.1111  -3.9793   6.8628   6.6371  11.8543
-#>   -6.6008   0.6008   4.6997  -1.5596   0.2766  -3.9507  -0.4521   3.7588
-#>   -6.1623   4.5682   6.6610   0.1241  10.9521   1.7854   2.6624  -2.3684
-#>    8.5402  15.9268   5.0533  -1.7578   0.7582   5.4772  -1.6012 -10.3979
-#>   11.4607   1.5538  -7.0335   1.5337   5.4602   3.1144  -6.8919 -15.1536
-#>   -0.9874  11.4116  -1.7802 -16.8985  -2.0688 -12.0846  -7.8504   7.7643
-#>   -4.8618  -0.8623   6.5522  -3.0675   0.4466  -6.2224  -1.0398   3.3669
-#>   -6.9103  -0.4912  13.6113   8.4300   5.0445  -1.1812  12.4340   5.0129
-#>    2.9676  -3.5193  -6.5441  -1.1419  -0.4146  -2.7309  -3.9625   0.2427
-#>   -0.5066  -4.1027   2.8202   1.5772  12.5458  -7.5701  -1.1209  -7.5376
-#>   -2.0766   4.6149  10.3450   6.7406  -4.9339   5.7498  17.3834   2.1269
-#>   -9.6193  -1.3648  16.9052   6.0183   4.4245  -0.6685   2.6400  -7.4417
-#>    4.9282   9.5929  -7.0839  -1.3978  -6.6698  -8.3408   0.2958   5.9292
-#>   -6.4539   0.5636  -5.9307  -1.6424   5.9629   3.1537  -4.6600   5.7245
-#>   -4.9213   0.7057   9.0855   3.7485   3.8237 -11.5914  -0.5496  -9.8527
-#>    2.9095   5.2565  -2.3803   9.6923  -1.2148  -3.3360  -1.4157 -13.0961
-#> 
-#> Columns 25 to 32  17.6035  -1.0098   0.4745   5.2585  -8.8529   8.4093  -2.0189 -14.7016
-#>   -4.3281 -14.1630   5.7770   3.3682  -7.6735   3.5629   0.3521  13.0974
-#>   -9.4802  -2.8482  -2.6903  -2.4335  15.1989  -3.3284 -11.1240  16.1645
-#>   -6.6805 -17.1317   0.4984   7.8714  -7.6200  -5.2047   4.7747   4.5009
-#>   -5.8401  -6.7577  -3.1531  -3.6990  10.3882  -6.1229   0.1506  14.0069
-#>   -3.9239  -1.7848  15.8552 -18.3749   4.1166   7.0067  -9.0793   7.1111
-#>    3.9034  -3.2623   6.3512   6.5142  14.1725  -5.2678  -7.8942  10.7899
-#>    4.7258   0.0566   4.4063  -9.2343  -4.1122  10.0212  -5.9066  -8.8272
-#>    7.2739  -0.5225  -9.5228  12.9427   8.1354   1.2012   1.9712  12.4760
-#>   -7.0641  -7.0924  -7.2254   6.4615  -2.3076  -5.9760   7.1168  12.0361
-#>  -15.8013   4.0385  -0.1108   4.2316  10.0294 -14.1306   3.5020   7.7976
-#>   -3.4861   0.6688   1.7526 -18.0820   5.2449 -15.6297 -13.8289   3.2798
-#>    5.3501   8.1111   3.2815   4.4101   8.1744  -0.5013  -2.3403   2.6503
-#>    1.1117   9.9882  11.2948  10.9378   7.5708 -11.2358   6.5127   2.0092
-#>   -9.1659  -7.0353   6.4879   2.5423  13.5808   1.9107   8.5529   9.1137
-#>    5.4336 -12.5380 -12.8151  16.0256 -12.6789  -4.6559  13.5395   1.8768
-#>    9.7476  -7.3086  -6.5087  12.6011   1.0711  -4.2273  -2.4932  -3.8373
-#>    7.3071  -3.0686   4.7835 -14.8320   1.5951 -10.5570 -17.9316   8.8383
-#>  -12.3674  -5.0916   6.1914   3.6960   2.2219   6.4669   6.3904  11.3875
-#>   -0.5713   3.2452  -1.6545   4.4855   6.2768  -1.4021  -2.1622  -7.2800
-#>    6.0130  -2.4392   2.8221   5.5003   2.2283  -5.5170   2.9483  -8.5641
-#>    3.1298   7.8611  -2.8450  13.6667  -4.9227   0.3805   7.6739  -5.1870
-#>    2.8829  -0.7680  12.2108   3.1766  -4.0264   7.3800   3.9469 -11.9077
-#>    8.4839  -6.2682  -5.1891  10.8249  -7.2002  -7.9541   3.9664  -1.7241
-#>    0.0130  -6.5531  -3.9633 -11.3358  -2.3152  -5.5856  -5.8848  14.1289
-#>    0.2054  -2.6318   3.5308   4.7872   6.3370  13.6606  -7.1487  -0.2621
-#>    5.9394   4.4685  -0.4925   6.0674   1.9031   6.0924   3.2171   2.2223
-#>  -12.7919   0.8670   0.7464   9.2208   5.1413  -3.4920  -0.8628  -1.1500
-#>  -10.9959   2.0829  -6.2836  -3.3308  -1.2404   0.3261   3.4622   6.7565
-#>   -1.7894   7.0095  -4.7989   5.9668  14.7276  -0.0832  -1.1292  -2.3796
-#>   -3.8813  -4.9567   4.5870 -20.2590  -3.6941  11.4605   5.8353   5.5101
-#>  -21.2222  -2.7852  -0.2706  -3.5601 -10.8011  -0.5643   7.5233   2.2526
-#>   -2.8226  -1.7783  -0.7867  -6.6398   6.8352   1.3618  -7.3804  -0.3211
-#> 
-#> Columns 33 to 40   4.6767  -5.6769  -8.2998   1.8646 -11.7082  -0.1739   0.7321   0.9858
-#>    9.6309  -4.0555  -4.5445  -6.0392  -1.8424  -4.5782  -2.2394   2.7480
-#>   19.3465   1.4504  -0.5011   0.6825   8.3566  -6.0887 -12.4131   1.4882
-#>   15.4626  -0.9147  -2.8020  10.4818   0.1867  -2.4863  -4.4813   8.9666
-#>   -2.2579  -4.4858  -2.6382  -8.7673  -2.4578   8.6333  -0.1472   0.8210
-#>   -3.8017  -4.5528   2.1585   0.3108   4.3367   4.4167  -4.1404  -7.1562
-#>    6.1018  -1.3892   2.6067  11.0037  -0.8936  -5.2789  -3.8202  -8.6666
-#>    9.7097   3.3408   0.3116   3.6396   0.1723  -5.4994  -3.0563  -3.6130
-#>    4.9959   1.7683  -1.3992  -1.3118  -4.7137  -3.1537   2.8307   8.6702
-#>    5.5646  -1.9904  -0.2973 -14.9815   3.3672   0.3944   0.3536   3.0087
-#>  -18.5556   0.8547   3.2588  -8.9740  -5.4412  -7.7150  -7.0575   5.6699
-#>  -12.5947  -4.6393  -2.0987 -10.0344   2.7829   5.2220  -0.2026  -6.7648
-#>   13.9009  10.4672   0.7112  -1.6072  -6.0489  -4.2060  -7.4302  11.4814
-#>    2.8680   2.9533   3.4821   3.1926   2.0164  -6.2120  -8.4158   2.3738
-#>    2.9694  -8.4894  -3.6779  -3.0176  -9.0022   7.1106   1.5780   7.5124
-#>    7.1181   7.5552  -9.9423   5.9438   4.1525  -1.6902  -6.9626   0.7560
-#>    3.2141  -9.8982  -6.5783  -6.5020   5.5751  -0.7571   1.3244  -4.2656
-#>   -1.6315  -6.8276  10.5276   2.9332   0.0299  -0.9580   0.6356  -1.0171
-#>    6.1094  11.5435  -2.5057   1.0803  -5.3372 -16.7403  -5.9370   0.2371
-#>  -10.7809  -6.6780 -11.6158  -7.0993   1.7148   0.5335  -0.7110  -5.7289
-#>    4.4232   8.1946  -2.5558   8.8174   9.0208  -4.4722  -3.7291 -11.3726
-#>   -5.6536   1.3000  -6.5301   3.3230  -7.4231   4.5805   7.0784  11.0849
-#>   -1.1154   0.2432  -2.7664   1.8290  12.1905   4.9875   2.9038 -13.9286
-#>   -4.1042   6.4505  -2.1113   6.7506   6.6225   9.8238  11.0429  -0.3648
-#>  -10.7701  -6.5126   3.9260   0.4233  -5.3073  -2.3215   4.7964  -4.2986
-#>   10.9507   4.4344 -11.7721   3.5976 -10.9204   3.9153  -7.7275  -1.1018
-#>   -1.1800  -1.4308  -7.2643  -2.7054  -0.8770   3.8441  -2.1754  -8.6870
-#>  -10.8437  -0.3657  -7.6537   7.2054   7.0568   0.1700 -12.9408  -9.0044
-#>   -0.5383  -5.7443  -2.7455  -9.1649  -1.6883  -5.2865  -0.0477   2.8188
-#>   -3.5584   3.7999  -4.8056   5.4437   8.3543  -2.5686 -14.0578   2.3122
-#>    3.3393  -6.5970  -3.9285  -7.5108  -5.0091   1.1976   3.0261   1.5317
-#>    6.3473  -0.3590   3.6051  -7.7109   6.6993   0.2420   0.8268   9.6148
-#>    2.3679  -4.2571  -7.5652  -2.6319  -0.2594   3.5161  -3.8648   1.3730
-#> 
-#> Columns 41 to 48  -8.9004  -2.4431  10.1769 -11.5539   0.9727   1.1449  -4.1664  -5.9406
-#>    0.7273  -1.5841  -4.8308   0.8003   4.3019  -3.4016   8.7224  -4.3127
-#>    9.4124  -5.4304  -7.3879  -1.5780  -0.8244  -9.7549   7.1619  -1.2881
-#>    4.4182  -9.6812   2.0931  -7.2892  -9.6957  -6.2069  13.2915 -14.1783
-#>   12.7210  16.2381   1.1936  -1.2641   2.7008   7.9641  -5.9156   2.2741
-#>   -0.3840  -4.3539   0.3929   4.1084  13.3946  -1.5061 -16.0275  14.1161
-#>    4.8527   4.2228  -5.5224 -10.0346   0.8754 -11.9943   3.7959  -1.2697
-#>   -3.2294  -7.6962  -0.5004   0.1692  -2.6538  -2.7955   4.5124   7.7744
-#>   -1.2764  -5.2906  -6.6582   1.0561   1.0314   8.8017  -1.1811  -5.1169
-#>   -0.8060  11.0308  -2.5343   4.9971  -3.6082   3.8307   1.2113  -1.5236
-#>    8.1704   4.0161  -3.9638  -0.7323  -7.8175   3.4363  -1.1549   2.2999
-#>   18.6318  -2.9746  -5.0073  -6.5272  -2.8055  -3.2255  -4.9634   6.8455
-#>   -2.7224  -6.3045   1.2384  10.8459  -4.7152   2.5874  11.1960   3.3570
-#>   -0.6983 -11.5129  -7.0210   0.7556  -6.1628  -4.8726   9.0647  -7.1177
-#>    6.3566   5.4712  -5.9771   5.7913  -4.8866   0.0971   5.9906  -0.9292
-#>    0.4678  -0.3558   2.8748   7.5943  -7.6338  -2.0429   9.8464  -6.7635
-#>  -14.0334   6.1741   0.1270  -3.7588  -5.0111   2.7787  -5.4135   5.7406
-#>   12.3765   2.0786   6.4566   3.8771   3.3312 -17.2266  -4.5957  -4.6832
-#>   -4.6396  -8.2818   1.9074 -12.3811 -14.7881   0.2181  13.7469   6.0143
-#>   -2.1437   8.6999  -7.6948  -5.9722   1.3065  -2.1438   1.2666  -2.7880
-#>    8.8771 -19.2104 -12.9171  -1.5808   9.4828  -5.6920   7.1086   1.3148
-#>   -3.7578  -9.0183   0.3922   3.3510  -2.0988  -1.6591   7.8891 -20.0991
-#>   -2.7687  -1.7540   1.9076  -5.1849   0.4355   5.3135  -2.5350  -6.0803
-#>    0.0246   7.8145   5.0346  -6.8477  10.7157  -0.9533  -9.1985  -8.1825
-#>   -1.9543  -8.1319  -8.0247 -20.4680  13.8195 -11.3091   1.6414   6.3563
-#>   -1.4511  11.1988   2.4813  -9.8037   3.3185   6.6449   6.7901  -5.9648
-#>   -7.7734  -3.5544  -5.1077 -13.9153   1.2176   7.7291   1.5552   1.4795
-#>    8.3889   9.3231  -4.9893   1.0248   3.7695  -7.8579  10.9412   6.6266
-#>   -5.4727  -5.4548  -6.1984  -7.9609   4.9564  -3.3660  19.5644   1.3405
-#>    0.5967  -2.9063  10.0813   2.3446  -2.4747   6.2081  -2.8591   8.8000
-#>   -7.9574  -9.9399   4.6892 -10.6437  -9.4975   6.5943   8.4337   4.7842
-#>   -0.9742   9.0712 -15.3261   2.3812  -8.8730  17.1299   4.1943  13.8919
-#>   10.7144  -5.8989   3.4063  -3.5016   9.2069   1.1498   4.2816  -0.5729
-#> 
-#> (17,.,.) = 
-#>  Columns 1 to 8  -5.1619  -3.0181   0.0799   8.0886  -8.0772  -1.7640  -2.0343  -4.7349
-#>   -9.5859  -4.2881  -1.5816  -0.3175   5.1352  -4.7297   4.2629 -17.4556
-#>    0.1543  -4.0294   1.6310  -3.1471  -1.8329  -1.6866   5.3869  -2.4516
-#>   -0.6062   7.6543 -11.9250  -3.3041   6.7607 -15.7408  10.0127  -9.7765
-#>   -8.9783   3.2641   6.1305  -6.3902 -11.9548   6.8638  -3.3556   9.9042
-#>    5.1950  -8.6193  -2.3250   1.6673  -5.1070   4.9004   1.0199   1.3473
-#>   -2.8416  -3.7654   0.7706   1.6454   7.8840   0.3698  -6.4068  -1.4886
-#>   -5.5793  -6.7922  -8.8654  23.0614  10.3158  -4.6445   0.7068  -2.0719
-#>  -10.3018  -3.6898   6.1985  -1.7263  -4.9523  -4.9590   4.9585  -3.1475
-#>  -15.3576  -6.5329   6.7014  -3.4137  -5.3419  -1.9853   5.9655  -9.1938
-#>   -9.9914  10.6697   9.6767  -1.4626   8.7081  10.7835  -8.2493   2.8484
-#>    4.6417  -4.1499   3.1414   5.5009   6.9503  11.8197  -5.4029   4.0689
-#>    5.8440  -9.3735   5.0352   5.2992  -9.7783 -13.5972   0.1664   2.9280
-#>   11.3548   6.7934  -0.6160  -0.2442  -1.3540 -15.6663   9.3547  -3.9605
-#>   -0.2968  -2.4413   7.3948   3.9985 -12.6692  -1.9594  -0.7751   3.0247
-#>    9.6975   0.2138   3.5373  -0.5736  -1.4443  -8.1797  13.9032  -4.1398
-#>    1.3615  -8.6565   4.6917   1.6749   7.3299   4.0300   1.2357   9.4734
-#>   -4.4466   5.9717   3.1726   4.6943  -6.6801   8.4973   4.4991  -1.2390
-#>   -2.3091  -0.4119  15.5392   3.3148   7.9750  -1.5371  -7.4485   3.0937
-#>    3.5304  10.8551  -6.4733 -10.7545  12.2401   2.2745  -1.5117  -1.7315
-#>   -7.3637  10.8645 -15.1257   9.1840  20.0610  -3.7527   0.8773 -10.2257
-#>    1.5459  10.0743  -5.4409   2.1041  -3.2769 -16.7276   4.5645 -10.5682
-#>   -2.6268   4.3734   0.6472   1.6766   0.9398  -2.4559  -1.0414  10.8499
-#>  -10.7332  -0.2520  -1.4968  -9.6132   3.9767  -2.8039  -5.3973  -5.8692
-#>   14.2206   3.5955   9.6403  -6.4169   9.3426   8.1698   2.8643  -2.2282
-#>   -6.4186  -4.2460  -2.9623   9.3977   3.6590 -11.1579  -3.4319   6.0393
-#>    1.2881  -1.7220  -1.5405  -2.2051  -1.8137   3.1895   2.4844   2.4990
-#>    8.5819  -4.9393   6.3451  -0.7898  10.0815   7.1112  -4.4164   7.8334
-#>    4.4946   4.7311   4.1540   1.7233   4.4152  -6.0693  11.5300 -11.6184
-#>    0.9200  -7.2788   8.5793  -9.7418  -3.6820  11.7213  -5.6273  14.2378
-#>   -2.9730  -8.6553   5.6509   4.2432  -3.6835   8.6053   6.7605   8.3489
-#>    6.7401 -10.0715  -0.1860 -10.4713  -3.0209  -3.4074  -5.3498   0.4218
-#>    8.3541   3.9618   5.6562   1.1163 -10.3233  -7.6167   2.0936  -3.4023
-#> 
-#> Columns 9 to 16   2.6554   7.9167  11.0304  -4.0841   8.3659 -15.0617   1.0746   0.0951
-#>   10.2653  -7.1887  -4.6526  -3.0189  -2.7806   3.2218   1.7490   0.8503
-#>   -2.5758 -11.4871  -2.7451   2.1423  -5.5929  -9.2981  -1.2714   5.3440
-#>   15.2209 -20.7210   7.8777  -4.8968  -3.0089  -8.5371   6.3820   5.9276
-#>   -4.4465   2.5734  -0.8134   2.3880  -4.4097   3.2617   5.6593  -8.9384
-#>  -10.4354  -1.8856  14.2443   0.8415   0.0422   6.6552  -0.5176  -6.7742
-#>  -10.1508   6.2166   2.2943 -11.7513   1.5491   8.8356   7.0511   5.0354
-#>    2.0353 -10.3984   6.9557 -10.8480  18.4747  -3.5018   3.2486  -3.2049
-#>    1.1831  -2.6879  -5.1247  10.9413 -21.6096  -4.6595  -1.9645   6.3144
-#>    4.4255  -5.3163  -0.3537  -1.4157  -8.4213   2.9359   3.8514  -9.3721
-#>   -4.2098   5.0282 -18.7914  -4.1104   8.0676  -1.0900   7.6226  -6.8346
-#>   -4.2074 -13.9924   1.3014  -3.5194   8.7787   6.4364   7.0054  -7.3548
-#>   -7.7226  -9.0022  -1.0856   5.7499  -1.0572   0.2483  -8.3625   9.5794
-#>   -1.4326 -10.7731   3.7636  13.0417 -10.8303   9.9199  -4.1907  -3.1137
-#>   -7.6242   1.8568  -9.7423  -1.0967  -1.8745   8.7429  -6.1002  -4.2548
-#>    5.0080 -12.6321  -5.0495  10.8333 -13.4834   1.3995  -9.5204   5.1213
-#>    2.6434   4.7095   0.6623   3.0407 -15.8481  -1.2436   1.1847  -6.4874
-#>    7.3450   6.5187   9.4047  -3.9367   3.4945  -6.0047  -0.9545  -0.7230
-#>   -8.0573 -17.1685  -9.0478  -9.2035  -0.3623  -1.2031   5.7464   6.7034
-#>   -6.2174   2.1358  -1.1809  -0.1612   0.1096  11.0414  -3.0298  -0.0556
-#>    8.8962 -16.2860   3.3317   1.7542   4.2848  -3.9125  -1.7668   1.5290
-#>   -0.6201   5.7843   0.1410   1.3853  -9.2887   3.0838  -6.3960   2.8845
-#>   -4.3300  -8.9639   2.3970  -1.8609   5.3062   2.0718   9.1274  -8.2183
-#>   -2.6742   9.8989   3.7277  -1.4399 -16.5996   9.0688  12.1121   6.0464
-#>    1.4948   6.4696 -10.6562  12.1046  -9.5973  10.5317   2.4813  -1.4759
-#>   -7.1796 -11.7486  -2.1333  -1.0915   6.6922  -7.2241   2.8495  -3.5509
-#>   -1.6976   4.0630  -2.1573  -6.1899  10.4746   4.8966   3.6920   1.2625
-#>   -5.1499  10.9102 -10.2700  11.9868   2.2540  18.0478 -14.5344  -3.5789
-#>    6.3958 -15.7501  -3.0503  10.2427   2.8136   3.6015  -5.5024  -8.6063
-#>   -4.2474   6.5401   6.0854   3.6492  -5.1034 -14.2622  -2.2923   1.7127
-#>    6.0082   0.6665   6.6702   1.6794   2.8281  -5.3877   1.5180   0.3557
-#>  -10.4044  -5.7640  -3.0339   5.7415  12.9303   6.6334  -7.6317  -9.7096
-#>  -11.3651 -15.4415 -11.4166  10.0479   1.4643  -3.6533  -3.1368  -0.9498
-#> 
-#> Columns 17 to 24   4.1676   7.5346 -10.3955  -4.6839   0.8027   2.8550   0.9751  -5.7473
-#>   -6.4199  -7.4061   3.9227  -1.3790   5.2387   9.0546   3.6188 -12.4515
-#>   -3.0704  -2.0439   9.9159   4.2188   5.0221   9.8404   5.9538  -6.1228
-#>   -4.2774  -4.6437  -1.9673   0.0261   4.6949  -3.4775  -3.4377  -1.4731
-#>   -0.7676   1.8158  -6.7767   1.5923  -8.7248  -6.2934  -4.0206  -8.6869
-#>    5.2254   0.5085  -4.9254  11.4029   3.0626   0.1749 -13.6412   2.6948
-#>   -4.2016   2.6745   1.2933 -11.6407   9.3893  -8.2027  -0.5136  -2.2459
-#>   -2.9029  -1.7087   6.3701   0.0140   2.1192   9.2754   6.8887   8.9853
-#>   -1.8979  -2.3461   1.6134   6.0133  -6.7247  -0.7846  -8.3433 -10.7952
-#>    5.7948  -0.8582  -1.8358  -7.5462   0.4105   4.3216  -1.8319 -11.7112
-#>    0.0190   3.6595   0.9984   2.8579 -10.6178  -6.5399 -16.9250   2.6083
-#>  -14.4299  13.9719   3.9606  -7.8635   6.7253   7.4283  -2.4251   9.9554
-#>    4.1462  -0.4103   8.6328 -13.0772  -3.6970  -0.9119   8.4376  -2.4958
-#>   10.0345   1.5690   1.6743  -1.7376 -13.0662  -8.2072   5.0286  -3.3454
-#>   -2.9310   1.0582  -0.1027  11.4803   2.2111 -19.6883  -3.5387 -17.9479
-#>    0.9670   0.4862   1.6640  -4.9562  -4.3110  -1.3840  10.0940  -9.0717
-#>   -4.9501   9.6821   5.8758  -7.8606  -7.7860  -1.8677   7.3119   3.8837
-#>    2.2149  -6.3069  -6.0500   2.8637   0.0099   2.4581   3.6942   2.2841
-#>  -11.5380  -3.9158  -1.4166  -2.4726  11.1947   6.1733   1.3971  -4.6914
-#>   -0.1422   1.8758   5.2678  -6.8755  -2.8271  -4.3313  -3.3322   0.3057
-#>    0.0091  -2.7056  11.6045  -1.7608  -1.0983  -0.1272  16.8650   4.9916
-#>   10.2011   2.1264  -2.9585  -1.9620  -9.1845  -8.9957  -7.8044  -9.0668
-#>   -6.6551  10.0978   2.3357  -5.9963   3.1873  -2.3427   5.4463  -5.4590
-#>    4.8718   7.0416 -10.5680 -16.6755  -2.4014   2.2601  -2.7843  -4.7637
-#>  -14.4118  -6.3285 -14.8899  14.0403  11.6604   6.6104   1.4341  -2.7390
-#>   -5.8211   9.2186  -4.2027  -1.8181   0.1697  -5.8698   3.5700   2.3641
-#>   -7.4844   6.4635   2.6496  -1.5543   4.3140  -8.8012  -2.4610   1.3795
-#>   -0.4812  13.2961   7.8098   4.3972  -3.9960  -9.7672  -4.6588  -7.2766
-#>   -6.0338  -7.8509   2.8048   7.2771   2.8969  -1.5515   6.3580  -4.0975
-#>    8.6986  14.3351  -9.7717  -4.0023 -13.9180  -0.9266  -3.7760   5.1942
-#>   -6.2422   2.0891  -3.1558   1.5595   2.0403   2.2207  -3.0024  -3.1902
-#>   -1.3010  -5.1145   7.8635  -2.7449  -3.6258  -4.4641  -4.6369  -3.9438
-#>    6.8597   5.6082  -5.5376   1.7434 -13.2703  -3.8859   0.5757  -2.0066
-#> 
-#> Columns 25 to 32   1.0254   7.0910  14.8846  -3.1377  -1.7441  -0.0101   8.2309  13.6490
-#>    7.2203  -9.8544   4.2957   0.8403  -5.4216   4.2901   0.5307 -11.0900
-#>   -3.2518  -5.0485  12.4350 -10.1260   3.0470   5.9382  -5.1991  -3.0149
-#>    0.9825 -17.6493  -3.7067   5.6643  -9.3720   9.9860  -2.0499 -10.5845
-#>   -5.4938  -0.9989  -7.3844   1.0720  -2.2592   6.6725 -11.4328   2.6314
-#>   -3.4160  -1.9221   6.9001  -9.0317  -1.7597   0.1183 -10.0590  -6.1533
-#>   -1.4411   2.9178   6.7159  -5.2199   3.7244   6.8215  -3.6982   7.9301
-#>   -6.6415   3.1656  13.6420 -11.6915  -3.5711   5.6936  -2.9921   3.3072
-#>   -9.9602   2.2012   0.1391   5.7160   4.5033   4.3581  -0.8962   1.0365
-#>   -2.7040 -12.5892  -0.7467   6.4007   3.1365   0.2761   5.1091  -4.8358
-#>   -6.5507  13.0072  -8.1460  -0.7292   9.5301  -1.2198   5.1274  13.7180
-#>   -1.8198   2.6547  17.5326 -10.1833  -5.6534   0.1305  -8.4429   6.1219
-#>   -3.2205   8.2305  -6.3872   8.7776   8.8897   2.1674  -2.3012   3.0600
-#>   -2.2893   4.2262   4.3550  -0.6973   3.3189   5.1070  -4.3752   0.7677
-#>   -1.8420   9.5921  -1.2874   5.4161   8.3827   4.1311   7.7046  -4.6980
-#>   -2.2840  -7.7288 -11.9842   0.1884   1.9551   0.8840   0.4875  -0.9882
-#>    5.5980  -6.7492  -0.0901  -2.6437  -0.4772  10.5760  -1.4162   2.2015
-#>    1.7957  -4.1445   0.9496 -10.8582  -6.8151   4.5452  -3.4300   6.9712
-#>  -12.0713  -3.2485  -0.1581   2.2096   9.8587  11.0674  13.6746  -0.3345
-#>   -0.0934   4.7471   6.2557  10.1419  -4.7220  -6.5351   3.8809  -4.5939
-#>   -2.1335   4.1217  11.6893   5.7794 -12.5167   3.2000   3.4059  -2.0594
-#>    4.6066  10.4228  -5.0172   5.2087   2.2536  -7.1337   3.2033  -2.7008
-#>    7.1901  -6.3060   3.5671  -9.6330  -5.9182  10.4501  -0.6683  -0.4104
-#>    3.0606   8.3018  -4.4403  10.3565  -6.3468  -4.1858   9.2453  12.9026
-#>   -1.5092 -10.2147  -2.1994  -0.7522   6.9462 -12.3251   4.1127  -4.6304
-#>   -3.3659  -2.5790   1.9965  -0.7867   7.8040  11.6107  10.2200   0.8229
-#>    3.3383   7.1378  10.7486  -3.0431   0.0634   2.2515 -10.1307  -7.2413
-#>   -2.3418  12.8547  -2.7102  -2.8552   7.1522  -3.7697   3.2059  -6.8201
-#>   -0.0820 -11.7380  13.9148  -0.6083   8.5250  -1.8080   2.8137 -12.9253
-#>   -5.3931  -4.7689  -5.3929  -7.5490   9.0840   3.3297   3.7127  10.0785
-#>   -0.8460  -2.7423   6.4142  -7.6215  -7.6490  -2.2948  -0.1514   5.8941
-#>    3.4158  -9.1364   1.2171   3.4272  -1.3323  -1.6521  -5.7197  -1.3973
-#>    1.8469   0.4327  -5.5370   1.6241  10.8228   0.9583 -11.2766   2.1041
-#> 
-#> Columns 33 to 40 -14.3575  -6.2215  -2.6464   3.5467   3.0407  -4.6165   3.2398   5.9551
-#>   -0.0480 -11.2296   9.0606  -8.4787   3.7471   9.2177  -4.2168  -8.3942
-#>   -7.6122  -3.3571   9.6595   5.2166   9.2910   1.9303   0.2418   2.0756
-#>   -5.9336  -9.8716  16.4470  -1.7783  -4.9414   9.2061  -4.3183   3.6377
-#>    4.0196  -1.7859  -6.7206  -3.6580  -2.8390  -2.7614   2.3717  -4.0672
-#>    8.6838  -8.0013  -4.4193 -15.6865   4.0136   5.3373   4.8009 -15.2233
-#>   -4.8134  -6.2068  -1.7466   5.2689   2.3783  -4.6048 -13.0424  -1.3871
-#>    2.1823  -1.4709   4.6472  -4.9556   3.6358  -1.6127  -4.5299  -0.7508
-#>   -7.7999   8.5013  -0.5993   8.4254  -4.4798 -12.0772  13.9128   7.1798
-#>   -2.4574  -7.7704  -6.0134  -1.1115  -2.3742   5.8104  -0.0299  -7.7799
-#>    4.7580   9.0302  -0.8664   2.2599  -4.7359   6.7417  -5.0842  -6.4733
-#>   14.2720   0.5971 -11.0018  -0.0784  12.7015  -7.7272  -1.3485   8.8818
-#>   10.4907   0.4184  -0.7159  -1.4922   1.9790  -9.6921   3.3352  -4.8613
-#>   -2.7084   3.6704   0.9111   5.4527  -7.8099  -1.5995   4.5562  -1.6489
-#>   -2.1861  -3.2265   4.8851   2.3370 -12.4613   3.2311  15.8262  -3.8380
-#>   -1.0990   4.2719   3.0757  15.0411  -5.6176  -3.2691  -6.7996   1.1006
-#>   -8.8203  -4.1936  -1.6645  -2.6186  10.6776   9.8482   6.9322 -12.3674
-#>    5.5215  -8.9044  -3.4960  -6.0829  -1.6044   2.5236   0.0788  -5.7065
-#>   -1.2669   0.4632  13.2928  11.7140  -7.4080  -6.8600  -8.0006  -4.2027
-#>   -4.0752  -4.7452   0.7452   6.4075  -1.4182  10.2600   3.5309  -8.4121
-#>   -4.2307   1.1806   9.6492  15.4468   5.8637   6.8539  -8.5205   1.0018
-#>   -3.8457  -0.7070   6.1170   2.9409  -7.0939  -3.4753   1.9503   6.2271
-#>   -5.9679  -1.6525   0.3704  -5.1216  -8.2476   5.7906  -7.2179   1.3314
-#>  -11.1109  -3.0333 -14.4224   1.7890  -2.4105  -0.1108   0.3998  -5.0452
-#>   -7.0083   4.6308  -0.7722  11.1411   3.5653  -0.4934   6.9186  -8.6089
-#>    2.0942  -7.0346   7.1834  -1.7625  -9.4952  -0.1650  -0.6904  -3.1321
-#>    0.0324   6.7720   4.1951  -2.1648   1.2437   6.1136   1.2496   7.0027
-#>   10.8015   5.0426   0.4105  14.0700   6.3069  12.8383 -11.9664 -13.9583
-#>    1.3122   4.0592   2.7471   6.4240   1.6898   1.8227   0.5049   4.1766
-#>   -1.7909  -5.0770  -1.5484   7.6894   5.3256 -14.3788   4.5347   0.7098
-#>   -3.2952  -1.6346   2.2955   2.4030   1.0500  -8.9837   6.7502   6.9802
-#>    6.8395  -5.0656   0.1906  -9.0577   9.8598   5.1781  -7.2335  -0.2278
-#>    4.4538   2.7797  -0.9497  -6.1691   5.6912 -10.2034   3.1724   3.2141
-#> 
-#> Columns 41 to 48  -8.6441   2.4011   5.3406   0.5169  -7.1458 -10.6428   3.4734   6.4883
-#>    3.6561  -1.3602  -4.9829  14.9695   7.3692   1.5609   3.6538  -0.9603
-#>    9.7197   3.6295  -1.4647   4.9346   9.6288   7.8971   3.1743  -8.6251
-#>    1.6472   2.3387 -15.3470  17.4664  14.3885   0.9156  -9.6962  -6.3569
-#>   11.7460   6.7559   5.7597   1.3397  -0.2342   7.9527   4.8621   5.7302
-#>    5.2752  -6.7882   7.0607   4.4035  -7.4108   2.5544 -11.0435   1.9892
-#>    0.3894   5.1207   3.2670   0.8916  -1.3623   0.8533  -0.3910  -2.0448
-#>  -14.1813   3.4252  -6.7175  -0.2159  -4.8677   0.9509  -6.7635  -3.2218
-#>   -4.9690  13.1662   4.1110   8.9466   6.0124   3.0692  10.9720  -6.5359
-#>    0.5593   3.6655   7.2326   6.2879   7.1621   6.6863   1.0141  16.3214
-#>   12.3352  -7.1740  -4.8923  -5.6806  -6.4280  10.6362  -5.3967   3.5949
-#>   -2.2432 -12.8000 -11.8296 -16.0490  -9.8189  12.8758  -2.4351  -7.8443
-#>   -4.5688   7.1990   0.5583  -1.7811  14.3203   4.1463  -2.6914  -4.7332
-#>   -1.3859   5.0359  -1.3397   1.9056   9.3781   7.6273  -7.1196  -5.7528
-#>   12.7389  11.5307  10.7647   0.5788  12.7882   1.0392  -4.6635  -2.4167
-#>   -7.0239  16.5722  -6.2209   3.7139   8.4041   1.7262  11.3945  -4.0328
-#>    2.1784  -2.5491  -0.0794   9.1677  -5.9280  -6.5202   5.9558  12.9000
-#>    0.6541  -2.0004 -10.7711   1.4766  -6.1706   7.5175   4.1388   1.1855
-#>   -5.8622  -0.4412   4.2065  22.7852  10.0497   7.5345  -7.5494  -9.1347
-#>    7.6072   8.8231  -5.0044 -11.2194  -0.6387   0.7680   1.4248   6.9167
-#>  -12.1577   3.3665 -14.2950  -4.3271   3.3356   6.4653   4.0655  -2.3738
-#>   -6.7812  10.3407   3.4721  -3.3608  10.9246  -4.7196   0.0555   2.4327
-#>   -5.3465  -8.1440  -9.7443  10.5662  -1.7553   4.5819  -9.2733   0.5123
-#>   -5.5077  -4.3240  -2.6952   1.6386 -13.8442  -3.9715  -3.8010  -1.9999
-#>   -4.1681  -4.8682   1.0707   4.2970  -5.5541  -2.3680  10.4819 -12.6713
-#>   -8.8408   7.5186   1.9883   4.9950  10.6310   3.2479   1.3208   0.8368
-#>    3.6220   9.5142  -0.6823   1.2705  -5.9588 -16.6398  -2.1621   1.5515
-#>   16.8932   7.8655   0.1815 -15.7166  -8.2259  -9.0776  12.2167  -0.3076
-#>   -6.6233   3.8283   1.9062  -0.7773   8.5527   3.2746   7.6873  -3.1137
-#>   -4.6619  -5.6619   7.5286  -3.1911  -1.0284  10.5853   6.7247   7.2033
-#>    1.2962  -2.9593   6.1153   2.5834  -6.6286  -5.0059  -6.8562  -4.3241
-#>   15.2824 -16.6599  -1.0556  -0.1420  10.4592   3.5020  -5.7064   8.5650
-#>   -5.1108   4.7232  -1.9738  -6.1196   2.2868   3.7609   9.3567 -10.4940
-#> 
-#> (18,.,.) = 
-#>  Columns 1 to 8  -2.1182  -0.7571   7.9467   2.1898   4.7717  25.2638  -0.8232  -7.9312
-#>    1.4757  -8.4381   4.6898  -6.4652  12.7088   6.1968  -6.5510  -3.3435
-#>   -1.0880   3.1956   5.9717  -2.7051   2.0740  -1.9199   2.2675  -1.2013
-#>   -1.3924   8.2906  -2.5384  11.9476  -3.7197   7.0748  -6.8147  -9.3733
-#>    5.5619  -1.2401   1.4920  -6.0486   3.3838  -1.9039  -6.3562   8.2120
-#>   -0.6116  -4.2774   9.4702   1.4873  17.8344  -8.8499 -29.3098   3.4220
-#>   -3.7875  -1.9810  -6.1796  10.2906  -1.9900  -2.1061   7.1188   6.1529
-#>   -2.5599  -9.5376  -2.6383   2.9431  -3.7980   5.2267  -1.7671  -3.9193
-#>    4.8052   1.8797   8.5406  -8.1130  -2.0360  12.9946   2.4048  -4.7191
-#>    2.9974 -10.4339  -0.2404 -13.4410   7.9839   3.1934  -4.1069   3.5502
-#>   -1.6361  -4.6846  -3.8339  -1.8360  20.3230  -7.6295  -4.0841  -7.0594
-#>   -9.9150  -3.2720   6.0693   3.1899   3.3531   1.6007   1.5613   1.2052
-#>   -8.4328  -4.1565  -8.0653  -2.6708  -9.2769   1.4444   8.3375   8.4450
-#>   -3.2125   0.9822   0.4917  12.6375   3.2021   0.8483  -0.6230   4.8014
-#>   -0.2609   3.1864  -1.5156   6.5577  12.8700   7.6108  -4.1719  -4.2897
-#>    3.2358  -6.7246   3.5930  -4.1780  -4.8662  -1.1375  -4.2261 -14.1053
-#>   10.4043   1.6688  -7.9842  -8.4965  -2.1641   5.7481  -5.0010  12.2231
-#>   -3.2635  -2.9199   6.9172  -5.2742   3.3919   0.9880  -7.8094   6.6050
-#>   -2.4746  -0.2135   7.4969  -0.3032  11.5303 -13.9905  -0.9217  -5.6705
-#>   -3.7569   2.5001  -6.3367  14.1089   8.5051  -3.3212  10.8588   5.3911
-#>   -4.0467  -4.7554   1.1179   9.3319  -3.1277  -0.4392  -0.8629   2.8097
-#>   -1.7547   2.4296   3.7441   5.2022  -5.6253   8.2018  16.8028  -5.5757
-#>    0.0685  -0.7017   3.6165   9.9757  13.0693  -0.2408  -5.1801 -11.3338
-#>   -5.8028  -5.4429  -7.2817 -11.3863  -0.9092  -0.2677   4.8687   3.4034
-#>    2.2659   4.6321   7.0772   4.8961  16.6779   0.9971  -7.9687  -5.9066
-#>   -4.8906  -2.4658 -11.6296  -1.2893   2.6321   0.9346  -0.6113 -13.0913
-#>    8.9434   9.2545   3.0081   7.0109  -0.4382  12.3121   3.2274 -14.7190
-#>   -0.4862   1.9033  -5.7876   4.4347   7.4138 -26.2398   9.5332   9.6503
-#>    2.2402   3.9792   3.3721  10.4018   6.3583  -4.3108  -1.3508  -1.7882
-#>    7.4722   2.3650  -1.8423 -17.8048  -1.3112  -4.7984   3.7357   9.3711
-#>   -4.0211  14.1959   4.6885  -7.2354   3.9748   7.9475  -8.8620  -6.2914
-#>   -3.7000  -6.9855  -3.8212   8.3691   0.8111 -11.8869  -3.7272   0.7643
-#>   -4.7139  -6.8645  -0.7060   2.7541  -4.5484   3.5338  -8.6274  -1.2171
-#> 
-#> Columns 9 to 16   7.2647  -8.6888  11.7926  -1.0136   1.5494   2.4462  -6.5419  11.3079
-#>    4.5663  12.5308   0.7116  -2.8596   5.5737  -9.2472   5.8513  -9.5661
-#>  -13.3512   1.3591  -1.0610   0.4476   5.8431   1.2887  -1.1533  -1.8306
-#>   -3.8562  -1.3592  10.7865  14.0710 -13.6829 -10.5686   3.1663  -0.1743
-#>   -6.0634   8.4946   1.7074  -4.0652   1.0906  -0.4083  -2.4810  -2.0329
-#>   22.3303   8.4309   5.9973  -7.3569 -11.3795   8.3505   2.1217  -1.1199
-#>   -7.1720   2.0795   6.0179   1.7678   7.1983   0.3938  -4.3298   5.9715
-#>    4.9156  -5.4124   0.4973  -9.1705   4.4407   4.9347  -1.4694  -2.2564
-#>   -4.8952   9.7767   2.9930  -5.5948   7.6400  -5.4677   1.7944  -2.3845
-#>   -1.0263  10.0324  -7.4797   2.0402   9.0892  -4.2136  -4.2335  -7.1426
-#>    2.9696  16.1909 -12.5469 -11.5150  -1.9482  11.0889  -3.9540  -3.3895
-#>    2.2270  -4.5721 -18.1313  -9.1965   2.6634  18.9085   7.9679  -2.6307
-#>  -20.6692   4.9193   1.0881  10.1452   3.7314  -4.5919   1.2086  -2.2627
-#>   -9.9781   7.9977  -5.2585  11.1988  -2.5901  -5.4626   3.3733   5.7326
-#>   -2.7484  12.7465  -0.2040  13.1155  -6.3980  -9.1547  -0.0957   2.8694
-#>   -0.1539  -3.9398  -0.4655   8.6836   2.9089 -17.5699  -2.7059   2.7102
-#>    4.0578  -1.0165 -17.0126  -5.9745   9.3175 -15.3041   3.0811   7.9012
-#>    3.4738   2.0146   2.5935  -9.4803  -0.1053   0.6432  -0.7223  -6.0844
-#>   -6.5118  -1.8916   9.7646   0.6670  -2.9842  -7.9055  -6.2935  10.3625
-#>   -6.9629   4.8729 -21.6642  -4.4527  15.1960  -1.6819   5.9706   0.5120
-#>    2.6332  -3.0433 -16.7360   2.7591  11.2852   1.8029   1.4326   5.5599
-#>    3.3250  -9.9069  -0.7334   9.8337   6.9544  -8.0702   4.9554  -4.5893
-#>   11.7242   9.3968   2.5485   0.8957  -2.3584  -5.4113   2.9040   6.0120
-#>   -2.5418   9.6976  -6.0312   4.0430  11.2914  -7.4692   2.5382   4.2934
-#>    2.3915  -0.9626  -0.1069 -11.9796   9.2055  -1.7904   0.1402   6.1305
-#>    8.3409   0.2522  13.4007  -0.3983   2.9108  -8.6546  -7.6775   7.6755
-#>   -1.1018   1.2193   2.2627 -10.3144   0.4891   5.3719   4.6967   9.7798
-#>    9.2502  -9.7482 -14.2606   5.1587   0.8067  -4.0890   8.0564   4.0071
-#>   -4.5599  -3.6730   0.2758  -0.8335  11.5419  -2.6657  -3.4265   7.6750
-#>    3.7316 -15.9019   6.4014   2.8236   5.3996  -3.1120 -14.9505   0.0564
-#>   -5.4596   1.8753   0.0063  -7.5509 -16.1388   4.6096   3.8762  -3.2336
-#>   -8.9335   8.1773   4.1663  12.9117  -4.6454   2.1488   2.8809  -9.6851
-#>    0.0477 -10.4034   6.2636  -0.4742   5.5335   0.5257   0.8917   2.3452
-#> 
-#> Columns 17 to 24  11.0573  13.7312   5.8543   4.8489  -2.6569  -3.6729  -2.0829   6.7676
-#>   -2.7833  -7.6586  -4.0285  -6.8365  -4.4145   3.2945  -7.1827  -4.5094
-#>    1.2952   1.4334  -2.6366  -1.1614 -13.9862  -0.6042   4.2555  -2.1690
-#>    6.4549  -7.4690  -2.0481  -4.5348  -7.2859   8.3759  -7.9426  15.4676
-#>  -18.3143  -2.0021  -5.8181  -0.4990   2.7677  -6.0340  14.8655 -11.5795
-#>    1.4829   5.7384  -3.3054 -11.3290  -0.5048 -14.0581  12.6753  -3.5082
-#>  -11.5949   1.0957 -17.6710   4.4140  -6.1736  -2.9811   9.0966 -13.4329
-#>    4.3069   2.3311  -6.4558   0.6288 -10.9957   1.1390   3.1576   1.7888
-#>   -0.2856  -6.8539  12.5494  -3.4223   0.8451   7.7194   3.4557  -3.7051
-#>   -7.3951  -7.1409  -9.8258  -3.3382  -3.3691   0.7643   7.8610  -1.5240
-#>   10.8178   8.4922   2.1662 -14.1131   4.2186  10.6684  12.8027 -13.3816
-#>   -0.2625   5.7901  -7.1812   5.2922  -3.0609  -7.8868  10.9752   4.4107
-#>    1.2552  -1.4243   1.2744   4.8681  -4.7870   8.5247  -2.4831   7.4701
-#>    0.1306  10.3847   0.1616   3.1268  -4.7955   2.5798  -0.0894  -5.6855
-#>    5.3339  25.9790  14.3555   7.6033   0.2488   0.1268  -0.5005  -2.1643
-#>   -9.2570  -6.3087  -3.6075  -7.1626   0.3360   6.7071   0.3442   1.5326
-#>    1.8577  -2.1719  -8.1049 -13.1077  -7.9606 -11.6935  -6.9597   5.5981
-#>   -1.2713   4.8206   3.2899  -1.6654 -19.0327  -9.0100   4.2865  -5.6547
-#>    4.1511   4.4826  -2.2632  -7.7519   2.3801   7.0907   6.0796  -5.8401
-#>    3.5021   1.0276  -6.6806   1.2568  -2.0366   7.1988  -6.7076  -1.0068
-#>   -2.5280   0.8024  -8.9132  10.7897  -2.8374   6.7471  -2.2640   0.6762
-#>    6.0122 -12.5526   0.2823   1.9227   3.5617  13.1899 -14.5711  -1.3615
-#>   -5.3664  21.6509   0.7484   3.8145   0.2056 -14.7323  -4.4145  -4.0317
-#>  -15.9122  -8.8177  -0.5277   4.1409  20.5291   3.4526  -2.0654  10.7065
-#>   -3.5763  -0.3758   5.0681  -7.6710   4.2845   1.7020   3.9231  -2.4412
-#>    2.4172   5.0061  -5.2222  -2.6070   0.8065  -3.1587  -3.6731   5.6538
-#>    2.0505   5.3885   6.5308   6.9079  -0.5731   2.4897   5.5510  -0.2221
-#>   -1.2687  -6.4637  -0.7992   1.5713  11.7883   4.8409   7.4817   5.2496
-#>    2.1072   2.8074  -2.4123   6.6889  -1.5585  10.1925  -1.7233  -6.7870
-#>   -0.0777  -0.0870  -2.5451  -2.8325  -0.0159 -10.7361   4.9466  -2.9215
-#>    0.8042   6.5573  -3.6933  -4.7547  -6.9004  -1.5150   6.4858  10.4649
-#>    0.4677   4.9717   2.9189   1.6511  17.4041  10.3360   7.9191   9.0941
-#>    1.1544  -7.1980  -5.6384   2.1168   6.5097   8.4443   3.2848  -2.8482
-#> 
-#> Columns 25 to 32   1.0632  -2.7192  10.7800  -1.4988   5.6075  -1.0201  24.4864  -4.5952
-#>    1.8964  -4.6640  -5.0406   5.0747 -20.3201   6.5519  -6.2392 -14.4074
-#>    1.1198  -9.0152   0.1442  -4.7465   2.0033   3.4777  -3.9870  -4.5185
-#>   -2.9254  -8.6776   1.1509   8.1511  -1.6471   1.3036  -9.9595   1.6227
-#>   -1.9584  -1.3598   0.2078   3.9571  10.6007  -5.8023   2.1676   6.2004
-#>    8.0944  -9.1235  -7.1985  -4.1770   2.2347  14.1464  -2.0111 -22.4899
-#>   -2.8796  -7.7015  -6.2215  -5.4362  11.5978  -6.0972  -7.0790  -2.9885
-#>   -3.1749   4.9953  -3.3213  -7.7443  -8.5786   4.4992   3.6853  -9.6041
-#>   -5.9079  -3.8342 -12.4933   9.6378  -6.8800  -8.5215   0.6795   3.3179
-#>   -2.3786  -2.7458   3.6221  10.2273  -4.5791   1.8903   4.0407  -1.8440
-#>    3.4746  -7.2704  -5.6237   2.9026  -1.7574  -6.0075  -5.9611   3.7171
-#>    5.2485  -5.6597  -6.8140  -4.6195  -1.5528  -3.7034  -3.9570  -0.2235
-#>   -0.6517   6.5427  -2.0068   0.1044  -2.7763   1.1070  -7.6801   3.5222
-#>    6.5342  -7.2092  -3.5259   3.8781   2.7967  -2.8619  -6.4781   1.9573
-#>   -3.2627  -8.2156   5.7127 -10.2309   8.4198   1.0317  -8.5008 -11.6586
-#>    2.6354  -4.9628   6.0390   5.1588  -0.0314   5.3271  16.1069   2.2121
-#>   -6.7892   1.7971   3.7045  10.1105 -11.8865  -2.2256  -2.0193  -3.8336
-#>    2.3820  -6.5229  -9.5870  17.5375   7.5020   4.4041  -4.9949  -2.7408
-#>   -6.9702  -5.4410  -1.7742  -8.2750 -13.7040  -1.1796  -7.8330 -20.4477
-#>    2.6058   9.6906  -4.7918   5.4476  -9.4555   6.6318   1.0639  16.5680
-#>   11.1500  -8.0351  -3.4691  -2.8308 -11.5355   2.5864   3.8273   3.3524
-#>    1.0210  -2.1057  -3.7304   2.2107  -3.7075  -5.8099  -8.6483   7.5339
-#>   -0.2247  -5.4163  -0.5882 -14.3024  -2.6323   1.0308  -3.6861 -16.4198
-#>    8.8517  -5.0068   4.0431   5.2185  -7.5437   2.1399   9.1219  -7.3401
-#>   -1.0232  -2.8021  -7.3895   2.2813  -8.9037   0.7960  10.9585   3.6894
-#>   -3.5527   6.3734  11.3317  -9.4079   4.1024   9.1470   5.1007 -10.9093
-#>    1.7934   2.0417  -7.4359  -0.9415  -7.7304 -14.8421   4.6599  15.0996
-#>   -3.7730   3.0577   2.7603 -14.7906   6.3743  12.6716 -12.3185   3.3731
-#>   -8.9482   7.4187  -3.6600   3.4782  -1.1062   7.5387   8.5087  13.0561
-#>   -3.1593  -1.4216   6.9764  -0.7125  14.8323  -3.5689   9.6071  -3.1786
-#>   -5.4040  -7.6017   4.8639  -5.5065  -6.7502   5.9897   1.7524   4.7436
-#>   -0.7224   6.5160  13.5542  -0.8484   4.8272   9.3635   0.0067   8.9889
-#>    3.8936   8.1327  -1.3547  -5.1174   7.6414   0.9399   4.8738   6.6594
-#> 
-#> Columns 33 to 40  -9.2635  -7.2035  -6.8341  -3.8920  11.1846   0.3289   8.8149   1.3694
-#>  -11.8951   3.4905   3.6577   6.1760   2.5567  -3.8852   5.0694   3.1206
-#>    5.2271   1.2251   9.4451   7.1253  -0.0516  -0.6160   2.3369  -2.2727
-#>    2.7091  -4.3054  17.7565  -1.2457  13.8621  -3.6399  11.4166  -8.5968
-#>    5.7349   1.3999   3.5504   7.1171 -14.1684  -3.7598  -9.0061   4.2168
-#>    0.1764  20.1954   2.7542  12.2646   6.3112  -0.7967   3.3466  -0.7696
-#>    8.9047  -5.0562   0.5008   5.8380  -5.7228   6.8445  -3.6734  18.9400
-#>    2.0629  -8.6991  -0.5225 -10.7714   9.7581  -8.4159   7.7689   3.5440
-#>  -17.0136   1.7361  10.8883   5.8647   6.5579   5.2743   5.7465  -1.6654
-#>   -1.0095  -2.9798   5.6213   9.2175  -6.1612  -3.8507  -2.1239  -0.5416
-#>    5.0362   1.0972  -7.7023  -1.9197  -4.8732 -11.5382  -0.7454  -2.0414
-#>    5.5874  -1.5368  -4.8733  -3.8002  -6.0718   0.0737   0.5264   8.3143
-#>    1.9344  -3.4319   5.4247  -5.5361  -2.4694   9.2598  -3.9390 -12.0095
-#>    7.2665  -5.3043  20.2393  -5.8789  -3.3490  12.6675   3.9846   1.7936
-#>   12.2226   4.7088  -0.0720   3.2021  -5.8620   5.8119  -6.5830  16.4504
-#>    4.5032   5.2409  10.1898  -5.6131   0.3540   1.0364 -11.1512  -9.0271
-#>   -6.4820 -11.0132  -6.5388   4.7742 -18.1064   6.5336  -3.4887  -0.7135
-#>   -3.6589  -0.3688  -0.8579  -2.5890  -5.6719  -7.9088   3.0846 -10.2442
-#>   -4.3303   0.2712  -4.2583  10.9800   3.5294  -0.0587  -5.5945  -1.6424
-#>    7.3768   0.5957   0.5841  -7.9975 -16.0498   1.6262   4.4650  13.6902
-#>   -5.2461  -8.1827  12.8812  -1.8316  -0.6669   0.6355  20.4224  12.9001
-#>   -1.3292   1.1440  11.3888 -18.0718   8.5401   7.8767  15.9205  -3.3331
-#>   -6.4445  -0.6811   2.3454   7.0941  -0.0060  10.7454  -2.9552   3.8102
-#>   -8.8245   6.8739   3.8587   8.6120   1.8066   5.9996  -5.2777  -3.7399
-#>   -3.8900  12.8585 -10.1478  14.4859  -4.3676  -2.4779   9.6047  13.7051
-#>   -0.3521 -13.9371 -10.2086   0.6534   5.3738 -10.6879  -4.5834   9.1824
-#>   -3.5589  12.1856   0.6645   2.0483   2.5583   8.6065   4.0014   2.7410
-#>   23.3597   6.5069  -0.3860   5.0167 -13.4162  -3.9814  -3.4062  14.9261
-#>    3.4224  -7.6012   2.4267   2.0239  -5.9643  -2.4389   8.4784  12.2254
-#>    8.1131 -10.8963 -11.4393   7.0195  -8.6953  -4.0921  -9.2943 -10.3610
-#>    9.7696   4.3104  -9.8152  -5.1547   7.7941  -1.7377 -12.3977  -1.6889
-#>   14.6476   7.0514  -2.3186   2.1118  -2.5128  16.7693 -11.8469   6.5283
-#>    7.4444  -1.3278   5.5839   2.1411   3.1325  -7.2029   6.9434  -0.3647
-#> 
-#> Columns 41 to 48   6.3361  -8.3823  14.6838  -2.5564   2.3135 -11.0018  -3.4430  -6.6691
-#>    9.5879   9.2590  -5.3786   3.0516  -6.5846  10.0990 -11.7321   3.3944
-#>   -3.6460  13.8559   1.0598  -0.7469   1.6902   6.6729  -1.9580   2.0151
-#>   -0.0767  11.7552   7.3769  -8.4430   3.5126   3.5257   5.5583  -0.5369
-#>   -3.4907  -0.4182  -8.7510  -5.0566  -3.7087  -1.6293  -1.2193  -4.2151
-#>    0.7292  12.5892  -7.2307  -1.5323   7.5259  -0.2826  -6.2068   1.0094
-#>    0.1214  -0.8100  -0.6030   6.5839  -7.0462  -0.7055  13.5199  -9.9241
-#>    7.8897  -1.4335   0.9386   3.6702   4.7301  -1.1972   6.3694  -6.8673
-#>    0.1027   7.4085   0.0746  -4.1141   1.1504   7.1094  -7.2309  -3.6300
-#>   11.9246   4.9357 -16.8775   4.4258   1.9653   3.3235  -9.4068  -1.7021
-#>   -2.9280   1.6684  -8.8813  -3.6751   1.3775   6.1987  -7.1506   7.4461
-#>   -0.2259   1.0554  -2.3421  -6.5382   4.4263   8.2247   3.2295   2.3804
-#>  -10.8960  12.0267   2.7160   8.1598  -2.4184   9.6094  -2.0357  -3.3505
-#>  -15.7383   9.7148  12.3839   4.5777   2.4778   1.9414  -2.6358  -8.7548
-#>   -5.0241   5.4350   5.5382   3.1705   5.4128  -0.5800  -2.6863   5.3659
-#>  -10.4657   2.7522  -7.5623  -1.2090   1.4477   6.0935  -0.7693  -1.6742
-#>   22.0229  -5.2352 -11.4991   9.3870  -3.7281  -6.6134  -0.3707   9.4902
-#>   -7.5351   7.1026  -4.9276  -8.8025  -3.7069   4.6289  -4.7223 -12.3302
-#>    7.2135   3.2702 -11.6549  18.1010   4.2276   4.9779   1.6759   7.6809
-#>    8.9515  -8.4539  -1.6544  10.5667 -11.8874  -8.4563  -1.8262   2.5703
-#>  -13.5672 -11.6889   9.8639   6.4763  -3.5790   4.2325  10.7076  -8.6775
-#>  -10.4942   1.1181  11.4040   1.1981  -9.4173  -2.4131  -4.2072  -2.0181
-#>   -1.2752  -1.4358   6.2455   8.8270  -0.5674   4.2308   0.0401   5.4706
-#>    1.2251  -9.2584   2.5148  13.4763  -3.8339   3.0532   3.8232   8.1582
-#>   11.2414   0.5207 -14.2519  -8.0120   0.9716  -5.9536  -3.9727   6.0368
-#>    0.1865  -8.3975  -2.1859   1.7352   6.8353  -3.5377   6.7373   3.0838
-#>   10.4572  11.4622   8.1965  -0.7065  -2.8599   2.4068  -5.4784   3.3010
-#>  -17.6197 -19.1179  -5.1196   8.2346  -3.9124  -1.4653   7.1807   3.3478
-#>   13.8698  -7.8992  -7.9530   1.0119   4.6210  -1.1669  -1.1689  -2.1323
-#>    0.2055  -7.5565  -5.8767   6.8536   6.6724  -9.7350   0.5113   5.2973
-#>   13.9984  11.7311   4.0220  -6.5534   6.5149  -0.9068 -13.3340   4.0862
-#>    6.8044  -8.1622  -1.4375   4.8625   5.6870   6.0109   0.3636   6.5547
-#>  -11.3849   1.3162   3.0693  -3.2302   6.4743  -1.3361   8.0931   2.7210
-#> 
-#> (19,.,.) = 
-#>  Columns 1 to 8   7.9069   8.2314 -15.8607 -17.8247   1.8460   3.3960   1.7360   0.4683
-#>   -8.9073   3.4705 -10.9638   4.2166   2.0623   8.7358   4.3075   7.7731
-#>   -3.2392  -4.2938  -1.1854   5.3059  -9.1970  -3.2163  -0.8529  -5.6473
-#>  -16.0983  -5.4826  -5.9025   7.2389  -1.9904  -0.2595  -5.9837  -2.8110
-#>   -0.7603  -5.9232   5.6491   1.1754  -5.2810  -9.1353  -5.6206   1.7050
-#>   -3.8353  -2.9147  -0.8285   3.0347   7.9952   5.0474   2.4767   2.0987
-#>   -4.2395  -7.5736   6.2816  -0.5787  -3.1490  -2.5334   3.8162  -0.3144
-#>   -1.0432  10.8327  -2.2433   3.8450   8.5869   8.0106   6.9709   5.5028
-#>   -3.6809   8.2100   1.3975   4.3335  -8.6595 -15.4878   2.6603  -6.5403
-#>  -13.4476   0.9709   6.4793   4.2022  -5.8665   1.9948   3.1041  11.2352
-#>   17.0529 -18.9924  -5.9974   1.5673  10.0528  -9.8677 -11.7180   4.0176
-#>    2.5034 -11.2738 -11.7232   3.2905   3.9547   0.6555  -2.0560  11.3394
-#>    2.0134   5.7511   4.8171  21.8050  -7.7353  -1.7456   1.4701  -0.3785
-#>   -3.1590  -2.1877   2.9348  11.7882 -17.0002  -8.5149   6.3678  -8.5830
-#>    7.2730  -5.6195   0.4421   3.1140 -16.6905   3.8076  -8.8719 -10.0481
-#>   -5.8159   3.5791   3.6580   2.3512 -11.1311  -0.7581   6.1737 -15.1651
-#>  -10.3767   2.9377   1.5156  11.9794  -1.1671   7.3783  12.5935  -1.0580
-#>    7.6018   3.8385  -9.4392 -16.1210  -4.3064  -3.2163  -9.3067   0.9529
-#>   -0.3496  -7.4393  -6.1523   4.5940   5.0168   5.7267  -5.7149   2.5463
-#>  -10.4903  -5.9530  -0.8680   1.7640 -11.1686   1.3398   8.3867   4.7253
-#>   -2.5409   5.8450   1.7522   5.4105   1.0961   1.1265   8.4920   2.9826
-#>   -5.9085  -3.9603   4.3299  -6.5190  -5.3264  -9.2896   3.0530   0.7985
-#>    6.2176 -12.0171  -0.4960   3.7811   2.7566  -2.6141  -2.2421  -2.5439
-#>    5.8788   3.4545   4.1160  12.4822   4.4258  11.6593   2.6726   0.7957
-#>   -9.7333  -7.4100 -12.2648 -10.8202 -10.6378  -1.1734   1.2867  -4.5883
-#>   -3.5399  -5.8196   2.7657   8.1652   3.0322   1.3477   3.9744   1.1902
-#>   -0.0897   5.3256   5.4750   1.2325   7.7582  -2.1776  11.7394   0.1351
-#>   -8.1216 -10.0971  15.2709  10.7041  -0.3764  12.1821  -8.2312  -6.5013
-#>  -19.4967   7.9488  -6.2680  -1.6368 -13.4774  -4.1064   9.2371   1.7931
-#>   -0.0632   3.0925   6.8879   8.9557   4.6627  -4.3163  -8.4271  -0.9522
-#>   10.2018  -0.9176  -4.9775   7.8032  15.2119   6.8883  -1.4054   0.6085
-#>   -9.2351  -1.6457   5.4440  11.2551  -2.2972   1.8527   4.7612   0.0586
-#>   -2.7470  -3.5868   2.2028   5.0616  -1.9473  -6.4087   3.4935  -2.2491
-#> 
-#> Columns 9 to 16  -5.4244  -2.9245  -1.2107  -9.0027  13.3219   1.9440   4.8561  -2.7183
-#>   10.7535   4.1678   1.8800   5.5829 -10.7058   8.6258  -1.2581  -6.0093
-#>    5.9274  -2.4308   4.2193   7.6270   5.6584  -2.0767  -6.9968  -5.9731
-#>    0.4131   5.1239   8.8736  -4.7305 -11.9331   9.0935  -3.7310 -17.8027
-#>   -5.1119  -4.9878 -10.6180   0.6381   8.9182  -6.2042  -9.7135  -3.2330
-#>    3.2885  -1.8013   2.8075 -17.3621  -5.3055   0.9743 -14.3689  10.6096
-#>    8.6763  -2.0296   1.8713 -12.6399   5.9778 -13.2539  -0.1304  -9.0192
-#>    6.8218   3.4581   4.1998   2.8683   5.3528   0.3076   4.8818  -2.1856
-#>   -1.0621  -3.8601 -16.4815   6.7831   0.5187  -4.3644 -10.2156  -4.1790
-#>   10.7836  -1.4334  -6.2758   8.9640   0.5866   5.1194  -1.1981 -10.0573
-#>  -11.2556  -3.9902   1.0401  15.9763  -3.9429   6.0554   8.2533  -3.9805
-#>    4.6950  -4.2295  14.3786  -0.8173  17.1867   5.5266  -0.8495  -2.7889
-#>    6.8932   5.7003  -3.2936  13.3559   3.7617   1.0504   7.2782  -8.0423
-#>   -0.6424  -2.4865   3.1472  -4.1264  -5.5375   4.5062 -11.4587  -8.3921
-#>  -10.7727  -7.5424  -0.4159  15.8691   2.7726  -8.9574 -10.0827  -4.1641
-#>   -3.5679   5.6040  -0.5242   4.5327   2.4081   1.7839  -4.5119  -8.2861
-#>   14.1972   5.9486   5.8421  -3.0795   0.4700   9.4044   6.9189  -8.0553
-#>   -2.5634  -8.9776 -10.4028  -6.9258   9.4904  10.9099   4.0087  10.6379
-#>    5.2233  -6.9021  14.9642  14.7190   4.9868  -5.5645   1.4895 -11.4390
-#>    2.5536  -1.9752   2.8385  14.3668  -6.3358  -2.8437   0.8566  -7.5364
-#>    3.3009   6.6402   2.2603  -5.8293  -1.8706   2.2362  -2.2903  -6.8235
-#>  -16.8731   8.3490  -2.8018   1.8419  -7.3747   2.7683  -6.5963  -2.6283
-#>   -2.8859  -5.0357  11.7609 -12.2117  -0.3431  -4.5218  -3.4076  -6.0098
-#>    8.7292   0.1320   3.4142  -3.4457  -2.2028  -1.9678   0.8707  -6.1005
-#>    7.0865 -11.2925  -4.8071  -3.5456  -3.9779  -6.9031  -7.8760   9.7066
-#>   -2.3910  13.3094   8.4412   7.7155   5.9992   3.4252   4.0100  -3.7850
-#>   -5.6154   2.0339   3.5498  -7.0280   4.2935 -12.5404 -11.6149 -14.0983
-#>  -10.1337  -2.7636  -8.9362 -10.0790 -15.9228   4.6759  -3.2626  -1.6180
-#>    4.8460  -2.3104   7.6842   5.6354  -3.9253  -1.7831  -8.9399  -4.0914
-#>   -2.5983  -0.2068   0.7000  -9.6218   5.8294 -10.0757   8.0538   4.3348
-#>    1.8360  -1.5328   3.6088   3.5472   9.7461  -3.1073 -18.6002 -10.2768
-#>    1.2851   0.6742   9.5674   7.6433  -6.4800  -3.8342  -3.2553  -7.6469
-#>   -1.3759  15.5534  13.5354   4.8163   4.8285  -2.8745  -5.3420  -1.2828
-#> 
-#> Columns 17 to 24  -0.5167  -3.2736   3.6042   9.2083   9.7675  -3.9386   1.3428   3.4470
-#>    2.4704   2.8545   6.8275   3.5003  -0.2367   5.6338   1.1046   4.6771
-#>   -3.0332   2.1101   8.6318  -4.8976 -14.9192  -1.6236   2.2431   2.0142
-#>    0.4408   1.3938   3.2056  -8.0020  -2.1119   6.5324  -2.4020   9.4199
-#>   -9.3392   9.3710  -3.2274  -4.2743  -0.2528  -5.7441  19.9280  -2.5542
-#>    1.9263   1.6250   2.8807   0.3688 -14.1765  -6.7254  10.7015   9.2226
-#>    1.9163  -0.4107   8.0362 -10.4422  -0.5098   3.4652   5.8927   2.2033
-#>    2.4038  -8.7245   2.6648   2.7030   1.3872  -6.1252  -9.6656   0.5002
-#>    0.1946  -2.0269   1.5145   1.2279  -0.1315   1.4630   0.6094   1.6623
-#>   -6.3521   0.4316  -4.7299   0.3104  -1.4829  -1.6261   0.6357   7.8922
-#>  -11.5962  10.6036   1.4772  -5.9238   0.6079  -0.2997  15.3069   3.6901
-#>    2.1367  -1.4953  12.1796  -5.4230  -6.6698  -5.3625   8.1637  -3.4637
-#>   -4.0125   3.2110   5.4136   0.7173 -13.9764   6.4144 -11.7281   4.7523
-#>   -0.8757   6.4271   6.8656   5.0033  -9.3574   4.0220   1.5025   8.2151
-#>  -14.3414   4.9516  -3.8332   1.1615   3.7050   3.6838   0.3889   7.1748
-#>    3.4352  -8.0321  -0.5548  -7.1979  -2.1331  -0.9266   0.7498   3.3833
-#>   -3.8955   1.0812  -7.4346   9.9988   8.9725  -0.6261 -14.2812 -16.3892
-#>    0.9259  -3.9578   9.6653  -1.8063  -2.1143  -7.3721   9.1487   4.5222
-#>   -7.4901   0.1527  14.3195   1.1760   7.2353  -5.5672   2.5676  13.1659
-#>   -0.1484  12.3372  -1.2780  -1.0691   1.3121  10.2539  -5.5109   0.9131
-#>    5.1531  -5.7138   0.8301  -4.5160   7.7156  -4.6745  -5.6056 -10.4448
-#>   10.0189   6.8799   3.0023   0.4315   0.7961  14.9472  -0.0927  -6.6039
-#>    9.3146   7.4606   6.3168   2.5325  15.7218 -14.6869   6.3143  -8.6685
-#>   -3.7615  -2.7482 -14.5030 -11.5257   2.6656   0.5920   4.0818  -3.0660
-#>    1.9609   0.6213   2.8665   0.1718   8.8866   1.9372   0.4757   7.3429
-#>   -4.1544   4.6766   0.7745   1.2428   9.3632  -0.3896  -4.7325   2.0222
-#>    1.3315  -3.0702   1.4970  -9.3075   5.4004  -2.4335   1.7001  -3.5945
-#>  -12.0965  -2.4795  -3.4041  -5.4235 -13.7871  -3.7242   2.2135   0.9609
-#>   -1.4595  -7.6355   2.5596   6.0541  -0.2605   6.7192  -2.9285  14.5957
-#>  -13.5363  -0.7604  -2.6655   3.2770   0.5550  -3.3883  -0.0947 -17.2695
-#>  -12.8532 -10.6036  -5.2803  -5.4334  -0.8143   0.9354  -2.8281  -0.3836
-#>    1.2841   8.5728 -15.6670  -2.3541  -9.3281  11.0663   9.8001   8.9577
-#>    1.2819   0.9044   5.6131   2.0612  -6.8050   1.4621  12.6808  -3.0845
-#> 
-#> Columns 25 to 32  18.7151  -3.8094 -10.6858  -7.2112  10.5903   2.8308  -5.1495  -6.9245
-#>   -8.1932 -10.3093  -4.9610   6.5884   3.5473  -1.5878   1.4735  11.9831
-#>    1.6399  -2.0138   3.1374   2.5061  -0.7592 -12.6079   5.2005   9.6991
-#>   -0.2601   2.4913  -0.1172   3.2673  -1.0057  -5.0404 -15.6786  29.6677
-#>    6.3120  -2.2729   8.2883   8.2263   4.6611 -11.1320   7.2619   0.2219
-#>   -0.0609   7.8129   3.8104  -6.3285   4.6025  -4.4485  -0.0333  20.5367
-#>   21.4471   1.3245 -10.1189   6.9064  -6.3160  -3.4137   5.9507  10.0966
-#>    2.1005   3.0113  -7.5420   1.7446 -10.8668   0.2345   2.9597   7.5663
-#>    6.8733   1.2057  -1.3649  -2.9656   0.2376   6.2730  -2.6192   3.3051
-#>    2.5055  -4.9166   1.1617  10.2103   5.4102  -3.6294   8.0282  -3.7121
-#>    6.1556 -18.8554  -6.8870  16.7942   0.8996  -4.3907  15.7691  -6.0708
-#>  -13.7109  -5.4970   2.0404  12.2095  -1.1657  -5.2565 -15.3310  13.6764
-#>   -2.0914   5.5632   5.5576   6.7898  -2.8911   6.3307  -3.9102  -2.4087
-#>    5.1732   2.5589  14.9252  -4.7789  -0.9729  -1.8489  -9.2545  10.3432
-#>    0.5507  -0.1272  14.3721  -2.8788   7.3230  -8.7260   4.2822  -8.2377
-#>    7.8071   6.3932   6.5513   3.2569   0.9689  -0.3189  -4.6282   9.9274
-#>   -7.9111   1.6218 -14.3454   7.2757   9.5468  -7.2799   4.2477  -2.9991
-#>    1.4842  -7.5389 -11.1014  -1.6944   7.2728  -9.5720 -10.4276   4.1355
-#>   -3.4725  -0.5684   0.1559   2.7386  -5.8742   4.7850   5.0468   1.2748
-#>    0.1762   6.7808   6.3146   7.9698  -2.0042  -0.3079   6.2638  -6.0213
-#>    7.8444  -1.9231   9.2642   1.0918 -10.7200   6.6738  -5.5230   9.9495
-#>   -1.0943   7.7873   4.2141  -8.8941   0.5661   8.1359  -8.9883  -2.9023
-#>   -2.6989   9.5537  13.1507  -1.4590  -5.3198  -5.8421   3.6497   2.1718
-#>    8.8567  -5.6070   5.4076  -1.1994   1.1786   6.1395  -2.2800 -11.5192
-#>    3.2517  -4.9864   5.4578 -13.7910   0.8607   0.3237   2.9753  -1.2993
-#>    3.8434   5.3917   4.0140   5.5966   4.2340  -6.8201  11.1240  -2.5896
-#>    3.2052  10.5745  -3.6056   2.5925  -1.4456  -0.6922  -3.8502  16.5866
-#>   -2.4905   0.8548   5.1917   8.9457   7.4349 -11.4280  17.5558 -10.8432
-#>   -8.6278  -1.5267  10.5918  -1.0952   0.6434   4.8316  -0.5702  -4.1505
-#>    2.0603  15.5785  -1.0938   0.4554  14.3984   0.0487   1.9924  -8.3948
-#>   -4.8434  -0.7336   7.9365  -3.0920  -0.1015  -6.8206  -2.6112   8.2260
-#>  -10.5381   6.8931   5.0467  10.2215  -5.0572 -10.9092  15.1842  -1.2059
-#>    3.0102   6.5824  11.8250   0.1481   5.1619   6.7905  -6.3298   6.1166
-#> 
-#> Columns 33 to 40 -13.0929  10.7086 -10.6413  -3.2434  -4.7143  18.9670  11.5604   0.6759
-#>    3.8253   5.3724  -3.8026   1.2284   3.1021   3.1633 -10.6655   4.8320
-#>    9.6050  15.6168  -0.1603   5.9876   5.5583   3.9613  -3.5428   2.9187
-#>    0.0542  11.3364 -16.0973   3.8528  18.8930   0.8423 -19.0941   3.0001
-#>    4.6167  -7.5432   1.4097  11.1482  -7.2509   2.3051  -1.6934  -2.9962
-#>   -1.2676   3.3272   4.4583   2.2233   0.6304  -1.1169  -3.7036 -12.3604
-#>   -7.3089   2.9711  -4.3308  18.0142  -7.9470  -0.1779  -2.5158 -10.6243
-#>   -8.7892  -0.0394  -2.9864   0.9498  -9.3213 -11.2037   1.7052   3.1814
-#>   -8.1533   6.6281   4.9492  13.8490  -3.8301   7.7220   1.4083  -1.9021
-#>    9.1308  -7.0064  -1.9722  -2.4951  -0.6262  -2.2968  -8.2641   4.3387
-#>    3.6544 -28.7199   7.4428  -0.3025   8.9910   8.9597 -18.8920  -2.7463
-#>   -4.9109   2.1997  -5.8295  -5.6553 -10.4926  -5.4169  -0.9827   5.2563
-#>    4.6452   3.1934   3.9736   3.3958   4.4744  -5.4590   7.4215   2.6005
-#>   -0.9471   5.3609   0.7467   4.1787  10.4314   0.9719  -8.7019  11.9533
-#>   12.1445   3.5598   5.1703   1.6544  14.4675   9.4715   3.7597  -2.1770
-#>   -8.2620  12.5158 -12.3281   0.5927  -1.2875   3.3224 -15.3013  -1.1989
-#>    2.0916  -0.5344 -13.9363  -4.2687   1.7251   2.1584   9.0830  -2.3336
-#>   -5.1937  -0.4773  -5.7731  -0.3001 -10.2828   9.0828   1.6291 -10.7334
-#>   -1.9560  -4.0323   3.8732   9.6128  12.4645   5.0971 -12.0427  -6.5134
-#>    7.2082 -10.4481  10.9988  -6.3425  -1.5460  -7.8643   8.3994  15.8663
-#>  -15.5418   7.9106   0.1194  -4.9896 -12.1432  -4.8835 -13.2222  11.1480
-#>   -6.0650  12.4503   1.2084  -0.6934   4.0231   5.1878   7.6225  16.0133
-#>   10.0894   3.7950   2.7662   6.9198  12.7698   9.4798  -1.6231   2.8290
-#>    3.9802   3.8951  -5.1209 -12.1517  -6.8346  11.1322 -12.7089  -8.3542
-#>  -14.0842  -6.4819  -3.2150  -0.6622  -8.4714  -3.2800 -11.8791 -14.5002
-#>   -3.6597   0.2235   1.5656   1.3861   8.3664  -1.1531   3.4840   4.8625
-#>   -0.0208   4.5306  -4.4620   5.6068  -3.5328   0.9448   7.0014  -8.7854
-#>    8.1730 -16.0610  -4.7102  -6.8084   6.8921 -13.9539  -2.5970   0.8457
-#>   -7.5805  -7.3242   0.9772  -3.8022  -2.8765 -21.1915  -8.8806   8.6112
-#>    2.7900  -1.2628 -10.5153   0.7526  11.9589  11.3937   0.9338  -0.7113
-#>    3.3273  -4.8510 -16.1544 -11.6446   4.8889   1.0366  -9.9487  -8.3986
-#>    7.2784 -17.8624   2.1096  -7.7101   3.0077 -21.8409  -7.6522  -5.3485
-#>   -9.4605  12.9928  -5.7377   8.1991  -3.6538  -4.7890  -3.8185   1.3486
-#> 
-#> Columns 41 to 48  -1.2806   4.4719   3.2787  -6.8226  -2.5020  -0.3241  16.2354   4.4872
-#>    4.2604  11.2756   1.4287  16.5132   1.0703  -2.5217  -3.6417  -3.5623
-#>   -5.9802  -4.1072   5.3458   8.4859   5.6440  -5.0054  -5.6247  -2.7213
-#>  -13.6875  12.1878   8.7390   5.3186  16.4883 -17.4650  15.0589  -7.1149
-#>    8.6370   6.1348   6.9977   5.9094  -3.5747  -0.2621 -14.0265  -8.1669
-#>    5.1393   4.4540  -1.6370 -10.1522   3.2873   4.1298   7.4150   9.1745
-#>   -3.5731 -10.3298   9.7454   5.4470   7.8061  -1.7168   3.2278   8.5204
-#>  -21.1075  -2.1749  -7.7596  -2.9712   1.7288   3.6917  16.6576   3.8277
-#>    2.7863  12.8813  21.6354   5.3553  -1.5177   2.0300  -9.4604 -10.3778
-#>  -14.4875  11.1262  -0.4382  11.3462  -7.3018   2.3005 -12.4743  -5.6966
-#>   12.2622   7.4871  -5.5479   1.3534   6.5592   7.8683   6.0947  -3.2516
-#>    5.1640 -17.4172   4.4021  -9.6653  10.3575   1.2316  -6.2373   9.5555
-#>  -16.6553  -1.8077   2.1062  -4.1136   0.4310   0.2189  -1.6960 -16.2303
-#>   -4.1715   1.8463   9.8158   2.6802   1.2722  -8.7310  10.8009  -9.0684
-#>    7.0178  12.7044  13.5289   3.1725  -2.0315  -5.1303   0.0483  -5.9967
-#>   -7.4459  -7.6461   1.2485   0.4100   4.6413  -3.3643  -1.9663  -8.7889
-#>   -1.4376   0.2320 -14.3036   7.3219 -10.1662  13.2323  -4.9297   6.0043
-#>   -2.9853   6.5275  -3.6477  -2.9087  -4.8125   1.1970   5.2914  -0.7746
-#>   -1.9150   0.9732   1.9533   8.5727  21.2805   1.1917   0.2164   2.1555
-#>    1.4902   8.4288  -2.4849  -2.1220  -5.5417  -1.3303  -0.5162   7.3754
-#>   -8.6690 -13.9623   0.6408  -1.8519   2.7357 -11.0654  12.4976  -2.1276
-#>    2.2020  12.4450  13.9324   1.6783  -4.6593  -5.5748   5.2634  -8.7318
-#>    6.9535  -3.0555  -0.3675  11.4615  -7.1063   8.8430   2.9909   8.8720
-#>  -12.3394 -23.7992 -11.7108   2.3299   7.7491   0.7197   2.0416   0.8095
-#>   15.6655   1.4036  12.8402  -3.1910   9.5950  -5.7898  -0.3393   4.2886
-#>  -11.1935  -0.6632  -4.5321  -4.1002   7.3329 -10.4798   8.2632  -4.2149
-#>    6.5294   6.4735   2.2356  -5.7527  -3.6129   8.0644   5.7421   3.7550
-#>    5.3824 -15.5725  -6.2125   6.5763   5.5347  -8.4960   2.9947   3.0953
-#>    0.0087  13.1358   1.9827   7.6309   1.6621 -10.9873  -4.9114  -9.1532
-#>   -5.0495 -20.4267   3.4558  -1.2676   6.2794  -2.6602  -7.8177   8.3995
-#>   -5.3475  -8.1220  -0.6322  -4.6946   7.1616   7.0391   7.9409  -1.3812
-#>    9.0524   1.8260 -11.0054   8.7754  -8.6430   5.8534 -23.3776   1.1368
-#>    8.2543  -9.6404   8.8720  -3.6102   9.5666  -5.0479  -3.4277  -8.2915
-#> 
-#> (20,.,.) = 
-#>  Columns 1 to 6  2.3410e+00  9.7388e+00  1.6557e+01  1.1382e+01  3.7641e+00  5.1080e+00
-#>   9.7553e-01 -8.4447e+00  1.3123e+00 -1.5725e+01 -4.9083e+00  4.1044e+00
-#>  -3.1703e+00 -2.2135e+00  4.4341e+00 -1.2445e+01 -3.2447e+00  3.6457e+00
-#>   1.2177e+01  1.5420e+00  5.5418e+00 -2.3071e+01 -1.8939e+00  1.7252e+01
-#>  -1.0726e+01 -4.2155e+00  6.5425e+00 -2.0108e+00 -4.7632e+00 -1.3778e+00
-#>   1.2218e+01 -3.6186e+00 -4.9115e+00  5.6926e+00  3.1619e+00 -1.5542e+01
-#>   1.1675e+00  1.8411e+00  1.7625e+01 -1.9243e+00 -1.8360e+00  5.9005e+00
-#>  -2.1542e+00  3.3940e-01 -4.9044e-01  7.3563e+00  9.6633e+00 -3.6952e+00
-#>   8.6159e-02 -9.9189e+00 -3.8149e+00 -8.9140e+00  2.6082e+00 -6.3848e+00
-#>  -1.0125e+01  1.6594e-01 -2.5587e+00 -6.3884e+00 -1.0598e+01 -5.6895e+00
-#>  -1.2456e+01  1.3919e+01  8.3685e-01  1.1546e+00 -3.0186e+00 -3.8498e+00
-#>  -2.6311e+00 -2.4535e+00  6.6286e+00 -1.7185e+00 -5.9649e+00 -2.6087e+00
-#>  -1.0975e+01 -2.2859e+00 -4.9682e+00  1.6251e+00 -8.6998e-01  9.8512e-01
-#>   2.8754e+00  2.6769e+00  2.8849e+00  9.1190e-02  4.8219e+00  5.3024e+00
-#>  -1.0107e+01  8.5539e+00 -2.2039e+00  1.1878e+01  3.7640e+00  1.7298e+00
-#>   5.3624e+00 -4.1503e+00  1.0721e+00 -9.7069e+00 -2.6360e+00  6.1107e+00
-#>  -4.1990e+00  5.3183e+00  9.7740e+00 -1.4839e-02 -2.9959e+00 -9.8873e+00
-#>   7.1682e-01 -6.7365e+00  1.7742e+01 -2.0493e+00 -4.0481e+00 -2.4705e+00
-#>   5.6811e+00  2.1429e+00 -1.9895e+00 -6.0990e+00  7.1933e+00  1.0245e+01
-#>  -1.0327e+01  1.1902e+01  8.1764e+00  2.9050e+00  3.5568e+00  4.8856e+00
-#>   9.8634e+00 -7.3801e+00 -1.8072e+00 -1.8462e+01 -9.1422e+00  8.2301e+00
-#>   7.4528e+00 -3.2333e+00 -2.7164e+00 -2.6745e+00  4.9729e+00  8.2682e+00
-#>   9.8821e+00  6.4739e+00 -2.9487e-01 -9.3617e-01  1.5004e+01  1.2282e+00
-#>  -4.8038e-01  7.8481e+00  3.1612e+00 -1.7786e+01 -7.0473e+00 -4.7113e+00
-#>   1.3829e+01 -1.1918e+00  4.7191e+00 -9.9064e+00  3.7553e+00 -5.6204e-01
-#>  -2.0430e+00  1.0027e+01  7.2484e+00  7.6063e-01  6.9578e+00  4.1638e+00
-#>  -1.2486e+00 -3.1506e+00  7.0924e+00  6.9274e+00  1.1315e+01 -6.7493e-01
-#>   4.5251e+00 -1.1089e+00 -4.6790e+00 -4.9297e+00 -2.0166e+01 -3.0425e+00
-#>   2.0341e+00 -5.0193e+00 -1.5881e+00  1.4271e+00 -5.1175e-01 -1.9088e-01
-#>   4.3895e+00 -3.2604e-01  2.8351e+00  6.5263e+00 -6.4667e+00 -3.6202e+00
-#>  -4.6403e-01  1.6272e+00  3.1020e+00  6.5796e+00  1.4924e+01  4.7444e+00
-#>  -8.6713e+00  7.4556e+00 -1.9416e+01 -6.8462e+00 -1.1393e+01 -6.3260e+00
-#>   8.3200e+00 -1.1023e+01  3.5368e+00 -1.8314e+00 -7.4983e+00 -1.0943e-02
-#> 
-#> Columns 7 to 12 -2.4804e+00 -1.8602e-01  7.1954e-01  2.9089e+00  1.7618e+00 -1.3436e+01
-#>   1.7067e+00  8.4794e+00 -2.6234e+00  7.7493e+00 -6.4802e+00  2.0458e+00
-#>   5.1771e+00  3.1818e-01 -1.7405e+00 -6.4634e-01 -7.8719e+00 -2.0841e+00
-#>  -8.7515e+00  8.5336e-01 -1.5769e+01 -5.3437e+00 -3.2970e+00 -2.0343e+00
-#>   2.2304e+00 -5.4520e+00  1.9013e+00  2.4374e-01 -2.1490e-01 -1.4346e+01
-#>  -2.6801e+00 -1.8525e+01  2.5054e-01  1.1545e+00  1.2923e+00 -2.2221e+00
-#>   5.4859e+00  3.2670e+00  5.4010e+00  1.0159e+00  2.6045e+00 -3.3450e+00
-#>  -5.5536e-01  1.9618e+00  3.5680e+00  8.0874e+00  9.1611e+00 -2.3273e+00
-#>  -8.0280e+00  1.5038e+00 -2.8943e-01  6.2901e+00 -5.8491e+00 -1.6066e+00
-#>   7.5733e-01 -8.2033e+00 -9.2451e-01  7.4625e+00 -6.7360e+00 -8.9020e+00
-#>   1.2189e+01 -1.8027e+00  1.6689e+01  3.4474e+00  2.9138e+00  1.7912e+00
-#>   1.3682e+01 -1.6062e+00  9.7473e+00  4.5117e+00  5.6799e+00 -5.4646e+00
-#>   6.8369e+00  1.3800e+01 -6.7010e+00 -2.2776e+00  6.9862e+00  7.4298e+00
-#>   5.5939e+00  7.5994e+00 -9.1281e+00 -1.0513e+01  3.1338e+00  2.2780e+00
-#>   4.0438e+00  9.6674e+00 -8.9905e+00 -6.4321e+00 -2.2459e+00 -2.7442e+00
-#>   1.7242e+00  3.3175e+00 -1.9010e+01 -5.4166e+00  2.1368e+00  1.3751e+00
-#>   9.9083e-01 -4.8365e+00  4.8378e-01  1.6902e+00  2.2087e+00 -3.4764e-01
-#>   5.4890e-02 -7.2534e+00 -2.5185e+00  1.2493e+01 -2.4334e+00 -6.3627e+00
-#>   9.5054e+00 -4.4612e+00  1.8223e+01 -3.7394e-01  3.6688e+00  7.5635e+00
-#>   2.2095e+00  4.6699e+00  9.9971e+00 -6.1401e+00 -1.0550e+01 -1.5797e+00
-#>  -9.8198e+00  9.8286e+00  5.4587e+00  1.0334e+00 -3.0479e+00  1.7580e+00
-#>  -1.2700e+00  2.6403e+01 -4.4089e+00 -8.6787e+00 -8.4800e+00  4.4394e+00
-#>   2.8741e+00 -1.9863e+00 -3.2514e+00 -8.6687e+00  9.8180e-01 -1.7195e+00
-#>  -5.3990e+00  2.8465e+00  1.2402e+01 -8.9504e+00  2.4825e+00 -1.1775e+00
-#>  -4.4550e+00 -8.1371e+00  6.7530e+00  3.7530e+00 -1.0986e+01 -2.4960e+00
-#>  -2.8496e+00  2.0082e+00 -4.6388e-01 -9.5946e-01  1.0177e+01 -3.2097e+00
-#>  -9.3333e-01  1.1898e+00 -4.7260e+00 -5.8988e+00  1.5007e+00  3.4544e+00
-#>   3.4987e+00  3.1301e+00  1.5726e-01 -1.7080e+00  4.4588e+00  4.7642e+00
-#>  -2.8878e+00 -6.6251e-01 -3.1073e+00 -3.9279e+00 -6.6434e+00 -1.0017e+00
-#>   7.0556e+00 -1.6597e+01  2.5636e+00 -1.2606e+01  7.0497e+00 -1.9386e+00
-#>  -1.7508e+00 -1.2447e+01 -1.0717e+01  5.4835e+00  5.0638e+00 -3.2764e+00
-#>   4.4112e+00 -5.0044e+00 -1.2282e+01 -1.1170e+01 -6.7855e+00 -6.1712e-01
-#>   4.2148e+00  1.2460e+01 -8.7559e+00 -2.6166e+00 -1.9169e-02  1.4165e-02
-#> 
-#> Columns 13 to 18  6.2091e+00 -8.8143e+00  7.2104e-01  7.2597e+00 -1.5449e+00 -3.9369e+00
-#>  -9.3598e+00  5.4552e+00 -2.3879e+00 -1.3020e-01 -1.2810e+00 -6.2295e+00
-#>   1.6856e+00  5.1225e+00 -6.9954e+00  1.0107e+00  1.1691e+00 -1.7933e+00
-#>  -3.2094e+00  4.2271e+00 -1.9460e-01  6.9871e-01 -1.8737e+00 -6.9162e+00
-#>  -5.5509e-01  1.1511e+01  9.7265e-01 -4.1372e+00  6.0545e+00  4.0498e+00
-#>  -6.5004e+00  3.4623e+00  3.2891e+00 -1.6722e+01 -3.2627e+00  1.3829e+01
-#>  -2.0242e+00  1.3205e+01 -8.8209e+00  9.6122e-01 -4.1757e+00 -2.7641e+00
-#>   1.6212e+00 -1.6687e+00 -4.6750e+00  2.5037e+00  2.8769e+00 -8.8224e-01
-#>   6.9457e-02 -9.3542e+00  1.1674e+00  6.2896e-01 -1.2544e+01 -5.5968e+00
-#>  -6.5634e+00  9.7293e+00  6.9997e-01 -4.0996e+00  7.8806e+00  1.4738e-01
-#>   8.3325e+00 -7.4368e+00 -1.0828e+00  4.0310e+00  1.0186e+01  1.6531e+01
-#>  -9.2470e+00  1.6434e+00 -1.0851e+01 -2.7858e+00 -4.7217e+00  1.2983e+00
-#>   3.9226e+00 -3.9145e+00  2.3867e+00  1.5888e+01  6.1320e+00  5.3633e+00
-#>   9.3941e-01 -3.2628e+00 -1.4717e+00 -9.6640e-01  9.1844e-01  5.7049e+00
-#>   1.4595e+00  1.0113e+00  6.7419e+00 -4.7996e+00  4.4700e+00  1.5168e+01
-#>  -4.1207e-01  7.1577e+00  7.2850e+00  3.0012e+00  6.6942e+00  3.8092e+00
-#>  -5.2720e+00  1.0192e+01  2.8500e-01 -8.5123e+00  1.5029e+00 -1.8070e+00
-#>  -8.1098e+00  1.7250e+00 -1.1236e+01 -7.7844e+00 -5.8833e+00  3.2155e+00
-#>   1.0499e+01  1.0083e+01  6.7967e+00  4.1068e+00  1.0419e+01 -5.3592e+00
-#>   2.2096e+00  2.1852e+00 -1.7720e+00  6.6781e+00  9.3576e+00  2.2509e+00
-#>   5.5971e+00  3.2116e+00 -1.7596e+01  6.4026e+00 -3.3209e+00 -8.8648e+00
-#>  -1.5210e+00 -1.3915e+01 -2.6886e+00  1.4786e+01 -4.6967e+00 -7.7192e+00
-#>  -7.6820e-01  8.3450e+00 -1.9640e+00 -6.6878e+00 -2.4184e+00 -7.9350e-01
-#>   7.4614e+00  5.3085e-01  5.5515e+00  6.6437e+00  5.0550e+00 -1.3595e+01
-#>  -7.7221e+00 -4.6417e+00  3.9403e-01 -1.3851e+01 -1.0311e+01 -1.3360e+00
-#>   6.6917e+00  1.2243e+01  1.4173e+01  2.8141e+00  1.1076e+01  5.8287e+00
-#>   1.0787e+00  2.0636e+00 -1.0247e+00  3.1193e+00 -9.4799e+00  8.2093e+00
-#>  -1.5041e+00  7.0780e+00 -7.8316e+00 -1.5371e+01  7.1263e+00  5.9010e+00
-#>  -8.2715e+00  9.9196e-01  2.1497e+00 -2.1559e+00 -1.2710e+00 -1.9854e+00
-#>   1.1778e+01  1.7334e+00  3.4908e+00 -2.5709e+00  1.1166e+01 -7.3452e+00
-#>  -5.5082e-01 -4.1049e+00  2.4903e+00  8.0472e+00 -3.3471e+00  3.5109e+00
-#>  -5.2418e+00  6.6022e-01  3.3127e+00 -5.4575e+00  1.7177e+01  6.8944e+00
-#>   1.4118e+00  3.3244e+00  4.7817e+00  1.2690e+01  1.1262e+01  5.2541e+00
-#> 
-#> Columns 19 to 24  5.7987e+00  9.9046e-01  1.3476e+01  2.2373e+00  4.8450e+00 -3.3785e-01
-#>  -6.4077e+00 -5.5996e+00  7.7242e+00 -4.9139e+00 -1.0795e+01 -2.7210e+00
-#>  -7.8080e+00 -9.8749e+00 -8.0328e-01 -5.6685e+00 -6.5051e+00 -7.3282e+00
-#>   4.4262e+00 -4.0880e+00 -3.1434e+00 -1.5922e+00 -3.5972e+00 -4.8698e+00
-#>   8.9376e+00 -1.6125e+00 -3.0707e+00  2.6647e+00 -8.6386e+00  1.0014e+01
-#>   1.0788e+01 -8.3395e+00  9.1440e+00 -2.9792e-01 -5.8211e+00  5.8759e+00
-#>  -6.4088e+00 -1.2216e+01 -6.1322e+00 -6.9714e+00 -1.8267e+00  3.8377e-01
-#>   6.6123e+00 -1.4050e+01  4.3107e+00  5.4709e+00  2.8144e+00 -4.5840e+00
-#>   1.3259e+00 -9.2734e+00 -6.1174e+00 -1.4116e+00  8.0506e+00 -1.7683e+00
-#>  -1.3442e+01 -1.9701e+00  7.1781e+00  4.8789e+00 -1.0749e+01  4.3147e+00
-#>  -1.0874e+01 -2.7071e+00  5.0782e+00  3.2553e+00 -3.3053e+00  5.0959e+00
-#>   4.7742e+00 -9.7450e-01  4.0312e+00  4.8259e+00  2.0546e+00  1.6256e+01
-#>  -6.2039e+00 -7.7300e+00  2.7282e+00  9.7221e-01  1.7280e+00 -1.1206e+01
-#>  -3.4114e+00 -5.4780e+00  1.9617e+00  1.9247e+00 -1.0711e+00 -6.1604e-01
-#>  -4.0607e+00  6.2097e+00 -6.0131e-01  1.2949e+01 -9.1161e-01 -5.7228e+00
-#>  -4.4931e+00 -6.3089e+00 -1.1048e+00 -3.4241e+00 -5.9472e+00 -1.1120e+01
-#>   1.4240e+00 -6.8421e+00  5.2643e+00  1.2383e+00  3.9429e+00 -1.4067e+00
-#>  -5.4825e-01 -1.4219e+01 -7.1825e-01 -3.0608e+00 -4.8196e+00  1.2558e+01
-#>  -9.2407e+00 -1.0295e+01 -4.5680e+00 -6.3542e-01 -5.6528e+00 -1.1106e+01
-#>   2.5570e+00  1.3100e+01  3.2021e+00  3.3889e+00  7.0477e+00  6.1366e+00
-#>  -3.4840e+00  6.5269e-01 -1.1508e+01  3.1331e-01 -2.6091e+00  8.6547e+00
-#>  -3.7476e+00 -1.5001e+00  4.7892e+00 -7.2544e+00  6.8576e+00 -2.3800e+00
-#>   1.2855e+01  4.0347e+00 -4.9090e+00  1.6522e+00 -3.1490e+00  7.2887e-01
-#>  -1.1071e+00 -1.1343e+00  7.0469e+00 -4.3051e+00 -6.4920e+00 -2.1248e+00
-#>  -6.6440e+00 -2.3582e+00 -5.1569e+00  1.7230e-03  4.1819e+00  2.6481e+00
-#>   8.3807e+00 -1.0780e+01 -4.4174e+00  6.4726e+00 -1.4760e+00 -1.2222e+01
-#>   1.2455e+01  1.3503e+01  6.7132e+00  3.2466e+00  9.0439e+00 -1.0962e+00
-#>  -7.0192e+00  1.2580e+01  7.1967e+00 -9.6692e-01 -2.7230e+00  7.7308e+00
-#>  -7.6719e+00  7.7843e+00  5.2237e+00  4.2453e+00  6.7228e-01 -9.5117e-01
-#>   3.6038e+00 -6.0937e+00 -1.4692e+00 -8.3313e+00  1.5697e+00  4.7399e+00
-#>  -1.2687e+00  1.0278e+01  8.8936e+00  2.8074e+00  2.1382e+00  3.5530e+00
-#>  -1.9756e-01  9.7066e+00  3.1387e+00  7.1569e+00  2.4309e+00 -8.4276e+00
-#>   7.7145e+00 -3.3162e+00  9.2129e+00  1.5138e+00 -6.6110e+00 -5.4324e+00
-#> 
-#> Columns 25 to 30  3.3448e+00  9.3022e+00  9.5999e+00  6.9354e+00 -5.0616e+00 -6.9058e+00
-#>   5.9573e+00  3.5726e-01 -9.9542e+00 -3.3779e+00  6.1937e-01  6.9114e+00
-#>   2.3182e+00  3.1634e+00 -5.6326e+00 -9.8146e+00  1.1031e+00  4.6291e+00
-#>   3.7577e+00  2.9056e+00 -1.2893e+01 -7.0990e+00 -5.8789e+00  5.4299e+00
-#>  -2.0569e+00 -1.2289e+01 -1.2072e+01 -8.9648e+00  6.1291e+00 -4.8387e+00
-#>   2.4939e+00  3.9368e+00 -3.3553e+00 -3.7930e+00  2.2403e+00  1.5174e+00
-#>   4.7722e+00 -2.3783e+00 -3.4526e+00 -7.4857e-01  4.7284e+00 -5.9305e-01
-#>  -4.4239e+00  2.2117e+00  1.6915e+00  1.6951e+00  6.1919e-01  7.1786e+00
-#>  -8.2375e+00 -1.1487e+01 -3.2686e+00  1.9129e+00  3.4600e+00  6.1822e+00
-#>   9.6635e+00 -9.8398e+00 -1.4344e+01  1.1570e+00  3.5877e+00 -3.4732e+00
-#>   6.8537e+00 -1.6598e+00  1.4938e+00 -5.8247e+00  1.0250e+01 -5.8323e+00
-#>   7.7056e+00  4.1740e+00 -1.0906e+01 -3.1704e+00  2.1178e+00  7.9223e+00
-#>  -5.4461e+00 -4.5399e-01 -2.1419e+00  3.8774e+00 -1.9193e+00  5.1422e+00
-#>  -3.3196e+00 -2.4690e-01  2.8738e+00 -2.3391e+00  8.6590e+00 -3.8078e+00
-#>  -1.1259e+01 -8.6266e+00 -2.9897e+00 -6.1473e+00  4.7037e+00  4.2867e-03
-#>   1.7899e-01  2.0520e+00  3.4490e+00 -6.9371e+00  2.1701e+00 -6.4096e+00
-#>   1.5743e+01  1.5727e+00 -8.0126e+00 -1.6293e+00 -6.9487e-01  9.7779e-01
-#>   3.3199e+00  4.9184e+00  9.6237e-01 -1.3034e+00 -6.9815e-01  1.5008e+00
-#>   1.7309e+00  1.7355e+00  3.3040e+00 -1.1595e+01  6.4257e+00 -2.0536e+00
-#>   9.0737e-01 -3.9525e+00  4.7663e-01  6.2257e+00 -3.6348e+00  7.5429e-01
-#>  -1.6843e+01 -2.9076e+00  5.7143e+00  5.1020e+00  5.1055e+00  6.6208e+00
-#>  -1.3249e+01 -3.3027e+00  1.0028e+00  1.5995e+01  2.1968e+00  4.1618e-01
-#>   1.8869e-01  4.9186e+00  6.6267e+00 -1.1144e+01  1.2518e+01 -9.4801e+00
-#>   7.2734e+00 -4.7865e+00  7.6977e+00 -2.5835e+00  6.9857e+00 -1.1586e+01
-#>   1.9629e+00 -7.9237e+00  8.7230e-01  3.6833e+00  1.0750e+01  7.8505e-01
-#>   2.2178e+00  2.6605e+00 -3.7676e+00 -8.0763e+00  2.7502e+00 -1.2980e+00
-#>  -3.3262e+00 -2.9508e+00 -6.5470e+00  1.2192e+00 -5.2483e+00  1.0321e+00
-#>   1.7810e+01 -1.1750e-01  4.5557e-01 -8.7464e+00  1.0301e+01  1.7402e+00
-#>  -1.9575e+00 -7.2531e+00 -5.6767e+00  4.7833e+00  1.0424e+00 -8.0447e-01
-#>   9.4049e+00  4.8694e+00 -3.4308e+00 -4.0935e+00  7.7406e-01 -8.3420e+00
-#>  -1.0877e+00  5.9013e-01 -8.3452e+00 -6.0349e+00 -2.7546e+00 -1.5708e+00
-#>  -4.6523e+00 -1.1413e+01 -7.7647e+00 -1.9390e-01  2.4253e+00  9.0860e+00
-#>  -5.4974e+00  2.3994e+00 -4.8689e+00  2.3564e+00  1.8606e+00 -4.4984e+00
-#> 
-#> Columns 31 to 36 -5.3996e+00 -4.0268e+00  1.0155e+01 -9.1129e+00 -1.8529e+00 -5.9859e+00
-#>  -1.1870e+00  9.0568e+00 -5.8302e+00  2.2678e+00  1.4472e+01 -7.0498e-01
-#>  -3.1906e+00 -3.1934e+00 -5.5015e+00  1.1824e+00  1.1202e+01  5.9822e-01
-#>  -4.0763e+00  5.6502e+00 -1.1138e+01 -5.5295e+00 -6.0794e+00 -9.0080e+00
-#>   1.4124e+01 -8.0207e+00 -1.2152e+00 -2.9232e+00 -1.2306e+01 -6.1385e-01
-#>  -1.5200e-01  1.1691e+00  5.9120e+00  9.2071e+00 -1.2299e+01  1.2687e+01
-#>  -4.5410e+00 -7.0710e+00 -6.6179e+00 -9.8775e-01  9.3494e-02  9.7834e-01
-#>  -8.4071e+00 -4.0261e-01  3.7053e+00  3.0223e+00  1.0222e+01  5.3209e+00
-#>   3.2476e+00 -7.2387e-01 -8.1169e-02  5.4877e+00 -4.6037e+00 -3.2524e+00
-#>   7.5073e+00  7.5331e+00  3.5078e-01 -1.7766e+01  6.9871e-01  2.0369e-01
-#>   2.2511e-01 -2.8367e+00  2.4152e+00 -2.4106e+00 -9.4229e+00  5.5880e+00
-#>   4.5797e+00 -3.7804e+00  4.6730e+00  1.4458e+01 -1.3785e-01  5.0291e+00
-#>  -4.1167e+00  7.9227e+00 -4.6733e+00 -7.1373e+00  1.6804e+01  4.3820e+00
-#>   8.5466e-01 -1.0724e+00 -9.3670e+00  1.3986e+00 -2.3463e+00  6.8835e+00
-#>   1.4297e+00 -4.2350e+00  9.0299e-01 -2.1366e+00 -5.3894e+00 -2.7163e+00
-#>  -5.1330e+00 -1.9521e+00 -1.0124e+01 -9.9508e+00 -5.3707e-01 -1.4966e+00
-#>   4.0181e+00  1.2901e+01  1.0945e+01 -1.3669e+01 -8.6388e+00 -4.9692e+00
-#>   2.7508e+00  4.1519e+00 -2.2471e-01  6.2458e+00 -1.2785e+01 -8.8307e+00
-#>  -7.6881e+00 -5.7679e+00 -7.7874e+00 -5.1019e+00  1.2219e+01  1.0732e+01
-#>   1.0853e-01 -5.1944e+00 -5.5256e+00 -4.7699e+00  6.8243e+00  6.7839e+00
-#>   6.1342e+00 -7.3736e+00 -4.8876e+00  6.9786e+00  1.0532e+01  1.9532e+01
-#>  -7.2484e+00  4.8691e+00 -6.1163e+00 -2.4217e+00  9.1861e-01 -7.0110e+00
-#>   9.6960e+00 -1.0111e+01  4.1335e+00  8.7717e+00 -4.5262e+00 -1.9480e-01
-#>   1.0519e+01 -2.2790e+00 -5.3841e+00 -1.5754e+01 -4.0010e-01  5.5570e+00
-#>  -1.7870e+00 -4.3120e+00 -8.3646e+00  5.1878e+00 -3.8105e+00  5.4791e+00
-#>  -6.1213e+00  2.6515e+00  3.8603e+00 -7.3960e+00 -2.4629e+00 -3.2340e-01
-#>   2.4277e+00 -6.0967e+00 -6.1727e-01  9.6043e+00  8.1445e-02 -1.4710e-01
-#>  -1.2415e+00  1.0929e+01 -8.9700e+00 -8.4613e-01  1.9625e+01  3.9038e+00
-#>  -2.6456e+00  4.6997e+00 -1.1893e+01 -3.1487e-01  1.3500e+01 -5.4387e+00
-#>   3.8680e+00  1.5947e-01  1.4209e+01 -1.6989e+01 -9.0062e+00  3.9879e+00
-#>   9.3004e-02 -1.7697e+00 -4.5250e+00  7.9326e+00 -4.7444e+00 -9.6158e+00
-#>   4.5809e+00 -5.6403e+00 -5.0796e-02 -5.9721e+00  7.1729e+00  1.8613e+00
-#>  -5.4809e+00  1.7845e+00 -8.3083e+00 -5.4888e+00 -2.0179e+00  4.4420e+00
-#> 
-#> Columns 37 to 42 -4.7855e+00 -8.5175e+00 -2.1857e-01  8.4637e-01  4.3660e-01 -4.9466e+00
-#>   6.6450e-01  4.6346e+00  2.6782e+00  1.2447e+00  3.8391e+00 -8.0498e-01
-#>  -1.1130e+01  4.2234e+00  7.1485e-01 -1.3026e+00 -3.9174e+00  4.1765e-01
-#>   8.3580e-01  1.3614e+01  3.9566e+00 -1.0713e+01  3.2567e+00 -8.4020e+00
-#>   7.6823e+00  3.8936e+00  3.1150e+00  1.7484e-01  4.1188e+00  1.7145e+01
-#>   2.6063e+00 -2.1592e+00  3.8965e+00  2.8466e+00 -1.9440e+00 -3.9970e+00
-#>  -4.6261e+00  1.2012e+01  1.5563e+00 -3.5953e-01 -1.2297e+01  2.0953e+00
-#>  -5.7136e+00 -1.6435e+00 -3.1510e+00  3.9118e+00 -6.4690e+00 -2.4583e+00
-#>   1.0818e+01 -1.7416e+00 -9.2704e-03 -7.6917e+00  7.7324e+00 -2.0817e+00
-#>   7.8892e-01  1.1853e+01  1.3597e+00  1.0491e-01 -9.5310e-01  5.4994e+00
-#>   2.7781e+00  8.3081e-01  1.2500e+00 -2.1802e+00 -1.1053e+01  1.8365e+01
-#>  -5.6311e+00 -9.8486e+00  2.1665e+00 -2.4590e+00 -2.2154e+00  6.3714e+00
-#>  -4.4185e+00  9.0754e+00 -4.9723e+00  4.0641e+00 -2.4477e-01  1.1559e+00
-#>   3.8127e+00  4.3389e+00  1.8134e-01 -1.1438e+01  1.4193e+00  9.3104e+00
-#>  -2.5857e+00  1.2633e+00  5.8720e+00 -2.4309e+00 -1.2344e-01  7.6392e+00
-#>   1.4477e+00  1.0082e+01  2.5753e+00 -3.5404e+00  1.1207e+01 -2.6013e+00
-#>   2.7662e+00  1.3309e+01 -1.3517e+00  8.3357e+00 -5.1607e+00  1.3805e+00
-#>  -5.8706e+00 -1.7318e+00  2.8528e+00  1.9490e+00  2.3670e+00 -3.3641e+00
-#>   2.8802e+00  1.3370e+01  1.8935e+00  1.2284e+01 -1.6036e+01  5.7118e+00
-#>   4.4686e+00 -3.7408e+00 -6.7829e+00 -8.1280e-01 -6.6447e-01  9.5033e+00
-#>   7.0788e+00 -7.5171e+00  1.4984e+00 -8.3525e+00  1.0497e+01 -4.3993e+00
-#>  -2.2460e+00 -9.2195e-01 -4.3723e+00 -1.5712e+01  1.6383e+01 -6.0502e+00
-#>   1.1910e+01  7.7961e+00  9.7424e-01  1.0935e+01 -9.6052e+00  1.5065e+01
-#>  -2.9370e+00 -1.0523e+01 -5.9286e+00  5.4506e+00 -9.7247e+00 -1.1499e+01
-#>   1.0424e+01 -1.3085e+01  6.8869e+00 -6.7083e-01  6.7912e+00 -3.1413e+00
-#>   9.4696e+00  1.3121e+01  8.3503e+00  5.9973e+00  1.9038e-01  8.1521e+00
-#>   1.2455e+01 -3.8683e+00 -1.1434e+01 -6.7846e+00  5.1093e-01  1.0225e+01
-#>  -6.8801e+00  1.1190e+01  5.6877e+00 -1.1347e+01 -8.6186e+00 -8.9230e-01
-#>   6.7188e+00 -1.9456e+00  2.7723e+00 -2.1238e-01  5.3955e+00  1.3889e+01
-#>   2.5153e+00  1.3421e+01  1.0657e+01  1.0813e+01 -4.0389e+00  4.3639e+00
-#>   2.3169e+00 -6.5107e+00  1.1071e+00  5.0577e+00 -1.9199e+00  2.6549e+00
-#>   3.7111e-01  4.1470e+00 -1.4876e+00  6.1349e+00 -3.8993e+00  1.3496e+01
-#>   3.9903e+00 -2.5372e+00  2.3113e+00 -1.3299e+01  1.5658e+01 -2.1184e+00
-#> 
-#> Columns 43 to 48 -5.3974e+00  1.1470e+01  8.0168e+00 -3.1737e+00  1.8088e+01  6.7016e+00
-#>   8.4993e+00  7.5901e+00 -3.8522e+00 -1.5282e+01  2.7711e+00 -3.9009e+00
-#>   5.4798e+00  8.2084e+00 -4.3640e+00 -5.6298e+00  2.8553e+00  6.0013e-01
-#>   1.5117e+01 -3.8010e+00 -6.0661e-01 -8.6124e+00  7.9563e+00 -8.5322e+00
-#>   1.0926e+00 -5.7196e+00 -7.8125e+00  2.9962e+00 -2.4641e+00 -3.9728e+00
-#>  -6.9750e+00  3.9074e+00 -9.7547e+00  1.8155e+00 -9.4942e-01  1.4033e+00
-#>   7.8986e+00 -2.0966e+00 -4.5563e+00 -9.9288e-01 -3.9173e+00  9.6752e-01
-#>  -2.1302e+00 -3.0563e+00  5.9163e+00 -7.8072e+00 -5.2031e+00  4.8729e+00
-#>  -3.2703e-01 -5.2523e+00 -1.7050e+00  6.8670e-01  6.2048e+00  2.3407e-01
-#>   3.8850e-02  9.0948e+00 -3.6616e+00 -6.1142e+00  7.1880e+00 -1.5549e-01
-#>  -6.8444e+00  4.9850e+00  2.1235e+00  4.4228e+00 -3.7574e+00 -8.3262e+00
-#>  -3.4195e+00  7.0761e-01 -6.9093e+00  2.0876e+00 -1.3823e+00  1.0538e+01
-#>   7.4552e+00 -2.3935e+00  1.1926e+00 -3.8090e+00 -8.4963e+00  6.3465e-01
-#>  -2.4364e+00 -1.1607e+00 -6.0250e+00  5.9558e+00 -3.0977e+00  1.4288e+00
-#>   1.1530e+01 -5.9603e+00 -4.1689e+00  1.2215e+01 -5.4808e+00  1.5768e+00
-#>   3.7910e+00 -1.1363e+00  2.6201e+00 -3.1267e+00 -7.8219e+00 -8.4765e-01
-#>   2.4809e+00 -2.6889e+00  9.0631e+00  1.3237e+00  6.6355e+00  4.8937e+00
-#>   2.8647e+00  1.6486e-01 -3.0638e+00  3.1515e+00  1.1612e+01  2.6685e+00
-#>   2.0890e+00  8.1913e+00 -5.3012e+00 -7.3151e+00 -5.1472e+00 -5.9437e+00
-#>   3.8042e-01  9.8934e-01 -1.1415e-01  4.0065e+00 -2.8091e-02  4.7139e+00
-#>  -1.7802e+00  4.7301e+00  1.3032e-02 -3.6905e+00 -6.6128e+00  5.0625e+00
-#>   3.5504e+00 -8.2332e+00  3.7096e-01  9.8524e-02  3.4324e+00  2.8376e+00
-#>  -9.2314e+00 -6.9470e+00  7.7655e-01 -5.2664e-01 -8.1657e+00  3.3885e+00
-#>  -1.4360e+01  1.4785e+01 -3.7140e+00 -6.3802e+00  6.6014e+00 -1.1859e+01
-#>  -7.1300e+00  1.4353e+01 -9.8361e+00  9.5068e+00  4.5462e+00 -4.0918e+00
-#>   2.4090e+00  2.6027e+00  8.3217e-01 -2.4805e+00 -4.2917e+00 -5.1048e+00
-#>  -3.8890e+00 -9.2622e+00 -2.4179e+00  9.1678e+00 -1.0741e+00  1.4275e+00
-#>   1.0725e+01  3.6622e+00 -1.0390e+01  3.2040e+00 -8.1849e+00 -1.4154e-04
-#>   2.5473e-01  1.4962e+01 -6.1097e+00  7.3571e+00  3.6925e+00 -1.6461e+00
-#>  -4.1509e+00  6.1366e+00  3.2962e+00 -5.4747e-01  3.5650e+00  2.8045e+00
-#>  -4.0917e+00 -2.1213e+00 -6.7569e+00  2.4749e+00  4.6886e+00  1.8253e+00
-#>   4.5376e+00 -1.0110e+01  1.1122e+01 -1.1604e+00 -8.3908e+00 -3.9016e+00
-#>  -4.6760e-01  1.4584e+00  5.2119e+00 -3.8599e+00 -8.4865e+00  2.6378e+00
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    bias
    NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: None
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    NA the stride of the convolving kernel. Can be a single number or a      tuple (sH, sW). Default: 1
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    NA implicit paddings on both sides of the input. Can be a      single number or a tuple (padH, padW). Default: 0
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    NA the spacing between kernel elements. Can be a single number or      a tuple (dH, dW). Default: 1
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    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    
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-filters = torch_randn(c(8,4,3,3))
-inputs = torch_randn(c(1,4,5,5))
-nnf_conv2d(inputs, filters, padding=1)
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>  -1.2050  3.5601 -7.8325 -0.6184 -5.9300
-#>   6.9992  0.3900  1.3901 -2.5574  6.5135
-#>  -2.3490  3.9786  4.1985 -9.6240  8.3693
-#>   0.1293  0.5186 -2.7322  9.4996 -1.1448
-#>  -5.2164 -12.5999  8.9623 -8.9888  7.3018
-#> 
-#> (1,2,.,.) = 
-#>  -0.8450 -8.2603 -2.9019 -5.5598 -4.9509
-#>  -5.6410  1.2666 -5.8790  2.1525 -1.3928
-#>  -0.2024  2.1010  6.3255 -0.5656 -2.5797
-#>  -0.3285  6.7826  5.8021 -1.7078  7.7191
-#>  -0.0922 -8.5321 -5.4983  0.7221 -3.5291
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-#> (1,3,.,.) = 
-#>   2.5094  9.2626  5.0731 -3.6366  3.3318
-#>  -0.2142  2.5216 -0.6932  8.3285 -0.4724
-#>  -2.7548 -2.3855  1.6016 -9.5280 -0.9662
-#>   1.6404 -7.5112  0.5470 -3.7868  0.9206
-#>   0.0776 -1.8698  3.2148 -2.8353 -1.9503
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-#> (1,4,.,.) = 
-#>   -1.0442  10.8465   5.7719   2.8028   0.3426
-#>   -1.9633   3.7087  -3.1929  -2.0919  -8.2913
-#>   -0.9261  -3.8192   9.3410  -9.4944   8.6766
-#>   -5.9383  -2.2824  -4.0878 -10.5887  -2.2831
-#>    6.7283   2.9823   0.6066   0.9479  -1.2099
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-#> (1,5,.,.) = 
-#>  -3.4877 -1.5824 -6.4475 -1.1290 -2.3183
-#>  -2.2229 -5.1762  9.2085 -3.5268 -3.8315
-#>  -6.3696  2.3858 -12.1739 -2.6510 -3.0961
-#>   4.4813  3.8243 -0.4043  2.4245 -0.3913
-#>   0.6648 -2.4358  0.4671 -3.7840 -3.5538
-#> 
-#> (1,6,.,.) = 
-#>   0.8825  0.3176 -6.6245 -6.0051  2.4430
-#>  -2.0265 -0.9768  6.7438 -7.4899  2.0777
-#>  -6.8935 -2.2333 -3.3011  2.0151  2.5786
-#>   0.2469 -4.8563  2.3537  0.5968  3.6600
-#>  -1.4781  0.2248 -3.1730 -6.7766 -3.0347
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-#>   1.5889 -4.5167  0.0906  7.6198  1.8365
-#>   5.0933 -2.3551 -2.6166 -7.5808 -2.0514
-#>   4.0381  6.9844 -2.3409  2.5503  6.2012
-#>   6.1860  8.5588  0.8248  1.6048 -4.7339
-#>  -2.7566  3.9998  8.0454 -1.6114 -0.9366
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-#>   4.3153 -3.2445  0.4090  1.5033  6.4462
-#>   6.4586  2.8058 -3.3607  5.9048 -2.4407
-#>  -3.9344  4.6081  5.3624 -0.2189 -0.3718
-#>  -4.3796  5.2653 -0.4628  8.5329 -6.1232
-#>   0.7405  5.8740  5.0339 -0.6710  0.8794
-#> [ CPUFloatType{1,8,5,5} ]
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    bias
    NA optional bias tensor of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sT, sH, sW). Default: 1
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    NA implicit paddings on both sides of the input. Can be a      single number or a tuple (padT, padH, padW). Default: 0
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    NA the spacing between kernel elements. Can be a single number or      a tuple (dT, dH, dW). Default: 1
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    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1
    
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    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
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    # \dontrun{
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-inputs = torch_randn(c(20, 16, 50))
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-nnf_conv_transpose1d(inputs, weights)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  Columns 1 to 8   1.6673  -7.0390   6.6254 -13.9097  -9.2569  16.1860   3.0554 -10.1673
-#>    0.5097   0.4955   5.0436  -2.2605   3.2619   1.0813   8.2840   0.2337
-#>   -2.5402  -1.4119  -6.6792   0.1580 -12.0189  -2.8290   5.7440  10.1772
-#>   -6.6198   3.2866  -5.4540   2.0456   3.6355   7.4837 -10.9005  -5.6650
-#>   -4.6747   5.7736  -5.4007   1.2693  11.4762  -4.7003 -11.3653  10.1400
-#>   -7.4153   3.9516  -1.1868  -8.5232   6.7928  -4.0613 -12.1954  -0.3727
-#>    3.3955   1.0648  -4.0864 -17.0257  -3.4968  -4.9347 -25.7271  21.9273
-#>    4.0335  -6.6207  -3.6258   2.1134   2.8998  -6.8774  10.1487 -13.0325
-#>   -0.7558  -1.2329   0.9330  12.2378  -7.8136  -0.6589   7.9927   1.7098
-#>   -7.7476  11.0338 -19.6308 -11.0443  -1.9108  -9.0969 -28.1443 -17.9797
-#>    2.7401   3.4970  -6.8374  -8.1578   1.5898   9.3208   4.2085   2.1895
-#>    3.1280  -2.2744   3.1126   3.8573  -5.6051   2.0268   2.0747  -4.4871
-#>    3.2771   4.8169   5.4240  -9.3082 -13.5079  -0.9002  -4.1523   5.5984
-#>   -3.5472   5.3228  -0.3093   4.4044   0.9189  -6.4752  -6.5480   7.4419
-#>    4.2013  -5.9650  -2.9532  -1.8045   4.5317   0.8231   5.2075  -0.9755
-#>    1.6888   6.3345   8.0313  -4.3153  -1.1394   6.0972 -11.7238  -4.2819
-#>   -0.4871   7.4014  -7.3978  -2.9028  -5.0449  -7.1657  -3.8541  12.1393
-#>    0.8300  -2.1989  18.5210  -6.7047   1.8052 -12.0026  -3.6008  12.7004
-#>   -5.7435  -4.3290  -5.1741  -4.9028  10.4417  -1.7813  22.8982   4.6736
-#>   -1.8184   1.6866   2.4972 -10.6010  -1.6615   4.2066   1.2256   1.8704
-#>   -0.1898   7.4767  21.4571  -1.9761   8.4871  -4.1088  -4.3248   3.2695
-#>    2.8483 -11.0110   7.3260   0.7999   3.4976   5.7566  12.8856   4.1038
-#>    6.9826   9.8364  -0.9694  -7.2072   2.1751  -9.7158 -20.3140  -4.7768
-#>    0.2967  -5.7820   1.4100  -1.7626  -0.5567  -2.8442  11.3496   0.9707
-#>    2.5259   7.6391   2.8814   8.4676  -8.6886 -16.8321   4.9696 -23.1633
-#>   -3.6094  -7.7429  -3.9817 -10.3633 -14.8211 -17.5294  -7.3543   1.4741
-#>   -3.6305   1.5408  -2.2006   8.1134   1.1769  -3.0465   5.4047  -7.1063
-#>   -0.2056  -3.0883   2.6383   1.4320  -0.4309  12.6957  20.8133  -1.0188
-#>    0.7445  -2.1118   1.9698  -2.5180   0.3591 -17.7520  -5.9807  -7.7320
-#>    3.2831  -7.6201  -3.1870  -8.1050  -8.9224  -8.1959  -4.7006 -10.0807
-#>   -1.7069  -4.7109   5.5420   0.4770   3.4143  15.3951  -8.6150 -10.6463
-#>    4.9333   8.1781   2.6415   5.0800 -18.8387 -13.3438   0.6360  16.3387
-#>    2.4939   8.4147  -4.0838  -7.4578  -5.3779  -6.8781   3.7359   9.3849
-#> 
-#> Columns 9 to 16   4.5019   6.9288 -21.3335   0.1317  -3.8836   8.3590  -0.8443 -14.3927
-#>  -16.2857 -11.4820  -1.4024  -4.5153   1.9758   0.6438  -2.7761  10.1361
-#>    6.2420  -0.6045  -9.7481   9.3241  -3.6135 -18.6855   6.7881 -13.9915
-#>   -5.0487 -20.3735  28.4884   3.9854  20.9909  -0.9224   7.0288   0.5621
-#>   -3.5373 -15.3569 -16.2579  16.5925  -7.9983  -9.0862  -6.5943   0.2070
-#>   13.7029   5.4600  -7.5984  -6.4349   7.3174  15.5161   2.0219  -1.0355
-#>    3.6252 -20.4715  -3.8647 -20.7537   7.9750   0.7964 -23.7706   9.1045
-#>   -8.7950   4.0828  -4.5432  -6.1630  -5.2080 -14.8888   9.1260  -5.9252
-#>    2.9966   4.8565   6.0109  -8.5393  19.8787   3.5916   4.0040 -11.9161
-#>  -11.2591  -1.4102   8.5272  -8.9441  10.3345 -13.4799  -0.9897  15.2467
-#>   -0.6632   8.7770 -15.7039 -13.8364   1.2821  -0.6675   0.4520  -0.1015
-#>   17.6055  15.4246  -8.5307  13.7545   6.1392   2.1994   5.0420  -3.2646
-#>    5.2685  -6.2487   3.4035  -6.7103   4.0975   7.0198  -4.6465  -7.1480
-#>    7.5268  -3.9966   9.9433  -3.9344  -6.8677   6.9691 -16.2891  22.7894
-#>    5.2153  16.0680  16.4236  18.3746  -5.5733  -1.2358  -6.8528   1.5499
-#>    4.9569  -3.0487  -1.9484   0.2809   6.0391   0.1817   2.8151 -11.2639
-#>   29.2747   7.6625  -8.7079   2.9776  -6.0190  -6.8860  -3.6460   2.3650
-#>    8.1998  -6.1037  -6.8587  -6.0042   4.2655  10.6292  -6.2967   0.6930
-#>  -14.4762  16.4459  -5.9406   2.0809   1.8835  -3.1317  -8.9321   7.7105
-#>  -10.7999  18.7483  17.0834 -10.3893  -8.3545 -21.9094   4.0324   2.8529
-#>   -6.2272  -3.0918   1.9956  -4.4372  -6.3339   5.5328  -6.0873 -10.6416
-#>   -5.9817  -6.9611  12.4265  -5.7492   3.2616  -6.5960   6.5149   9.2240
-#>    8.0986  13.9525  21.4184  -4.3470   9.5946 -18.5461  -4.5604 -15.4378
-#>    3.7024  -0.7089   2.5686  -0.7054   6.0643  12.1628   1.9096   6.4987
-#>   10.7530   8.5200  -5.6235   2.7819   6.0993 -11.6488  18.3623  -5.3475
-#>   -0.5536  -8.5774   0.8194   7.4602 -11.3240   5.9768  -0.3840  19.7081
-#>    0.0850   0.9243   2.3494  12.6000  -6.7075   8.3957  -7.9544  15.4305
-#>    1.4948   1.6475 -10.0262  -0.6992 -20.6828   5.6993   3.6267  -8.4266
-#>   -3.9348   2.5818   4.7236   2.2962  -8.2360   3.9196   1.1975  14.9994
-#>    7.2291 -14.4824  -7.3407  26.6378   4.6584  -1.7192  -4.7126  -6.6773
-#>    3.6147  -1.7117   3.2719  -0.1214   3.2613 -12.0086   0.1217 -11.8695
-#>    2.6794  -8.8757  20.7463  13.6375 -16.4280  14.3362 -13.4708   9.1810
-#>   11.7337   6.5971   4.8227  -7.0015 -12.9038  22.7038 -19.3795  -5.8170
-#> 
-#> Columns 17 to 24   1.2900 -12.9553  -5.3724  15.7169 -10.8972 -11.2200  15.5498  -5.0368
-#>    2.0881   8.0263  -3.9599   9.3983 -10.2026   2.6811  -5.9319  -7.8959
-#>   14.9066   7.4782  -0.0191  -0.6530  -7.1498   9.4691 -11.9981  15.5602
-#>   14.5616   5.2064   2.5051   8.9144  17.6842 -13.5671  -3.9489   7.7915
-#>   -9.7128   6.7477  -3.1993   1.6146   4.5125   5.5407  -8.2644  13.3508
-#>   -1.6102  11.9611   3.4677  -4.9038 -10.2262 -14.4330  -1.2227 -12.5192
-#>   12.0007 -14.9848   0.9463   3.5031  17.4539   9.3853  -1.6176  -9.6670
-#>  -18.4721   3.3622   8.1907  -2.8999  -0.2637  -4.8769   1.5459  16.6135
-#>    2.1102   5.4149  -1.7087  -8.4103  12.7386  -9.9078  -9.3740  12.7742
-#>    5.3657   5.1944  12.6113   8.4549  15.0561 -10.8250  11.2875  -8.3614
-#>    4.6304   9.5156 -12.6676  -0.9019 -10.6211   9.9370   3.3044   3.2810
-#>   -4.6163  17.0280   1.7505   6.2276 -11.3447  -6.5945   1.7422   0.4499
-#>   -3.0405  -5.3669  -1.8315  -2.1907  -6.5904  -0.9806  -0.6572  17.8599
-#>    4.4193  18.8522  10.2451   2.9497  21.5558   0.1101  -5.1855  -9.5943
-#>   10.4257  13.2620   2.2022 -19.5122   2.7581  -3.0927   1.9384   7.3883
-#>   10.6498  -4.7587   2.6286  -6.5227  -4.8643 -16.4109  11.9423 -26.0725
-#>    3.3000   9.5719  11.0230   9.4557  -4.9966  -4.4449   4.4763 -18.4214
-#>  -15.8333  -7.3848   4.0740   6.5697  -5.7416  -4.4711   8.0335  -3.5673
-#>   11.2547  11.7943 -27.4378  -5.9246   8.8010  20.3112  -3.0324  16.8786
-#>   -2.6789   4.0453   0.9162  12.4067   5.2146  -2.5160  -9.9002  16.7632
-#>  -19.3769  -4.4897   5.8856   5.8640 -14.5450  11.0825   1.5232  -3.6449
-#>    6.0268  12.5299  13.8870 -16.7846  -9.3446   0.2853   0.7077 -11.7750
-#>  -12.7468  10.7160  31.0325  34.9867  -5.8856 -11.4765 -10.4610  -2.5913
-#>    9.3896  -6.9599  -7.6279  -5.4562   0.0194  -6.9496  -2.8263  -5.2451
-#>    4.0190  -7.9305  -7.1466  13.0450  -5.5047  -4.4661   0.9274  -4.7775
-#>  -13.4600  -0.2809  -0.7337   9.6314   1.7702   3.1068   9.6836  -3.6410
-#>   -0.2111   0.3837 -10.4137  -3.0894   8.8677  -2.2090   7.1865  -3.8667
-#>   13.4651   7.1765  -1.6467  -4.9560  -9.6018   8.1471  -7.0450  17.0260
-#>  -17.4430  -1.8719  -8.0025 -15.9723  11.6767  -7.6636  -7.0144   3.6021
-#>   18.1120  -0.0250 -18.4019   9.8127  -2.1751  -2.7134   8.3464  -1.5143
-#>   -3.8167  10.2692   6.5966   7.9059   1.1906  10.1638   1.1750  -2.4618
-#>    0.1265 -15.9027   2.7108  -6.4089   4.9851   1.2107 -11.7264  -1.2454
-#>    0.4524   0.2076   8.5678  -6.2080   0.6733  -7.2406   0.8338   3.5937
-#> 
-#> Columns 25 to 32  -0.0293  14.3552   3.6278   7.7933  -5.4766  12.6476   1.8703  -2.8825
-#>   -3.2107  -9.0870   7.0099   5.4586   2.7359   2.2581  -3.8164   8.9624
-#>   13.3699  -4.2088  -4.7406  -8.7636  -5.9517   2.2329   1.8833  21.2654
-#>   12.8826  -7.6350  -5.2774   2.4938   9.3194 -13.5257   5.9729  20.7365
-#>   10.1223   4.4283 -13.0912  -9.2791  -1.8947   0.4621   3.4288   8.0413
-#>   -9.1683   7.7603   3.0516  -5.1836   2.5889   8.7409   5.4927   4.1831
-#>   -1.1417  10.9090  -7.1233  -3.5366  -1.2165  -3.2714   3.8553   1.1704
-#>    6.5912   4.1697   7.7293   7.9883  -9.9514  -5.4976  12.7131  -0.1949
-#>   -4.4844  -5.7362  -3.9648  10.1807  -6.2640   5.3054  -4.7080   4.0852
-#>   12.6666  12.5517  -4.1955   7.5249  -0.7672   3.2016   6.5978  20.8497
-#>   -7.9074   7.2881  -4.8798  -1.4548  -8.7517   3.9573   9.5329  -4.8086
-#>    0.3169  -3.8601   1.0305   0.5803   1.2628   4.3491 -12.1400  -2.7427
-#>   -0.3467  13.3886  -6.3979   2.0452   2.3282  -2.0779  -2.4584 -16.2387
-#>   -5.7151   9.2725  -0.0182   1.8764   6.9025  -7.1871 -10.9513  -6.7978
-#>    2.4439  -1.6886  -7.7059   0.1886  17.1840  -6.1218  -5.3057   5.4783
-#>  -16.9525   6.0691  16.1657 -11.4185   3.4004  10.6037   4.7744   2.5440
-#>    9.5940   2.7641   7.8744  19.0521  -3.7261  14.7517   3.5746  -0.4117
-#>    0.4406 -12.6979  -6.8351   3.1805 -11.3419  -1.0762   9.5960  -4.1767
-#>  -11.9938  -6.4985   9.3770   6.9766  13.3734  12.1043  14.3930  -7.0519
-#>   -2.6697  -2.4679   1.1577   3.0986   6.3140 -21.5625  -0.3885   0.4263
-#>    0.3668  -3.9383  20.8603   6.6954  -2.7366  -0.2231  -0.5172 -13.0651
-#>   19.9507   1.6934   7.1942 -15.3310   3.2378   8.5819 -14.4676   0.2683
-#>   -2.5942   1.2458   9.2131   9.8679   8.3943 -12.4564   4.6223   3.0705
-#>    2.0545  -1.3666  -0.5177  -9.2698  -7.6781  -1.9081  -3.4399  -3.9338
-#>    8.1274  -0.1854   5.2153 -11.7028   8.3669   4.8400 -13.4963  11.7893
-#>   -0.1239   6.0862  -7.2274   0.1266   2.1916  -9.3351 -11.9587  -7.4673
-#>  -20.4607   4.0279   7.3662  -0.7345   5.7650   3.7344  -0.5221  -2.1129
-#>   11.4275 -12.4868  13.0824  10.5353   4.0451   1.8503  18.2519  -0.7406
-#>   13.0047 -15.2421   9.2275   1.4917 -11.4757  -5.3868   3.8877   4.4101
-#>  -10.2052  18.6921 -13.4181 -20.0699  12.8999 -12.0904   0.3806  -5.5696
-#>   -2.4651 -10.7724   8.9007   1.9734  -6.8980 -11.9957   6.5855  -4.4643
-#>   -1.0077  -4.1095  -2.1013  -0.5406   5.8052   9.8388 -20.2044  -2.6470
-#>  -13.4139  -2.1838  -1.5024  -6.3311  -6.9503   3.3761   7.3990   7.9767
-#> 
-#> Columns 33 to 40   1.8592   0.9761  -2.6963  -3.8153  -1.8090  29.3572 -11.8402  11.8519
-#>   13.3483   5.1859   4.9052  -0.4473  12.8364   2.1392  11.2240   1.1636
-#>    2.9127  -0.4139  -9.8866   2.1386  -0.3520  -3.4992  15.7872  -1.9768
-#>   -8.7932  10.8440  -0.3113   9.1708   9.1623 -17.6051  14.4912  -2.6571
-#>   -5.4569  14.1891   1.8065  17.6668   2.9545  -3.7614  26.8721   6.9820
-#>    5.6159 -18.5910  -0.6680  -9.2854   2.9860   3.0446 -10.3332  -9.6543
-#>    6.3406   1.3053  -2.6143   0.2845   7.4428   8.5844   1.7969  -1.5125
-#>   10.9239   9.0027  22.6637  18.8975  -4.2188  12.3108  26.5707   8.6210
-#>    1.2633  -9.0421   0.9745   2.6553 -11.1772 -12.4188   1.3170  -5.5598
-#>  -12.8508   6.2380  -4.0621  14.0566  16.2944  -9.8447   8.1251  11.8212
-#>   10.8548  17.6606  -0.6996  -0.6495   1.4041   2.4018   7.0875   3.6098
-#>   -3.0109  -0.2962   6.1093   6.9493  -6.2817   4.9856   9.9639   0.5589
-#>    0.5565  -0.0766   1.2112   1.3211 -15.6654   0.7365  -1.7787 -11.4870
-#>    1.2893 -10.7174  -7.8088  -3.3270  -0.2953 -10.0667  10.5948  -3.7164
-#>   -7.5186  -7.9848 -14.5698   2.6338   6.9504  -1.8349  14.9450   9.0353
-#>   -3.8914   2.3522  -3.9384 -14.0598  11.8851   3.7743  -4.7451 -18.7160
-#>   -7.9114   4.4290   1.2984  -6.1245  10.8040   9.1649   9.3862   3.8943
-#>   13.5192  -0.3540  11.7615 -10.4322 -10.5759   5.1959 -10.5739  -2.5550
-#>    4.1348 -11.0223  -7.5457  -1.8305  10.4051   9.8118   5.1208   5.6186
-#>   -4.8263  -6.7968   5.5902  12.7691   7.9257   1.9996   6.5328   8.3577
-#>    0.7681   7.4137  12.9171  -3.9795  -2.3992   5.0543  -2.1804   6.8998
-#>    7.7960  -0.6246  -0.4729   2.6995   5.4509  -0.4801   2.1876   4.9933
-#>   -6.3639   0.3813  12.9830  -2.4914  -6.7726   1.7444   4.6179  18.1880
-#>    6.7140   2.4883   8.6126  18.9305 -10.5894   3.3141 -21.9243   6.1176
-#>    0.2413  -8.9027   3.3162 -13.0962 -12.3184  -3.8753 -23.0307   9.0538
-#>    0.0690 -15.0865   1.8602  -8.6315   0.5089  -0.7437  -5.9029   3.0767
-#>    7.1451  -8.2692 -11.5116  -8.8415  -2.0170  -7.0324  -3.6529 -10.3245
-#>    6.1436  -0.5680   4.2794  -0.3316  -7.4252 -10.7011   7.0821   7.7291
-#>   -1.3997   6.5191  -0.3735  -6.5781 -10.0386  -6.9226  -2.5003  -9.5659
-#>   -0.0958  -0.5062   2.8923  -3.4108  13.0374  -2.9370 -10.4329  -3.1501
-#>   -8.9609   2.8826   0.2373   5.7849  -1.4304  -5.0798  12.6846  12.8611
-#>   -0.7236  -1.0731  -3.0510  -9.1343 -18.2925   1.9743  -0.5286  -7.9524
-#>   10.3919  13.0850   0.9242  -0.9788  -6.7773   0.8554  -4.7509  -5.3867
-#> 
-#> Columns 41 to 48   2.9985 -15.5869  14.0373  10.4037 -11.1567   6.1454   3.8270   7.7380
-#>   -1.6961 -13.4523   2.7170  -5.3499  -5.4221  10.5192   9.8349 -12.1479
-#>    6.2730  22.6802  -7.0960 -14.5570 -13.0734  24.1451   3.9818   4.1208
-#>   -1.6407   8.4934  -6.2687  -4.5555  -9.5029  15.1876   8.3357  -3.0061
-#>  -12.4818  22.4958  -6.0299  -8.6670 -10.3577   2.4604  10.0247 -10.8954
-#>   -6.8458  -6.1891   1.1851   2.8739   2.8533 -10.4645   1.1883  -5.4889
-#>  -18.7008  15.1108   8.5953  -5.3394  -2.5032   6.4586   3.9125   3.6250
-#>    2.3395   4.7180 -12.1364  -2.4026   6.8779   8.2134 -22.8381  -3.5587
-#>   15.7945  -2.3467   5.5252   4.7041  -6.9610  -8.8453  -2.2314  11.2754
-#>   -4.7374 -10.4254  10.9580 -13.0100  -6.7119   0.3756  16.1945   8.9202
-#>   -6.7409   4.5078   4.3692  -4.6630  -5.9395  -3.9312  -5.5752   1.4460
-#>    1.1583   8.8820   2.3125  -8.9832   1.3572   5.7707   4.3816 -12.6141
-#>    1.1008   6.2556  -5.6111  -7.0873   0.3703   3.5847   0.6188  -5.6186
-#>   -5.2835  -8.3637  -0.4618   5.6316  -1.7833  -8.5794  27.2491 -10.4892
-#>   20.8741   8.0639 -15.5463  -7.4028   3.6106  -1.1026   0.9431  -4.4649
-#>  -11.5054  -5.7812  19.4337   8.4593   7.6994 -14.5653   3.4009  -3.5659
-#>  -13.8089 -11.7804  10.2397  -9.4642  -1.2912 -14.0060  -6.6083 -11.9624
-#>   -8.1229  -1.9699  11.3045   4.4870  -1.1037   9.2278  -1.8346  -6.5917
-#>    8.1041 -13.1606 -10.0848   2.4079  -2.9753 -14.2536  -8.2830  25.1459
-#>   -0.6315  11.0338 -11.0389   4.5759  -1.7932  18.5247  -1.9834  -1.4612
-#>  -11.3549  -0.3709  18.6718  14.9252  -3.7786  -3.9237  -4.9956  18.7164
-#>    9.1790   5.0087  -4.8780 -10.6788  -4.4748  -7.9521   2.3956  11.5146
-#>  -14.9233  15.5074  13.5288   6.9501  -3.6375   6.9654  -4.0685  -8.8993
-#>   -1.0245   4.7855 -17.6042   4.1208   1.0548  -8.5788  -1.7149   1.0637
-#>   -5.1705  13.9681   1.5986  15.8308  -3.9914   8.3166   1.1126 -11.7575
-#>    2.7832 -21.6206   0.3791  -5.4350  18.9783   0.2022   6.9384 -23.6424
-#>   -1.1030  -4.5934  -1.3159   2.3002   2.8800  -6.3804  19.1025 -11.9186
-#>    1.8217   2.0252  -7.3752   7.9834  -7.1141  -2.5236  -6.1048  14.2583
-#>   -7.0049   7.8784 -14.4774  -6.2137  10.1269 -15.6804  -6.8904  -4.6614
-#>   10.0098   8.4671   2.8254   9.2529 -16.7819  -3.6309  18.5456 -10.9203
-#>   -0.6490  -6.3074   0.1846   4.3354   9.1514 -12.6226  -3.6332   2.4962
-#>   11.7775  -1.8143   1.0238 -24.0774  13.3624   0.3635   1.7397  -2.5824
-#>  -13.1913   2.5277 -12.8994  11.0974  -3.0293   2.3099  -0.8103  -0.4676
-#> 
-#> Columns 49 to 54  -8.7345 -14.2799  19.6350  13.5499   1.2315  -0.6506
-#>    1.8484  -3.2535  -1.7324  -4.3937   4.1733  -0.5664
-#>    4.5108  13.2596  -4.4172   1.1377   3.8892  10.2091
-#>    7.5502   1.0322  -9.1656 -15.8731   5.8408  -5.2419
-#>   -2.2194   8.1269  11.4847  -5.2739  -5.0256   0.6433
-#>   -4.1628  -0.1577   1.1322   9.3402   4.0405  -2.5579
-#>   -2.4551  21.4342  -0.1518   0.2222  -0.9053  -2.7780
-#>   10.6593  -5.1491   0.3012   6.6177  -8.4856 -11.1581
-#>  -11.0929   5.7385  -7.6207  -0.4959  -1.9499   2.3844
-#>   -4.4337  -0.3689  -3.9270  -5.0881   3.1659  -2.1273
-#>    1.6437   0.2100  -3.8414  -0.0949  -6.4092  -2.9916
-#>  -10.3230  11.3656 -11.9414  11.8128  -5.7713   2.6200
-#>   -4.4394  -3.3487   2.7528   3.8788  -1.4185  -3.0869
-#>   15.5630  -6.5025  -3.7371  -6.6396  15.1147  -0.3786
-#>   -3.5993  -1.2830 -12.9225  -9.8014   6.9267   7.7926
-#>    1.7097   5.5124  -1.5683  -0.6643  -3.9920   5.6530
-#>   -6.5705   5.0899  10.4892  -1.9026   2.3790  -1.7570
-#>   -9.6865  -3.1503   5.3201  -1.7256   1.1293 -11.1168
-#>   -4.3125   4.4370 -12.0474  -2.7226  -2.3004  -5.8733
-#>    6.1387  -0.5534  -3.6291   9.1612   7.0297   1.6557
-#>   -7.3084   9.0659   4.9779  -3.0977  -8.6063   0.9893
-#>   20.1320  -8.8056 -12.3684   6.0840  -1.6641   4.9350
-#>   -3.0457   9.5258 -15.0322   7.7813   1.9794  -3.8203
-#>    0.5432  11.5130 -26.2873   3.9583   8.1876   1.2593
-#>   -5.0762  12.8825  -2.9607   6.7716   2.5277  -4.8843
-#>   -3.7113   7.3289  -1.8610   6.7798   4.0303  -2.3700
-#>    1.6202  -2.6948  -2.9366  -5.8387   7.3190   3.1959
-#>    1.5668  -3.8051  15.0303  -7.3860  -2.8871  -3.9352
-#>   -2.9899 -11.8343  -5.7724  -2.9391  -0.1798  -5.7964
-#>   -2.9931   4.5864   7.4006 -21.0496 -10.1332   5.1122
-#>   20.1991  -6.5378 -11.0948   0.1378   7.5001  -2.8968
-#>   -2.7824 -17.3546  -6.3185  -4.0368  12.6433   7.2955
-#>    0.4172  -4.4497   9.9093  -2.1454   8.5557  -5.9379
-#> 
-#> (2,.,.) = 
-#>  Columns 1 to 8   4.7596  -2.3708   1.3418  10.7392 -17.9585   1.6765   9.6306   3.8409
-#>    2.5987 -11.9234   5.6093 -15.4016   8.0875 -11.7553 -11.3902   8.7209
-#>  -11.8239   2.1317  -9.5167   8.5529  -5.5999  -8.7316  -8.2675  12.9377
-#>  -10.1575   1.3394  -2.0061   6.9250   0.4979  -6.0003   2.4058   8.1599
-#>  -12.7172  -1.2893 -14.0448  -3.4130   1.7811 -20.7635  -1.9964  -6.3883
-#>   -1.9728  17.3032   8.5093   4.1580  -6.2205  18.0032  -1.7450   2.6589
-#>    3.5242 -16.6260   7.9907  -1.1886 -14.1955  -7.1470   7.9479   7.5243
-#>    0.5199  -7.8098  -5.8438  -1.0146  -5.0564  12.4495  -1.4228 -13.6972
-#>    0.1183   2.4802  12.7326  -6.0799  -1.6831  -6.8250   9.4823  10.3730
-#>  -12.6137  -9.4658  -8.8782  -6.6302  -1.4513   2.4207  23.0161  -8.5375
-#>   -1.3446  -0.7345  -8.8468   5.0346   0.8501  -2.3404  -1.5003  -9.4550
-#>    0.1520  -2.6062   4.4482  -8.2256  -5.3967  -2.2162  -3.8969 -12.9057
-#>    7.7093  -0.7853   7.2272   3.5096   1.8355  16.3071  -9.8703  -3.0199
-#>    1.7039  -7.8727   4.3188  -6.3996   9.3464 -11.0304 -13.0502   8.8442
-#>   -5.0000  -0.4485  -5.8625   0.0639   0.5489  -8.9442   3.0572 -10.4764
-#>    2.1024   1.1575  -7.3141  -3.1504  16.4440   1.9490   4.5007   1.5405
-#>    2.2730   3.5882  10.6477  -8.7302   5.1281  -5.5718   1.3978  -8.2277
-#>   13.8522  -0.6762  10.2911  -0.2770  -7.3265  -7.6784   1.6779   2.8196
-#>   -3.8064  -8.9778  -0.1520   1.8026  -7.8690  -4.4951  12.5004   1.7195
-#>  -14.5756   4.0345  -6.2921   5.8808 -14.3969   9.0118  -9.3838  -3.6941
-#>    4.7236  -4.2033  -8.3519  -4.8034  -9.7167   9.4050  -4.0289 -11.0073
-#>    5.3240  -7.0412   1.8191 -11.3021  12.7159   9.0398   8.1060   2.1124
-#>   -6.4629  -2.5036  -7.5835   3.1017 -11.9905  -8.9950  11.2799  -2.9150
-#>    4.2767   4.3477   1.0800  -2.5998   2.8277  10.7435   9.3785  -9.6822
-#>   -4.4303  20.7324 -13.0990   6.1930  -6.2529   0.7377  -7.4001   1.3349
-#>    4.4720   2.8303  13.6389  -7.9152   8.4899  -3.1532   8.3466   0.4572
-#>   -8.3623  15.7370 -10.7719   4.4032   9.9055   3.7759  -2.8595  -1.7905
-#>   -4.8775   1.8915  -0.8530  10.2657 -15.9343  10.3426 -17.7020   2.0729
-#>    0.5613   7.1272  19.9651  10.9519  14.0965   2.4315  13.4485  -1.4175
-#>    4.8714   8.1319 -10.5989   0.3411  -9.3492   7.6087   9.2715   9.6984
-#>   -1.3565  -7.4533  -4.8182 -17.3904  -0.5276  10.7067  -8.2810  -3.0651
-#>    5.9905   1.0488   8.9671  16.8183  11.3654  -6.4070   3.9435  19.8416
-#>    3.5665  -2.9801  -1.3262   8.9776   9.5841   2.2091  -2.9954  -3.0973
-#> 
-#> Columns 9 to 16  -8.8073  -4.6426 -13.9884  -8.2320  -8.5217  -0.4775  -7.4151  -0.6725
-#>   10.0546   4.3717  -1.5863   1.3989   7.1575  32.8837   3.4143  -0.7769
-#>   -8.8521  -9.9139 -19.5135 -24.9521   0.7116 -11.7181  -5.9171  -5.1497
-#>   -3.9503 -13.6314  -5.8326  17.8691   6.6317  -7.6286   4.8017  10.8725
-#>  -12.6786 -19.4017  -9.4109   2.1331   6.1469  11.5832   4.8107   5.5907
-#>   23.6291  -7.2367   7.5458   1.4301   9.4437  -3.4435   9.0182   6.6427
-#>    2.9611   0.3858  -5.4467   4.0315 -21.9230 -10.6043   3.1202   1.5852
-#>    3.9447   3.1710 -12.9762   8.3977   9.4892  -0.5182   2.5873  17.2142
-#>   -1.3194 -13.1223   6.6894   7.0331   6.4437 -12.6453 -14.5226  -3.8072
-#>  -10.4038  -7.1690  14.0111  -8.5985  -3.2720   0.6783   8.7759   8.8802
-#>    2.2255   7.0874  -0.6579  -2.0451  -3.7498   0.6559   5.8038   3.7002
-#>   14.7065 -16.0033  -5.9809   5.9349   7.9587  -9.9855 -17.9542  -3.7742
-#>   -1.4432   1.0548 -14.3342  13.1105   2.1546  -9.1868  11.2042  -0.9932
-#>   12.1984 -12.8600  -2.5919  -0.8897  11.8771  10.0617  -8.9914 -11.5302
-#>    7.5468 -12.1222 -26.0768  -6.2028  -0.7591  -1.2010  -6.0798 -11.1108
-#>    8.7007  20.9135  -2.9024   1.6970 -11.4094   8.3639  10.0946   6.3963
-#>   12.4040   4.4726  24.0844  -5.9889   9.6856   1.4792  -1.8792   2.4935
-#>    2.5080  18.3730  -7.6850   9.7258 -11.3493  13.4590  -1.7244   9.6702
-#>   -5.4163 -14.2738  -4.3608  10.3636   3.8761 -12.1021  -6.0958   1.4944
-#>   -5.7651 -10.1263 -17.2028   5.7702  -3.2691  -9.1889  -2.5732  -3.3792
-#>  -10.0545   4.1091  14.0733 -10.4982  -5.7342   2.3171  -6.4773   3.3353
-#>    2.6782  -2.8125   2.7590  -1.9897  -9.1181  11.7984  -9.0359   1.9233
-#>   -5.2344  10.2287  -2.8782   1.6119  -0.4775  -8.1459   1.1576  -9.2421
-#>   21.9949  -8.0431   1.7145   4.7288 -22.4366   4.0880 -12.0808   1.6562
-#>    7.5187   9.7269   0.6820  -2.6167  -1.1286  -1.0346 -18.5821  -5.9096
-#>    9.3526  -5.6035   5.2310   9.7429  -3.3496 -10.7880  -7.9118 -15.5445
-#>   13.6427  -0.6605  11.3279 -13.0369   9.5204  -4.8265   1.9385 -12.8543
-#>   -1.0619 -13.6995  -2.8276   4.1690  18.6644  -5.6350  -5.1124   1.0305
-#>   17.6611  11.3794   4.5687  20.1350   3.8588  -1.0378   1.2913   5.7805
-#>   -9.2243   5.0020   3.6845   5.8329 -21.8466  -0.6807   0.5413  -0.8968
-#>   -0.6459  -3.9581  -4.2478  -0.6667  -8.4845  -8.5001   2.8692   8.0881
-#>   -4.8527   8.2332  -5.3847 -15.9781  14.2163 -13.9345  -3.4176  -7.2562
-#>   -1.8429   9.3069  -3.8057  -1.8225 -20.4709   2.7353   2.2712  -5.1033
-#> 
-#> Columns 17 to 24  11.0662 -14.5526   0.0697 -19.0220   4.6790   3.9423  -2.6753  -2.9578
-#>   22.7239   6.9675   0.6424   3.7429  16.1882  -1.5087 -16.0539   0.1969
-#>   -2.5550  -3.6210  -3.0403   3.2895  -9.0184  -1.8333  -1.3289   3.3007
-#>   -0.8008 -13.9414  -3.3939 -18.7868  -4.2458   9.7082   1.1865 -18.0323
-#>   -5.0873  11.8188   7.8642  11.5776 -16.0727   9.4908   8.4264 -15.9399
-#>    9.5338   0.5156   0.8218  24.7352  21.4326  -1.3723  -3.8085   4.0040
-#>   -3.6941  13.9830   3.1614  -2.4057  -5.2300  12.6373   0.7486 -10.2209
-#>    9.6279   5.2914  -2.1667  -2.6882  -8.5979 -17.3494  14.1774   4.5505
-#>   -4.9814 -17.5009 -15.7751   2.3943 -14.3330  15.0807 -10.8200  -6.3016
-#>    1.0375   7.5819   1.8242  -2.1025   3.4078  -3.6889  -3.6870  13.7473
-#>  -10.5505   8.0478   7.4250 -13.4552 -14.5107   3.5241  -1.5158   4.6435
-#>  -17.6329  -8.9843  -0.5239  11.9668 -16.0605  -6.9913  -9.6883   2.4846
-#>    3.9703  10.5117   2.4437  -9.5863  -8.5419   4.4804  15.5980  -8.0862
-#>   -3.8186   2.0125   2.0543  10.0792  -7.5268  -4.8376   5.2885   7.0757
-#>  -12.5689  -8.2614 -13.2759  12.4033 -19.5609   3.0369 -12.8527  -5.7830
-#>   -4.6646   0.1257   6.0242   4.2538  14.7014  -8.8032  -5.5753 -10.4496
-#>  -10.2244  -0.1577  13.0210  10.7659  -2.0343  -4.4945  -2.3216  -4.1657
-#>    0.5649  -8.4057  26.7812  -6.8665   1.8525   5.4294  -5.2585   4.8750
-#>   -3.3987   2.3718 -11.2156  -6.4853 -24.5315   2.5088  -9.4710   4.6227
-#>   -7.9132   1.8410  11.1107 -11.2720  -4.2379  -2.7606  -5.2530   5.4750
-#>   -1.3218  10.2854   1.9285 -11.3502 -11.5231 -10.5135  -5.7751   1.5308
-#>   -4.0835  -6.0362   1.4618  -5.1609   6.1913 -23.7162   6.2317  14.7307
-#>  -17.7677 -17.0161 -13.5697   2.4813   8.5010  -6.2259 -17.0806 -15.3741
-#>   11.8556  -4.8654 -11.5342  -4.0967  11.5691   1.4684   7.8547   9.3808
-#>    1.8603 -12.9068   8.3558  -8.8895  23.3471 -17.4276   1.8093   7.6683
-#>    9.5079   3.8055   7.7854  20.9576  -6.5707  15.5529   2.2398  21.4755
-#>   -5.8233 -11.2661 -21.2273   1.8201   1.9201   0.9845 -15.9731   7.6355
-#>    4.1302  -6.0695  -8.4113 -10.1005 -17.6905 -16.4190   6.3698 -12.3462
-#>  -17.9427  15.6452  -5.7567 -10.7185  12.8775  12.0741  21.4769   6.2621
-#>   17.3807   3.2854   2.0233   0.4028   7.7633   4.6181   7.1242 -18.0502
-#>   -2.6197 -14.2332   0.0485   9.7610   3.9811 -18.7429  12.5354  -6.3579
-#>   -9.0786   0.7897   3.0636 -13.3762   1.8459   8.2314  11.0418   3.6651
-#>   -2.2425   2.0437  -5.5021  -3.8781   9.9642   2.8891  -2.2660 -11.2317
-#> 
-#> Columns 25 to 32 -32.6698  -8.6093  -5.0129 -11.3094  24.4550  -0.5704   4.8933  -5.5201
-#>    1.2721   0.6487 -10.9427  11.6122  12.3350   0.6388   1.1434   3.0152
-#>    8.1651 -14.7024 -10.9904   2.9376   5.4284  11.7591  15.3603   5.0108
-#>   21.2289 -14.6108   3.7578  23.7265   0.1149   3.1634  -7.0561 -25.7672
-#>   -6.4865   1.1524  13.9062  17.8211   4.4666   6.4054   4.1235   7.4927
-#>  -24.0737 -10.2488  -0.7837 -19.7711  -3.5405 -14.7599 -11.0616  -5.3042
-#>    0.7789 -17.5263 -15.1668   8.9913  -2.4021   9.1531   9.4817   7.4751
-#>   -6.0258  -2.9461  13.5621  15.0060  -5.9288  -7.2522  13.6002  -4.5906
-#>   -5.3995 -12.3453  -2.9668 -10.6944   0.0278   3.2880  -3.7492   5.9309
-#>    7.2386  11.6521   6.7375   4.7726  10.0783  -2.9344 -19.2203  -3.8975
-#>   -3.8018   5.9055  -9.5356 -17.2161   1.6056  -3.9030  -2.1836   6.9117
-#>  -22.5193   1.6938  -3.8648   6.0373   6.0638 -18.1423   2.2888   1.0071
-#>   -0.0912   4.5126  12.5903   5.7911  -4.7895 -15.4148  -1.4881  -7.6777
-#>    8.1668  -1.7108 -10.8280  16.8029   8.0118   0.1464   0.8490 -14.1483
-#>    9.1446   1.4189   0.1367  -3.3847   9.2224  -2.9130   2.0731 -13.4869
-#>   -1.2765  21.6822   5.4889  -5.0004  -3.4493  -2.9209  -9.9478   7.0307
-#>   -4.0638   6.2533  -9.5217  -2.7831  -0.7303  -1.4290   7.1134   8.5808
-#>   -5.4981 -17.6882  -6.5339   9.6844   6.3867   3.6524   4.6800   2.2742
-#>   -1.4602   1.0576   5.5146 -13.2516 -14.4008  13.8107 -18.6747   5.9339
-#>    4.9861  -4.7771  20.6300  15.5541  -6.0074   5.1128  -0.4189  -2.9462
-#>   -1.4486  16.5589  19.4602   1.9526   3.5924  -7.0477  -4.7282   1.1304
-#>   -0.3443   9.8070 -16.0824  -8.1581   3.0776 -12.2659  11.0605 -22.4701
-#>    8.4359  13.0354  13.1142  -1.8719 -10.8537   3.0869  11.9608   4.5571
-#>  -11.3341  -7.3854  -0.7040  -8.9241  13.4767  -0.2498   1.4465   7.7940
-#>   -9.5916   2.3081   9.4823  -5.6463  -4.7764 -17.7450  14.1080  17.3467
-#>   -0.5413   2.8060  10.5147   1.7546  -3.5436  -6.1599 -14.4421  12.4286
-#>    1.0812  -9.0801  -1.7089 -18.4518   2.6798  -9.2463  -9.5171   6.6413
-#>    4.7788  -0.4133   2.3009   0.4398  -5.0574  -4.7202  -5.6557 -13.3648
-#>   12.6261  -3.5589  -2.4662   1.1209 -23.9096  -2.2812   6.0163  -7.3213
-#>    1.1171  20.2254  -3.8290 -15.7041   5.4705  -1.6709  -2.7804  10.5089
-#>    9.1564   5.4900  -7.7571  14.2231   5.9711  -1.7668  -1.0377 -10.1896
-#>    4.7588  -0.2267  -7.0410  -8.5436 -12.5272 -17.1023  18.5268  -5.4560
-#>    8.0987  -4.0980  -0.5273 -11.0374  -7.9019  10.1087   0.3397  -2.1266
-#> 
-#> Columns 33 to 40  -0.6891   5.1512  -5.9616   8.3684  -6.5541   6.7590  -1.5969  -3.3233
-#>   -7.1381  -5.8954   2.3722   1.1176  -1.5182  -0.4858   4.1055   0.5757
-#>  -12.1200   1.3603   4.2367   6.1071   7.8496   0.7433   7.9517   7.7278
-#>   -6.7656  -8.5868   2.2282  -0.5914  -4.4096   6.2110  -1.2708  26.3763
-#>    1.6977  -3.0817   0.9547  11.4690   8.4347  -0.5228   9.5944  -1.6138
-#>    4.3577  -4.5182  -3.2134  -9.1625  -9.4456  -3.9361  -7.6599  -1.6247
-#>    2.2651   6.7368   9.1694   7.3736  11.9476 -25.9909   7.7422   7.7991
-#>    6.8059  -2.5797 -14.0586  -0.4316   4.3446   1.4599  14.3628 -19.3045
-#>   -2.8454  -5.5364  -2.1157  -6.6131  -0.8127 -10.4145  -5.6694  10.0690
-#>   12.9586   1.9260  -3.7913   6.1513   1.6149   0.6863 -12.8163  14.7614
-#>  -17.7495   0.5206   3.7497   1.8556   7.8428  -7.2770   0.9337  -3.4212
-#>  -13.5710   6.9932  -2.0936  -2.0107  -1.9852  -1.7724  17.9422   7.7792
-#>   -3.7335  -7.4195  -2.4935  -3.5388   2.8353  -8.7390   6.6221  -8.8004
-#>   -9.6807 -15.3747   4.5706  -4.4656  -1.0659  -1.2077  -4.3275   0.8064
-#>   -1.5919   8.4855   4.3687   2.5481   3.1221   1.6635   7.3577  -9.0880
-#>    0.5298   7.4672   4.1995   9.2957   0.1546   5.4831 -13.1099  -8.7616
-#>   -9.7556 -26.8600  -9.2641 -11.0434 -10.5407 -10.8140  -0.6515  -5.8294
-#>   -0.2582   5.4815   3.4851  -1.8528   9.1895   5.7904   2.5789  15.6803
-#>    6.5863   3.9697   9.1019  -7.4410  -7.5782 -16.5352  -7.4639  -8.7216
-#>    3.1715   6.5688  -5.2957  -4.3494  -4.3843  17.6800   6.5693 -15.0530
-#>    8.9078  -1.9871   4.1219   7.5827  17.6163  14.7036  -0.9792  -9.6755
-#>  -16.9230  -0.4241  -8.5176  -0.6277 -15.3566 -11.9403 -10.3961   3.3311
-#>    1.5606  -8.6993  -6.6938  -7.1477  12.3101 -12.5791  -3.6695 -11.3222
-#>   -7.9792   4.5259   9.5120   1.5035   1.6864   1.8410  -7.3137  -4.2054
-#>   -5.8180  10.1696   4.6879   5.7464  -1.5702  28.5283 -13.1240   6.4315
-#>    2.2603   3.7824  10.2709  -9.1696   6.4035   7.2668   7.3593  -2.1308
-#>   -0.7404  -3.1743   9.5376 -12.0170  -5.0920   3.1090   5.7006  -8.4534
-#>   -6.4615 -25.2439 -10.9172  -2.2400   4.7356  -2.1891   2.6827 -10.5127
-#>    9.5131   7.4656  -6.5773   4.6907   4.1973  -3.3962   7.0490  -7.4300
-#>   16.4267   3.5051  10.1579   7.0891   9.1914   6.3337  -2.7389  14.1222
-#>    1.3694   0.3559   1.9932  -3.0715   0.0079  -0.8437  -4.3533   3.1633
-#>    2.0113  -3.4719  11.3453  -3.5211  -6.7155 -10.9955  -4.1942   2.4041
-#>   11.0641 -13.8099   4.6128   2.8432  11.7525   3.2607   1.2708  -2.1138
-#> 
-#> Columns 41 to 48  -0.1047   7.1639 -12.7312 -14.9411  -1.7243 -12.6823  -5.2909  -1.5054
-#>   -0.6198  -5.0780   3.3587  -3.7096   9.7318 -11.5688  -6.0776  -5.0235
-#>    3.5723  -9.2005   2.5989 -21.5485 -12.2536  17.0624   1.4070  -7.9798
-#>  -12.1266   2.2245  -1.4824  -2.2374   6.7505  22.1302 -10.3626  16.0516
-#>   12.9833 -10.8692   8.1235   6.8576   1.9169   5.8651  10.7781   1.8127
-#>   13.4862  14.4447   7.6561   0.8456  -6.2709 -10.2808 -14.2315  -5.1059
-#>  -11.6531   3.8384 -13.5760 -15.2587  -0.4386 -11.6019  -3.1254   1.9812
-#>  -14.4318  -8.3302  -5.7316   1.3431   3.1469  -0.5234   2.0978   5.0587
-#>   11.3827  12.2602  13.5625   8.1668 -13.3577  -5.9555 -10.2404  -0.4668
-#>  -20.0554   6.6514  -5.7848  -3.7281   2.9242  -1.2270  -3.3500   2.0820
-#>   13.5036   4.9700  14.1329  -3.3614  -2.4387   4.3636   5.2511 -26.7314
-#>   12.3755  -6.0302  13.0706   7.0045  -4.3136 -20.3503   6.1174  -7.6721
-#>  -11.2236   2.2306   0.2531   6.8231  -1.9899   3.6850   2.7379  -7.5945
-#>    7.6483  -7.3374   9.9872   4.2223  12.5574   4.1922  11.5467  -1.8445
-#>    6.6880   1.3918   6.2407 -23.0986   3.5472  15.1577   6.0838  -9.5015
-#>  -25.3432  21.5193   0.5058  -5.4625 -14.2084  -2.0676  14.9385   2.1116
-#>    5.4331   0.7524  -4.1156  10.5738 -15.1925 -10.3904  11.5019   8.5438
-#>   -0.5497 -12.5371   5.6004  -4.6952  15.8923  -8.7153  -3.9162  -8.2549
-#>   -0.2119  -0.2621   3.6054 -26.5559  -0.8848 -10.6993   3.7716 -17.5107
-#>  -19.2452  -4.8849   2.6568 -10.7631  -3.6147  18.3777  10.0724  -3.3694
-#>   -8.3158 -15.7886  12.6284   4.6058  -7.6070  -6.1975  23.2880  18.2722
-#>   -0.4041  10.9108  -5.3681   8.0119  -4.9525  14.1613  12.6936  -3.8161
-#>  -14.9089   6.0440  14.4983  -9.9542 -14.4793  -4.1608   2.9876  28.2071
-#>   -7.5434  14.3462 -13.1011  -0.0543  -0.1406   1.4744  -9.4161  -4.9771
-#>  -14.9983  13.5030  10.9352 -10.9594  -6.3634   6.1297 -18.0191   1.8998
-#>    3.8310 -10.9236  -3.8793   6.5608   5.8434  -7.0148 -15.9521 -13.2939
-#>   17.6064  -5.7444  -0.3266  -0.0692 -11.4574   5.3437 -12.4108  -8.0583
-#>   10.7164  -5.7833   1.8178  15.0389   3.0004  -2.2704  -3.4458   4.3220
-#>    4.8007   3.8398  -4.0033  10.0028   4.0875   9.7484 -17.8828 -12.7776
-#>   10.3227  -1.1501  -2.4754 -20.4586  13.4596  -9.2950   1.5691   2.9662
-#>   -6.1917  -0.8440  -3.0441  14.1943  -4.7117   2.9086   9.5871   7.8146
-#>  -20.5611  12.6771  -7.1091   2.4571  -3.8733  -0.2544   7.2356  -6.9050
-#>    3.2720   2.4587   0.8837 -15.8807   4.8638  -8.7061  -8.4504  -8.2424
-#> 
-#> Columns 49 to 54  21.8557   4.4202  10.8168   7.5174  -3.7758   2.1122
-#>   -2.8456 -15.9854   2.0339   7.9619   0.4806  -0.4653
-#>   -9.5906   5.3457   2.7396   8.0575  -3.6745   1.8922
-#>    9.1292   8.5034 -25.1697  -2.3926   3.7174  -3.3314
-#>    5.9401  -1.1539   3.8906   7.1575   0.5660   2.4047
-#>   -5.3541  -2.6976   4.3906   2.5245   0.3295   2.7928
-#>  -10.4245   7.0469  -6.8186  -4.2369   2.9327   2.8947
-#>    3.0145   0.9534   5.5193   7.3609   0.7635   1.8795
-#>    1.0380   8.5256 -16.1735  -1.1572  -3.3035  -0.8527
-#>   -5.8692 -11.7066 -13.6596   4.0496  10.6453   1.8254
-#>   -5.8070  -8.3313  21.3003   0.7767  -2.3350   3.4440
-#>  -12.4272 -14.2899   1.9738   2.6648  -7.6391   2.7511
-#>  -16.9396  -9.4806   4.9570   0.1588   3.8355   3.2884
-#>    5.2529  -7.1969  -4.2218  -6.2748   2.3049  -1.3600
-#>  -13.7700  15.0260 -11.5880  -3.3049  -0.6822  -3.0384
-#>  -10.5514  -9.3660   5.5534   1.1381  -2.6856  -1.1547
-#>   -2.5532  -0.4677   5.0003   5.1773   0.7936  -0.4262
-#>    6.8253  -5.2287  -6.6104   1.5442  -2.8864  -0.4845
-#>   11.6100  14.6819  -9.3433  -6.4061 -13.6912  -0.5850
-#>    0.3866  -5.1808   4.5291   7.7504   1.9457  -1.1015
-#>    0.5982  -8.9206   4.5865   0.8641   1.1949   1.2615
-#>   -5.3683  -4.6591   0.5351  -8.2208  -2.7109   3.5416
-#>  -13.6418 -10.2297  -9.1510   5.2640   2.1182  -4.0404
-#>    4.0355  -8.2871  -9.8668  -5.2217  -8.3299   0.6824
-#>   -3.4248  -5.1316   9.4518   0.5314   0.5506  -2.2042
-#>    1.9089  -0.8906  -2.7357   3.9258   4.9725  -0.2202
-#>    7.4031  -6.5030  -0.5144   0.6044   1.5107  -1.8649
-#>   -2.6418   0.4153   3.0424   8.7083   1.7534  -0.0252
-#>    4.0239   3.5966  -7.6770   0.8856   5.5774  -2.7345
-#>    2.3516 -11.4263   0.2037  -6.9024   2.5088   3.9246
-#>   11.4006  -1.0101  -2.1060  -2.4315   0.8993   1.6014
-#>  -14.6229  -1.0623  11.6151  -9.1313   6.1282   1.5521
-#>   -2.1762 -12.8544  -2.2436   5.0173   2.8554  -0.1953
-#> 
-#> (3,.,.) = 
-#>  Columns 1 to 8   5.0065  -7.5648   4.3697   5.4119   1.1572  17.5422  -0.4933   3.3253
-#>    4.5796   7.3903  -1.7693  -2.0851   2.1409   3.3564   1.4481  -4.8963
-#>    1.6970  -2.0240   0.1673 -13.3189  10.1225   4.5772  12.1823 -12.9104
-#>    4.4423   0.3706   1.9800 -14.1760   1.7023  -7.2400   0.9277 -10.3556
-#>   -4.9464   5.5538   3.4204  -2.9786  -1.0806  -6.1542  -0.2451  -9.0744
-#>   11.9943  -1.9920   5.2217  -4.9694  -3.6795   7.3552  11.9307   8.9454
-#>   -6.8636   5.1646   3.3317  -2.5187  -3.2920   2.8625   8.4681  -1.5043
-#>   -3.4725  -0.3777   0.8911  -0.7854  -1.9812  -1.6493 -27.8762   2.8427
-#>    4.7066  -3.2704  -0.3828  -6.5696   7.0008  -5.8864  13.5979 -16.6016
-#>    5.4407   3.1746  19.6670   0.1120  -2.7427 -11.9844   1.0637   5.8977
-#>   -7.6526  -4.7646   7.5172   2.7917 -15.0073   1.5688  -6.8070   5.4306
-#>    2.9191   1.0430   0.1736  -4.6040  -3.2349  18.2567  -0.5415 -12.4009
-#>   -2.2711   4.5532   4.4744  -0.5926   2.2176  14.9981 -11.5919  14.1614
-#>   -5.8899   2.6850   0.1409  -2.7625  -0.4703  14.8921  -3.4326  -3.3055
-#>    0.4423  -8.7097 -10.6624   8.3148   6.7439  -2.4797  12.4699 -20.2065
-#>    8.0021   0.3554   0.3091  -3.3935  12.4163  -1.2953   4.3594  -4.7604
-#>   -2.1517  -1.1949   6.7236  -9.2434 -11.6224   6.0957  10.3943  -1.4627
-#>    0.7060  -1.5135 -20.7254  -6.8302  -3.2998  15.6868  -5.5653  -9.2229
-#>   -5.0176  -0.9663  -1.7610  15.8706  -1.0764 -10.6421  -5.5809  -3.3236
-#>    1.9067 -10.7770  -3.6526  10.3817   9.1903   6.5684 -17.1229  -7.5234
-#>    1.2750  -3.1628  -1.8613   8.5306   1.5362   0.4427  -4.1968  -0.8201
-#>   -2.1752  -1.8308   6.5429   7.1769  10.1203 -15.6256  -3.5803   4.7920
-#>   -3.9520   1.5366  -1.0068  -8.6163  -5.8413   5.2089  -2.8411  -5.1499
-#>    4.3731   3.5496  -1.5140   6.9102  -1.4003   8.9362  -6.4642  -4.5631
-#>    4.2081  -9.6222  -3.5010   0.5712  -2.3747   6.0067  -4.5879   8.2174
-#>    6.5613   5.2677  -1.1577  15.0782  11.2545  -8.9979   2.4309   3.5133
-#>    4.0774  -0.2183  -7.1818  -3.6122   4.8120   5.3588   7.6044   0.1746
-#>   -8.7328 -10.5805   1.2447   3.5645   6.8815  -6.6055   1.7821  -3.7090
-#>   -6.2692  -0.6318  -4.2597  -8.9461  -2.7332  -6.6875  -7.8548   4.0098
-#>   -0.3510   3.9007 -14.3480  -0.3358  -9.3153  -3.4372  -6.5250   6.8921
-#>   -1.2713   2.7064   5.8096  -5.4592   1.9853   5.8544  -6.8093   2.7735
-#>   -2.0104  -1.4024  -2.1828  -2.0720  10.3598   8.6430  -0.3503   3.7573
-#>   -4.4944   2.3952  -2.2312  -6.9977  -4.7600   2.7363  -0.5609   6.7697
-#> 
-#> Columns 9 to 16  -6.9998  10.6761   8.6290   1.0619   4.6502  -8.7584  -8.5783   5.7564
-#>    1.2974  -3.5524   6.9653 -19.2955  -4.2965   3.1079  13.4470  -4.0555
-#>   -5.2354   4.2810  -0.9473  18.6950  -8.9680   9.0909 -22.2445 -16.3682
-#>   -1.2449   6.2986 -12.2026  19.6986  -8.0719   4.8052 -13.2800 -24.9230
-#>    3.1749   5.6027  -5.8446   4.7225  -1.5331   9.1026   6.1800   6.6361
-#>   10.8301  14.3328  -7.5849  17.7657   6.8040   2.6532  -6.7402   2.3008
-#>   -2.4575  -5.3909   3.3260  12.3631  10.6478   1.0650  -6.7919   6.5352
-#>   -6.5352  -1.1951  -2.9045  -4.2426   1.1777  -9.2049   1.3871  22.7300
-#>   -6.4794   8.2347 -10.4154   4.7242  -8.1403   7.2454 -13.2459   0.8178
-#>   -1.7903  -5.8577   0.6970   9.9278   5.4227   9.8878 -14.7169  -0.7110
-#>    6.4007  -7.5623 -10.7833   4.0560   5.7243   6.1790   6.8927  -6.2734
-#>    2.0545  -3.5389   2.0349 -10.0992  -3.0352  -4.9398   2.8822   0.6213
-#>    9.8403   9.5956 -10.4498  -1.1934   5.3889  -9.9235  -3.0167 -12.1756
-#>   -1.9861  -9.2124   6.4746  -6.2048  12.4562  -2.5963   7.2102  -7.0540
-#>    6.5246   4.7411 -12.6090   2.8226 -13.1843   7.1695  -9.3513  -5.5817
-#>   12.1244   6.2580  -2.8042  -7.4370   5.9493  12.8463   5.8486  -8.9301
-#>    2.3902  -0.9217   6.3379   3.5433   7.7250  -5.8269   6.4756   0.2072
-#>    4.1890   0.4687  13.0917  -8.7310  -4.4070 -12.8233  -3.4196   5.6194
-#>    7.1521  -6.5397   0.6541  -6.3224  10.2388   4.7956 -12.6320   4.4017
-#>   -1.5785  10.3988 -10.7020   4.6607  -7.1149  -2.0043  -1.2336  -6.8194
-#>   -2.8968   2.4503  -5.4942 -14.4237   6.6551  11.6778   8.5156  11.1045
-#>   -1.1568  -2.7098   1.0471  -3.7183   4.8246  -1.9663  -4.6640  -1.1049
-#>   -8.5518   7.5451 -15.9437  -1.2995   0.7863   6.2623   1.3795   1.0162
-#>   -1.5262   8.2184   1.0894   1.9374  -4.0241  -0.2278 -15.7278 -12.5403
-#>   -3.6396  -0.5381  -9.3408   4.3941 -12.5852  11.0130   7.4217  -5.1584
-#>    3.5167  -0.9388   8.3914  -6.8105  -2.8391 -21.0392   0.0891   6.5242
-#>    6.7332  -3.6219 -10.7829   7.0740   7.6049   5.0294  -0.9974  -3.7430
-#>    8.4995  11.3926 -10.2824  -2.8517  -9.0784  11.1800   2.0026  -2.0633
-#>    4.7858 -14.6920   7.6764  14.7249   7.3598  -4.3916  -7.6974  -6.0448
-#>    4.9977  -2.4565   6.1780  -5.7729  -1.9491   7.0341   9.1896   2.2460
-#>   -3.9617   0.7266  -8.6600   0.5977   9.7916   8.5833  11.3199  -4.5682
-#>    3.0461   7.4758   2.2648   3.1652  15.9415  -9.5217  -7.9929 -14.9009
-#>    0.0593   8.0995   5.7684  11.2536  -1.8930   0.8747  -0.8771 -17.4723
-#> 
-#> Columns 17 to 24   2.9991  -7.0031  -4.3098   3.9696  -3.8698   3.7981  -3.4638  -5.8145
-#>    9.8929  -0.6985   1.5678  -7.6750  -1.0575   3.5687  10.1861  -2.8345
-#>   -3.5003  11.2459  -9.4071   7.3753  -0.4676  -7.4027  -6.5143   4.4750
-#>    2.7744   3.1267  -5.5507  -3.7926   4.7195  -4.2368 -16.2356  11.4313
-#>   14.7289   3.2852  -5.1273 -26.9714  -5.7715 -11.4138  -6.7957   3.3423
-#>   -0.2818   5.4439  -0.5775  -0.4554  -2.5376  18.6859  12.4762 -13.8969
-#>    4.9449   1.3183  -0.1317  -5.1529   3.8452   9.2586   1.8964  13.9726
-#>   -1.6536   0.3676 -13.3478  -4.2053   0.0647  -6.0252   9.3051  -6.1649
-#>   -5.5615  14.3067  -1.6848  12.2309  14.4537  -8.1157  -0.8579   1.5930
-#>   -0.9635  -1.4980   4.2655 -21.3527  -7.6679  -7.1719  -0.3917   1.2707
-#>    3.1651   2.3176   4.5623  -2.1620   2.1102   1.5631   6.1667 -12.4041
-#>   -3.5800  15.3647 -13.4311  -7.2799   0.5813  -0.9263  17.0942 -18.8560
-#>   -1.9807   1.5200 -10.6795  -0.0452  -4.0646  16.5639  -0.9831 -21.0682
-#>   -0.0563   2.6941 -12.5726 -15.9221  -5.0472  11.8357   9.6567   2.6775
-#>   -0.7082  15.5344 -10.7737 -14.3932 -12.7231 -17.7728  -9.7606   3.9705
-#>  -11.7585  -0.7036   6.9729   2.0480  -1.0557  13.8488  -3.0825   1.0844
-#>   -5.2574   2.7561  -4.0692  -7.9613  -7.0095  11.1284  14.2156  -2.0798
-#>   12.7254 -12.6184  -4.4636  10.5271   8.7149  -3.2069   5.7323   4.0469
-#>  -14.2750   1.7038  10.0903  -2.5975  15.6578 -16.2574  15.1585 -11.0779
-#>   12.1564  -2.2589 -12.5739 -10.4091  -6.1980  -5.2244   4.4709  -3.7656
-#>  -18.4174 -15.6443  -1.5797  -5.4252   6.9559   2.8517  16.7162 -15.9417
-#>   -9.5823  11.4253   6.4563  -7.3173  11.0476   4.3020   4.4361  -3.1137
-#>    2.2054   7.7500   4.4457 -11.8929 -18.6036  10.0097  11.4196   5.3231
-#>   -1.3877 -14.1901  10.9835   5.7080   2.7517  -2.3153   2.5598   3.1702
-#>   11.1839  -7.5466   6.8821  12.2485   3.6899  10.8344  -6.7247   2.7138
-#>    6.8281 -12.7237 -12.4240   0.8836 -10.6440   2.3363   0.7084   4.0937
-#>   -3.7046   3.6566   3.7686  17.9101   2.9497   3.7957 -10.0212   7.2813
-#>  -15.1208   3.2913  10.3041  11.0956   2.9664   5.2545   3.8653   5.1548
-#>    0.9173 -13.3204   5.8748   4.8021   4.3568  -2.2284  -1.8059   4.8992
-#>   -1.8480  -7.9347   0.0099  11.5403  -5.5167 -14.6196 -13.5348  -3.0772
-#>    6.2677   3.2872  -3.0857 -13.0179 -17.1925  -3.1941   4.9096 -10.0129
-#>   -4.5396   7.1775  -5.0795  -2.1652  -4.6862   9.7565   4.7690  -8.4607
-#>   -6.4992 -17.9611  -0.7486   8.4730   0.2359  14.2265  -4.9010  13.5942
-#> 
-#> Columns 25 to 32  -0.4502  -2.3367   5.3966  -1.9736  14.0377   3.0119  -8.4109 -13.9355
-#>    3.9575  -4.0850  -0.7513  -8.3361  -0.8708   9.1271   0.5462 -14.4554
-#>   -6.7282   1.3936   7.7278  15.0878   3.4429  -3.3617  -7.3842  -6.6403
-#>   -6.4748 -13.2080   3.8655  10.9370  -5.1134 -14.1147   8.0535 -11.6929
-#>  -12.5487  -1.3291   3.7207  -1.6684 -11.1489  -1.3516   9.4046  -2.0490
-#>    5.3655   9.5782 -17.2964  -9.7356   0.9319  11.7845  -0.2503  -2.2241
-#>   -4.9538 -22.1325   8.2270  17.2945  -4.1733  -6.3520  -0.8176  -5.2402
-#>   -4.1229  10.7951   3.8800 -14.9516 -13.4749 -13.0130  -2.2828  -2.7846
-#>    4.6756  -9.9426  -6.9022   4.8072  15.7269  12.2702  -3.2373  -5.9807
-#>    4.7910  -8.1889   3.8486  -5.5455 -18.6664   8.1700   7.6792  -5.5944
-#>    5.4961  -5.3140 -14.3234   2.9961   0.9553   2.8977  -3.2754  10.3027
-#>   -0.4717   7.9712 -12.2355  -3.7001  11.2613   4.8899 -19.9430   3.0561
-#>   10.5788  11.4136  -4.8488  -8.5414   1.8690   1.0350  -0.8902  -6.1415
-#>   10.3371 -10.8624 -12.7971   1.8257  -3.5686  -1.8141  -6.7775  -5.4592
-#>    4.4009  -3.1121   4.6466  18.3852  11.2915  -3.8269  -7.2640   2.5717
-#>   13.3067  -0.2645  -2.1777  -3.1643   4.2687  12.0935  10.4656  19.6666
-#>   -8.0351  -4.7948  -8.3152 -14.3852  -4.9098   0.2674 -12.8918  -5.0915
-#>   -6.9013  -3.8669   6.1630  -0.5587   1.0668  -7.0706   6.9699   8.8962
-#>    4.4448  -4.7124 -12.8896  14.1769   3.3869  -5.7435  -8.1179  -5.2362
-#>    4.2559  16.4625   2.6017  -3.6140  -1.8479 -22.0223  -7.7596  -0.5762
-#>   -7.4018  16.2872 -12.9994  -7.2869  -7.2918  13.4027  18.9048  17.6126
-#>    1.9429   6.2656   1.1654  -6.0094   7.2305  -3.4192  -6.5580 -11.7218
-#>   -1.4432  -3.2813  -9.4318 -10.5674  -1.7319  17.8822  14.0319  -6.5661
-#>   -4.2170   9.2337   2.8840   1.7105  -0.0033   4.7875   7.6331   0.7086
-#>    1.5496  -4.3581 -11.1832  -1.6342  15.4827   0.6272   0.9326  19.2356
-#>   -2.5731  -0.6965  15.7973   2.5878 -13.0473  -8.7761  -7.2038  10.3990
-#>    0.1765  -2.1546  -2.9154  17.0378   1.3992   1.8228 -11.6180  11.1169
-#>  -19.8042   5.9646 -15.4726 -11.0236  -6.0788   4.6658  -4.0302  -6.4444
-#>  -16.1552  12.7106   8.4312   1.0798  -3.3709 -10.7590  18.7330   1.0471
-#>    2.8258 -19.5791   2.6700  24.3105   0.5782  -0.9183  -3.7057  -0.9869
-#>    2.3721   4.8013  -6.0742  -5.4514  -2.3220   0.5604  13.2491   5.0343
-#>    8.4863   3.8528   6.6870   0.3344  12.9178  28.9992   1.1368  -3.5024
-#>    7.6834  -2.3466  -9.0723  -5.1307  -6.5264  14.4130   9.0037  -5.7521
-#> 
-#> Columns 33 to 40   6.1166   3.4755   3.0107  10.4820   2.2313  21.4302  -0.3769   7.2492
-#>    7.5885   2.9510  -5.9707  -3.1287  -6.9030   0.4736  -9.9255 -11.9223
-#>   -7.3547  -2.6745  16.3513  -9.9079  -7.4035  15.0191   6.9734  -0.3176
-#>   -3.9057   7.4514  -3.5686  -0.0520   5.5190  -8.5440  16.0547  -6.0800
-#>   -3.9551   9.4788   2.8746 -14.6228  -8.6366 -17.5136  -2.6790  -0.4776
-#>   -9.5146  -5.2213  11.0260 -14.8032   6.0196   1.7087   6.6660  14.5329
-#>   -5.6591   6.8087 -15.9567  -2.8290  -4.5220  -7.0795   4.1778  -3.3880
-#>    2.3287   9.4812   1.4361   2.4778  -4.0854 -14.8061 -15.7904 -13.1929
-#>   12.3652 -12.8175   4.8446   9.9093   2.9067  24.6350  18.0183   2.1561
-#>  -12.6300  -1.2267  -5.1545  12.1703  -2.8878  -1.3358   8.3011  -2.3132
-#>   -0.8691  -1.6677  17.0963   2.4648  -7.9536 -11.2632  -4.9028  -4.6315
-#>   -2.7268   0.6535  15.4282   8.6797   3.8988   5.2600   4.8597   1.6352
-#>   -8.4937  -0.0901   5.7381   5.0044  11.3519  -8.4415  13.9622  -1.7646
-#>  -16.1659   4.4432 -11.6796  -0.2280   7.2947   0.4916  23.7616   4.4628
-#>   -7.2801   7.3802   3.8974  -3.3115   3.7231   2.3252   9.6789   3.7708
-#>   -3.8670 -11.5865   1.2037  -4.6438   7.7230  -3.4565  -6.9513   5.7064
-#>  -12.7542 -12.4351   2.0368  -1.3465   7.0032   5.8571   1.5565   0.1240
-#>    2.4653  -3.8384  -6.6116  -0.0328  -2.6110   9.6295  -1.0983  -7.9041
-#>   18.4993  -2.1162  -7.7738   0.2191   8.9795  11.0376   7.3325   9.2761
-#>    3.1431 -11.9472   0.6696   7.9047   1.0182   4.9934  -7.1324  -9.2449
-#>   12.1020   1.9057  -1.7627   9.8111   6.0275  -2.3225  -1.9654  -5.9898
-#>   -5.4913  -1.6481  21.1171  15.0078  -6.5409  -2.4634  -9.6712  -3.9974
-#>  -27.1950  -3.4378  -0.7142  -6.0477   9.2275 -22.3768 -14.0585   7.8989
-#>    0.9481  11.1605   5.6543  -4.2198   5.2720   9.0667  -8.2835   0.0860
-#>   -1.8083 -11.5910   3.3375 -21.1419   0.6927   7.6406 -25.4208  12.1868
-#>    2.0968  -8.3505   5.4435   8.9627  10.5742  14.8554  12.7098  -5.0643
-#>   -0.4590  -4.4083  -0.0300  -4.9880   8.3520  -3.7739  19.8174  -0.7580
-#>   10.4876   2.0805   1.9403   2.2905  18.1642  -4.9934  -3.4718  -1.4246
-#>  -16.6475  11.8381  -9.6935 -20.3861   8.1342  -1.1187   8.6158  10.3533
-#>    5.5304  -1.2605 -15.8330 -13.0794  11.5950 -10.1415  -4.5288  -9.6068
-#>    1.0786  -3.2658  16.3202   0.3046  -4.1398  -7.0007  -3.2427  -0.0090
-#>    2.3637  -7.6107   4.8096 -14.9503  16.2826  -1.3325   3.9388  11.2878
-#>   -5.3557  16.9209   0.8013  -1.4738   3.4657  -9.9022  -1.0349  -4.0073
-#> 
-#> Columns 41 to 48   1.3345 -11.9278   4.8337 -20.2754   3.6607  -7.6054 -10.6370   0.7393
-#>   18.4758   4.1840   5.1066   0.5228   4.7843  25.5798   1.4430  -4.8073
-#>    2.2567 -12.8952   0.7386  -6.6147 -16.7057  21.4841  -9.8661  -0.3491
-#>    0.1938   8.7895 -10.5219  -5.8892  -1.2568  25.8267  -9.5847 -17.0484
-#>    0.5941  13.5316  12.5077  11.4497 -15.2622   7.9676   3.8970  -5.2587
-#>    3.5475  -0.9832   4.7096  10.4700   7.5481  -6.0933  -1.1715   0.8234
-#>  -15.7558  10.3998  -3.9478  -2.2627  -3.1319 -13.5418  22.4130   5.0423
-#>  -15.0093   0.8777   8.9129  -0.8943   6.5934  -8.7079  -9.1901   1.2953
-#>    0.7930 -18.1611  -9.1900  -1.9323  -4.9864   0.0681  -2.9314   1.2715
-#>    8.6339   5.4117  14.6414   2.9027  18.3501   9.9014  -8.9372  -0.7209
-#>    4.2320   3.9845   7.9440   3.8098  -7.0927   6.7669  -6.9893  17.8406
-#>   -7.9320 -18.7648  -7.3420   2.7295   5.3951   3.3670  -8.2192   4.4860
-#>   -2.9330  20.8977   5.6355  -2.1919 -12.0907  -3.9535   6.5782   8.8423
-#>   -9.3347 -10.6555 -17.5191  -6.0478  -6.2106   1.9033  17.4623 -13.6784
-#>    1.0635 -20.0535  -9.3549 -19.6059   0.9665   5.6258   2.6148 -15.1398
-#>    7.1171  13.7309  10.8487  -5.9154   2.0694  -2.6388   2.8409   7.6188
-#>   -9.5625  -5.8672  -2.7635   9.1451  -1.4415 -10.1499   8.3045  -3.1570
-#>   -0.8245  -1.6012   8.4346   7.0643   0.2006  -6.0378  -5.0282   7.1434
-#>  -20.1260 -14.5046  -4.1219 -16.9426   2.5031   7.4219  -3.9130  -0.1316
-#>   -9.7300  -6.6407 -14.1674  -9.5041   4.6654  13.3797 -17.6050   5.9562
-#>   -5.4223 -14.4687   7.3019   5.0101 -12.4661 -10.5039  -1.3772   9.8727
-#>   -1.9127 -18.7884   8.1959   1.4009  -0.5677  -3.3554   7.6146  -4.5400
-#>   -8.3890  -4.7103 -10.4115 -10.5468 -11.2971   6.0386  -0.3137  -4.2605
-#>   -0.4814  11.7382   8.6822   6.9266   7.6994  -3.8786  13.3594   5.3836
-#>    6.7302  -2.0560   2.8992  -1.6217  -7.0485  -3.6673  -8.4619  20.2970
-#>    1.4750   1.6396   1.5047  14.3273  12.3591  -3.2715   6.9656  -9.5265
-#>   11.8076   1.5431 -13.8370  -2.2326  -7.8101  -4.1678   6.3293 -11.9680
-#>   -4.2533  -2.1013 -11.6261  -1.8696  -1.4513  -3.3382   0.7499  -3.3817
-#>    1.1298  16.2342  -3.1134   0.6689   2.9858  -5.9440   6.0355  -6.6737
-#>   -2.8073  11.9571  12.6337   8.9343   4.8625   2.5022   5.9198   4.3786
-#>   -1.4268   0.1202  -1.7580   5.3506   5.1749   1.9549  -8.0605  -6.0836
-#>   -2.4020   8.9237   7.2497 -13.5399  -2.3874  12.1784   4.0796  16.0535
-#>    0.6426  23.0241   2.1876  -4.6716  -2.3914   4.1827   5.9588  12.7554
-#> 
-#> Columns 49 to 54 -14.4496  -8.8349   1.4433   2.1690  -3.7491   0.9587
-#>   -7.7497  -6.5779  -8.7464   9.5712  -5.7366  -5.5686
-#>    4.6983   5.0069  -3.8794  11.1549  -4.5174   4.5328
-#>    1.5492  -0.2539  -1.7878  -1.7838   2.4953  -0.5657
-#>    3.1202   0.9545   3.0343  -1.6115  -8.4593   5.7329
-#>   -4.5750   7.9842   3.8035 -13.5802  -5.4309  -4.1773
-#>   -5.8305   6.6706   5.9399  -4.3707   6.1034  -0.6240
-#>    3.9163  -7.3941  -6.3986  -1.3349  -4.0819  -4.4972
-#>    4.2833   1.5292  -2.7047   0.2604  -0.3884   3.3314
-#>    7.3679  18.8472  -7.1299  -6.6655   1.7560  -7.6865
-#>   -8.4181  -8.1825   2.7599   5.0031  -9.8373  -0.7030
-#>   -1.7533   7.0408   1.4097   4.1171  -2.0185   0.9093
-#>  -15.0229   2.6766  -0.4257  -7.9381  -2.5720   4.4902
-#>    0.2040   1.1599  13.5789  -7.6558  10.2325  -6.6165
-#>    4.6438  -3.2434  10.0951   3.6194  -4.4035  -0.0836
-#>   -9.2463   2.0903  -0.0023  10.8511  -6.4154  -5.2644
-#>    2.7451 -13.9360  -9.0759  -7.8088   2.8181  -5.4235
-#>    3.7136  -6.3668  -1.2021   2.3239   7.6207  -3.6235
-#>   -0.2203   3.0907 -11.3325  -1.1252 -11.7583  -3.8903
-#>    7.2152   0.2197  -1.1622  12.1529  -2.1452   3.1754
-#>   -9.8983   2.5051  -0.5053   1.2530   1.9963  -3.8447
-#>   -6.6937   8.3050   0.6103   1.0726  -4.1585  -3.8931
-#>   -0.9202   5.0524   0.0446   8.4999   8.9261  -1.5784
-#>    4.6479  -1.3398  11.7661  -1.6349   0.1920  -0.1233
-#>    8.7547  -3.9087   3.6918   5.1973   3.0588   2.7503
-#>   16.5450   9.8928   0.2408   0.5691   6.6154   1.0948
-#>    0.1952 -11.5720  13.2281  -6.8009   3.6701   0.9840
-#>    2.0523 -17.0915  -1.5819  -4.7299  -3.5012  -0.7101
-#>    7.6151  -2.8723   3.9770  -5.7623   2.6486  -0.8233
-#>   -3.1527   4.0325   8.9116  -5.7383  -0.0428   7.4691
-#>    7.4466   9.5679  -3.7968   6.6464   2.2457  -3.8793
-#>  -13.5084   7.2839  -5.4541  -4.2447   0.3259   3.5467
-#>    7.9819 -10.1110   6.5147  -0.2204   2.2264  -2.8546
-#> 
-#> (4,.,.) = 
-#>  Columns 1 to 8  -2.1050  12.3998  -0.4774   1.2383   5.8022  -6.9281  -5.4728   6.0727
-#>   -1.8157   0.5752   1.5176   9.4034  10.4397  -3.4623  -8.0909   8.9566
-#>    2.8523  -1.4397  -2.0096   0.4399  -2.9058  13.6328   1.0217  13.0719
-#>   -1.5294   2.2925  -6.5809  -2.0880 -19.8867  14.5897 -13.2773 -11.1911
-#>    6.7109  -7.0110   0.6445  -0.2109   1.6469   9.4859  -5.7427 -10.0133
-#>   -4.8364   5.8289   4.6019   3.3430  -1.8501 -10.7135  21.6716 -15.6815
-#>    7.1862   0.7821  12.1798   8.3415  -3.7003   6.7931  -1.0732  -6.0702
-#>    6.0033  -0.7861  -5.6532   8.0808   9.2785  12.5299  14.3445   3.3772
-#>   -2.2405   4.2441   2.1795   2.5741 -12.6495   2.4456   6.2146  -5.1256
-#>    3.2793  13.7299   3.0425  -3.4000   3.7587  10.1255   7.9639 -18.8988
-#>    0.8084  -8.2614   1.0017  10.9275  10.0781   5.7191  -3.9948   0.1607
-#>   -1.2355  -1.4259  -3.7475   1.8783   0.8166   0.9565  -4.1186   5.8185
-#>    1.4094 -12.0985 -11.9980   7.8775   9.0769  15.9720  -4.7439   2.9385
-#>    1.5571   0.0629  -0.1035  -1.5539  -3.8787 -14.4867  -3.6049 -22.4549
-#>    4.0708  -4.1490   0.1908  -5.8443  -4.5887  -0.4553  -0.2272  -8.7493
-#>   -4.0910  10.3983   0.2122   2.4811 -10.1094   2.2425 -10.9766   5.7353
-#>   -1.1070  -3.0927  -0.9344   0.5191   3.5854   0.6241  -0.4108  -3.0465
-#>   -6.5693   4.7755  -9.9835   3.8776  -1.0840   8.8799 -12.0977  14.3494
-#>    1.4439   5.9479  14.3636  -2.7812  -2.9505  -1.6697  -2.3293 -10.8652
-#>    5.3586   0.2086  -5.2899  -5.4280   9.1076  -3.8572   5.6281   6.1843
-#>    2.3577   3.8243  -3.7754   0.7548  -6.3366  -7.6611   0.5793   1.8813
-#>   -1.0511   7.6017 -20.3287   7.5251  -7.2557   5.9654   8.1025  14.8469
-#>    7.8334   0.2638  -5.3560   5.1233  -0.2849   1.2744  -9.1953  -2.2904
-#>   -3.4615   2.4915  -1.4092  -8.7137   1.1823   4.8117   8.3513  16.4244
-#>   -3.6477   8.8516  -8.0689   0.4341   8.3788   2.0504  -8.3104  11.5212
-#>   -5.6302   0.9044   3.8023  -4.9904  18.6028 -11.3102   7.7670   3.6899
-#>   -0.8651   1.9924   8.2783  -7.4464   4.8625 -15.3295   0.5469  -9.0181
-#>    0.7463  -6.4614   1.3538   3.3951   2.1724  12.7520   7.3761 -11.6228
-#>    2.6962 -12.6195  -3.5614   6.3831  -0.7390   4.9695   7.9886   9.1374
-#>    0.5365   6.8245  -1.8546  -7.4207 -12.1718  12.5915  -8.2897   5.3908
-#>    0.0312   4.4716  -0.3820   3.3666 -15.6750  -0.7308  -5.7290 -10.0158
-#>    0.0913  -9.0728 -10.2081   0.3705   0.5208  -4.9135 -13.3187  -3.3307
-#>   -2.4412  -8.5937   2.6922   0.0637   7.2697  10.2367  -0.7692   7.1194
-#> 
-#> Columns 9 to 16   1.4035   1.4040   4.8057 -16.6953 -10.7570  15.9873  -3.9211   2.1780
-#>   -1.6966  -5.1725  19.3307  -6.3238   6.2951   4.6960  -2.6081  20.1427
-#>   -1.5471  -7.7275   3.1516  -0.9020 -10.0211  -7.9036   1.2798   0.7613
-#>   -0.2585  -4.4660  -6.3808  -6.9989   7.8038   0.3097   3.0570  -5.2005
-#>    1.4197   1.7325 -17.5488  -3.1597  13.1648   2.9228  -3.4837 -14.5672
-#>   15.8725  23.6275  -5.4269 -23.7333 -21.2530   0.0843   1.8968  14.8612
-#>   13.6775  10.2984 -13.2271 -13.6772   5.7547   2.0044  -3.7326 -19.6737
-#>  -16.4962   6.2314   5.0742  -0.6590   0.0642   0.3928 -11.1059  -7.1018
-#>   11.2382   0.8385  -1.0045  -4.1813 -10.6938   4.4621  14.5863 -18.2117
-#>    1.6609  -5.2850   5.2872   0.7011  -0.5309 -15.8560   4.4899 -23.4390
-#>  -14.5825   4.0461  11.6341 -17.4078  -7.0555  -5.2670   0.8999  -5.6901
-#>    2.8568   6.2582  10.6664 -14.0320   4.2543   4.6557  -4.4138   2.9445
-#>   -5.7549  11.1731  -8.0999 -14.7524   7.7781  -2.1008 -18.4997   2.9257
-#>    2.8536   8.2467  13.3471   0.4846   5.7097   1.9348  -6.6551  -8.1418
-#>   -3.3299  -4.0816  13.1217  16.2571  -1.7483   7.3860  14.2170 -11.9363
-#>   11.0684   5.0762   5.3195   0.1432  12.0400  -3.7210   2.7910  -1.7928
-#>   19.4745  -2.3785   8.6529  -4.5426  -2.4921   7.6448   0.1893  -0.8252
-#>   -1.9241  -9.2395   5.6588  -7.6330  -0.7623  -0.6414   9.5515   8.5972
-#>    9.1962  -6.1070   6.8629 -12.9698  -1.6276  24.5720  16.0840 -17.1156
-#>  -19.7998   6.6669  12.4325   8.5895   4.8028  -7.0879 -12.0306   1.7905
-#>   -9.7381 -10.4174  12.7223  17.6191  16.4640   1.4797  -2.2388   4.2665
-#>   -3.5749   1.9684   4.4351   0.8422   3.2918 -15.7919 -17.1336  -9.5118
-#>   -5.4865  18.5721  13.7783   1.7104   9.1968   8.7916   5.1292  -8.2372
-#>  -10.1325  19.9818   2.2621  -2.6759   1.3810  10.1955   1.6041   0.8605
-#>   -8.0232   8.5082  -8.4552  -5.3418   2.3370  13.6287   2.0805   8.9783
-#>   11.1102  -8.4891   2.2244   2.1921  -7.0178 -18.9312 -15.8038   1.8319
-#>    6.3255  -1.7980 -12.3073   0.5514   2.2092   2.9887  -5.8563   2.6762
-#>  -15.6557  -3.5616   2.8305  -8.2303 -19.6977  11.7540   1.1495   1.8021
-#>   -2.1927  17.4035  -0.7762   0.3718  -5.3043  -6.2854  -6.9760 -15.9494
-#>   15.0398 -13.4662 -24.0948   1.7141  14.0322  16.5744   0.9768  -0.3527
-#>  -14.3824   5.7630  15.4500  -0.7237   2.4711   6.4872   2.1575  -3.1848
-#>   10.7607  -2.2199   4.4435   4.3157   6.8573   2.1049 -20.8255  23.3302
-#>   -2.3852  14.4152   1.5751  -5.2666  -9.6740  -7.5906   0.5588  12.2082
-#> 
-#> Columns 17 to 24  -0.3194 -14.9899 -11.1432   7.6206  -2.6515  20.3641   6.9513   1.0261
-#>    8.1171  -7.2204 -20.3917  -8.9583  -0.4736   9.6779  12.4881  -6.0153
-#>    7.8626  -5.3077  -1.0013  -4.2854  -3.4871  12.5138  -4.1042  -4.1187
-#>   -0.2872 -14.5971 -15.9643   6.7193  11.3281  16.3837  -6.2956  -8.5059
-#>    7.8821  -2.9166  11.2147 -13.4587  -1.7634   8.1924  -8.6742  10.9291
-#>   -5.3366   3.4671   7.0700  10.0996  -3.0505   5.1970  -6.2224  -0.0521
-#>    3.1443  13.5610   8.5994 -11.0686   4.1030   1.0456 -10.7196  -4.6311
-#>   -5.1903   1.7303 -10.9142  -3.1094   1.8742 -16.7151  -0.4450  14.1182
-#>   -5.8055  -7.5304   3.0536  -5.1155  -7.8851  18.8317  -2.9984  -7.9829
-#>   -9.4636   0.1752   1.8338  -3.9810 -18.1357   7.3099   3.1274 -19.7982
-#>   -1.5617  16.0187  -1.5374 -20.1662  -5.5443   7.1038  -4.8856   3.5684
-#>    4.4075 -20.2555  15.7781   2.6051  16.9903  -1.7933  -4.4984  -6.9582
-#>   -0.3479  13.9096 -15.6244 -11.1793  -3.5875 -15.4238  -7.4633   8.6940
-#>  -12.2850  -4.8526   8.2597  -7.7774  10.2423  10.6756  15.3577   3.9057
-#>   -9.5595 -12.0798   5.7455  13.8555   2.1398  11.5047   2.4477  -7.0594
-#>    6.9720  -3.8493  11.8396  11.0641  -2.8332  -0.1463  -7.4761  -6.3588
-#>    5.5957   1.3788  12.8145 -16.4845  -2.3937  -4.1697   2.6650   9.6846
-#>   16.3475  -1.5722  -3.2263  -1.4248   2.0256  -1.4739   4.0604 -10.2989
-#>   -0.6586   3.4046   4.6824   0.4668  -9.5215  -0.6462  -1.9205   0.3976
-#>  -16.8921   9.5151   0.8390  14.2056  -3.7218   9.2721  -5.7017  -8.5205
-#>  -14.3077   9.3742   3.4875   9.6195  -6.0802  -3.7222  10.8635   5.9279
-#>   -2.1217  -0.1907   4.4814   6.3114   4.5167  -3.5535   2.8435   9.9919
-#>  -13.9939  15.1288   4.2053  -9.5740   1.5628   3.6481  -1.3641  -3.7322
-#>    8.7557   8.7261  25.1453   0.8178   4.6255  -6.7057  -8.6511  -0.1091
-#>    7.4447  -1.8325   3.1293   7.0337  -2.7863   1.6286 -16.0444  -3.8087
-#>   -2.8873   2.8116   0.0587   3.4188  -9.5266 -14.8540   4.2804  -8.2651
-#>   -9.8895  -6.4253 -10.2173   5.8122  14.4629  -1.7608 -11.0493   3.2803
-#>   -1.0160 -13.9493  -9.9600 -15.3795  -3.2849   5.6923  -7.4746  12.0561
-#>    2.1805   4.5089  -2.3862  -4.1503  10.5217 -16.1267  -6.3030   6.9384
-#>   -1.3073   7.8117   7.1677   2.6476  -8.0955   8.7233   2.8246   4.7810
-#>  -18.4368  -2.3778   0.1608  13.3684   6.7485   6.4731   6.1167  -1.2516
-#>   -0.6043  24.9120  -1.4423  -6.7582   8.9642  -8.6571 -14.0482  16.8610
-#>   17.7286  20.2208  18.4647  -7.7855   1.3575  -9.3874  -0.5325 -11.0693
-#> 
-#> Columns 25 to 32   5.6326   5.8350 -13.5859   2.6041  -4.8413  -0.6250  -6.9518  -5.0680
-#>  -12.4608  -1.0832  -3.9246  -6.0014 -14.6060   9.3799  -5.2067   9.0054
-#>   -9.7539   1.2320  -1.4374  -8.5207 -10.2753  -2.1604  -1.0834  -9.2973
-#>   -2.7700  -3.0238  -6.5836  22.4792  -6.0836  -3.0262   9.0822 -13.0279
-#>   -3.6857  -8.2444  -9.6928  -5.6210  -3.9370   3.0351   7.6838 -11.3569
-#>   -0.2301   6.7172  -1.5944  -1.4519   0.7078 -22.6055  -0.0719  -9.2618
-#>   22.1931 -19.8122  -7.2434  -1.8270  -7.0690 -20.1464 -11.8704   2.4956
-#>    8.5558 -13.4251   9.5341  -4.0036  -0.1540   8.6128   7.1111   7.8143
-#>    7.0072   4.0444 -14.5680 -15.6934  16.3534  -8.4037   2.4889 -12.2590
-#>    4.0391  13.6764 -10.0684   6.1171  -5.3206 -15.5514   5.7784  -2.1322
-#>   -0.0031  -7.7351  -9.6109   6.7777  10.3609  -5.9732  -5.8301   0.0472
-#>   -7.5225  -8.9016  -3.5149 -12.1527 -13.5420 -10.9205   1.6544 -16.7605
-#>    0.7409  -9.4100   4.2403   6.1743  13.0698   4.1074  -2.2450  11.6677
-#>    7.2575   5.4975   1.5036   8.9499  -6.6605   1.1732  10.0563  -2.5328
-#>  -13.7980   2.2803 -11.4877  -8.7844 -11.2398 -18.5173  21.1523 -24.5849
-#>  -13.6781   4.3191  -1.2661   3.1900  -9.3190  -2.6247   2.3197  -7.3712
-#>   14.6047  -0.4196   6.9357  15.3953   5.5046  -5.5453   0.5608  -0.1292
-#>    7.9832   2.5611  -7.4931   8.8954 -12.2209   9.0451 -13.8076  15.4440
-#>   16.5112   0.9398   2.9589  -0.9302  10.1620  -6.0823  -2.3119  11.8207
-#>   -2.4773  11.4658   4.3734 -14.3540  -4.6328   6.7320  10.7169  -2.2529
-#>    5.1290   7.3429  -6.4964  -1.1527   9.2697  11.9739  -7.9363   7.1066
-#>   -6.5489  -4.9425  -4.4167  11.7435  -4.6573   3.6331  -4.8614  -0.2404
-#>    4.7502 -13.9168   8.9244   1.8197  -3.4975   2.8928   5.0875 -16.2945
-#>    8.9716 -22.2495  -7.6811 -13.3519  -6.7208  -3.6837  -6.2659 -10.6813
-#>   10.0090  -2.3997  -3.9190  11.7128 -11.4862   2.7779  10.6134 -17.2960
-#>    5.2759  10.7843   6.3188  -9.5183   3.7632 -13.0112   7.6870  -9.9708
-#>  -17.1446   0.4763   1.7853   8.8129   0.8247 -12.7194  13.7973 -10.3702
-#>   -2.9073   7.9717  -1.6721   4.9187  17.0543  27.8806  -3.1481   3.5462
-#>    0.6223  -5.8077  21.9198  14.0404   1.6793  11.8098  -2.5856   5.5326
-#>   -1.5546   8.4663 -19.7711  -3.5745  -9.0930   2.5057  -8.8058   1.5028
-#>    2.9323   2.4301   0.4083   1.3978   2.1401  -3.1432  -9.2657  10.6113
-#>   -8.3944   2.1382   3.2571   5.1617   4.1159  11.8319  -2.4654   3.5631
-#>   -9.5940  -3.8198  -0.0708   0.2324  -0.3559   5.0562  -9.3026  -0.0156
-#> 
-#> Columns 33 to 40   5.4751  -4.9685  -1.1140  12.0255  -2.7069   0.1429  -3.9142  -1.8313
-#>  -21.6627   7.3637  -1.2541 -10.8029  -6.5101   4.8416  -2.5360   5.8329
-#>    0.8076  18.1017  -4.4381  -0.4216  -1.6496   0.0826   8.9062  -9.5129
-#>   11.0231  -3.6290  -7.2440   1.0281   5.9202  -2.9915  -0.1684  -6.0654
-#>   -1.8620   4.0979   3.5261  -2.2876  -3.8825  -4.7818  30.0266  -5.1267
-#>    9.2627  -9.3801  -6.3897   4.1681   3.5414  -1.2313   2.2550  -7.4358
-#>    3.8479   0.4866  21.6011  11.6342  -7.4530   9.2773   4.7980   4.0954
-#>  -12.4142   0.9372  -1.0704  -8.8271 -10.6478   7.2930   3.4420  -7.5093
-#>    0.1698  -2.8374  -9.1786   3.2934   9.4190   9.5889 -12.3461  -6.6323
-#>    6.0238 -16.7210  -2.1618   6.7680   9.0894 -11.5117   9.2601   4.0035
-#>    7.0340  -5.0686  -6.7381   6.7828   3.2271  -1.7524   6.9165  -1.2493
-#>   -2.5774  -9.4541   5.8497 -10.5635   7.0962   4.1460  -3.2895  -4.5333
-#>   -0.0374  -5.8546   0.1963  -1.3632 -10.0908   9.7387  -1.9190  -8.9648
-#>    2.5153  -6.6226   7.6842  -8.7523  -0.5876  -4.2538   0.6357   7.1436
-#>   15.0600   8.3653  -5.3016  -8.9727   1.9099  -4.7034 -14.8080   6.8321
-#>    5.2180   1.6998   9.6705  -1.0297  -0.2300 -18.0583  -5.9676   2.8278
-#>   -6.6438  -3.8793  10.7047   1.1690 -10.4911  -4.8902  -8.9453   2.6225
-#>    2.6865  -7.0715  17.3035  -1.0292   8.6419   1.5127  11.3743   4.6567
-#>   -0.8823   1.9922  -1.6805   2.6441   7.0532  -3.5863   1.5426  -9.0900
-#>    3.4192  22.3566 -13.5973  -0.9803  -0.6945 -13.5652  -6.6271  -8.6951
-#>   -4.7106  -1.8860   4.7969  -3.3994  -0.0136  -1.0474   7.1799 -12.5076
-#>   -3.5109  -6.8928 -10.2952 -27.2561  -4.6149   1.7142 -12.5523  -1.0221
-#>   -6.7093  10.3142   8.2573   2.4934  -1.7516 -11.4688  -2.4966 -15.7521
-#>    7.1038  -8.7811   1.0369  -5.1111  10.7257  -0.7682  -2.9512  -2.0453
-#>  -11.9724  15.1011  -3.2683   5.3593  10.3720  -2.2955   6.3947   4.5287
-#>   -6.6439   6.2459  -5.5014  -5.6457   0.1835  12.0601   2.1755  11.0305
-#>    1.2823  -1.0151   0.4840   9.5057   6.6848  -7.5415   3.5770   1.3109
-#>    7.9927  -9.9826   1.0853 -13.3779  -7.0886   3.5114  -5.8115  -1.8867
-#>   -1.5721  -1.4694   5.1739  -5.5387  13.7887   3.5508  14.0987   8.0543
-#>   11.2043   6.1202   1.7812  17.8911   9.0120   2.1963  15.4762  12.7197
-#>    8.6884 -13.1554  -4.0781   6.2083  10.8189  -9.2288  -7.2779   3.9982
-#>   -8.0184  16.5070   5.8675  -2.3072 -16.1820  18.5364  -5.8738 -16.4123
-#>    3.2305  -0.1945   8.0007  15.5950   6.0886   3.3087  -5.3623   0.7048
-#> 
-#> Columns 41 to 48  -8.6006  -4.8646  27.5523  -1.5430 -15.4093   7.7829   7.3981  -0.3793
-#>  -15.8642  -0.2972  -3.1149  -0.9991  -3.9445 -12.1184 -10.9670   6.3001
-#>   -0.7089 -11.8555   2.6656   0.5943  16.2644  -5.9812 -12.9371  -1.6014
-#>  -12.7581  -0.4289   6.1141  -8.4463  -2.0868  -6.8926  -2.6126  -9.0016
-#>   -8.2506  21.2704  -4.0491  -2.2546  12.1547   0.1667  -8.4576  10.7930
-#>   16.0594   4.1562  10.0196  -7.5173  -0.3256  16.0685  -0.1030 -16.9110
-#>   -0.6021  12.6787   2.3527  -8.8386  -7.3754  13.4047  -8.9777  -5.7262
-#>   -2.1249 -14.3050   5.0873   6.6320  -6.5322 -16.8134  -7.4323  -3.4885
-#>   12.5125 -10.3117  18.7218   4.0142   2.5446   8.3031   0.4404   3.8229
-#>   -0.8576   1.2573   0.1647  -5.6551  25.1561   2.0695 -17.7567 -22.7935
-#>   17.9452 -11.9911   9.8603   7.5845  -2.0050   6.4060  -4.7783   0.6968
-#>    3.0729  -2.5976  17.7593  -0.2200 -14.6376  11.7562  12.4318 -22.8368
-#>   -3.3752  -8.7275  22.4086  10.6214  -6.4601  17.0018   1.2447  -9.9543
-#>  -17.1848   7.0295 -20.1981  15.9027 -13.7001  14.6122 -12.5801   8.7190
-#>    7.3749  -8.2294 -10.3922   5.2209  10.2326  -1.1034  -2.3768   5.0779
-#>    6.5220   6.8331  -5.5146 -26.3487  10.0968   7.4076   4.2510  -0.1241
-#>  -16.3551  23.9117 -12.9491  -6.2878  -8.0376  -8.9809 -11.6969 -16.2643
-#>   -3.3320  -7.2563   7.0096  -5.1591 -10.8903  -8.3051  11.5121  -4.8205
-#>   30.1463 -27.2953 -10.3255   1.2407  -3.8466 -11.4208  10.3090 -21.3064
-#>   16.6340 -28.7060   7.5365  -0.2053  -9.2341  -6.5734 -10.7766  -2.7444
-#>   11.6982  -2.6341 -11.3039  -7.5007   5.1457   7.6688  26.6237 -15.1442
-#>   -9.7540  -8.5738 -14.3518  -5.7013  13.9404  -3.8985  -0.1138   8.8463
-#>  -10.4308  11.0512  -5.5217 -22.5346   1.6473  -5.2179  -9.4511  -7.9082
-#>   -2.4912   0.3161 -11.0292   6.5892   0.1307  -1.6834  -1.0987   1.6923
-#>   -3.9673  -7.9401  22.3718  -2.3691  11.7177 -11.3505   6.1523   3.1161
-#>    2.6399  13.7468  -0.3763  18.8045   8.5708   4.0262  -8.7736   4.2290
-#>   -2.8576   7.1759  -8.0101  10.8422   6.3865   5.0936   1.2168   9.2354
-#>  -10.8633  -1.0824  -0.8987  11.4758 -11.9743 -10.4646  13.7240  -1.7358
-#>  -14.1178  21.2416   3.7955  24.8035   6.0983   0.3335   3.4862   4.4295
-#>   11.0182   5.3442  -3.7926 -20.3325  23.6837  -3.2902  -8.2127  -9.5710
-#>    1.1583   2.6508 -16.1883   2.1579 -12.3988  16.1296   1.6853   2.6362
-#>   -4.6801  -1.2721   3.6902   8.4217  20.9060 -13.4412  18.0424  -1.5549
-#>   -8.8554  17.1304  -6.1151   8.3124 -10.3070  -2.1585 -10.7280   1.6565
-#> 
-#> Columns 49 to 54  -6.7632   9.0205   3.9832   1.1877   3.4006   5.9775
-#>   -8.4520  -5.9237   2.4574   0.5103  -1.2747  -0.2187
-#>   -6.3469   0.8673  -5.0039  -2.8374  -1.5800   0.5130
-#>  -15.8223 -10.4915  -2.9986  -0.9827  -1.1368  -2.5114
-#>  -10.8001   7.0442   9.2466   8.6179   2.7842   0.3649
-#>   20.2535  11.2160   3.6390  13.7110  13.4839   8.3034
-#>    4.1736  -9.9043  -5.7932  -7.1145   1.6513  -1.3660
-#>   -2.9477  -6.7830   2.2032   4.6185   4.4635   2.1922
-#>  -19.0045   9.1668  -3.2519   5.0947  -1.5906   1.6095
-#>   -5.0198  -7.6808 -18.1146   5.1944   4.6247   2.0399
-#>    8.0772  10.3144  -3.3351   4.8508  -1.3147   1.5389
-#>    6.1753  -1.1323   7.2598   9.2451  -4.0144   4.3835
-#>    2.9787   0.5584   7.9027  -0.5738  -0.4446   3.5570
-#>   -2.4050 -19.9975  -0.9384  -1.0172   1.1957  -3.5614
-#>   16.0283  11.9335   1.4864  13.4882   0.2626   2.1828
-#>    6.7011   1.7985  -3.5908   3.4675   0.3625   1.4105
-#>   14.4403   6.5076   8.2722  12.7859   4.8887   5.4573
-#>   -4.6987  -9.9025  -0.4740  -6.8387  -0.7232  -0.5840
-#>   -0.4707   3.2551  -7.0753   1.6114  -1.6163  -0.1045
-#>   -3.9348   7.0254  -7.9377  -5.2363   0.3976  -1.0753
-#>   -5.7204   6.0980   1.9556  -4.8694   0.6084  -1.2481
-#>   -8.0397   4.9181   1.6892  -0.0915   0.5730   0.9259
-#>    5.1109  -1.9765   3.3636   4.5646   6.2376   3.4933
-#>    6.6943 -21.3563  -4.1890  -4.2812  -1.6969  -2.0259
-#>   15.2933 -11.0076  -7.0053  -1.5960  -3.4357  -2.7896
-#>   -1.0028 -14.8088  -0.0199  -5.7920  -1.0041  -0.3028
-#>   11.7314  -0.1637  -1.2221  -3.7700  -1.0029  -3.3862
-#>   -2.0893   6.2374  18.0681  -0.3367  -1.2742  -0.1907
-#>   -0.0101  -6.8336  -1.6090  -7.7200  -3.4319  -1.2397
-#>    5.7481  -7.0355   4.3746   0.9063  -8.7526  -4.9835
-#>    7.4789  -5.0154   6.5255   6.2043   1.7799   4.7899
-#>   -6.0105   7.1342  -0.0061  -2.5677  -8.1663   1.6100
-#>   -0.1814 -12.1102  -1.1473  -2.8114   7.7898  -4.2462
-#> 
-#> (5,.,.) = 
-#>  Columns 1 to 6 -6.4262e+00 -1.0302e+01 -8.0426e+00 -3.3373e-01  1.1466e+01 -1.5697e+01
-#>  -1.1519e+00  2.0001e+00 -4.2894e+00 -7.6826e-01 -1.1227e+01  2.5261e+00
-#>   1.6970e+00 -3.3100e+00 -4.4895e+00  1.3259e+00  1.8962e+01  8.1508e+00
-#>   2.2075e+00  2.1765e+00  1.4384e+00  1.5917e+01 -1.1309e+01  7.0364e+00
-#>   3.8093e+00 -3.8368e+00 -1.6455e+01  1.2298e+00 -1.0367e+01 -1.5784e+01
-#>  -9.8932e-01 -5.0788e+00 -1.5136e+01 -9.8048e+00 -6.8520e+00  1.4605e+01
-#>  -4.7910e+00 -5.1260e+00  1.6453e+00 -5.3460e+00  8.2288e+00  2.0704e+00
-#>   6.3422e+00  9.8608e+00  5.8286e+00 -5.4466e+00  5.3061e+00 -8.3891e+00
-#>  -4.0661e+00 -2.3066e+00  2.0202e+00 -1.3354e+00  8.2298e+00 -7.6327e-01
-#>  -1.5477e+00  7.6396e-01  4.5311e+00  3.5211e+00 -1.4649e+01  7.8986e+00
-#>   2.5916e+00  2.3080e+00  6.7754e+00 -1.1042e+01 -1.3366e+01 -3.8321e+00
-#>   2.5285e+00  4.0808e+00  8.8407e+00 -6.2941e+00  1.3328e-02  3.6616e+00
-#>   4.6916e+00 -5.0228e+00  7.1309e+00 -4.2475e+00  1.1867e+01 -8.2675e+00
-#>   8.0642e+00  7.6726e+00  5.1226e-01  4.8608e+00  2.9767e+00  2.1985e+00
-#>  -4.2219e+00  6.4403e+00  1.7694e+00  4.1442e+00  7.6349e-01 -1.0233e+01
-#>  -9.3862e+00 -3.3176e+00 -1.1481e+01  2.5465e+00 -3.9686e+00  1.6527e+00
-#>   8.8190e-01 -3.4220e+00  1.1836e+00 -2.9330e+00  1.0608e+01  1.6665e+01
-#>  -1.4457e+01 -2.0386e+00 -7.8769e+00  9.1628e+00 -1.2645e+01  8.8112e+00
-#>   3.3646e+00  2.8787e-01 -2.8648e+00  9.7906e+00 -4.8234e-01  2.3953e+01
-#>   5.5200e+00  8.0760e+00  8.1474e+00  1.6808e+01  3.8783e+00 -2.1914e+00
-#>  -2.4432e+00 -6.1003e+00 -3.4941e+00  9.1001e-01 -1.7641e+01 -9.1526e+00
-#>   7.2679e+00  2.8391e+01 -5.7809e-01  6.8612e+00  1.4567e+01  3.5080e+00
-#>   6.7239e+00  6.1337e+00  1.0413e+01  1.2382e+00  4.5004e+00  2.7951e+00
-#>  -2.6557e+00 -6.5517e+00  2.6339e+00 -3.3769e+00  4.9165e+00  1.9107e+00
-#>   4.2102e+00 -3.3543e+00 -1.2687e+01  2.5395e+00  2.5638e+00  1.9196e+01
-#>  -3.8726e+00  2.0134e-01  4.1404e+00 -6.0846e+00  3.6685e+00 -6.0531e+00
-#>   2.7909e-01  1.0917e+00 -7.4160e-01 -2.7103e-01  8.1912e-05 -3.0494e+00
-#>   7.3136e+00  6.4701e+00  1.0309e+01  7.2169e+00  2.8521e+00 -2.0348e+00
-#>  -2.8647e+00 -3.3199e+00 -1.1787e+01 -2.0081e+00 -4.9858e+00 -7.1792e+00
-#>  -9.1410e+00 -1.2388e+01  3.4625e+00  1.7626e+01 -1.7789e+01  2.6632e+00
-#>   5.0764e+00  6.8630e+00  8.2448e+00 -1.5367e+01 -6.3745e+00 -8.8637e+00
-#>   3.8776e+00 -6.5466e+00 -1.8389e+01 -6.6696e+00  5.8418e+00 -6.7904e+00
-#>  -3.2302e+00 -8.0956e+00 -5.7334e+00  1.4126e+00 -2.7921e+00 -3.9389e+00
-#> 
-#> Columns 7 to 12 -4.0465e+00  8.8923e-01 -1.4303e+01 -9.4046e+00  6.4015e+00  1.5997e+01
-#>  -2.9243e+00  1.7137e+00  4.3121e+00 -7.3565e+00 -1.0214e+01 -1.2201e+01
-#>   7.1019e+00  5.0987e+00  1.9010e+00 -2.4699e+00 -3.0563e+00  2.9805e+00
-#>  -8.1475e-01  8.9229e-01  1.4172e+00 -3.4651e+00  5.2722e+00  1.2982e+00
-#>  -5.4192e+00 -9.6536e+00  6.7028e+00 -2.9846e+00  3.8598e+00 -8.2039e+00
-#>   1.7103e+01  4.1835e+00 -1.9952e+00  5.5272e+00 -1.5506e+00 -9.4011e+00
-#>   2.2505e+00 -1.9357e+01 -2.2002e+00  2.4192e+00  1.0321e+01  1.7182e+01
-#>  -1.0851e+01  9.3675e+00 -1.4618e+00 -1.9284e+00 -3.1685e+00  9.2756e+00
-#>   1.1996e+01 -8.0681e+00  3.1487e+00  4.6738e+00  7.7214e-01  9.2540e+00
-#>  -2.0899e+01 -2.4664e+00 -1.0292e+01 -2.3162e+00  4.3699e-01  3.0183e+00
-#>  -7.5234e+00  4.0622e+00  2.8611e+00 -5.0612e-02 -2.7615e+00  1.0727e+01
-#>  -6.7269e+00  1.7070e+01  1.3479e+00  2.8591e+00  1.3784e+00  1.5057e+00
-#>  -2.5754e+00  1.4918e+00 -1.6116e+00  9.1770e+00 -2.8859e+00  6.1093e+00
-#>   4.5459e+00 -6.6762e+00  1.0926e+01 -8.5546e+00  5.9326e-01 -4.5555e+00
-#>   2.1496e+00 -6.6026e+00  2.0679e+00 -2.2635e-01  1.2354e+01  8.0611e+00
-#>  -1.0398e+00  2.4987e+00 -6.6752e+00  1.6018e-02 -5.0908e+00 -7.8365e-01
-#>   2.1959e+00  6.0708e+00  4.3137e+00  2.1468e+00 -6.7604e+00 -2.5864e+00
-#>  -7.0599e+00  5.5481e+00 -4.1306e+00 -1.1196e+01  7.3052e-02  2.0858e+00
-#>  -1.9106e+01 -4.3791e+00 -1.5648e+01 -3.9139e+00  4.2754e+00  1.4006e+01
-#>  -1.2236e-01  1.2929e+01  1.3368e+01 -7.0120e-01  8.7745e+00 -6.1214e+00
-#>  -1.8933e+01 -2.1145e-01 -5.3975e+00 -3.9796e+00 -9.0409e+00 -8.7610e+00
-#>   7.7373e+00 -2.1278e+00  3.6165e+00  7.6281e+00  4.9305e+00  7.7253e+00
-#>   8.2651e+00  1.7974e+01  1.0775e+01 -1.3353e+00  8.9461e+00 -3.0251e+00
-#>   4.0353e+00 -5.0770e+00  7.8504e-01  8.8365e+00 -3.8695e+00  1.3443e+01
-#>  -7.6197e+00  1.1457e+01 -8.3000e+00 -1.5143e+01 -4.2876e+00 -8.1806e-01
-#>   3.5782e+00 -2.8537e+00  9.9314e+00  2.0893e+00 -3.4795e+00 -1.2507e+01
-#>   6.0520e+00 -1.2071e+01  9.9488e-01  2.2217e+00  5.7808e+00 -1.6132e+01
-#>   1.4790e+01  1.4483e+00 -1.9808e+00 -3.8774e-01 -8.9233e+00  4.7249e+00
-#>   1.2036e+01  2.3744e+00  3.1593e+00  1.5919e+00  5.9775e+00  2.4469e+00
-#>  -1.0775e+01 -9.3839e+00 -1.6414e+01  5.2102e+00  1.5185e+01  5.9463e+00
-#>  -3.5521e+00 -7.9817e+00 -2.9207e+00  5.7468e+00 -1.1332e+01  8.4551e+00
-#>  -1.0195e+01  1.2005e+01 -3.6723e+00  1.1780e+01 -1.6271e+00  7.6696e+00
-#>   2.1713e+01  5.2783e+00  9.3111e+00  8.0497e+00 -1.6087e+00  4.5775e+00
-#> 
-#> Columns 13 to 18 -1.8652e+00 -1.6296e+01  1.8492e+01 -8.9194e+00 -2.5883e+01  1.1869e+00
-#>  -6.7993e+00  5.6378e+00  1.4581e+01 -4.0838e+00  6.1944e+00 -2.9574e+00
-#>   2.2431e+01 -2.6491e+00 -1.2315e+01 -4.1996e+00 -2.0293e+00  9.9904e+00
-#>  -1.8455e+00  3.2875e-01 -6.2301e+00  1.0648e+01 -3.1354e+00  4.8623e+00
-#>  -2.4562e+00 -4.0854e+00 -1.4479e+01 -4.5397e+00 -1.3717e+01 -3.2325e-01
-#>   1.6040e+01  9.0584e+00 -8.3440e+00  3.6967e+00  9.8770e+00  2.2980e+00
-#>  -1.2911e+01  1.2589e+01  6.9104e+00  3.5571e+00 -5.1544e+00 -7.4146e+00
-#>   6.1413e+00 -1.8190e-01 -3.3456e-01 -2.6882e+01 -1.4690e+01 -1.1079e+01
-#>   1.1708e+01 -9.3690e+00 -1.1440e+01  9.3169e+00  7.7772e+00 -1.8925e+01
-#>   1.5197e+01  1.3778e+01 -5.0570e+00 -9.0739e-01 -6.1582e+00 -2.7271e+00
-#>  -2.1772e+00 -1.6658e+01 -6.2780e-01  5.7757e+00 -1.0697e+01  2.5060e+00
-#>  -1.1280e+01  1.2305e+01  5.9221e+00 -3.2115e+00 -7.5718e+00  8.3008e+00
-#>  -1.5762e+01 -2.2615e+00  1.2355e+01 -1.1957e+01 -9.3039e+00  6.8012e+00
-#>  -8.7712e+00  8.6509e+00  7.4090e-01  9.2350e+00  1.9766e+00 -1.6338e+01
-#>   1.4290e+01 -3.2918e+00 -1.5809e+01  1.0007e+00  6.8876e+00 -3.0997e+00
-#>   1.4291e+01  1.4412e+01 -8.0414e+00  9.9127e-02 -4.8554e+00  3.1393e+00
-#>  -9.8472e+00  9.9958e-01  2.5397e-01 -4.2138e+00 -1.1304e+00 -1.6261e-01
-#>  -1.1017e+01 -4.3670e+00  1.7037e+01  8.7364e+00  1.3894e-01  7.6550e+00
-#>   3.4132e+00 -3.2857e+01  2.2227e+00  5.0719e+00  2.8524e+00 -1.2048e+01
-#>   1.4289e+01  4.6767e+00 -1.4386e+01 -8.2073e+00 -1.0569e+01 -1.3552e+01
-#>   3.5160e+00  1.1464e+01  7.5946e+00 -5.3213e+00 -1.9107e+01 -1.2373e+01
-#>   3.4990e+00 -7.3915e+00 -1.1307e+01 -6.4806e+00 -5.4687e+00 -2.5775e+00
-#>  -2.8884e+00  2.8935e+01 -6.5814e+00  1.8260e+00 -8.2682e+00 -1.2801e+01
-#>   1.1532e+00  3.3882e+00 -1.7850e+01  2.8981e+00  1.5333e+01 -9.5075e-01
-#>   5.5637e+00  1.7700e+01  7.4352e+00  8.8628e+00  7.4742e+00  2.0415e+01
-#>   2.9779e+00  2.2275e+01 -1.2943e+00  6.2881e+00  2.5515e+01 -1.5408e+01
-#>  -1.9378e+00  5.2831e+00  2.9522e+00  1.0344e+01  4.0745e+00 -1.2546e+01
-#>   1.8626e+00 -2.3987e+01  4.6421e-01 -5.2962e+00 -1.8111e+00  4.6379e+00
-#>  -8.5860e+00  9.5225e+00  9.0563e+00 -7.0315e+00  4.4661e+00  8.3436e+00
-#>   9.7812e+00  5.3378e+00  1.3720e+01  1.2092e+01  1.2569e+01  6.4263e+00
-#>   6.8753e+00 -2.3651e-01 -7.5481e+00  5.3557e-01  2.3002e+00 -2.2790e+01
-#>   2.4410e-01 -1.2898e+01  1.6192e+01 -1.8877e+00  3.8370e+00  6.8946e+00
-#>  -5.2318e-01 -5.6432e+00 -6.6090e+00  4.1998e-01  5.3408e+00  1.3252e+01
-#> 
-#> Columns 19 to 24 -1.4445e+01 -7.1449e+00 -1.5830e+01 -8.5493e+00 -1.3246e+01 -1.0818e+01
-#>  -5.8257e+00  2.5380e+00  4.0747e+00  3.3717e+00  1.2595e+01 -3.3998e+00
-#>  -7.3894e+00 -4.2604e+00 -1.2218e+01 -4.3442e+00  9.4085e+00 -2.5070e+00
-#>  -4.1200e+00 -3.6416e+00  9.7555e+00  2.0301e+01  4.9697e+00 -1.1485e+01
-#>  -3.8639e+00  3.4220e+00 -1.6872e+01  3.9867e+00  2.3141e+01  1.2290e+01
-#>   1.7509e+01  1.1767e+01 -8.2266e-01 -3.9060e+00 -1.0922e+01 -1.0966e+01
-#>   7.7470e+00 -1.4266e+01  3.9405e+00 -6.0676e+00  1.2489e+00  8.5741e+00
-#>  -1.4520e+00 -7.8036e+00 -8.0052e+00  4.7120e+00  1.9998e+00  9.7785e+00
-#>   7.1853e+00 -5.4099e+00 -1.1661e+00 -9.6124e-01 -3.5060e+00  2.8660e+00
-#>  -4.3456e+00  1.4829e+01  1.3369e+01 -6.0624e+00 -6.6249e+00 -4.8913e+00
-#>   6.3133e+00 -5.3194e-01 -1.5952e+00 -2.1070e+00  2.6445e+00  5.3696e+00
-#>  -1.2118e+01  5.5716e+00  4.6971e-01 -4.8059e+00 -5.0967e+00 -9.4180e+00
-#>   1.5625e+01 -2.5577e+00 -1.1879e+01 -4.4374e-01 -8.2021e+00  2.3238e+00
-#>  -4.9951e+00  1.4574e+01  1.0015e+01  6.7822e+00  1.8982e+00 -9.4548e+00
-#>  -1.3176e+01 -7.8126e+00 -1.8460e+00 -1.2364e+00  8.4664e-01 -1.9703e+01
-#>   1.4479e+00  1.4478e+01  5.8062e+00  9.4571e-01  8.8584e+00 -5.1504e+00
-#>   1.1455e+00 -6.0675e+00  9.1297e+00 -1.1173e+01 -4.6109e+00  5.5358e-01
-#>  -3.5382e+00  9.8266e+00  1.4570e+01  7.3645e+00 -3.1711e+00  8.0725e+00
-#>  -1.3910e+01 -1.2044e+01 -1.6904e+01 -6.0675e+00 -4.3612e+00 -8.3484e+00
-#>  -4.3631e+00  5.4232e+00  3.4341e+00  2.0606e+00 -5.7591e+00 -3.5410e+00
-#>  -8.8304e+00  2.7338e+00  5.3586e+00 -4.3458e+00  8.0168e+00  4.1562e+00
-#>   3.4144e+00  1.1629e+01 -4.1753e+00 -2.2655e+00  2.4787e-01 -1.4080e+01
-#>   1.0627e+01  7.8785e+00  1.1896e+01 -7.2031e+00 -4.6135e+00 -2.0067e+00
-#>   1.1385e+01  1.1738e+01 -1.7749e+01  1.7336e+01  8.0784e+00  7.6059e+00
-#>   1.2071e+01  9.1543e+00 -9.2724e+00 -3.8312e+00  7.5362e-03 -8.5426e+00
-#>   1.0760e+01  6.5716e+00  5.0156e+00 -6.8049e+00 -1.5221e+00 -1.8318e+00
-#>   5.8628e+00 -5.8969e+00 -2.5412e+00 -7.0412e+00 -7.1329e+00 -4.1548e+00
-#>  -5.9491e+00 -4.2658e+00 -1.1386e+01  4.8017e+00 -4.1747e+00 -1.5459e+00
-#>   6.2477e+00  3.3169e+00  1.0762e+01  8.2661e+00 -4.6610e+00  1.3204e+01
-#>   1.6609e+00  8.2084e-01 -2.4961e+00 -1.3010e+00  5.8499e+00  1.2042e+00
-#>  -1.8975e+01  9.0164e+00  6.3102e+00  1.0104e+01 -4.1855e+00 -2.9264e+00
-#>   1.7456e+01  2.2383e+00 -7.5770e+00  1.1101e+01 -7.0678e+00 -7.7324e+00
-#>   1.2254e+01  1.9148e+01 -9.5767e+00  1.3521e+01  5.4999e+00  1.3138e+01
-#> 
-#> Columns 25 to 30  4.7672e+00  3.9393e+00  5.8296e+00 -3.1557e+00  9.4979e+00 -2.1713e+01
-#>  -6.2923e+00  1.0280e+01 -9.7095e+00  2.7831e+00  3.1785e-01  1.5338e+00
-#>  -1.8244e+00  2.1574e+00  1.1629e+01  8.0770e+00  4.8884e-01  1.8520e+01
-#>   1.5629e-02 -1.8364e+00  1.8026e+01  1.5302e+01 -2.8889e+00  1.3968e-01
-#>   1.1179e+00 -9.3442e+00  2.7496e+00 -6.8790e+00 -4.7582e+00 -1.4035e+01
-#>  -6.6229e-01  6.1338e-01 -6.1479e+00 -9.7994e+00 -1.1166e+01 -3.6618e+01
-#>  -1.2571e+01  2.6986e+00  3.4589e+00  1.0057e+01  6.3649e+00  1.6529e+00
-#>   1.3031e+01  4.0839e+00  2.0510e+00 -1.5870e+00 -9.3332e+00  9.5409e+00
-#>   1.5391e+00 -2.6677e+00  9.4479e+00  1.2192e+01 -1.5544e+01  4.3714e-01
-#>  -5.6149e+00  1.4890e+01  2.8167e+01  2.1890e+01  3.7060e+00 -2.3475e+00
-#>   3.1602e+00 -6.4318e+00 -5.0051e-01 -7.0032e+00  2.8286e+00 -5.1985e+00
-#>   2.1729e+01  4.4250e+00  5.0968e+00  6.9575e+00  1.7531e+00 -2.5253e+00
-#>  -1.0086e+00 -2.7643e-01  4.9174e+00  3.8020e+00  1.1939e+01 -2.0344e+00
-#>  -6.7084e+00 -3.3351e+00  7.7175e-01  2.1211e+01  2.6425e+01  6.7001e+00
-#>   8.5745e+00 -6.3598e+00  4.2359e+00 -6.8517e-02 -8.5386e-01  5.5816e+00
-#>  -8.9783e+00  4.1437e-01 -8.1550e+00 -8.5186e+00 -3.6995e-01 -4.0838e+00
-#>  -9.3662e+00 -9.4380e+00 -1.6104e+01 -1.6740e+01 -1.4065e+01 -1.4101e+01
-#>   4.0266e+00 -6.0926e+00 -2.1354e+00 -1.2275e+01 -3.8985e+00 -4.7642e+00
-#>   7.2370e-01 -8.8531e+00  3.0463e+00  1.1427e+01 -4.3989e-01  3.9344e+00
-#>   1.6464e+01  1.1835e+01  9.4309e+00  1.2652e+01  4.9765e+00  2.0312e+01
-#>  -5.0763e+00 -5.5975e+00 -5.2981e-01 -9.1223e+00  7.8726e+00 -1.1486e+01
-#>  -5.5547e+00  4.3114e+00  1.8073e+00  1.1570e+01  9.9499e+00  6.8600e+00
-#>  -6.6898e-01 -3.5121e+00  1.2643e+00  3.2227e+00 -3.4610e+00  1.8418e+01
-#>   4.0002e+00  1.0894e+00  8.4338e+00 -5.1922e+00  3.8902e+00 -6.6107e+00
-#>   3.9277e+00 -6.1210e+00 -1.3713e+01 -1.8275e+00 -3.6425e+00  1.0173e+01
-#>  -3.0410e+00  1.9244e+01  1.1407e+01  2.2155e+01  3.5196e+00  1.0021e+01
-#>  -1.0214e+01 -3.6439e+00  5.5281e+00 -1.3027e+01  1.2913e+01 -8.8776e-01
-#>  -1.2979e+00 -2.1701e+01 -3.9833e+00 -1.3435e+01  1.7802e+01  5.0667e+00
-#>  -8.7162e+00 -4.8520e+00  4.9603e+00 -6.5037e+00 -7.7042e+00 -2.6138e+00
-#>   2.7359e+00 -1.6787e+00  1.8766e+01 -6.5021e+00 -6.3636e+00 -1.6554e+01
-#>  -2.1235e+00  3.6546e+00  2.7977e+00  2.9845e+00  4.1591e-01  1.8891e+00
-#>  -1.6986e+01  5.1112e-01 -5.8523e+00 -8.3724e+00  1.6497e+01  1.3320e+00
-#>   3.8910e+00  1.3113e+00 -4.9165e+00 -1.6934e+01  1.6983e+00 -9.6708e+00
-#> 
-#> Columns 31 to 36 -2.1679e+01 -2.4109e+01  8.4593e+00  7.2496e+00 -7.6315e+00 -2.8294e+00
-#>   9.3554e+00 -1.6916e+01 -9.3243e+00 -4.0114e+00 -7.3527e+00  4.0655e-01
-#>   1.4200e+01  1.5864e+01 -5.9292e+00  1.3862e+01  2.3485e+00  2.4782e+00
-#>  -1.8995e+00  2.3539e+01 -2.6220e-02  1.8460e+01 -1.2601e+00  1.1129e+01
-#>  -1.4494e+01 -9.6573e-01 -5.0733e+00 -1.0772e+01 -2.2918e+01 -3.4051e+00
-#>  -2.4427e+01  5.6512e-02 -2.4875e+00 -1.4292e+01 -5.5592e+00 -1.3762e+01
-#>  -1.4701e+00  8.7661e+00  3.7526e+00  3.6377e+00 -1.2703e+01 -9.5928e+00
-#>  -1.0701e+01 -1.4778e+01 -3.5459e+00 -1.4791e+01 -1.0391e+01  1.0136e+00
-#>  -9.1675e+00 -1.6266e-01  1.1090e+01  1.5069e+01  1.9667e+01 -7.0169e+00
-#>   2.6015e+01 -2.2708e+00 -1.2905e+01  1.6435e+00 -2.3520e+00 -6.1251e+00
-#>  -1.7792e+00 -2.3453e+01 -1.9723e+00 -4.5929e+00 -1.6097e+01 -4.4947e+00
-#>  -5.4744e+00 -6.4192e-01  1.6510e+01 -2.6668e+00  2.4133e+00 -2.6066e+00
-#>  -1.9293e+00  1.5536e+01  1.1604e+01 -1.0907e+01 -1.0784e+01  1.3530e+01
-#>   1.2010e+01 -9.5282e+00 -7.4836e+00  8.5354e+00  1.5636e+01  6.4028e-01
-#>  -8.9697e-01  1.3402e+01 -8.8782e+00 -7.5711e+00  1.0922e+01 -1.6372e+00
-#>  -8.5683e+00  7.1512e+00 -3.3600e-01 -8.1503e+00 -1.5620e+01  2.9184e-01
-#>  -7.3744e+00 -9.9613e+00  3.9222e+00 -3.9316e+00 -4.0211e+00 -1.2877e+01
-#>   3.4622e+00 -2.0458e+00  1.0160e+01  1.9177e+01 -1.0452e+01 -7.6392e+00
-#>  -1.7070e+00 -1.3017e+01 -3.2206e+00  4.0881e+00 -4.6195e+00  1.0797e+00
-#>   1.7468e+01  9.2982e+00 -1.2351e+01  2.2376e+00 -3.9956e+00  1.3261e+01
-#>  -4.4173e+00 -1.4719e+01 -9.7518e+00 -1.7765e+01  1.4858e+00 -3.2349e+00
-#>   3.7165e+00  5.9601e-01  1.4029e+01 -1.0961e+01  8.7351e+00  7.7419e+00
-#>  -6.1524e+00  1.3197e+01  3.9808e+00  4.3193e+00 -1.2020e+00 -1.2825e+01
-#>  -5.0571e-01 -1.0672e+01  1.8364e+01  8.0442e+00  1.3081e+01  8.4952e+00
-#>   4.4564e+00  5.5969e+00  5.6686e+00 -1.1134e+00 -7.3456e+00  7.7669e+00
-#>   1.3500e+01  4.4614e+00  4.9841e+00  8.9211e+00  2.0098e+01  3.8844e+00
-#>   3.6128e+00  1.1122e+01 -4.4044e-01 -3.6093e+00  1.3210e+01  8.5443e+00
-#>  -1.8090e-01 -1.0985e+01 -4.1344e+00 -5.0467e+00 -1.5144e+01 -1.9789e+00
-#>  -1.3420e+01 -7.0009e+00  8.1848e-01 -5.0013e+00  1.8268e+01  8.2374e+00
-#>   6.3996e+00  1.8506e+01 -1.1341e+01  7.0303e+00 -1.0490e+01 -4.4531e+00
-#>   5.5464e+00 -1.7529e+01 -7.0866e+00 -8.8303e-01 -5.3377e+00 -7.3746e+00
-#>  -8.8998e+00  1.7904e+00  4.3107e+00 -1.4987e+01 -1.3366e+00  5.2522e+00
-#>   8.7654e+00 -7.8483e-01 -7.9464e+00  1.0005e+01 -1.1502e+01 -2.4911e+00
-#> 
-#> Columns 37 to 42 -1.2982e+01 -4.2126e+00 -3.8256e+00  3.6204e+00  3.1385e+00 -3.8542e+00
-#>  -7.1096e-01 -1.1357e+01  1.9772e+01  3.5039e+00  4.9852e+00  1.4891e+01
-#>   1.5609e+00 -2.5245e+00 -8.8441e+00  4.7378e+00  3.7088e+00  8.7780e+00
-#>   7.1751e+00 -5.4980e+00 -1.6342e+01  8.2999e+00  2.4324e+00 -7.6220e+00
-#>   1.0249e+01  1.2807e+01 -6.5801e+00 -5.8649e+00 -4.9927e+00  2.6560e+00
-#>  -2.1048e+01  6.4206e+00  1.0280e+01  5.0778e+00  1.0445e+01  4.2637e+00
-#>   1.2026e+01 -6.7137e+00 -8.5624e+00  2.7173e+00  3.2165e+00 -3.5499e+00
-#>  -2.1791e+00 -2.1789e+00  4.3917e+00 -1.3265e+01  5.4625e+00  1.8335e+01
-#>   7.3245e+00 -5.1060e+00  7.6107e-01  6.8573e+00  1.4891e+01 -4.1370e+00
-#>   1.1617e+00  9.7119e+00 -6.5697e+00  3.8368e-01 -1.1239e+00 -7.7994e+00
-#>   4.0031e+00 -3.4288e+00 -1.2127e+00 -7.2412e+00  2.3004e-01  8.5065e-01
-#>   5.2983e+00  5.3690e+00  5.7870e+00 -2.9230e+00  4.3193e+00  4.7594e+00
-#>   1.4966e+01 -4.6703e+00 -2.1251e-01 -1.6869e+01 -6.9216e+00  9.3986e+00
-#>   1.6732e+00 -2.7369e+00  5.2217e+00 -2.9938e+00  5.3384e-01 -3.6932e+00
-#>   6.5449e+00  4.4988e-02 -8.0596e+00 -1.0725e+01  9.6437e+00 -1.2317e+01
-#>   1.0285e+00  8.7999e+00  1.4183e+01  1.0116e+01  9.0127e-01 -5.8294e+00
-#>   4.4251e-01  1.2525e-01 -8.3928e-01  8.1308e+00  1.8535e+00 -1.1609e+01
-#>  -1.7475e+01 -2.8897e-01  6.4601e+00  9.6699e+00  2.5221e-01 -1.0648e+01
-#>   3.5728e+00 -6.6077e+00 -1.7886e+01  4.0742e+00  1.7618e+00 -3.7558e+00
-#>  -2.0264e+00 -2.7089e-01  4.3377e+00 -5.0581e+00  2.4265e-01  8.6139e+00
-#>  -9.2931e-01  6.4229e+00  8.4352e+00  6.6326e-01  1.1871e+00  1.4929e+00
-#>   6.7805e+00 -1.0345e+00  1.5609e+01 -1.3094e+01 -1.1205e+01  5.9305e+00
-#>   3.6864e+00  1.0229e+01  1.1679e+01  4.0239e+00 -4.9793e+00 -2.2810e+01
-#>   1.8718e+00 -2.4655e+00 -4.8008e+00  9.0004e+00 -3.6858e+00  6.8290e+00
-#>  -5.7660e+00  2.6562e+01 -6.6253e+00  5.0441e+00 -3.5265e+00 -3.7034e+00
-#>  -1.9097e-01 -7.5070e+00 -2.1687e+00 -1.3804e+01 -1.7359e+00  1.9275e+00
-#>  -7.2095e+00 -9.9872e+00  3.7455e+00  4.0918e+00 -9.8750e+00  3.0196e-01
-#>   5.1022e+00  1.0027e+01 -1.4910e+00  1.2692e+01 -6.9118e+00  7.6790e+00
-#>   1.3758e+00 -7.4730e+00 -1.0146e+01  1.7413e+00 -2.0952e+00 -3.0648e+00
-#>   3.3219e+00  4.3010e-02 -1.1769e+01 -5.4523e+00 -8.0371e+00 -1.5348e+01
-#>  -4.3930e-01 -1.0443e+01  8.5679e+00  1.2639e+00 -1.0131e+01 -7.0613e+00
-#>   1.1237e+01 -1.5008e+01 -1.1160e+01 -1.6202e+00 -1.6306e+01 -3.6980e+00
-#>  -4.0589e+00 -8.3484e-06  2.1629e+00  1.3816e+01 -3.1079e+00  4.6911e+00
-#> 
-#> Columns 43 to 48  9.5938e+00  9.8600e+00  9.7126e+00  1.8094e+01 -1.2962e+01  4.5320e+00
-#>   8.8597e+00 -3.8651e+00  3.1434e+00 -1.8626e-01 -5.8093e+00  1.1049e+01
-#>  -2.7338e+00 -1.1493e+01  7.7413e+00 -7.4347e+00 -2.8650e+00 -4.0432e+00
-#>  -5.0291e+00 -3.8119e+00  7.6550e+00  4.3292e+00 -3.0579e-01 -3.6069e+00
-#>  -5.2778e-01 -7.3304e+00  5.8848e+00 -1.2196e+01  7.4105e+00 -1.9333e+00
-#>   2.8526e+00  1.9088e+01  4.3373e+00  1.6240e+01 -1.1476e+01 -6.8856e+00
-#>   1.4744e+00  1.0112e+01  8.1093e+00 -3.6238e+00  1.1439e+00 -7.5707e+00
-#>  -5.3667e+00 -7.7414e+00 -1.0272e+01  6.6667e-01  1.0756e+01  7.8223e+00
-#>  -1.2314e+00  1.2076e+01 -2.8305e+00  7.6454e+00 -1.7534e+01  7.1417e-01
-#>   7.0782e-01  2.2438e+00  1.0972e+01 -2.3305e+00  3.3615e+00  8.5989e+00
-#>  -2.4608e+00  1.9173e+00  4.5344e+00 -9.2493e+00  9.1561e+00  3.1954e+00
-#>   5.0468e+00  7.4412e+00  9.2711e+00 -8.5012e+00 -9.2720e+00  4.4542e-01
-#>  -6.5111e+00  6.7097e+00 -4.1257e+00  2.3944e+00  2.2047e+01  7.6683e+00
-#>   6.7561e+00  7.3840e+00  7.0542e+00 -1.7891e+00 -1.0972e-01 -1.3271e+01
-#>  -7.1963e-01  1.1568e+01 -4.8449e+00  2.3278e+00 -1.7470e+01 -4.0480e+00
-#>   4.8309e-01 -5.2807e+00  1.1232e+01  6.8060e+00 -2.8304e+00 -1.1613e+00
-#>   3.3958e+00  5.9262e+00  1.4133e+01  1.1143e+01  3.2042e+00 -6.2356e+00
-#>   7.5310e+00 -2.0502e+00 -2.2026e+00 -8.8068e+00 -4.4770e+00  2.5733e+00
-#>   6.1012e+00  4.0045e+00  3.6754e+00  3.0534e+00 -8.3425e+00  1.6813e-01
-#>  -1.0076e+01  1.3927e+00 -2.6783e+00 -9.6086e+00  3.5074e+00  1.0511e+01
-#>   1.0308e+00 -4.1848e+00 -5.3140e-01  4.9027e-01 -1.4362e+00  1.7054e+00
-#>   9.5634e+00  2.1233e+00 -2.3149e+00 -2.8003e+00 -1.4120e+01  1.0925e+01
-#>  -8.9510e+00 -2.0107e+00  1.3626e+01  7.5999e+00 -1.0009e+01 -8.6540e+00
-#>   5.8128e+00  4.3800e+00  5.1025e+00 -3.1124e-01 -9.5418e+00 -8.0206e-01
-#>   4.5954e-01 -1.9820e+01 -1.0856e+01 -2.4205e+00  3.2851e+00  3.5446e+00
-#>   7.4073e+00  1.0443e+01 -9.5225e+00  9.4974e+00 -3.1614e+00  8.7247e+00
-#>   6.9447e+00 -3.9645e-01 -7.9139e+00  6.0300e+00  8.5722e+00 -1.7283e+00
-#>  -1.0806e+01 -1.5662e+01  2.5612e+00  4.0088e+00  5.8340e+00  7.7349e+00
-#>  -4.5984e+00 -2.4696e+00 -5.8806e+00  3.0326e+00  1.4204e+01 -2.3575e+00
-#>   2.8538e+00 -1.9296e+00  3.6106e+00 -2.7019e+01 -9.1201e+00  1.2406e+01
-#>  -1.4059e+01  6.0087e+00  7.9251e+00 -2.9546e+00 -1.3986e+00  1.0299e+00
-#>  -6.0447e+00 -2.5707e+00  3.1745e+00  9.1589e+00  4.0167e+00  9.2205e-01
-#>  -3.8542e+00 -1.4597e+01  1.0325e+01 -2.1651e+00  9.4333e+00 -1.0715e+01
-#> 
-#> Columns 49 to 54 -2.8126e+00  6.2290e-01  1.2094e+01  5.7506e+00  6.0703e+00  1.8163e-01
-#>  -6.2051e+00  1.0590e+01 -1.1221e+01  4.7619e+00 -6.3597e-02  5.8108e+00
-#>   7.0618e+00 -3.9845e+00  1.2555e+00  5.8426e+00  9.2383e+00  8.9532e-01
-#>  -1.9856e+01 -6.1150e+00 -5.9260e+00  2.8530e+00  9.8016e-01 -2.5336e+00
-#>   1.4864e+01 -6.8976e-01  1.2750e+00  2.0616e+00  3.1718e+00 -6.2053e-01
-#>   4.0523e+00  1.1325e+01  1.2280e+01  3.6007e+00  3.4541e+00 -3.6653e+00
-#>   8.3939e+00  1.1663e+01  1.1109e+01  8.8074e-01  5.1258e+00 -6.6330e+00
-#>  -2.4700e-01 -5.0268e+00 -2.8844e+00 -7.5633e+00 -1.9285e+00 -1.2111e+00
-#>  -3.1770e+00 -1.8981e+00  3.9011e-01  7.7184e-01  5.0163e+00 -5.7405e+00
-#>   6.6273e+00 -1.8221e-01  1.1584e+00 -2.7757e+00 -2.0168e+00  2.6052e+00
-#>  -5.7066e+00 -5.4887e+00  2.3780e+00 -6.5638e-01  7.6786e-01 -7.7979e-01
-#>  -5.6415e+00  6.8836e+00  2.1560e-01  7.0334e+00  1.0236e+00 -6.6481e+00
-#>  -1.0836e+01  4.5798e-01  9.5420e+00  7.7963e+00 -3.3061e-01 -1.6403e+00
-#>   4.2271e+00 -1.2098e+01 -1.3656e+01  2.4545e+00  1.2756e+00  2.7649e+00
-#>  -2.0405e-01  4.1072e+00 -4.2119e+00  9.9203e+00  2.4864e+00 -4.2951e+00
-#>   7.1166e-02  1.0848e+01  3.1254e+00  7.9914e+00 -3.9279e+00  3.5528e+00
-#>  -5.9704e+00  1.2113e+00 -1.4374e+01 -3.6522e+00 -7.1488e-01 -3.2987e+00
-#>  -9.3306e+00 -2.4419e+00 -2.1639e+00 -3.6770e+00 -6.2138e-01  5.5423e-01
-#>   4.0496e+00 -1.0450e+01  3.8506e+00 -3.9680e+00 -7.0082e+00 -1.1381e+01
-#>   5.7722e-01 -1.5393e+01  1.1042e+01 -1.5000e+00 -3.2943e+00 -7.7883e+00
-#>  -1.3542e+01  7.2040e-01 -1.4342e+01 -1.2992e-02 -1.2799e+00  5.8970e+00
-#>  -1.1502e+01  1.9461e+01 -8.2887e+00  5.0860e+00 -2.7199e+00  5.8708e+00
-#>  -5.4900e+00 -1.0352e+01 -9.3241e+00  5.3902e+00 -1.1108e+01 -4.9735e+00
-#>   1.1056e-01  5.0094e+00 -1.0799e+00  4.5442e+00 -6.3421e-01 -1.9181e+00
-#>  -5.7869e+00 -1.0112e+01  8.2043e-01  4.2532e+00  4.8572e-01 -3.0658e+00
-#>   1.2075e+01  5.5917e+00  6.5248e+00 -3.1185e+00 -3.3772e-01  3.6188e+00
-#>   9.3177e+00 -2.4290e+00  6.1698e+00  3.9114e+00 -8.6027e-01  6.6250e+00
-#>  -1.5134e+01 -1.6484e+01 -1.0285e+01  9.0815e-01  6.7053e+00 -6.5309e-01
-#>  -8.6335e+00  3.7887e+00 -6.0815e-01  7.2834e+00 -4.3596e+00  1.6165e+00
-#>   7.0482e+00  1.2581e+00 -2.9573e+00 -1.2959e+00 -5.8671e+00  1.1073e+00
-#>  -1.2857e+01  1.1212e+01 -6.9868e+00  1.1672e-01 -4.6736e+00  7.4065e-01
-#>   1.4155e+00 -7.0236e+00 -6.6755e+00  1.3262e+01 -4.7395e+00  4.9263e-01
-#>   1.0220e+01 -1.6622e+01  5.0489e+00 -5.7797e+00 -5.4130e+00  2.3046e+00
-#> 
-#> (6,.,.) = 
-#>  Columns 1 to 8  -1.2906  13.5946  10.1609   1.2739   2.2736   2.0222  -0.3510   3.4442
-#>   -5.7275  -1.5648  -8.0162   0.4344   3.3434  -8.8662   5.8548  -2.1173
-#>   -2.5602  -3.4971   7.1341  12.4275  -2.3825  -1.8784  -1.5559  -8.6028
-#>   -5.2301  -2.8007   0.5347  11.6950 -20.4631  -3.5238  -4.4829   6.4217
-#>    6.0463 -12.9998   3.9110   5.6952   9.0395   5.3502  -2.5195   1.1746
-#>   -6.3904   2.9205   7.7085   5.1703   9.8267   7.5979   9.2738   5.0525
-#>    5.5847  -6.8299  11.9292  -0.2180   1.3347  11.5043  -1.4268  -6.0069
-#>    0.2869  10.9916  -0.4642   1.6550  10.1435   9.0110  -5.8498  -6.5407
-#>   -5.0244  -0.8937   5.7387  -3.5939 -20.8283  -1.1995   2.6885   0.3491
-#>    1.9330   0.2644   4.1328  18.1709 -11.5749   5.7500  23.1029   3.4215
-#>    6.1167  -2.6856  -1.9285  -4.0633  15.5307   0.2766  -2.4825  -7.5930
-#>   -1.4727  -6.1985   1.0367  -9.4084   5.9970  -5.9196   8.1893   2.3675
-#>   -0.8975  -0.4740   2.3347   2.8178  11.5984  -0.6683  -8.1911   2.5873
-#>    6.2782  -5.5500   4.2726  -1.2471   3.3512  -4.0309  -6.6749   0.4875
-#>    6.7282   6.7253   4.8038   6.0504 -10.3696   4.3334  -2.6760  -1.5284
-#>   -5.7392  -2.9991  -1.2506   5.1530  -5.3925   8.0087  -2.9595   2.1494
-#>    3.1805  -3.1789   3.7345  -1.2337  14.6059  11.3571  10.6334  -5.8997
-#>   -7.5094  -1.0523  -0.3500  -2.5702   0.7187  -4.1282   4.0303  -7.1820
-#>    8.0910  11.2153  16.4467  -2.5688  -4.1481  -5.9578  -9.3378   5.4305
-#>   -2.5413   9.1377  -0.1849   0.8289  -4.1381  -8.1889  -7.9123   7.7997
-#>    6.4039   9.6086   6.6445   6.9689   9.6904  -8.8979   9.8943  -1.0717
-#>   -2.0200  10.0184 -16.7365  -9.6430  -1.2828  -0.5102  -8.9521  -8.7141
-#>    1.0829  -0.9323  -1.9788 -11.2347   1.2045   3.7669  10.7768   6.0730
-#>   -6.5144  -1.7845  -3.0716 -19.3201  -3.5435   1.3338  -0.8777  -3.1286
-#>   -6.1790   8.0965  -6.8826   2.2282   4.6184  12.5645   3.0924  -3.1263
-#>   -4.5024  -0.9420   1.5130  -8.1597  12.1098   2.0007   5.8567  -2.0337
-#>   -0.1062  -1.1714  -0.1613 -14.2301   2.1413  -3.3113   0.8350   2.4379
-#>    8.3067   7.4910  11.0588   2.2626   5.9629  -6.2164  -7.1244  -0.4086
-#>    3.5105   1.5456  -4.8592   7.5274   3.0848   3.7589  -1.5261   6.4598
-#>    7.7199   1.7536   2.7952   3.6789  -5.1802   6.6892  14.5219  10.6791
-#>    0.7732  -0.6465  -3.0562   6.5930  -9.0988   1.7741   1.0375  -0.2780
-#>   -3.2239  -0.4057   0.9958  -8.9499   0.6236   0.7190 -18.1055   4.8374
-#>    0.3086  -7.5484   5.2433  -0.9087   9.3846   2.9502   5.1732  -3.6332
-#> 
-#> Columns 9 to 16  14.4922  -9.0520   1.7655   4.2005  -0.9748   1.0361  -3.8319  -3.0438
-#>    0.3332   6.1866  -1.8103   2.5202  -4.6763   7.3212  11.9548  13.8410
-#>    2.7913  -6.1743   9.6053 -13.5915   9.1463  15.6517   1.2033  -8.0341
-#>    6.7896  -3.6931  -1.0360 -15.0298   2.2860   7.6720   0.8738  -1.3508
-#>    0.9471   3.8266  20.7929  -9.0604  18.7446   7.6388   0.2071  -9.4922
-#>    6.1561   8.5326  -1.2114   5.5687  -5.6578   1.3177  13.0493  -9.0726
-#>    1.6027   9.1719   5.6655  11.2977 -10.5055 -12.8792   2.7278   1.8168
-#>  -15.2426   1.5844  -3.5033   7.4899   3.4438  18.5491   8.6613  -1.9612
-#>    7.7457  -3.5828 -14.3712   6.5652 -15.7928  10.0078   8.9950 -14.0851
-#>    6.0489   2.3172  -9.3602   2.7904  -3.2131  13.4063  19.7302   6.5122
-#>   -7.6034   1.8109   8.9422   7.0092  16.0588  -1.2942   2.1396   7.1782
-#>   -0.0329 -13.2314   5.2734  -9.0207  -6.3200  15.1783   5.7580  -5.6126
-#>  -13.0665  -0.2777 -12.8806 -10.6230   9.8472   0.1550  -2.8208  -4.6425
-#>    8.4266   7.6153  -4.0710  -7.4449 -12.7797 -12.6976  11.2124  -1.8019
-#>    2.2260  -0.8911  -8.0302  -6.1254  -6.7894   3.7435  -2.2531 -15.0217
-#>    4.3711   7.2751   3.4861  -9.9310  -9.6459   5.1806  -3.0555  -1.2542
-#>    7.7591  -1.4780  10.0699   6.3439 -10.2799  -1.5439 -16.2412  -3.7900
-#>   -6.9606   2.6252  -1.3341   3.2982   0.2523   4.2664   3.4766  -0.9335
-#>   17.7835   2.1302  -7.5523   3.5047 -18.0717  -6.1698 -13.9270  -5.4293
-#>  -11.9347  -2.7808 -11.9411 -15.3471  18.3825  10.5389  -3.8798   0.3755
-#>    3.5219   5.9095   0.5010  -5.5723 -11.3272 -13.9500   0.7060   7.0516
-#>    2.6564 -11.4641  -5.9555  -2.0962 -14.1869  13.4808   7.7111  -4.0473
-#>  -11.7709  -7.2278   3.2929  -4.9161  -5.4113   6.6190   9.6433   6.6927
-#>   -1.6251  -0.1537  -0.8085  -4.9274  -2.5047  -7.8605  14.4556  12.5811
-#>   10.3315  -6.0728   8.8520   6.2369  16.2882   6.1643 -18.3501  -9.8453
-#>    1.9906   3.0279 -15.3214   9.9443  -2.8701   1.5651   1.0282   9.6610
-#>    3.4091   0.9268   2.7886  -4.3620   4.8404 -10.7781  -2.5085   7.5129
-#>    0.1259 -11.2227  14.8560  -6.3446  18.5227  -0.2072  -1.8997 -10.7735
-#>   -2.5384   0.8208   1.6124   2.8615  11.5942  -3.2394 -12.9641  12.4291
-#>   -2.6944   2.3566  -2.2074  -2.1300   4.6018  -9.9700   1.1242  18.0932
-#>   -2.4919   4.8262   3.4302  -6.9013  -7.1076  -2.1004   8.5142   1.9163
-#>    5.3395  -0.6464 -13.5380  -5.0712   0.9941  10.4185 -17.7384  -5.8762
-#>  -12.3580  -1.6421   9.2891   3.8499  21.0648  -9.4984  11.6074   4.4902
-#> 
-#> Columns 17 to 24   5.7558  -5.1015   3.7358   3.3919  10.1574   4.8178 -21.1594 -12.2321
-#>   -3.0562   0.7140  10.4205  -6.7750  -5.5420   2.6335  11.1883 -11.4260
-#>    3.0956  -0.0853   5.8817   8.6862  -4.5400   7.8507   6.7873  -6.8118
-#>   13.3198  -0.1248  10.6121  -0.0760 -18.0245  11.9826   0.3320  14.2314
-#>   -4.0194   2.2549   2.3223  -6.7249   4.2722 -14.4571   9.4587  11.3563
-#>  -17.4306   4.3258 -21.1685   8.0124   7.3428  -5.9854 -11.9992 -12.7875
-#>   15.3551  11.2782  11.0832  -2.1655  -4.6535  -4.5613  -4.1144   8.5404
-#>    7.6964  16.3118   2.3128  -8.6316   0.7952   4.3366  -0.4963 -19.6819
-#>    8.0289  -7.0709 -13.0450   5.5670  -1.3630  -7.3526 -19.8146  -2.7441
-#>    8.7356   7.6161  20.4846   4.1788   1.2975  10.3680  22.0978   6.2228
-#>    5.9167  -9.3964  -5.7256  -9.7319  -4.4493 -12.1559   1.1821  -4.4289
-#>    9.2950   0.7511  -0.2363 -12.1582  11.4554 -10.0148   5.8216 -10.9486
-#>    9.9000  16.4000  -0.7640   1.1786   4.3074  10.8577   0.4916  -6.8029
-#>   10.3147   5.5437  -7.1659  12.2463   2.2565   0.3769  20.3979   8.4227
-#>   -0.3663   2.1757   5.1778  -5.3807  -1.6984   2.3133   2.5216  -2.2583
-#>   -0.9128 -14.7464  15.0432  -9.6708   0.6328  -7.2687   6.0182  12.2746
-#>   -3.4113   5.8405  -1.8712   9.3081   5.1289  -6.4269 -10.4528   8.3959
-#>    8.7077 -19.5687   9.4994  -7.7430  -1.2161  -5.4623  -9.8941   1.4983
-#>   13.8759   4.1344   9.4766  11.2207   2.2961  -8.7867   5.9747  -7.1402
-#>   12.1295  12.9967   0.6639  -1.5193   0.4410  10.6945  13.6656  -3.1914
-#>    5.4100   3.8496   1.6437  -3.5398   7.0846  -9.9937  15.4963   5.8231
-#>    7.5987   3.5565   4.1458  -7.8678   0.6812  -4.4799  24.1990  -5.0146
-#>  -10.0771  -3.8329   4.9017   7.5790  -6.6872  -0.7476   9.1992   7.2369
-#>   -5.0835   5.0043   3.1980  -5.0508   3.8299  -4.9766  -9.6292  -1.7594
-#>  -15.1777  -5.5674  -3.6914  11.2230  -6.3116  13.1206  -4.2019   3.9232
-#>    4.7549  14.9044  -9.8537  17.3155   9.6950   7.1425   4.4975  -5.3993
-#>  -16.9693   2.4203 -20.4703   8.3267  -2.2390  -6.7381  -2.9412  12.7916
-#>  -11.6139  10.8966  -3.9312  -0.5212  -6.7344   3.3800 -12.3245  10.8742
-#>   -5.0495 -12.9478  -1.5114 -13.5049   9.3209  -2.9167  -2.9070  -9.1915
-#>   -0.3623  -6.9541   8.1159  12.0325  -9.8860  -8.7181  -2.5255   5.7182
-#>    1.4827   2.1857  -3.2521   2.9576  -7.1097   4.2946   8.4858  -0.9879
-#>   -0.2729   2.7021 -13.6980  -0.0209   6.9267  -1.2844   7.1979  -2.7620
-#>   -6.2110 -11.8811   5.1298   3.0679  -5.8170  -4.0543  -4.4873 -10.4857
-#> 
-#> Columns 25 to 32   2.5510  -0.2038  14.5818  -4.6291  -0.7677  -1.7932  12.4390  -4.2010
-#>   15.1813   0.3858   5.0331  -2.6030 -12.5818   8.9777   6.5527 -18.1512
-#>    6.1780   5.2303   5.6359  -4.6120  15.2766  -8.3173  -5.6248  -1.8824
-#>    8.1504  -1.0806  12.6728  -0.5206 -17.4869  -0.0462   8.9059   6.3680
-#>  -12.1255   4.2941  12.3835  -4.6695   2.1640   9.4901  -1.9697 -12.8155
-#>   -2.8595 -11.3560 -23.4308 -12.8827  -3.2424 -22.6272   4.2050   7.6287
-#>  -11.3116   4.9729   6.0999   3.8416  -6.1895  -9.4986   3.3053  -7.4039
-#>    3.3873  -0.7899   4.5732  21.9898   6.3325   6.0113   2.0164  15.7458
-#>   10.9122   2.3831  -5.2849  -9.6523   7.8680  -2.9722  -0.3612  14.7341
-#>   -0.2008  11.2113  -2.5775  -2.3837  -0.5849   0.4306  -6.9612   1.6097
-#>  -14.1493  -8.8999  -1.6389  11.8062   4.1949  -5.4295 -10.4731  11.8199
-#>    8.0969  -8.5132 -24.8994   4.6152  -3.4242  -2.6175  -4.3879  -4.9619
-#>   -9.4342 -15.1767  -5.6920  21.9019   5.1764 -10.5333  -5.2888  16.3868
-#>   15.5493  11.3486  -0.2930   4.1731  -7.7393  -3.6599  -7.6600  -5.2174
-#>   -3.5141  -4.6415   0.3840  -5.3498  -2.1020  -3.9604   0.0056   3.5127
-#>   -4.7510 -12.2849  -4.9326  -5.1865 -12.1735  -7.7928   4.1647  -2.0988
-#>   -1.1027  -9.5895   4.5745  -5.6741   4.7831   2.0540  10.7514 -14.1080
-#>   -2.9644   5.5836  -8.3494   5.9076  -1.2470  -1.0219  11.5192 -13.4809
-#>   13.2887   6.7956  -0.6082  23.5026 -13.5884  -6.3495  15.0499  18.1156
-#>    3.5274   6.5587   2.6680   5.6992   0.6544   7.6869 -19.7288   5.1232
-#>   -0.5296  11.7520  -6.7091  -1.0494  13.8793   6.6161  -9.0327  -8.9474
-#>   -3.4582   1.6826 -11.8708   2.1495  11.1554   2.5380   1.2823  -2.7771
-#>   -0.4756   8.0583   0.6827 -11.9828  -2.3517  11.3830  -0.6355  -2.7412
-#>  -11.4423  -5.4743 -15.1870  12.4140  -9.7539 -17.8683  -0.7848   7.7927
-#>   -0.5499  -6.9762 -12.1503  10.2159  -0.7098  -0.9954  -0.4118 -11.7689
-#>   -4.6858  -6.1957   0.0519  -7.1924  -2.5395   2.1236   1.3794 -15.5540
-#>   -9.3657  -0.6970   5.4592 -16.0231   8.2241  -4.5828   1.4532   4.9496
-#>   12.8412  -3.9305   7.5273  -3.0112  15.4585  -6.6945  15.4474   1.7287
-#>  -22.4385  -0.7342 -11.1161   4.6486  -2.7083  -9.0396  14.7538  -1.0047
-#>    4.5947 -21.1098 -16.8718  -6.1116  -9.9848   6.5708  -4.7937  -2.0188
-#>   24.0556  -8.9986 -12.3429  17.4882  -4.6548 -13.4062   6.2575  10.7854
-#>  -31.1434   1.9465   0.7855 -15.7536  10.1426  -1.6422  -8.7910  -0.0079
-#>  -10.3111   0.0067  -2.4724  -6.9653  -3.8297 -21.0870  -3.7484  -4.1639
-#> 
-#> Columns 33 to 40 -13.3018  11.4347   4.3881 -15.3451 -14.7796 -10.7482  -9.8222  -7.6255
-#>    5.7348  -2.8622  10.3386  -4.3851  -3.2253   2.3243  10.5710  -9.1831
-#>   10.8954  -7.3811   7.6372  11.4209  -8.9989  -3.6543 -15.2089  -7.5075
-#>   -4.4141 -10.8794  11.8271  -1.5246  -4.1511  -3.5885   0.3060   1.0262
-#>    1.4067 -11.2596  -3.3783 -10.3016  -5.7747  -2.5181 -14.6493  -8.2378
-#>  -10.3898  -6.8135  -0.2176  13.7231  -7.8071  -5.1213  -9.7749 -10.0052
-#>  -14.7091   9.5471   3.8610  -1.7426  -9.3497  -2.6168  -0.9749   1.7457
-#>  -22.4270  -0.7327   3.1410 -10.7137  -3.9862  -6.6951 -16.3995 -10.5144
-#>   -3.1132   3.0083  12.4543  13.9422  -2.3628   2.5201  11.7110  -3.1082
-#>  -16.7121  -8.7479   7.5455  19.0781   1.5504  -4.3607 -11.3715 -11.4387
-#>    1.3740   2.8587   1.4961  -0.6484  -5.6926  -0.5601  -9.7146  -4.9405
-#>    3.9314   3.1979   5.8303   0.5067   5.4296  12.0537  -3.3515 -11.3496
-#>  -24.2184   7.8634  14.0017   4.3131  -0.1126  -7.2120  -9.5349   0.8077
-#>   -4.7040 -15.6956   7.7416   8.2861   8.7321  22.4792   2.6020  -9.6495
-#>    9.5377 -11.9823  -6.4117   8.4295   0.3117  -2.6650   7.8302  -3.7887
-#>    5.5278  -2.7329  -2.2512  -7.1119   1.3326   8.0848   2.5164   0.9978
-#>   -1.0899  14.8330 -14.3933  -7.3525  -7.7192  -7.0869  -3.4828 -12.8309
-#>    8.4395   4.9174  -4.2364  -3.0753  21.9154   3.6303   6.2544   0.7341
-#>    2.3007  -6.2655   5.5432  -5.3002  -9.3214  -8.0130   7.6070   1.5934
-#>    4.3254 -11.5373  -4.2664   2.0801  -0.6847   3.1335  -5.3338   1.9036
-#>   12.3775  -7.0790 -28.5314  -0.3746  18.1311   2.2420   0.0830 -12.6389
-#>   -3.0356 -13.9939  14.8970   4.8902   7.3808  -2.6823  -6.3736 -12.9497
-#>  -16.7200  -2.1116  -2.9996   6.8710   2.5699   5.4890   8.0162  -2.9067
-#>  -13.6207  -4.2545  11.4654  14.8817  -2.2713   0.8434   7.1582   1.3200
-#>    5.0770  -2.2040  12.7372  11.4328  -1.7731   5.3843  -4.7258   5.3155
-#>    9.3367   7.5425  -4.8037   6.9640  18.1435   8.1916   6.8829  -1.8885
-#>   13.0478   2.9280  -0.1942   1.3672  -6.5255   2.8133   1.1775   7.4638
-#>   11.6807  -0.5185  -1.4964  -4.7613  -6.2701 -10.8524   3.5170  -3.0822
-#>    3.3242  -5.4642   4.7088   5.5320   8.1024  -5.8204  -3.0596  10.6272
-#>   -1.0657   3.4552   2.3027  -7.0692  10.1231   0.8105  -8.5921  13.5097
-#>  -15.2484 -10.5065  -3.6962  -1.5426   0.5034   8.4190   1.9529 -18.8593
-#>  -11.2276  11.5599   1.5902   2.1219  -1.6046   3.9939  -3.8218   6.1057
-#>   -2.9041  -0.2319  20.5079  10.3067 -19.0063  -7.7289   0.2409   9.4719
-#> 
-#> Columns 41 to 48 -14.8274  11.1019  -2.0624 -16.6093  14.9514  -5.6329  -3.2294 -11.8248
-#>  -25.6942 -11.7620  15.1268 -16.4583  -2.4520  -1.4868  -7.3511 -11.5673
-#>   10.0451   9.0156  -5.6282   1.9462 -13.0216  -7.2568   5.5590  13.4736
-#>    4.9235  -5.8640   5.4821  17.9450  -0.4013  -9.8630   3.4781   6.9655
-#>   15.0701 -16.0830 -11.3884   5.5517  -4.9922  -9.6732  -1.6430   6.1311
-#>    3.8547  13.6872   1.2339   1.6836   8.5749  -8.7089 -14.2435  11.3817
-#>   12.4370   6.2683  -8.8350   5.0442   2.9360  -2.7908 -10.8230  12.0263
-#>   -3.1593  -1.5564   3.6364  21.1648  -2.1498  -4.9529   5.2285   9.4477
-#>   15.8922   8.4917   1.9509   1.4087   7.6780   7.7388  -8.4368 -12.4227
-#>  -16.1673  13.1916 -15.0930   1.8549   9.6663 -17.2185  -1.4829   7.0265
-#>   16.0285  15.2274   3.2461  -3.6231  14.0717  -8.7764  -1.0086   3.2117
-#>    9.4831  -2.8242   3.4838   5.2793  -4.4744  -3.2964  -3.9987  -9.1133
-#>    7.7553 -12.7563  10.4662   5.3940   4.2116  -5.8236  -9.4847  -3.2442
-#>    1.3194 -12.7428   4.0818  -1.5940   1.5243   5.1842  -7.2948  -5.7164
-#>    4.8879  -2.9317  -6.2709  -9.3459 -11.3165   5.0118  -0.1904  22.1940
-#>  -21.0022   6.7985   4.4091 -10.0046  -6.7588   3.2733   2.6421  -4.7261
-#>    3.9414   9.6563  -5.8217  10.3224   2.6729 -11.2405  -0.9543   5.5213
-#>   -1.2199  -1.8814   4.7612   1.8873   8.5311  -6.8704  -0.2994 -12.2560
-#>   17.0057   1.9599 -10.4429  -0.1507  -9.0125  11.3883  15.8209  -4.7667
-#>   -3.3215  -1.4828  -8.9326 -14.8883  10.0406  -0.3560   2.7622  10.9950
-#>  -19.2001  -0.0726  -1.9566   5.3143   7.0787  -2.4969  -4.9595 -22.0236
-#>    1.4301   6.5826   6.1883   3.8234  -0.1367   5.4273   4.0576 -11.0166
-#>   -7.8817   0.6544   6.0732  11.9236   7.7939   0.3150 -18.9965   1.4273
-#>    2.6034 -10.2298  -4.7003  -5.3183  -3.9666   9.8251  -2.7840   2.1820
-#>    8.2280  11.6911  -6.6334   7.4555   8.5682  -8.2273  -3.4682   0.9171
-#>  -14.0598 -11.5994   1.5577  -7.6353  -0.4864   5.9775  -6.4676   8.4718
-#>    9.0035   1.8517  12.9204   1.1901  -7.3776   9.1212  -8.3669  13.1364
-#>   17.7747  10.1759   8.1650  16.2932  -3.2379  10.2901  -3.4087   0.9077
-#>   26.6333  -1.2646   5.5650  27.8534  -8.3948   2.2099  15.4857   8.4266
-#>    5.2051  -7.7246  10.6129  -5.9848   1.8629   1.8079  -2.9143   2.8773
-#>   -4.5634  -6.9991  -0.1841  -1.9090  -3.3952 -11.1046   1.2958  -5.0358
-#>   -1.5619  -4.0569   0.3958  -2.8646  -3.9297   5.8160   6.0127 -14.5869
-#>   -2.1185   6.3288  14.7029   5.6887  -7.8463   9.2926  -5.1830  11.8611
-#> 
-#> Columns 49 to 54   7.1016 -15.8208  -2.0877   8.7288  -3.1307  -0.8803
-#>   -0.4871   0.7266   4.5678   4.0105   1.2923   5.1693
-#>   20.2181  11.8863  -7.4998  -6.5552  -5.5006   2.7607
-#>    8.0890  -2.5741   1.3882   7.7029  -3.1784   0.5210
-#>    2.2496  -4.2309   5.2862  -3.9217   4.9795  -0.7292
-#>   -8.6583   6.5319   0.0370   2.8436  10.4838  -0.7236
-#>   -1.1649  -4.4350  -6.3319   5.5223  -1.6165   1.1725
-#>    4.8659   3.2011  -8.5719  -1.7483  -1.6685  -1.5847
-#>   -2.2247  13.8979   4.1977  18.8982  10.7389   0.0871
-#>    3.9782  -7.1217   0.0354  -1.4489  -5.5193   1.8505
-#>   18.8135  -5.2025   0.9429  -3.0978   1.4687  -2.5012
-#>   11.6686  16.0039  19.1609   9.8649  14.1246   6.5454
-#>   13.3683  -8.2871  -6.7784  -8.3795  -0.2653  -3.5511
-#>  -21.7449   8.3611  -4.8974   7.8489  11.9476   5.2951
-#>   11.3051   4.7276  -3.7736   9.6208   4.5921   6.3180
-#>   15.5787 -15.6322  -1.9335   2.3981   0.3116  -0.4815
-#>  -12.1117   9.1798  -4.6968   0.7476  -1.0237   3.7553
-#>   -7.8043  -0.7208   8.2035   3.4730  -4.0109   1.5793
-#>    3.9935 -16.5139 -31.5667   0.2528  -0.0504  -9.1094
-#>   11.5752  -5.3425   3.3273  -0.7484   2.1920   2.7529
-#>    0.3867   1.0408   2.0689  -6.1926   0.9572   1.3587
-#>   14.7566   4.6171   8.8172  -5.3254   3.3079  -2.3980
-#>    8.7173   8.6772  12.3852  11.0251  11.5906   0.0299
-#>    4.6733  -7.0466  14.9059  -4.1834  21.6208  -1.9280
-#>    3.1884   6.7935  18.1165  -7.7078  -2.9576  -5.1778
-#>  -18.3818   6.2766   7.1564   2.9596   0.0514   5.4900
-#>  -11.5391   3.1277  -0.8124  -2.9626  -9.9937  -3.3890
-#>    2.6400   7.5140 -11.0859  -9.9491  -5.3755  -7.1824
-#>   -5.2797  -3.5233   2.8756  -5.8039  -2.0460  -5.4051
-#>    4.4817   5.8428  14.4519   2.6131  -9.7038  -6.9624
-#>    1.5933  -4.5518   0.6980  -4.8872  14.2196  -2.3431
-#>   13.2308  -9.6762  -9.5056  -3.2635  -4.0156  -6.7775
-#>   12.9216   5.4440   0.0763  -1.1853  -3.6075  -3.9249
-#> 
-#> (7,.,.) = 
-#>  Columns 1 to 8   2.9533   1.0860  -8.1119  -0.0494  -5.6527  -3.9036   1.4153  14.9349
-#>   -0.0426  -5.0686   0.9671   9.3674  -1.9175  -4.4150 -20.3450 -11.1734
-#>   -5.7712   0.4861  -5.7229   2.3722  -0.5183   4.7114  -5.0389   9.6950
-#>   -4.9070  -3.9710  -4.8358  -5.3924  -1.4841 -23.6475   3.8715  10.7274
-#>   -2.5420  -0.1729   3.0972   2.3531  -9.6480  -8.7437  19.5345  -5.5293
-#>    4.4810   5.3877   7.0822   7.6412  12.4621 -10.4872   4.7130   3.0768
-#>    0.2558   8.5553  -1.4754   6.0410   3.9560  -4.5169  11.5759  20.3742
-#>    8.0860  -2.6291  -2.9121   7.6992   3.2772  -6.0553   4.4048   6.1588
-#>   -4.3041  -3.4693 -10.2610  -6.9206   8.7375   2.1002   2.5182   1.9419
-#>   -4.9646  -0.1113  -1.3192  -9.4292   4.6407 -18.0276  -4.2440   6.6633
-#>   -5.8289   5.9275   2.1018  -1.3827  -2.2998   1.0696   4.6979  -9.5232
-#>   -2.5592 -10.3871  -4.0104  -1.7658 -10.1056 -11.3087  15.4066  -7.7634
-#>    6.3138  -1.6091  -5.9848 -13.2303 -23.1200  -7.9112  10.0865  -1.6915
-#>   -0.5376  -1.2372  -2.6146   1.1744   3.0030   2.3626   0.6140  -4.1495
-#>   -1.0276   4.0819   1.0181 -17.9625  -1.2514  -7.6360  -2.6066   4.1145
-#>  -11.0159   6.8261  14.0863   4.3507  -5.7617  -3.7358   2.9299  -8.3246
-#>    2.3352  -3.0110  12.2271  18.0495  -4.5932  -7.1297   5.2973  -8.6118
-#>   -0.5394  -6.9015   5.5024  -7.8668  12.8111   1.7763   9.8438  -1.7404
-#>    3.3877  -4.2540  -2.2542  -6.0180  18.3600  -5.4755   3.0592 -11.9726
-#>  -10.2080   7.4090   4.1472  -2.6033  -4.1521   3.2111   8.6643  11.6759
-#>    1.0144  -5.1425   0.0963  -7.1641  -0.3397  19.8292  -0.3321 -16.5396
-#>    4.8501   8.3368   3.3213   1.6215  -0.6207   0.4590 -15.0183 -14.6612
-#>   -2.5804   3.4855  16.8857   9.2382   3.0253  -6.7524   0.1960  18.0831
-#>    1.7473   0.6880 -11.5274  -8.9468   3.7359 -17.2174   1.2827   8.5942
-#>   -2.4625  13.4675  13.3387  -0.6154   4.2879 -11.0969  -5.7258  21.5706
-#>   -4.4280 -14.9012  -8.8041 -14.0777   4.9153  13.6668   5.2954   3.0269
-#>   -1.2733  -3.2601  -1.8351  -5.1108  -3.5288   1.6957  -8.3590  -5.3381
-#>    4.1251   0.9375  -3.7061  23.1854  -1.0771 -12.4512  -2.0129  -4.0775
-#>    4.0202   0.9914  -3.9503  -4.0601   7.2828  -3.2260   9.1126   8.2552
-#>    4.5288  13.4074   1.2214  -2.5435   6.7230   2.8578   9.0410  -2.8760
-#>   -3.6678  -2.4159  -3.7118   9.2260   8.0656  -4.5218   7.3985  -8.2251
-#>    5.0364  -9.3228  12.9259  -8.1170 -17.5855   3.5425  -3.0200 -10.1435
-#>   -6.1906  -4.4283  -0.9003  -0.7803   9.6682   3.5322  -0.8297   8.7635
-#> 
-#> Columns 9 to 16   1.5017  -8.6068  -6.9181   0.8914  -5.0705  15.9220   5.7279   0.2399
-#>    7.3119   1.3518  -5.9874  -0.2507   7.6702  -9.8376  -3.9085   7.4234
-#>    8.6301   2.0793 -11.9055  -3.7911   2.2005  -7.8006  -5.2940  21.5218
-#>    3.0038  -1.6677   1.7950  -4.5508   2.2488   5.3606  -9.1701  11.3947
-#>   -5.7708   0.4336  14.7154  -3.9453   7.6350   4.7351  -8.6861  15.0580
-#>   -1.3754 -17.9018  -1.1444  12.8072  -1.4358  -3.2275  -3.6352   7.4320
-#>   -5.0755  -5.4128   1.8288  -5.4710  -3.5631  14.3366  15.0004  -6.0529
-#>  -10.4102  -6.2087   0.9492   6.0118   7.5276  -6.7697  10.3562 -11.1823
-#>  -11.1926 -13.0750   9.3369  -2.0888 -19.6530 -11.2297   0.6006 -12.6120
-#>    3.0238 -16.1934  -2.7229   1.8922  13.2706   7.3099 -13.3667   1.7925
-#>   12.4466  -0.6007   5.8977  -3.2004  -0.3359  -1.2151   0.8104  -3.3276
-#>   -1.6860 -14.1189  11.2619  -6.9821   4.1982 -13.8986  -2.2289  16.0245
-#>    7.7072   3.2524   2.7273  -0.8512   1.0577   2.9917   7.9595  -3.3827
-#>    7.0726  -7.8132   0.4509  -2.2803   6.6153  -4.2307   3.3257  12.5965
-#>   -7.9716 -23.9474  -3.5218   1.5840   9.3275   1.9662  13.4866  -2.4736
-#>    2.7921  -3.6859  -3.9322  -4.6172   9.0890   2.9150  -0.3922   4.2977
-#>   -5.9871   9.6314   9.5661  -4.8246  -8.7754  -3.9940 -18.3416   4.9380
-#>   -0.0987  -0.1037  10.6268  -0.9204 -13.3450  -1.6316  10.4843   2.5481
-#>  -14.0458 -11.3099   3.7968  -0.4273  -0.2426   5.3267   2.5750 -17.5287
-#>   -2.2571  -2.0932 -14.2280  11.7172  17.4334   3.4308   0.5894   6.1905
-#>    0.2811  15.8760   2.4301  -2.2227   7.1458  11.6980  -1.3617   1.6834
-#>   -1.4061  -1.7128  -5.3377 -13.2902  -0.3802 -10.0704  11.9042  -0.1962
-#>   -7.3468  -1.7626  11.2446   4.4840  10.7810 -18.4213 -13.0671  14.1763
-#>    2.9852 -13.0677  -2.5682  -2.7484  -0.3983   0.4290   6.8680 -12.5134
-#>   13.3903  -0.9157  -2.4934   3.4122  -3.4299   2.6442 -15.6976  18.5193
-#>   -6.8927  -4.6039  -1.2142  -5.0980  -6.1670  -5.9587   2.6590   0.4891
-#>    9.6014  11.4081 -11.5250  -7.0517  -0.4210   3.0345  -4.5130   0.2101
-#>   -0.8494  15.2047   9.5280   1.9386 -13.3232  -3.7443 -10.3539 -14.5014
-#>   12.9063   7.2197  -0.1236  -8.5376  -2.7291  -1.1630 -10.6046 -17.5495
-#>   -0.0908  10.3624  11.3390   0.9138  -0.4849  13.4013  11.7459  -8.2455
-#>    1.9956  -6.9150   2.7161   1.0772   2.4628   0.0884   9.3908   5.6051
-#>    5.1412  20.5724   7.5165   1.3354   9.4225   1.5700   0.4186  19.6713
-#>   13.9985   7.4152   2.8320  13.5761   6.1628 -12.5014   2.0404  -2.9885
-#> 
-#> Columns 17 to 24   9.7553 -13.1200   7.1868   4.8436   2.1946  -6.9015   1.4137   9.7216
-#>   10.7145 -15.9968  -7.6058   3.1467   9.2966  14.3546  -0.1738   9.8027
-#>   -9.2212  -0.5251   3.5527   5.3623 -11.8983   4.0554   4.5675  -6.3613
-#>   -2.6589   8.1325   7.2018   2.1848  -4.5962   2.5252   1.0252 -14.3119
-#>   -7.3349   2.4963   6.5116 -10.1706  -3.1291  -7.2340  12.6099  -0.4696
-#>   -0.1394  -7.5460 -22.2137  -2.3903   2.1341 -22.2161  17.2772  28.6837
-#>   -2.1571   5.4578  -1.2019 -22.6302  -2.6689   9.1606  -3.8338   2.8104
-#>    4.4399  -4.1536  -0.9956   9.4338  -4.5337  -8.7534  -5.4436   5.9705
-#>  -11.4283  -1.5159  -6.5968  10.1358 -15.0663  -4.5839  -3.3741   7.3909
-#>    5.8492  11.3488  -2.2672  12.7561  -7.0420  -2.7220  15.0504   8.2648
-#>  -18.0644   3.9038   1.3762 -20.8657 -11.8200   4.8428   3.3412 -21.4536
-#>   -5.5596 -23.4892  -6.5741  11.1924 -15.3357  -2.2479   6.0471  -0.6416
-#>   -5.0136   0.5910  -2.1618   6.7253  -1.5704   6.2009   1.3919   9.3597
-#>   10.9548 -13.9191  12.5985  -1.2022  10.8440  -5.5861   4.4983   3.3493
-#>   -1.2791   3.0089   9.8931 -13.5114  -4.6666  -1.9426   4.4158 -10.9277
-#>  -12.9990   0.6456   8.3221  -4.9207  -0.7035  17.5786   7.1658   5.9181
-#>   17.2244 -19.4162   2.3888  -0.8057 -11.6695   4.9724  13.7872   1.6801
-#>    5.4351  -6.6086  -7.4150  -2.8655   4.8108   5.5622   6.3642 -10.0064
-#>  -11.7144  11.1556   1.1453  21.3122 -11.9525  -6.5990   9.2495  -5.2196
-#>  -15.3457   6.0296   4.1158  -0.5219   1.6137 -11.4727   8.4630  -2.4792
-#>    2.8535   0.0787   1.0286  15.6627  13.0209  -6.1135  14.4283   9.9701
-#>    2.5635  -3.7947  -1.3162   6.7904   0.7413 -17.1797 -21.7695   8.1626
-#>   -3.6684  -4.9924  -2.0509  -4.8486  -2.1283  -4.5470  -1.8739   9.6315
-#>  -14.5102   9.8342  -1.4152 -10.0871   9.7394  12.9369 -12.2200  -8.0341
-#>  -10.4517  -4.9812   2.9651   7.1184  -1.4927   6.9153  16.0767  -8.4478
-#>    4.6687   3.0401  -1.0172   2.2060   1.4107   2.0808 -11.0041  -0.6075
-#>   -4.7864  -0.0843   2.4379 -12.3772   9.2019  -3.9495  -1.5036 -11.3102
-#>    0.9289  -7.3573  -4.8422   5.4141   8.8234 -12.1827   6.7583  -5.4115
-#>    4.5193   7.5225  -6.1402 -12.0619   3.5380   2.5877 -13.4143 -13.8412
-#>    4.8879  11.3728   8.5100 -22.6038 -11.1467   9.6744  10.0143 -21.5391
-#>   10.9835  -5.5687   1.3013   2.8671  -5.4365  -4.9046 -20.4971   2.6865
-#>  -18.5385   4.6653  -6.3887  -0.4565   3.7783   1.8561  -6.8878   2.6661
-#>  -20.1066   1.6469   2.6033 -19.8456   8.6572   3.5506   8.2641 -13.4063
-#> 
-#> Columns 25 to 32  -1.2997   2.5660  11.4786  21.9807  10.5563  12.5488 -16.1947   8.9865
-#>   30.3387   4.3806  -2.5467   6.4973   8.2565  11.6147   1.4779   1.1958
-#>   -8.6803  -5.0393  -4.5055   2.4810  -7.9344   7.4099  -4.2127  -3.3463
-#>   -4.6563  23.6231 -14.3282  -0.1461   2.4661  14.0052  -8.7004  -1.3035
-#>    5.4801  16.1340   1.9629  14.6541   3.6288  10.3801  -2.2284   3.0719
-#>    9.1457   4.7753  26.7420   1.9238  16.1791  -2.2101  -5.3664  11.4996
-#>   -2.6553  23.0783  -3.7477  -4.2019  11.1723  -4.5220   4.4127  -4.7281
-#>   -2.5818   4.4406  10.7327   2.0380   2.0969   9.6335   3.9120   8.2685
-#>  -33.8974 -12.8143  -6.0901  -9.0709  -7.8182   8.9614  -2.5334 -12.7631
-#>    3.2786  -1.6036   7.5980  -3.9095   1.2602   2.3739   3.2521  -4.8686
-#>   -7.3327  10.4040   5.5009   4.0083  16.3784  -8.6116   5.7804  10.3379
-#>  -14.2673  -6.2080  -4.0674   0.1512   8.3497  -9.4116  -4.0423 -14.0406
-#>  -20.3385   3.9015   2.0622   6.2539   6.8554  -7.8619  -3.0550  -4.7275
-#>    2.5882 -12.9390 -22.0963  -3.0532   1.9954  -0.9987   3.1953  -8.4127
-#>   -5.0772  16.6097  -9.3671  -0.4289  -0.8395  -4.9621  -8.1858  -7.3859
-#>    7.6867  20.9398  -0.7564 -10.0955   4.2113   3.5465  -1.3963   8.6430
-#>    1.0266   7.9831  -4.8256   2.7832  20.5387  -2.9075   1.2404   7.3119
-#>    4.3127  -3.5009   1.9082   1.2073  -3.6349   0.1758   7.0910   2.4704
-#>    5.2905 -13.3534  18.8257 -26.3553   2.9182 -13.3157   3.7616  -7.0972
-#>   -9.0565  -9.4113  -7.3716  -6.4189  -1.4251  -0.6970 -11.1698   6.4259
-#>    9.8357 -20.6985   6.5946  -6.4993   1.4075 -10.1752  -2.8046  -0.5371
-#>   -7.0588 -15.2924  -4.0809  17.3121  -5.1022  14.4247  -6.8520  13.0136
-#>   -5.4876 -12.7528 -31.4428  -4.2435   5.3583   0.4308  -8.7641  -7.9566
-#>    2.7864   3.8519  -3.5083  -1.5176  -2.9368   9.7348  -5.4117  -6.1777
-#>   -1.2136 -10.0024  -8.1429  -4.3171  -6.6987  -1.9511  -9.4266  -5.6240
-#>   -7.5783  -7.4562   6.2961  -6.0867   1.4181 -16.5222  -2.1903  -8.6514
-#>    9.0910  -3.3295   1.5234  -4.1993   1.2210  -0.2420   2.8981  -2.0521
-#>   -5.5707 -18.4647  -0.6560  -2.7191   1.2130  17.2127  -7.8321   4.2030
-#>    4.6750  -2.4481 -14.0304   6.0465  -5.7216   2.8683   3.5524  -2.6627
-#>   -4.3774   4.2675  11.3429  -9.4706  -2.9024  -6.9048  -0.8443  -2.9756
-#>   -6.5006   5.7395  -0.4179  -7.7384  -0.1501   9.6269  -4.5812  12.0688
-#>   13.9179 -15.6514  -3.5161   5.1031  19.9115  -3.0878   4.0127 -15.4881
-#>    6.5677   1.2792  -3.8230   2.6097 -13.5379  10.4018   0.3715   7.4503
-#> 
-#> Columns 33 to 40   5.3052  -7.0661   4.7852   8.9610 -15.2011  -3.8709  17.9290   1.6281
-#>   -5.1879   5.2382  -1.0851   8.2228   4.6034  -5.9043  -1.7003 -11.1631
-#>   -6.9663   6.2021   3.7609 -16.0715   0.8179   4.0172   2.3748   2.4703
-#>  -18.8565  15.3300  -2.0820  -7.5073  -6.7966  15.5286 -13.4748   6.2408
-#>    0.8957   3.5834  12.3402 -17.6054   6.3492   7.4437   3.7392  -8.5734
-#>   -2.9738  -7.2329  -2.5599   1.7708   2.4251  -5.8812  -5.8491  -5.3014
-#>    6.4677   0.8822   1.9027  -8.3738  -8.0058  12.0594  -8.5700  -5.1170
-#>   18.9312  17.7423  -3.8196  10.4943  -0.9489  -7.5846  -2.0368   1.1635
-#>   -2.4501   4.5884  -6.6799  -4.8542   5.8958   1.9316   7.5522   4.8995
-#>   -9.1462 -12.1461   8.4455  14.3685 -16.3043  -9.3552  -8.0756  -6.7934
-#>   10.4133 -12.1937  15.6228  -9.6192   2.0900  -1.8004  -6.6100  -7.5872
-#>    8.8462   0.4353  11.5399  -4.4965  -6.6862  -1.5440   7.6812  -2.9164
-#>    5.2504  -0.7551   9.2843  -1.7086  -3.6233   0.1112  12.3992  -0.2854
-#>   -9.8543  -2.7453   2.0754   1.1477   6.3524  -7.0851   0.0442  10.8561
-#>   -9.9474   9.1364  -0.2938   2.6699   1.5531  15.9298  -7.0656  -4.3737
-#>   -2.8141 -23.9811   2.7360  10.6291  -3.0334  10.8106   0.6688  -9.3946
-#>    4.9860   0.2085  -0.2466   1.6133  -3.4966  14.4860  -2.2270 -23.5474
-#>   -2.5455  -2.8775 -15.3958  -7.0275   3.9818  12.7210 -13.5770  16.3814
-#>   11.6903  -1.1662  -3.7867  17.0957  10.7837  -6.2491   7.7903  -1.4236
-#>    6.9868   3.5043  10.6076  -3.3494   9.3721  -1.1851 -16.0425   7.7467
-#>    6.9866  -8.2193  -5.6146  15.1631   4.8171  10.9608   0.2572  11.4145
-#>   -4.2670   7.7636  13.9072  14.0630  -3.8046  -5.2689  -9.9894   1.9107
-#>    9.0186  -1.3330   2.4330  20.1800 -10.8788  -3.1260  -5.8801  -9.1647
-#>    5.5179  -1.6877 -19.2072 -14.4144  -5.0496  -7.9839   6.6813  12.4073
-#>  -12.5223   2.0891 -12.9087  -7.6491  -1.4437  -8.5859   1.9822  11.2169
-#>    9.9112 -18.6615  -7.3252  -5.5876  -1.6898  -3.7585   0.5299 -15.4517
-#>    5.1917  -6.7378   0.6070  -1.0705   0.7240  -6.5471  -4.8843  -0.6816
-#>   -7.0180  16.8131  -3.6724   3.8947  11.0605  -0.7513  -1.2543  -1.7039
-#>    8.3646  10.8441 -21.4233 -16.3048   6.7304  -8.0358 -16.3618  14.3174
-#>    3.0343  -7.0327  -5.2200   8.3443 -11.4185 -15.7410   5.5705  -2.6389
-#>   -1.9251  -4.4846  10.0314   4.7699   2.6357   2.8893  -2.4603  -1.3923
-#>    1.6812  -1.4077  -6.8064   0.4776  -2.6904  10.8166   4.8353 -27.0714
-#>   -7.4296  -3.7325  -5.3549  -8.5688  -9.1646  -6.8415  -2.5583   6.1067
-#> 
-#> Columns 41 to 48   3.1174  16.3425   7.7411   4.7303   6.4072  -0.6967 -15.2469   4.0689
-#>  -19.8591  20.3703 -22.3870  -0.0186  -3.8217  -4.0515 -10.3993   8.4956
-#>    6.2955  -6.7682   1.2646   1.5836  -2.6305   5.3780  -4.7371  -6.3778
-#>   -3.3552   3.5098  -1.0505   8.3239  -8.7252   8.8065   6.4772   8.2973
-#>   -3.9051  -7.4153  -2.2978   4.8423  -6.7482   2.8870  -4.6284  12.2837
-#>   -3.4785 -24.9972   3.0788   1.1094   2.2645 -26.4156   3.6031  -6.0978
-#>   -5.8234   3.4496  -2.9199  -8.7993 -10.5120  -4.7851 -11.1427 -23.1279
-#>  -11.7796  -0.4631 -13.1887 -11.4661   2.6153   7.1247 -10.4822 -15.9656
-#>   13.4413 -11.6371  -0.9134  13.4668  -4.4373  -6.4646  -1.3270   2.8753
-#>   -2.1881  -6.9783 -10.5101 -10.4630  -3.0733  -2.1742   8.1180  -6.3409
-#>   -1.8502  -9.7822   2.1889  -0.1678  -5.9537  15.3674  -0.7551  -4.7234
-#>   -8.0885   8.0203  -4.0589  -8.8241  -8.3500  -2.8535   9.3532  10.9975
-#>  -18.7868   0.3839  21.5336   9.5365  -2.7460   8.3454   6.3590  -6.5452
-#>    1.0877  11.8411  -7.7273  -3.8096  -1.4145 -16.3855  13.4120  -4.7363
-#>   15.1865   5.0968  -3.6312   1.6900   9.4821   2.4396   3.0815   5.8806
-#>  -10.7069   8.0684  -4.1255  13.1902  -7.9577  -1.6689  -4.5992   7.6382
-#>    2.7812  -9.5274   8.5902  -4.6735  -2.9463   1.7383  -6.5527 -14.1294
-#>  -17.0843  14.5295   7.6360  -1.0162   0.3424  23.1627 -11.0931   6.9220
-#>   -9.3723   6.4530  -1.4624  -0.4699  -5.1367   4.7219  -9.0282   4.5032
-#>   12.0693   1.0159 -23.7530  -1.8362  -2.2409  -5.2844  -4.3661  -0.5389
-#>   -0.3056   3.2738   3.7991  -2.3939   8.0024  -1.8377   5.1208  10.9426
-#>   -9.6241  14.9390 -16.1278  -9.0459 -16.5176  -9.3462   0.4452   4.7589
-#>  -10.3488  -1.6794  -5.0653  -8.7524  -7.6349 -16.4446 -11.3896  -7.0746
-#>    1.4225 -17.2559   4.2896   8.8625  10.0452  -8.2771  -0.2759   7.0084
-#>   -8.0495  -7.6525   2.5955  -0.5583  15.6281 -17.6703   0.5897  11.1602
-#>   27.0729   5.4696  -1.3197  -9.3038  13.9750  -2.9348   4.7927  -2.7989
-#>   29.5099 -20.9963  24.6360  -6.2216  15.2439 -11.4710  16.2514   6.2927
-#>   -3.5272 -19.5338   3.2136   7.2999   0.0861  -2.9485  -9.8887   0.4744
-#>   -1.2962  -1.0744  14.8624  -2.6720  19.5613  23.0580  -6.7557   7.1725
-#>  -17.8225  -0.3951  -7.6426  10.5787  -2.1145  -5.5434  20.5069   3.5584
-#>   -2.0788   2.3581  -3.1871   7.5324 -25.1909   4.3661   7.6013  -0.0651
-#>   12.2884   8.4565  20.5663   9.6368  18.6797  -2.5369  13.3668   1.8755
-#>  -10.1990 -10.4719  -0.4565  10.4234   4.8150   1.9160   2.6964  -5.6770
-#> 
-#> Columns 49 to 54   4.7604 -17.0443   4.2362  -0.1506   5.7847   4.8275
-#>   12.6656   2.2082 -15.0857  -9.7875  -1.2137   7.7689
-#>   -7.9383   3.0470  -2.7657 -15.1841   7.5981   3.2548
-#>   13.3431  -7.0009  -9.5811   3.9706   8.5158   0.8813
-#>   -2.0129  13.4008  -1.1993  -6.5928   6.5611  -4.5572
-#>  -13.2717  12.4965  -5.4569  -0.3310  10.9039   5.8270
-#>    8.9575  -5.0186  -2.2719   0.3510  -4.7358  -3.2742
-#>    6.5051   0.5433  -8.9381  -0.5561  -6.3295  -1.4019
-#>    3.8464  -4.6707 -13.7753  -0.9268   5.7068  -1.6860
-#>    3.8926  -6.4982  -3.0241   1.7773  -3.8354   2.0144
-#>   -6.7829   7.4228  -0.6287  -1.7426  -6.6208  -2.8569
-#>  -12.3822  14.1381 -11.0661   9.7871   2.0780  -1.8625
-#>    8.6162   4.5793  -2.0453  10.8411  -8.9352  -2.9064
-#>    8.2304   9.6105 -18.9576  -9.7135   8.1245   6.5796
-#>    4.2747  15.7482   0.5116   0.3624  10.7449   3.9772
-#>   -4.2663  -3.5648  -1.8642   7.4056   0.9312   5.9999
-#>   -2.1748  -9.0366  11.5350  -4.8309  -3.1486   9.7313
-#>   -3.5441  -3.7128   3.9071   1.9930  -5.4614  -4.3329
-#>   -3.4657  11.1438 -16.1882   8.2909   5.3822 -11.1595
-#>   -6.5959  -4.9608   1.6281   5.4256   1.1819  -7.4167
-#>  -12.6978   0.9185   9.9471  -9.0970  -8.7337  -0.1304
-#>  -14.7662   1.3690   5.9851  -0.7951   3.1230   6.4365
-#>    7.6856   0.4292 -14.4094  -0.8849   0.1469   3.4671
-#>    5.4269  22.9641 -11.5888  -0.5557  12.3558  -2.9540
-#>  -17.0178   1.4444  -3.5835   1.6870   7.0000  -9.2378
-#>   -3.3226  -4.0039   4.7945   6.7741 -10.1068   4.7550
-#>   -3.1680 -10.0941   1.2355  -4.2952   3.2572   4.4980
-#>   -1.9808 -11.6065  -7.0829  -7.6412   7.5375  -9.0860
-#>   -5.1760  -1.4036  -5.5432  -5.4713   3.0101  -2.3162
-#>   10.2289   1.8357  -9.1445  22.0267  -3.5414 -11.8593
-#>    4.8033   6.7724  -6.2342   3.3569   0.4787   4.3952
-#>   10.7536   3.2190  -5.0490  10.7763   0.7685   2.7364
-#>    3.8563   5.1250  -8.5096  -8.6343   6.7572  -4.6396
-#> 
-#> (8,.,.) = 
-#>  Columns 1 to 8   7.1508   6.7372   5.9819  -0.0601   2.6715  -5.1312  -4.7280  -6.3784
-#>    7.6682  -5.1930  -3.7598   6.8804  11.7423  -9.1464  10.0633   6.0598
-#>   -5.6035  -1.0263   1.6200   0.2141   4.6984  -2.8773 -12.6556   2.3026
-#>   -2.9972   0.1561   7.8324   2.6244  11.8952   4.3623   3.7364 -25.3673
-#>   -9.6151   1.6634  -0.2185  -7.4899   4.8237  -4.0181   4.5082  -0.4462
-#>    4.0094  -2.8231   0.4799   3.3922 -11.7035 -13.0875 -10.3550   5.3582
-#>    3.9708   2.3987   2.3578  -3.6164  -9.5522   6.8756 -12.5137 -12.8285
-#>    0.9439   0.7691   1.1446   3.5504  -5.8556   2.2269   1.1489   3.9464
-#>    1.8752  -4.9550   3.1504  -6.5187  -8.1246  12.7283 -14.8513  -1.5619
-#>    0.9727  -0.8928  15.7404   4.6312   8.3961  12.8783   7.6629   6.6705
-#>   -6.8592  -0.9109  -4.1087  -2.7627 -13.6369  -0.1231   2.1088  -2.5499
-#>   -3.5121  -2.3167  -5.9088   3.2831  -0.5246  -4.8987   2.8872  16.5708
-#>    1.6531   6.3598  -0.8847  22.2141  -2.2368  -8.2070   1.2624  -2.6903
-#>   -1.7068  -4.0863  -5.5869   3.6179   9.6339  -3.1257  29.5030  -1.4866
-#>   -2.4773  -5.0205   4.1964  -6.4092  -4.1407  -5.0958  -4.7861  14.8317
-#>    6.5156   4.4029   6.6035   1.2587   1.1257 -16.5518  -3.6082  16.4726
-#>   -1.0780   3.4886   6.4448  -3.6593   0.8148  -5.3412  -0.4133   4.7233
-#>    9.4846   7.6809   6.4711   2.0481  -1.3859  -1.0715   4.2044 -20.6968
-#>   -4.3761   1.6670   2.4490  -8.4354  -2.7067   9.7746   2.3280   3.9501
-#>   -3.8817  -6.5747  -2.0872   2.3250 -12.6315  -5.6352   1.2802  15.7961
-#>    4.2989   1.5583   4.6880   5.4209   1.1403  -0.6907  -0.5091  16.0139
-#>    1.2061 -14.1669  -8.0375 -17.3101   5.6985  -0.8953  12.6394  -9.9687
-#>   -5.8559  -8.4070 -11.6112  -4.7989 -24.5551 -16.0300 -10.0286   9.7208
-#>    8.2044   6.9436  -3.6526  23.1866   8.5349  11.6573  -0.4488   2.0429
-#>   -3.9382  -3.7899  -5.7767   5.4737   2.7066  -1.5215  -7.6963  -3.5784
-#>    9.5496   8.5969  10.5426  15.4591   5.8838  14.7248   8.8217  16.1680
-#>   -1.1191  -2.1120  -6.3763   1.0351  11.4906  -7.1137 -11.8626  -0.3931
-#>  -10.9741  -4.9215 -13.0459 -15.8233  -3.4078 -12.5638   2.9955  -5.9202
-#>   -0.1297   8.3024  12.6717  10.2556  13.5064  13.4313   0.2281  -1.7045
-#>    1.6663   3.6732  -3.0221   3.8763  16.6424  -5.3196   4.7441 -17.1505
-#>   -6.5405  -3.9878  -7.3507   1.7192  -9.0308  -9.1152  14.1827  -6.6667
-#>    0.8203   4.1849   3.5764   1.3127   3.9395 -12.8159 -17.8305   2.2912
-#>    3.0475   8.3816  -2.4021   6.5701   4.2364  -9.9423   0.9770  -9.7009
-#> 
-#> Columns 9 to 16 -10.0151  -9.5474  16.3357   8.2495  -1.5964   0.5695  -4.5643   9.8078
-#>   30.0856   0.3242  -7.8498   8.6488   2.6957  -5.3110  -8.6937  -1.7189
-#>    1.6592  -7.2534  -2.4388  12.4133   5.6499  15.6555   9.9671   1.4648
-#>   11.3157  -6.9102  -4.3567  -1.6289  -1.9218   3.2060   9.5204 -16.0418
-#>    7.6599  13.5402   0.1472   3.2803  -7.8039   5.7619  12.2552  -0.9779
-#>  -15.4246  20.6626  18.6428  11.2379   3.3039   9.3777  -0.9136   1.9426
-#>  -14.6024   3.6175   9.5540  -3.6661  10.4562  15.3631  18.0100  -3.4185
-#>    1.6321  -2.9346  -6.9568  -8.9791  -3.5516   2.2035  -0.5501  -7.4288
-#>   -4.0139  -5.5305 -11.6019   8.2809 -23.6624  -2.0877   3.3591  -1.3592
-#>    3.6416   8.6007 -10.4185   7.1283   4.3517  13.6260   5.8551  -3.1176
-#>  -11.1471 -15.0331  -2.3048  -1.3674  -2.6871   3.3351  -2.7968  -7.0620
-#>   -6.1378  14.2888  -1.8011  -2.4272  -3.7110  -0.9645  -4.7425   9.3068
-#>   16.9800  25.1799   9.1233  -7.2462   4.7891  -5.0251   0.7113  -9.1539
-#>    9.0895   3.0683 -24.9544   6.3863   5.4586  -9.7773  -0.5664  -7.1917
-#>  -10.7116  17.3930 -13.5514  11.1032  -0.2145  12.2160  -5.1363   2.3689
-#>    0.3445  -2.2634   0.8781   3.2789   4.5291  12.1612   6.0505   2.4993
-#>    5.3868   3.0195  -0.2482  -5.4812   8.4448   9.9567  -0.4724  -6.5618
-#>   10.9629 -12.1021  13.6602  -3.9014 -15.5234  -8.2013  -9.3046  12.3432
-#>  -14.5482  -5.0352 -10.1014  -4.7969  -0.9369  10.4475 -10.6851  -0.4472
-#>   12.1259  -6.1691  -3.0353   4.5765  10.1662  -0.7835   2.3517 -10.8543
-#>   -8.3979  -4.6937 -15.2274 -22.2141  -8.5639  -6.7124  -0.9374   7.7074
-#>  -17.2386  -7.9246  -6.5068   3.9143  11.5533   3.2035 -11.3446  -2.0618
-#>    9.9309 -13.9416 -10.4132 -16.5851   3.7950  10.6597   2.1974  -4.4408
-#>   -0.4629  -0.9287  22.9732   1.8167   9.5406  -3.2515   1.6038   1.8185
-#>   11.0190 -21.9261  22.7242 -17.9158  -7.3786  -7.2195  -0.3001  18.1530
-#>    5.9330  19.3633  -2.7365   9.7048  -4.1241  10.0479  -3.0356   8.2367
-#>  -10.5649   2.1628 -13.9123   9.5574  -5.5361   3.2461   1.5565   3.8338
-#>   -3.5414 -14.0745  -3.8302 -14.1880  -6.9356  -9.2233  -1.5608 -11.7840
-#>   -5.1279   1.8206  -1.8614   5.3786   8.2715   2.3127  -1.2312   3.2170
-#>   -1.4440   1.4858   9.7767  -1.3624  -2.6655   5.8860   5.7552   9.4817
-#>    1.4178 -16.0870 -17.0147  -6.7973   3.1098  -0.3210   7.8234   1.2850
-#>  -10.4717   1.5837  -1.9258  -2.0323  13.3036   7.7405   7.7084  -5.0524
-#>    9.6043  -7.5064   0.8189   0.4072   9.6478   1.5139  20.2461  -1.0598
-#> 
-#> Columns 17 to 24   8.1001  -1.0351  -6.7461 -11.0419  -8.1504 -15.6931 -19.7889  -2.3716
-#>  -10.0340  -5.0651  14.4485 -11.9413   6.1078 -14.9792   2.4590 -15.2920
-#>   -3.9651   6.6120  10.5014   7.5160  -9.6985   4.0468  -7.4204 -11.1921
-#>  -13.8032   4.4341  -0.3191  14.4921 -10.4447  -1.8446  12.3376  -0.8948
-#>   -8.0753  -1.2589  -8.4688   3.2476 -26.8242  -4.4277 -13.5342   2.0415
-#>   -4.3877  -0.1280 -20.6544 -12.8968  10.8765 -10.9568 -11.3636  17.6642
-#>    9.3794  -1.1094  -9.6291   3.5754  -2.1581  -8.7307  -2.9342   4.1637
-#>   -6.2747 -11.1756  -8.4495 -13.4040 -13.1809 -13.8689  -7.2018 -13.4809
-#>   12.8036  16.1512   5.4492  15.9207  -0.3596   0.6861  13.7002  -4.8839
-#>  -10.9333   2.2218 -14.8068  13.8960  -5.6335  -0.0825   5.0305  -6.7653
-#>   14.1881   0.2138  -1.2540   2.3505  -2.9398   3.0173 -16.3710   2.6998
-#>    3.5745  14.2715  15.6316   2.2813  -7.2114  -1.7036  -5.6726   6.8289
-#>   -1.3762 -16.8109   1.8873   5.7485  11.4382  -7.3215 -15.7174   4.9650
-#>    0.9686   5.8856  -8.1113   7.1000   5.8756  -9.7769  10.2416   7.2427
-#>  -10.5910  22.1120  -8.2317  -3.1513  -9.2054  -6.5088   1.6777  -1.6041
-#>   -9.1672   1.9800 -16.2627  -9.7974   3.4530  13.2416  -4.8867  -3.5655
-#>  -12.8684 -11.0718 -13.4428 -13.2373 -10.2448  -2.0117   7.0435   3.1549
-#>   -1.6896   6.6603  15.4529   7.5885   4.8641   7.3906  -2.2209  -6.2453
-#>   -2.5293  -9.9172 -13.6721  -0.2927  -6.7671   8.2823  19.9966   7.8450
-#>    2.8503   4.1798   1.5580 -14.8536  -6.3334   2.6650  -4.5798  -8.2165
-#>   -4.0300 -14.2928  -2.4017 -20.5895   4.7263  14.9711  -2.4875  14.4660
-#>    5.1115 -13.9482  -3.6005   4.3206   9.7899  -5.5324   8.6255  -7.1614
-#>    5.7668   2.7724   7.4530 -12.2066   3.6582  -2.3568  -3.9457   3.7212
-#>   13.5755   5.8316   0.1492   9.8553  16.3314  15.4315  -7.3261   3.8208
-#>    5.3305  12.3318   1.8238   2.7613  11.6012   8.4931  -3.4332   5.4188
-#>    8.1775  12.1450  15.1162   2.3753   2.2250  -3.6509  12.8902  -2.8555
-#>    7.8905  10.0615  -0.4912   6.3662  -0.6782   9.5253 -15.2438   2.5168
-#>  -11.2149 -10.0954   3.3422  -2.7070  -7.0759   2.4042  -3.7015   1.2223
-#>    2.3879   6.8082   0.2533  11.3374  -1.4567  14.0965   2.2258  -1.8460
-#>    3.4320   9.3158  -0.6690  17.7832   9.3512  13.3587   9.3576   5.0262
-#>    9.3727   1.7643   2.7273  -7.8212  -7.6490  -2.7739   7.7751  -6.1483
-#>    0.2295   0.1979   2.4044   6.5403   8.0871   1.0703  -1.9011   7.0253
-#>    4.3509   5.3380  -7.0635  10.6731  10.6836   0.4986  -7.3226   1.9557
-#> 
-#> Columns 25 to 32   6.7455   1.7868  -8.8835  -8.2019   4.5433  -1.0239  -3.1092   1.6769
-#>    8.4804   5.0411  11.5685  -0.9770   2.6991  12.6585  -1.4397 -14.9938
-#>  -10.9128  -5.3340  -9.6483  -6.0048   9.7009   3.1092   5.1960   1.1162
-#>   11.1955  -2.2595   9.9399  -9.6134  -1.3063   9.5693   3.4519   1.7172
-#>    3.1671   1.4267  -2.8816  14.6492   7.2395   1.8898   5.8684  13.9336
-#>    4.4651   8.7700  -5.7401   6.6528  -5.2405  17.1295   8.9922   8.9185
-#>   -3.7940 -18.7592 -11.9993 -18.3577  10.6142 -13.1228   3.9631  11.6180
-#>   -1.4272  -1.2968  -1.0754   3.7228  16.9619  -9.0493  -7.8127   6.2468
-#>   -7.6175   2.0480 -11.3912 -27.6616  -2.1672   1.8430   2.4369   0.5673
-#>   -3.9454   5.5963   8.3102   2.1835 -15.3158  24.8253  -5.0833  10.2927
-#>   -8.1090   8.9425  -9.0912   7.1353   5.2831  -4.3799  -4.0059  25.5929
-#>   -4.5726  -0.3739 -18.7746   9.9125 -14.6715  15.2602  -3.1135  14.5251
-#>    6.0025   3.2679  10.2790   5.0409   1.6172  -0.0848  -5.3120   4.0880
-#>   10.8606  -4.5095  20.0533  -6.8515 -18.1393   7.5030  -8.9183  -7.8453
-#>   -3.4917  -7.7637  -9.8252  16.4568  -2.8053   8.6339  13.7757  -0.1181
-#>   14.0185   9.1554   5.6943   0.7750 -10.0743   2.1277  -2.2721  13.1413
-#>   10.1655  -4.2152   1.7572   4.1830   2.1623  -8.7048  -3.9587   1.0580
-#>   -6.0097  10.9930   5.1062   8.3181   0.8457  -0.1577 -18.2184  -2.3131
-#>   -5.5882  -2.2534 -22.0129   4.8418 -15.4079   5.5410  -4.4979  -0.6346
-#>  -14.6428   7.7315   6.3648  -5.0603   5.9393  -1.0989  11.1776   6.7368
-#>    9.9365  24.6246  -1.8262  14.6768 -21.6443 -14.6404 -12.6476  -2.4409
-#>    1.4070 -16.3869 -19.9686  -6.7619   4.0466   3.5202   4.3108   2.6778
-#>    9.7351   1.3257  -9.6109 -21.4991  -2.8457  -9.2122  -3.7684  12.5494
-#>   -1.6200  -0.4281  -3.1035  -1.5048   1.5300  16.4868  -5.0592  -1.0714
-#>    8.1314  15.9266  -7.8683  -6.8566  -8.5155  -0.3033 -16.4273  -7.1421
-#>  -13.8999  -5.4036   8.2061   1.4796  -2.7902   4.8665  -6.9505   0.0025
-#>   -7.4138  -4.6629  -4.7055  -3.7802  -9.6058   6.1842  -2.8387  -6.5854
-#>   10.8963  14.8133   0.6713  -1.0068   0.1435 -19.0033   6.1731 -15.9684
-#>    3.5799  -7.0449  -4.7986   4.5287  -7.1192  -8.4011   5.7986   1.1786
-#>  -13.1699 -20.9364  -9.5993   5.8298  -7.7431  23.6490   4.8607 -11.3314
-#>    6.8217  -2.7287  11.7833 -12.5530  -7.4013   7.5500  -6.6971  10.5138
-#>   10.9274  13.0032  -9.6783  15.5270  -2.9641   2.4258  -6.9870   2.7114
-#>   12.2095  10.0677  12.3627  -0.1106   3.4960   3.6681   2.2930  -6.1216
-#> 
-#> Columns 33 to 40  -3.3622 -10.3849  10.2555  11.8728   5.8306 -10.4878   0.4853   8.9719
-#>   18.4105  -7.9307  11.4194  -0.1296  -5.5568   3.6137   9.7678  -2.1159
-#>   -3.3697   0.7608 -12.6738 -10.1236   4.4349  10.4471  -4.0326  -0.8935
-#>   -0.3232  -0.7280  -0.2048   5.9241   0.5660 -11.9498  -1.1860  21.1517
-#>   -2.7066   3.8992  -7.7092   2.9674  -2.1234  -1.3887   0.8307  -2.0074
-#>   -0.5790  16.3288  -1.7148 -13.5067  -1.4230  -3.2069  -0.7859  12.1890
-#>   -4.6540  -1.9929  -0.4003   0.7257 -10.6383 -10.1322   6.7866   7.2317
-#>    3.8970  -3.4188 -11.7834  -4.6428   4.0508   2.1324   4.1699  -4.1890
-#>    7.3724  16.4938  10.6386  22.3735  -2.0468   7.5446 -19.1999   2.6527
-#>  -13.2517  -5.3879 -10.5804   5.9759  -2.5601  -3.3287  12.2843   6.9087
-#>   -0.9873   0.0392  -3.8649   9.3947   2.5094  -8.7465 -14.7925   5.2627
-#>   12.9071  17.7511   6.9966  -1.4220 -10.6707   4.9446  -6.7811  -4.1808
-#>    3.2866   2.8973  -1.3446  14.3974   7.3142   4.2670   1.8701   1.2805
-#>    9.1203  -7.5222  -4.2375   3.9771   1.2064   2.9481  -2.7665 -11.2163
-#>  -11.7766  -6.0367 -11.9295  -6.2336  21.8730   1.4385 -13.9398   0.4111
-#>   -4.0622   4.9269  -9.6263  -9.0863  15.4455  10.2114  -8.1764   5.1174
-#>    7.1776  -7.6390   4.4247 -12.3476 -15.5966   7.9522   2.4543  -2.4679
-#>    2.6628  -2.8408  15.8058   2.4088  -8.0436  -3.6906   9.7034  -3.1776
-#>  -18.7927 -17.2439 -23.2489  19.8174  22.7149 -10.6282  -5.0924  -4.8641
-#>   -8.5925   1.8419  -9.6410  -8.1455   9.6088  -5.6521   3.3799  -1.7245
-#>   -3.4918 -10.8292  -2.7675 -10.0738   3.9026   9.0146   3.3701 -11.1880
-#>   17.8822   4.8984   0.0398   2.5109   6.7534   4.2931  -5.9973  10.8163
-#>    1.7897   5.7650   2.3038 -28.0216 -12.0954  -4.8501  -7.7992   4.6672
-#>   -0.3011   3.7991  -4.8132  16.1519  13.6011   4.7395  -7.8466  -8.0761
-#>   -6.7641  -1.3968  -2.4330 -26.8625 -14.1981  -3.8306   5.0209   4.4976
-#>    6.8127   7.9355   5.9901  -5.3127  -5.0978   8.3256   0.7121  -8.4116
-#>   -1.6115  12.8354  10.4227   0.5847  -5.0641   2.4588  -4.8166  -3.2331
-#>    4.9475  -2.3680 -10.0948  10.5419   2.5680  -7.0339  -1.7302  -0.8638
-#>  -13.0108  -5.0026   6.1649   5.7417  -4.4212 -12.3816  11.8004   4.8924
-#>  -11.2446 -16.1600 -13.2574  -0.8420  -4.7720  -2.6097  13.6350  -3.3475
-#>   17.9535   1.4541  -8.9213  -3.8055   1.3003   2.3363 -12.3381  -5.6345
-#>   -9.3921  -4.6415  -0.2370 -11.6773  13.2384  -5.2224  -6.8675   5.8824
-#>    5.9682   6.0117 -12.0649  -5.3713   2.1772 -21.1429   3.9779  -3.2444
-#> 
-#> Columns 41 to 48  -7.4203  -4.3417  -8.7223   9.8122   3.8623  -7.4629  13.9484 -23.2190
-#>    3.4445  -1.3355   7.6531  10.1566  -4.0611  -8.9193  12.1846  13.4072
-#>   -9.1451  -2.1885   2.3641 -12.4758 -16.5542   0.9120  -3.5647  12.2927
-#>    4.6303  -5.2585   1.0651  -4.5631  -7.7577  -0.9423  -7.0766  11.9225
-#>   -3.1262  -4.0675   2.1898  -8.6319  -2.6677   4.4339  -0.6671   5.0801
-#>    6.2554   2.5598  -0.1234  13.0930  -1.8810  28.3096  -6.6054  -0.4741
-#>  -11.8179 -12.9049   4.6068   2.6987   2.6213 -11.2886   3.4658  -5.2631
-#>   -2.5020   5.5396  -3.7473   0.5592 -12.7861 -18.4386  20.1443  -9.0295
-#>   -6.0943  -6.2892 -15.0037  -2.9393  -1.0958  15.7839 -17.5685  -4.1025
-#>  -14.5351 -11.2419 -10.0126   8.4245  -3.8231  13.4642  -2.1626  19.3573
-#>   -2.6913   1.2570   0.0191  -5.7630  -1.7038 -12.0102  -2.8262  -1.9102
-#>   -9.3014  -2.7707  -5.6902   3.2790 -12.9711   6.5359   6.9999 -10.7766
-#>    2.9156   8.3690  -0.5395  -6.0872  11.8689  -4.0404   2.1184  -9.6522
-#>   12.3743  -1.0288  12.3481   1.4885  -0.7266   4.8815   0.7529  -0.9136
-#>  -19.4519  -7.9898   7.5399   2.6351  -5.8580   6.3750   0.1136  -2.1771
-#>    1.6941  -6.4103   0.5496  11.4846   7.6194   3.6779   1.9675   5.1756
-#>    3.4512   6.0714   4.5734  27.0274   3.3834   8.3279  -5.7394  -1.7655
-#>   -2.3163   7.9076  -5.1612  -5.3446   9.2760 -25.0731  13.2667   8.3713
-#>    6.8684  -9.4366 -14.1991   8.6951  10.7399  -2.8100  -2.2233 -10.4838
-#>  -14.9936  -2.4823  18.8716 -10.5186 -11.2061   2.7645   9.7941 -20.5271
-#>    2.9695  10.8395 -10.4739   5.1254  -4.9681  -5.0584 -18.1055   2.5028
-#>    2.7110   0.8587  -4.6111   0.3774 -11.9666   3.8982  -5.3369   6.5355
-#>  -12.3133 -15.5227  -2.7682   4.9590   1.8040   0.7331  -1.1232  -6.3707
-#>   -0.3749   5.4597 -11.5046   2.4737   4.4048  14.9951   5.7543 -16.8669
-#>   -2.6534  -2.9687   0.4743  -3.9876 -11.8613   8.7067  11.5766 -13.5679
-#>    0.9820 -10.8301   6.7273  -1.5765  -8.2368  14.5064   6.3943   7.6894
-#>    3.5332  -6.1911   6.6732   4.3271  -9.1047  14.4457 -19.7375  12.9010
-#>   15.6519  13.4092  -7.4260  -5.9673 -16.3715 -12.2971 -15.9744  -9.7981
-#>    8.2944   9.8776 -10.1648 -10.2498  -0.8161  -5.1461   8.3299   0.3224
-#>   -5.7386  -5.1824   0.2582  -9.6135  15.1354  -0.5289 -12.7296 -13.0442
-#>   13.5382  -0.9888 -13.6341  -5.3329  14.3248   0.2444  -4.2781   4.5478
-#>    9.2803  -0.4922   5.8904   9.9608  -0.0805  11.7786  -7.9851   1.9345
-#>    2.3301  -4.4072   2.7353   0.9836   3.9349  -6.1501   1.5588 -10.2998
-#> 
-#> Columns 49 to 54 -20.8314  16.5843 -13.9129  -2.0479   6.8089  -3.7914
-#>    6.2111   6.6074  -5.1333   2.0526   0.3237  -2.0470
-#>  -14.5700   7.4540  -0.3076   7.1954  -6.8094  -4.0095
-#>  -15.0809   0.6740  -0.7762   0.2321 -12.1556   2.6609
-#>   14.3694  -1.3963   6.5437  10.2236  -8.9210   2.5166
-#>  -12.3399 -26.4408   2.1555  -2.6613   3.1474  -2.0783
-#>   -1.7603  -6.9358   7.6874   1.6465 -10.0625  -5.6285
-#>   -2.7790   3.9554  -1.3829  -9.3238  -5.7191  -2.2334
-#>  -11.1100  -4.3702  14.1653   4.4415   3.3352  -1.5379
-#>  -14.6190  -1.4964  -1.2528  -9.3638  -8.0048  -4.8460
-#>    8.4043  -8.5918  -5.1574   0.0703   1.7428  -1.1935
-#>   -0.7531   1.1208  -4.4809   4.9666  14.7488  -3.2977
-#>    4.3365   3.2914   4.2250  -2.9052 -11.3092  -4.1889
-#>    3.3596 -16.7166   3.0995   1.4962   1.6846  -3.7894
-#>   -9.7414   8.1937  13.6883  -2.5184  10.6000   3.3366
-#>    2.6495   2.5624  -7.4683   2.6998   0.0631   2.7538
-#>   -8.5029  -4.9118 -18.4429  -3.2778   8.0432  -8.2741
-#>   -1.9020   6.6837  -5.4427   6.1116   7.2660   0.4438
-#>   -7.0378  -4.7777  15.0084  -2.7419  -2.9724   3.1454
-#>    4.6097   4.8506   3.8411  -4.8637 -13.3390   3.0000
-#>    9.8125  -6.7690   6.7033   5.7351   3.2553  -1.0613
-#>   -0.3601 -13.9705  -1.0009   4.9286  -7.7348  -2.9671
-#>   -2.5662  -6.6644  -7.4420 -13.8237   5.5117   3.2655
-#>   14.5174  -5.0537  21.5743  10.0272  -5.0012   3.7732
-#>  -15.3295  16.5149   0.0156  -2.6484   1.1358   2.8868
-#>   13.3596 -12.8918  -0.3230 -11.0566   6.0814  -0.1005
-#>   -2.1543 -12.3791   9.6814  -9.1736   1.9768   0.4381
-#>   -2.3409  -1.2850 -13.5981   0.2571  -8.0662  -3.2677
-#>    7.2799  -3.7789  -4.2514   2.2253  -5.6518   1.1974
-#>    2.7535  11.8568   1.0693  -0.8272   2.2264  -0.9253
-#>   14.5961 -14.8933  -9.8182  11.7285   0.6507   0.6431
-#>   -2.7609   6.4170  -0.6261   1.7213  -7.1515  -4.1353
-#>   11.0345  -4.5719  -3.2490   0.7722 -13.8753   4.9701
-#> 
-#> (9,.,.) = 
-#>  Columns 1 to 6 -3.0844e+00  3.9575e+00 -8.8483e+00  7.1485e+00  1.4732e+01  3.7237e+00
-#>   1.3299e+00 -3.2750e+00 -3.2767e+00 -6.6849e+00  3.0363e+00 -1.1108e+01
-#>  -5.3153e+00 -1.0945e+00 -2.6950e+00  6.9211e+00  4.3921e+00  4.0269e+00
-#>  -6.4988e-01  2.1014e+00  9.9262e+00 -3.4379e+00  1.2616e+01  2.0861e+00
-#>   1.9079e+00  1.3251e+00 -2.1251e+00 -5.3450e+00  3.2996e+00 -4.3239e+00
-#>  -6.0243e+00 -9.6375e+00 -1.1247e+01  6.9145e+00 -4.1306e+00  2.5840e+00
-#>   6.0369e+00  6.4653e-01 -2.5032e+00 -1.9317e+00  1.9454e+01  5.1159e+00
-#>   6.6771e+00 -9.7476e-01  4.0785e-01 -4.2783e+00  2.9150e+00  1.3538e+01
-#>  -9.8473e+00  1.0038e+00  7.7229e+00  3.0641e+00  5.7014e+00 -1.2515e+01
-#>  -4.6366e+00  1.6778e+00 -6.7110e+00 -2.1135e+01 -1.1663e+01  9.1680e+00
-#>   2.4572e+00  1.0856e+01 -8.8700e+00 -6.6691e+00 -9.6360e-01  1.3725e+01
-#>   1.5261e+00  2.6883e-01 -3.6022e-01  6.8276e+00 -1.1354e+00 -5.7788e+00
-#>   6.0398e+00 -3.6515e+00  1.9147e+00 -9.9658e+00  1.8608e+00  1.6815e+01
-#>   1.0912e+01 -1.1609e+00  4.9686e+00 -8.8477e+00 -5.8313e+00 -2.2854e+00
-#>   4.3442e-01  6.8882e+00  7.6787e+00 -8.1718e+00 -5.9922e+00 -2.9733e+00
-#>  -2.9825e+00  2.4150e+00 -1.2465e+00 -7.3483e-01 -1.5889e+01 -1.0715e+01
-#>   2.2427e+00  3.7664e+00 -2.2868e+00  4.5995e+00  1.0914e+01 -1.2551e+00
-#>  -9.6294e-01  5.3982e-01  1.4011e+01  6.6027e+00  3.2266e+00 -1.1214e+01
-#>  -1.2467e+00  5.1257e+00  2.9094e+00 -4.5639e+00  3.2488e+00  4.4284e+00
-#>   2.6206e+00  8.2820e+00  3.8331e+00 -4.8418e+00 -1.3598e+01  9.1072e+00
-#>   6.8628e-01  1.1245e+01  1.8393e-02 -2.6590e+00 -2.7642e+01 -3.6005e+00
-#>   2.3190e+00 -2.0259e+00 -1.0040e+01 -6.6561e-01 -1.7592e+01  3.9268e+00
-#>   7.6194e+00  5.9051e+00  1.9740e+00 -8.0803e+00 -1.1348e+01  5.4960e-01
-#>  -8.1681e+00  2.1004e+00 -3.6650e+00  1.0278e+01  5.2578e+00  2.8381e+00
-#>  -4.0629e+00  3.3020e+00 -6.8705e+00  6.6090e+00 -5.2204e+00  8.8572e+00
-#>  -2.8817e+00 -7.1743e+00 -5.8274e+00 -1.4024e+00 -1.6453e+00 -1.2242e+01
-#>  -1.8511e+00  5.2588e+00 -3.8408e+00  1.7996e+01 -2.0355e+00  4.0652e+00
-#>   2.0675e+00  8.8085e+00  1.3759e+00  7.3567e-01 -9.5449e+00  3.0625e+00
-#>   1.1249e+00 -2.9889e+00  1.9286e+00 -3.4670e+00  1.6181e+01  1.6632e+01
-#>  -9.4529e+00  2.5716e+00  7.1519e+00  3.4283e+00 -7.8391e+00  4.5298e+00
-#>   6.1931e-01  1.0553e+00  6.9422e+00 -1.0306e+01 -1.4759e+01 -7.0191e+00
-#>   9.1682e+00 -4.0056e-01 -9.7737e+00  9.0546e+00 -5.7929e+00 -5.6782e+00
-#>   2.3222e+00 -4.0269e+00  3.3736e+00  3.6830e+00  1.0166e+01  6.4749e+00
-#> 
-#> Columns 7 to 12 -5.3888e+00 -2.9333e+00  1.5363e+00  5.4292e+00 -9.1061e-01  1.2076e+01
-#>   1.2150e+00  1.0525e+01  6.0796e+00  2.6328e+00 -1.1000e+01  2.9484e+00
-#>   3.1673e+00 -8.9752e+00 -7.2490e-01  1.5361e+01  2.7796e+00 -7.5944e+00
-#>  -1.4430e+01  9.7151e+00 -9.1587e-01  3.2778e+00  5.2619e+00 -3.7392e-01
-#>   3.7892e+00 -5.1312e+00 -9.0128e+00  5.1808e-01 -3.1414e+00  2.7353e+00
-#>  -6.7068e+00 -9.0294e+00  1.1638e+01  1.0029e+00  1.1856e+01 -2.6498e+00
-#>  -1.1853e+01 -9.1865e+00 -9.7623e+00  7.9592e+00  1.4931e+01 -5.3246e+00
-#>   1.1537e+01 -4.9769e+00  5.7990e+00 -1.7946e+01 -2.5516e+00 -6.2747e+00
-#>  -1.0631e-01 -1.8550e+00 -6.2494e+00  1.0040e+01  1.2557e+01  1.6425e+01
-#>   1.5632e+00  8.7546e-01 -1.0170e+01 -1.5403e+00  8.9363e-01 -3.8374e+00
-#>   1.6753e+01 -1.7494e+01 -1.5118e+01  6.6152e+00 -3.2020e+00  9.7728e+00
-#>   1.8719e+01  7.2675e+00  1.6485e+01  2.5752e+00 -2.4923e+00  1.7008e+01
-#>   6.3191e+00 -6.5026e+00 -2.9797e+00 -1.0865e+01 -5.6393e+00  2.7777e+00
-#>   2.7979e+00  8.5942e+00 -2.8034e+00 -1.0326e+01  1.2389e+01  1.6556e+01
-#>   1.3892e+01  6.8219e+00  7.3136e+00  1.1366e+01  8.1391e-01  4.3374e+00
-#>   4.8084e-01  4.3842e+00 -4.2161e+00  1.8518e+00 -3.7542e+00  9.6281e+00
-#>  -2.2650e+00  7.0146e+00  1.1047e+01  6.1887e+00  1.8057e+01  1.4751e+00
-#>  -2.4461e+01  9.5519e+00 -8.2912e-01 -1.0050e+01 -8.2641e+00  4.6827e+00
-#>   7.4679e+00 -3.5030e+00 -1.4055e+00  1.9115e+01  2.4359e+01  8.1015e+00
-#>   2.4131e+01  2.4365e+00 -6.8482e+00  2.9907e+00 -4.4200e+00  2.4605e+00
-#>  -2.3553e+00  6.7272e+00 -1.4875e+01 -1.3162e+01  9.2889e+00  6.4767e+00
-#>   2.3963e+01  1.2350e+01  8.0211e+00  1.3432e+01 -1.4131e-01  6.6445e+00
-#>   1.3471e+01  7.7444e+00  7.6033e+00 -2.4399e+00  2.5683e+01 -6.2176e+00
-#>   8.8612e+00 -4.4346e+00  1.4746e+01 -2.9410e+00 -6.8655e+00  6.2316e+00
-#>  -3.5537e+00 -7.2698e+00 -4.7409e+00 -7.2148e+00 -5.8450e+00 -5.2691e+00
-#>   1.3115e+00  1.6191e+00  4.7505e-01 -1.5181e+01 -1.1890e+00 -1.3413e+01
-#>   2.0220e+00  4.9279e+00  2.4231e+00 -5.2500e+00  4.8225e+00 -1.3004e+01
-#>   1.6325e+00 -8.4463e-01 -5.2737e+00 -4.4634e+00  9.0230e-01  1.5081e+01
-#>  -9.6546e+00 -1.6568e+01 -2.3547e+00 -9.3497e+00 -9.6521e+00 -4.5624e+00
-#>  -1.1359e+01  4.4158e+00  7.5889e+00  5.7622e-01 -7.3438e+00 -1.0305e+01
-#>   3.7111e+00 -3.7338e-01  1.4702e+01 -6.3014e+00 -6.5463e+00  2.1742e+01
-#>   9.0815e+00  2.7954e+00 -1.5308e+01  1.0759e+01  3.7131e+00 -1.3179e+01
-#>   3.3553e+00 -8.0263e+00 -4.2763e+00  8.2564e-01 -3.5557e+00 -6.8022e-01
-#> 
-#> Columns 13 to 18 -7.4942e+00 -1.4010e+01 -5.3054e+00 -8.4081e+00 -3.8957e-01  7.1862e+00
-#>  -7.1702e+00 -7.3959e+00  2.7816e-01  9.9267e+00 -2.1226e+01  2.2359e+01
-#>   3.7178e+00  8.9062e+00 -1.9239e+00  3.9103e+00 -3.0656e+00 -2.7693e+00
-#>  -6.8793e+00  6.7350e+00  7.0029e+00 -1.1243e+00  9.9269e+00 -6.0519e+00
-#>  -1.9346e+01  9.3772e+00 -1.6757e+01  8.7048e+00  2.2695e+00 -9.9442e+00
-#>  -2.1270e+01 -1.9596e+01 -1.2583e+01 -2.6858e+01 -2.5704e-01 -2.3715e+01
-#>  -1.5152e+01 -1.1151e+01 -1.4880e+00 -1.2560e+01  1.4956e+01  1.3154e+00
-#>   1.4843e+01  1.0410e+01  6.9810e+00 -2.2508e+01  5.1875e+00  9.2162e+00
-#>   8.8139e+00  8.1885e+00  1.5398e+01 -6.9044e+00  1.1073e+01 -4.3035e+00
-#>  -5.6897e+00  1.0101e+01 -2.1102e-01 -8.2544e+00 -7.0694e+00  1.1567e-01
-#>  -5.6823e+00  4.4739e+00 -1.0758e+01 -1.8899e+00  3.8160e+00 -8.9889e+00
-#>  -6.3853e+00  3.2104e+00  5.0729e+00 -2.8138e+00  2.5488e+00  3.5416e+00
-#>  -1.3139e+01 -1.6489e+01  1.7487e+01  5.5499e+00  9.9309e+00 -5.3077e-01
-#>   3.8411e+00  6.1458e+00  6.2721e-02  8.6752e-01  1.6274e+00  1.2679e+01
-#>   3.6154e+00 -6.3606e+00 -7.1458e+00 -9.3122e+00  5.1054e+00  3.1354e+00
-#>  -8.9520e+00 -7.4094e+00 -1.3353e+01  1.1036e+01  3.9082e+00 -9.9292e+00
-#>  -8.0426e+00 -2.7335e+00 -1.6823e+01 -1.5870e+01 -2.4214e+00 -1.9141e+00
-#>   3.9491e+00  5.3443e+00  1.4690e+01 -2.9426e+00  4.3608e+00  7.3118e+00
-#>   4.9603e+00 -4.8410e+00 -8.5335e+00 -2.0889e+00  2.1733e+00 -6.1265e-01
-#>   7.4755e-01  1.5469e+01  6.2350e+00 -9.7556e+00  1.9131e+01  4.6571e+00
-#>   1.3613e+01 -8.7656e-01  5.4168e-01  9.9604e+00  1.6535e-01  9.2111e+00
-#>   4.1108e+00  1.3246e+00 -3.6717e+00 -3.7175e+00 -1.5989e+01 -3.8839e+00
-#>   1.0627e+01  1.4176e+01 -5.0590e+00 -2.0990e+01  1.3081e+01 -3.2022e+00
-#>   2.4884e+00 -8.7539e+00 -1.5358e+00  9.2289e+00  6.3881e+00 -6.5302e-01
-#>   8.6044e+00  5.5272e+00 -3.5769e+00 -4.3029e+00  8.1571e+00 -1.5234e+01
-#>   1.0456e+00  1.2951e+01  1.2300e+01 -7.5131e+00 -8.2078e-01 -5.4459e+00
-#>   1.1622e+00 -3.2786e+00 -2.4809e+00  9.2949e+00  4.5887e-01 -5.6015e+00
-#>   1.0684e+01 -8.1112e-01 -7.1593e+00  6.1027e+00 -6.0294e+00 -4.8785e+00
-#>  -3.0858e+00  1.3670e+01 -2.5882e+00  7.5500e+00  7.6051e+00 -9.8345e+00
-#>  -1.4309e+01 -2.4152e+01 -4.3382e+00  2.0233e+01 -6.3286e+00 -1.8082e+01
-#>   1.5897e-02  3.6208e+00  2.3562e+00 -1.3223e+01 -9.8380e+00 -4.8735e+00
-#>   2.0443e+00 -1.5113e+01 -3.1743e+00 -4.9740e-01 -1.1086e+01  3.2641e+00
-#>   1.5150e+01 -5.8453e+00 -1.1136e+01  6.0637e+00 -6.3859e+00 -2.8524e+00
-#> 
-#> Columns 19 to 24 -5.6893e+00 -1.9453e+01 -3.5957e+00  1.5872e+01 -1.3028e+01  5.1139e+00
-#>   6.9340e+00  1.2190e+00  1.2480e+01  1.2942e+00 -1.6315e+00  1.4260e+01
-#>   1.2432e+01 -4.2904e+00  2.6480e+00 -1.8259e+00 -1.5810e+00  3.2235e+00
-#>   1.4535e+01 -7.8076e+00  1.7186e+00 -7.7738e+00 -7.1399e+00 -8.9696e-01
-#>   8.6471e-01  5.4114e+00  1.1150e+01  5.3790e+00  7.0586e+00  1.9771e+00
-#>  -9.0428e+00 -6.1166e+00 -1.1130e+01  1.3702e+01 -7.3280e+00  1.9078e+00
-#>  -1.2262e+01  4.3546e+00  6.0670e+00 -3.4008e+00  8.6816e+00  7.8010e-02
-#>  -2.0900e+01  4.2239e+00 -9.2654e-01  7.3594e+00  8.6544e+00  2.1153e+00
-#>  -1.1320e+01  1.2950e+01 -8.8325e+00  5.0883e+00  1.2992e+01 -1.0941e+01
-#>  -5.0212e+00  2.9902e+00 -3.3185e+00  5.6894e+00 -1.4010e+01 -1.0241e+01
-#>  -1.1013e+01 -3.3661e-01  2.2183e+00  1.8726e+00 -1.7061e+00  5.6472e+00
-#>  -8.8008e-01  4.3267e+00 -4.8622e+00  7.6512e+00 -5.9359e+00  2.2392e+01
-#>  -3.8189e+00 -8.8454e+00 -2.0480e+00 -1.0577e+01 -4.6067e+00 -1.5634e-01
-#>   1.4468e+00 -4.0155e+00 -7.7699e+00 -6.3497e+00 -1.2672e+01  6.8436e+00
-#>   2.3073e+00  1.2243e+01 -9.7741e+00  1.7353e+01 -8.2820e-01  1.3717e+00
-#>   1.4566e+01 -1.2031e+01 -2.8944e+00  1.1853e+01  5.4134e+00 -1.2361e+00
-#>  -8.3577e+00  2.4359e+00 -1.3276e+01 -4.0730e+00 -1.0948e+01  1.3474e+01
-#>   3.8182e-01 -6.4262e+00  3.8115e+00 -2.2974e+00 -5.7632e-01  9.6762e+00
-#>  -5.3428e+00  8.6593e+00 -6.0399e+00  1.8404e+01  2.2284e+00 -9.9725e+00
-#>   4.7909e+00 -2.2855e+00 -4.3346e+00 -5.4306e+00  2.7422e-01 -2.0013e+00
-#>  -8.8541e+00 -6.9699e+00 -1.3400e+01  8.0493e+00  6.8156e+00 -9.7870e-01
-#>  -7.3828e+00  2.8325e+00  7.9561e+00  8.8198e+00  1.1244e+00 -1.6940e+00
-#>   4.4528e+00 -1.8023e+00 -6.6527e+00 -3.9291e+00 -5.5194e+00  1.8781e+00
-#>   6.1969e+00 -1.2150e+01  1.4234e+01  1.0549e+01  1.6503e+01  5.4961e+00
-#>   1.9726e+01 -1.9853e+01  1.1585e+01 -2.1674e+00 -1.4682e+01  5.4222e+00
-#>  -7.7661e+00 -2.6476e+00 -8.8573e+00 -3.4332e+00 -9.9211e+00  8.6490e-02
-#>   1.4378e+01  4.0144e+00 -9.0094e+00  2.5586e+00 -7.2253e+00 -7.1700e+00
-#>  -1.1073e+00 -2.0247e+01  1.6292e+00 -1.5077e+01 -1.4288e+00 -1.5738e+00
-#>  -1.2809e+01 -8.0958e-01 -3.1857e+00 -1.3108e+01  4.3087e+00 -1.0828e+01
-#>   1.0087e+01 -8.5014e+00  8.2723e+00  5.4861e+00 -1.5241e+01  5.1375e+00
-#>   4.1856e+00 -6.3674e+00 -6.0491e-01 -3.4339e+00  3.0145e+00  7.3578e+00
-#>  -2.0565e+00  7.9100e+00 -1.1618e+01 -1.0194e+01 -1.0763e+01 -1.8640e+01
-#>   6.7430e+00 -1.1374e+01  2.1704e+00 -7.5976e+00 -8.7040e+00 -7.4410e-03
-#> 
-#> Columns 25 to 30  1.3558e+01  3.3122e+00  2.0959e+00  1.5647e+01  1.0997e+01 -1.9436e+01
-#>   2.6344e-02  7.2531e+00  1.2503e+01 -9.3091e+00  3.1167e+00 -1.1548e+01
-#>  -2.7886e-01  7.5301e+00  4.8612e+00  2.8948e+00 -2.8620e+00  6.4170e+00
-#>  -6.6708e+00  1.7084e+00 -1.2577e+01 -4.7185e+00 -4.7590e+00  1.1023e+01
-#>  -5.3235e+00  1.5145e+00  8.6505e-01  8.3207e+00 -2.0379e+01  4.0297e+00
-#>   1.5575e+01 -2.6839e+00  7.3206e+00  1.3451e+01 -5.3136e+00 -4.7260e+00
-#>   6.1753e+00  1.2691e+00 -7.8798e-02  1.5560e+01 -1.3044e+00 -2.4853e+00
-#>  -5.1093e+00  2.5675e+00  4.8341e-01  2.7719e+00  5.3591e+00 -2.0743e-01
-#>   8.9852e+00  9.6871e+00 -6.3932e+00  3.0404e+00  7.4364e+00 -1.0637e+01
-#>   4.8914e+00 -6.7086e+00  3.4064e+00  8.1951e+00  6.9123e+00 -8.8660e+00
-#>  -7.4082e-01 -3.7787e+00  7.6736e-02  1.8565e+00 -6.1790e+00  1.2534e+01
-#>   9.1540e+00 -4.7321e+00  5.9293e+00  1.2966e+00 -1.2415e+01  1.3284e+01
-#>  -1.3991e+01  9.3310e-01 -2.2098e+00  6.6424e+00  4.0365e+00  1.3118e+01
-#>  -1.1929e+01 -4.9660e+00  8.5513e+00 -3.3803e+00 -1.0103e+01 -2.6659e+00
-#>   3.5112e+00 -6.5770e+00  5.2533e+00  6.0158e+00 -4.1765e-01  9.3664e+00
-#>   3.5893e+00 -5.5666e+00 -8.5346e-01  1.0235e+01 -2.8308e-01 -5.9867e+00
-#>   1.2652e+01 -2.2574e+00  5.1448e+00  1.3336e+00 -1.3777e+01 -2.0938e+01
-#>  -9.7569e-01  7.9474e+00 -7.8825e+00 -3.6059e+00  4.1351e+00  1.9781e+00
-#>   4.8112e+00 -4.8876e+00 -1.4709e+00  4.2883e+00  2.9530e+00  1.8854e+00
-#>  -1.3174e-01 -6.2074e+00  4.1328e+00  6.5042e+00 -9.7857e+00  6.5055e-01
-#>  -3.8156e+00 -1.3507e+01  1.6801e+00  2.0514e+00  5.6115e+00  3.8388e+00
-#>  -6.6274e+00 -7.6941e+00  1.3906e+00 -8.0568e+00  2.6097e+00 -1.2881e+00
-#>   6.0680e-01 -3.7622e+00  1.9647e+00  1.7416e+01 -1.8234e+01 -1.8430e+00
-#>  -4.6856e+00 -4.4744e+00  3.9488e+00  9.2486e+00  5.6437e+00  1.2768e+01
-#>   5.5231e+00 -5.7111e+00  9.3682e-01  5.9836e+00 -9.2493e+00  3.0670e+00
-#>   9.8058e+00 -2.6402e+00  1.1336e+01  6.2685e+00 -2.6886e+00  7.6441e+00
-#>   5.9363e+00 -6.5192e+00  7.8321e-01 -4.0427e-01 -1.3133e+00  3.0544e+00
-#>  -6.3091e+00  1.0040e+01 -1.2751e+01 -1.1534e+01 -5.8298e+00  2.7647e+00
-#>  -7.4592e+00  9.4103e+00  1.4476e-01  5.4149e+00  6.6634e+00  1.8745e+01
-#>   2.3166e+00 -2.2831e+00  2.4495e+00 -1.4437e+01 -4.8693e+00  4.2169e+00
-#>  -2.0170e+01 -3.3310e+00 -8.3002e+00 -5.6621e+00  2.9494e+00  1.0479e+01
-#>  -1.6103e+01  2.7127e+00  1.2102e+01  1.2768e+01  1.7274e+01 -4.4694e+00
-#>  -8.0425e+00  1.6162e+01 -6.0518e-02  1.0420e+01  4.4894e+00  6.6914e+00
-#> 
-#> Columns 31 to 36  3.6761e+00  1.0637e+01  1.4562e+01  5.9846e+00  4.0722e+00  9.0006e+00
-#>  -5.1966e+00  1.5071e+01 -3.9094e+00 -8.8287e+00  1.9831e+01  1.1739e+01
-#>  -1.7767e+00  5.8233e+00 -7.6486e+00  4.5091e+00  7.6294e+00 -5.1123e+00
-#>  -6.5812e-01  5.4449e+00  9.8120e+00  3.2020e+00 -5.4120e+00  8.7914e+00
-#>  -1.8320e+00  2.6157e+00  2.6970e+00 -2.7039e+00 -6.0996e+00 -3.1244e+00
-#>  -3.5625e+00 -4.4053e+00 -2.8956e+00  1.3660e+01 -5.8036e-01  1.0343e+00
-#>  -8.0378e+00 -1.0343e+01  1.2668e+01  3.7481e+00 -5.9861e+00  1.0574e+01
-#>  -5.5252e+00  6.8272e+00 -8.0569e-01  4.2449e+00  7.8909e+00  1.2782e+01
-#>  -5.3252e+00  7.3852e+00 -6.1989e+00  8.6960e+00  2.6933e+00 -1.5599e+01
-#>   1.6819e+01 -2.1453e-01  1.8835e+01  4.1777e+00  5.2485e+00  1.8756e+01
-#>   1.0849e+01 -3.0162e+00 -1.2584e+01 -1.1527e+00  1.0408e+01 -3.3177e+00
-#>   6.6933e-01  6.6165e+00 -7.3807e-01  7.7241e-01  7.5175e+00  8.1878e+00
-#>  -2.0470e+00  1.7072e+01  1.0649e+01 -1.3827e+01 -7.3002e+00  7.4366e+00
-#>  -1.2941e+01 -1.1538e+01  7.9025e+00 -1.5590e+00  4.0351e+00  4.5150e+00
-#>   1.2127e+01  9.3004e-01 -7.7179e+00  8.3346e+00 -8.8323e-01  3.2883e+00
-#>   1.3323e+01  4.5492e+00  4.5777e+00  3.5815e+00 -6.6529e-01 -8.3083e-01
-#>   6.3809e+00 -1.5644e+01  1.8017e+00 -5.0205e+00  4.5602e+00  1.4026e+01
-#>  -1.9529e+00  7.3174e+00 -2.8223e+00  8.9153e-01 -5.3674e+00  3.2901e+00
-#>  -1.2805e+01  2.5653e+00 -2.9729e-01  8.0468e+00 -4.1089e+00  1.0789e+01
-#>  -8.7715e-01  1.3078e+00 -1.4629e+01  8.8644e+00  1.8758e+01 -1.1461e+01
-#>   8.1886e+00  7.9778e+00  1.5112e+01 -6.0972e+00 -7.2719e+00  5.6152e+00
-#>  -1.0019e+01 -2.1547e+00  6.7985e+00 -1.1222e+00  3.7693e+00 -3.7409e+00
-#>  -1.3928e+01  1.2986e+00 -1.6339e+00  2.8692e+00 -1.0651e+01  1.3526e+01
-#>  -6.4898e+00  6.9418e+00  1.5826e+01  1.5640e+01 -1.0684e+01 -6.8203e+00
-#>   1.0347e+01 -1.3085e+01 -1.5079e+01  2.2178e+01 -1.2524e+01 -2.0429e+01
-#>   1.3768e+01  6.9692e+00 -7.8130e+00  3.4595e+00  6.7786e+00  1.5439e-01
-#>   1.0279e+01 -7.5763e+00 -6.7798e+00 -5.3758e-01  3.0871e+00 -5.8363e+00
-#>  -6.6974e+00  9.5524e+00 -5.5794e+00  4.1938e-01  1.8463e+00  4.4021e+00
-#>   3.0242e+00 -1.3227e+01  6.3768e+00 -3.2215e+00  2.1403e+00  1.8556e+00
-#>   1.7823e+01  5.1552e+00  3.1660e+00  1.7945e+00 -2.3683e+01  9.8308e-01
-#>  -1.4320e+01 -3.1440e-01 -9.9361e-01 -8.1371e+00  8.5137e+00 -2.5419e+00
-#>   1.5456e+01 -1.1247e+01  9.6495e+00 -1.7449e+01  1.5162e+00  3.9664e+00
-#>  -4.0748e+00  1.2323e+01  1.0367e+01  1.8966e+00 -6.5433e-01  9.7306e-01
-#> 
-#> Columns 37 to 42  6.3193e+00  4.1580e-01 -5.5026e+00 -5.2077e+00  3.8273e+00 -1.7859e+01
-#>   6.0152e+00  4.8147e+00 -3.1357e+00 -1.1225e+01  4.4666e-02 -2.4823e+00
-#>   1.6241e+01 -1.2788e+01 -7.8586e+00  4.2744e+00  8.6902e-01  1.9180e+00
-#>   4.2309e+00 -1.2683e+00  1.1844e+00  5.9364e-01  1.2986e+00  7.0099e+00
-#>   1.8031e+01 -1.3735e+01  3.0254e+00 -6.7109e+00 -8.0463e+00  4.5866e+00
-#>   1.2389e+01 -1.3599e+00 -1.3221e+01 -1.2302e+01 -1.9856e+01  4.3722e+00
-#>  -2.8307e+00  1.1498e+01  3.3417e+00 -2.7400e+00 -1.3726e+01  8.2211e+00
-#>   1.5877e-01  1.0815e+01  2.8379e+00  4.1172e+00  1.7293e+00 -2.6715e+01
-#>   5.9303e+00  1.5277e+01 -1.0294e+01  1.1066e+01  2.9799e+00  4.0886e+00
-#>   9.0479e+00  9.2719e+00  1.0262e+01 -6.9908e+00  7.9677e+00 -3.2507e+00
-#>   5.7520e+00  8.5322e+00  8.9157e+00  4.7481e+00 -7.1603e+00 -1.1803e+01
-#>  -2.4535e+00  2.2372e+00  4.7389e-01 -2.4764e-01  6.1815e+00 -6.0736e-01
-#>   2.7749e+00  9.9736e+00  8.4133e-01 -1.1915e+00  1.6670e+01  5.8009e+00
-#>  -1.0435e+01  1.1925e+01  6.6147e+00 -1.0507e+01 -2.8868e+00  5.8958e+00
-#>   4.8700e+00 -1.6851e+01  1.1281e+01  3.3564e+00  9.3250e+00 -8.3811e+00
-#>   4.7449e+00 -6.5033e+00 -9.0380e+00 -7.3736e+00 -1.5892e+00  5.9191e-02
-#>  -1.1771e+00  7.8254e-01 -4.0813e-01 -2.0650e+00 -2.8252e+01  2.5435e+00
-#>  -8.5939e+00  1.0028e+00  5.0527e+00 -2.2370e+00  7.3478e+00 -1.6387e-01
-#>  -3.6050e+00  1.1006e+01  2.0796e+01 -8.6269e+00  9.8430e+00  1.6179e+00
-#>  -2.2736e+00 -2.3937e+00 -1.7850e+00  1.6858e+00  3.9957e+00 -1.4926e+01
-#>  -6.3791e-01  8.6191e-01  1.3340e+01 -8.5667e+00 -2.6413e+00 -1.2077e+00
-#>  -1.3345e+00  9.3251e+00 -1.2439e+01  2.0178e+00  3.7639e+00 -8.5700e+00
-#>   4.7723e+00 -2.5637e-02 -4.7521e+00 -6.5413e+00 -1.6969e+01  7.9883e+00
-#>  -1.3204e+01  4.1165e+00 -1.6554e+01  8.3988e+00 -7.9562e+00 -3.6346e+00
-#>   4.0281e+00 -1.1415e+01 -1.5121e+01  1.3674e+01  9.2672e-01  7.8058e+00
-#>  -6.4940e+00  3.4464e+00  4.2818e+00  1.0922e+01  1.1316e+00 -3.7089e+00
-#>   5.0213e+00  9.4807e-01 -2.1677e+00  5.5849e+00  1.1946e+01  1.0366e+01
-#>   1.1274e+00  5.3332e+00  1.5231e+00  1.3328e+01 -1.3047e+01  8.8516e+00
-#>  -3.4887e+00 -7.2093e+00  1.3508e+01  3.4285e+00 -2.4273e+00 -2.3980e+00
-#>   1.1403e+01 -1.4971e+01  2.1474e+00  2.2323e+00  5.8092e+00  5.7479e+00
-#>  -3.5859e+00  1.2391e+01 -3.6762e+00  5.5437e+00 -1.0012e+01 -1.2154e+01
-#>   6.0459e+00 -9.5193e+00  1.4791e+01 -2.6113e+01  7.2202e+00  6.5619e+00
-#>  -3.8888e-01  8.4880e-01 -5.9651e+00 -1.3199e+01  1.5273e-01 -1.0037e+01
-#> 
-#> Columns 43 to 48  1.2266e+01 -2.8834e+01 -8.0280e+00 -2.0428e+00 -4.9001e+00  9.2407e-01
-#>   9.0277e+00  1.7013e+00  5.1573e+00 -5.6153e+00  2.1339e+00 -2.9950e+00
-#>   7.0761e+00  1.5237e-02 -1.3783e+01 -5.3301e-01  3.7376e+00  1.3496e+01
-#>  -1.1369e+01 -1.5329e+01 -7.7484e+00  5.5542e+00  1.0380e+01  5.9031e+00
-#>   1.7468e+00 -3.4204e-01  7.3675e+00  4.2309e+00  6.3147e+00  4.0213e+00
-#>   1.1571e+01  1.5676e+01 -6.5670e+00  6.0116e+00 -9.0544e+00 -1.0544e+01
-#>  -8.3220e+00 -1.5512e-01  1.5130e+01 -5.3928e+00 -4.0473e+00  1.3477e+00
-#>  -1.3731e+01 -9.9624e+00  1.8703e+00 -3.4599e+00 -1.0661e+01  6.7202e+00
-#>   9.7868e+00 -5.0907e+00 -1.8674e+01  2.8963e+00  2.7677e+00  1.6540e+01
-#>  -2.8286e+00  2.6729e+00 -7.6072e+00  9.1315e+00 -3.8775e+00 -7.6609e+00
-#>   1.5656e+00 -6.6805e+00  9.2606e+00  1.1768e+01 -1.4334e+01  2.7171e+00
-#>  -7.4580e-01 -3.4155e+00 -4.1006e+00  8.6670e+00  9.1879e-01 -1.4253e+00
-#>  -4.0258e+00 -1.1773e+01  4.4728e+00  8.1287e+00 -1.2450e+01  6.1224e+00
-#>  -4.0809e+00  1.3555e+01 -3.5414e+00  8.0674e+00 -5.2967e-03  6.7733e-01
-#>  -8.5613e-01  8.3502e+00  4.0190e-01  6.8232e+00  1.3272e+01 -1.0222e+01
-#>   6.6294e+00  3.8793e+00  6.6421e+00 -3.5255e+00  1.1354e+00 -1.6485e+01
-#>  -1.1456e+01 -6.4231e+00 -6.0706e+00 -1.6325e+00 -9.0896e+00 -3.1131e+00
-#>   6.9775e+00 -1.0652e+01  6.8487e+00 -4.7825e+00  9.5261e+00  5.9431e+00
-#>  -5.6337e+00  2.5851e+00 -1.9914e+01 -2.2390e+00 -8.6217e+00 -3.2185e+00
-#>  -3.1673e+00 -7.3470e+00 -8.4587e-01  1.4544e+01  4.8756e+00 -1.0150e+01
-#>  -1.8867e+01  1.3802e-01  2.3707e+00 -4.2196e+00  5.3611e+00 -7.3660e+00
-#>  -1.3701e+00  4.8789e-01  8.8058e+00 -1.4194e+01  5.8139e+00 -2.0546e+00
-#>  -1.4534e+01  3.2997e-04  8.3650e+00  4.9874e+00 -1.5715e+00 -2.5045e+00
-#>   7.4516e+00 -1.2611e+01  3.1515e-01 -3.5712e+00 -2.8321e+00 -1.1929e+01
-#>   1.7093e+01 -5.9745e+00  2.7888e+00  1.9365e+00  1.1775e+01 -1.8040e+01
-#>   2.6475e+00  1.1220e+01 -6.5698e+00  4.0266e+00 -2.2043e+00  3.9965e-01
-#>  -8.9387e+00  1.1510e+01 -1.3995e+01  6.5618e+00 -8.3189e+00  2.2141e+00
-#>  -4.2109e+00 -1.1936e+01 -7.9612e+00  5.2782e+00  5.8308e+00  1.4404e+01
-#>  -3.3679e+00 -5.2825e+00  9.1787e+00 -1.5533e+01  2.4056e+00 -6.0696e+00
-#>   1.0202e+01 -1.0092e+01  1.2604e+00  6.0711e+00 -6.1118e+00 -7.1285e+00
-#>   6.6391e+00 -1.0309e+01  9.7536e+00  9.0842e+00 -5.4748e+00  6.3011e+00
-#>  -1.3415e+01  3.2043e+00 -8.2222e+00 -7.4362e+00  7.4519e-01  1.9304e+00
-#>  -3.7220e+00 -4.5726e+00 -2.2351e+00 -1.9753e+00 -1.2845e+00 -7.2767e+00
-#> 
-#> Columns 49 to 54 -7.8283e+00  7.6700e+00 -6.4372e+00  6.7907e-01  7.5293e+00 -2.7664e+00
-#>   6.8952e+00 -1.7029e+00 -1.9042e-01  3.6731e+00  2.5942e-01  4.6866e+00
-#>  -1.9880e+00  5.6396e+00 -7.6415e+00 -3.5026e+00 -3.4724e-01  4.2378e+00
-#>  -2.9653e+00  3.0887e+00  4.5493e+00 -7.4611e+00 -1.0166e+01  9.6342e+00
-#>   6.3533e+00 -2.0191e+00 -3.0445e-01  6.5535e-01  6.8669e+00  2.7846e+00
-#>  -3.0310e+00  1.6618e+01 -1.5621e+01 -2.1432e+00  7.1358e+00  5.5269e+00
-#>   5.7609e+00 -4.8679e+00  7.3759e+00 -9.2898e-01  2.1645e+00  8.0198e-01
-#>  -1.5698e+00  9.1562e-01  4.2051e+00 -5.0667e+00 -6.5677e-02 -1.8864e+00
-#>  -1.6002e+01  4.7873e+00 -2.8521e+00 -6.0687e+00 -2.3322e+00  8.8416e-01
-#>   1.2803e+01  2.7640e+00  4.7702e+00 -3.9992e+00 -2.7644e+00  5.7228e+00
-#>   1.3082e+00  4.6939e+00 -4.8850e+00  1.8737e+00  7.4326e+00 -8.6145e+00
-#>   1.6210e+00 -7.7442e+00  7.6699e-01  3.9052e+00 -7.2906e-01  8.8425e+00
-#>  -3.1784e+00  1.6128e+01  7.4539e+00 -7.2732e+00 -2.3566e+00 -4.6841e-01
-#>   1.8711e+01 -2.0399e+01 -1.7557e+00 -6.4544e+00  7.8731e-01 -3.3787e+00
-#>  -3.0505e-01  1.3782e+00 -2.6054e-01  2.7923e+00 -9.2549e-01  1.0817e+01
-#>   1.4942e+00  7.8411e+00  1.3749e+00 -8.3084e+00 -1.3907e-01  7.3425e+00
-#>   3.9848e+00 -4.5565e+00 -1.3949e+01  4.5808e-01  4.6363e+00 -5.0544e-02
-#>   4.7378e-01  2.4725e+00  8.0652e-01  8.6825e+00  3.1991e-01 -3.7330e+00
-#>   2.5099e+00 -7.2661e+00  4.7621e+00  3.0879e+00 -5.1366e+00 -1.6244e+00
-#>  -1.0469e+01  7.9192e+00  2.0401e+00 -1.2957e+01  3.4234e+00 -2.3505e+00
-#>   6.6999e+00  7.5522e+00 -2.8123e+00 -3.0786e+00 -1.0235e+01 -2.7743e+00
-#>  -1.6822e+01 -1.2617e+01  9.4496e-01  2.7541e+00  1.7316e+00  6.2106e+00
-#>   1.0395e+01 -2.0956e+00 -1.2255e+01 -4.5416e-01 -3.9487e+00  4.3195e+00
-#>   4.6914e+00  3.1649e+00  4.1611e+00  6.1286e+00  5.0255e+00  7.1065e+00
-#>   7.5021e+00  6.8406e+00 -9.6136e+00  1.7223e+01 -1.3089e+01 -3.5942e+00
-#>  -5.5354e+00 -5.7653e+00  4.2610e+00  2.9217e+00  7.4059e+00  1.9449e+00
-#>  -3.5394e+00  8.6149e+00 -2.3641e+00 -5.7500e+00  1.5863e+00 -2.8873e+00
-#>  -1.4569e+01  4.0575e+00 -9.7085e+00 -3.8323e+00 -1.4526e+01 -3.2623e+00
-#>   4.0992e+00 -5.9664e+00 -5.2388e+00  7.7750e-01 -5.2460e+00  1.2358e+00
-#>   6.7283e+00 -1.1768e+01  4.6034e+00  1.5952e+01  1.1168e+00 -9.1776e-01
-#>  -3.8493e+00 -2.4761e+00  5.4825e-01 -8.8532e+00  1.0645e+00  9.2228e+00
-#>   1.3682e+00 -4.5516e+00  8.4050e-01  9.9312e-01 -2.7629e+00  3.6249e+00
-#>   1.8553e+01  3.0052e-01  3.0743e+00 -8.5578e+00  2.4675e+00 -3.0139e+00
-#> 
-#> (10,.,.) = 
-#>  Columns 1 to 8   4.1819  -2.7002 -18.1760  -1.1440  -2.9460   6.0683  -1.4644  -8.8791
-#>   -5.2517  -2.8884  -7.8021   1.1429   1.3565  18.4330  21.5672  -6.8964
-#>    7.6318  -6.9620   8.9294  -8.1504  -0.3623   0.9137   4.5002  -3.5007
-#>   -5.7043  -0.1398  -5.9344  14.1726   4.8862   3.5814   5.0882  -6.4967
-#>   -0.8601  -6.4114  11.4836  -7.3367  -7.5688   7.5060  -5.5756  -1.5425
-#>   -8.2925  -6.0803   6.6156 -13.9416   1.9437   3.9939   1.1387   7.1872
-#>    2.5452   2.3789  -6.3848   1.8546   1.7084 -12.1336  -3.9954  13.7758
-#>   -1.8303   3.3219   5.7164  -3.2491  -1.4877   9.1777   8.8536   5.8444
-#>    8.2554   5.8091  -6.1897  -8.2495   1.8647 -10.6258  -0.8845  -2.1385
-#>    1.7795   7.0254  -9.9362   6.2154  -3.4837  -0.7500  20.6577   2.2969
-#>    2.0167  -1.8900   0.5460  -1.2813  -1.4822  -3.7381  -4.6472   6.2117
-#>   -2.2446  -0.4886  -4.3657   6.1555 -10.2198   4.8470  -7.4376  -2.0771
-#>   -5.2713  -1.6133  -1.1467  -4.1871   5.4227   9.1644  -3.4754  -5.3385
-#>    0.3023  11.8181   1.3283 -12.7279  -7.5569  10.1936   6.1990  11.6441
-#>   -3.3104   3.9960  -2.4172   2.2504  -7.6548 -12.2355  10.1682   7.0920
-#>   -6.2455  -2.9252   1.0112  10.4484   1.2471  -0.9377 -12.7561  -5.3247
-#>   -3.9030  -9.2627   1.0247   1.9701  -8.9632   0.5715   2.5766   1.9814
-#>   -2.1366   5.8496  -6.1677   8.8440  -5.9129   0.9059  -8.7046  -2.5762
-#>    5.2249   5.9455  -8.3770  -7.0487   1.7812  -7.4599   0.4756  18.2719
-#>    2.1648  -0.6042   5.4310  -3.9076   1.8279   3.3609   8.3237   4.8889
-#>   -3.8500   8.1186   3.1748   0.4045  -3.5455   1.0117  -5.7007  -7.4015
-#>    2.6253  10.7632  -3.3398   4.9756 -20.4394   3.7262  11.1934  17.5135
-#>   -0.7312   4.0369   6.8090   3.6341  -0.8025   1.7529  -4.8715  10.0601
-#>   -1.1232  -2.8163  -2.6153   0.1292 -13.4160   3.9119  -7.7021  10.4302
-#>    2.2927  -2.3197   3.5505   8.4843   7.7590   6.3685  -6.6163 -17.7623
-#>    2.3017   5.6381   0.4991 -10.5783  -4.1324 -13.1374   5.4518   6.6804
-#>   -2.0478  -7.5091  11.3796  -7.9909  10.7743  -3.7296  -2.3580 -15.9901
-#>    4.0426  -9.7816   8.3604   2.4883   8.6378   9.9463  -3.2728  -0.6286
-#>    1.2885  -0.2170  12.5709   1.1797  -8.4373  -3.5454 -11.9339  -8.7778
-#>    5.4442   1.5278  -9.7229   1.7396  11.1748  -4.8533  -4.4837  -3.0608
-#>    3.1077  -2.4470   7.1603   5.6823 -21.7576   0.4642  -6.7589  10.2302
-#>   -4.3704   2.2297   0.9107 -17.3731   2.7938  -0.4466  -1.6011   1.0906
-#>   -1.2622  -2.4540  -1.1643  -8.5674   9.3976   2.6191 -14.8639   1.3717
-#> 
-#> Columns 9 to 16   1.8556   0.0946   0.1899  11.7107  -5.4183  11.5789   9.7279  -2.8268
-#>   -3.3962   4.7751 -14.7356  11.8734   2.1015   2.1329   2.1923   3.2486
-#>    1.4306   0.3409  -9.0292  -0.4911  -3.6641  -0.5287  18.8181  -1.0346
-#>   -9.8678  -5.0219  15.4837 -13.4747  -5.8500  17.1277  -0.1490   2.6720
-#>   -6.8133  -7.6474   5.9233  -0.4694  -1.0901  -7.4239  10.4323   3.4986
-#>   -5.5519  -0.0618  11.3379   8.0623  -3.5955   1.7862   7.5723   2.4541
-#>    1.3072   0.6549   6.0913 -18.9477   1.6956   7.7507   1.1176  -0.0589
-#>    6.1180   3.5580   2.0954   3.3366   4.7224  -3.3512   9.1941  -3.5992
-#>   -8.3560  -4.7540   1.6382 -13.4815   3.3727  -1.9814  -0.0188  -3.9449
-#>    4.9662 -12.4013  19.1299 -15.2008   8.3711   3.6924   4.5674  -2.7297
-#>    0.6927  -5.4917  -5.8683  -2.1278  -9.2548  -8.4762   9.8112  -6.1467
-#>    0.5953  -0.1684  -3.7758  -3.3063   3.1845 -12.9019  -5.7863  -2.8776
-#>   -9.7528  18.2416  19.8239   8.8325 -16.4710 -13.3338   4.2949   7.3125
-#>   -3.6096  -7.3529   4.0815  -0.1361   4.7710  -6.8037 -22.0151  11.4496
-#>   13.0295 -16.0956  20.0523   0.4107  -3.8567   6.1604 -10.1262 -17.2949
-#>    3.1475  -0.2741  12.1048  17.4119   4.1416  -7.9165  10.1866   7.0605
-#>    7.9490   6.5653  -0.1566   6.7108   8.9105   4.9730 -11.4455   1.9475
-#>   -7.0497  -1.4302  -0.2031  -0.7717  10.2553  -5.8846 -12.2485   5.1642
-#>   -1.4445  -1.0975   4.7951 -10.3304   3.8726 -11.5951  18.1794  -1.8722
-#>   -3.5374   1.9384   7.5930  -1.7373  -1.3728   0.1740   9.8508 -17.4885
-#>    5.2514   1.7544   7.5342  21.3597   2.5470 -13.2217  -2.4640  15.8839
-#>   15.8121   1.9745 -12.0076   9.3390 -10.4766 -11.5816   5.4715  15.8169
-#>    2.7863   4.0846   3.5614  11.5852  13.7292  -3.9139  -1.5637  -4.1002
-#>   -8.6593   5.3422  -7.6377  -6.7198   2.7386  -2.0494  -7.7664   1.0160
-#>   -7.9545  11.3175   5.0165   2.6514  -4.5948   4.2351  -5.6257  -0.6661
-#>    2.0352   8.5044 -11.0207   3.9361  -3.1783  -5.0761 -10.5843  -6.2035
-#>   -9.2982   3.4657  -8.0456   1.1406  -5.7957  -0.0046  -2.1324   8.1229
-#>  -16.6553  10.8810  -0.9489  13.3421  -8.0725   9.1832  -4.2330   7.3050
-#>   -8.2410   7.6877   4.3861 -15.3545  -0.3129   0.7203   1.5005   6.7024
-#>   -6.5903  -1.3819  -6.9875  -4.7477   4.7515   0.0780 -13.0780 -12.4752
-#>    0.5467   0.2573 -11.7447  11.3582   0.7454 -11.0607  -4.9295   6.2202
-#>    0.9685   0.8027  11.2603   1.4244 -10.0601  -9.5701  14.1312  11.6304
-#>  -11.8415   2.6648   0.9239  -9.2368  -4.5917  12.4541   8.8730   4.3792
-#> 
-#> Columns 17 to 24  17.7241  16.4431  -4.4976   4.1706 -12.8776 -15.2067 -10.9706   4.8594
-#>   -2.4854   4.0536   0.9395   0.8457  -2.1911  -0.2284   2.7090  -5.2006
-#>   -1.5690   2.2409  15.2344   2.3106 -15.5675   4.6727 -18.2378  -8.5262
-#>  -19.8752   9.8127 -10.5200   8.9852 -14.8782  -3.1370   4.1668 -10.8551
-#>   -3.6712 -12.0466   7.5911   7.8817 -14.2461  -1.8762   6.1248  -4.2913
-#>    7.4452  -0.9572  -3.7197   1.3354   6.7883 -13.2608 -13.4355  14.7262
-#>   10.5368  -0.6395  15.0316   7.2175  -6.9461 -20.6147   1.1905  13.3449
-#>    9.9587  17.1341 -11.5921  -1.7717 -12.4661   6.2366  -9.3802  -1.1792
-#>    0.3752   8.5826 -13.2740  -0.0306  13.3570  17.4113 -12.8242   9.3084
-#>  -11.8180   9.5951   6.7905  12.8367  -8.0408 -15.6782   6.5608 -18.7112
-#>   22.3179  -7.4422   1.5256  11.6816   5.9146 -10.3219  -3.7901   7.5836
-#>    3.8820  -7.6058   8.6871  -4.0585   8.9200  -4.1044  -1.5731  -3.6648
-#>   -1.7718  -1.6068  -6.4439  11.8292  -2.6022 -16.4736 -11.8727  11.3917
-#>  -17.9264  -4.7390   6.3397  -8.9043   8.8302 -16.6837   2.7382 -14.5637
-#>   -1.9014   7.9378   1.7300   0.9099  -7.4772  -0.5940  -6.2970 -17.5786
-#>    1.4715  -0.3907  -9.3192  10.1859  11.9984 -14.9277  12.3849  17.7064
-#>    2.3705 -17.2180   4.6233   4.2500  -4.6967 -11.6569  -5.3638 -11.5411
-#>   -1.3751  12.4701  -7.0619   4.3700  -0.2691  -0.2517   6.5075   2.4046
-#>    9.9320  -0.5399   3.7089  -8.3076   4.6503  -6.4728 -24.8052  24.1327
-#>    1.9584   7.0046   1.5319  -5.6831 -14.7582   1.4350  -3.2149  16.2855
-#>   12.0401  -0.7776   3.8262  -6.7161   0.6593   2.0989  -4.3371  12.7094
-#>  -19.7485   3.2476 -10.9042  14.1623   8.4786   7.8983   0.4614 -12.9178
-#>   -7.0698   1.4244 -11.8563   1.1273  15.1136  10.7658   2.1623  -7.2914
-#>    4.7051   3.1767  -8.4571  -4.8364  14.5448   8.8465  -1.5965   9.6834
-#>   -4.4673  -7.8511   0.8354   5.9707   3.3181  12.2863  11.1134 -10.8450
-#>   -7.2237   0.3425  17.3627 -14.8729   2.3539 -13.4249   3.3829   4.3209
-#>    1.4075  -4.3673   1.7913  -1.9425  10.2997  -7.9903   1.3192   5.3896
-#>   19.7331  -9.6629  -7.2495  -9.8141   5.0245   8.9532 -18.5641   8.1088
-#>   -7.1797  -1.4011  -7.3396   2.2222  -7.3143   7.9236   2.3504 -12.2137
-#>    6.8541   2.4263   0.0412   8.6535  -3.8287   4.7261   8.2146  -1.9722
-#>    0.9613   2.0930  -8.1847   8.7364  11.5909   4.4428 -15.8480   2.7165
-#>  -14.3310   3.1957  13.1065  11.7513  -4.6397   4.3785 -23.1525   2.6458
-#>    8.8166   9.1156   0.1519  -5.9758   6.7014  -3.0737 -10.1020   1.6576
-#> 
-#> Columns 25 to 32  10.5292  -3.9645  12.8190   2.1494  10.7930  11.1448   9.9790  12.4497
-#>   11.4912  12.2822  -0.5843  -8.5035 -10.4811   7.8215   5.3842   1.7837
-#>    9.6353  14.6377   3.8013  14.2778  -5.8059  19.4480 -11.1792   9.9659
-#>    1.2615 -12.5058  -1.1939  -1.1402 -15.7723   5.3684   1.1663  -5.7907
-#>    2.3630  -5.0501  13.3084  18.5605   1.1618   8.6902  -2.5698  -1.1850
-#>  -13.9340   4.3304   5.8433  -7.3367   4.6003  -6.4036   4.8493   5.0689
-#>   -1.3893  14.5972  16.2413  14.9188  -0.5374  -7.7302  -0.7117  -2.1723
-#>    1.4423   2.1201   1.2344  -0.5692  14.9963   3.6928  -3.9090   0.6049
-#>    0.7095  -7.0357  -2.1930  -3.7832  -5.9221  -8.0483  -6.4077   2.4676
-#>   -1.5388   0.4704  12.8991  -6.0182  14.1894  -0.9701   9.0996 -16.5178
-#>  -10.3212 -10.7765   4.8461   4.5388   4.3799 -10.6147   0.8329  -6.4080
-#>    7.8106   2.1017  -9.8434  -0.4331   5.9101  -3.3945  -2.3179  -2.2783
-#>  -13.8799   0.6262  -4.3279   3.8890  -3.8753  -8.0789  -0.7328  -4.0891
-#>   14.3206   7.7849   3.6460  -4.0085  -7.5287 -11.1466   4.8729 -11.5882
-#>   -0.0881  -2.9461   2.1459  11.0899   3.5483  -0.2515   2.0340   1.7700
-#>    9.2189 -13.4063  -2.4231   3.0836   5.5822   2.4684   2.2476   1.8075
-#>   -2.4374   2.6486  -5.4516   1.9154   5.0113 -13.6252   9.9636 -11.3138
-#>    2.4418  -4.0569  -8.3882 -19.7733  -2.0071   4.8646   2.7268   6.5430
-#>   -5.4768  -4.4051  17.2406   1.0203   7.2529 -12.8022   1.9017   4.4126
-#>    5.6344   7.9751   3.8124  -4.3246   6.1148  15.3043 -18.8127  10.1895
-#>    1.9013   4.9514   4.0854  -3.0271  19.8358  -0.8026  14.0764  -0.8553
-#>   -2.2414 -11.0523  18.3026  -1.5447   0.4390  -9.4300   0.1453  -6.5077
-#>  -12.4334   6.4966 -14.2762  13.4793   6.2123   0.7264  -8.5034  -7.8488
-#>    3.6735 -10.9852  12.3695  -3.4940   3.7098 -15.8378  -2.1686  -8.8639
-#>   -6.1870   6.3384 -17.9945   4.0391   5.4411   3.9668 -13.1839   6.2893
-#>  -12.5335  10.5731  -9.3001  -6.2328 -10.8204   4.7457   3.1881   6.8758
-#>   -9.5590   4.8932  -1.7767  -4.7540  -8.1867   0.3086  12.7548 -12.7002
-#>    3.6840 -12.3161  -6.0951  -1.2251   4.7612  -1.1004  -1.5123  -2.2366
-#>   -3.0114  -9.8084  -9.4691   0.2194  -9.6904 -13.4815  -1.6036  -6.8736
-#>  -13.0158 -10.7943  -6.4755   4.5214   0.5924  -7.7346   8.6752  10.1047
-#>    7.4194 -13.7749   5.0595   5.8050   5.8419  -0.0406   9.7345  -1.6591
-#>  -12.7815   0.2335   2.2811  -2.3682  -7.0829 -12.3729  -3.2147   1.8516
-#>    8.9504  -2.9962   8.6639  -3.1886  -5.3588   5.4321  -9.9454  -5.3727
-#> 
-#> Columns 33 to 40  -2.6904  -3.1793  -3.9982   2.2110 -15.0660  -7.8316  -4.5374  -3.6815
-#>    6.3429  -0.5526 -12.4770  10.9454  10.4932   2.0863   7.1767 -15.6242
-#>    9.9699  -2.9062  -7.5707  -4.7522  -8.1896  -2.9347  -0.8152  -4.8127
-#>  -12.2653  11.1087   0.8183  -3.0673   5.4707  -7.3395   8.2259   0.0790
-#>   10.7333  11.7357  -0.5174  -2.7349 -11.8983   4.5970   4.7187   7.6155
-#>    0.8824  -0.0948   2.7482 -16.1985  -3.4809 -13.0749  -5.6538   1.3078
-#>   -4.6005  10.9233  -8.3767 -23.2996  -9.9544   4.9645   6.6196  -1.6382
-#>   -4.8016   1.2827   2.5463  -9.7602 -11.3491  -6.9045   8.6490  -3.3517
-#>   10.8844  -4.3575   4.9099  25.9703  -7.6978  -8.9655 -17.4221   1.0042
-#>   -4.2847   2.7185  -2.9281  -5.9636   7.4574   0.7742  -1.9685   3.4123
-#>    9.2045   5.2980  15.7517  -5.8331 -15.0311   7.7835  -9.3464  -1.9853
-#>    6.4127  -4.6117  19.0878  -3.0422  -1.5535  -2.1965  -1.4020   1.2183
-#>  -11.4244   7.5634   8.4587  -1.8272   3.1815  11.3702   1.6303   8.1204
-#>    1.2992  -2.9209  -2.0812  11.5782  22.2623   7.6336  -5.1908   5.6859
-#>   -2.1399   3.7254  -9.4623  -7.0854  -2.7738  12.2496   1.6377   2.5113
-#>  -12.5850  -4.9262   3.3072  -5.5994   4.4245  -1.8885  -0.8919 -10.0518
-#>   13.9544  -2.2759   4.0960 -12.4514 -14.1780  -0.7708  -2.8591   2.4331
-#>   -0.4444  -4.2921   7.0387   6.5836   9.4148 -14.9592   4.8886   7.0435
-#>  -19.0743  -3.3144 -13.8473  -2.4842  -1.5997  -0.7481  15.9668   0.6583
-#>  -10.1261  -2.2332  -7.1975  -2.3450   9.0592  10.7116  -0.8987  -8.9685
-#>  -11.9545  -8.5190   4.8090  13.1186  10.6559  -4.5238  -2.1798  -8.8657
-#>   -1.9783   8.6018   3.2554 -16.5278 -14.0102  -3.3953   9.9409   3.1031
-#>  -13.6221  -1.6317  17.8017 -13.5058  -0.0398 -20.7438  -2.9618   4.1430
-#>    0.8359  -2.0096  -1.9416   4.9894   4.1868  -2.3571   5.3722   7.2971
-#>    2.0086 -12.1338  17.6228 -10.7163   5.5852  -7.9251   1.4832 -14.7635
-#>    5.7109   5.6109  -7.2849   1.9554   5.1607   2.8878   1.7395   1.9746
-#>    1.1878  -8.7317  -0.0576  13.3530   1.2356   4.8184 -11.7238  -1.7304
-#>   -5.7051  -4.8007  10.1910  18.0028 -12.7282 -13.6544   2.4402  -9.5052
-#>   -2.6643  14.4130  10.9647  -9.3912   3.2169  -7.6773 -12.9542   0.5395
-#>   -7.9634  -6.3668  -8.8339   3.0626   3.5328  -5.4092   6.0550  -2.6830
-#>   -5.5458   1.8084   3.9153   6.7767   5.4337 -10.1347  -0.7966   9.2530
-#>    5.2944 -15.6789  16.6287   0.8781  -8.6488  12.3779 -11.0757  -2.0095
-#>   -1.9817   1.1022   3.5618  -4.4506   0.1626  -8.8249  -2.2472  -2.0069
-#> 
-#> Columns 41 to 48  17.5704   3.3890 -14.7766   8.8433  -8.1115  18.5641   5.5488 -15.7888
-#>    1.3682  -0.3895 -13.8270 -15.9155  -4.3090  -8.9518  10.9437 -12.4455
-#>   17.5254   1.6004   1.1251  -3.3617   6.1294  14.9661 -12.5143  14.6155
-#>    2.5869  11.4185  -0.2229 -15.8317  16.1505  20.4406 -10.4021   4.9527
-#>   -4.8332   2.2539   2.9690 -19.8020  -0.6848  10.3750  -3.9140  -7.7243
-#>    9.0005  -4.9784 -14.8227  -5.6539 -22.5182   3.3849  -6.7069  -9.1133
-#>  -13.7044   8.7026  15.7930 -21.8754  14.5765  -6.7201  -2.8081   2.8092
-#>   -8.3891  -2.3845   8.5504  12.8548   1.4612  -3.3912 -12.1353   5.9169
-#>   -0.5118   0.2147   5.5487  -9.9571   7.8578  31.2956  -7.6141   6.0582
-#>   -4.1179  17.5565  -1.9947   7.3969   1.8679  -1.0288 -10.8080  -6.1185
-#>  -12.9663  -2.5697  12.2865  13.0955  -2.8212 -13.4418  -3.3537  11.4065
-#>    0.6306  -6.9157 -22.3353   7.5794   5.3888  -1.9895   6.8287 -15.9672
-#>   -3.7175  -7.1140  -7.8213   0.2052   7.8420  23.6224  -1.2615   5.3317
-#>   -2.8885   7.7248   5.9880 -12.1741  -8.5161   3.1150  20.8556  -8.3987
-#>    2.4623   2.5093   8.3615  -4.6639   6.6286 -13.4688  -6.7489   9.2497
-#>   -9.4874   0.9945   5.8155  -4.5071  -3.0293 -14.6485   0.9375  -9.3388
-#>    5.6274  -2.6494   2.2440   6.4652  -6.9652  -8.7051  -4.4503 -11.7992
-#>   -7.1215 -11.7251  -0.0189  -7.7000  20.6908  -9.6292  17.5161   0.8517
-#>   -1.1748  16.3685  17.1124  -0.1526   9.9417 -15.2438  -2.5329  -3.7418
-#>    9.6564   9.9983 -10.8354   6.3171   5.9041  -1.7046 -10.1057   1.9377
-#>   -8.3920  -5.5874  -3.4125   9.7132  11.8479   8.2080   4.9230 -10.1816
-#>    9.3801   3.2812   4.9593   2.1379 -10.3081  -8.9848 -15.6181  -5.7878
-#>    1.7352  14.5038  -3.1819  -9.0198   9.3146   9.1462 -12.0767  -4.1429
-#>   -7.9687   1.5227 -10.3528  -6.1670 -12.7228  -8.4186   9.4608  -7.8613
-#>   -0.3986  -0.5350 -20.1001   7.9296   2.2427  14.5613  16.7416  -7.5759
-#>   -4.4657   1.9143  -2.0119   9.2431  15.5990  -9.8887  -2.0631  -5.6964
-#>   -4.3807   2.3498   2.3197   2.3938  -1.6953   5.2110   4.9993   2.3942
-#>    8.3853 -10.0567  17.5188  -0.2648   6.5264  23.4170   0.1300   4.5117
-#>  -16.0887   2.2805  16.1597  -5.0918  14.6784   0.1509  17.1091  10.7455
-#>   -5.0488  -1.5862   3.4866  -7.5867  12.9261   1.0782  -9.2606 -10.5018
-#>    4.2590  -8.3886  12.6921  14.9392 -12.0440  -5.4093 -14.8714   9.9903
-#>   10.3134   7.1416  -2.7204  -8.9261  -3.6273  29.5756  -9.7316  12.0568
-#>   -4.3794 -11.0773  10.8934  -7.9329   5.4392  -4.8761   4.0482   7.3628
-#> 
-#> Columns 49 to 54 -14.5902  -0.2382   4.3159  -2.6877   5.0703   2.4193
-#>   -7.1263  -7.5356  -5.7498   7.8019  -0.7866  -2.3049
-#>  -12.3100  -1.9089   0.3939   9.3053  -5.7823   1.2514
-#>  -10.8058   5.7169   6.6433  11.4855  -1.1410  -4.5655
-#>    8.8070   5.1104   8.8327  12.1930   5.0258   5.7235
-#>  -25.6938  -3.5064  -2.6577 -15.0395  -2.9256  -3.4516
-#>   -6.6522  -0.1651 -11.4028  -6.1774   2.9720  -3.9651
-#>  -11.4142 -13.1631  14.7867   5.8335   5.6973   0.3327
-#>  -24.5765   5.7319  12.5413  -2.6588   1.5041  -1.9145
-#>    6.3423  -8.7484  -4.6776   1.1261  -4.7972   0.9373
-#>   13.7444  -5.1163  -2.2804  -7.3522   3.7646   6.7636
-#>   -6.6618  -3.2632  10.3643   0.2059  -1.5956   2.7000
-#>  -20.9447   4.3667  -0.4386 -11.0839   1.1866   3.1471
-#>   10.5578  -6.5905  -5.1642   6.4413   0.7218  -5.2311
-#>    1.3020   9.0326   7.8362   0.2074  -1.2691  -3.3912
-#>    0.0193  11.9974  -4.4291 -10.2958 -13.7476  -0.6264
-#>   -7.6380   2.1646 -13.6631  -4.7001   5.4647  -4.1669
-#>    5.9041   4.0855  -2.6119   2.6817   6.5380  -0.4950
-#>    6.0665   3.0488   7.5467 -16.5522   1.2797  -2.5643
-#>    2.4455  -9.6235   7.8516  -1.5448   2.5017   3.9083
-#>   10.5918  10.5633   4.1331   3.3853  -0.5743   0.0424
-#>    6.7426  -6.8340  -1.2416  -1.1304 -13.0831  -2.6119
-#>   -9.2593  -1.9201  -3.0986  14.1559  -0.0596 -11.1140
-#>  -11.5428   2.5954  10.6733  -4.6600  -3.8252  -3.4938
-#>    1.3032  -0.2393  -5.7651   3.2347   1.5240  -3.7687
-#>    4.9202  -6.3740 -17.5235   4.7132   2.0002  -0.2020
-#>   11.3402   5.2820 -17.9162  -0.2594  -7.4186  -2.8355
-#>  -19.4105   5.8406   7.7503  -7.5794   1.5874  -2.1943
-#>    1.6365  -4.2029  -6.0806   1.8311  -1.0763   0.0894
-#>   17.7446  21.0740  -0.0280 -10.0488   5.2612   4.8506
-#>   -4.6811   9.2956  18.5009  -6.1713  -1.4305   0.4427
-#>  -10.9019   7.2243 -14.8779  -8.3666  -7.9722   0.3536
-#>   -8.8841   2.5430  -2.5944   1.4092  -9.3180  -2.0870
-#> 
-#> (11,.,.) = 
-#>  Columns 1 to 6 -3.7849e+00 -5.0196e+00  2.1432e+00  1.7021e+00  2.1228e+00 -4.3782e+00
-#>   4.1938e+00 -3.9595e+00  4.0635e+00 -5.5167e+00  5.6394e+00  4.2589e+00
-#>  -2.8545e+00  1.2807e+00 -6.3746e+00 -3.4508e+00  1.5402e+01  5.1446e+00
-#>   2.8870e+00  3.8449e+00  9.1714e+00  6.2121e+00  8.3932e+00  8.3983e+00
-#>  -2.2077e+00  8.2990e+00 -1.5067e+00 -5.9537e+00 -1.3866e+01 -6.3206e+00
-#>  -3.9909e-01  4.0141e+00 -1.2573e+01  8.3191e+00  2.9710e+00 -2.4224e+00
-#>  -1.8828e+00 -2.7120e+00  4.2654e+00 -1.2382e+00 -1.4503e+00  5.6398e+00
-#>   3.4461e+00 -6.0116e+00  2.6112e+00 -4.3507e+00 -6.3859e+00 -4.9195e+00
-#>  -1.6851e+00 -9.8991e+00  5.0588e+00 -2.4422e+00  6.6687e+00  1.7778e+00
-#>   4.5697e-01 -7.3823e-02  4.4258e+00 -4.7368e-01  1.0315e+00  3.9583e+00
-#>   1.1417e+00 -1.6804e+00 -2.8100e+00 -2.1802e+00 -3.9562e+00 -1.0400e+01
-#>  -5.8564e-01  1.0935e+01  2.6781e+00  4.0670e+00  8.1068e-01 -3.8475e+00
-#>   4.1610e+00 -5.0571e-01  2.9245e+00 -7.9322e+00 -1.6292e+01 -3.1278e+00
-#>  -1.4342e+00 -2.3585e+00  4.6606e+00  1.9605e+01  2.9582e+00  1.8324e+01
-#>  -5.5404e-01  7.1800e-01 -1.4267e+01  4.0446e+00  5.6713e+00 -1.4284e+01
-#>   4.1463e+00  7.7104e+00 -1.1590e+01 -4.9941e+00  6.1806e+00 -1.0090e+01
-#>  -2.4521e-01  8.6493e+00  3.0875e+00  1.5020e+00  7.0473e+00 -7.6942e-01
-#>  -1.9347e+00 -1.0450e+01  3.0863e+00  2.8111e+00  1.0976e+00  1.5948e+01
-#>  -2.6669e+00 -1.8782e+00  2.9217e+00  1.3300e+01 -1.6403e+00 -8.4687e+00
-#>   1.7010e+00  6.6925e-01 -2.5950e+00  1.3026e+00  8.2395e+00  4.8721e+00
-#>   2.0192e+00  2.7153e+00  2.1160e+00 -7.7253e+00 -8.3825e+00 -3.1612e+00
-#>   3.9173e+00 -6.8199e+00  1.5681e+01  8.2122e+00  2.8327e-01 -9.1162e+00
-#>   1.6034e+00  3.3712e+00  1.5284e+00  1.3668e+00  5.2616e+00  4.2150e+00
-#>   9.3535e+00  5.1441e-01 -5.8771e+00  6.4237e+00 -7.5995e+00 -1.9427e+01
-#>   1.0015e-02  5.9925e+00 -4.5338e+00 -4.7200e+00  5.7863e+00  3.5718e+00
-#>  -3.8353e-01  1.7491e+00 -1.1367e+01  1.3499e+00 -7.0512e+00  1.4846e+00
-#>  -6.6765e-01  6.4164e+00 -7.2331e+00  8.6983e+00 -4.7788e+00  9.2212e+00
-#>  -3.8685e+00  3.6740e+00  4.8216e+00  1.3614e+01  2.4227e-01  1.1748e+00
-#>  -1.2426e+00 -6.0361e+00 -5.0742e+00 -1.0331e+01 -1.2833e+01  7.2044e+00
-#>   8.5433e-01  3.0204e+00  6.8311e-01 -1.3152e+00 -1.2573e+00  1.1791e+00
-#>   1.0658e+00 -1.8278e+00  2.1841e+00  1.3825e+00  5.6781e+00 -1.4014e+01
-#>  -3.4659e+00 -2.0254e-02 -6.6509e+00 -2.2007e+00 -1.0925e+01 -3.6717e+00
-#>   3.3764e+00  2.2750e+00 -8.4900e+00  7.7153e+00 -4.4704e+00 -1.6245e+00
-#> 
-#> Columns 7 to 12  6.0973e-01 -1.4816e+01 -1.6339e+01 -3.2286e+00  4.9790e+00  3.0585e+00
-#>   6.8249e+00  3.8479e+00  1.2093e+00 -4.8146e+00 -1.4413e+01 -2.8921e+00
-#>  -1.7814e+00  6.0191e+00 -1.1324e+01  5.8392e+00 -5.0403e+00  3.6101e+00
-#>   4.6124e+00  2.8248e+00  9.4244e-01  2.1540e+00  5.7656e+00  1.0509e+01
-#>   1.6468e+00  8.3349e+00  1.0383e+01  1.0796e+01 -1.1249e+01  4.1644e+00
-#>  -1.0202e+01 -1.2100e+00 -1.3321e+01 -1.6383e+01 -1.6935e+01 -4.5484e+00
-#>   8.5930e-01 -5.8933e+00  7.5169e+00  5.9695e+00  1.0066e+00 -6.7549e+00
-#>   2.3309e+00 -4.0142e+00 -2.3207e+00 -2.2595e-01  3.0484e+00 -2.7808e+00
-#>   2.0428e+00 -6.2656e+00 -7.2521e+00 -5.2489e+00  9.3829e+00  1.3080e+00
-#>  -6.5883e-01 -4.0317e-01 -1.2260e+00 -1.4979e+01  2.1824e+00  2.7155e+00
-#>   7.3020e+00  9.7149e-01 -1.2680e+01  9.1245e+00  1.1231e-01 -1.4746e+01
-#>  -8.0618e+00 -2.8863e+00 -1.4427e+01 -2.7446e+00  3.0282e+00 -7.5802e+00
-#>   7.9672e+00 -3.7802e+00 -5.4363e+00  7.7521e+00 -1.0090e+01 -7.8178e+00
-#>   1.2089e+01  5.3239e+00  4.9814e+00 -5.6864e+00 -3.6484e+00 -4.6010e+00
-#>  -2.8823e+00 -1.1789e+01 -1.1138e+01 -1.0029e+01  1.0326e+01 -1.0063e+01
-#>  -5.3812e+00  3.2276e+00 -3.6145e+00 -3.5016e+00 -4.2648e+00 -4.8598e+00
-#>  -8.2244e+00  2.2045e+00 -5.3808e+00 -9.6569e+00  1.8693e+00 -3.7419e+00
-#>   4.7899e+00  4.2961e+00 -9.5148e-01  1.1050e+01  2.0492e+00 -6.1241e+00
-#>  -9.6661e+00 -1.4662e+00 -1.4761e+01  4.7909e+00  9.8864e+00  5.9882e+00
-#>   1.0315e+01 -3.5138e+00 -1.0110e+01  8.8079e+00  6.5133e+00 -5.1630e+00
-#>  -1.4707e+01 -1.8352e+00  8.1046e+00  3.2276e+00  5.4078e+00  1.4720e+00
-#>   4.0316e+00 -4.7272e+00  2.2854e+00 -1.0523e+01 -9.7445e-01 -2.0957e+00
-#>  -8.9254e-01  4.7154e-01 -3.2894e+00 -4.5754e+00 -3.8137e+00 -7.3894e+00
-#>  -2.3613e+00 -1.3534e+00  1.3022e+01  4.2130e+00  3.0146e+00  1.2287e+00
-#>   6.0422e+00 -2.9257e+00 -8.1346e+00 -5.4069e+00 -6.5148e+00  1.1687e+01
-#>  -4.2277e+00  6.4720e+00 -2.5719e+00 -5.1238e+00 -6.9550e+00 -1.9845e-01
-#>  -4.8493e+00 -3.1173e+00 -3.0814e+00  4.2641e-01 -2.9877e+00  4.9414e+00
-#>  -6.0841e+00 -4.0842e+00 -1.1773e+01  4.2059e-01 -8.0204e+00  2.0235e-01
-#>   3.8129e+00 -5.0090e+00  8.0143e+00  7.2605e+00 -8.3539e+00  1.9997e-01
-#>  -5.2110e+00 -2.1361e+00  5.1565e+00  5.2963e+00  7.4855e+00  8.4635e+00
-#>  -6.7656e+00  1.0277e+01  3.9130e-01  1.8962e+00  5.5895e+00 -7.6272e+00
-#>   4.1073e+00  4.8892e+00  1.0822e+00 -4.1000e+00 -1.1186e+01  1.5630e+00
-#>   2.1621e+00 -1.8975e+00 -1.0544e+00  7.2115e+00 -5.1892e+00  1.8350e+00
-#> 
-#> Columns 13 to 18  1.2055e+01 -1.5823e+00 -4.0961e+00  4.4132e+00  1.6397e+01 -7.4848e+00
-#>  -8.9138e+00 -1.2013e+00  8.9636e-01 -2.3079e+00 -9.8273e+00 -3.5474e+00
-#>  -4.1584e+00  9.4283e+00  3.3601e+00  1.1121e+01 -2.0212e+00 -7.8219e+00
-#>  -5.1769e+00  3.6503e+00 -1.1306e+01  4.6026e+00 -6.1186e+00  4.3157e+00
-#>   7.2794e-02 -3.5959e+00 -6.4562e+00 -4.8976e+00 -8.0606e+00 -6.6448e+00
-#>  -2.9705e+00 -1.6580e+00  9.4389e+00 -1.0084e+00 -1.6057e+00  5.3935e+00
-#>   1.2491e+01 -4.7120e+00 -5.2667e+00  1.6226e+01  3.9891e+00 -1.4046e+01
-#>   1.3069e+01 -3.1612e+00 -9.8630e+00 -2.3612e+00  1.6367e+00  2.9848e+00
-#>   2.8621e+00  1.2066e+01 -8.5787e+00  1.0951e+01 -5.8907e+00  8.6545e+00
-#>  -3.6769e+00  8.8309e+00 -4.3361e+00 -2.2998e+00 -5.8985e+00  1.7773e+00
-#>   5.7171e-01  4.1866e+00 -3.4568e+00  2.9769e-01  1.8227e+01  2.3260e+00
-#>  -4.9518e+00 -4.5572e+00 -1.9396e+00 -3.8327e+00 -4.9439e+00  1.4059e+01
-#>  -1.8056e+00 -1.0121e+01 -1.8802e+01 -3.8292e+00  1.4599e+01  7.9980e+00
-#>  -5.1516e+00 -6.4763e-01 -1.7462e+00 -1.9015e+01  5.6483e+00 -3.0024e-01
-#>  -1.0154e+01 -8.1130e+00  3.3937e+00  6.0944e-01 -1.4344e+01  9.7953e+00
-#>  -4.8702e+00  2.4856e+00  7.8282e+00  3.4010e-01 -1.4870e+01  1.3035e+00
-#>  -1.1545e+00 -3.1283e+00  5.7233e+00  2.2559e+00 -1.8827e+00 -2.2008e+00
-#>   2.7410e+00 -2.7028e+00  4.8085e+00 -5.9716e+00 -6.9213e+00 -6.9172e-01
-#>   1.5735e+01  4.2947e+00 -3.2594e+00  7.8355e-01 -4.0335e-01 -2.7748e-01
-#>  -1.2516e+01  9.3661e-01  8.7588e+00 -1.0998e+01 -1.0668e+01  3.7740e+00
-#>   2.1174e+00 -1.1302e+01  2.5620e+00 -1.2488e+01 -3.4072e-02  1.3111e+01
-#>   1.3338e+01 -2.0520e+00 -1.5102e+01 -2.9870e+00  5.0300e+00  6.6853e+00
-#>   9.5776e-02 -1.1173e+01 -7.8260e+00 -7.9570e-01 -4.0905e+00  3.1859e+00
-#>   9.8249e+00 -8.4933e+00  6.2898e+00  1.8266e+00  4.9972e-01  1.1476e+00
-#>  -3.1772e+00  5.4599e+00  9.6864e+00 -9.3830e-01  1.3744e+01  4.6697e+00
-#>  -7.9928e-01  9.6497e+00  1.1326e+01  5.6546e-01 -4.4016e+00  1.2161e+01
-#>  -4.6892e+00 -2.7207e+00  1.5347e+01 -1.3501e+00  1.0858e+01 -1.9357e-01
-#>   3.9911e+00 -2.1637e+00  3.5168e+00 -5.6583e+00  4.2209e+00  4.6337e+00
-#>   9.9214e+00 -7.2976e-01  1.0678e+01  1.8799e+01  1.7413e+01 -8.4330e-01
-#>   2.3533e+00 -5.9228e+00  1.3134e+01 -1.4971e+00  3.8762e+00  1.5080e+00
-#>  -7.7518e-01  1.4636e+00 -7.8376e+00 -1.2756e+01 -3.8406e+00  3.6612e+00
-#>   2.5242e+00  8.6385e-01 -6.2545e+00  4.3963e+00  9.6608e+00  1.5203e+01
-#>  -4.8494e+00 -6.9702e+00  4.1564e+00  4.8794e+00  1.7624e+01 -7.4009e+00
-#> 
-#> Columns 19 to 24 -1.1275e+01 -8.8870e+00 -1.1503e+00 -1.3656e+00 -1.4318e+00 -4.2171e+00
-#>   1.2225e+00  6.3358e+00  1.4911e+00 -9.2090e+00  2.2159e+00 -3.0175e+00
-#>  -6.2561e+00 -6.9127e+00 -1.5702e+00 -1.1056e+01  3.5933e+00 -2.8720e+00
-#>  -8.9248e+00 -5.0138e+00  1.0471e+01 -5.2612e+00  5.8908e+00  1.0312e+01
-#>   9.3393e+00 -1.1216e+00 -9.9527e+00 -5.3251e+00  3.2107e-01 -2.6164e+00
-#>  -1.4038e+00 -2.8797e+00  3.0678e+00  1.7291e+01 -1.7825e+01  1.5292e+00
-#>  -1.1628e+00 -1.6507e+00  4.8153e+00 -1.1936e+01 -1.5914e+00 -1.2476e+01
-#>   5.0162e-01 -2.5429e+00 -3.8954e+00 -1.0717e+00 -7.4392e-01 -8.1214e+00
-#>  -2.0364e+00 -3.6251e+00 -1.8497e+00  4.7399e+00  1.0754e+01 -1.1310e+01
-#>  -4.8160e+00  1.7677e+01 -2.3417e+00  1.8043e+00 -1.5608e+01 -3.7794e+00
-#>   1.0231e+00  9.7939e-01 -4.3045e+00 -5.1458e+00  3.6919e+00  4.1636e+00
-#>   3.0280e+00  6.1041e+00  9.0048e-01  4.3932e+00 -1.2203e+01  2.1182e+01
-#>  -2.6326e+00 -5.6641e+00  6.3962e+00  8.7000e-01  1.0775e-01  1.2407e+01
-#>   9.1635e+00  2.8067e+00  2.2553e+00  2.4540e+00  5.8933e-01  1.7804e+01
-#>   3.9390e-01  1.5576e+00  2.1263e+00  3.5894e+00  7.0876e+00  1.6979e+01
-#>   2.2544e-01  4.1435e+00  1.8176e+00  3.9213e+00 -1.5278e+01  2.8989e+00
-#>  -4.3914e-01  1.2226e+01 -4.4174e+00  2.6144e-01 -5.7790e+00  1.1095e+01
-#>  -1.0954e+00  3.1233e+00 -4.9339e+00 -5.5239e+00  1.2868e+01 -1.3096e+00
-#>  -1.7473e+01 -3.2913e+00 -9.9370e+00  1.6746e+01 -1.0182e+00 -1.9162e+01
-#>   6.0977e+00 -7.1840e+00  9.8135e+00 -1.5442e+01  4.1905e+00  4.2416e+00
-#>   8.6695e+00 -1.1826e+00 -1.3659e+01  1.9926e+00  3.4558e+00 -8.9040e+00
-#>  -7.1326e+00  3.0948e+00  5.0819e+00  8.8750e-01  8.3385e+00 -1.4594e+00
-#>   1.3645e+01  5.1997e+00  3.6020e+00 -8.2307e+00  2.1137e+00  1.2046e+01
-#>  -4.8727e-01 -7.2430e+00 -2.7028e+00  1.1680e+01 -7.5035e+00 -5.5648e+00
-#>   7.9166e+00 -2.5712e+00  1.0126e+01 -5.6492e-01 -1.6257e+01  8.2240e+00
-#>   1.1700e+01  1.9525e+00 -1.7176e+00  7.5046e+00 -9.1546e+00  6.5096e+00
-#>   5.9320e-01 -5.0605e+00 -1.0223e+00  1.9240e+01 -1.0675e+01  2.9595e+00
-#>   1.3353e+01 -1.2302e+01 -2.9855e+00 -1.1250e+00  5.4672e+00 -7.9218e+00
-#>  -2.6932e+00  6.7082e+00 -1.1248e+01  5.8509e+00  1.2224e+01 -1.1559e+01
-#>   6.9015e+00 -6.2887e+00  1.2609e+00  3.2606e+00 -1.2129e+01  2.1034e+00
-#>   5.9783e+00  2.8002e+00 -1.0461e+00  2.0890e+00 -1.5872e+00  4.5608e+00
-#>   2.3425e+00  9.2480e+00 -1.0278e+01  8.0220e+00 -4.5289e+00  1.0035e+01
-#>   1.3217e+00 -2.8492e+00  4.2375e+00 -8.9814e+00 -8.6686e+00 -8.7894e+00
-#> 
-#> Columns 25 to 30  8.3360e+00 -3.7383e+00 -1.0755e+01 -4.4215e+00 -5.5599e+00  4.4972e+00
-#>   9.7614e+00  3.3047e+00  8.1004e+00 -1.7579e-01 -2.6047e+00  6.5980e+00
-#>  -4.3140e+00 -9.9903e+00  3.5220e+00 -1.1364e+01 -2.4828e+00 -1.2487e+01
-#>   7.3264e-01  1.2492e+01  1.8758e-03 -1.5940e+01 -1.7799e+01  7.2498e-01
-#>  -6.0802e+00 -2.9302e+00  5.6184e+00 -7.8101e+00 -3.4038e+00  3.7225e-01
-#>  -3.1649e+00 -8.9998e+00  2.3312e+00  7.6182e+00  8.3816e+00  9.3550e+00
-#>  -1.1552e+01  2.3792e-01  1.4321e+00  3.3447e+00 -1.7611e+01  8.7142e+00
-#>  -8.7107e-01  8.1181e+00 -1.4570e+01  6.2605e+00  7.8271e+00 -4.8895e+00
-#>  -2.9786e+00 -2.9680e+00 -2.1442e+00 -1.3693e+01  2.4202e+00 -1.5083e+00
-#>  -3.8475e+00 -6.6983e-01  1.0256e+00 -1.1677e+01 -4.5688e+00  1.2044e+01
-#>  -1.4389e+01 -1.1644e+01 -2.4801e+00  2.1499e+00 -5.2939e-01  3.5510e-01
-#>  -2.3637e+00 -3.1054e+01 -6.8584e+00  9.6814e-02  1.5122e+00  9.6570e+00
-#>   4.2619e+00  5.8534e+00 -1.5839e+01  1.4565e+00 -1.7065e+00 -9.5515e+00
-#>  -8.0406e+00  5.8786e+00  7.9152e+00 -6.5251e+00 -1.3515e+01  1.7494e+01
-#>  -5.5358e+00 -1.6714e+01 -1.0522e+00  5.3414e+00 -1.4394e+01  7.4968e+00
-#>   5.9624e+00  7.6743e-01  4.9624e+00  9.7006e+00 -5.8005e-01 -2.2949e+00
-#>   3.7649e-01 -1.2126e+01  1.4155e+01  8.3035e+00  1.8507e+00  4.5304e+00
-#>   2.6175e+00  1.0547e+01 -5.2089e+00 -1.7335e+00 -1.1900e+01 -3.6790e-01
-#>  -2.6824e+00 -1.0599e+01  1.6807e+01  1.3984e+01  1.0876e+01  1.8125e+01
-#>  -6.5827e+00 -2.4784e+00  2.5894e-01 -8.1518e+00 -5.2204e+00  7.2081e+00
-#>   9.8621e+00  4.5915e-02  2.3873e-01  1.2638e+01  1.6099e+00  3.0094e+00
-#>   3.0689e+00  5.8365e+00 -1.1364e+01  3.0868e+00  1.1472e+01 -3.2644e-01
-#>   4.4706e-01  6.1634e+00 -8.9733e+00 -1.2721e+01  6.9967e-01  1.8322e+01
-#>  -5.6052e+00  6.5106e+00  8.6718e+00  4.4310e+00 -9.5444e-01 -6.2647e-01
-#>   4.7944e+00 -5.0659e+00  5.1348e+00 -5.5745e+00  2.4925e+00 -1.3703e+00
-#>  -1.4523e+01 -1.3315e+01  7.2122e-04 -1.1568e+01 -4.8835e+00  4.6232e+00
-#>  -3.9652e+00  6.9381e+00 -4.8351e+00 -1.2155e+01  8.0029e+00 -8.4143e+00
-#>   6.9426e+00  3.5703e+00 -9.3369e+00  3.6161e+00  2.8410e+00  4.1312e+00
-#>  -6.9064e+00  9.6734e+00 -1.0496e+01 -3.5655e+00 -8.3905e+00 -1.7785e+01
-#>   3.1462e-03  1.9450e+00 -5.5292e+00  2.8397e+00 -6.2833e+00  3.3583e+00
-#>  -5.6672e+00  3.4510e+00 -1.5409e+00  8.8068e+00 -5.1308e+00  2.6718e+00
-#>  -3.3111e+00  1.4582e+01  5.8425e+00 -1.7964e+01  6.5661e+00 -2.8826e+01
-#>  -6.9143e+00  1.0431e+01 -5.4161e+00 -2.9465e+00  3.6363e+00  3.3457e+00
-#> 
-#> Columns 31 to 36  1.2633e-01  2.0837e+00 -1.7995e+01 -7.4558e+00 -3.4899e+00 -2.1166e+00
-#>   5.1230e+00  9.6372e+00  4.8431e+00 -4.0710e-02 -1.1583e+01  2.3542e+01
-#>   3.2173e+00  6.4572e+00  8.2509e+00  8.3110e+00 -1.1414e+01 -1.0241e+01
-#>   1.9424e+00 -8.8026e+00  1.1571e+01  6.9345e+00  2.2727e+00  1.0056e+00
-#>  -7.7589e+00 -1.0472e+01 -3.7097e+00 -1.0164e+01 -3.7912e-01 -2.1021e+01
-#>   1.9517e+01  4.7494e+00  4.4957e+00 -1.0816e+00  5.4489e+00 -6.5487e+00
-#>  -8.7826e+00  7.8509e-02  9.0676e+00  6.6272e+00  2.6549e+00 -1.5556e+01
-#>   9.7232e-01  1.4177e+01  7.6577e+00  1.4539e+00 -1.0341e+01 -4.9364e+00
-#>   5.9784e+00  5.7458e+00 -1.4109e+01 -5.6991e+00  9.1061e+00  1.4150e+01
-#>   1.6477e+01 -3.1146e+00  2.9849e+01 -1.8320e-01 -2.9251e+00  4.5001e-01
-#>  -5.6236e-03  1.4766e+00 -6.0240e+00  9.4568e+00 -1.5395e+00 -1.5155e+01
-#>   4.9929e+00  6.7127e+00 -8.6386e+00 -4.3836e+00 -5.1465e+00 -2.8905e+00
-#>  -3.2138e+00  6.1757e+00  1.0868e+01  2.0150e+01  2.3389e+00 -1.8949e+01
-#>  -3.8071e+00  9.3911e+00  1.2564e+01 -9.0786e+00  4.9773e+00  9.1801e+00
-#>   6.5899e+00 -1.0259e+01  2.5031e+00 -6.8856e+00  2.3481e+00 -2.8523e-01
-#>   7.7059e+00 -3.3388e+00 -1.0783e+01  3.0331e+00  1.0327e+01  1.5650e+01
-#>   5.5370e+00 -2.3466e+00  7.0102e+00  3.5636e+00  5.4982e+00  2.2237e-01
-#>  -4.8712e+00 -8.5445e-02 -6.6527e+00  1.0074e+01 -9.5377e+00  5.8579e+00
-#>   7.7541e+00 -5.7797e+00 -1.3062e+00 -1.3028e+01  1.2405e+01  7.4622e+00
-#>   7.9735e+00  1.0001e+01 -2.2889e+00  2.2752e+00 -8.3279e+00 -1.2083e+01
-#>  -6.8952e+00 -8.5599e+00 -8.3242e+00  5.5835e+00  2.0972e+00  5.6569e+00
-#>   1.2612e+00  1.4267e+01  1.1097e+00  1.6704e+01 -3.4209e+00  7.5645e+00
-#>   2.4637e+00  4.9550e+00 -2.2320e+00 -6.2892e-01  5.8074e+00  2.5013e+00
-#>  -1.9164e+01 -3.9155e+00 -9.6766e+00 -1.0788e+01  1.0344e+00 -4.6280e+00
-#>  -5.2133e-01 -5.1429e+00  4.8088e-01  2.4318e+00  4.7498e+00  1.5048e+01
-#>   9.6296e+00  7.1187e+00  7.5106e+00 -1.4648e+01 -6.7761e+00 -9.6407e+00
-#>  -6.2040e+00 -5.7022e+00  3.5299e+00 -7.0126e+00  2.2069e+01 -1.1930e+01
-#>   1.0790e+01  1.6324e+01 -2.4648e+00  8.3412e+00  1.5067e+01  2.6021e+00
-#>  -5.1471e+00 -6.1570e+00  1.7403e+01  9.0279e+00 -1.1493e+01 -2.4385e-01
-#>  -6.8378e+00 -2.1936e+01 -8.3022e+00 -7.4845e+00  9.9043e+00  3.4503e+00
-#>   3.5370e+00 -4.7399e+00 -9.0786e+00 -8.4300e+00 -1.1417e+01 -2.3562e-01
-#>   1.6061e+00 -6.5874e+00  6.4658e+00 -7.2971e+00  1.1293e+01 -1.7372e+01
-#>  -6.1479e+00  4.7600e+00  2.8297e+00 -4.8681e+00 -5.9525e+00 -2.5531e+00
-#> 
-#> Columns 37 to 42 -4.1066e+00  2.3703e+00 -5.9486e-01 -4.4666e-01  1.8248e-01 -8.1558e+00
-#>   8.1020e-01  1.9982e+00  1.4639e+01  4.4896e+00  7.8410e+00  9.7412e+00
-#>  -1.4223e+01 -1.3682e+01 -1.7861e+00 -6.5508e+00  8.4454e+00  7.9100e+00
-#>  -1.8200e+01  1.3744e+01 -1.3529e+01  1.0235e+01  5.1733e+00 -1.4189e+01
-#>  -6.4968e+00  1.8096e+01 -2.5983e+00  3.8399e+00  5.0918e+00  3.8648e+00
-#>   1.0602e+01  1.2529e+01  1.3578e+01  3.7652e+00 -2.2580e+00 -5.7485e+00
-#>  -7.6017e+00 -2.7962e+00  7.1716e+00 -1.3455e-01 -1.4039e+00 -4.6689e+00
-#>   3.3376e+00 -8.9963e+00  1.1831e+01 -7.4716e+00 -5.3106e+00  4.3870e+00
-#>   7.4744e+00 -2.4187e+00 -2.0261e+00 -6.3718e+00 -6.1785e+00 -9.6643e+00
-#>  -1.2513e+01  1.6945e+01 -3.6329e+00  6.4687e+00 -5.0772e+00  8.4799e+00
-#>   7.7864e+00 -1.5982e+01  9.4983e-01  4.9537e+00  3.2252e+00  7.0543e+00
-#>   1.2666e+00 -4.2042e+00 -1.7502e+00 -2.3269e+00 -1.6726e+00 -9.4753e+00
-#>   6.5305e+00 -8.1240e+00  9.4768e+00  4.4920e+00 -6.1574e+00 -5.3549e-01
-#>   2.2090e+00  4.4112e+00 -7.0229e+00  1.0717e+01 -6.2096e+00  3.4585e+00
-#>  -1.1082e+01  6.0274e+00 -2.1499e+01 -3.2891e+00  2.8027e+00 -1.1171e+01
-#>  -1.3268e+01  3.0344e+01  6.3035e-01 -4.0915e-01 -1.5139e-01 -1.0354e+01
-#>   6.5644e+00  7.3598e+00 -6.2678e+00 -1.0709e+00 -4.2123e+00  2.6543e+00
-#>   1.8287e+01 -1.2104e+01  3.0959e+00  6.8876e+00 -9.4723e-01  1.1671e+00
-#>  -4.3596e-01 -9.0169e+00  1.3417e+01 -4.2153e+00  3.0050e+00  1.0411e-01
-#>  -3.4339e+00 -1.8052e+01 -1.4217e+00 -3.8098e+00  7.3061e-01 -1.7107e+01
-#>   1.3988e+01  9.8941e+00  7.7778e+00 -1.4597e-01 -3.0472e+00 -5.8861e+00
-#>  -1.7668e+01  2.0067e+00 -1.7894e+01 -7.7294e+00 -7.6481e+00  1.0407e+01
-#>   2.3894e+00 -8.2646e+00  9.7261e-01 -2.3514e+01 -1.4048e+01 -1.6880e+01
-#>  -2.4144e+00  2.2946e+00  4.3116e+00 -5.1044e+00 -1.0307e+01  5.4957e+00
-#>  -1.0266e+01 -6.9912e+00  2.0645e+01 -1.2542e+01  2.4918e+00 -9.1484e+00
-#>   3.8444e+00  3.6482e+00  6.9221e+00 -6.6864e+00  7.9277e-01 -6.1825e+00
-#>  -7.1886e-01 -1.9885e+00 -8.9606e+00  1.2146e+01 -6.4248e-01 -6.7472e+00
-#>  -5.4758e+00 -1.1466e+01 -2.0698e+00  1.4037e+01  9.3900e+00  4.5658e+00
-#>   5.3529e+00 -4.9850e+00  4.7814e+00  1.0499e+01 -1.3354e+01  1.0404e+01
-#>  -1.2554e+01 -6.0901e-01 -4.0208e+00 -2.2119e+01  5.2214e+00 -1.0635e-01
-#>  -4.0157e+00  5.0545e-04 -4.0805e-01 -6.4190e+00 -9.8647e+00  7.2780e+00
-#>  -3.5262e+00  1.7072e-01 -1.3999e+01  4.5315e-01 -1.7660e+00 -1.6332e+00
-#>  -5.1083e+00 -4.2060e+00 -2.6048e+00  2.4064e+00  3.4505e+00  1.4462e+01
-#> 
-#> Columns 43 to 48  7.4749e+00  3.2896e+00 -6.5771e-01 -2.0536e+00 -1.5341e+00 -2.1485e+00
-#>  -1.4133e+01 -5.2030e+00  1.2760e+01 -1.2155e+01  1.1957e+01 -8.8063e+00
-#>   5.0999e+00 -6.1652e+00  3.1785e+00 -6.0359e+00 -4.3141e+00  2.7696e+00
-#>   4.1555e+00 -1.0005e+01  7.6561e-01  1.1265e+00 -5.4895e+00  7.6430e+00
-#>   4.9313e+00 -1.8493e+00  5.9208e+00 -9.9984e+00 -4.2710e+00 -1.3521e+00
-#>   5.3910e+00 -4.5412e-01 -9.8172e+00  2.3860e+01 -1.8580e+01  1.1603e+01
-#>   5.4036e+00  3.2782e+00  3.6406e+00 -7.9052e+00 -3.6935e+00 -5.4650e+00
-#>   4.7777e-01  1.8790e+01 -7.1115e+00 -1.1741e+01  5.1975e+00 -9.0889e-01
-#>  -4.3182e+00  9.0281e+00 -8.1416e+00  7.9910e+00 -9.8128e+00 -1.0565e+01
-#>   5.0018e-01 -4.6825e+00  5.4171e+00 -1.3931e+01  1.5320e+00 -8.1043e+00
-#>   1.3373e+01 -5.6451e+00 -6.6508e+00  1.5060e+00 -8.8349e-01  1.1201e+00
-#>  -5.6232e+00  3.6242e+00 -1.0497e+00  2.5834e+00 -2.0360e+00 -7.3847e+00
-#>   6.6888e+00  2.3927e+00 -3.8077e+00 -2.1639e-02 -5.8558e+00  6.8943e+00
-#>  -7.8989e+00 -2.6013e+00  5.0901e+00  2.8595e+00 -5.5578e+00 -2.1903e+00
-#>   9.7989e+00  5.4863e+00 -1.1755e+01  1.3804e+01  6.5772e+00 -2.0106e+00
-#>   4.7886e+00  4.1859e-01  1.0380e+00  1.2049e+01  3.2355e+00 -6.1523e+00
-#>  -1.0745e+00  4.7642e+00  2.3202e+00 -7.0219e+00 -1.0187e+00 -7.0501e+00
-#>  -5.4901e+00 -3.2077e+00  2.0884e+01  2.2532e+00  3.0159e+00  7.5106e+00
-#>  -1.1099e+01  1.3630e+01 -7.1980e+00 -1.1558e+01  1.6250e-01  7.4032e+00
-#>   1.2947e+01  5.4460e+00 -1.5756e+01  9.4801e+00 -1.2139e+00  6.2564e+00
-#>   1.1998e-01  5.7220e+00  1.1314e+01 -3.5625e+00  3.5268e+00  1.1134e+01
-#>   1.3069e+01  1.1677e+00 -8.4681e+00 -1.1823e+01 -1.4532e+01 -5.3117e+00
-#>   3.4807e+00  1.9474e+01  8.1345e-01 -4.3280e+00  3.3045e+00  8.0262e+00
-#>  -4.1458e-01  2.9176e+00  3.3451e+00 -2.6143e+00  8.6070e-01  2.2524e-01
-#>   4.1285e+00  9.9700e-01  9.8412e+00  3.2570e+00 -8.5901e-01  4.1803e+00
-#>  -1.1954e+01  5.9839e+00 -1.7945e+01  1.3585e+01 -3.0892e+00  6.8838e-01
-#>   2.6369e+00 -5.5210e+00 -1.5965e+01  1.8688e+01 -1.1416e+01  9.1712e+00
-#>   1.5720e+00 -3.2047e+00  3.7386e+00 -1.0319e+01 -6.0105e+00  6.2452e-02
-#>  -1.0030e+00 -3.8978e+00  8.4415e+00 -6.1983e+00  9.5013e+00  7.3072e+00
-#>   1.0870e+00 -1.7976e+00  1.0758e+01  6.4537e+00 -3.5953e-01  8.7095e+00
-#>  -1.2589e+01  1.6091e+00  4.8148e+00  3.5849e+00 -3.8170e+00 -3.7377e+00
-#>  -1.0399e+00  1.7239e+01  3.7850e+00  8.8687e+00  1.6787e+00  1.8082e+01
-#>   9.9256e+00 -5.7379e+00  6.7655e+00 -4.6335e+00  1.2040e+01  4.8663e-01
-#> 
-#> Columns 49 to 54  2.0588e+00 -1.1653e+01  1.0608e+01  2.7881e+00  5.6513e+00 -5.9351e-01
-#>  -1.5856e+01 -1.0242e+01 -7.9768e-01  2.1046e+00 -4.7630e+00 -2.5084e+00
-#>   5.8313e+00 -1.4300e+01  4.8191e+00 -3.9928e+00  3.9323e+00 -1.7959e+00
-#>   7.1854e+00  7.4052e+00 -3.5649e+00  3.0255e+00 -6.5195e+00  4.0929e-02
-#>   7.3524e+00 -4.1666e+00  1.4574e+01 -8.3825e+00  1.2557e+00  2.2931e+00
-#>  -1.6590e+01 -4.2074e+00  6.5455e+00 -2.3427e+00 -5.9171e+00 -2.4614e+00
-#>   8.4701e+00 -4.9499e+00  2.8532e+00  1.2714e+00 -1.0051e+01 -2.1670e+00
-#>  -4.7380e+00 -4.1595e+00  7.7442e-01  4.5593e+00 -3.1357e+00  1.0338e+00
-#>   1.7061e+01 -1.1289e+01  8.1655e+00  5.5929e+00  5.5192e+00  6.9233e-01
-#>  -1.1707e+01 -4.1463e+00 -4.4399e+00 -5.5704e-01 -5.4864e+00 -4.6775e+00
-#>  -1.9277e+00  4.0682e+00  1.0254e+01 -2.4243e-01 -1.0002e+00 -1.2724e+00
-#>   6.7865e+00 -1.4280e+00  1.7221e+00 -4.3760e+00  5.2218e+00 -1.1768e-01
-#>  -3.3110e+00 -9.4838e-01 -1.9602e+01  9.0612e+00 -1.4708e+00  4.8763e+00
-#>  -3.5278e+00  4.1600e+00 -1.1134e+00  7.8087e-01 -2.6953e+00 -1.4792e+00
-#>   6.3919e+00 -8.5068e+00  1.0860e+01 -2.0888e+00  4.6862e+00 -2.6934e+00
-#>  -1.3631e+01  3.2825e+00  3.6291e+00  2.1424e-01 -6.6992e+00 -2.3271e+00
-#>  -8.4119e+00 -8.5404e+00  4.4918e+00  2.3728e+00 -2.8177e+00  2.3407e+00
-#>  -5.7681e+00  4.3214e+00 -1.0287e+01  3.5729e+00 -4.2341e+00  1.4796e+00
-#>  -8.7989e+00  5.0641e+00 -4.5184e-01 -7.1411e+00  4.0992e+00 -2.9186e+00
-#>  -5.6270e+00 -6.4904e+00  3.5192e+00 -4.1002e+00  5.7519e+00 -9.8820e-01
-#>  -4.6634e+00 -5.7731e-01 -5.6198e+00 -2.6096e+00  5.3468e+00 -2.4138e+00
-#>   1.9903e+01 -1.9132e-01  3.7509e+00 -2.5854e+00  8.5715e-01 -1.6367e+00
-#>  -1.8672e+01 -6.1275e+00  6.0109e+00  6.0365e+00 -3.1170e+00  2.3840e-01
-#>  -3.7971e-01 -6.9648e+00  2.7984e+00 -2.4435e+00 -4.0945e+00  3.8353e-01
-#>  -1.4058e+01  5.9574e+00 -1.9671e+00 -5.1292e+00  1.5424e+00 -5.5177e-01
-#>  -8.7823e+00  1.3229e+00 -7.1644e+00  1.2158e+00 -3.8932e+00  3.1650e+00
-#>  -5.6919e+00  1.2080e+01  1.2179e+00  4.1779e+00 -5.4655e-02  2.0174e-01
-#>  -8.4263e+00  3.1701e+00  2.4079e-01  5.1354e+00  2.8352e+00 -2.6872e-01
-#>   4.9421e+00 -5.0250e+00  2.6910e+00  2.7623e+00 -6.1163e+00  1.4400e+00
-#>  -1.1406e+01  2.4924e+01 -9.8252e+00 -1.6379e+00 -2.1415e+00 -1.5942e+00
-#>   5.9189e+00  6.6855e+00  5.4495e+00  8.0702e+00 -6.0936e+00  1.2601e+00
-#>  -7.1622e+00  7.4579e+00 -1.6247e+01 -1.7368e+00  3.5875e+00  8.7810e-01
-#>  -5.6202e+00 -4.4285e+00  1.1572e+00 -4.1747e+00 -1.2560e+00 -6.9329e+00
-#> 
-#> (12,.,.) = 
-#>  Columns 1 to 6  2.2343e+00  2.4861e-01  1.3061e+01 -1.3344e+01  1.2442e+00  1.6980e+01
-#>   5.8112e-01 -1.1779e+01 -2.8015e+00 -1.2982e+01 -3.2822e+00  7.1146e-02
-#>  -4.3058e+00  1.0108e+01  2.0428e+00  4.8671e+00 -7.7594e+00 -1.3086e+01
-#>  -1.4351e+00 -5.7256e+00 -4.4247e+00  1.3467e+00  1.5534e+00 -1.1222e+01
-#>   1.7914e+00  6.6421e+00 -2.5117e+00  2.6416e+00  4.7904e+00 -3.9372e-01
-#>  -8.3136e+00 -1.5627e+00 -4.9528e+00  6.6515e+00  5.2495e+00 -1.7103e+00
-#>   5.3299e+00  6.1510e+00 -7.8576e-01 -3.3475e+00 -1.0934e+01 -3.2163e+00
-#>  -2.7892e+00  7.9525e+00  1.3028e+01 -3.7640e+00 -2.0863e+01 -2.6415e+00
-#>  -4.3488e+00 -8.0201e+00 -5.8985e+00 -3.7523e-01  1.3658e+01 -5.0732e+00
-#>  -7.9534e-01 -8.1160e+00 -4.3030e+00 -8.5787e-02 -1.4162e+01 -1.9195e+01
-#>   1.7526e+00 -1.3950e+00 -3.5435e+00 -3.9843e+00 -5.4550e-01  1.4017e+00
-#>  -5.9295e+00 -1.8240e+00  2.3624e+00  2.2988e+00  8.0792e+00  5.5243e+00
-#>   3.5374e+00  1.2951e+00  1.0387e+01 -7.4211e+00 -1.6104e+01 -9.4376e+00
-#>   4.2853e+00 -5.5056e+00 -4.3562e-01  1.1677e+00  6.9683e+00  3.3208e+00
-#>   7.1005e-02  8.5128e+00  3.6922e+00  7.9138e+00  5.0410e+00  3.8249e+00
-#>  -7.2560e-01 -9.5460e-01 -4.3871e+00 -4.4477e+00  5.1031e+00  5.3250e+00
-#>   9.0075e-01  9.4957e+00  5.7318e+00 -5.9370e-01  7.3959e+00  8.1217e+00
-#>  -6.2461e-02 -8.1013e+00 -2.5499e+00 -1.3845e+00  3.1540e+00  1.0728e+01
-#>   5.4301e+00  2.6902e+00 -5.4021e+00  1.0105e+01 -1.1946e+01 -8.0718e+00
-#>   1.5006e-02  5.5758e+00  4.5327e+00  3.3208e+00  3.3094e-01 -2.0927e+01
-#>   7.4586e+00  4.6525e+00  5.8436e-01  1.0973e+01 -2.3610e+00  4.2508e+00
-#>  -1.2983e+00 -1.1301e+01  1.2430e+01  4.0250e+00  4.1832e-01 -1.6513e+01
-#>  -7.8391e+00  2.4531e+00 -1.8300e+00  1.0450e+01  1.5860e+00 -2.5208e+00
-#>   5.6105e+00  5.5700e+00 -4.3786e+00 -9.1806e+00 -5.7021e+00 -7.4651e+00
-#>  -9.2395e+00  6.9638e+00  6.7176e+00  4.8742e+00  1.5557e+00 -4.7856e+00
-#>   1.3244e+00 -1.5630e+00 -6.8633e+00 -2.1048e+00 -4.5452e+00 -1.2455e+01
-#>  -3.4415e+00 -3.6602e+00 -8.8038e+00  1.2900e+01 -1.6583e+00  3.5402e-01
-#>  -5.4946e-01  4.2501e+00 -3.8394e+00 -1.1170e+00 -8.1395e-01 -3.9050e+00
-#>   2.5022e+00  8.3729e+00 -2.6832e+00  2.7693e-01  3.8717e+00 -6.5552e+00
-#>   7.8093e+00  1.3572e+00 -1.4310e+01  5.0189e+00  3.0458e+00  8.0139e+00
-#>   1.3326e+00 -6.4450e+00  1.0170e+00  5.7269e-01  1.9765e+01 -6.6270e+00
-#>   1.9113e+00  5.7325e+00  7.8303e+00 -8.4500e-01 -7.9439e+00 -6.7554e+00
-#>   2.8425e+00  8.1149e+00  6.1998e-01 -6.4198e+00 -1.3863e+01  2.7923e+00
-#> 
-#> Columns 7 to 12  1.0106e+01 -8.0998e+00 -1.6541e+00 -1.0441e+01 -6.2678e+00  1.5084e+01
-#>   4.2660e+00  5.1183e+00 -4.0667e+00 -2.3064e+01 -4.2162e+00 -1.1576e+00
-#>   1.3151e+00 -4.1282e+00  1.2276e+01  2.8656e+00  1.3460e+01 -8.0012e+00
-#>  -4.6559e+00 -1.3864e+01 -4.5554e+00 -3.6413e+00 -5.4804e+00 -1.7346e+01
-#>   9.5870e+00  7.6032e+00  8.3130e+00  1.2399e+00  2.7644e+00  3.0823e+00
-#>   7.4200e+00 -1.7400e+01 -1.1742e+01  7.4787e+00 -2.5101e+01  5.8978e+00
-#>  -1.8558e+00  2.3167e+00 -5.5227e+00 -3.7291e+00  4.4580e+00  1.3574e+00
-#>   4.9338e+00  1.0376e+01 -5.2100e+00  6.7813e+00 -3.7380e+00 -1.6642e+01
-#>   1.8014e+01 -2.5866e+00 -5.0389e+00 -3.0290e+00  1.5388e+00 -1.0736e+00
-#>  -2.3015e+01 -4.0512e+00 -2.0893e+00 -4.2346e+00 -2.2044e+01  1.4695e+00
-#>   4.4195e+00 -1.5963e+00  2.3283e+00 -7.8490e-01 -1.9061e+00  9.3110e-01
-#>  -1.0458e+01  2.6943e+00 -1.1518e+01  3.4626e+00  9.9623e-01  1.5833e+01
-#>   5.4454e-01 -1.8906e-01 -4.6633e-01  8.7036e+00 -1.3710e+00 -1.1635e+01
-#>  -3.0853e+00  1.0756e+01 -2.1256e+01 -1.3527e+01  2.0147e-01  8.2447e+00
-#>  -2.6700e+00 -2.6537e+00  3.8012e-02 -4.0746e-01  4.2514e+00  1.3912e+01
-#>   4.6423e+00 -1.3562e+01  3.3131e-01 -4.1649e+00 -7.3361e+00  4.3607e+00
-#>  -1.1995e+01  7.7007e+00 -1.9198e+00 -1.5712e+01 -1.6685e+01  9.2774e+00
-#>  -5.1702e+00  1.3754e+01  1.1154e+00 -2.0734e+00  1.8528e-02 -3.4237e-01
-#>  -7.5284e+00 -9.7365e+00 -3.6080e+00  6.4859e+00 -8.1583e+00  6.2707e+00
-#>   1.2302e+01  4.8155e+00  6.7645e-01 -9.2286e-01  7.5249e+00 -1.2391e+00
-#>  -3.8789e+00 -7.4176e-01 -3.9320e+00 -6.7518e+00  2.9841e+00  2.0185e+01
-#>   3.6928e+00 -1.8126e+01 -9.5705e+00  2.5406e+00  7.8560e+00 -1.4716e+01
-#>   1.0955e+01  3.9481e+00 -1.9212e+01 -2.1933e+01 -1.2778e+01  1.1302e+01
-#>   7.4397e+00 -7.9327e-01 -9.3109e-01  1.9953e+01 -1.2216e+00  1.0269e+01
-#>   1.7136e+00 -1.5817e+00 -5.9169e+00 -6.5075e+00  4.2173e+00  1.1803e+01
-#>   6.3738e-01  4.2163e+00 -1.4027e+00  9.7656e+00 -2.3720e+00  1.1708e+01
-#>   4.1512e+00 -6.7453e+00 -7.7274e+00  3.5501e+00  8.7879e-01  4.4375e+00
-#>  -3.6729e+00 -1.0202e+01 -6.3316e-01 -1.5827e+01 -5.6612e+00 -1.3232e+01
-#>   3.4021e+00  1.2152e+01  4.2543e+00  1.6077e+01  7.1638e+00 -9.5322e+00
-#>  -1.6027e+01 -1.2267e+01  9.8984e+00  5.8649e+00  1.5113e+01  2.3995e+01
-#>   6.6292e+00 -6.8196e+00  4.2903e+00  7.1128e+00 -1.0787e+01 -1.3526e+00
-#>  -7.1549e+00  1.3952e+01 -9.2232e+00 -6.3592e+00  6.6941e-01 -9.2557e+00
-#>  -3.7317e+00  9.6946e+00 -6.1778e+00  9.9506e+00 -5.2459e+00 -5.7252e+00
-#> 
-#> Columns 13 to 18  7.9775e+00 -3.3716e+00 -1.6398e+01  1.0290e+00  1.4219e+01 -7.9817e-02
-#>  -2.8099e+00  9.6442e+00  5.9975e+00 -8.3840e+00 -1.2924e+01  1.7883e+01
-#>  -2.9478e+00 -4.1491e+00  6.5517e+00 -3.4311e+00 -2.5219e-01 -4.7551e+00
-#>  -1.7489e+00  4.8127e+00  1.7067e-01 -1.0031e+01  4.8095e-01  1.7671e+01
-#>   3.3080e+00 -5.0196e-01  1.6195e+01  2.1735e-01 -1.1563e+01 -1.8092e+01
-#>   5.8877e+00  8.8385e+00  4.5792e+00 -1.3592e+01  3.6850e+00  3.2994e+00
-#>  -3.6112e+00  6.8737e+00  3.0362e+00  3.9409e+00  1.3417e+01 -7.3520e+00
-#>  -1.2533e+01 -5.9631e+00 -6.3714e+00  2.5638e+00  1.9757e+01 -1.3020e+00
-#>   1.8081e+01 -3.9881e+00 -2.4899e+01 -1.1685e+01 -5.0189e+00  2.7581e-01
-#>  -1.0677e+01 -1.7292e+00  6.1302e+00  9.3948e+00 -5.9134e+00  1.0920e+01
-#>  -1.5548e+01 -9.7880e+00  1.5163e+01  3.2234e+00 -1.4654e+01 -2.7111e+00
-#>   2.6025e+00  1.6772e+01 -9.5342e+00 -1.7929e+01  6.9729e+00  1.7990e+00
-#>  -4.6987e+00  1.8096e+00 -4.8640e-01  1.7515e+00  1.6400e+00  8.6115e+00
-#>   5.8794e+00  2.3319e+01 -1.2410e+01 -1.4471e+01 -2.5027e+00  1.3834e+01
-#>   3.6492e+00  1.0097e+01  6.0911e+00 -1.6952e+01  3.7765e+00  1.2470e+00
-#>   9.0154e+00 -7.9646e+00  1.0940e+01 -2.5419e+00 -1.2839e+01 -3.2985e-01
-#>  -1.0842e+01  1.9563e+00  2.8683e+00  6.0006e+00 -4.8396e+00 -3.4927e+00
-#>   5.7079e-01 -9.4869e+00 -7.2187e+00 -2.3264e+00  1.1500e+00  1.3042e+01
-#>   9.3394e-02 -1.3834e+01 -1.0763e+01 -9.9645e+00  1.9327e+01 -3.2349e+00
-#>   1.6126e+01 -7.6733e+00  7.4829e+00 -1.7533e+00 -4.0451e+00  6.5047e+00
-#>  -7.6359e+00 -1.2013e+01 -7.0419e+00  7.2818e+00 -9.0434e+00  6.7979e+00
-#>  -7.6891e+00 -3.2007e+00 -1.9260e+01  1.1613e+01  1.1378e+01  1.1982e+01
-#>   1.8505e+01  1.5860e+01  1.0207e+01 -1.2126e+01 -3.8591e+00 -5.2286e+00
-#>  -1.0642e+01  9.9338e+00 -4.5486e+00 -2.5227e+00 -4.0303e-02 -1.1448e+00
-#>  -2.2505e+00  2.7698e+00  1.3077e+01 -9.0763e+00  7.3168e+00 -5.4729e-01
-#>  -1.9906e+00 -1.5236e+00 -5.2221e+00 -6.9238e-01  9.1291e+00  2.5066e+00
-#>  -3.4526e+00  1.7163e+00 -1.1267e+00  4.2589e+00 -4.5121e-01  1.1336e+00
-#>  -8.9524e+00 -9.5551e+00 -4.5162e+00 -2.4994e+00  1.2130e+00 -2.3913e+01
-#>  -4.9009e+00 -8.7625e+00  3.8590e+00 -2.1340e+00 -7.0136e+00 -7.2723e+00
-#>  -4.2614e+00 -1.5789e+01  1.1984e+01  1.4028e+01  2.2274e+00 -9.6963e+00
-#>   1.0833e+01  1.0684e+01  3.9278e+00 -2.4064e+00 -7.7770e+00  8.4217e+00
-#>   5.8047e+00  1.9016e+01 -1.1636e+01  5.5353e+00 -4.7503e+00  1.2186e+01
-#>  -5.2927e+00  1.4010e+00  1.2860e+00  6.6502e+00 -5.9610e+00 -1.4549e+01
-#> 
-#> Columns 19 to 24 -7.8774e+00  9.3987e+00 -3.1508e+00  3.2035e+00 -1.7237e+00 -2.2337e+00
-#>   9.8753e+00 -5.1873e+00 -1.5979e-01  7.0176e+00  1.4790e+00 -2.2652e+00
-#>   2.4684e+00 -1.5629e+01 -1.0845e+00  2.5782e+00  5.0271e+00 -1.5199e+01
-#>   1.1172e+01  1.2462e+00  1.2406e+01  7.3422e+00 -1.3610e+00 -7.2763e+00
-#>   1.1630e+01 -6.2654e+00  1.3605e+00  3.8456e+00  9.3753e+00 -9.7017e+00
-#>   2.6302e+00 -4.7561e+00 -1.1254e+01 -2.3001e+01  4.0595e-01  6.5323e+00
-#>  -1.6812e+01 -1.8110e+01 -3.6096e+00 -3.5465e-02 -1.4968e+01  9.3158e+00
-#>  -2.4737e+01  8.3503e+00 -1.7397e+00  2.9828e+00 -8.2400e+00  4.5024e+00
-#>  -3.7867e+00  8.1052e+00 -8.3192e-01  8.8176e+00  4.7793e+00 -1.0590e+00
-#>  -3.2457e+00  2.4234e+00 -5.5022e+00 -5.7761e-01  5.2855e+00 -7.4730e+00
-#>  -1.1944e+01 -6.8048e-01 -8.7536e+00 -1.1724e+00  3.2814e+00 -1.1310e+01
-#>   2.9503e+00  1.9739e+00 -8.4449e+00 -1.2881e+01 -7.4179e+00 -2.1671e+00
-#>  -7.6667e+00  9.0520e+00 -4.4530e+00  3.8394e+00 -4.5973e+00  1.5565e+01
-#>   1.2824e+01 -3.3694e+00  1.1752e+01 -2.3582e+00  5.0714e+00  4.2392e+00
-#>   8.0841e+00 -2.5142e+00 -1.0000e+01  1.6647e+00 -8.9167e+00 -1.6243e+01
-#>   1.3602e+01  2.1327e+00 -1.9909e+01  2.4326e+00  1.2900e+01 -3.7971e+00
-#>   1.8914e+01  6.4058e+00  2.3033e-01 -8.2163e+00  3.4782e+00 -1.0740e+00
-#>   6.3810e+00 -4.0515e+00  1.0759e+01 -8.3499e-01  7.3965e-01 -9.1388e+00
-#>  -5.8142e+00  4.5707e-02  1.1224e+00 -4.1406e+00  6.5294e+00 -6.1837e+00
-#>  -9.5804e+00 -7.5517e+00 -4.6984e+00  1.7209e+00 -6.8727e+00  1.3254e-01
-#>   8.7612e+00 -5.2527e+00 -6.8012e+00 -9.3108e-01  6.9843e+00  1.3341e+01
-#>  -1.4546e+01  9.7236e+00 -6.3226e-01 -4.3340e+00 -5.9874e+00 -3.2339e+00
-#>   3.9066e+00  2.2284e-01 -1.4156e+01 -3.8453e+00 -8.6821e+00 -1.9315e+00
-#>  -1.5990e+00  7.5505e-01 -1.8868e+00  1.4217e+01  1.5455e+00 -2.3546e+00
-#>  -1.0250e+01 -7.7651e+00 -7.1299e+00 -3.6324e+00  3.5405e+00 -4.7819e+00
-#>   1.4602e+01  1.3240e+00  9.4274e+00 -8.6895e+00 -5.1178e+00  2.1310e+00
-#>   8.2942e+00 -2.1216e+01  9.4975e+00 -7.5825e+00  1.0006e+01 -2.7521e+00
-#>   1.1945e+01 -1.4659e+01  6.7279e+00 -6.1521e+00  4.0640e+00 -6.7216e+00
-#>  -6.6725e+00  2.7873e+00  8.7322e+00  9.3220e+00  5.0820e+00  5.8792e+00
-#>   3.6809e+00 -1.6383e+01 -1.5499e+01  1.1922e+01  3.0115e+00 -2.6662e+01
-#>  -3.1499e-01  7.1257e+00 -1.5790e+01  1.0345e+00  2.9703e+00  4.5194e-01
-#>  -1.3069e+00 -1.4656e+00 -2.5011e+00  8.5862e+00 -3.4828e-01  1.6991e+01
-#>   2.6757e+00 -2.0333e+01  1.2204e+00 -5.4096e+00  6.6630e+00 -6.8385e+00
-#> 
-#> Columns 25 to 30 -1.5597e+00 -4.3700e+00 -8.6704e+00 -4.0963e+00 -1.6321e+01 -4.3423e+00
-#>   2.9396e+00 -5.9848e+00  2.3327e+00  9.8268e+00  1.4889e+00  1.8608e+01
-#>  -3.4125e+00  9.3013e+00  1.5506e+00  1.0031e+01 -5.2014e+00 -6.4869e+00
-#>  -3.6186e+00 -5.2734e+00 -8.8278e+00  1.1643e+01 -1.0888e+01 -7.8657e+00
-#>   8.9388e-01 -3.4935e+00 -1.1399e+01  8.2926e+00  1.1714e+01  1.8860e+01
-#>  -2.4677e+01 -1.4618e+01  1.2835e+01  2.7846e+00  3.1225e+00 -7.5194e+00
-#>  -3.2098e+00 -1.2272e+01 -7.2635e+00  2.9061e+00 -1.4480e+00 -7.6684e+00
-#>   6.1910e+00  1.7474e+00 -1.5868e+00 -1.4324e+01 -3.1537e+00  2.5270e+00
-#>  -1.4938e+01 -2.9654e-01 -5.9241e+00  4.5311e+00  5.1367e-01 -4.2442e+00
-#>  -1.3926e+01  1.4841e+01  1.2362e+01  3.4737e+00 -3.6818e+00  6.5808e+00
-#>   5.6785e+00  4.2856e+00 -4.1781e+00 -4.9512e+00  1.3393e+01 -4.2968e-01
-#>  -5.5229e+00 -1.6487e+01  6.4846e-01  7.7945e-01  1.2717e+01  1.9683e+00
-#>   4.8212e-01 -1.0946e+01  5.6029e+00  1.0808e+01  1.0435e+00 -4.6982e+00
-#>  -8.2592e+00 -5.3752e+00 -5.2363e+00 -5.4708e-01 -7.7221e+00  1.0334e+01
-#>  -5.4707e+00  2.1817e+01  6.3952e-01  2.8722e+00 -7.4404e+00 -9.4587e+00
-#>  -9.4711e+00  3.9907e+00  2.3350e+00  6.3608e+00  1.4797e+01  8.6591e+00
-#>  -1.5397e+01 -1.2832e+01 -1.2764e+01 -9.1527e+00 -1.3297e+00  3.4214e+00
-#>   2.7281e+01 -1.0290e+01 -8.7485e-02 -3.3851e+00  6.6595e+00 -2.6885e+00
-#>   7.5860e+00  4.2211e+00  1.8520e+01 -1.6380e+01 -5.6852e+00 -1.8309e+01
-#>  -4.2865e+00  1.0817e+01  6.2852e+00 -7.2758e+00 -4.1168e+00  3.6913e+00
-#>   2.4672e+00 -1.1057e+01  2.4086e+00 -4.3137e+00  9.9728e+00  1.6034e+01
-#>  -6.7063e+00 -6.8820e+00  4.8363e-01  1.1105e+01 -1.0333e+01 -1.6367e+00
-#>  -2.1280e+01 -4.1415e+00  6.0902e-01 -1.1766e+01  9.4662e+00  1.6602e+01
-#>  -1.0490e+00 -9.4947e+00  8.6725e+00  3.2525e-01  4.2604e+00 -2.7379e+00
-#>   5.9630e+00 -5.8807e+00  1.1127e+01 -7.1153e+00  1.0029e+01 -1.1379e+01
-#>  -2.1555e+00  6.7623e+00  2.2071e+00 -1.9433e+00  1.1161e+01 -7.5301e+00
-#>  -1.9755e+00  7.1774e+00 -2.1621e+00 -4.2744e+00 -1.2805e+01 -1.0321e+01
-#>   1.6206e+01 -9.0574e+00 -1.3386e+01  1.4836e+00 -3.6717e+00  1.9019e+00
-#>   1.8684e+01 -1.0844e+01 -3.7766e+00 -1.4817e-01 -6.8664e+00 -1.0706e+01
-#>   4.5852e+00  1.4066e+01  9.4515e+00  7.7569e-01 -4.8496e+00 -1.4866e+01
-#>  -2.0469e+00 -4.9087e-01  7.5441e-01 -2.6077e+00 -3.6706e-02  1.2476e+01
-#>   1.8071e-01 -7.4970e+00 -3.3489e-01  6.6271e+00 -2.0375e+00 -1.4445e+01
-#>  -1.8088e+00  4.1301e+00 -2.2527e+00  2.1262e+00 -3.4904e-01  6.9723e+00
-#> 
-#> Columns 31 to 36 -6.5040e+00  5.8752e+00  3.1383e+00  1.1837e+01  6.4853e+00  1.0323e+01
-#>   4.4193e+00  2.0463e+00  5.9618e-01  7.4200e+00 -1.3114e+01 -1.4656e+00
-#>  -1.0508e+01  3.8826e+00  5.1851e+00 -9.6904e+00  1.1859e+01  4.9136e+00
-#>  -4.1396e+00 -7.9817e+00 -5.4383e+00 -1.7414e+01  7.0854e+00 -9.6429e+00
-#>   2.2758e+00  6.2357e+00  9.3515e+00 -6.7403e+00  1.8872e+01 -5.0694e+00
-#>   5.8505e+00  4.4309e+00  1.3236e+01  1.0722e+01  8.9095e+00 -2.9298e+00
-#>   6.5115e+00  3.5489e+00 -9.1628e-01  2.5802e+00  7.0845e+00  1.8733e+01
-#>  -3.0491e+00  3.2534e+00  1.1997e+00  8.8833e-01 -2.1584e+00  2.3641e+00
-#>   1.6468e+00  4.4843e+00  3.3471e+00 -1.8669e+00  5.9386e-01  8.3609e+00
-#>  -2.0089e+00 -1.9370e+01 -2.9176e+00 -5.4528e+00  5.9043e+00  8.6076e+00
-#>  -9.3280e+00  6.0964e+00  3.1025e+00  1.0701e+00  1.0776e+01  9.5514e+00
-#>   6.1485e+00 -3.3274e+00  8.4756e+00 -4.8317e+00 -2.7427e+00  1.5232e+00
-#>   4.6530e+00  9.2008e-01 -6.9505e+00 -6.6205e+00  7.9752e+00 -1.4570e-01
-#>  -2.1591e+00 -1.0310e+01 -1.2721e+01 -2.1465e+01 -1.4941e+01  5.3263e+00
-#>   8.0429e-01  9.5050e+00  5.3638e+00 -5.6013e+00  2.7263e+00 -3.2744e+00
-#>   2.6271e+00 -7.3372e+00  6.6152e+00  2.8711e+00  1.1847e+01 -3.4755e-01
-#>  -3.4368e+00 -9.0752e+00 -1.5055e+00 -3.1626e+00 -9.8245e+00 -4.7415e-01
-#>   2.9191e+00  2.4938e-01  1.8888e-01  9.8061e+00 -1.7531e+01 -6.0103e+00
-#>  -4.7865e+00  1.6937e-01 -5.0390e+00  4.5317e+00  2.9974e+00  1.9773e+01
-#>   2.9439e+00  4.0795e+00 -3.9380e+00 -6.3246e+00  1.0128e+00 -4.5317e+00
-#>   2.6851e+00 -5.0098e+00 -6.2676e+00  6.7208e+00 -1.0663e+01  5.7691e+00
-#>  -1.2311e+01 -6.5510e-01  7.9312e+00 -1.9693e+00 -6.8400e+00  1.4329e+01
-#>   9.2061e+00 -5.5078e+00 -5.9201e+00 -1.1016e+01 -1.0204e+01 -9.2475e+00
-#>   1.3350e+01  1.9800e+00 -9.1722e+00 -3.6948e+00  8.2696e+00  8.0762e+00
-#>   4.6315e+00 -7.5339e+00  1.5947e+00 -1.1322e+01 -8.3186e+00 -6.6386e+00
-#>  -5.7139e+00 -1.3925e+01 -2.3077e+00  1.1494e+00 -7.2648e+00  2.1355e+00
-#>  -4.2203e+00  2.6027e+00  1.7255e+00 -5.4968e+00  3.3107e+00 -7.9746e+00
-#>  -3.7495e+00  1.9044e+00 -5.6979e+00  1.2216e+00  2.1435e+00  4.0822e+00
-#>  -4.3472e+00 -1.2315e+01 -5.7846e+00 -9.3757e+00  6.5540e+00 -5.8813e+00
-#>  -4.1403e+00 -3.7812e+00 -4.1460e+00  3.9681e+00  1.9608e-01  3.2650e-01
-#>   1.4559e+00 -1.7231e+00  1.6940e+00 -5.9361e+00  9.1474e+00 -6.8446e-01
-#>  -5.8992e+00 -3.6607e-02 -4.8166e-01  4.3366e-01 -1.3624e+00  8.6887e+00
-#>   5.1203e+00  4.1813e+00 -9.7441e+00 -2.3176e+00  6.0868e+00  8.1336e+00
-#> 
-#> Columns 37 to 42  1.1062e+01 -2.0296e+00  8.4705e+00 -3.9911e+00  8.3573e-01  1.9506e+00
-#>  -1.1974e+01  5.3647e+00 -1.1954e+01 -5.3444e+00 -1.2646e+01 -1.6711e+01
-#>   2.1595e+01 -4.4778e+00  1.0384e+01 -5.3072e-01 -9.7432e+00 -1.0414e+01
-#>  -1.0581e+01 -4.4263e-01  4.7731e+00 -2.6030e+00  6.7749e+00 -1.6233e+00
-#>  -9.3047e+00 -1.5634e+01 -1.5564e+01 -1.4220e+01 -1.4309e+01 -6.0601e-01
-#>   5.9982e-01 -4.9061e+00  3.9769e+00 -6.1671e+00 -1.0324e+01  4.0731e+00
-#>  -4.3588e+00 -5.6208e+00  6.1008e+00 -9.9672e+00  1.3759e+00  4.8355e+00
-#>   7.6434e+00 -1.1719e+01 -4.9626e+00 -5.4398e-02 -9.2290e+00  5.2844e+00
-#>   1.2625e+01 -1.1581e+01  1.8633e+01  5.7880e+00 -1.1121e+01  2.0442e+01
-#>  -3.0447e+00  5.1596e-01 -1.4120e+01 -2.8625e+00  5.8997e+00  4.8108e+00
-#>  -1.3048e+00  1.6969e+00 -6.9976e+00 -2.0704e+00 -1.4876e+01  1.3682e+01
-#>  -1.0865e+00  3.1554e+00  1.2313e+00  8.0548e-01 -2.0513e+01  7.1541e+00
-#>  -1.4557e+00  2.9544e+00  2.4083e+00  1.6820e+00  7.4094e+00  2.2584e+01
-#>  -2.5237e+00  1.0195e+01 -2.6441e+00 -3.7556e+00 -3.9881e+00  2.7679e+00
-#>   1.6696e+01 -7.1908e+00  2.1827e+00 -2.7923e+00 -1.9770e+01  5.8063e+00
-#>  -1.1795e+01  1.0671e+01  2.6017e+00 -8.2290e+00 -1.6968e+00  7.3746e+00
-#>  -1.9721e+01  3.6066e-01 -2.9877e+00 -8.9195e-01 -2.4205e+00 -1.4037e+01
-#>  -1.7180e+00  2.5656e+01  1.3118e+01  2.6885e+00  2.4311e+00 -1.9627e+01
-#>  -1.2209e+00 -1.7275e+01 -1.0422e+01 -1.7955e+00 -3.1315e+00  2.8352e+01
-#>   5.5952e+00 -1.3472e-01 -1.9804e+00 -2.7732e+00 -3.5788e+00  1.0023e+01
-#>   4.8103e-03  1.3171e+01 -7.5450e-01  8.6808e+00  7.5673e+00  1.6787e+00
-#>   7.9544e+00  8.2651e+00 -1.1695e+01 -1.4135e+01 -3.2763e+00  6.9574e+00
-#>  -4.4499e+00  4.4580e+00  1.5868e+01 -4.1087e+00 -8.7055e+00 -4.7099e+00
-#>  -9.6114e-01  9.5251e-01  1.4457e+01  2.6207e+00 -2.6928e+00  1.6989e+01
-#>   1.2193e+01  7.8603e+00  5.9201e+00 -2.7639e-01  9.1031e+00 -1.2982e+01
-#>   5.6224e+00  6.9850e+00  1.0509e+01  1.2624e+01  1.2922e+01 -1.3763e-01
-#>   1.1296e+00  5.3891e+00  2.9965e+00 -7.8478e+00  1.1889e+01 -2.1520e+00
-#>   1.0341e+01 -1.0417e+01  7.8661e+00 -8.1170e+00 -4.0068e+00 -7.5216e+00
-#>   4.2167e-01  9.3623e+00  6.6411e+00  2.1331e+01  1.4457e+01  8.5066e+00
-#>  -5.5189e+00  5.7353e+00  8.0404e+00 -3.5269e+00  1.4370e+01  8.0252e-01
-#>  -5.0023e+00  8.6836e+00 -1.6432e+00 -3.7779e+00 -1.8732e+01  7.3776e+00
-#>  -2.2673e+00  1.3026e+01  1.6185e+01  5.9499e+00  8.0218e+00  1.3399e+01
-#>   4.0683e+00  4.2663e+00  1.6034e+01  2.7645e-01  7.4202e+00 -2.4128e+00
-#> 
-#> Columns 43 to 48 -1.2115e+01  1.1046e+00 -1.2515e+01  2.2028e+00 -7.2036e+00 -4.0879e+00
-#>   3.2179e+00  1.7676e+00 -1.1806e+00  1.7817e+01 -4.8552e+00  4.6124e+00
-#>  -4.8195e+00  9.0433e+00 -4.6589e+00  7.1531e+00 -1.0476e-01 -2.9681e+00
-#>  -9.5588e+00 -2.9691e+00 -3.7199e+00  8.4445e+00  5.2903e+00 -3.8193e+00
-#>  -5.4232e-01 -3.8415e+00 -7.7434e+00  2.5880e+00  1.6367e+00 -1.3412e+01
-#>  -8.0844e+00  3.3727e-01  5.1215e+00 -1.5609e+01 -5.1258e+00  8.3984e+00
-#>  -2.3635e+00 -3.6531e+00 -5.6427e+00 -8.5791e+00  5.5740e-01 -1.5238e+01
-#>  -4.3731e+00  1.1792e+00  1.4618e+01 -2.5758e+01 -1.0487e+01 -2.2543e+00
-#>  -1.5963e+01 -1.3927e+01  2.1588e-01 -3.2920e+00  9.5727e+00 -5.3125e+00
-#>   2.9978e+00  8.8854e+00  6.2099e+00 -4.2905e+00 -9.6987e+00  1.4991e+00
-#>  -2.1656e+00 -7.8803e+00 -4.0464e+00 -1.5159e+01 -1.1318e+01 -4.2191e+00
-#>  -6.2049e+00  5.2066e+00  1.2489e+01 -7.5448e-01  7.2532e+00 -5.8020e+00
-#>  -5.8869e+00  1.3076e+00  1.9979e+01 -9.7389e+00 -1.3736e+01 -1.6782e+01
-#>   5.1603e+00 -6.6834e+00  5.8808e+00  9.0616e+00  1.1712e+01 -6.1902e+00
-#>  -6.4048e+00  7.1058e+00 -1.6456e+00  2.8912e+00  3.1004e+01 -1.2531e+01
-#>   6.8006e+00  1.9610e+01 -9.1857e+00  4.4045e+00  2.9447e+00  1.6091e+01
-#>   1.6478e+01  4.0959e-01 -2.3223e-02 -7.2793e+00 -2.9199e+00 -1.1647e+01
-#>  -9.9995e+00 -8.1696e+00  1.4737e+00  6.5148e+00 -1.0667e+01 -3.2807e+00
-#>   2.2444e+00 -2.0464e+01  3.0094e+00 -2.6361e+00 -3.3323e+00  1.1333e+01
-#>  -1.0368e+01  7.5013e+00  1.0117e+01  1.3035e+01 -7.9614e+00  8.9583e+00
-#>   5.1484e+00 -3.3187e+00  4.4564e+00  4.0747e+00 -1.4273e+01 -4.7209e+00
-#>  -6.8290e-01  5.9370e+00  1.9618e-01  2.6352e+00  8.7978e+00  1.3552e+01
-#>  -3.7305e+00  2.9977e+00  1.2201e+01  8.4139e+00  1.1424e+01 -4.4993e+00
-#>   1.6751e+01 -8.0573e+00  1.2008e+00 -3.8915e+00  1.2050e+01  1.0548e+01
-#>   6.2575e+00  1.6294e+01  3.5308e+00 -1.2285e+00  4.1870e+00  8.4701e+00
-#>   3.4958e+00  1.4690e+01  8.2981e+00 -1.0359e+01 -1.0257e+01 -7.3372e+00
-#>  -6.5664e+00  9.0567e-01  1.2777e+01 -3.5199e+00  2.0997e+00  4.1423e+00
-#>   8.9526e+00 -1.3091e+01 -1.2533e+00  6.2042e+00 -7.8835e+00 -1.1464e+01
-#>   9.2127e+00 -3.9388e-01  7.7766e+00 -1.4394e+01 -1.3689e+01 -1.4557e+01
-#>  -3.7454e+00  3.2210e+00 -1.3385e+01  4.2240e-01  8.1938e+00 -5.3303e+00
-#>   2.7591e+00 -4.6040e+00  2.7475e+00 -5.0884e+00 -1.3997e+00  4.3406e+00
-#>   4.6150e+00 -9.1322e+00  2.9752e+00  1.5551e+01 -1.3454e-01 -1.5530e+01
-#>   1.6372e+01  1.1288e+00 -3.9405e-01 -3.8099e+00 -6.5638e+00 -7.1677e+00
-#> 
-#> Columns 49 to 54  2.5190e+00  1.1175e+01  1.1601e+01 -7.7790e+00 -5.6342e+00  3.8789e-01
-#>   7.7460e+00  1.1136e+01  1.7623e+00  6.0582e+00 -1.1285e+00  2.3664e+00
-#>   3.6941e+00  5.5731e+00  1.9186e+00  4.5717e+00 -1.0011e+01 -3.9924e-01
-#>   1.0815e+01  2.3231e+01 -4.9671e+00 -4.7148e+00  7.6496e-01  2.7236e+00
-#>  -8.7334e+00  7.1061e-01  2.8245e+00  7.4412e-01  1.5410e-01  5.2671e+00
-#>   9.0524e+00 -4.5650e+00  6.4491e+00  1.6855e+00  7.0050e+00 -9.4149e-02
-#>   8.5457e+00  1.3220e+01  7.7125e+00 -6.8548e-01  3.0141e+00 -5.4146e+00
-#>   4.9509e+00 -2.3485e+00  1.4033e+01  9.0368e+00  3.3652e+00  5.8068e-01
-#>  -2.0652e+00 -5.7014e+00 -3.8886e+00 -5.5111e+00 -5.6780e+00 -3.0847e+00
-#>  -2.7895e+00  3.7967e+00 -1.0079e+00  1.4597e+01  4.4697e+00  3.1462e+00
-#>  -5.1495e+00  2.1150e+00  5.2226e+00 -1.7447e+00 -4.7373e+00  2.6336e+00
-#>   1.9226e+00 -1.4160e+00 -4.5898e+00 -6.5440e+00 -2.6794e+00  3.3858e+00
-#>  -9.6982e-01  4.6653e+00  9.2560e+00 -5.5485e-01 -1.1931e+00  1.4302e+00
-#>   3.4949e+00 -6.4405e+00  9.6045e+00 -2.4561e+00  5.1457e+00 -4.2440e+00
-#>   4.8166e+00 -3.9878e+00 -3.9815e+00 -1.3134e+01 -1.0717e-04  3.4807e+00
-#>  -6.2559e+00 -4.7228e-01 -6.3469e+00  6.4103e-01 -3.5107e+00  3.2496e+00
-#>   2.0834e+00  1.0204e+01  1.1521e+01  1.6283e+01  6.7450e+00  4.0019e-01
-#>   5.8669e+00  8.3477e+00 -2.4140e-01 -5.7797e+00 -2.8879e-01 -3.9505e+00
-#>  -5.4113e+00 -2.5000e+00  6.8480e+00  2.3189e+00 -2.7195e+00  3.8497e+00
-#>   3.4269e+00 -3.1728e+00 -1.1029e-01 -9.0684e-01  1.3045e+00  1.5835e+00
-#>  -8.3938e+00  4.9229e-02 -2.7664e+00  3.1438e+00 -3.9460e+00 -2.1360e+00
-#>  -4.3670e+00 -3.5643e+00  2.9556e+00  8.9076e+00 -6.1031e+00  1.0562e+00
-#>   4.2641e+00 -9.5336e-01 -3.9316e+00 -3.7728e-02  7.7775e+00 -5.3253e+00
-#>   2.4582e+00 -7.1300e+00 -3.4889e-01 -3.3291e+00  2.4103e+00 -5.9407e-01
-#>  -2.3643e+01 -9.1169e+00  2.0369e-01  1.8650e+00 -5.5027e-01 -2.4543e+00
-#>  -2.8845e+00 -4.8286e+00  8.2757e+00 -1.1491e+00  7.9805e+00 -1.8078e+00
-#>   6.6483e+00 -1.0365e+01 -8.0959e+00 -8.7907e+00  1.4690e+00 -7.9068e-01
-#>  -8.7006e-01  1.8929e+00  4.0245e+00  1.9152e+00 -2.2162e+00 -2.2415e+00
-#>   3.6591e-01 -5.5330e+00 -8.4923e+00  1.8120e+00  5.9263e+00  3.9024e+00
-#>  -6.2291e+00  8.7485e+00 -1.9414e+00 -7.9282e+00  4.7886e-01  4.3102e+00
-#>   5.4525e+00 -1.2424e+01 -7.3165e+00  2.8767e-01 -1.4393e-01  4.6123e+00
-#>  -1.2733e+01  4.2735e+00 -2.6760e+00  1.1882e+01 -5.1786e+00  5.2276e+00
-#>   6.1855e+00 -8.2518e+00  8.2563e+00  8.2125e-01  1.9322e+00 -2.0311e-01
-#> 
-#> (13,.,.) = 
-#>  Columns 1 to 8   3.3697  -1.6417  -2.5881   1.4601   4.3656  -6.9630  -4.3418   0.4763
-#>    1.1765  -1.5559   2.3349  -1.8196  -0.6485  10.1627  -6.8879  -7.2488
-#>    0.8502   1.8956 -11.6411  -5.9180  -5.4087  14.6186 -24.2376   0.7743
-#>   -1.4274   3.4590  -9.0911   8.5080  -2.2069   9.8465  12.1775   6.1672
-#>    2.4107   3.6783   0.7586  -9.3225   4.0492   3.9703  -6.0808   0.2643
-#>   -2.4323   1.7145   6.8121  -6.2586   8.6515  -8.2777   9.1493  -2.4481
-#>    4.9859  -0.4675 -15.7742   6.5457  -1.6402   0.0901  -6.2409  -7.5844
-#>   -4.2467   2.7866  -5.5029   6.2417  -0.0762  -4.2140  -9.9406   8.5973
-#>   -1.1883   7.0216  -1.1023  -6.5494  -6.9241   4.7190  -8.5682   6.3939
-#>   -0.1639   5.8292  -8.6185 -16.1194   3.0383   6.4299  13.7305 -25.3119
-#>   -3.3431   1.2020  -3.0795  -5.2280   8.8131  -1.5852  -3.8657   6.2194
-#>   -0.8473   0.6341  -5.4182  -2.6758  -2.1707  -6.6575  -0.5795   0.3148
-#>   -0.2078  -1.1004  -7.5125   7.7188   4.9043 -11.1906  -0.4454   6.0457
-#>    2.4375   2.4327  -6.1229  -7.1326   7.1260  -7.1742   4.7441  -3.1286
-#>    0.3591  -2.6065   1.6676  -8.6926   7.5336   5.8167  -6.4093  -9.9438
-#>    0.9972   8.1101  -0.7143 -10.3995  -6.7773  10.8206  11.4318  -4.4148
-#>   -1.1437  -0.7181  -3.4500  -4.8707  -1.9937   5.6570  16.0217   5.5252
-#>    2.4227   8.3839  -2.4364   3.3629  -5.9968 -12.7720   7.5399  -6.6289
-#>   -1.8409  -6.0536   1.2139 -12.4322   8.8837  -0.7812   8.3287  -2.8039
-#>    1.9115  -4.3994  -9.7979  -8.3191   7.2758   4.4275 -16.5625  -4.0027
-#>    3.4311   2.6624   2.9892 -10.4464  -2.2261   1.0225   0.3791  -8.0550
-#>   -4.4709  -0.7903   0.7995   3.1568  -1.2654  11.3205  -1.4539  17.6271
-#>   -1.7305   9.0243 -13.2431  -1.9026 -16.2815   0.5502   5.2861   7.0291
-#>   -4.2350  -1.8609   0.3718   4.0760   0.5236  -8.7421  -6.9448   7.3357
-#>   -1.3744   3.5020 -12.2297  13.4129   1.6466 -11.8234  -8.9527   1.1651
-#>    2.0638  -4.1548   8.5688  -6.7970   5.9547  -7.3101   3.1767 -17.7759
-#>   -1.1274  -3.6897  10.8214  -4.8866  15.7748  -7.5586   4.6709   0.7997
-#>   -4.9754  -8.4870  -0.9146  -1.8098  -2.3627   9.0146  -2.1974  12.3374
-#>   -0.2340  -3.5367   2.8041  15.3531   0.9489 -13.3056  -0.0813  -1.9383
-#>    0.3238  -4.0122  -1.6916   9.1604   8.2029  -4.6214  -4.9758 -12.1864
-#>   -3.2851   6.1557   6.3449  -3.0137 -10.8634   5.8261   7.5779   5.0891
-#>    7.6729  -5.9596   2.6869  -4.4836  21.3971  -9.8410  -1.4419   5.5365
-#>   -2.3335   1.0808  -6.9140  -9.3190 -13.7158  -4.9634  -1.6641   9.4183
-#> 
-#> Columns 9 to 16   1.4272   6.9005  -8.1758  11.1291   1.9131   0.3419 -16.7107   2.3946
-#>    2.9979   9.6438  -1.1152  10.2243  10.0655   2.6768   0.7035  -7.3764
-#>    0.7594 -11.4686  -0.8667  -6.9460  -3.0995 -10.4431   9.9436  -4.9980
-#>  -12.8362   6.3509   8.7563   1.5577  -6.4292  -1.0006   0.2837   2.4161
-#>   -0.8063 -11.6076   5.4064  -1.6572   5.0197  -0.9882   6.2821 -11.3196
-#>   -0.4334  -5.9995   8.1809   9.3257  -7.2008  11.0680   9.0294  11.7098
-#>    1.7393 -10.8395  -3.5343  -2.6829  -6.3353   9.3118  -4.4164  -0.7906
-#>    3.3009  10.0124  -0.3810   5.4467  -6.9107  -2.5141   2.8867  -7.0754
-#>    9.0013   2.4283   5.4354   6.2346  -2.8392   3.9953   0.3989   7.1242
-#>   -1.1859   9.7886  -9.4103   4.9508  -3.2787   7.1588   0.6914   9.1727
-#>    3.9983  -4.8992  -4.3854   5.9145   8.3597   1.4387   5.1238 -11.0169
-#>    6.1850 -18.2773  16.1798  -0.8564   4.5464 -12.6405  16.6850  -5.5169
-#>  -14.1683  -3.7150   8.3266   3.9403 -10.1845   3.7448  14.6025 -10.5982
-#>   -2.1483  -1.9087  -4.0616   4.0809  -7.5876  -1.6227   6.5654 -12.7282
-#>  -12.4211  -2.9235   4.4465  13.7550  -3.7480 -11.1861  -4.5634   0.1438
-#>   -4.0812  -0.1487   1.5998  -2.6823  -1.8722   3.0849   0.9512  -3.3778
-#>   20.1330  -4.9240   0.1899  -1.7735  -2.1026   5.9774   1.7584  11.2253
-#>    2.7877   9.6033   2.8122 -10.5991  -4.5545   7.3662   3.2882   1.9574
-#>   -1.0704 -11.4845  17.3092   0.6163   7.4801  -0.4920 -11.8827   3.2849
-#>  -13.0745   8.8030  -2.1587  -2.8709 -12.8417 -10.1367  -7.5423  -9.6992
-#>   -3.0761   7.7702  -2.4629  -3.4838  -3.1326  -6.3012  -0.3314   0.4033
-#>   -0.9980   0.8173 -13.4021   5.3338   4.5769   0.6703   7.1546  -5.3624
-#>    6.4712  -7.9052  -8.2864 -10.3298  -5.9160  -9.6548  -6.2402  -3.2102
-#>    8.4025 -13.5483   4.8272  -1.4405  -7.0647  -2.4812  10.6723  -6.7507
-#>   10.1343 -10.1757  -5.0400  -3.4735  -3.4416  -8.9320  -2.7094  17.5644
-#>    9.3466  -0.2112   6.1917 -14.8537   7.7931   7.1170   8.1069   0.8100
-#>   -0.9844  10.4327   9.0464  -0.4420   0.5409  -7.9996   4.3075   0.8815
-#>    4.1023  -5.1221   1.8569   2.7524   1.9987  -3.7410   1.0878   3.2000
-#>    4.1913   2.2717  14.7937  -8.9496  -7.7735   1.2565  10.5878   8.3647
-#>  -19.0380   2.2726  -6.1166   9.6046   5.4028  -8.7725  10.7857 -15.7318
-#>   -6.8082  -8.1418   2.6750   3.2514 -13.1861   3.7538  14.1491 -20.7132
-#>    2.5634  -7.6944  -1.8655  -0.1787  -5.3450   5.7111   1.7207  -2.0251
-#>    2.7810  -2.1352 -14.3000 -14.9511  -4.4896   2.1630   4.6799 -10.1312
-#> 
-#> Columns 17 to 24   4.1233 -17.0780  -3.9538  -2.8758  -3.5483  -7.9644  13.6918   7.0413
-#>   -4.7687   0.5470   1.2882  -1.0949  -0.6276   1.5143  -9.6125  -1.6341
-#>  -15.2562  -1.1686 -16.4718   8.9318   7.9628   9.2988  -2.5182  -9.5424
-#>   -0.6619   5.0462 -12.8017   1.7106 -15.0936  14.4036 -13.6283 -22.3025
-#>   -8.0790  -0.9867   3.7640   7.9434   1.0856  11.9036  15.1968 -13.2958
-#>   -4.3092  -4.7185  -0.5972   7.3328   5.9813   0.2981   7.5770   5.4590
-#>    9.5222 -13.4433  -6.4562  -3.4667   3.6051  19.5683  -0.8224  11.0804
-#>   -7.5286  -8.2387  -3.8117   9.0431  -1.1148 -11.5416   2.0163   2.2055
-#>   -2.1770   6.3546  -7.6080  -5.0374  -6.2474  -7.7696  18.1866   6.6824
-#>   -7.2299  -4.4548  -7.4621   2.6389 -14.8909  -8.9010 -13.5682  -8.0851
-#>   10.6522  -5.2483   4.8124 -14.9675  13.2051   7.2541  20.9926  -3.5390
-#>  -17.9549  14.9953 -11.2778 -11.2486  -0.9213   1.1407  15.7856   1.4826
-#>   13.3178  -6.0633  -3.3993 -16.2574  -7.6736   4.8620  11.2303 -21.1926
-#>   15.5492   3.6144   6.5018  -6.6918 -13.9382 -14.1719  -3.3112  -1.1664
-#>  -12.5781   6.3902 -10.8792   6.3373   8.9104  -0.7249   0.7847  -9.6297
-#>    5.4024   6.1987   0.7445   6.3244  -7.0532  13.4351  -0.4791  -7.8003
-#>   -8.9566   4.4521   3.5503   0.7959  -2.4220  -4.2371 -16.6129  -3.5669
-#>   -5.0858  -3.2332   4.4177  -2.7194  -5.7067   0.9085  -0.5359  23.8047
-#>    6.6007   1.4694 -14.1240   5.0184   2.4091 -14.1085 -10.9890 -13.9345
-#>   -4.3680  -6.0325   5.6439  -3.9697  12.0833  -1.6750  -1.3681  -2.9893
-#>    3.2750   1.4166   7.3507 -13.6353 -13.9985 -15.8404   9.4659   5.0599
-#>    7.0789   1.9307  -7.8050   5.8272  -6.3961 -15.0296  10.2564   8.1358
-#>    2.6946  -0.3610  -6.2785  15.8479   1.6669  -1.4408  10.4534  14.8757
-#>    7.3278   3.8470  -8.3954  -5.5899  -7.4549  -2.1057   4.4085  16.0832
-#>   -4.0996  19.4165  -3.9743   0.4099   4.5863   8.4134  -2.0642   4.1411
-#>   -7.2185  -5.7259  -2.9592  -1.7083  -2.6126 -13.4320 -22.0603   7.3150
-#>   11.2710  -4.9157   6.0592  -2.3185   2.4728   6.5344   0.5997  -0.1768
-#>    1.0739   4.0677  12.0173  -1.5614  -6.8807   1.5222   8.9047  -7.8059
-#>    1.2063   7.1509  -8.9546   8.0253  -1.3432  -5.8292  -8.4954  -1.0355
-#>    7.4239  -4.5134   1.7243  -5.2007 -10.9802  16.0628  11.1323  -8.3712
-#>    4.0806  -0.9152  -2.2090   5.6934  -4.1941  -4.5412   9.3974  -0.6221
-#>   15.5935   0.7072 -10.0603   5.7345   1.6642   8.9193   5.1053  -2.7519
-#>    7.1138 -12.1894   0.6080   7.8461  -8.7989  22.0221   6.3918   9.6505
-#> 
-#> Columns 25 to 32  -2.6800  -5.7408   5.5992   2.6666   5.3303  -4.8828 -11.9063   3.5820
-#>    6.6322  -2.1588  -2.1263   1.4245  18.6545  -7.0352 -17.6687  -3.6482
-#>   12.9900   1.3050 -11.1928  -9.5810  -5.0537   8.9391   2.4537  -3.9121
-#>   11.7591   9.5109 -13.7638  -6.9739   6.2391   7.8595 -18.5428 -13.9119
-#>   -7.2973   4.5046  -8.6841   0.8250  -1.5067   9.7065   7.1418  17.4973
-#>   -5.0957   4.2618   6.5113  10.0076   2.1287   1.5233  13.4246   1.1008
-#>   13.6883  12.8369   2.2686   1.3899  -3.6863  -4.7827 -18.9132   0.2136
-#>    0.2509  10.7896  15.7100  -2.1480   2.9210 -11.5030  -2.3106   1.9727
-#>    1.2314 -13.5424 -16.2364 -15.6695  -8.0330  -4.8299  -2.7412 -10.9407
-#>   -7.7083   9.8008  -1.3087  -8.5017 -15.5218  -5.4738 -14.0310  -5.9766
-#>    3.9822  -3.2992   4.6238  -6.0679  -6.0140  -5.3986  -3.0122   7.2745
-#>    0.5601   2.7388 -11.0853  -0.6145  -2.6784  -3.7761   5.2345 -10.7856
-#>   -4.9741   3.9108  19.4934  -4.8232   9.6452   1.3108   0.9940  -2.7460
-#>    1.2843  -7.5330  -7.7228  12.3964  -4.2323   1.3258  -1.2630 -16.1458
-#>   -1.2016  12.5133 -14.4585   5.6997  18.9993  10.7756   5.3936   7.2677
-#>   -1.4915   2.3885   3.8869  -7.1487   7.6098  -3.9885  -3.6000   1.5721
-#>   -9.4018   6.0380  -8.7007  15.1696  11.1714   3.3346   8.8122  -1.4180
-#>    8.4743  -7.6606   2.2928   6.4204  -2.3149   1.3092   0.0668 -23.7015
-#>  -19.6935   7.9098   4.7952  -0.2914  -5.0775   5.1258 -13.0558 -19.8275
-#>   14.4639  -7.6469   1.4158  -1.6512  10.6585   7.2336 -15.5307   4.4070
-#>  -20.4726  -3.5960  16.8774  12.4712 -10.9533 -14.0708   0.3716  -0.7920
-#>   -6.4765   1.7232  11.5085  -4.0904  -4.2319 -14.4870   3.9247  12.0280
-#>    8.0161  -5.6225  -4.3256  -0.7993   5.5562  -0.6457 -10.7856  -8.2703
-#>    5.5643  -2.1936 -12.2500   1.4277  -7.6654  -3.2104  -5.2612   9.9382
-#>    5.8360  -5.2719 -11.2102   3.7008  -4.7814   4.4835  -3.3795 -11.0323
-#>   -5.5010  -5.6203  -4.4000   7.3807  -1.5143   0.2017   1.5784  -7.5409
-#>    2.7717   3.4020 -14.4807   5.5100 -13.9762  -4.2596  -3.8514   7.4326
-#>  -16.6801  -2.2176  -3.4166  -3.9260  -0.4806   1.3265 -18.2089  -8.6009
-#>   -0.0372   9.5898 -11.3903  -1.9474  -2.1608   0.1678  10.6816   0.2370
-#>    7.6345   9.2515  17.8873   3.6420  -7.1994   5.8380  -6.9410   6.9322
-#>    7.5231  -5.2615   4.9636  -4.4058  -2.2565  -5.9325   5.9708   1.2859
-#>    4.2971  -4.3269  -0.0417   0.7345  21.4405   3.9511  12.8331   0.6572
-#>    6.0391  -3.2032  -9.0262  -4.9702  -5.1867   6.1670  -3.9718   0.8178
-#> 
-#> Columns 33 to 40  10.8348  14.7557   3.3659  -0.1846 -11.8096   4.6553   4.4372   3.0805
-#>   -1.4661 -23.1553   9.9883   2.0956   4.4233 -18.6846  -8.7162  -8.2316
-#>   -7.0449   2.0095   3.8353  -5.8263  19.1000 -13.2257   3.6385   7.9960
-#>   17.4574 -10.0265  -0.9992 -17.1924  -1.2527 -11.1004   3.5991   4.4013
-#>    0.8899   2.4346  -4.6935  -5.2829   3.8203  -5.5734   3.9520  16.4752
-#>    4.7666  15.8362  -2.0676  -1.8818   1.2573  20.5019 -14.3063  17.9730
-#>   17.2786 -12.8405  -3.7920   5.0189   1.6979   0.9932  12.1372   0.0466
-#>    3.7036   6.8519  -9.6157   6.8130  -9.7540  -3.0379   8.6012  -5.9710
-#>   -5.5511  15.5132  -6.4415 -14.1951   3.1228  -9.8643  -1.0604  10.2775
-#>   14.2856 -10.0348 -12.9017  -7.8481   0.7524 -14.6779 -16.8381   8.3535
-#>  -10.8446   4.1617  -6.2735   5.5409   2.7467 -14.3498   7.3312   9.8202
-#>   -3.1855   6.0698   9.7219  -3.7233  -9.9178  -3.9497   1.5279  18.1157
-#>    2.7665   3.5974 -11.1750  -2.1840 -10.3358  -0.4430  14.4890  -2.0752
-#>   -7.7241  -2.5540   8.6497   8.3299   2.8415 -20.8746 -24.1512  18.1693
-#>    9.1557   9.8385  -3.6903   7.4441   0.4999  17.7083 -16.9265   3.5991
-#>   23.0757  -8.6160   6.5006  -4.3907  10.3004   0.7045   1.5834 -10.7031
-#>   -4.5615  -7.9168   2.6719  -0.5778   5.2735   1.1896  -2.8907  10.4492
-#>   -9.2825  -7.2149  -0.4448  14.7885  -2.6618  -8.3046   4.2973  -6.7991
-#>    6.1126  20.5788 -26.1840  -1.7718 -11.1458  -7.2249   2.3674  -9.6824
-#>   -1.3163   5.0976   3.1525   6.4770   6.3339   0.9214  -2.7082   5.1429
-#>   -3.1457  12.5447   8.5838   2.9981  -7.9624 -12.5725 -10.0365  -9.2533
-#>    0.9133   5.9405  11.3644 -18.7311   4.3938  -7.6613  12.7711  -2.6682
-#>   30.8365  -7.4982   0.9603  21.9556  -6.2884 -11.5021   4.4946  10.3176
-#>   14.4685   0.7428 -16.6316  -8.9628  -4.4752   3.2429  -1.9568  11.5486
-#>   15.3273  -3.5033  -3.1176   4.2342  -2.5407  10.6061  -6.7412   0.8499
-#>   -1.9486 -13.6522   1.0336   7.3432   0.7010  12.5901  -9.1793  10.5238
-#>   -7.0167  -0.1492  -8.6312   5.7787   2.9533  14.0871 -16.4314   2.4016
-#>  -18.2783  16.0457   8.4394  -4.0634  -8.0765  -7.8682  -6.6131   3.6371
-#>    3.8207  -4.2039 -16.9315   3.1379  -6.0499   1.3322   9.1753  -1.0800
-#>   15.7705 -12.9205 -13.6020   8.6659   6.2298   7.5931  -2.9307   1.7454
-#>    3.2957  16.8939  11.6730  -2.9389   8.2990 -14.7993  -2.8298   9.3996
-#>    1.9768   2.8376   3.7889   3.5501 -10.1581  -5.4513   8.9801  -7.9636
-#>   10.2112 -16.3559  -7.1759  -0.9930   9.6437  -1.5389  -1.1243  10.5625
-#> 
-#> Columns 41 to 48  -3.3578  -1.1139  -2.4762 -11.2209   9.3577  -8.8033  13.6489   4.8650
-#>    6.5548   1.7133  -0.1935   5.5874 -10.3480  -6.8775   3.3956  -6.4977
-#>   13.0252  -6.4013  -1.1414  11.4570  -7.0205   6.2467  -4.4751  -4.3536
-#>    1.8241  28.0728 -12.6101   8.6788   2.0485   6.1478 -12.4244 -16.7476
-#>   -8.6532  -8.1284   6.3043 -12.8859 -12.4685   9.6203 -18.7328 -18.8922
-#>  -15.2930  -5.9777  -0.0329 -28.5118  -3.1383   2.8168   0.3067 -13.5621
-#>  -14.5134  -3.2274  -0.0055   4.0627   2.8727   1.3799  -6.3114   0.0714
-#>   -0.6586   5.8188  -6.9768  -4.4756   2.2888   2.8688 -13.7075  11.0588
-#>   10.5744  13.1215  -2.0282  11.4851   2.1987   9.1812   3.6154  14.4257
-#>    3.2649  20.1229  -6.1985 -15.9335  -3.7413  -2.8519  -9.2608 -15.2161
-#>   -6.5173  -7.6655  -0.2815  -4.8623   2.1578  -1.2280   9.3485  -0.0405
-#>   11.4033   7.2183  -8.9260   1.6441   0.8568  -7.4824   1.2641   3.8432
-#>   -6.9809   6.6826  -9.4452  -2.2702   3.1859  -2.7236  -9.9576   1.2124
-#>   -5.0526  25.2570   3.2705  -8.0824   0.9330  -8.9063  14.1930  -0.4507
-#>    1.8832  -4.0172   0.1316 -12.6761  -5.1909   9.1281 -10.9429  18.3555
-#>   -3.4178   0.2063   1.9687   3.5427 -13.6490   3.5390  -2.1170  -7.5645
-#>   -4.9405  -9.4943  -8.6500  -7.4028  -4.2744 -15.9314  10.8900  -7.9999
-#>   -5.6152   0.7457  -8.4452  15.5308  -9.3691  -2.9904  10.8867   2.9986
-#>  -20.7752  -1.4896   7.0495  -8.1634   6.6833  18.6335  -6.1095  18.3737
-#>   10.9329   1.9044   6.1540  -2.9243  -0.0544  -3.7527  -3.9820  10.0857
-#>   -1.5593  -0.9560  16.8549  -3.6897 -10.3189  -0.4447  11.5176  -5.2639
-#>    6.5579  13.6265  -3.9144  10.4601   3.4433   9.1518   6.4529  -4.3566
-#>   11.3136  12.5980   7.1762  -5.4726 -20.0146  10.0288  -6.0000  -3.1094
-#>   -6.4761  -7.8402   9.1078   1.4559  -1.1450   2.7579   4.0419  -2.2974
-#>    3.5403  -6.0334  -3.7099   3.2879   6.2914  -9.1560   0.7688 -11.4910
-#>    4.6251  -0.9901   1.4971  -9.3215  -6.6628  -6.7178   8.7838   6.6056
-#>    2.6455   0.9620  13.9202 -10.0320  13.9161  -1.2996   6.1395   2.7685
-#>    0.3381   3.3796  -0.1898  -3.0536   7.6887   1.7112  16.5639  -2.5761
-#>    5.5124  -6.3711  -2.4787   7.4375 -13.7518   3.8744  -4.3973   2.8493
-#>   -2.6076  -9.5805  -3.0601 -13.9946   4.2460  -6.1053  -2.4415  -2.1601
-#>   -3.5259   8.7255  -4.6512   9.3553 -13.5940  -4.1108  14.2099 -19.3789
-#>   -9.5535   7.4221  11.2101 -15.1180   6.3322  -0.1481 -11.3736  12.0167
-#>   -4.7469   3.2653  -3.6165 -11.0855   5.2520  -7.3072  -6.8884  -0.6938
-#> 
-#> Columns 49 to 54  -9.5841  -3.1883   4.8819   0.9323   2.6546   3.5816
-#>   -0.0657  -3.6876   6.0528  13.6310  12.1754   0.8895
-#>    6.4921  -1.6472   0.3413  -4.9985  -5.0039  10.1445
-#>   17.9599  -1.9585   2.2322   8.4005  -2.2082  -4.5209
-#>    1.2629  -0.2790  15.5301   7.7452   7.6565   8.6430
-#>   -8.3178  18.0239   1.7017  -4.8925   6.3538   1.4147
-#>    1.9469   6.5868  -6.3767  11.1967  -4.1276  -3.9215
-#>   -6.0379  11.6491   8.1192  14.2532   2.8171  -3.9183
-#>   -7.5273  -5.7284  -2.2543   5.3219 -13.6635  -1.2019
-#>   11.3169  16.1229   5.3380   5.9345   7.8532  -1.9193
-#>   -1.1561   0.6397  -5.8041  -5.6023   3.9126   4.1689
-#>  -11.1197   9.0098   0.2090  -4.2656   5.6267   2.1441
-#>    0.2229  23.9966   4.7711  -3.8389  -2.0389   0.9057
-#>   -0.7366  -1.9622  -0.0843   5.3703   7.7059  -5.1786
-#>    7.1206  -9.2183 -13.2013   2.0726   5.0482   0.8396
-#>   14.2594   7.7721   3.2311 -13.0711  -4.3741   0.9782
-#>    3.7075  11.6253   2.4502   1.5064   0.6112   1.8650
-#>    0.5343   4.5191  -2.2040  -3.6395   3.1832  -3.9657
-#>  -10.2922   0.4036 -10.7387  -0.4683   7.3636  -4.6882
-#>    5.1110   2.4479  -8.0885  -0.8889  -1.2094  -0.3844
-#>   -4.1646  14.5772  16.0076  -7.5377  -3.2482  -3.9920
-#>    0.1385 -18.6015   0.7036 -10.1109  -3.2682   3.0401
-#>    4.0277   3.9081  -8.8889   2.1189  -7.2020  -7.7930
-#>   -5.4221  -2.3047  -2.3612  -5.3930   1.0088  -1.1463
-#>   10.4664  -0.2838   2.3142  -2.4206  -3.5280  -7.2229
-#>   -5.9612  11.5086  -9.4708   9.8642   0.9766  -3.3587
-#>   -0.9830  -5.6997   0.4194  -5.3989  -7.7813   0.5598
-#>    5.4461 -11.4660  14.4418  -5.9451  -5.2957  -3.8965
-#>    9.3387   3.2802  13.7992  -1.7918   2.2744  -0.3253
-#>   -5.5320   6.5413   0.8090   0.4279   1.2553  -0.8095
-#>   -6.7395   4.1986  -5.4247 -12.3497   4.4859  -1.4505
-#>   -0.5004 -13.5893  -4.6648 -16.0753  -8.4585  -0.8199
-#>   11.9082   0.3645  -0.3096  -5.3894  -1.3357  -2.1738
-#> 
-#> (14,.,.) = 
-#>  Columns 1 to 8   0.3770  11.4530  12.3254  -8.1717   2.0064   9.2537  -6.0489  -2.5420
-#>    3.3567   0.3686  -2.1060  -8.9118  -3.2569   8.7885  -6.6666  -0.7948
-#>    1.4497  -6.2063  -8.3565   5.7694   0.5264   6.2934  -8.0693  -3.4899
-#>   -3.0604  -2.0974   3.9263  -2.7745  -3.1499  14.5660  10.1938  -4.8976
-#>   -5.2067  -7.2476  -3.7690   2.6146  -5.7421 -14.2176  -5.4898   9.6478
-#>    2.7384   9.4170  -1.8324   9.9095   6.4603  -9.8938  -1.7702   5.1115
-#>   -4.0225   2.1869   4.1249  -6.0901   2.7495  -9.6204   1.9513   6.8786
-#>    6.3655  -0.8163  -3.4434   8.3752  -2.3094   6.4114  -4.9764   0.9664
-#>   -0.0141   2.9467   9.7410  -3.6164  -2.9697   3.0740   1.8683  15.1830
-#>    4.5287   7.7451   1.3771  -5.9612  18.3195   3.0365 -13.9349   0.0934
-#>   -2.5749  -7.0213   3.1655   8.8867   7.6434 -14.2088   6.4947   0.1312
-#>    0.2012   0.2631 -14.1806   9.5833   2.1844  -8.8791   6.7946  -6.6629
-#>   -1.3827  -1.3199   4.2433  13.1405  -5.5283   5.0318   7.1572  -6.3521
-#>   -1.7635  -9.2385   1.3914  -0.1005   3.9263   0.8530   2.5017  -0.9691
-#>    6.6711  -7.8012  -4.7570   8.3643  -3.3958  -8.0918  -3.4167   2.7507
-#>   -0.3402   0.0913   6.1923  -0.4942   9.8246 -10.3545  -3.3388  -2.1711
-#>   -0.1663   2.2720  -4.4339  11.3540  13.9740  -2.5846   2.5917   8.9986
-#>   -7.9547   3.4470   2.9946  -0.8519  -3.0121   5.5671   2.6166  -6.7560
-#>   -2.8500  14.7417  11.9077   0.6807  -3.3666  -5.2585  -1.2208   5.3802
-#>    1.9767 -10.0252 -11.7052  -0.6985   8.8981  -7.7196  -6.4009 -11.7299
-#>    0.5899   8.1488  -1.2487   3.1090  11.6321  -1.5359   4.3982  -9.4264
-#>    4.3843   3.0785   5.5632  -9.2855  -6.0409   0.6462   0.5483  -0.7019
-#>   -1.0347  -1.7948 -18.4069   4.8440   3.1485 -12.7971   2.4637  13.6364
-#>    1.7668   1.3412  -1.5615   5.8986   1.3487 -17.8216   3.9517   0.0958
-#>    0.1322   0.1879 -17.0802   7.1159  13.9271  -5.1603   1.4898   8.4073
-#>    1.5698   9.0832   4.5893   6.2004   6.9843  -1.5408  -6.6988  -3.6267
-#>    3.2580  -1.6388  -6.9130  -4.1207   3.7113   7.9904  10.7252  -1.3340
-#>   -2.4975  -2.1435   4.3989   9.1296   7.1549  11.2097   6.9807   4.6930
-#>    0.9505  -3.5208   3.4347  18.2746   4.8642  15.6792   8.6931   6.0316
-#>    2.9614  -2.0693  -0.3657  -4.5516   2.1412  -5.9561   0.8259   7.0303
-#>    0.2068  -7.9595   0.9035  -1.1417  -6.3314  -5.8986  -2.8805  -7.1754
-#>   -6.0468   1.5532   7.6197   4.5963  -9.7855  12.0498   4.3925  12.1035
-#>   -1.9815  -5.6976   4.5649   0.0077   1.1741  15.6724  -8.5468  13.0375
-#> 
-#> Columns 9 to 16  14.8517  -7.5338   2.0898   7.6614   9.8378  -4.6831 -10.0600  -2.1377
-#>    0.2623  -4.5503  -9.4165   8.9472   5.1810   1.5789  -4.1377  10.3595
-#>  -11.9591 -22.1948  -3.1148   9.7955  11.0973  17.6188 -16.4940   6.3169
-#>  -28.6606  -0.8674  -1.7593  -0.1612   7.4730   0.9168   9.7108   4.6269
-#>  -14.9942  -7.2785  -5.0295  -3.6556  -1.4412   3.0655  -4.7327  -2.4419
-#>    7.7481  20.4307   6.6899   3.0492  14.3508   6.8140 -18.2257  -9.2996
-#>   -6.0070   6.3098  -6.6324   9.5148  -2.5147   7.1132   6.0873 -21.6238
-#>    6.2963   4.4553   4.4620  15.4355  -2.1113  13.9785   5.0768   3.0637
-#>   -0.0940 -11.8444   1.7085   7.9298 -21.2709  12.9068  -1.5473   1.5120
-#>    3.5864  -1.8245  -3.1646   3.0038  17.3009   3.9642   4.0612 -17.3035
-#>    6.0550  -2.8465   6.7859   5.7545  -3.3479   7.4782  -0.4281   0.6236
-#>   -7.2762  -8.2545  -3.9465   1.5299   2.3039   3.3165 -10.5609  16.3569
-#>    1.4526   8.4141  -3.7688  -0.0326   6.0030 -10.1505  -4.4883   0.2529
-#>   -8.2809  -3.0374 -28.4246  -4.4572  15.7466 -10.8104  14.7449  -3.9600
-#>  -15.1718   4.2424   9.0816  -0.5463  -4.5732  -3.3233  -6.8652   7.9014
-#>    4.3162  15.7039  20.4732 -12.0980   7.2798  -3.6740  -1.9739 -12.1692
-#>   -2.6370   0.7528  -9.7031   1.6575   8.0723   8.4717  -2.0160   3.2745
-#>   -4.5273  -0.0800   3.9357   9.6299 -14.8624  -5.6028  -1.8641   6.1496
-#>    8.7650   8.5841   8.5647   4.0912 -27.8494   6.4642  -8.0686   4.8601
-#>   -5.1022  -7.4816   0.6639   0.6033   2.0572   4.6621  -1.3301  -3.6485
-#>   12.1442   3.4068  -2.5187 -10.2796 -14.7906   5.9140   0.7293  -5.2769
-#>    8.1061  -4.8040  -2.1649   0.6224  11.7789  16.9427   2.5081   6.6288
-#>    4.9209  -7.8456 -26.9416  -0.1341   0.9170  12.5050   8.5160  16.3678
-#>   -3.3597   8.2125   5.1914  -7.7349   7.0574  -6.7789  -8.3004  -8.7751
-#>    5.3466  -3.3574   6.1179 -13.2464  14.9734  -9.8865 -19.7086  19.1938
-#>  -11.9796   6.3637   5.0602   7.1964   1.0075   5.9998   6.7007  -9.5336
-#>   -4.5889  13.0579   5.0471 -13.9253   1.0684   3.8819   6.7362   5.6673
-#>   -5.1198  -4.9673  10.4607   0.9514  -6.6400   6.9641  -0.0188   8.3754
-#>    2.1722  15.0798   6.2502   5.0564   9.8680  10.7211  16.0995  -4.0884
-#>   -9.4826  -2.3942  14.4484  -1.4258   4.7605 -15.5945  -9.1665  17.3647
-#>   -9.1650   1.6061  -2.9621 -12.2872  -5.8349  -4.7291  15.9064   3.0888
-#>    8.6125  -0.0677  -1.3918 -11.1935   5.1555 -11.0749   7.4689  -3.5442
-#>   -2.2461  -0.5258  -7.8684   1.0981  11.2391  -1.9608   1.9035  -7.2017
-#> 
-#> Columns 17 to 24  -4.9053  -2.2667  -1.0283   9.6957   0.2477 -10.0500  -4.8617   0.0063
-#>  -13.0942   1.2088  -5.3956  -1.5164  15.8247   4.6247   3.8747  -0.5828
-#>  -10.8099   4.3983  -8.7463  -3.5179   2.2671   5.8489   0.7554  -5.0317
-#>   -1.0272   8.7758  -6.7657   4.8201   6.0469   6.8353  -1.8373  -8.9972
-#>   -4.4041   5.9650   6.4497  -1.7451 -12.9368  10.2491   8.8281   8.5340
-#>    9.2032 -14.1359 -18.1122  13.0184   6.0143  -9.3855   0.8192   7.0896
-#>    3.8926  -9.6341   4.3712  14.9302 -10.4555   2.5968   7.1624  -5.0525
-#>   -6.0445  -0.9045  10.0906  -2.3694  -1.8475  -3.1114   7.8476  -1.0478
-#>   13.0187  10.4528   4.5314   4.6289  -6.1119  -3.1832 -17.6575  -9.1404
-#>   15.6379  -0.9983 -15.1494  14.3846   1.5959   3.0012  -1.9903  -2.3397
-#>   10.5089  11.0922 -10.5190   3.0268  -6.0590   8.4724  -7.9977   6.2165
-#>    2.7381  -5.1110   5.9924  10.3621 -14.0646  -2.0999 -11.6789  -1.5407
-#>    2.9158  -4.4157   1.3596   9.1929  -6.0317  -2.0577  -0.3624   8.0497
-#>    0.9831 -17.8929  11.4422  15.9151  10.2596   0.3767  -3.8407  -6.7646
-#>    8.4754  -5.1325  -5.9244  -4.3481  -2.2820   9.5119  -3.3970 -18.9483
-#>    6.4051  -5.7837  -3.7830   2.3501  -1.5908  -1.0771  -7.2282   1.1143
-#>   -4.5312  -6.2030   1.3716   0.0105  -0.7657  11.8283  14.8129   4.3418
-#>  -20.4536   6.8824   3.8435  12.0614   7.8767  -3.0026   5.1493   6.4343
-#>    6.8865   8.7946  -7.3705  10.7327  -6.2733  -3.3637   3.3572 -12.2461
-#>    6.0896   1.9243  -4.6876  -1.3111   9.2689  -1.3074 -17.4840  -7.6172
-#>   -6.5077  24.6859   1.1896   6.5526   5.2203  -8.3758  -0.7854   2.7145
-#>    0.3053  -0.3795   5.7081  -9.1353 -10.9340  -7.9618  -7.4539   0.8218
-#>    1.8895   0.8432   4.5600  -7.0166  -5.9432   3.2469  -1.9923  -5.0270
-#>    2.7993  -9.1268  -3.3086   0.9909  -9.4538   3.3323 -20.2697   5.6602
-#>  -11.5537  -6.1840  -5.3921  17.5635  -2.1432   1.7829  -1.8565   8.2781
-#>    0.6469  -8.2766  10.0507 -10.9065  -8.3207  -8.7234   2.6877   1.4146
-#>    3.9395 -10.7987  -2.3744  -0.4842   9.9037  -6.3236 -16.1055   1.0521
-#>    0.3880  21.7167   8.6837  -1.8329   9.5305   7.4091  -7.5914  -9.3135
-#>  -11.2178  -0.9932   4.0680 -18.4544   7.8578   4.7529  -9.2014   3.2285
-#>   -5.4010   8.5502  -7.7846  -0.5676  -3.9020   7.2746  13.7788   4.3461
-#>    7.3959   3.2083  -5.1978  -0.5509   8.3545  -5.1124 -16.1492   1.5927
-#>   -5.4056  -7.4793  -1.7978  -0.9334 -20.6932   3.2113   8.4319 -12.3685
-#>   -1.3231  -0.5456  -8.2329   1.9113  -5.4219   1.5084   1.4032   7.3739
-#> 
-#> Columns 25 to 32  -4.0028   4.8457  -5.0337  -5.2634   5.4246  -7.4923  -6.1144  10.9337
-#>   -5.0902  -2.3824   0.2125   8.0339   1.0811  -8.2466  -3.7509  -5.0870
-#>   -9.6053 -12.6881   6.7873   7.1377  11.8440  -9.8080   9.0595  12.2712
-#>    0.4937   4.3730   8.4381  12.5765 -13.4349  -1.9148   0.0590   5.0614
-#>    5.8021  -0.0977   7.5855  16.1178   5.8871   1.0429  -3.0092  12.8243
-#>    2.4016  -4.3190  -6.2966   2.1748  -1.2029  -5.1658 -15.5438  -2.8358
-#>   -2.3391 -11.2238   2.7683   0.0513  -6.0206  -8.7212   1.2179   9.4017
-#>   -4.2383  -3.6747  -0.1034  -1.2900   2.7896   3.6094  -5.0779 -11.1294
-#>   -0.4860  -1.2960  -7.0285  -3.7450   0.6067  -2.7440  -2.7454   2.7484
-#>    5.7242   6.1890  -6.7480  10.1540  -5.7611  12.0798  -8.7057   7.2471
-#>   -4.3361 -14.2966   9.0196  -4.6395   7.8449   8.9978   2.8720  -6.5707
-#>   -2.1374  -4.8998  -0.3253  -1.8154  -4.7756   0.1213   4.8799   0.6199
-#>    3.0059 -20.9024   2.4299  -4.7674  -3.9045  -1.9628  -6.2359  -4.9126
-#>    4.3799   7.5832  -0.6820  13.5544 -13.8275   6.0846 -12.6547   9.0053
-#>   -0.1091  13.7221  -3.1154   7.0069 -11.3168   7.5186  -4.2445   2.5850
-#>    1.4133   1.5797   0.1667   4.9029 -14.3867   0.2739   0.6428   3.6051
-#>    1.4212 -10.1993 -11.1234   5.9852  -9.9299  -4.6147  -6.8913  -3.7936
-#>    1.9547   2.7585   2.8571  -8.3228  -8.9590   4.0832  -1.1072   3.0466
-#>   11.6103  -2.7016   6.9391  -3.3514  -8.2382   7.7049 -13.1237   2.9195
-#>    6.5049   0.7257  -1.6754 -13.8499   6.7940   6.9524  14.0003  -0.5845
-#>   15.9858  10.4259  -1.7270   4.0562   6.4757  -2.5022  -2.3366  -8.7249
-#>  -20.8702   7.3881 -10.0172  -4.7277  -2.9041   6.2343   0.9083  -6.3604
-#>   11.2073 -18.9086 -10.6599  -6.8272  10.4249  -2.8861   9.5068   3.4434
-#>   -3.0630  -3.0321  12.9202  -5.8672   4.3428 -16.0467   6.4028  16.5198
-#>    2.1288   4.1729   1.9260  -6.8011  27.9381 -14.9567   5.8274   0.3583
-#>   -0.3553  -4.5055   6.6590 -16.1631  -3.7251   4.1277   3.0556  10.5262
-#>   -1.7344  -8.8326   2.0646  -6.5968  -3.3695  -2.0245  -6.8629   4.3947
-#>   -3.8936  -4.9729  -9.2678   7.3137  13.4529  -1.2636   0.7179  -4.3780
-#>    7.6871   2.5940  -2.2730  -2.1216  -6.6604   2.8323 -16.7128  -0.9180
-#>   11.0534   0.7482  -5.2661   6.5815   3.0277  -9.2092  12.3954  17.9892
-#>    1.9958   1.8362  13.7950   4.5558 -13.7289   8.9340   4.6768   2.5235
-#>   15.4579  -0.8263  -0.8164   9.3055 -23.9530   6.0654   5.2620  -3.2954
-#>    7.7474 -13.7643   0.0998   3.7900   0.5168   5.0000   1.4169  17.5255
-#> 
-#> Columns 33 to 40   3.7398  11.9164  -2.1994  17.3545   7.6919  18.2535  16.2565   3.0534
-#>  -22.1746  -2.5972 -10.5328  -5.6384  17.1634   0.1492   2.6765  -8.7797
-#>   15.7078  13.5311  -1.9342   4.0391  -6.3492   0.9819   9.8864  -8.1232
-#>   -7.4723   1.2027   5.8385 -15.9131 -16.5342   1.8749   7.4802 -13.5046
-#>  -14.5844  -6.6044   6.7171  11.3866  -1.6240   4.8207  14.8608  -5.6832
-#>   -5.3270  -8.5717 -11.8968  -5.8006   8.5304   6.1275   5.4905  -1.5662
-#>   20.9067  -3.2596  16.0589  -8.4640  -4.7471   0.1439  -9.1014   1.8498
-#>    6.4862   0.3508   5.7172  -7.2685  16.5987  -5.4466 -11.0539   9.2751
-#>    8.8171   3.6017   4.7839   6.9964 -12.3529  -3.2227   6.5608  18.1055
-#>   -1.8451  -7.4598   2.2739  -2.4590 -23.3320   3.6801  -1.7517  -0.7305
-#>   -2.1438  -0.6471 -15.6980   9.3079  -3.2956   3.1527   1.0967   6.6124
-#>   -9.3272   5.5347   5.8071 -15.6016  23.7244  -3.1802   2.5827   2.6769
-#>   -1.8480   3.5504  -9.2644 -16.1763   7.2894  13.7280  -0.3345  -6.5412
-#>   -6.9716  -2.2138   7.0869 -13.8287   5.5485 -20.5346  -9.7861  -2.8131
-#>    0.6799 -17.7559   2.9644  -3.0908   8.0651  -5.7967  17.4581  -7.6221
-#>    1.4858  -4.2815   9.1155   3.8075   5.8086  -0.4867   3.1360  -3.1349
-#>  -10.7100  -4.7337  -4.7890  -9.3010  11.4710   5.1431  -6.5741  -6.5903
-#>    1.1697   2.8402   0.9476 -12.3607  13.7122  -3.1704 -12.2792  -3.6520
-#>   -1.7845 -10.8223   1.3488  -4.9230  -6.2594  -8.1701   8.8945   7.6211
-#>    9.5109  13.8461  -2.3755  12.3814  -1.8163  -7.5476  -0.9768  -0.7303
-#>   13.7919  13.6527  15.5735  10.0841  10.3817  10.6021   9.0033   1.7628
-#>   13.4732   5.4684  -8.3357 -10.4179  -7.9186 -15.1649  -8.5268  13.5443
-#>    7.3305  -0.4604  10.7282  -6.5767   3.2823  -7.0912  -6.9034  -7.9658
-#>    3.3321  13.0385   3.1378   1.2988 -11.9930  11.8531  -7.0118  -3.9382
-#>    2.0293   9.2086  -0.8242  12.7483 -19.4353   0.8570  -3.8385   5.4355
-#>  -11.7978   4.2577  -3.3972   3.6214  11.9412 -15.4736  -8.1722  -5.0488
-#>   -9.0630   1.7378  -9.9009   3.4573  -3.5296   6.7400   1.7715  -6.7341
-#>   -8.6042  -2.0558   0.2023  -2.2278 -11.8652   1.5105  11.7174   2.7020
-#>    7.3962  -5.1905   4.9489  -4.2631   3.5248   0.8108  -4.9632  -8.9748
-#>  -14.8573 -14.3859  14.9262  -2.3562 -20.1310   4.1151  13.4385 -11.9751
-#>   -5.2578  13.2722   0.9739  -5.3776   3.9516   3.5788  -3.9147  -5.6340
-#>    7.1877   3.0640 -10.6718  -2.7446  -0.6904   8.7616   6.3383   7.2771
-#>   15.3132   2.0735   4.8753   0.2851   4.6359  -0.4220   9.4668 -14.1132
-#> 
-#> Columns 41 to 48  -2.7489  -8.7637   3.4835   1.0170  -0.7160  -1.7874   4.3668  18.0269
-#>   11.4088  -3.2957  -4.7056   7.7861  -8.8527  -3.9654   9.5578  -0.8020
-#>   -2.7851   4.7159   8.3065  -5.3754   2.2233   9.1180   4.2429   3.4977
-#>   -3.0859  -0.8083 -13.5866   6.5434 -10.6219 -11.0563  -5.6027   2.0945
-#>    6.7164   0.0129   3.6310   3.6835  -9.5113  12.8007  -0.6328   5.7456
-#>    3.5358   2.2530   1.3245 -20.5415  -1.0149  -3.6555 -11.8244   4.6888
-#>  -13.7008  -0.9236   1.7582  16.2404   3.8166 -10.3989   1.1112  -3.0338
-#>    1.4287  -1.9917  -9.1448  -2.0913  10.1029  -1.3184   1.9773  10.6877
-#>   -2.1908   5.9729  15.2308   7.5356  -2.5093   7.4100   4.0253   4.1294
-#>    5.3162   4.4217   3.4451  -7.0947  -6.7558  10.9257  -5.3132  -9.3164
-#>   17.2181   6.2738   1.8721  -4.4210  12.1152   8.9523 -14.2943   5.3058
-#>   10.6419  -0.2886  10.8269  -5.1502   0.3061   0.6123   6.4105  13.0537
-#>   -6.0470  -2.5747  -5.8937   1.1997   6.6017  -6.8245  -4.9771   8.0405
-#>    8.6029  -4.6612  -0.0321   4.4581  -7.2621  -3.3948  -4.2746  -0.9244
-#>    3.5538   9.7629  -6.2850  -0.0550   0.5231  11.7813   1.8676  -2.0707
-#>   -7.6222  12.5437   7.6741  -3.4922   1.6843  -1.0553   5.5238  -7.2334
-#>   -2.4571  -6.9917  -8.7695 -11.9418 -12.1425  -9.5730  -2.0288   7.2764
-#>   -0.2375  -6.6255  -1.9505   2.1925   2.3345  -6.5458  12.4992   6.5844
-#>    9.2619 -10.2640   2.0741   7.4089   4.0091   7.8756 -13.4588   0.8496
-#>  -10.6017   9.2146   6.2082  -4.6360  -7.2165  18.5288  -2.6394  -6.6095
-#>   -1.1088   4.6886  14.3204  -1.6789   2.7128  12.7741  12.5627  -7.0749
-#>    3.5440  10.7971  -7.8047  -4.8617   6.3549   1.0599   7.3522  -7.8855
-#>  -16.5681  12.9976  14.0391 -18.0699  -2.0621  -1.0039  -7.8445  -5.6196
-#>   -3.3615  -7.7826   2.4972  -9.4898   7.0115   2.9747  -2.6037   1.7966
-#>  -16.6786   0.8662  10.1673 -12.8165   6.5936   3.3037  -1.5560  -8.5763
-#>    3.9353 -11.2481   4.8464 -17.0819  -7.2871   3.1291   1.1024  -1.7613
-#>    4.4568  -2.9537  16.3754  -1.8688   1.7240   0.7697 -11.0913  -7.0720
-#>   15.3738   2.0966   1.3261   8.6218   5.8933  -2.8206  -0.0873   7.9319
-#>   -2.3210   3.2512 -13.6099  -1.0325  -2.7013 -14.6160  -2.2551  -1.8460
-#>  -10.7967   1.6568  -6.5915 -11.5341  -9.9567   2.6287   4.8549 -18.6987
-#>   17.4152   9.9368  -4.7196  -2.0201   7.0575   1.9245 -10.3459   5.4035
-#>   -9.8628  -1.2139   0.5197  -9.0915  -9.3666 -17.6017 -11.3053 -11.6865
-#>    5.1867  -0.8220  -2.7756   3.3252   5.9480  -6.7896  -3.5799   0.5004
-#> 
-#> Columns 49 to 54  -0.6389  -7.4733 -14.9716 -11.8120  -1.9556  -5.3680
-#>   10.7929  12.4003 -14.2223  -7.3193  -3.7665   0.6283
-#>    8.6197   2.0170  -6.0246  -6.1153  -0.3049  -7.7032
-#>   11.1266 -22.0888   0.6137   2.3653   1.6914   6.3109
-#>   -0.6324  -5.6238 -12.4644  -6.6811  -6.2189  -8.1869
-#>   -2.6817 -15.2342  -6.2547  -0.4114  -9.7645  -5.8096
-#>   -2.0674 -12.6191 -15.4729  -7.7481  -3.3053   3.9714
-#>    4.4544  -6.5520  -3.6067  -3.6903  -1.3885  -1.8380
-#>   10.1238  23.3828   6.2061  -0.9916   9.4449   1.7633
-#>   -1.6249 -16.9761   3.6877  -4.3206  -9.6594  -4.2910
-#>    0.2759  -0.0149   6.6960  -3.9407  -4.2733  -0.9428
-#>    8.0797  -1.3322   3.3517  -1.2963  -1.9725  -2.5632
-#>   -3.1325  -3.4439  -1.0952  -1.0389   5.6795  -2.8524
-#>   10.0878  12.1763  -3.0694  -2.4060  -6.0147   8.5415
-#>   -1.3119  -6.9195  10.8808  -0.8992  -4.8843   1.3601
-#>   -4.5548 -20.3759   1.4613   8.7679   9.1140  -1.0856
-#>    0.3173   3.1479  -8.8415  -2.1779 -12.6805  -1.6526
-#>    6.4048   6.2706   4.1337   3.4527   3.3755  -1.7199
-#>   -0.6586  -5.9617  12.3254   5.0271   8.6379   6.4460
-#>    4.2861 -12.6578   9.2266   1.6816  -0.8269   4.5423
-#>    9.5541  -5.7773   2.6169   3.7411   8.2549   1.6539
-#>    5.7854   0.5161  -1.5401   2.0400  -0.6564  -5.9205
-#>   15.7457  -0.5584  -4.0106   2.0536  -0.5748   2.0987
-#>   -1.1142   8.8274  -2.3402   0.8552   8.2448  -0.9579
-#>    4.9794   2.9381  -5.4608   0.4388  -2.2405   1.1353
-#>   -1.2917   1.1117  -4.4265   3.8347 -12.0188  -2.6317
-#>   -6.2574   2.3697  -2.6166   2.7013   3.3212  -0.2425
-#>   -3.8129  19.2050  -4.5047  -5.3278  10.3334  -2.1496
-#>   -0.3943  11.1524   5.1407   9.3953   3.7767   0.9381
-#>  -13.8334  -2.3486   1.6269  -4.3620   4.8251  -1.9973
-#>    0.3519  -7.7991  -6.3443   3.6354  -2.9999  -4.0078
-#>   -1.0298   7.8492  -4.6288   1.7104   0.7321  -3.8820
-#>   -2.9105   6.6443  -1.8968  -0.9760  -1.5631   4.2653
-#> 
-#> (15,.,.) = 
-#>  Columns 1 to 8 -10.0990   9.5713   1.5318 -12.0500  10.1011  -0.1370  13.3657 -12.0867
-#>   -2.0152  -2.9317   2.3648   0.7224   7.9930   5.1094  10.4382   2.3264
-#>   -0.3481  -1.4523  -7.0102   3.4108  -2.3078  -5.3298 -15.2268   9.4144
-#>   -2.6055  -9.8104   9.6568  12.2556 -10.5023  -2.5403  -8.1588  11.3824
-#>    4.8885  -1.2973   0.0989  20.7426   3.1836   2.7362  -4.7865  21.5729
-#>    4.6923   1.6641  -1.2218  21.5251   2.9839 -10.0373  15.2247   6.9074
-#>    0.9719  -3.3859  -6.3710   0.5712 -14.3356   8.1015  10.3925  -7.1472
-#>   -5.1478  -0.4332   7.1794 -11.7585  11.1017  -0.9514  -0.9117  -7.5852
-#>    0.8523   4.0596  10.2042   8.8919  -6.7690  -3.1127   2.1410   5.3746
-#>   -0.7264  -9.5159  13.2099   0.0314  -0.9318 -12.5969   2.5364  13.7676
-#>    3.2831   2.4765  -0.7692   0.4533   3.5371  11.6717   4.3137  10.3082
-#>    1.7459   3.0613   4.4691   3.0374   1.8916  -4.2943  -4.3574   2.6327
-#>   -5.2546   0.7602   5.5456   2.5400  -7.4997   3.5122   8.7494  -3.9388
-#>   -4.8197  -7.6028   1.1249   9.1811 -12.9616  -2.8928   4.7218 -12.8941
-#>    7.2280  -6.4193   0.2792   0.6822   6.5073  -7.9635  -0.0429   5.5636
-#>    7.6099  -0.7829  -5.8236   3.8139   2.2075  -0.5448   3.2761   4.8688
-#>    3.5679   0.7753 -15.0284  -4.2804 -11.6611  -5.5340   8.6002   5.5089
-#>    3.7578  -1.4981   0.1193  -5.3650  -1.1050  12.6927  -6.2565  -1.5469
-#>   -0.8684  -3.1628   2.5079 -10.9548 -12.2026 -11.2409  -5.4725   6.1974
-#>   -4.8493  -5.0138   4.2155  -2.2080  -8.8043  -5.2325  -5.2402  -5.7408
-#>    3.4794   1.2526   1.7075  -2.2464   5.5673 -13.7139 -11.0148  -7.4443
-#>   -8.2279   0.0555   3.0568 -11.7474  12.0560   3.7196  -3.5746  -5.4535
-#>   -1.3951   1.4963  -0.4092 -11.0516 -22.7744   3.8872  -2.3623 -20.0583
-#>   -4.2623  -9.1239  -3.2667   3.5099  -9.2813 -14.1512  11.7609  -1.7458
-#>   -4.4801   2.6365   7.5302  -6.0688 -19.6205  -5.4619   2.6827 -14.5390
-#>    1.4995   4.9967  -4.6511   2.8508  -1.2898   4.5070   0.4309   7.5099
-#>    1.0764   1.9196   0.5463   3.8593  -3.8163   9.3600   7.4160  -3.2611
-#>   -3.6800   4.9303 -10.1005  16.2065  -2.8412  -9.7141  -9.2764  13.5852
-#>    2.1092  -2.0972   6.2155  -2.3583  -1.2559   1.8911 -17.7634  18.2820
-#>   -0.5072  -1.4840  -1.8773 -13.4366  -9.4894  -5.1573  19.5537  -5.9230
-#>   -3.0255   3.9880  -7.6719   6.5883   9.1100  -8.1304  -5.2865   6.3455
-#>   -1.7676  -3.3332   2.5160  -3.1491 -18.3137  -9.7269  15.7431 -19.0633
-#>    3.7121  -0.4173  -9.2082   0.8137 -20.6655  -6.5320   1.3618  -3.3141
-#> 
-#> Columns 9 to 16  -5.0178   2.8376 -13.4590  -1.7824  -3.2256   6.0948  10.5693  -7.3420
-#>  -13.4998   9.7929   7.6963  -5.6667   5.6835  -5.7594  17.1983  -8.0135
-#>   -6.6259   1.8900   0.4777  -2.9068  -1.9392   2.3020  12.7189  11.0737
-#>    9.7699  -1.4176  19.3781  -8.8971   1.0619   1.5661  12.3683   6.8381
-#>   -8.8433   6.7435   5.3905  -2.1816   7.6027 -13.1408   9.6943  18.9325
-#>   -3.7578 -20.4967 -12.1749  13.7635  -8.2955  10.1202  -9.7523   0.7279
-#>   -1.5795 -27.3397   6.2327   4.8520  -6.9096   7.8854  11.5034  19.2436
-#>   21.0009  10.6960   0.3235  18.1828   1.3996   1.6413   9.7474 -12.8684
-#>    3.1225   3.5732   4.3682  10.1331  -2.1940   1.4707   6.3428   7.3753
-#>    5.8743  -4.9510   7.6673   3.3482   0.4906   4.1957  21.5036  -0.4971
-#>    4.3325   4.0696  -7.1543   4.7685   9.2816  -8.0765  13.1303   2.2542
-#>    1.3199   8.9509 -14.1791   0.3937  12.9080   2.3078  13.6441  -7.4704
-#>   14.1569   5.6918  -2.8820   5.4940   5.2787  -3.9716  11.0201  -9.0025
-#>   -1.4026   0.8986   0.0281  -1.1187   0.9862 -23.6242  12.1266   2.6198
-#>   -5.9391  -5.4533   2.9304 -14.2286   8.5711   6.9260  -5.7363   9.8813
-#>  -13.7160 -13.2488   6.5590   0.5550  -0.6141   0.2143 -11.7267  -2.9618
-#>    8.3337  -8.7911  -4.3042   0.2995  -3.9446   2.9244 -13.8378  13.2256
-#>    8.2129  -0.0243  -2.5634   4.2056   7.3335 -13.9953 -12.1880 -10.9778
-#>  -15.5198   3.0817   4.3212   2.8397  -4.5304  -2.8506  18.1735   0.9459
-#>   -0.1223  -5.3029  -0.6803   1.7909   8.2157   9.3911   3.0801 -20.6149
-#>    6.4214  -5.0558   1.5302  -1.3221   6.9508 -19.6272 -11.9980  -1.2132
-#>   10.1410  15.7525   0.6530 -15.2663  -6.4518  -4.8851  16.6364   9.8039
-#>    2.4659  -9.5107   5.0274   3.2727 -20.7507  -2.8210   0.5230   9.5059
-#>  -17.5149   7.4447  -0.9244   5.0719  -8.1361   2.1893   7.6107   2.9166
-#>  -15.9416   3.8260  -6.9479   2.5022 -14.7595  12.8274 -20.2647   1.7912
-#>    7.4356  -4.8601  -4.9859  13.4862  -3.5921  12.9788  -8.9852 -11.1844
-#>   -9.6747   2.3929  -1.8941  -0.1248   1.6079   8.6074  -6.0711  10.0755
-#>    5.3568  16.4666   6.2013  -1.3843   0.6055   2.8061 -12.3449  17.9458
-#>   11.9440  -0.8353   3.4883  10.3915   7.5584  -1.4332  -4.6735  12.5461
-#>  -22.4059  -4.5566   7.3376  -4.3847  -1.5582  -7.5045   7.5009  -1.9745
-#>    5.6077  -5.3056 -13.0279   1.4611  -4.6591 -15.2655   4.6496 -10.6508
-#>    0.5694  -4.6875  -1.8906 -10.3570 -15.9350  -3.2111  -2.0236  -6.8917
-#>  -11.4327  -4.4946  11.8158  -3.7137  -5.7878  -3.2148   2.5095   5.8133
-#> 
-#> Columns 17 to 24  23.9548   4.4337  22.5554  10.9288  -2.2020   3.8517   1.3465  15.9809
-#>   -2.5955   9.0179  -3.8016  -8.0012   1.7868  -3.1316  -5.1833 -18.7513
-#>  -11.2852  13.7604   9.7052  -4.6647   9.1436   1.4140  15.3762  -5.4790
-#>  -30.2964   9.0454   3.9029 -30.8797  -6.0035   6.8617  12.3477  -8.1561
-#>   -7.8357  -2.4154   0.1894 -17.0568  12.7654  -1.0467  -1.8494  -2.1127
-#>    9.1948   0.0944   3.3596   1.7337   7.1465  -3.6636 -13.6780   2.3150
-#>    1.4789  11.8322  15.1057  13.5677   2.5585  10.9729   1.6040 -10.0423
-#>   18.6681   6.5529  -2.8593   3.3970   6.8450   0.3297 -10.0932  -0.2324
-#>    2.9418  -4.5070  17.7137  -1.9664   2.8364 -10.0479   6.7950  10.6355
-#>   -3.8491  14.8961   0.6555 -14.6044   7.5461  19.3217  -9.9193  -8.7238
-#>   13.7032 -13.1803  -1.7174  11.4773  12.4560  -9.4533   6.6293 -14.7780
-#>   10.5144   6.1302  -7.4889  19.8148   5.8770  -0.9864   6.1714  -4.5893
-#>   10.9235 -19.2067  -7.2074   1.9999  -0.9868   0.1538  -3.3172 -12.9561
-#>   -8.1066  -9.5891  -5.8863  -4.7349   3.4509  -1.9967  -0.9951   2.1819
-#>   -0.1153  -1.2667   7.1105 -10.0341 -10.6734  25.9604  11.4678   6.4583
-#>   15.4342   3.8854  -0.8302  -5.8226 -13.3946   1.0479   6.4934 -15.2569
-#>    7.5051   3.4195   1.0269   7.6924  13.4544  -7.2745   0.9785  -1.0435
-#>   -7.2453  10.1602   2.5436 -12.7391   0.8896  -1.8076  -5.2475  -9.5786
-#>    0.9025  10.1723   8.4099  17.3126  -3.8429   0.2009  18.8467  17.4271
-#>    5.6315  15.4214  -6.9537 -16.5940   5.3793  19.0690  -8.0471 -13.2905
-#>   11.9089   3.0548  -5.6932   7.0078  -2.3373  -3.9093   5.7294   4.5638
-#>   11.4385 -25.3665   0.1245   4.7662   6.2247  -4.7778   0.2985   1.8013
-#>   11.1348  11.0020   0.1509  -5.2465  10.6244   8.7351  -2.8962  -4.9217
-#>    4.3799  13.4892   4.0956  -2.8105  -4.9192  -1.0366   1.2922   0.6402
-#>   -8.3492  24.5169 -10.7443  -2.3853   8.8224   1.1678   9.0221   0.4602
-#>   -0.2645   2.5483  -1.9621   6.9814   6.4197  15.2534 -17.7681  -2.2454
-#>   -7.4653   4.4360  10.7323  -7.0816   3.3647   9.4408   9.3030   8.1634
-#>   -2.1583   1.4271  -0.0035  -8.9739   5.9935  -9.9652  15.8939   2.9064
-#>  -22.6930   3.8092  -0.5091  -9.1934  10.3580  13.3964  -6.4586  -0.6747
-#>   -6.0896   5.1857   3.3376  -4.1952  -0.2352  -1.3639  27.2648  -4.6405
-#>   13.2255  -4.4454  -0.9535  -1.1588   5.7364  -5.5923   8.2029   1.3131
-#>    8.5942 -18.0943  -3.8576  14.8559  -0.2151  -0.5652   5.3782   5.7814
-#>   -2.4000   7.8375   8.4794  -0.1797   8.0042  -5.4103  -2.8932  -6.7073
-#> 
-#> Columns 25 to 32   9.5944  -3.9121  -0.6456  -4.2796   4.8669  -3.1430 -16.6505  10.3947
-#>    6.2774  -4.8735 -10.7184  -1.3617   8.1813  -6.5940   9.6497  11.8253
-#>   -0.8060  -7.0873  -5.1033   3.6967  -5.9224  -1.5106  -3.2493 -22.3877
-#>   -2.1715  16.7673 -14.4711  -6.9998  -2.7722  -5.2056  -5.5870  -3.6650
-#>  -10.9918   9.5981  -4.8545   1.3297   1.6327   6.8057  17.8017 -10.4679
-#>   -5.9841   5.0507  -1.8719  16.6006  -8.6540  18.5502   6.4155  -0.4553
-#>   -1.2806  -4.6073   1.7010  -2.0524  21.2555   1.6784 -14.3942  14.7029
-#>   -6.6802 -10.5134  -5.3347   1.4647   6.6026   7.7703  -8.3399  -1.4836
-#>   -6.5434   0.1531   0.9292   2.4402 -17.5561   5.7259   9.4887  -2.3714
-#>   -0.8271   9.9857  -0.1178  -1.8616  -7.1793   6.0239  -7.9769   7.4283
-#>    7.1402   4.4461  -0.9369  -2.3136  -0.0208  10.2257  -4.8433  -9.9684
-#>    5.9708   1.4622  -4.8579  -8.3934  -7.1991  14.0736  -2.2637   0.5849
-#>    4.8735   8.7754  -5.3562 -14.4586  11.7871   3.8676 -26.7182   6.0868
-#>   -1.8051  -0.7627   7.4101 -11.8195   9.1956   3.0181   1.0301   0.4012
-#>    2.3346   5.5885  -6.8960   2.6455 -11.1043   1.1019  -3.3641   1.5160
-#>    0.4052   2.8550   9.9087  -9.2560   4.5426   3.6349   1.1024   1.1567
-#>   -3.4402   4.1977   2.2295   3.9754  -5.8424   2.3379   9.0788   1.3631
-#>   -0.3071   4.4000   0.4199   6.6844   9.9604  -3.5819  -0.1863  -0.4231
-#>  -12.1294  12.6241   4.2895  -2.8436 -20.4353  -1.1826 -21.5068  -3.8060
-#>   -1.0920  -4.3573  -8.0907   1.3174  -1.0410  -4.7343   3.5406 -20.6220
-#>   11.1171   5.0765  12.8293  -2.8107  -9.1112  10.5481  -5.7296 -10.1943
-#>    2.2845 -14.3124  -0.5741 -18.6881   7.3470   8.4301 -16.0972  -4.9773
-#>   14.1851  15.5705  -8.0232 -14.2519  -8.7095   5.3573   8.2457   3.0091
-#>   -8.0929   0.0361 -15.0765   1.3317  -9.0183   0.3250  -2.7328   4.0589
-#>   -0.5257   6.8122   2.5629  23.2483  -1.5479  12.1290   3.9113 -14.1497
-#>   -3.3007  -0.4385   0.9678  11.9494  -6.6841  -8.8127   8.3065   2.3145
-#>   -2.0953   4.7034  -6.3555   4.3592  -7.7332   2.2606  10.0530  -0.9074
-#>   -8.5917   8.7902  11.5992   4.3040 -12.4374  -3.4665  -0.0276 -18.1506
-#>  -13.3506   1.1399  -7.6358   3.4748  12.0200   1.2798   1.8368   6.4288
-#>   -1.7819  13.8193   9.8684  -1.6199  -5.4542  -4.0736   1.5121  11.8665
-#>    3.2625  10.5987   1.5617 -10.7859  -7.1992   1.7146   5.2890  -3.0862
-#>    6.1506   0.8400   9.1340  -6.4293 -11.4344  13.4539 -25.8874 -12.8097
-#>   -0.2576   2.2089   0.5780  -3.6492   9.1683  -5.4479 -13.5015  -8.2683
-#> 
-#> Columns 33 to 40   8.8396  -4.0908   3.3033   5.9178  -2.1426   4.7535  11.7253   2.1089
-#>   -7.8518  -0.6864   5.4062  -0.0704   2.2593   1.2757  -3.3115  -2.8540
-#>    8.0880   8.8613  -0.2046  25.4886  -0.0231  -0.6460  -3.0581   3.7206
-#>    1.3567   2.0511   3.5708  -3.5456  -7.6781  -1.4215  -6.5459   3.4979
-#>    8.6816   6.3005  -2.1095   7.5814 -10.5794   2.5243   2.6340   2.6379
-#>    2.2035  10.3515 -11.2097  -9.3520   7.2193  16.8672  -1.1143  -6.3585
-#>   -6.3557   2.2419   4.5310  -5.1427   4.8296   0.9774  17.5645  -2.1679
-#>    0.7955   3.9828  19.1163   7.6492  -5.3007   4.0388  10.3170 -10.0017
-#>  -10.3534   6.4167 -19.4180   3.1978   5.7691 -21.6791  -2.2751   3.2430
-#>   -1.7144  -2.9233  -4.6692   2.2329  24.4980   3.3718  -0.1364   3.9223
-#>   15.9999  -5.0894  -0.0592   5.6503  -1.0933 -18.6162  13.1086  -2.5467
-#>   -8.7221  -2.9894   1.3795  11.1901  -4.6743  -4.0438   6.3607 -14.1750
-#>    6.4703 -12.0389  15.9626 -14.4179 -11.5250  11.3063  18.6204  -6.1838
-#>   -9.8206  -8.7003   8.7307   6.8307   5.9919  -0.1534 -13.9388   3.1807
-#>   -5.1773  -2.5748 -13.1049  17.7876   2.0671   7.2958   1.2341  24.2162
-#>    2.9031  -0.5510  -8.2014 -11.4646  -0.3466   5.5836   5.9189  -2.3536
-#>   -7.3025   9.3635  -6.8624   3.7640   1.4827   4.0607  -1.9772   0.5987
-#>  -12.8694  -6.1116   8.6849 -18.8294 -10.3818  -5.0519 -12.2981   6.4965
-#>   22.5084   4.1215  -7.5574   0.8125   7.2067  -9.4430  14.0021   5.2206
-#>    2.0273   4.4054   6.1284  13.6395  14.3388  -8.0690   6.9133  -0.9929
-#>   -6.8994   3.5835   4.0496 -10.4464   2.8428  10.3485   3.0069  -3.1604
-#>    2.0785   9.0722   3.1944  16.6751   0.8630   4.7099   2.2799 -12.4083
-#>  -14.1698 -11.5007  -2.5575  18.4120  12.5671   0.3347  14.5842  -6.3854
-#>   -0.4839   3.4926  -0.2347  -4.1269  -8.5848 -17.0848   5.6182 -10.1792
-#>    6.5714  -2.6046  -5.7125   6.4672  -3.9295 -19.8987   6.2386  18.6289
-#>  -18.1906  13.5562  -6.0317  -7.6967   9.0732   5.7860   3.1072  -0.3426
-#>    2.8831  -2.1550  -8.5623   6.4899  -9.2266   7.8874 -10.9110  -7.0155
-#>    0.4335  12.8337  11.4560  11.9060 -20.1756   0.3952  -4.7485  11.1188
-#>   -0.0864  -0.7748   5.9271  -9.5802 -24.4697   3.9133  -9.2256 -10.1374
-#>    9.4869 -26.5448  -4.7492   1.5490  -0.8644   4.6959   0.7020   6.8054
-#>  -11.8460  -2.2999   8.3583  -4.7632   5.6372  12.6364   4.8529 -11.5818
-#>    7.3621 -14.5742   5.1122  -4.9590  -6.8797   0.0120  15.1780  -1.1519
-#>    9.1480  -8.6119   5.6794   1.8353  -1.2049  -3.6436 -15.5298   6.1394
-#> 
-#> Columns 41 to 48  -0.9185   1.0292   2.3816 -19.0016   3.2346   5.1427  -9.2602   3.5470
-#>   -4.6642  -7.5193  -6.2972  -3.6340  -4.3782   3.5069  12.3738   3.8607
-#>   -1.7061  17.2409   0.5289  -6.2155  -9.2250   7.7128   2.6069  -4.1813
-#>  -22.3711  15.8590 -12.1630  -1.9723   3.4912   0.7936  -0.4754  -4.8637
-#>  -10.7078   8.7035 -17.7855  -6.9211   9.6944  -0.4565  14.0562   7.5147
-#>   15.2351   6.8293  11.9645   9.6385  16.1149   5.4100  13.9070  11.3935
-#>  -18.9558   4.3913 -11.3429 -11.7447  10.8551  -1.7770 -10.4569   2.5707
-#>   -5.0401  -9.8150   1.7656  -1.7277 -14.3881  -3.8345   2.5442   7.6936
-#>   -3.8712   7.5262   5.4346  -3.8012  -4.1631   3.5016 -10.7002   2.8133
-#>  -12.1691   9.2626 -20.9391  -0.4648   0.0210 -11.2584  -9.1199  24.2553
-#>   -4.7094  -2.3166  -9.8245   9.5187  -3.5064  16.1850   3.9799   2.8132
-#>    6.3814  -3.9819  -1.0964   3.3634  -8.4593  17.8737   5.7258  -2.5306
-#>   13.5904  -5.1623  -4.3888  -9.1726  -5.7060  13.3960   9.3317  11.0526
-#>  -15.8183   3.6202   8.2642  -3.5512   7.4538  -7.7091   6.9085 -20.2944
-#>   -1.1283 -18.0089   9.1768 -19.2064  13.2384   0.6168  -3.4532   1.7254
-#>    1.0596   4.8809  -7.4798   9.9955   2.8880  -0.7054  -1.5500   3.2815
-#>    3.6454  -0.9967   1.9723   7.5453  10.3257   9.4845  16.9860  -0.8021
-#>   -7.3195  14.6915  -0.9854  -4.1521  -0.8937 -13.6568  -3.7865 -23.4725
-#>  -14.4901  -3.1384 -17.0815  -4.9651   5.3681  -3.0924 -18.9295 -15.6902
-#>   -2.3697  -4.0433 -11.8967  13.1645   5.7599   3.2860  -4.1032   8.4708
-#>    5.5353  -7.1651   5.3395   2.2028 -13.1871 -10.0166 -14.7682   1.3285
-#>   12.3502   2.8922   8.9898  -1.6964 -13.0801  -0.3030  -7.0138  11.3802
-#>   -8.0877   3.5271  -2.7746  -7.2920  11.5408  12.2708   5.6332  15.9228
-#>    7.5764  11.6671  -3.1646  -0.9293  -8.9805 -10.0701   3.1878   3.2721
-#>    3.0567   9.6918   0.9289   9.2551 -17.5344  -4.5718  11.4749  -8.6895
-#>    9.8700  -5.0108  11.2222   2.5289  14.6404 -18.5415 -10.1208  -7.4381
-#>   -5.1425  -8.1582   5.6891   1.3202   9.9968 -10.1401   9.7812  -5.5545
-#>    6.0983   3.3249   4.1080   3.2006   5.4616   6.3398  14.2389  -3.0887
-#>   -5.4333   9.9757  14.0214   0.1371   9.8365  -4.8653  -3.6701 -17.4995
-#>   -2.9224   1.7300  -6.4158  -4.8144   3.8159 -14.3869   1.6443   3.7402
-#>    5.6625  -1.9869  -0.9393   2.7615   0.5217   1.2785   0.9142   7.9043
-#>   13.3689  -3.6108   0.6681 -16.7220  -0.2150   9.0452   3.9819   8.0725
-#>  -11.0557  21.5327  -4.6524  -2.5301  15.3207  -1.6733  29.7789   0.5129
-#> 
-#> Columns 49 to 54   1.5166   8.2477   6.7136   6.3118  -4.9695  11.5162
-#>    1.4363 -12.9781   0.1371   0.9093  -3.6536   4.9203
-#>  -18.0463   5.2483  -0.5983   5.6387  -2.6682   1.3394
-#>   -2.6935  -6.1874  -6.5942  -4.2045   6.6405  -9.5767
-#>    0.8123   0.8789 -17.2362  -1.3923   9.8621  -4.7030
-#>    0.9047  25.0544   4.3639   7.7567  19.5197   8.8219
-#>   18.4562  11.7296   5.6156   4.0337   8.2729  -3.8409
-#>    6.9517  -5.0020  -3.8453  -0.1510   1.8457   4.7716
-#>   -7.2966   8.9733  -7.9880  -5.7693  -1.0765  -3.2241
-#>   19.3973  -9.3490  -5.5793   2.3670   4.4611   6.6473
-#>    2.0844  -0.0334  -8.0439   5.7844   1.4116   4.2696
-#>   -0.1985   5.3961   6.0418  -0.2969  -0.8843  -2.1523
-#>    2.0317  -4.4195  -3.6542   0.5706  -2.4710   0.7280
-#>    7.4199  -3.0333  -4.4803  -2.6479   6.0699  -2.7246
-#>  -11.5188  16.6455  -0.1031  -2.7591   3.5070  -0.1705
-#>   -3.0831  -4.5135  12.9683   1.0926   1.7067   2.5385
-#>    5.8785   6.7963   7.6884   5.5664  11.5878   2.8810
-#>   13.8876  -7.7859   1.2779  -7.9931  -4.2584  -0.1300
-#>   14.8720  15.5157   3.7080  11.8443  -3.9134  -2.1870
-#>   -5.7535   7.5996  -4.3604   2.5150  -6.0650  -3.4270
-#>    3.9493   1.7726  -3.3017  -2.0998  -8.4011   8.5350
-#>    0.7961  -3.1373  15.8146  -4.7486  -1.0243   4.7894
-#>    8.8234  -8.4761  -7.7498   9.6531  10.3125   0.0289
-#>   11.9533   9.9974   0.7059 -16.6528   1.1749  -7.9864
-#>  -10.4745  -5.0384  11.4629   2.7173   4.2650  -1.2628
-#>   11.0660  -5.0848  -0.7776  -6.1537  -0.6618  -0.3784
-#>   -9.9422  -1.2817  -1.6077   5.3577   3.3337   0.4067
-#>  -12.0042   5.7682  -6.7125   4.8503   6.7699   2.9700
-#>   -3.3479  -7.8093  -2.1469  -7.6515  11.3454  -7.3593
-#>   21.3429   2.5380 -10.8956   2.5114  -3.3077  -1.5169
-#>    1.3157   5.7816   2.1688  -8.4880   6.3563   1.6823
-#>    4.5685  -1.2746   8.4463  10.5379 -11.6546   5.8264
-#>    9.5908 -13.6034   2.6211   4.8941   5.8864   2.5896
-#> 
-#> (16,.,.) = 
-#>  Columns 1 to 8   5.7149   2.6410   0.4331  -1.4741  -3.6267  -1.4720  -1.6433 -14.4904
-#>   -4.1965  -2.3145   2.4730   7.7392  -2.5778  -1.9739   5.9185   6.2945
-#>   -0.3824   2.3342   5.6595  -3.6659  13.7773  -5.9059  -3.7858   3.5306
-#>   -2.4134  -1.4280  -7.1424   2.0948   2.2874  -1.0853   5.7903  10.8578
-#>    3.2607   0.0906   0.4323  -3.3052  13.3146  -0.6935   5.2667  13.1755
-#>    1.6097  -0.1559   1.9115   5.1168   6.6133  13.6786 -12.4811   5.9716
-#>    7.3616  -5.4578   2.2418   8.3101   8.4899   7.1793   4.6639  -8.8759
-#>    1.4888   3.3524   4.0891   7.2668  -7.6339  10.0442   3.4466   1.9793
-#>    2.9624  -0.7599  -4.4550 -16.5763  -7.2150  -9.3511 -14.4847   3.8877
-#>    5.4535   2.1113   0.6556   8.3213  11.4949   4.0772  -0.9157   6.0573
-#>    1.3422  -2.3339   2.4545   2.0971   8.9639 -10.1366  -1.9312  11.8052
-#>   -1.4407  -5.1037  -0.8735  -7.5622  -6.1879  -0.0690  -6.2049   6.1181
-#>    1.4357  -3.5222  -1.9755   9.4500  -1.6771   4.4376   0.3270   3.4174
-#>    2.5677   0.9643  -0.4635  -1.1444  -5.9775 -13.5607   9.5219  -1.5825
-#>   -3.4731   4.3262   7.8018  -1.1242  -0.3613  -4.4380 -17.4636  11.9620
-#>   -4.2367  -0.5903  -4.0691   4.3360   6.9549   5.8410  -7.6657   5.2381
-#>    0.2624 -11.5379  -1.8299  -6.6303  17.5205   6.7721   7.8004   2.3381
-#>   -3.8974   1.7662  -4.8138   9.5514  -9.4977   0.3022  -1.0301  -7.1786
-#>    8.0423   3.0451  -5.4620  -1.9462 -11.0921   2.8610  -8.4385  18.6906
-#>   -0.7504   8.3239   2.5793   2.7778  -8.9122   6.5091  -6.5768   5.6288
-#>    4.3457  -1.8958 -10.0769  -3.2070   0.9191  15.5076   1.3231  -1.1706
-#>   -3.5223   0.8839   0.7376   4.0680  -6.0472 -10.2105   2.2769   2.2336
-#>    3.7377  -5.7436  -0.2260   4.7655   3.2836   9.0940   3.0380   4.9916
-#>   -0.6492  -7.3590  -3.8933  -4.5155   3.0945 -17.9669   0.7992  -2.5830
-#>   -4.1239   3.6299   0.7977   1.3964   0.1331  -2.0033  18.6986  -2.7015
-#>    0.3150  -1.0361   4.5186  -2.9666  -1.6695   4.2238  -9.6508  -5.4337
-#>   -2.3472  -2.5659   5.6535  -6.8268   0.4131 -15.7032   4.3612  -4.8776
-#>    5.7511   1.5074   4.6386 -14.9877  11.4655  -3.4371   6.8665   4.7969
-#>   -1.2161   2.2089  -1.8261   3.9251  -1.3776  -0.7568   0.8804  -8.0807
-#>   -0.7410   0.6941   2.5636  16.1539  17.1623   4.5810 -12.6779   0.4777
-#>   -0.5473   0.5947  -0.6145  -0.4223   4.8203  -2.9468  -8.0156  16.5324
-#>   -2.8870  -0.4101  -8.1284   8.2239 -12.4741   9.3738   0.8917   2.3292
-#>   -0.0762  -4.8836  -2.4877  -2.8065  11.9033  -6.2954  12.8217   4.6448
-#> 
-#> Columns 9 to 16 -10.8486  19.0040  15.5205   6.3897  12.7509  18.7076   6.7341  24.7735
-#>  -12.0438  12.6606  -0.3553  -4.9212  12.0861   4.6795   6.6952   5.5794
-#>   -4.6007   0.9672   8.7212  11.1086   0.4787   8.4304  -7.2332  13.4946
-#>   -8.7545   2.6975  -7.5476  -0.5051 -26.9210   7.1168   7.2227   7.0064
-#>    5.0392  10.5801   3.8094  10.7401   4.8611   6.8573  -2.9970  10.8328
-#>    2.7897   2.6428  -3.0699  -1.1935   1.6653   3.4466  -6.4703  15.9608
-#>   -4.0934 -13.0422  30.0203   9.2027  -0.0284  -8.5338  17.6166   5.7459
-#>    0.6521   6.9662   3.7325  13.7420  22.0283   3.8541  -6.6730 -10.8181
-#>    8.4981  -0.7829   1.4430   7.1065 -20.1671  -2.2740   4.0409   1.3867
-#>    1.0489   7.8033 -10.2676   6.4569 -12.2644  27.0497 -12.3430  -4.7140
-#>   -4.2990  -7.0005   9.4127  10.5111   5.6435 -16.3177 -19.4785   4.6994
-#>    7.1485   0.1711  -2.3173   8.7568  13.6645  15.3494  -2.7278  -6.3752
-#>  -22.2274 -13.7903   2.3464   9.1934  12.2973   1.7982  -8.8251   1.1781
-#>   -1.8933 -11.6445  -5.1614   2.8326   7.9728  -9.9083   1.7724   6.6256
-#>   -0.4213  -2.8628  -9.6671  12.5749  -0.2841  17.4236 -15.0692  12.7773
-#>   -2.8915   4.1267   3.7853  -2.7139   6.6256  11.3734 -20.9926  -4.2265
-#>    2.4341  -8.6435  -2.8338   0.5613   7.2049   6.5807  -2.2237  -2.7587
-#>  -10.2696   0.0926  -3.1129 -10.9694   7.9198  -8.8719  13.5763  -9.7744
-#>  -15.7203  -4.0138  12.2160  -7.0447 -18.8194 -24.3269  12.7430   9.2565
-#>    1.8916   2.4190   4.8938   8.2787  25.5384  11.2692  -9.0044  -2.9264
-#>    4.7253  -1.1566  -7.7629   6.2313  19.2309  12.9958  -1.3933 -23.9209
-#>    4.0398   3.1965   1.0156   1.8936   0.3614  -6.0729  -5.9055  -5.6367
-#>   12.6499  -8.3206  -7.9043  -2.4800  19.1821   5.0694  -7.8344 -22.8667
-#>    1.9533  11.7377  11.7026  -3.2078   4.5977  -9.8745  -2.6903  -0.1726
-#>    0.5964   2.2094 -20.7317  -7.7429   8.8774  -9.4002  -8.3394  -5.2582
-#>   -7.7434   2.7373  -0.4027  -2.3704  12.3224  -3.0172   3.5563 -13.3000
-#>   -6.1673   2.1377  -9.6263  -7.3032  -8.1092   4.8288   0.9269   6.6680
-#>    7.1307 -11.4082  -1.8733  -3.4725   0.7833 -11.1207   1.6904  -3.3928
-#>  -13.8459  -8.0944  -4.5687  -1.3178 -12.7660 -14.1142   2.3761  -1.5851
-#>   -4.4088  -4.7370 -13.4434 -11.3929 -10.0721   0.3293  14.4283  -5.7123
-#>    1.6628  -4.8135   4.1967  -3.6642   2.6072   8.6424  -5.3487  -5.5875
-#>  -15.1982 -16.2075 -11.1081   2.1837 -15.1037   2.5881 -16.0889  19.7138
-#>   -1.3740  -0.5834  11.2036  -2.6599   8.2420 -12.5969 -12.3959   1.4875
-#> 
-#> Columns 17 to 24   2.0507   2.6598  -8.2677  -0.3819  -9.7047 -13.0155   0.8416  11.0928
-#>    9.5006 -16.9117 -12.5434  -9.1153   0.4889  -1.8486   0.6586   7.6950
-#>   15.1938   3.1147   0.5621   1.7471 -14.8611  -2.8270  -0.4675  11.0833
-#>  -21.2756 -17.3433  -3.7241   2.3021   1.9242  17.5036   2.0321  11.3387
-#>    4.0239  -4.6242   1.3642   0.7304  -4.2136   6.3788  -5.1951   1.0344
-#>   -0.0384  -2.6888  -6.1012   6.7180 -12.0463 -12.4807  -5.0917  -9.4530
-#>    3.7491  -1.2887  10.0114   0.4980 -19.6182 -11.8975  -6.1498  -9.0285
-#>    3.5560   4.8208   2.8959  -9.5669  -5.5017   1.9822   8.5510  -2.1044
-#>   -2.2116   2.3504   2.2195  14.9792  -9.9029  -1.9384  -0.5256   3.0722
-#>  -22.9213  21.1300  -9.5968  13.4872 -10.8303   0.4232 -12.2590  -4.7347
-#>   19.0331   2.7374   4.2416   4.2413  -0.5785  -2.3002  -2.3245  -8.5098
-#>    3.4308  -4.5625   2.0586   6.0886   5.3807  -1.9928  -3.4943   0.4703
-#>   -1.6577 -15.8600   8.2561   7.0638   1.7736 -16.2779   7.8722  -3.1787
-#>   -0.3687 -16.4942   6.5590  -1.1787   3.5540 -14.2099  12.5565   4.2178
-#>   13.0011   0.9513   4.4265  10.9985  -0.1402   4.5048 -13.1231   7.3450
-#>    7.5692  -4.8010 -20.2641  10.6312   6.1431   9.6376  -9.9602  -9.9894
-#>   11.8609   6.2776  -2.6604 -13.9893  -0.5079  -3.7566  -5.1813   4.9594
-#>   -5.7975  -2.5242   2.6223 -15.3010   8.8385  15.4189  13.6916   2.9766
-#>   15.4826  14.5025  -1.2235   9.1774  -3.8551  -1.9364 -10.3641  -6.1060
-#>   -4.3016   0.8259  -4.8363   0.1938  -5.1897  -5.0624  -3.6538  14.0120
-#>   -5.6653  12.6322  -1.3424   7.4415  10.4254   5.4954   5.7740   5.2493
-#>   10.3612 -11.3761  -4.5315  20.6901  -4.6147  -4.6538  -6.1524  -4.4536
-#>    1.1449  -1.2692   4.8713  10.5046   4.0514   1.0056   1.6884  -9.5293
-#>   -4.5487  -7.4370   0.9980   0.2414  -9.4703   0.0699   6.6126   1.3606
-#>   -7.5593  -4.8113   8.1871  15.3340  16.2282   0.7743 -10.5867  -0.0072
-#>   -7.5182   2.6224  -0.3788  -3.3806  -4.6903  -6.0372   5.0801 -11.2094
-#>   -0.7491  -4.1805   6.0432  10.6777   0.9205 -10.8444  -2.9452  -1.8480
-#>   10.1078  -2.1582  -1.2274  -9.9255  15.0571  17.5290  -3.8803   3.5488
-#>   -7.4319   5.0795  13.8641  -1.4426   1.3595   0.4998  -3.2554  -2.9333
-#>   -3.0166  -4.6155  21.9036  -3.6327   0.2526   4.0147  -3.2307   2.0131
-#>    2.7417  -0.6201   4.8743   2.8442   3.4621   6.0141   0.4053 -16.9837
-#>    0.8861   1.6744  -1.4065  14.6046   1.5714 -24.4373   0.3676  -3.4827
-#>    4.0054   0.5725   8.7190  -8.5844  -1.3806  -6.2709  13.3831  -4.2304
-#> 
-#> Columns 25 to 32  -4.1254   9.3742 -11.9406  19.7302   6.2971   2.7894  13.8389   6.6408
-#>   -1.9426  -1.7817 -12.3767   1.1051   3.3208  -1.9366   2.5308 -11.9070
-#>    8.8551  -2.2768  -3.4851  -2.1931   4.0168   3.8313   7.3140  11.3613
-#>    5.8380   1.4407 -14.6187  -6.7719 -12.8975 -12.0475  -1.8910  -8.9767
-#>    1.2515  -0.0015  11.6763   6.6943   3.9679  -4.1267 -11.1167   6.0731
-#>   -7.4304  -0.4224  -1.8240  -6.7562   3.3597   3.2047  14.7926   3.0720
-#>   18.2255  -5.5199  12.2591  12.3209 -10.4560  -6.3152   0.1159 -13.3165
-#>    3.0514  -6.1623   1.4615  -2.6432  -3.0623 -16.2089   3.5304  -2.6365
-#>    1.8521   3.1686   1.3424   0.4103   7.6215  -2.8771   5.7098 -15.4748
-#>    7.0573   8.4287 -15.0557  -2.5131 -13.4608  -2.3850   1.0915 -15.5075
-#>   -7.8992   0.6289  -4.1602   5.5693  -4.8073  -8.5766   4.1574  -1.1716
-#>   -7.6856   4.9891   0.0264   8.6593  12.1591 -10.1124   4.3597  10.4212
-#>   -2.5367  -9.9828 -11.1830   3.8636  -6.6999  -9.8037  14.4445  18.1645
-#>    3.0433   8.5416  -6.2895  -0.2634   7.1224 -22.0405  -3.1111   5.2978
-#>    2.5170  -4.6569  -3.9122 -10.7506  13.1106  -1.4986  13.1161  14.7422
-#>    1.7151   5.4397  -4.4998   0.5734  -3.2140   8.3545  -6.4990 -22.1587
-#>    1.0871  -0.3733   6.6648  -1.9736  -3.1345   9.0372  -7.9000   3.6416
-#>   -5.7346  -5.5697  -7.7569  -6.2873   4.6999  -4.1704   1.8441  -8.9156
-#>    1.9548   6.3252   3.0557  -9.0061   5.7293  -4.4918  10.9426  -2.5873
-#>   15.6885  -2.0274 -10.3964   4.3669   5.7090 -10.1337  -2.0274  -5.9175
-#>  -10.9127   8.2897   1.2689  -2.7881   5.2909  -7.6923  -7.8837   2.5822
-#>  -12.6344  11.2309  -6.9455   4.0729  -0.8880 -14.4394 -11.9550   5.4840
-#>   11.6747 -18.4813  -0.9224  14.7047 -13.1532 -16.9584 -14.5604  -3.2588
-#>    7.1224   7.6081   7.8043   6.9435  -4.3951  19.4718   0.1496  -0.0237
-#>    3.6916  -3.1680 -14.2709  12.8420   5.4446   3.6627 -16.5807  15.2178
-#>    5.9516  -3.0243  14.8711  -3.7670   6.9118   9.2722   8.1439   7.4384
-#>   -5.9544  -2.2944   6.3805  -9.6320  12.2830   6.9966  -4.4074   0.8192
-#>  -18.1687 -16.2847 -10.2525  -7.6339  -7.2074   9.0036 -15.0035   4.3509
-#>    1.0724  -6.5366  11.6397  -5.2278  -2.8765   4.4298   5.9241   6.1974
-#>    3.5181   1.3551  -1.8297  -4.7495  -8.6474   3.3282  16.5622  -4.5502
-#>   -8.1147  12.4193   6.6285   7.0230  -7.9623  11.4584  -7.5197 -14.5441
-#>    4.9385  -5.5366 -15.0028   8.5349   0.1998 -13.5030   7.5491  10.4168
-#>    6.9734  -9.1558   2.4214   2.2550  -6.5530   0.7602   9.4171  -5.7679
-#> 
-#> Columns 33 to 40 -16.2546 -23.7417 -17.0193  -1.3142   2.8238   7.1652  -5.3280  -1.3377
-#>   14.1420  -1.5059  22.5524 -21.2378  13.9141   8.4371   9.6989   5.8117
-#>   -7.3609  -3.2967 -12.9083 -11.0725   4.1133  11.0140   9.3464   7.2597
-#>  -10.0718  16.3289  -1.2712  -3.5266   5.6203  -5.0587  -0.1206 -14.1769
-#>   -5.1636   5.6576  10.7469  20.5651   8.4264  10.4495  -3.8374   5.2713
-#>   15.6951  14.4955 -11.6119   9.4762  -3.1124  -2.3191   2.1196   3.5569
-#>  -10.7368 -10.5437 -12.2213  10.9230  -4.4011  -1.6173   0.0974  -0.5739
-#>  -14.4136 -15.9359 -10.0872  -3.8252  16.9249  -4.7211   7.9440   1.1553
-#>   -3.7608  -0.2942 -17.8180  -3.6190   8.0015 -14.3660   1.6582 -10.8504
-#>   21.1495  16.3938   9.4025   1.9902  -3.1828  10.3133  -0.6980 -12.5243
-#>    3.1599  -1.0148 -10.7063   4.5622  -6.2987 -11.1163   8.4843   1.3144
-#>    3.5440  13.5956   5.0636 -11.6090   9.5306  -3.8764  -3.9018   7.6265
-#>    5.8936   0.2157  -4.4326  12.8327   8.0861 -25.1949 -14.3138 -12.4501
-#>    5.9666  15.9317   3.8190  -4.7217  11.1876   5.2509 -10.9786  -0.5133
-#>    0.6909  18.5410   4.4026  -8.1224   8.0649  18.7591  18.1166 -18.0383
-#>    9.0911  14.8643  11.4708   4.9207  -8.1068   7.9500   4.9009  -6.7330
-#>    9.7842  -4.6255  20.4290  -4.4621   1.0605  -4.9756   0.1691  15.5058
-#>   -2.8511 -16.5230 -13.0380  -6.8763  -5.0870   5.3321  -3.4553  10.2239
-#>   -4.1570 -19.2809 -12.6503   4.1567   6.9719   5.7000  -1.0137   5.4218
-#>    3.9844  23.7523 -20.5564   2.7967  13.0207   1.8161   4.7416  -9.0043
-#>    0.8970 -10.7307   9.0940  10.2688   1.0261   2.0990  -3.9108   3.1225
-#>   13.4368 -10.2608   8.3270  -0.6738   3.3290  -3.8392  10.7277   0.5851
-#>   -2.8274  21.8511  17.5014   7.3997   2.9204  -2.4399  -3.8114  -6.8817
-#>  -12.0053   9.8954 -13.6067  10.2075  -0.5364   4.7130  -2.0404   2.1682
-#>    7.2322   1.0930  -6.5927   1.4633  -1.6856  11.1873 -12.6404  -4.7624
-#>   21.7513   1.6566  -7.0826  -9.0371 -14.3815 -19.6564  -6.0599   8.9391
-#>   -3.6946  -2.1719 -14.3045   3.9593 -11.4235  -0.6905 -10.8632  -6.2279
-#>   -7.9132   4.1077  -7.4057   3.9989   2.7405  10.1765 -11.8403   0.2625
-#>  -12.8951  -8.4539  -3.7227  -8.7636 -19.2086 -20.4250 -23.3362  -6.3952
-#>    9.0696  -1.4293  -5.9031  -7.8074 -13.0599   0.1288  -5.4286  -6.5128
-#>    3.3542  21.9397   1.2557   0.1547  18.7915  -2.2035  12.2932   4.3940
-#>   19.6359  -5.6027   3.4627  -4.9635   4.0945 -17.2492  -8.7759  -7.4648
-#>  -14.4994  13.8843  -3.1236   9.1751  -7.8411  -6.0328  -1.2764   5.8990
-#> 
-#> Columns 41 to 48  12.4684   1.5413  -0.9921  -2.4225  15.1364  11.2646   9.6565  18.3759
-#>   -2.2483  -9.2219  -9.1508   8.3533  -7.1090  -0.2778  -0.5484   4.5380
-#>    6.2917  -1.6471  -0.3049  -6.6333  -9.6016  11.6996  -4.1160   8.6289
-#>    6.0482   2.3693  -8.5061   6.0234  -1.0929   8.3549  -6.7385  -3.8282
-#>    3.0921  -0.1369   2.3729  -4.0608 -12.5768   6.9210  -2.2395   0.3691
-#>   -3.9794  -9.2462  -3.3190  -5.6941   5.3892  -5.3677   0.8627   1.9602
-#>    5.0847   7.7314  -8.0740  -0.1435  12.0844  -1.2089  18.1352  -6.2714
-#>    4.8262   2.1688   6.4618  -0.6966  14.3354  -0.1032   7.7291   7.0555
-#>    3.2609  14.0137  -2.2330 -13.2423  -4.2445   1.9784  -8.1714  -6.3727
-#>   -4.8374  -6.5268  12.9251  15.1205  -8.1213  -1.3245 -10.2242 -14.8910
-#>   -6.9816   8.6871  -3.6590 -14.3809  -9.3692   2.6268  -1.2327   8.6051
-#>    1.6206  13.5033  -5.4795  -2.0907  -5.7077 -14.7999  -5.7586   4.2999
-#>    8.7203   3.3506 -14.3992  -5.7758   3.1561 -16.5495   5.3469   3.9362
-#>   -4.2642   8.1815   4.7881   2.3056  -6.4157 -12.4372   8.1750 -13.3807
-#>    9.0167  12.7056  11.6007 -11.0262   4.2971  -3.2547   1.4945   4.9691
-#>  -16.8074  -6.4873   3.3881  13.5651   3.2480   2.2724  -7.9075  -0.5044
-#>    6.9120   5.2717   2.6873  10.3306  -8.6791   4.5743  11.1047  -7.8470
-#>    3.4473  -9.3260 -13.5324  -2.1988   3.6893   9.5879  -2.7626  -3.1742
-#>   -1.0179  15.3903  16.5882 -15.5050  10.5391  16.1257  -4.2574  -0.6911
-#>    0.2554  10.1870  11.1322  -0.8859   7.5715  -6.3537  -6.4601   8.0480
-#>    1.9401  -4.3159  13.1100   7.9316  -0.6930  12.7543  -0.0876   2.2636
-#>    1.8901  -1.4288   1.4151  12.0978  -8.4133   1.3448 -13.1126  -4.5250
-#>   14.9244  15.4586  22.7820  14.1672  -0.7533  -3.9422  -0.6175  -6.9593
-#>    2.2502   8.3303  -2.0124  -6.4444  -1.9069  -9.1721  -9.1885   0.4218
-#>   -1.2529   4.6876   3.5248   0.3786   6.1484  -2.2346 -12.1829  25.1356
-#>   -3.8372 -10.7091  -7.9372   2.7616  -1.8327  -8.7472   3.2606 -12.9457
-#>   -0.8361  -8.9647  -5.8202   0.8926   5.8276   0.1345  -3.0728   7.0463
-#>   -6.0486  10.4944  -4.7435  -7.4233  -1.6023   2.5923   0.8755   7.7830
-#>    6.3221 -10.5719 -15.1768  -0.5792  -4.6111   5.8933   1.8873   1.3806
-#>    4.9719   2.2166  -5.4620  -3.4779   8.0047 -12.0724   4.7335  -3.8603
-#>   -5.3593  -5.7105   7.5636  13.7879  -7.3514   4.2796  -1.3703 -14.9948
-#>    1.2680   6.3434   4.6993  -5.8200   6.6031  -3.4956   5.4723   8.7672
-#>   -8.4542   3.9480  -2.9003  -7.2767  -2.1211 -11.5426  -1.4058  -4.2086
-#> 
-#> Columns 49 to 54   6.9345   6.8547   3.0552  -3.7366  15.4697   0.1524
-#>    6.2922  -0.7933  -6.4809   2.7974  -5.4409   0.0367
-#>   -3.3257  -0.0392   2.6232  -1.5331  10.6490  -2.2028
-#>    4.5382  -9.6036  -9.4649   6.2032  -3.8240   1.4407
-#>   13.5199   6.7690   2.6443  -3.3702   5.4460  -3.5104
-#>  -16.6280   2.0464   1.1608  -5.5702  -3.6791  -5.3750
-#>    6.2361  -2.5801  -0.8141   5.3224  -2.5808  -0.4248
-#>    6.5722   4.9813  13.0259   3.5532  -0.2108  -0.3481
-#>   -8.5610  -4.7566   0.1911 -19.4216   8.7068   1.1852
-#>   16.1358  11.3908   0.7024  -5.9319  -4.2298  -3.7900
-#>    0.7456  -2.5285   2.6793   0.9100   4.2093  -1.8958
-#>  -14.6449  -7.2198   2.0372  -3.0012   0.7265  -2.1829
-#>    4.9887  -4.5485  -7.1482   0.7946  -0.2220   1.1991
-#>    7.3561  -6.5854   0.4942  -9.0523 -14.6778  -0.6867
-#>    1.8704  -5.6577  14.4754   0.7415   2.1388  -1.6480
-#>    1.0187   7.2594  -7.2277   2.4263  -2.5897  -0.5953
-#>    5.8552  -3.0989  -4.6741   2.1087  -6.3685  -5.4360
-#>   -4.7954  -3.0337  -2.6234   9.1441   4.1686   1.3206
-#>    0.5679   9.4099   5.8011   0.1202  -0.3942  -0.2886
-#>   -2.6996  -0.6632   0.7895  -1.5725  -0.1924   0.0885
-#>   14.5838  10.1875   0.2721   3.2883   1.1273   2.9354
-#>   -8.6839   4.6683   4.7676  -4.0084  -3.8531  -2.5571
-#>   -2.7880   3.9429   3.5918   2.5719  -6.0650  -0.8717
-#>  -10.4243  -2.6173  -0.4466  -6.1775  -4.4496  -1.5418
-#>  -13.9890   3.4637 -13.8115   7.9119   2.7131   0.7817
-#>   -2.7265  -7.1634   4.9050   0.6368  -4.2845   0.3284
-#>    6.9923 -13.7115   2.1192  -0.0309   3.4392  -0.0764
-#>   -2.5682   6.7601 -11.6196  -0.8245   5.3279   2.1712
-#>   -1.3624  -9.8982  -3.0243   3.6376  -3.2003  -0.3407
-#>   -1.5863  12.3002 -15.6227   9.8221  10.8348   1.3270
-#>   -0.2349  16.1233   7.4516   5.6809  -5.4817  -1.3903
-#>    5.5133   7.0013  -9.5816   2.0696   2.0649  -0.7826
-#>    4.8998  -2.3120  -3.3847  -3.6179  -0.9100  -0.9852
-#> 
-#> (17,.,.) = 
-#>  Columns 1 to 8   6.1443   6.9194  -1.0562  -4.3711   4.4654   0.5805  -9.1360 -14.1596
-#>    2.9841   6.6422  -2.7177  -1.1271   0.7274  -5.4302   2.3300  -5.4333
-#>    1.9327  -5.6299  -6.9753  -3.4866   7.1639   4.7113  -8.2368   9.1127
-#>    4.4792  -8.6570  -7.9390 -12.0866  -1.9326   4.4371 -24.3383   4.0757
-#>   -0.5831  -6.2944  -0.5927  -0.3604  11.4115   2.1684  -7.3916   3.0542
-#>    4.4370  -0.6599   3.2872   9.1564   2.6958   6.7784  -1.4288  -2.8477
-#>   -2.1455   4.1315 -10.8879   1.0214   7.5736   2.6349 -12.0602 -16.0574
-#>    1.5223   4.4186  -2.2786  -1.9884   0.6818 -11.9779  -7.7711   0.4184
-#>   -0.6403   9.8892  -0.0395  -1.9764  -3.8331  12.8968  -8.1312  12.7658
-#>    4.0269   4.3379  -2.1609 -17.5246   8.5467   2.9727  -6.4218  -1.9910
-#>   -3.8391   2.7261  -0.3629   0.9506   2.2703   8.2623  13.7309  -7.8259
-#>    2.4645  -2.5920 -12.7258   0.0863  -2.0993  -6.0942   0.4621  13.7128
-#>    2.4760   1.2588  -9.4273   8.8638   4.6503   1.9964   4.9189  -3.0370
-#>   -0.5484  10.3669  -6.2906  -7.2585 -16.6554  -2.5892  -7.9406  -4.5755
-#>   -0.7397  -8.5692   4.6649  -3.7718   0.4087   4.4101 -11.1031  -4.4849
-#>    0.1002  -2.6376   2.6019  12.8042  -1.8125   2.6265   8.1862   2.4251
-#>   -1.0834  -3.4857  -4.6050   9.3387  -5.0314  -9.9726  -9.3422  -8.8175
-#>   -1.5390   0.1313   9.5713  -3.0821  -2.9337  -3.8771   3.7128   4.3213
-#>   -7.7632   6.8190   6.7207 -13.4798  14.6956   3.4435  -2.8333 -21.4868
-#>    3.2687  -4.6805  -4.7812  -3.1980   1.6897 -13.9128  -9.4960   3.1243
-#>   -1.6829   3.7035  14.6709  -2.2077  -3.2603  -1.1370  10.1571   1.2648
-#>   -1.0883  11.0762   1.3416 -12.7511 -21.1933   2.9799   1.6736  15.2179
-#>    4.4480   2.8421  -5.4820  -4.7026  -5.0694 -11.4562 -23.3585   7.1575
-#>   -2.9082  -1.0902  -8.4298  -1.1375  -2.0630  -7.2376   8.0495  15.6140
-#>    7.1673 -10.4637  -7.5146   0.0550  10.0286  -4.0913  -6.3031  20.5785
-#>   -5.4081  -0.1117   2.7705  12.1973  -8.7742  -0.2077   8.2314  11.1816
-#>    1.1246  -5.8824  -3.4459   2.8865   3.2704   4.4080  -1.6483   5.7580
-#>   -0.6552   3.7956  -3.3537  -1.6918   3.8882   4.0538   3.1555  -8.2176
-#>   -2.3748  -6.2723   5.2245   4.1894  10.9072  -3.6395  -0.2955  -1.0144
-#>   -3.5075  -5.5204  -6.1432   0.8693   9.1727 -10.3702   6.5089   3.1676
-#>   -2.1095   6.3571  -2.2335  10.9228 -13.9975  -2.3345  22.6371  -9.1804
-#>   -0.1568  -0.9048   5.5829  -2.9037  13.8580  -8.1319  -1.9552   3.5291
-#>   -3.8153  -0.3087  -7.4113  -8.1043   7.0150   4.7633  16.0928   3.2886
-#> 
-#> Columns 9 to 16  -3.1810  -3.2627   1.0612   4.0951   1.5249   5.4468  -0.3204   7.8181
-#>   -4.6727  -5.7859   0.5801   3.7623   0.3317  -1.4330   1.5571  -0.2559
-#>   -7.3271   5.7157  -5.1694  16.8466  -2.7004   2.6971  -3.5376 -10.4199
-#>   -6.5211  11.6065  -8.2419   5.3917  -1.8777  -7.2023   5.5177   7.2373
-#>    6.9915 -10.4649  -8.8021  -0.0459  -0.1659  -1.2228  -1.3253   8.1643
-#>    3.4482   6.1002   3.7333  -9.9588  11.2046  11.5108  -6.2545  -9.3857
-#>    8.2876   1.7361 -12.5369   2.4079  16.4401   5.7171   2.8861  13.5347
-#>    3.4647 -11.3662 -21.3331   7.0156   6.9562   6.3768 -10.0717   0.1385
-#>   15.1363  16.1886  -0.6350  11.5681  -6.6108  -8.0296   2.9866  -5.0846
-#>    3.5970  18.8378 -11.3572  -4.2139   0.3005   7.6319  18.2322  -4.4625
-#>   -0.8092  -5.0381   4.6445   1.8130  -9.0166   8.6400   7.3903 -17.6204
-#>   14.0589  -4.6007  10.7953  -7.4705 -12.3759   9.3497  -3.1502  -5.5020
-#>   -6.2404  -2.1810  -2.7716   0.7559   2.4455  11.5034   4.8724  -9.3390
-#>    1.5732  -4.2609  14.5878 -21.0616   2.1890  -5.8857  -4.9004   8.9875
-#>    1.0933  -3.6500   3.7066   5.4885 -13.8248  -0.8643 -16.2240   7.1034
-#>  -17.7197   5.6153  12.2955  10.4517  -3.1934   4.9312   5.7033  13.4370
-#>    1.3269 -10.6556  -6.0935   6.4030   2.2019  -2.4704  -3.2745  -7.5166
-#>   -6.9070  -1.5901  -3.8122  -5.1475  -3.1792   1.8839  -6.9131   7.7553
-#>    9.1986  -4.2436  -2.6430   4.5851 -18.2240 -13.6614   2.8320  -6.5608
-#>   -3.7126  -0.7393  12.5292   5.0482  -8.4647  -1.1843  15.3833 -12.8543
-#>  -13.4071  -6.8954  -6.4824  -6.8973  -8.1641   7.5570  20.1569  13.7861
-#>    7.7510  10.6749  -6.2694   0.7353   1.9931   3.6837  -7.3125   1.7338
-#>   -0.2942   6.2285   5.4998  -8.2894  14.3482  12.4478  -3.6528  -4.6218
-#>   13.3558  -4.2591  18.4636  -8.0938   5.3546   8.5646  -4.5114   3.6857
-#>   -1.7329 -15.8077  12.7673 -10.2201   9.5951   8.8423 -11.7141  -9.4775
-#>   19.4649  -6.8292   9.3490  -7.6591   8.2599  -1.1958  -1.2143   9.2001
-#>   -9.5506  -3.6073  14.2369  -3.8460  -1.9205   5.9566  -4.4946  -9.4718
-#>    2.9816  -0.1607  -3.5225  14.3550  -7.2591 -11.0014  -2.8438   3.6171
-#>    3.2333  -0.8371   2.9425   2.9499   2.0615   9.2706  -8.6544  -7.0034
-#>  -16.4620   2.1289   0.1354  -8.4689  -1.2499   4.8122   2.5817  13.4833
-#>   -0.5567   1.8226  -2.1932 -11.4949  -8.9891   7.0014  11.6330   0.0430
-#>  -15.2057  -1.4595 -11.9441  16.5283  16.2629  -5.4902  20.6133  -0.9546
-#>   -7.3588  -4.2087  -0.1877   4.6742  10.1569   4.0011  -0.8341   8.5255
-#> 
-#> Columns 17 to 24   4.7876  17.2720   3.3702  -1.0435 -10.8395  -8.1406  -7.5049  -5.5539
-#>   -9.9691  -1.1988  -0.3827   8.6837 -16.2213 -13.0124  -2.5975  -7.0430
-#>    4.5661  17.4118   7.9166   2.5864 -10.3547  -1.7683   9.1435  10.5543
-#>  -23.2334  10.3631 -25.5210 -12.3129  -4.8079   9.2390  -4.7016  -1.0820
-#>   -2.2913  -9.7322  -9.6260   5.5965  -2.5656  -0.5326  -6.9418 -10.7495
-#>   -3.2222   8.0219   5.9834 -14.8087  -6.2356  -2.7075 -13.7779 -38.2064
-#>    1.0871   0.2784   4.6760  -6.9322  15.0898 -14.0151  -7.9154 -12.2438
-#>   20.5265 -13.0739  -4.6356 -14.1243   1.4672  -5.4715  -0.2433  19.5680
-#>  -12.5312  -2.4462  12.4738 -14.6028  17.5852   3.3055   0.7279   2.9435
-#>   -9.0759   7.4424 -13.4984  -3.4864  10.9933  23.4347 -12.4106  -6.8116
-#>    0.2945  -2.3298  -7.9317  14.3155  -0.0490  -8.9765  -2.0218   7.3739
-#>  -13.2542   5.0205  -2.1731   1.3804   1.0738  -4.6849 -13.5458   2.3084
-#>    7.1460  -7.8515   2.2562  -6.2295  11.3502  -1.0303  -5.2992  15.8323
-#>  -15.0881   0.1767   6.4603   2.2481  10.8668  -1.9336  -3.0059  -3.9025
-#>   -3.4863   0.9929  17.7379  -0.4459   4.1941  -0.3456   7.3584  -9.6679
-#>   -7.0207   0.9989 -17.1677  12.6743  -3.4119   5.4517   4.1034  -7.5619
-#>   -6.6729  -7.0634  -8.5295  -6.7227   7.5573 -20.6982 -17.9593  -6.2486
-#>   -0.9564  16.8376 -17.7000   8.7197  -8.0258   3.5434   3.8272   8.6623
-#>    4.7000  -2.5202  -4.5670  -4.7674  -4.1558  19.1030   0.3822  -3.8329
-#>   -4.2104  18.9013  -0.6283  -5.7242  -0.6763   4.5063   4.3415  -4.9077
-#>   -6.0676  -5.2026  -9.6267  16.3435  -8.3280  16.4242  10.7939   5.0139
-#>   -6.8391   1.9071 -11.6442  21.1259 -11.9573  -2.9056  -0.0309  -0.9524
-#>  -15.3838 -14.4158  -5.6303  10.7407  18.0120  -5.8079   3.7584  -4.3958
-#>   12.5217  -0.7652  -7.5056   1.5211 -10.9556   9.6789   1.6204  -0.5635
-#>    8.5732  20.9318 -13.5805   8.2473  -2.1256  13.2180  -0.0080   2.3703
-#>   12.6771  -0.0184  15.6403   1.7141  19.6619 -13.3052  17.5606  -7.7566
-#>    2.2863   0.1965   7.5713  -2.2330   0.8285 -10.7955   3.7508  -3.1906
-#>  -12.9624  -5.9707  -4.7057  -9.0526 -14.0223  20.6805   0.6815   6.7069
-#>   11.4943  -6.2175   7.6624   8.1907   3.7794   3.9137  16.4800   9.7840
-#>   -6.1348   4.4264   4.3409  11.0234  -1.3618   5.7269 -19.5008  -0.8807
-#>    3.5661  -2.3396  -6.4453   4.2679   7.8474  10.3957  -0.1799   1.4265
-#>    3.1961  -9.3156  25.0814   8.3016   9.0736  -3.5149  -4.4687   4.5316
-#>   -2.1196   1.2412  -5.3532  -1.9141   1.0356   2.0371   3.8051   3.0251
-#> 
-#> Columns 25 to 32 -12.7784  -3.1151  -4.1067 -17.6687 -13.4600   6.3249  -9.3811  -7.3388
-#>   -0.7619 -11.6666  12.7568  -7.8023 -13.0570   0.8027  15.5756  -0.4485
-#>    0.7670  -3.6165   1.0185   6.6977   0.9419   0.6155  -6.7409   9.6993
-#>    5.1670   0.1816  -5.8841  10.4241   8.7812  -0.6064  17.1802   5.0143
-#>   -4.9780  -2.7439 -14.4820  -3.7913  -4.8708  -2.0714  -3.2589  12.7400
-#>  -11.4598  -1.2145 -10.0119 -10.2576  -8.2680 -11.5806  -3.2187  -2.6170
-#>   -2.6593   3.2811 -11.8429   6.7609  -2.9523 -20.6242   1.8380   5.0370
-#>   -0.6210 -23.1475  -2.7063  -5.0878   3.5943  -1.2283  -3.5178  -7.9973
-#>    3.6137   9.2104   9.5769  -2.2702  11.1843  -0.4034   1.9130  11.4164
-#>    7.6460  -0.3827 -20.7806  -0.2912  -2.5550   2.5483  -0.9074   3.8046
-#>  -10.1698  -1.4984  -7.6352   1.1972   3.4435   4.5729   2.1873   2.6382
-#>    2.0410  -4.2316   1.8286   4.2103  -2.9651   6.1363   9.6191  -2.9329
-#>   -2.6176   1.7511  -8.1758  -4.8099  -9.2014   3.0600   4.1247  -6.5042
-#>   -0.7695  -2.7357   6.5961   0.2550  -8.4879   5.1099   7.8306  -3.8784
-#>    7.1554  -8.0673  -4.7431   4.5292  12.5880   0.7548  17.6900  -0.6163
-#>   -9.0725   9.8556   5.5272  -7.2525  -4.6794  -9.3108  12.0713   6.8277
-#>  -15.2457 -11.3464  -0.8299  -5.3889  -7.3931 -14.3264  -2.2502   4.6353
-#>   10.7940   4.0557  14.0390  -9.8524   2.6743  -4.7332   5.4225  -1.7788
-#>    5.1810  -9.9234   5.5246   1.2374   7.2331  -0.2130  10.8272  -0.3448
-#>    4.7325   4.7821  -2.9912   4.3711  -2.0756  12.5127  -0.9461  -8.1639
-#>    8.8977   8.8578  -9.6155  -7.2892  -4.1856   4.3503   5.4361  -3.7193
-#>   -3.5420 -11.7217   5.6519  -1.5706   1.9119   4.2155   4.5332 -15.1637
-#>    9.5145   6.2907  -9.3909   7.1549   4.9775   3.4711   7.4276   7.5582
-#>   -4.8574   7.1038   5.1234   8.3762  -4.2527  -6.3355   6.4191   1.9055
-#>   10.1519  12.8104   4.5860   0.7293   0.0193  -1.5512  -3.6323  -3.6445
-#>    9.6038   3.8469   3.8026  -2.4492  -5.6320  -7.9020  -0.5373  -3.5500
-#>   -3.2464   6.0656  -0.9350   3.5552   3.2878   8.9093   4.0319  -3.2805
-#>   -0.9716  -4.9932   1.2699  -2.3636   9.6565   2.8870  -2.7157   6.6711
-#>   -4.0200   2.8403  11.0463   5.4527  13.8217 -11.7949  -6.8832 -15.9980
-#>   20.2676  10.8881  -7.9728 -13.7948   4.5471  -4.2364   4.6383  12.1830
-#>  -13.1566  -7.2088   3.0935  -5.6241  -1.0032  -1.1888   5.7101   9.9611
-#>  -16.2755  11.5573   6.1171  -1.1188  -9.2500   7.4123   6.8200  -2.3877
-#>    0.3761   1.2744   1.5478   4.8301   3.0607  -1.7451  -8.4018   5.3828
-#> 
-#> Columns 33 to 40  -2.6798  10.7408   5.4904  -4.9847  -6.8072   5.9453  -7.2387   9.5456
-#>   -1.6163  -9.3078   7.8399 -18.4102   7.3172  -3.7820 -10.7607   6.2403
-#>  -10.8727   3.4745  -0.8042  18.6595   2.5602   7.4274  -2.0725  -3.4180
-#>   -4.3281  11.4767   6.3640  10.9480  -6.1661 -18.5870  -4.1403  10.0234
-#>   -2.4974   1.1080  -6.0992  10.2662  -2.3050  13.6306  16.0703   9.8415
-#>   -3.2326   1.2055  -2.1907  -1.9525  -3.4973   8.5366 -14.1278  -2.0794
-#>    1.5418  12.4918  11.6216   2.1373   6.5673   4.2851  -1.3019  15.2588
-#>    6.4475   4.5054  -8.5098  -8.8409  -0.7175  -3.4654  10.5672   2.5838
-#>    5.9579   0.1376   4.9462   1.3211   9.5206  -8.4694   0.2855  -4.4919
-#>    5.8723   8.9215   9.1594  -7.8355   5.6397   2.0405  -0.8956  -3.4817
-#>   -0.3586   0.7275  -0.6278   6.3590  13.4949   3.0733   3.7876   0.0623
-#>   12.0649   0.3028   0.3616  -4.0420  10.4038  -0.6742   2.6561  23.8164
-#>   -9.3511  -1.2811   1.8103  -9.9004   8.7360  -3.9421  -0.2291  15.3043
-#>   -2.3802  -7.0159  11.3137 -13.9367   6.5566  -6.2087 -11.2825   7.4017
-#>    5.6427  -5.7506  -0.9135   1.0892  -2.6045 -13.5775   4.7687   0.4022
-#>  -10.2733 -16.6287  -0.3390  -2.6910   0.1103   0.9711   2.0200 -10.0394
-#>    0.4027  -8.1790  -1.2274  -1.8224   6.4739   0.3178 -10.8276   5.8462
-#>    5.6329   0.4834   1.0717   2.2724  -4.3132  -6.7506   1.2790   9.4726
-#>   -6.6045  10.1189   6.9280   1.8852  -2.8820  -9.5778 -18.9174  -7.5127
-#>   -4.6235  11.4260 -15.3983   4.8237   0.5430   3.1613  17.0547  -7.7714
-#>    0.9165 -15.7357  -2.9726 -16.5025  -4.2553   8.5064   4.5877   6.8933
-#>   -1.0045  -1.5503  11.9903 -16.7153   1.7860  -8.8464  -1.3527  -5.7166
-#>    1.9460   4.4425 -13.1039   0.3578   8.0725   8.0274   9.0462 -14.6979
-#>   -1.4275   7.5097   6.2892   0.8070  -3.7205   0.7155 -12.4836   7.3437
-#>   -3.1733   9.4923 -12.6845  17.5650  -1.7871   8.4914  -5.2970  -6.5126
-#>    5.3399   6.7639   7.6913  -3.0564   8.4512   2.6952   8.8310  -0.6163
-#>   -4.9734   1.6869   3.8372  11.4282 -10.7808  -0.1684   8.9313  -4.8800
-#>   -8.7450  -6.8279  -6.4785  21.0190 -10.2943  21.6078 -29.3649   4.0950
-#>   -2.7746  -2.4019   1.7970   8.5955  -9.3389  -1.3879  13.5859  -5.6466
-#>   11.1821  -5.2228  13.2041   1.8326  -6.4913   5.4415 -16.9786  -5.4153
-#>   -2.8319  -4.7344   9.0530  -7.0999   4.7814  -6.2008  -4.4767  28.7513
-#>  -19.1089  -8.6855  13.1355  -9.2310   6.0743  -9.0933  11.9112 -10.9186
-#>   -9.4442   0.6115  -4.7476   4.9595   2.3222  31.3597  -0.4587  -3.3852
-#> 
-#> Columns 41 to 48  13.2335  18.4072   3.8452  -6.9916  16.0191  -1.4387   7.3699  14.8535
-#>   -4.2993  10.6820   7.0353   1.3130  -8.7711  10.1553   4.1372  -0.5599
-#>    3.2517  12.1267 -15.7250   0.9289 -12.1262  -4.4340  -5.4977  10.2717
-#>    2.3578  -1.1384 -13.6929   1.9523  -9.0392  16.9055   5.2991 -21.7345
-#>   20.1297   9.7540  -4.4430  17.1155 -10.7662   3.5038   7.8592   3.0781
-#>    8.2474  -7.9042   2.5285   4.0943  11.3423  -2.9282  -3.7371  -5.7120
-#>    4.6633  -6.2643  -2.6094 -16.2379   1.1435   4.4389  17.2909   8.6930
-#>   -0.7847 -14.8175  14.3593  -0.9707  13.6857   8.9951  -1.7974   5.3307
-#>   -2.7323  13.8403  -4.1151  -8.8232  -5.2754   3.0921 -12.9723  -7.8679
-#>   12.0113 -14.4334  -7.5176  -4.4632   3.3497   4.9875 -13.5565  -1.4509
-#>    2.6535   2.2753   3.7532   1.5156   2.9532 -11.4222  15.0870  12.4004
-#>   -0.8589   2.4802  -6.1565  -4.2074  -2.5356 -13.0278  -3.9227 -18.4011
-#>    6.0597 -10.3485   1.7872  -6.2897   0.9800   3.4572  17.1559  -0.8167
-#>   -3.0977  -4.0392   5.5219  -7.0214 -15.3351  -7.9001   5.0116 -29.1513
-#>   24.2113  14.1920   3.8048   5.9459  -8.5433 -11.8677  -0.5119   5.3183
-#>   -1.5524  -1.9231 -10.8412  14.6299   6.4453  -4.2846  -3.5581   6.6648
-#>  -17.1757  -4.2006  -2.9053  -7.5145  11.6744 -18.1325   5.4189  14.1761
-#>  -13.5461   6.5545  18.4231   9.6028   6.9905   7.2333  -4.9760  -7.3707
-#>    4.2127  -3.3927   8.6950 -22.2378  -4.6125  -7.6642   4.6457  22.9384
-#>    1.9780 -16.4488  -4.6691  -0.0215   3.3830   1.5566 -12.8181 -11.4244
-#>    4.7052   4.0998   3.6235  -2.1365  11.1460  -6.0001 -11.2796  -2.0067
-#>   -9.5723   6.5852  -8.2466   3.4642 -19.0371  -1.4680 -10.6882  -6.0754
-#>    1.5706  -2.1467  -8.1451   4.2954  -5.1098  -9.6729  -8.2277 -16.7589
-#>    4.9486  -7.8326  -9.1857   4.9922  -6.8312   2.1194  13.4255  -7.9282
-#>   -2.9973   4.8711 -14.2213  12.5082  -8.9272 -13.5373 -10.6331  -1.7991
-#>  -20.9875 -15.0079  -0.1355  -4.0733   3.4722  -1.1602  -1.9319 -10.5716
-#>   10.5513  -6.3437 -10.0204  -5.1811  -7.3454   3.2951  13.2194 -10.6875
-#>   -9.0411   9.6437  -0.7267  -8.0317  -3.7791 -14.4714   3.3345   0.9388
-#>  -12.0010 -11.0004 -18.0084  11.7813  -4.8742  17.4474  13.8801   2.0438
-#>   31.7265 -13.4833  13.5332  -2.5016  -7.9255   4.0851   1.6553  21.2569
-#>   -1.0274  -0.3764   3.9221   0.6137   4.0974  -6.4963  -6.7259 -11.1689
-#>    7.8031   0.8675   5.7429   2.3580  -2.4164   2.3665   5.2437  22.7314
-#>   -4.2878 -10.5383  -9.9768   1.3139  -9.4152  -6.3573   9.6883   5.9264
-#> 
-#> Columns 49 to 54  -0.2671   9.7701   6.0322   4.6811   2.9762   2.2722
-#>  -14.4196  -1.0555  10.7776  -8.0051   1.9966  -1.7877
-#>    3.2285 -12.2791   6.1325  -8.0227   6.4722   2.3322
-#>   -9.7100 -12.2404  12.3004  -3.0280  -5.8205  -3.4416
-#>    1.8904   1.2133   7.7791   2.6255   1.2190   0.2403
-#>   13.7775  11.0335 -12.7964  -3.6531  -5.7224  -0.2613
-#>    2.1140   0.6198   8.4620   2.8346 -13.2978  -4.7245
-#>    6.4016   6.4218   5.0143  -7.5094 -10.5561  -1.5737
-#>    5.6732 -21.9615   9.2310   6.7673  -3.2778   0.9200
-#>   10.3036  -3.3432  -9.5236   6.6128  -3.7361  -1.9285
-#>   -3.4186  -3.4549  -3.8103   2.1477   0.9592  -0.1993
-#>   -4.8919  10.2478   8.8289   1.0471   5.5893  -2.8752
-#>    1.7449   1.9779  -4.1833   2.3643  -0.7714  -1.5880
-#>   -7.5838  -4.1429  -6.4595  16.2080  -8.8949  -4.2480
-#>   -7.4641  -0.9506  -3.1015   8.1307  11.6222   0.3097
-#>   13.7682   8.3304   1.3823 -18.3744   4.5722   3.6273
-#>  -11.2872   3.8188   0.5564 -11.4159  -6.8905  -0.0975
-#>   11.2198  -1.0655   6.9872  -7.0629  -3.6281  -0.3584
-#>  -10.5131  -2.6208  -5.8299   0.5071   9.3178   3.8768
-#>    3.3297   8.2219 -13.3294  -0.0814   1.1981  -1.7593
-#>    7.4771  22.3231   3.6667  -4.9007   1.9783  -1.0990
-#>   11.3080 -15.7802   1.4606 -12.4734   2.3892   1.7499
-#>    0.4649  -9.0525   7.7570  -7.6014 -11.8574   0.6533
-#>    5.2104   1.1293   6.9246   6.9781  -3.4111   0.2557
-#>    6.8321  -5.3332   0.8903  -1.9140  -0.1111   0.7168
-#>   11.2020  13.3858  -6.2562  15.5736  -7.2203  -3.6565
-#>   -2.5946  10.7949  -9.5117  13.1883   0.9728   2.0467
-#>  -16.0799 -13.1516   0.1700 -12.3154   1.6330   4.2724
-#>    1.1213   8.2665  14.9941  -4.0737  -4.9093   1.5110
-#>    4.0347  -4.5001  -0.1521   7.3414   6.3084  -0.2847
-#>    3.4297   8.9231  13.8903  -1.4625  -3.8002   0.3066
-#>  -15.8880  12.6141   4.3672  -4.6317  -0.2330   2.2429
-#>   10.0463   3.7442  -3.5000   0.3014  -3.8686  -2.9811
-#> 
-#> (18,.,.) = 
-#>  Columns 1 to 8   1.6420   1.2879   5.3164  -7.8571  12.7048   5.8433  11.3036   3.7826
-#>   -2.5109   0.0431  -1.9696   4.5404 -18.1270  -4.8284   9.7173   6.8896
-#>    3.3734  -1.0133  -1.5317  13.5069   8.2392  -2.2201  18.2343   8.3940
-#>   -0.6494   1.1275  -0.7992   6.7130   6.8398  -0.1860   3.1122  -4.9259
-#>   -0.1896   1.4126  -7.5902   8.6638  -6.4634   2.9422   6.8588  -8.1542
-#>   -2.5423   1.3982   8.7002  -5.7975  -3.0473 -11.9556  13.9886   8.3534
-#>    0.0925  -1.9056   4.1929   9.6189   3.0107  18.1900  -4.0006  -0.3840
-#>   -1.9065 -12.0112   0.1455  -5.1230  -5.6154  -0.0627  -6.9732   7.9400
-#>    3.1135   9.0919  -4.3371 -19.9986   6.5665 -11.0074  -8.8907  -3.8055
-#>    7.1416   3.7126  -1.0907  10.3849  11.9761  -1.6818   9.6157   1.0954
-#>    1.6181  -7.9607  -6.3046  11.3903 -19.3071   0.1217   6.8742   7.1179
-#>    3.1030   0.0477  -3.0999  -4.6140  -9.5560  -5.0644  -0.4219   9.1221
-#>   -0.3628  -5.0311   7.3550   5.5133  -4.0872  -4.3923   8.5265  11.5909
-#>   -2.2950   5.0372   6.1929   0.2985  -9.3952   2.1639  -2.5889   0.1801
-#>    0.5811  -7.8022  -0.1743  -1.9489  -9.0799  -2.7091  12.2969  -0.8607
-#>    2.0249   1.1647  -2.1831  13.7438  -3.9637  -1.8503   8.7329  -7.0915
-#>   -3.8079  -6.8342  -6.9091  -2.8699  -1.6679 -16.3355   2.1067  -1.9638
-#>   -8.4592   8.9154   0.8565   0.4704  -4.0822   7.5768 -11.9635  12.3542
-#>    3.6177   3.4600 -10.9865   1.7910  -2.5461  10.9428 -11.2756  -9.3878
-#>    2.6046  -3.8795  -1.0403   8.5378   3.9353   3.5416  -1.2939  14.9872
-#>    2.5174   1.1112  -1.4658   5.9015   6.6519  -5.6944  -8.8193  -4.0332
-#>   -1.5362   0.8535  -5.1453  -6.1017  -5.8288 -13.3209 -13.5367  -0.0891
-#>    5.5770  -5.0353  -3.9072   9.3719 -15.7994 -14.6314   3.9531  14.4180
-#>    4.4054  -8.4694  10.0511   8.5610 -10.1093   4.8425   1.0703  -1.3042
-#>    6.2056  -1.5487  -4.2585   6.9884   1.3044  -2.5759   5.4127   9.3555
-#>    2.2354   6.1777   8.6935   0.8330   7.0591   9.0817   4.4968  -2.9316
-#>   -0.3657  -2.1088   1.9124  -2.1532   0.5189  -4.1221  10.8716   4.1775
-#>   -0.4629  -0.4061  -5.8569  -5.7979   8.3717  -0.3196  -4.8337  -5.8717
-#>   -6.2881  -4.1826   0.7540   6.1380  -4.3152  -7.9107   6.6223   2.5199
-#>    8.2005   6.7751  -7.6609  12.9753  -3.5936  10.3625  -2.7662   0.3874
-#>    2.6145  -1.6590   1.1084   6.4919 -10.4815  -7.4845   5.0912  -3.3561
-#>   -5.6482   6.7101   0.6589  -2.6008   9.2927  -2.4136  25.8413   4.2583
-#>   -1.5522  -0.6731   3.7094  13.6084  -3.4717  14.0455  18.0751   9.0139
-#> 
-#> Columns 9 to 16   5.3804 -12.1351 -10.3660   0.5842   8.7693  14.6643   1.6818  -4.7866
-#>   -6.3455   3.6485  -5.2393   8.7314 -12.4487   4.7045   7.5412  -7.6969
-#>    8.4230 -14.6234  11.1057 -14.9826   4.4700  -5.2711  19.1685  -4.3796
-#>   17.0186  -6.2625   7.5988  -5.7124   6.4860   5.1624   2.9079  -1.0623
-#>    5.3565  -3.7169  29.1445   0.5628  -6.2952 -15.5886  -2.4912   1.7751
-#>    4.5112 -12.6647  -5.2051  -5.0987  -2.6762 -12.0308 -17.8213   2.3569
-#>    9.6305   7.1402  -9.4125   4.1921   5.6178  -4.1310  -7.4195  20.3406
-#>  -23.4526   6.1727   4.8096   6.1797  -0.8832  13.5739  10.5601   1.5159
-#>   11.5793   3.3354  -4.7421  -2.3175   7.3162  -5.4094   5.8834  10.9601
-#>   21.4171   3.9900   5.2486  17.4438   4.1869 -10.7578   9.4085  15.2720
-#>    3.6663  10.5914   9.0197 -12.5738   3.7574  -8.8465  -1.0220   3.9162
-#>    8.9307  -4.4353   2.4044   3.1607  -5.1845  -4.3000  -9.6301 -17.7922
-#>    4.1468  -5.9985  -0.0510  -0.9246   8.0400  -0.8469 -19.3457  -0.0925
-#>    2.5744  -1.3962  -4.2692   6.4907 -18.3116   3.7004 -19.8901   2.7946
-#>   -0.8892  -3.9821  -2.5982   1.1824  -8.8158  -0.4670 -10.6899   7.9036
-#>  -11.7663  -8.2280  10.0735  12.8687  -3.1658  -1.1699   5.9914   4.3675
-#>   -2.5636 -13.9672 -10.1241   6.0991 -17.4340 -10.4532  -0.0374   6.1176
-#>   -7.6453   9.5692  -7.7252 -12.5117   6.0954  -0.0058   2.8967 -21.3386
-#>  -13.3735   5.3355 -13.9365  10.3804   8.2995 -10.9733  33.1979  27.0988
-#>  -12.7816 -11.8679  11.4570  -3.9373   5.9092   7.1102  -6.8729  -6.7231
-#>   -6.3768   3.6131   2.9764  -1.4754  15.5975   8.9042  16.6951  13.3923
-#>   -0.5956   8.4284   9.3541   6.6690   3.1478  15.4808  10.6831  -8.6146
-#>   -2.4373 -18.7960  18.9351  15.9599  11.4891  -7.5023 -15.6213  -6.1754
-#>    3.8959  -1.9634   2.8737   4.6599   4.4457  -1.1856   4.8629 -12.7711
-#>    6.0529 -14.6800   2.7329  -1.2511  -7.1787  -5.7072   6.1981 -25.1700
-#>    4.8224   1.9774   8.1258   1.0748 -27.5141  -2.1715  -2.2420   0.7899
-#>   11.7260 -18.2582   6.0380  -6.0932  -3.7677   3.0523   4.9454 -11.8659
-#>   -0.3142  -1.3074  -2.0408  -8.9408  15.0563  -0.6220  19.6902   1.4908
-#>   -6.0120   9.0143 -10.1494 -11.0386  -5.0090  -5.1131  -9.2287  -8.1093
-#>   14.9981  18.3641  -4.4207  -3.9998 -10.9331 -15.5465   0.6011  -2.4611
-#>    5.7311   2.3118   7.1591  16.2519  -0.1902   6.0938  -4.1115   0.3160
-#>   16.1710  -7.7254   5.3234  -6.6723   0.1106   3.8326   3.5976  -9.7607
-#>   -1.7634  -1.1571   2.4107  -5.2687  10.5207  -9.7934  -5.3621  -4.9413
-#> 
-#> Columns 17 to 24  12.7919  13.8965   7.2914   7.1090   2.6619 -11.1852  20.6129  -0.3397
-#>   -6.1425   4.7137   0.3191  12.4660 -12.7929 -10.1171   1.9036  -9.9026
-#>   -2.9329   5.4450   8.1227  13.2214   2.8128 -23.6955  13.6789 -10.9361
-#>  -21.1594   3.2425  14.4772 -10.3611  21.0172  -5.6483   9.4607   6.3364
-#>   -8.1113   2.5801  -8.5287  -0.6597 -14.0022  -0.5884   3.7643 -12.5078
-#>   15.2535  -7.8894  21.2285 -13.4478 -13.0378  -9.4753   6.7139  -6.2283
-#>  -18.2535   2.6662   2.6795  -2.7393   4.5147   2.3993 -10.3161   9.5086
-#>    9.5280 -14.5483   4.6656   3.5450 -12.7685   5.6188  -4.8503  11.8668
-#>   -8.0276  17.9217  -7.9395  -7.7207  20.0763 -10.4341   0.3116   6.6646
-#>  -12.0671   3.4923   3.1873  -8.6960  13.8439 -15.9396  -5.4830   9.7459
-#>    6.6190  -1.9811  -8.0694   9.4840 -16.2822   2.9730   1.0627   3.1966
-#>   20.4468  -0.6983   4.6333   5.5524 -14.3997  -1.8202  -4.0220 -12.3155
-#>   11.8901  -5.3156   4.3961  -4.3514 -17.1259  10.1295  -3.1806  -3.7633
-#>  -22.2255   5.1331  19.4986   9.8792   1.8949  14.8938 -12.4817   3.8372
-#>   -2.5785  -4.3861   9.4344  -4.7644   2.7722 -10.0445  -4.9544   2.5280
-#>   10.1179   4.8086  -4.7714   6.3682  -4.8846  -8.6565  14.4215  -2.6060
-#>    2.7266  -6.6072   7.6766   8.2032  -2.2043   1.3790   2.5085  -2.5679
-#>   -9.4860  13.6252   0.5499   4.3637  -0.8586   3.5431   2.8470   5.7581
-#>    8.7090   8.6967 -25.1840 -12.8700  -0.1018   8.5919   1.0733  10.8271
-#>   -6.4616   3.3153   5.3792   7.9816  -1.5986  -3.9117  -6.9535   3.2914
-#>   19.0527  -3.1730   3.6325   3.6042   9.1775   6.4150   2.8763   3.6961
-#>    7.6531   1.3698  -9.0558  13.6710  -0.5876 -10.8151  11.8866 -19.2419
-#>   -8.6815 -17.6992  12.5567  12.1497   6.0738   3.7060  -3.1910  -0.9991
-#>    4.7524  -5.6268   2.4945   1.2878  -8.5590  -1.7245   2.4662 -12.2543
-#>   20.1751   1.5843  12.8122   2.2665  -0.8076  -7.3859  17.5358 -16.9012
-#>   -4.6932   9.3886   0.5833  -5.7472  -2.2443  -9.8603 -19.1601   3.3404
-#>    0.8012  -0.8414   2.3527  -1.0912   8.3260   2.9054   2.3910   3.4724
-#>   22.5107   3.1786  12.0413   1.5933   7.6991  -9.4353  13.6481  -2.4569
-#>   -3.8804 -11.3571  13.3741   1.0029  -6.2457  12.8943   0.5813   3.3167
-#>   -0.2584  -1.0563 -17.1628 -12.4293   1.2945  11.4786  -6.6795   6.9568
-#>   -6.6891 -13.8328   2.5390  -0.3918  -2.6157  -3.9123   7.7240  -1.6569
-#>   -9.8826  -1.3782   1.5560  13.3592   0.2965  17.9120   7.0649 -11.1058
-#>   -5.5006   9.0927   8.0763   2.2972   0.9185   5.0729  -2.5752   2.4860
-#> 
-#> Columns 25 to 32   2.6157  12.1656  -3.4293  11.2619  29.0579   9.0954  -7.2592   2.0037
-#>    0.8193  -0.0767   3.5949 -10.6917  -5.6788  11.6697  -5.4333  -6.3205
-#>   -3.6810   1.5525  -5.6771  -0.1137   4.2578   9.2257  -7.5001   1.5330
-#>   -0.0569  -4.2848   9.9154   6.3289  -5.2351   7.7988   4.8216  -2.9912
-#>   -3.8000   6.5655  -2.0004   1.7156 -17.9381  -4.2242   3.4018  -1.0467
-#>   -9.4344  -9.8031 -12.0416   8.2862  13.2678  -0.7302   0.4335  -8.4655
-#>   -7.8939   3.8292   9.9277   0.7376   1.4882   4.2665  10.4770  -8.0951
-#>   10.8728   9.1898   1.6196  -2.5027   1.6465  18.7318  -5.1867   4.6003
-#>  -11.2066  -9.4481  -5.4285  -4.1906  -7.5989  -2.8678 -16.7686  -2.0709
-#>  -17.2278 -10.1598  10.4174  -3.8264  12.3131  -2.5120  18.8831  -7.7288
-#>    0.3052   5.7338  -7.8968  -1.5548  -9.5453   4.5017  -5.5146  12.2713
-#>   -9.3960   3.2968 -12.8012   4.0913 -15.1407  -0.3459 -14.7093  -7.6451
-#>   -1.7817   4.8734   5.0182   4.4158  -6.2907  18.4289 -16.1908  10.3766
-#>    0.4862   5.4331  -6.9836   3.9721  -4.2630   9.7002   2.3095 -14.5059
-#>   -4.1786  -5.7285   2.5321   6.2680  -4.6553  -2.1756   6.8678   2.6513
-#>   -8.6665  -6.0416  -0.5409   0.5216   4.5455  -1.9902   0.3366   0.8028
-#>  -10.9652   6.0440  -2.8763  -5.2005  15.1445   1.7218  19.8993  -3.4327
-#>   10.6043  -3.2512 -10.1111   4.6157  -4.0006  -2.1343  -1.4302  10.6029
-#>   -0.7327  -0.6984  -2.8468  22.2596   8.2914   2.4505  16.9699  -3.4296
-#>    2.0949   2.8143   5.6126  -0.4120  -5.6877   3.9536 -10.9128   0.8354
-#>   16.6468   7.1970  -5.7594   1.3074   7.8698   0.9892   6.8667  -4.1368
-#>   -2.2990   3.1578  -5.4826  -1.8124   2.9626   8.2702 -14.0820  -6.5329
-#>   -5.5275  -8.0167  -3.2093  -3.3525  -9.3943  13.0748  -7.8546  -4.9310
-#>   -1.6238  -7.5335  -0.1831   3.1611  -7.2427  -1.5588  -5.7129  -0.7068
-#>    8.0404  -3.7601   4.9198  -5.5980  -1.6015 -12.4899  -1.4713  10.5558
-#>   -7.2427   2.0596  -5.3566  -7.9559  -4.5101  -5.5048  18.3094   4.4071
-#>   -1.3398   2.4388   6.1397   1.8313   3.2439  -6.4362   6.1027   1.0680
-#>    9.4973  -1.7636   0.4137   8.9540  -8.8187   0.1385  -0.4692  -5.5934
-#>   14.0594  -1.4364   3.2645  -2.2201  -1.2013  -4.2073  11.8217   9.4594
-#>    7.2463  -7.3954  14.7617  11.0248 -11.8653  -7.9344  -2.0461  17.9101
-#>   -4.8893   0.5295 -10.6414   2.5047  -1.8547   8.1770 -11.2773   3.5899
-#>    6.6188   5.2276  17.5914 -12.3284  13.5262  15.2202   2.1484  -6.2322
-#>   -3.2337  -6.4030   2.5477   0.6266  -1.7585   4.5958  -3.8904  -2.0269
-#> 
-#> Columns 33 to 40   0.0029  -3.3509 -12.6342   0.0077 -13.8816  19.4735   6.4888  -4.3073
-#>    1.5395   0.2632  -5.4625   0.9212  -9.7440   5.6657  -5.8855  -1.8879
-#>    5.9266   0.1231  -6.3651   0.3660  -1.8050 -17.6215  -0.0248 -11.7501
-#>  -10.3363  -1.2273   6.9968  -4.5623  24.2431  -8.3985   7.2473 -31.0217
-#>    1.2550   9.8227   3.1240 -18.6046 -13.7359  -3.0107  -1.1276  -2.6916
-#>   -8.9907  -9.6516  14.6502   8.2522   3.6585  -7.0430   2.7615   4.5177
-#>  -26.4023  -3.6227   6.8321  -8.3929 -11.2319   2.2254  -2.6124  -8.0238
-#>    6.9105   7.0635  -1.4849   2.4072 -19.7923   6.6321   3.8779   2.5356
-#>    6.3276  -8.5924  -5.0837  13.8570  12.6960  10.3987  11.6815   2.7757
-#>    2.8557  -1.6411  -8.9849 -14.0671 -11.5407  -7.7375   5.4139 -15.6535
-#>    0.7287  -7.0148  -4.2302  21.0221 -13.1672  11.1729  -5.9700  -4.3374
-#>   10.4731 -10.1309   3.3727  20.1838  10.8264   5.5776 -10.2286 -10.2985
-#>    3.3903  -8.1233  -0.6413   4.7653 -13.4528   3.2653   1.5537  -0.2125
-#>   -4.8917 -23.8506   8.8801  -4.8572   4.5650   8.1567   8.8407  10.9621
-#>    2.9578   9.7805  -9.9187   2.7422   9.3902   0.2009 -16.6340  10.2006
-#>   -3.8805  -6.7501   7.9872   2.7867  -0.0815  -7.9427  -3.5837  -0.0629
-#>    1.6030 -13.3641  11.1395  -1.3434  18.6885   3.3939   9.9179  -1.1933
-#>  -18.0162  -8.5544  -0.6633  11.2242  -0.5546 -10.2144  -3.5952  -0.8228
-#>   -0.0304   3.3971 -13.0760  12.1379 -18.7442   6.0984  11.3735  13.1533
-#>   -1.3424   7.4699 -16.1672  15.2030  -1.1397   1.4846  -9.6994   0.7061
-#>  -17.6452   7.4832 -12.2968  20.7037  -5.9765   6.0838  -3.7101  14.0650
-#>    9.8086  17.2381  14.2677   5.9241 -12.9382  15.8107   0.3432  13.0134
-#>   -7.9321  19.8009   6.0164 -11.8849  21.6689   7.9013  -0.1456 -14.9485
-#>    3.3430   3.6445  10.6610  -2.1489 -17.0001  -7.2675   6.2436  -3.5449
-#>    3.4334  -2.1735  13.1118 -14.9150   6.0767 -11.2966   4.3785 -12.3123
-#>   -2.0132  -6.5839  11.9638 -18.3315  -3.5849  -8.2416  -3.7682   6.0796
-#>   -0.0251 -11.1150   7.8212   1.7854   7.5301  -4.5684   0.8110   4.1804
-#>    8.2421  -1.7128  -1.8108  12.8005  17.7682   4.3181   8.1897  -3.9628
-#>    0.6283   2.0559  12.3124  -3.8224  -3.3201 -12.3931   6.2987   3.5806
-#>   -2.0786   6.6837  -3.2043   3.0021 -12.1649   0.8647 -19.2996 -11.3476
-#>    3.8575 -17.6842  13.0417  20.2883  -6.0133 -10.4405  -0.8185   3.9150
-#>    6.9854  10.4905   7.3135  -9.4315  -7.8653  -0.3362  -3.7238   3.9443
-#>    7.3930  -1.5779   0.2267  -8.8062   2.0794  -3.5313  10.0632 -14.0367
-#> 
-#> Columns 41 to 48   4.0510  -1.2702 -10.7254  -0.1518   6.2255 -16.2182   0.0292  13.3147
-#>   -2.1117   7.7726   4.7524  -6.3753   9.8414  -0.9707  -1.7454   8.6750
-#>   -0.9473  -6.6488  25.7680 -21.5618  -1.7513   8.2230  -4.7134  -9.3620
-#>    2.9891  -9.4650  26.0017 -24.0466  -3.2031  16.5410  -1.8369  21.9893
-#>   -1.7402   8.2179  11.1601  -4.2580   2.8483  13.6395   5.4053   2.7420
-#>    8.2215   6.4315 -10.5551   6.0965 -29.5574   9.0842   4.3430  -8.8682
-#>    4.0511  -4.8678  -7.8091  -7.2168  -3.2019   0.7052   2.3832 -13.7279
-#>    3.7785   7.8156  -3.6508  -6.4915   9.9509  10.5353   0.7613  -1.9243
-#>   -3.4082  -3.2314   7.5694  -9.4198   1.2483 -10.8518  -0.4544   5.1223
-#>    2.7928  -4.2954  13.6518  -4.1961 -32.9449   8.2825  10.6235  -6.5547
-#>   -2.1706   0.5347  -4.1825   9.5515  -1.3363  -0.9141  -8.9849  -2.0354
-#>   11.3627  -0.6651  -7.9567   1.5583 -23.5124  15.5929   0.6318   0.4863
-#>    9.5106  -0.9316  -1.4384  -7.9514   3.3732   2.6686  -4.9989  -3.0646
-#>    1.5673  -3.4351   3.2348   5.1354 -16.7270   2.7156 -10.4553 -14.2158
-#>   -2.6128   9.3098   6.3065  -3.1190  -4.8262   8.8057  -9.8614  -5.6171
-#>   19.3820  -7.3091  -1.5507  -3.3476   1.9809  10.8549   2.1270  -3.7262
-#>    7.5950   5.9518 -14.4187   1.6114  -7.7032   7.1640  17.7226  -8.4070
-#>    0.6154   5.7930 -13.4261   5.2674   9.2733   3.7338  -7.8101   1.1300
-#>   -8.1761  12.1908 -21.7284  13.0551 -16.3943  -5.5680 -10.0639   3.2579
-#>    1.8726   4.2444   4.9620  -6.5421  -9.1609  10.7864  -0.0778 -10.7919
-#>    9.7009  -2.5378 -15.6215   8.3960  -7.2251  -3.9734   8.8126  -4.3367
-#>    2.8564 -32.7612  16.8721   0.2182  11.6929  -9.1474  -5.9281   3.8051
-#>    1.7788   1.4127  -1.8833  -6.9912 -12.5033  18.4125   3.6896 -15.0041
-#>   -5.9166  -7.0237  -7.5080  -8.2371   7.0100  13.8009 -11.6932  -1.6627
-#>    4.9167  -2.6230   0.3647  -3.4979 -20.9022  16.2120  -6.7058 -14.1063
-#>   -1.9939  -0.7363 -17.2596  14.2294 -19.4004   8.7278  -0.1109  -9.6073
-#>   -4.8946  -0.0505  -6.9824  15.6053  -5.0343  -2.1242  -2.9937   0.5938
-#>   -4.5974  10.0005   5.7879 -12.7138   4.0694 -22.5153  -8.6040  19.5947
-#>   -7.7176   9.4326  -0.3602  -1.8544   9.7682   2.2847 -11.9673  -4.2708
-#>  -16.3190  -5.7646  -6.2865  -0.0257   2.4428  12.3580 -13.9446   2.4780
-#>    8.4663  -8.2000   9.8663 -13.7863  -1.1844  13.4650  -4.9049   5.9373
-#>    2.4027   0.5915 -21.0702  -1.0956   6.1958 -18.1889   5.0352 -18.4239
-#>   -9.9235  -7.0443  -2.4528 -12.1563   3.5734  -3.8522  -9.3329  -9.5187
-#> 
-#> Columns 49 to 54   2.4848   8.9600  -6.6746 -12.3711  -0.5049   2.8786
-#>   10.6893  -5.4225   9.1650 -13.3324 -13.8708   3.7911
-#>  -16.2479  27.7164   0.7434 -11.4350  -8.6765   0.6951
-#>   -0.2513  -5.2872  -8.1689  -6.9968  -0.3131   2.7240
-#>  -20.2858  15.1985  -7.0279  -9.8774  -2.8880   7.5356
-#>   -5.0441  -5.9110  -3.6796  10.4714  -2.4352  -5.0220
-#>   -2.9365   3.6362  10.7610  -0.0537   1.7815  -1.1395
-#>    1.9543 -10.6093  -5.5753  -3.9769  -3.0863   1.6045
-#>    3.8588  11.3072   2.7108  -5.5991   4.0178   0.1168
-#>  -22.2331 -13.1465  -2.4980   5.7941  -6.1919  -8.3823
-#>   -0.9479   7.0255   0.0698   1.3584   0.6481   0.1080
-#>    4.3669   5.3655  -2.4001   3.0138 -11.6708  10.0307
-#>    5.1871  -7.0168   0.4818  -0.0774  -0.6100   2.0541
-#>   17.7132   6.4588  10.1605 -10.7837  -6.7728  -7.9383
-#>   -7.6739   6.9783  -1.2624  -0.2311   1.1638   3.9497
-#>  -17.5456  -9.7086   2.0341   9.9349  -3.0648  -0.5089
-#>   -3.1795   4.3665  17.0024  -8.8965  -1.5841  -8.8056
-#>    2.2149  15.9212  -4.4968   0.0624  -3.1900   2.9269
-#>   11.9618  -2.4289 -17.1797  17.8757  -0.4401   7.4848
-#>    0.8993   9.7873  -2.5482  -0.0111  -6.4800  -1.8389
-#>  -12.8026  -5.3223 -10.4207  -7.1540  -3.7932  -0.5174
-#>    3.0748  -8.7669   3.6482  -0.7108   2.5877   2.6914
-#>   -1.5469  -5.7033   1.4735   4.2395   1.8257  -3.1212
-#>   20.0404  -0.7424  -2.4644   9.7535   1.2855   5.3932
-#>   -4.9952   6.6275  -9.0983  11.9380  -6.4644  -2.4656
-#>   14.4117 -11.4367   6.1705  -2.5055  -4.0090   1.6790
-#>    1.7852   2.5159   5.6664   7.5982  13.7174  -5.9481
-#>   -0.8873  -0.4220 -12.1559  -7.4173  -7.3533  -0.6071
-#>    3.2437   1.4743  -7.6123  13.1501   8.1817  -3.4226
-#>    3.7093 -13.1205  -7.3952   9.8814   4.7580  10.3219
-#>    3.5487 -23.3564   5.8674   6.2407   0.5246   4.0238
-#>    8.0665  28.6886   2.2886  -3.1141  11.1435  -2.9868
-#>    4.3882   5.3014  -3.7234   8.4248  -0.0245  -8.0384
-#> 
-#> (19,.,.) = 
-#>  Columns 1 to 8   2.2563  11.1571   5.1803   2.0074  20.4880   1.9959 -11.7897  -3.3816
-#>    1.6023   4.9350  -9.8570   2.5549   3.4270  -6.4630  12.5979  -3.4586
-#>   -3.7042  -7.2734  10.1350  -2.1168   4.5737   4.7926  -6.3151  13.1638
-#>    6.8103  -6.0863   3.9821  23.3459 -23.1046   3.2218  -2.8127  -2.0219
-#>   10.0323  -1.5816  -0.3515  -3.7162  -1.0843  -8.9585  -9.5895  -1.1016
-#>    4.5691   0.3556   6.5799  11.4361  -5.5251  -0.7265  -4.4567   7.4877
-#>    3.7387  -5.0253   6.2016  12.1069   1.3906  -3.1792   5.3994  -8.8015
-#>   -6.3128  19.6994   0.8121  -5.3457   2.7312  -7.0969   0.8557  -5.9668
-#>   -0.6554 -17.8413  -3.6626   7.7413  -3.0338   1.9055   2.3498   4.4129
-#>    2.2045 -17.2951  14.3557   5.4896 -13.9269  13.9771   0.1688  13.7065
-#>    1.4103 -10.9335   6.4542  -7.1951   0.4260   6.0540  -8.6265 -10.6153
-#>   -2.4367  -3.0208   1.7582  -3.9473  11.5795  -9.8707   8.2718   3.9334
-#>    2.6910  13.7177 -12.4472  -0.7891  -4.1189  -4.9260   1.9796   6.3284
-#>   -2.2626   0.8278 -16.8472   8.7072  -1.5437  -2.5874  18.4442 -12.6984
-#>    0.2770  -5.7886  -1.6831  -6.2280   0.3892 -10.7264   1.6883   0.5731
-#>    5.9888   2.8220   5.1822 -10.9491   2.9278  -1.2400   6.9684  -3.0588
-#>    2.0853   6.4697   9.1610  14.2869   3.9100   7.3812  -5.6622  -2.6049
-#>   -2.8214  -2.8717   3.4763  -4.1231   4.3919  -7.9700  14.0622 -10.4217
-#>    0.0360  -7.2489   7.1088  -5.2261   7.6209  -6.4602   3.4370  -3.3627
-#>    3.0146  -8.4109   7.3443   6.8336 -16.3648   7.2043   7.3347  -7.1156
-#>   11.6654  -2.6083  -1.8932 -11.5787  -7.3925 -12.9529  -8.5728   6.7741
-#>   -7.3200  10.8262  -1.1320 -12.8965  17.0946  -4.7953   1.3419  -5.9471
-#>    0.9198  -4.3271  -5.4991   3.7542 -11.3688   7.0314  -0.1410  -5.9312
-#>    5.9632   8.4848   2.1999   4.6207   0.4360   1.6097  17.6682   7.0791
-#>   -3.5695  -2.5270  -0.5916  -8.6880  -8.8335  14.8022   4.9419  -2.7857
-#>   -5.3153  -5.2006   1.6115   9.8521  -2.7637  13.6420  20.2294   2.9587
-#>    0.1992  -6.7249   2.8040   3.9956  -7.6735  16.6353   0.2961  -0.4600
-#>   -0.9324   4.6584  -9.8605  -3.0216   3.8279 -18.3609 -14.6428   2.9325
-#>   -2.3640   2.2242  -7.8729  -2.3704  11.0849  -3.0016  12.9536   4.4597
-#>    0.6707  -3.5127  -1.1921   7.2875   5.6279  -5.3606  16.7637  -0.5393
-#>   -6.0561   6.4866  -2.1347   2.0663  -9.1948  -4.9570   4.9678  -2.9562
-#>    1.7940   8.1519  -9.7820  -8.1634   8.2479   7.6515  -7.6138  20.4446
-#>    1.0375   8.2653  -6.3321   4.4135   7.8574   2.1843  10.2095  -3.0581
-#> 
-#> Columns 9 to 16  -6.7446 -19.2839  -5.2996  10.6448  -2.2137  -9.8432  16.7385   1.8806
-#>    2.7721  -9.6319  15.9549  -3.9562  -2.7533  -1.4063  17.1808  -3.8886
-#>    0.8954   3.4040  -4.2748  -7.6926  -1.9848  17.2263   0.3864  -4.7782
-#>   13.6218   3.0880  -9.0650  13.7402  14.2595  14.3139   8.1423 -10.8839
-#>   -2.8056  -1.5642 -10.9384   2.4840 -14.6773  15.3640 -11.8735   8.1836
-#>  -12.2327   8.0373  -9.9830  -7.0531  -2.4499  -2.4429  -9.1589   7.2055
-#>    6.7662   1.5342   1.1651   4.3661  -5.1278   7.6165   2.6917  -7.9761
-#>   -4.9940 -12.7029  -0.5018   4.1232   7.7914  -3.3340   3.7179 -15.3366
-#>   -2.8687  11.0463 -19.0638   0.3108   8.7410  -5.9274  -4.5910  10.8263
-#>    2.7317   2.1383 -13.6276  31.3021   5.1940   6.0216  -4.0457  -5.5339
-#>   -8.8773   1.3894   6.0943   8.2040 -22.9392   6.0407  -5.8266   9.3254
-#>   -5.3709   6.6576  -0.3668   0.5234  -0.9117   8.5379  -2.6543   5.2700
-#>   -4.0529  -2.2297  -3.6299   8.0911   7.2778  -1.6554  15.2207  -8.6982
-#>    2.9273  10.0753   1.0093   2.4645   6.0291   2.2682   1.5076   3.4788
-#>  -16.8549   8.1650  -5.1286  -7.7697  -3.0778  13.3012  -7.2841   0.8980
-#>   -4.7963  -5.0447  11.0234  19.7087 -13.5973  -1.4497  -6.0398   3.9615
-#>  -11.6929 -12.9310   3.0861   3.8349  -0.8498   0.3676   7.5970 -15.8111
-#>   10.5982  -8.7752  -4.8810   3.8208  -2.7940   0.2052   2.5400   8.1477
-#>   -3.8651  11.3115 -15.3378  -0.9253   7.8896  -0.5798  -9.4853  21.2292
-#>    1.5519   2.7537   7.5006   5.2048   3.9256   3.6954 -14.5668  -1.0811
-#>   -6.4954 -15.8022   2.8659   9.0508  11.4639  -2.6907  -8.2854  -1.4667
-#>    7.8183  -1.5043   9.2190  -3.2909  -3.6400  -7.3343   4.0588   3.0736
-#>    1.6113  -7.8219   7.8065  20.6163  -7.6171 -11.7764   8.7982  -8.1107
-#>    4.7483   9.2704  -0.8942   2.4555   7.9658   2.8396 -11.1332   6.9196
-#>    9.2414   3.9240   7.5839 -10.3013  -8.0429  -0.9105   7.5698   5.5207
-#>    9.1179   9.3893  -2.6667  -0.5141   6.6394   2.4894  -3.3466 -12.0105
-#>   -7.6842   7.3149  -2.5730   4.5251  -6.0077   4.9602   5.9953   2.1281
-#>  -10.2351  -0.4251   2.1078  -6.7500   6.6732  -0.6379  -4.8256   7.8820
-#>    7.6989   5.7581  -0.8032  -1.8760  -1.4233   4.0420  -9.7446 -10.8131
-#>   13.1536  -3.2820  -5.9773   0.4688  -7.2200  -1.9802   6.8597   7.1834
-#>   -6.9597  -6.3075 -10.8629   6.5933   5.1967  -2.3144 -13.2033  -6.3124
-#>  -15.1643  11.3891   1.7059  -1.9721   6.4254  -8.3010  13.8574  -8.0904
-#>   -0.6400   0.8845   9.1436  12.9765  -0.8867  -5.4252  -2.4219   2.5684
-#> 
-#> Columns 17 to 24 -11.6251  -3.3042  -9.5897 -19.7662 -11.3639   2.9764  -7.6847   5.3094
-#>    5.5535  12.3224  -0.4621  -1.7751  -6.2082  -2.8243  -3.3639   4.4086
-#>    4.9465  -6.1456  13.8044  -2.3682   0.1131  -1.0469  -0.6537 -18.7963
-#>   -0.2615   3.3738  12.8766  -0.7729  13.0538  -3.7030  21.3555  -0.0547
-#>   -5.4725  -2.8746   3.7941  -7.9571 -14.6536  -3.4342  -0.7045   1.1588
-#>    3.2876  -3.8150   2.2217  -7.6296  -6.1589   9.3530  -2.3499  17.2963
-#>   -8.1793   7.5804  12.0472 -14.9795   2.5250   4.4984  18.0541   8.3617
-#>  -10.3372   2.5190 -17.6749  -9.9759  -7.6969  -6.4078  13.4594  11.9478
-#>   -5.4648  -6.1133  -3.1051  15.8897  -0.9202   8.7116   5.8395 -20.7721
-#>   -4.9633   7.0661   1.5125  -9.6190  -6.7895   3.5231  -0.0329  23.3810
-#>  -15.8677  12.4208   2.0475   6.4192  -9.7269  -0.6899 -15.3368  17.5331
-#>   -7.8009  13.4144  -4.8032  15.5606 -10.1968   9.3907 -12.3619   0.2271
-#>  -17.6470   3.5627  12.0824  -4.9767  -6.5277   0.2517   1.5120  17.2585
-#>   -7.6810   2.1964  14.6920  -5.9527  10.4770  -6.3552   7.7612   9.4945
-#>    0.4429  -3.4509  -2.8073   0.4457  -8.6056  -3.4702 -23.0327   0.9529
-#>    0.8179   4.0997   2.5110 -15.3957   3.0363  10.0195  -9.8834  -8.2598
-#>    0.9310 -15.8169  -2.7237  -3.8845 -11.1570   3.2370  -4.0259  -0.8774
-#>   -5.0513  18.1145  -1.9949  18.7507  16.7231   7.7012  -4.9110  -4.4177
-#>  -32.6358   0.2473  -4.0207   2.8901 -18.4278  11.5049 -11.9611   8.3352
-#>    3.0942   8.3002  -8.3140  -5.4769   9.5909  -5.1340   1.8389   9.0214
-#>  -16.4604 -16.0312 -13.7154   5.3215 -13.6139  12.0238 -12.3383  -9.2225
-#>   -5.7482   1.7096  -0.1163  14.3944   0.9409 -12.6108  -5.2439  -6.4736
-#>   -2.8593  -0.6078   5.9682  -8.9257   6.8423   6.6952   1.2522  -8.5496
-#>    0.1467   9.9529   4.9536   4.2114  14.9164   8.9636 -11.2608  -6.2331
-#>    7.4674  -1.1952  -6.9978  -3.2490  11.2160  -6.2838  -1.1349   5.1366
-#>   11.8175   3.2338   2.4789  -7.7572   9.2989  -6.1385  -4.1347   4.6263
-#>    3.8211  -2.9451  12.8165 -13.0431   2.9364  -0.6053  -3.3417   0.7684
-#>   -5.6006 -21.6590   0.9646  13.6898  -1.2653 -14.3905   4.7676  -5.7793
-#>    3.3701  -2.2316  -6.6199  -1.5730   0.6153   2.5474  14.7909  14.1248
-#>    1.4418   6.3772   0.3450   6.5393   9.4165  15.8391  -5.4509  -7.6689
-#>    9.1360  -3.4296   4.2381   0.1421  -6.0637 -10.6114 -10.5209  -1.9886
-#>    8.9036 -12.5969  22.4951  -0.5840 -12.5121  15.3569 -18.5965   9.6492
-#>    1.6620  -3.6564  29.1041 -15.0301  24.3040   2.8217   5.5570  -3.0949
-#> 
-#> Columns 25 to 32   0.6754  -5.4351  -1.7427  -2.1690 -12.6507  -7.9582  -2.0196  -5.9914
-#>    6.2957  24.2517  -0.6646   5.8407   4.2708   0.6678   0.8753   7.4855
-#>    9.4350  -1.2963  -3.6902  -5.5026   2.7794  -3.1165  -1.7660   1.7011
-#>   10.9539 -17.5292   4.3171  -8.1743 -14.3399   9.7072  -7.5103   9.4845
-#>   -3.8901   7.8685  11.0553  -7.6921  -0.4697  11.2485  -3.0967   7.2322
-#>    5.9094   3.8987   8.5515  24.0678  -3.1706   2.4028   0.1868  -4.1452
-#>   -4.8616   5.1777  23.9718 -11.5407   7.5438   3.0655 -15.7713  -8.2458
-#>   -0.8644   5.6632  -3.6068  -3.0043  14.2817   7.5471   5.6906  -4.2427
-#>   -7.6225 -10.5315  -8.6553  -2.2209 -15.1343  -2.7813  14.5287 -10.1209
-#>    8.1794  -1.0438  17.5802   1.3836   6.7183  10.8328  -2.5115   2.8458
-#>    2.9136 -10.5741  12.6177   0.9100   2.0254   3.3510  -2.8068 -13.4172
-#>  -13.1170   1.6400 -13.0929 -10.3412   5.5702 -11.1293   2.2765   3.6344
-#>    1.1890  -1.9082  -2.3892  -8.1190   2.6411  -3.1444  -6.9475  -6.7670
-#>  -14.5015  11.7778  -2.8347 -13.3463  -5.1831   4.0238   0.1387   2.6846
-#>   11.0696 -13.9293  -6.1347   7.4526  -5.8511  -3.2405  -0.3918  13.2905
-#>   17.0813  -5.0977   6.4885  12.9523   8.3253 -10.0022  -6.1836  14.6672
-#>   -9.5167  -0.1452   8.0347   9.8185  -4.8826   7.0202 -13.3114  -7.3593
-#>   -3.5866  -2.1113   6.1158  -4.5314   0.5352   3.9646  -8.2554   1.0600
-#>   -6.3913  -3.4983  -2.0942   2.1771   1.5844  -3.6043   4.8755  -9.8164
-#>   11.0548 -20.8779 -16.4557   5.0648  -3.3323 -10.0792  18.6829  -5.9163
-#>   -2.9553  -3.4663   5.8928 -17.9741   7.4210  14.4276  12.8937   3.8324
-#>    6.7334  12.8358  -6.2878  -5.9928  12.9341   1.9918  -0.3133   0.8225
-#>   -8.5431  -4.5959   5.7668  -6.6540   6.5829   6.1331   1.0447  -7.4261
-#>   -5.5379   0.1180  -2.7121  -5.2564  -3.0441  -9.8881   4.7905  -2.0531
-#>    1.8131  -4.7013  -3.3520   6.5667   3.2757   5.0640   9.5182   2.7596
-#>   -7.9812   6.5552 -21.8246  15.2338  -1.6433  -0.1308   4.5212  -3.7304
-#>    1.0232   1.4512  -3.1897  -7.0585 -15.2776   3.4033 -12.1728  -0.5707
-#>    6.9714  -8.0632  -3.9023  -1.7853  -7.5021   9.3818   9.8124  -7.2047
-#>   -8.3226  -3.2363   5.5813  -9.9950  -1.7240  17.5094  -6.7417  -5.7870
-#>    9.7854  15.8732  13.8283  -0.1509  -6.0653   9.1286  -3.0200  -1.7873
-#>   -1.2083  -4.4204  -5.7270  11.8766  -5.3162  -0.7704   9.3954  -1.7334
-#>   -2.2619  11.5894 -10.0027  -5.0707   4.5968  -5.3793 -13.2128  12.2694
-#>   -9.4825  -1.5247  -0.3769   3.1203   5.3710  -5.7538  -8.5316  -1.2781
-#> 
-#> Columns 33 to 40  -9.2212  17.9095   8.4176  -3.7203  -6.5719  -1.9328  -3.5606   4.5551
-#>   -1.8634   6.8070   5.5377   7.7612  -0.3723  -8.5310 -11.2221  12.8836
-#>   -0.5835 -20.5723   6.2568 -13.6192   6.2027   2.5340   8.3277  -1.0690
-#>    9.4813 -13.0169  -6.5955  -9.6504   3.9302 -10.4059   0.3368  11.0317
-#>   13.2242  -6.7998  -7.4696  -4.5022   7.5813  -2.4320  18.8617   6.3256
-#>    5.4512   5.7200   4.0642  -1.2367  -0.7577  13.4109   1.9488   2.7067
-#>   17.0755  -5.8967  -4.9994   2.3772  -3.3988   7.8152  -7.2953  -7.5021
-#>   -8.0343  -4.6180   2.3566   9.2143   5.1217  18.9750   7.0116   0.8720
-#>    8.8868 -12.8316  32.2617   1.5173 -29.2014  -9.1697   1.7793 -11.5384
-#>   -8.6272  -4.4338 -21.9003   4.1937 -13.0200   8.8821   0.1906  13.0268
-#>   11.8491  11.2913  -2.7882 -20.0160  22.0333  16.8525  -3.0531  -2.2457
-#>    3.9485  -1.9158   2.2538 -21.1789  -3.8281   0.7445   2.8729   5.1457
-#>    4.8669  -7.2527  -3.9915 -13.5313  13.7822  12.5903   8.1406 -10.5367
-#>   -2.3059   2.7685  -3.8104  11.5816 -24.4048 -18.5444  -8.2961   3.2680
-#>  -16.0640 -19.7496 -16.1403  -6.2305  15.5044   0.6679   0.0522  19.7908
-#>   11.9814   5.5630  -0.8154 -19.5417   9.2743   8.0217 -15.5096  15.4365
-#>    4.4094  14.3800   3.7023  -2.6637  -6.9884  15.1600 -10.9028   8.2918
-#>    8.1691  17.8184   3.8099   3.3481 -12.3396  -3.6603  -0.4540  -1.5425
-#>  -15.0172 -12.5406   2.6438   1.7852  10.4317  16.6561 -11.3111 -20.8243
-#>  -12.4438 -10.9364   3.7333  -6.6735  13.5679  -1.7383  -2.9847   6.8149
-#>   -1.8302  17.7674   5.9496   1.7236 -20.1008   2.4569   4.4831  -5.7613
-#>   -0.7342  -9.7959  -7.8026  -6.3387  -2.7167  13.1393   7.8315   9.4000
-#>    9.4051   9.6574  14.3628 -27.0737 -19.5745 -15.6395  -3.8150   2.3063
-#>   -9.8362   7.6502   0.7495   5.7295  -0.4509 -10.2346   3.5372  -6.7736
-#>  -10.6270  14.9071   3.1708 -15.4192   2.3833 -13.9320  -9.1408   3.5938
-#>   -9.1457 -11.3525  -5.3337   4.7483  -4.7272  -3.0666   0.1602   2.4069
-#>   -8.1658   3.7447  -3.9244   9.3006  -1.0673  -7.4670  -3.5135  -1.1845
-#>   -3.0652   9.2276  17.3474  -4.8320   0.7358  -4.0601  13.5146 -17.2049
-#>  -12.7639   1.8174  -5.2838  10.5123  11.9389   1.5286   5.6593   0.2460
-#>  -12.6807   2.0698 -15.8524  -7.2091  -7.4212   0.1610  13.4838   2.1601
-#>    7.2512  -1.3859   0.5661 -15.6594  -3.3170  -2.9406   0.2242  -3.9722
-#>  -25.7610  -6.7176  10.3714  12.6513 -11.1680  15.6171 -19.9599   1.9016
-#>   -2.6267  20.8037  -2.0124   4.1648   1.2791   6.2882  -3.8924   3.8411
-#> 
-#> Columns 41 to 48  12.6592 -29.1091   4.9542   6.5500  -6.5881  -9.1306  -3.5637   6.9740
-#>   12.5785   4.9390  -7.1512   9.5374  19.1770  -2.1619  -8.8319  -6.1387
-#>    1.6069   0.0530  -7.3822   0.7928   4.1775  -0.2300 -11.3976  15.9961
-#>   -0.9627   5.4817 -15.4045  10.0725   5.5603 -11.5714  12.0004   1.3725
-#>   -2.4268  17.9863  10.2603   6.4608  12.7709   1.9074  -2.4812   2.6753
-#>    6.0541   0.7232  12.2480   5.2808   1.4006  10.6693  12.6639  12.8870
-#>   -6.6617 -14.0500 -14.0936  -3.0179 -11.9357   5.9934   3.5963  -6.5327
-#>   -1.3355  -2.3099  -5.0284   8.5370   3.2054 -19.0103  -6.5679   6.2471
-#>   10.6122  11.9086  -0.1262   6.2764  -3.3073   0.9882   0.3496  13.5524
-#>    1.8818   3.3550 -19.4137  -0.5076   8.5231   5.5520   7.0628  -4.7189
-#>    2.0409 -11.5721   1.2794   4.2566  -1.2600  11.6436   8.4207   9.9688
-#>    7.2530  -0.5176   9.8714  18.3778   1.0340   4.8684  10.6200   8.9634
-#>   -1.2232  -8.9204  -2.1755  -1.1896  -3.3770  -2.9910   0.3296  -5.7852
-#>   -7.5940   5.8494 -15.8490 -10.0028   6.6867   3.4989   2.2816  -7.2253
-#>    6.5804  -0.6134  -2.1385  15.5190   8.1329 -11.0707  11.2124  -2.0956
-#>   20.2437  -4.8852  -6.5738   2.1046  -1.0111  -4.7278   1.7603   7.4301
-#>    4.8448   1.4679   3.6642  -7.8998 -14.3289   8.8894   7.9565   2.9185
-#>   -2.0559   3.1594   5.7790 -13.0144 -14.4777  -5.6680 -10.7241  -4.0730
-#>   -2.1987  -0.4621 -29.1733  -3.3608 -10.2317  -5.5618  -2.7433  16.9024
-#>   -2.5571 -18.0090  -6.6635  17.0925   7.7247  -9.7994  -3.9254   9.3806
-#>   -2.6549  11.5325   1.9270 -18.3873  -4.6849   3.4927  -5.3377   8.0902
-#>   -6.9130   0.0039   0.7236 -23.0587  10.3118  17.0714  10.3968   9.7206
-#>    7.9891  -6.5689 -13.5171   4.7222   1.5046  -7.6745  -7.4960  19.0716
-#>   18.9985   2.7868   8.8201   3.8623   4.7100  -1.8755   1.1543   0.4049
-#>   -1.9884 -16.9054   6.6718   6.4880   5.4618 -16.1783  -5.3956   5.4232
-#>    2.3665   6.1933  11.9676   5.5544  -6.5978   9.0037  -4.2192  -6.4146
-#>   -8.1628 -11.2495  -7.0179  -6.8217  -1.2933   6.0335  -4.5630  -6.4349
-#>   -9.8572 -11.5498  -4.4926  -3.8380   0.7976  -1.0803   8.9509  24.5363
-#>  -16.4516  13.6568  10.2520 -10.9182   7.4908 -13.2014  -1.2110 -18.3038
-#>   -0.4914   4.0439  -2.5239   7.8569  -3.8332  -2.2241   4.7996 -14.7188
-#>   10.2891   6.7213  -2.7195  -8.7093   0.4224   8.2590  14.4842   5.9226
-#>   -2.3089 -15.3783  -6.7664  -7.4489   5.3329  -1.4130  -3.1034  -6.9561
-#>    0.3502  -1.1984 -10.8877  -5.4941   5.8245  -7.2559   2.3815   5.3173
-#> 
-#> Columns 49 to 54   1.2747   5.2395  13.4698  14.2738  12.2964  -1.5817
-#>    1.6910 -12.1397 -12.7877 -10.0018   7.3331   0.6355
-#>    0.7433  -5.8941  -6.2776   4.1104   2.8945   0.8537
-#>  -12.5226  -6.5539 -10.7138   7.6140  -1.7271  -7.8163
-#>    3.8362  -6.7615   2.8636  -6.1179  -3.3340  -9.7257
-#>   -1.7788   4.3488  22.8753   1.8028   5.4304  -1.8112
-#>   -1.4235   3.3501   5.2342   4.2336 -14.7682  -7.2064
-#>   10.1970   3.3946  -1.0360  -1.1210  -4.4042  -4.1440
-#>   11.1277   0.9043  12.8983  13.0145   5.9844  -1.5443
-#>   -4.2875  -5.3223   3.4411  -5.3851  -1.3105   0.6720
-#>   -6.5519  12.1854   0.0411  -1.8376   0.8744  -3.2010
-#>   -0.1420   7.3235   7.9048   7.8784   4.8304 -10.4523
-#>   -3.6911   4.2254   5.7063   8.9996  -4.4160   0.4081
-#>    8.5811  -7.6967  -5.5423  -5.7741  13.3399   1.0483
-#>    7.6063  -7.0702   3.5762  -6.5361  15.6316  -0.6609
-#>   -9.8814   1.9607  -0.4300  -0.0419   2.0097   9.4041
-#>   13.4482  10.9558  14.7211   0.1087  -0.7896   2.0498
-#>  -16.6676  -4.7743  -7.8918   1.7374  -4.2081  -5.8012
-#>   -8.2581  -6.0684   8.2776 -10.0404   9.0940  -0.6857
-#>   -3.9399   6.9471  -3.0039  -4.1868   0.1530  -1.3818
-#>    5.2030   3.0198   0.3190   4.3111  -2.7122   9.6921
-#>    8.5626   9.0417  -1.1888   6.9106  -3.1359   5.2059
-#>   11.7007   7.5871  -0.9854  -0.6513   0.4028  -3.6121
-#>   -9.2621   4.3883  -7.0322  -1.0245  -1.7525  -3.5827
-#>  -20.7991  20.7254 -10.1940   0.9200   5.2781  -3.8300
-#>    1.3659   0.4175  12.2263 -13.9632   9.0599  -8.4849
-#>   -4.6873   1.5768   7.5582  -0.5245  10.7458   5.4537
-#>   13.1642  -0.6127  -1.5006  20.3494   3.8420   6.7404
-#>   -3.1472 -10.0608   7.6488   4.2702  -4.5538  -2.1601
-#>  -27.4567   4.2973 -14.8549  -6.8283 -10.6547  -6.2378
-#>    4.6752  -3.8682   5.5952   1.8841  -0.3845  -1.9298
-#>    5.4148 -13.7245   6.6207   5.3980  -1.0152   7.5767
-#>    4.4342   1.4319   0.4299  -7.3339  -1.4901  -0.7209
-#> 
-#> (20,.,.) = 
-#>  Columns 1 to 8  -0.1621  -1.1462   5.5624  -3.2040 -15.1401   7.7637   2.6860   5.4238
-#>    0.8396  -3.5203   9.1231  15.4953  11.7898  -1.6306  -9.3022   1.3270
-#>   -1.3684  -3.0398 -10.3844   3.6498  -5.7445 -19.0700  -0.8349  10.7056
-#>   -0.0485   5.6198   3.5489   9.6158   1.7576  -4.0273 -13.8786 -16.4744
-#>   -2.5919  -8.6451 -13.4473  -2.8219   6.3921  -3.4349   3.5949  -5.9716
-#>   -4.9905   4.9731  10.2412   1.4691  -4.3466   7.6242  16.1750   6.4483
-#>   -1.5315  -0.1067   0.8477 -12.7635   2.7935  15.6729  -8.6513  -7.0986
-#>    4.7082  -2.4865  -1.2656   4.2381   1.8470  -8.7176  -0.1376  -1.4295
-#>   -4.5697   1.0897   4.0721 -16.2454   0.7888 -14.9746  -1.8295  -3.1963
-#>    0.1823   1.8564   6.2780   3.6687   7.3347  -1.5156  -4.9225 -16.4798
-#>   -1.4463   1.9402  -1.1858  -8.6737 -12.8343  -5.3619   6.5962  -7.5694
-#>   -2.6235   4.2898  -3.5294   4.9846 -10.0827 -30.8690  -0.3993  -3.9279
-#>    0.2410  -3.7194   5.1257   8.5350  -3.8493   3.5762  -2.3895 -23.1393
-#>    1.4401   2.6178  11.1458  10.0722  10.3905  -0.3786  -1.1106 -14.5141
-#>    1.5678  -0.4691  -5.4716 -15.0261  -3.4612 -16.8536  10.2880   0.9811
-#>   -5.6570   0.1170   5.6482   8.2850  -9.1239   2.9178  14.5901  -0.7696
-#>   -1.4326   3.0316   5.5369  10.9070  17.6759   0.0452   7.9348   3.0246
-#>   -1.1936   2.5122   7.0014   1.9653   0.1634  -1.7728  -1.8913   9.4284
-#>    1.6027   5.4183  -3.9848 -12.1217  10.1409  13.7953  21.0640   2.8829
-#>   -1.6356  -3.6014   0.0734  10.9810 -12.7295 -14.2390  -3.9708   6.6003
-#>   -2.8815  -3.3364   9.8267  14.1771   5.8635  -0.1086  -6.7500  11.7749
-#>    7.1709   8.3964   1.4076 -13.0953  -0.5085   4.6746 -12.6335   1.4956
-#>   -0.1749   3.9424   3.2652  16.9313 -10.2269 -11.3709 -13.7632  -4.8729
-#>    2.5850  -0.2872  -5.2173  -7.3208 -12.8036   9.8185   4.2883  -4.7945
-#>    2.4509  10.4656  -9.4248   8.9418  -7.5186   1.9619  -2.0981   9.3907
-#>   -2.6993  -4.5462  -2.2453  -4.4655  -2.1707  -3.4057  -9.1211   7.5554
-#>    2.3860   0.4197  -4.8111  -3.5332  -5.9333  -0.6345  -5.2828 -10.2688
-#>    1.8418   5.4025   3.4547   8.0757  -2.6860  -1.5656  -6.3146   9.1767
-#>    3.1711  -3.4898  -6.5597   0.5113   0.2847  -9.3321   5.6376  -4.6058
-#>    8.5215   6.1679  -2.6810  -5.5771  -6.6683  12.2519   2.2518 -16.5943
-#>   -0.2038  -2.1986   2.9182   1.5978  -9.3678  -4.8177   3.7600  -1.1258
-#>   -2.5421  -3.1019  -1.8657   3.2041   3.9845  -5.3289   8.3623  -6.0053
-#>   -2.2026  -4.4154   1.7093   1.2705 -11.6828   5.0229   7.7633  -4.8920
-#> 
-#> Columns 9 to 16  -9.6379  -8.8353  -2.0164  -3.9967 -14.9024 -13.2522   5.6158  -8.8205
-#>   -1.7650   3.1118  -1.6893  16.0329  11.6731   0.8990   9.0909  -8.5504
-#>   -4.1310  -3.4250   2.2022  -3.6775  -2.3736  -1.0813  -7.3223   2.0234
-#>   -7.7183   5.4651  -2.1186  -0.2175   2.3976  -5.4979  -1.3650   7.4051
-#>    0.3214  -4.2397  -1.5178   7.8210  -2.7930  -4.5397 -10.7320  11.6219
-#>    3.3722   8.3514  12.6042  -6.2435   9.9449   6.8076  -7.1994   7.9202
-#>   -0.7087  -3.7693  -5.8845   2.2852   4.7547   7.6629  -0.9728  -5.5279
-#>    0.5844 -13.7592  -3.8658  -4.3545   1.7405  10.1863   3.6145   7.0898
-#>    1.1853   1.7078  11.3930  -4.2465 -10.0186  -9.8318   6.1848   2.0908
-#>   14.1360   3.3278   8.2308  20.9610  -2.2449   2.7065 -13.6995  12.0065
-#>    8.1762   5.8692  -1.0152  -1.7100  -3.0871   7.8233  -0.6612  -0.3667
-#>    0.7398   2.9508  14.0272 -14.8148  -6.7409   8.5475  -8.1818   7.2170
-#>    3.1297  11.1152  16.2606 -10.3462   4.2060  -4.3647  -0.5266  -2.5029
-#>    6.4961  -8.4803  -3.9518   3.8498  10.7788  -2.2223  -5.9765   1.3549
-#>   -5.1063   4.6187  -2.1797 -22.5261  -7.0005   6.2757  -9.0270  25.0982
-#>   -0.1420   1.3720   7.4795   9.6153   4.3483   7.4302  -9.9186  -5.4024
-#>    3.4592  -8.3573 -17.2632  -6.2594   4.1314  10.3204   7.1232   6.3911
-#>   -5.9489   2.3011  -0.3504  15.1394   0.2342   6.0624   4.9731  -7.1409
-#>   -6.4556  -3.4254 -15.0925  -5.4326  -7.9049   5.4858   1.3068  17.6305
-#>  -11.9726  -0.1709  -2.0448  -8.8095  -3.7666  -1.9334  -3.4856   8.0408
-#>   -5.5254  -2.6180  -5.0557  11.0289  -9.9996  10.3354  -9.6572  -0.4148
-#>  -10.8925 -12.7567   0.0669   4.5049  -2.1159   7.2722  -7.0065 -12.0945
-#>    0.5809  -2.2126  -3.6979 -12.0575  -1.5567  -1.7114   6.3236  -0.1161
-#>   -9.8552   5.4610   0.4954   8.4186  16.3263   8.1864  10.1633  -7.8072
-#>   -5.9334  17.6178  12.9692   3.4399  -1.6990  -7.9805  -3.4114  -7.6213
-#>   19.0645  -4.7203  11.0164  -0.9063   4.8046  -8.6888  11.7567  -3.8675
-#>   11.7528   2.8265  -1.7707  -4.1104 -10.3084  -4.3146   2.8402  -6.7274
-#>  -16.2602  11.0432  -2.8828   0.1409  -2.7798  -4.6135   4.9228  -6.8063
-#>   13.7149  12.8582  -4.4341   4.5278  10.9037   6.1661  -4.7417  14.1037
-#>   -0.3279  21.5061  -1.1329   3.3908 -11.6120  -4.9290  10.7559  11.6238
-#>    4.9558  -5.5311  -8.7086 -15.1959   8.1782  -3.1170 -10.8156 -11.8758
-#>    9.2265  -7.8700   8.2888  -2.7848  -6.6362  -3.8733   6.7170  -9.3142
-#>    7.6552  11.7999   4.7896   7.7007   8.5952   2.0664  11.5924  -1.0443
-#> 
-#> Columns 17 to 24  -0.5321  11.0464   0.0856   0.5655  14.5991  12.9319   0.4836   7.6641
-#>   -6.7579  -5.3101  19.7148   3.8677 -14.5008  -9.3260  -5.3839   3.1493
-#>  -11.6755  -0.6071   2.5071  10.7974  -6.7518   1.3822   3.6109   4.1592
-#>   -2.6031  -5.6818  18.6989  -2.4348  -9.8791   6.2369 -15.8756   9.3603
-#>   -8.1665   6.7317  12.1462  16.9517 -15.2266  -5.7444   1.5100   6.8900
-#>   14.4074   8.8029  -5.4545  15.4322   6.9543   0.2443   2.1472  21.9676
-#>   -1.3419   6.2277  11.9838  -7.4835   2.0090   2.4590   2.9050  11.5184
-#>   -4.5372   1.9943  13.6520  -3.9550 -19.4712   0.5986  25.6229   7.6501
-#>  -12.0105  14.3773   0.4855  -3.1355  12.9980  17.6869  -7.5678 -20.4467
-#>    5.4924  -7.7566 -11.8491   7.2381   3.8651 -11.9726 -13.3154  30.7087
-#>    4.9715   3.6618  -5.9609   6.0388  10.1822  -0.1636   2.9248  21.1720
-#>    4.4321  13.4352   5.0641  11.5861  -9.4310  -1.9816  -6.3792  13.6577
-#>    5.9904  -4.2367   2.1718   3.8697  -2.1954 -12.3323   0.6447  11.7598
-#>  -12.3906  -5.8361  15.9661  -4.0532 -19.7730  -7.9416 -13.6731 -14.6051
-#>  -11.5482  -6.3060   0.4562  10.2639  -7.1850   2.3008   8.4813   4.6316
-#>   16.3523  -1.7841 -12.6560  -0.8742  14.8849  -1.3381  -6.6948  20.4880
-#>   15.8185   0.9278   5.1805  13.5886  -8.4595 -12.5742  -5.5707   6.3068
-#>   -1.7460  -6.1560  -0.1155  -1.3805 -11.9903   5.2742  -2.6962   1.2403
-#>  -20.6755   8.6058 -19.3932 -14.9239  14.5644  24.0100  -0.9756  -3.3321
-#>    2.8825   1.0881  -2.3569  -6.5990  -1.0636  -9.5998  -2.6571   2.1721
-#>   -0.2178 -13.1473 -11.7695 -11.7659  11.5251   6.0879   0.9299   4.1882
-#>    9.5220  -7.2763   9.1356  -7.0332   5.8918  -4.9225  -2.3877  -2.8981
-#>   -3.8195 -11.3067  17.5316  14.3856  -2.0751 -10.7862 -10.6612   0.7596
-#>    4.8557  -3.7417   2.7421  -3.9924  15.1623   5.9950  -3.4114  -5.8673
-#>   12.6678 -10.7893   5.1291   5.0086 -12.7731  -7.5149   1.4797   4.7836
-#>    6.8793  15.9510  -0.8191 -11.1323 -21.8725  -1.1746   9.8490  -1.9807
-#>   -6.6938  11.1216  -5.1915   9.9398   6.9210   0.1600   7.1468  -3.6307
-#>   -9.6642   2.1299  -1.9093   4.7243   2.9268   8.8768  -1.7267  -6.9847
-#>    5.6409   0.9974  22.4437   3.0573 -12.4774   7.3325  14.7486  -7.2137
-#>   11.1331  -3.1796 -14.4943  -6.3280   4.9811  12.2918  -0.0304  -3.3065
-#>   12.5455 -14.9207  -2.9549  -0.1376   7.9348  -4.7665 -13.3366   7.2516
-#>  -17.0092   3.3592  12.2121   0.9171   9.6731   1.8164  10.6970  -3.2334
-#>   -6.6542   7.6337   4.5601   4.0772   9.9508  10.3802  -0.0720  10.9570
-#> 
-#> Columns 25 to 32  14.7814   2.0855 -11.5443  -8.8636  -3.2921  -8.8975  -2.2202   2.0542
-#>   -4.3575  14.1877  21.3428   7.5668  -0.0204   0.6640  -1.0708 -12.3276
-#>   -5.2300   6.1240   7.4403   2.9730   2.8976   1.5309  -4.3767  -3.9842
-#>   10.1208   5.2596  -2.1734  -1.7602   5.0769 -13.1904   3.5458   7.7316
-#>    2.8890   4.8204  -0.0606  -1.4020   6.3128  -0.8638  10.7315   4.2823
-#>   12.1047  -4.3720   4.1811  11.6347   9.3276   9.7779 -12.5684  12.7005
-#>    8.1034  -5.7556   1.0095   7.1351  17.4726   3.1717   7.3035   7.1441
-#>   -1.0227  13.4178  12.7260  -4.6267 -11.1069   3.4204  -7.7886   0.5724
-#>   -1.9399   3.8624  -1.8930  -7.9153  -3.2695  -4.7378  16.7488  10.2630
-#>   17.3373  11.5215   4.4709   2.6323  -5.6626 -22.0692 -10.8594  -5.0825
-#>    8.9432  -0.7131  11.7734  -3.8360  12.8653   9.0661   5.6882 -10.2663
-#>    3.6119   5.3201  -1.0089   7.1640   6.3922   5.7196   4.8207  -6.0330
-#>   16.9539  -9.5322  -8.6009 -10.1206  11.5182   2.3165 -12.3131  -6.9663
-#>   -7.6772   5.6404  11.2296  -6.5533  -3.4842  -1.0550 -11.3338  -2.7795
-#>   -3.0589   2.6489   0.0237   3.8240  -3.4378  -0.5842  12.3299   5.6363
-#>    1.8475  -3.6678  -7.2582   2.2023   7.2895  -5.9783  -3.0408  -9.8344
-#>   -3.9609 -16.0397  -2.3379   9.4380  -2.0241   6.7547   4.8268  -3.5794
-#>  -10.1694  -6.8895  -1.8487  10.7999  11.4416  -8.0720  12.5670  -3.0657
-#>   -5.8882  -6.5137 -14.9760  -9.7027 -10.1098  -4.3701   2.7771  -3.2866
-#>    4.8151  -0.7031  13.0597   0.4731   2.4569   5.2606  -7.8424  -6.6531
-#>    4.7037  -5.5518 -13.4641 -14.5033  -9.6714  -9.2366 -10.1225 -18.6002
-#>   -4.9474  23.4492   7.2622  -3.3553   0.5726   4.0995  11.6019  -6.0000
-#>    7.2762   6.6382  14.5240   2.3849   1.0163   9.1811   4.0564   7.0639
-#>   -7.9470   1.7570  -2.2795  -8.6340 -10.8866   4.2379   2.3539   4.4703
-#>   -1.2421  11.0018   1.2535 -10.1757 -10.0624   4.5975 -10.5913  -5.5541
-#>   -6.7632  -9.0438   3.3362   3.3541  -7.8718  -7.5242 -13.5980   5.6186
-#>   -0.8035  -7.1194   3.7788   0.8013  -3.8723  13.9522  -7.8633   2.2792
-#>   13.8338  21.8256   3.2970  -3.9498 -10.6964   2.5834  -0.7092  -9.7314
-#>   -6.8580  -4.3072 -12.3198  -4.7211 -11.5913   0.8492  -7.2394 -14.1186
-#>   11.9156   4.7210  -0.4337  -4.2159   6.3054  -5.5326   2.0532  23.0029
-#>    4.6552  -2.2351   4.9680  -2.8215   2.8679   3.2595  -1.1829   7.9291
-#>    3.0608  -3.2694  -4.6975  -0.3494  -3.4043   1.4205   2.0749  -7.1137
-#>    0.9281   0.6515   3.0646  -1.4669   3.3878   3.2400  -1.6345  -0.2212
-#> 
-#> Columns 33 to 40   9.4695  -4.2720   5.6363  -3.0229   1.0772   4.3404   2.7501   8.8858
-#>   -0.6602  -6.7124   0.2431   9.1193 -10.9290  -5.7383   0.3658  -0.0696
-#>    4.7950  -5.0814  -4.8913 -10.7810  17.6417  -3.0259  10.1834   9.6151
-#>  -12.9637   1.7315   1.6938   0.9733   4.8371  -3.4247   5.4597  -3.4403
-#>   11.6400  -6.0170   0.6291   4.2209   8.5032 -11.3803   3.6310  -2.7111
-#>   18.9459  -0.5712  -1.3361  -2.9936  -7.7226   4.8759   3.8769  -4.6880
-#>   -6.3998   2.6967   1.3923  18.0601   2.7739   5.9172  -8.6638 -17.0653
-#>   -0.1278   1.9916  -3.3368   4.4702  -4.1882  -0.3656   0.7066 -15.3845
-#>    7.9409   5.3684  -7.0261  -2.6445  -0.9552  -8.4829  -3.8520  -2.4927
-#>   -6.1056 -10.0198   1.6371   9.4007   2.8853   0.1508 -15.6866  -5.7417
-#>   16.0239   3.5886   2.7665   3.9043 -11.8213  -0.2662  -9.6202   2.5200
-#>   22.2557 -10.0216  14.6203  -1.1390 -17.5313   8.1871   0.2831   6.0888
-#>    1.0272   0.8753   7.7316  15.5033 -12.1568  -7.3910   1.0686 -14.9231
-#>  -10.8576 -14.7991   0.4624  17.4074  -3.3230  12.4914  -1.6869  -6.4875
-#>    2.1269  -1.6013 -18.9873  -4.1625   1.2891   0.9499  10.2098  -8.4856
-#>    5.0698  -2.1100   4.8530  -5.0091  -0.4301   4.8984   1.7890   1.5331
-#>   -0.8267  -1.6853  -1.6022  -2.2362  -6.0568  11.0617   0.2674   8.6695
-#>   -0.0252   9.0893  -7.1012   0.4103  -7.2297   3.1142   4.9596  -2.1848
-#>   -3.7477  -5.8867 -15.8268   7.3226   8.2175   2.2897   1.7719  -8.0674
-#>   -5.4004  -0.0574   4.5258   0.9831   2.4997   3.1957   0.6281   5.7712
-#>   -4.2081   5.1848  -1.3553  12.6855   7.1704  -0.9142  -4.2758  -1.6022
-#>    0.9877  -2.9343  -1.7928  -5.7885  14.6740   1.5706  -5.1352   1.3063
-#>    4.1812   1.9999  -0.0702  -3.6045 -12.3558  -4.5112  13.6995  10.0074
-#>   19.6984 -15.9481  -2.8311  15.3651   4.1220  -0.2127   5.8846  -8.1123
-#>    4.0772  -9.8475  13.4586  -2.3518  -0.8136   2.3596   2.0469   3.7419
-#>   -9.5606   2.4306  10.7676 -10.6189  -7.2922   4.6915  -7.9171  -0.7868
-#>   -3.5749   1.9010   0.1177   1.1493  -5.8188  -1.8577   6.5739   0.6710
-#>   -7.3241   4.7530  -3.0966   2.7295  13.0151 -16.4036   2.9880  -4.5060
-#>   12.3952   3.8545  -4.4082   4.3266  -0.9524 -16.2677  -0.3679  -8.6198
-#>   -1.7516  -3.5273   2.3961  16.9297  -8.4036  -5.4464  -2.6408   2.6277
-#>    8.5223  -5.4636   8.0825  -4.6614   0.9806   6.6712   1.4723   6.5970
-#>   -8.9293   8.8014   4.4584   9.0321  -5.9880   0.2031   1.6938   2.3761
-#>    2.8712   1.0475  -3.0858   9.3976   3.4289  -2.6453   8.2908  -2.6215
-#> 
-#> Columns 41 to 48   1.0282  13.6111  -7.0692  -6.5561  -6.1215   4.2179 -15.8656   1.0944
-#>   -6.8862  -1.9006  -0.4087   1.5739  -1.7935  -7.1098 -11.9831  -3.2336
-#>   -8.8803   7.4065  10.8403  -8.0686  -6.7777   1.9696  -6.3820  10.2839
-#>    6.9485   3.3956   3.3277   3.0010  -5.4892   9.3176   2.4230  -8.8501
-#>   -1.8685  -6.0866  -7.0440   1.3316   0.4566 -10.7386 -12.7538   2.4884
-#>    1.0826   3.4354  -2.3901  -2.5510   5.8950 -18.2303  -2.8210 -15.3365
-#>   12.4550  -8.2105  -6.3106   9.6198   2.0183 -11.0000   7.1958 -12.3621
-#>   -6.6099  -6.4649   5.2101  -5.0436  -0.9363   1.8431   7.5087  17.3076
-#>   11.8163  -9.2981  12.9937  -7.0190   0.4832   7.5841  13.7286 -11.5256
-#>    4.6048  -7.5720  -0.0066  -1.8987   7.8794  12.4534  -2.0041 -15.7481
-#>   -6.0992   1.4299   0.5921   7.1506   3.3198   2.5087  -5.8267   5.3067
-#>   -7.0652  -5.6709   8.6364   1.6091   8.9214  -6.8446  -8.9418   2.2526
-#>   -4.1896   4.0452  -8.5550   5.1426   8.1493  -5.5370  -0.8277  20.2468
-#>    0.6847  -4.9379   5.2159  13.2771   9.5256  -5.6407   5.5542  -9.0216
-#>    1.2668 -10.6842   7.2327  -6.9216 -10.3167  -6.8236  11.4308  -6.7491
-#>  -16.9219   8.2406 -14.5830   4.0330  -4.7996  -2.9334 -18.2929 -10.6340
-#>    6.6933 -19.0212   1.3064   5.3452  19.1179  -4.2772   6.7768   6.4978
-#>   -2.2510  -1.6161  -3.3499  12.3447  -8.1975  -5.6954   0.0561   4.9685
-#>   -2.0489   4.4903  12.4049 -11.0199  -7.8862   3.5034  22.8815  -1.3020
-#>  -11.0295   7.3003   9.1650   0.5886  -5.0501  16.9966   3.6578  -1.9543
-#>  -12.0994  -8.8277  -8.1078   9.0297   0.4771 -14.6433 -13.8196   8.1879
-#>   -8.8032   8.7606  12.5391   3.2308  -3.6012  13.6578  -6.5386  11.2676
-#>    1.1408 -15.7426  -9.8566  11.0532  18.8391  -5.4709  -7.6888  -3.2865
-#>    9.8008  15.5037  -2.0344  -2.3890   6.5953   4.7175 -13.4495   5.5906
-#>   -8.6769   9.2517  -4.9664 -12.0861 -10.7764  16.0101 -19.5229  -1.9862
-#>    4.7044   0.4214  14.4001   0.7839   2.9285  -1.7922  27.9028  -4.0251
-#>    4.2268   2.6263   4.2395  -9.2513  -2.2608  -0.8726   6.1136  -7.9764
-#>   -8.6987   3.2674   1.5147   1.2253   1.7308   8.3466   3.8393   8.1229
-#>    3.5640   1.9986  -0.7368  -4.6264  -1.0220   6.6309  12.8355  23.6585
-#>    9.5552  13.7640 -17.8714  -0.0075   1.6017   6.0655 -11.4160  -1.1558
-#>   -8.3849   9.1110  -1.7346   7.3672  14.7431  -1.6898 -14.6558   5.7885
-#>    5.6095   2.8757  10.7389   9.7179  -6.1931 -11.3480   3.8011  14.7688
-#>    9.2174   6.8806  -3.9769   1.5860   5.7901   7.8094  -5.1846  -2.5478
-#> 
-#> Columns 49 to 54   2.4065 -13.3140   0.6704  -2.4768 -12.8549  -1.7246
-#>    0.2845 -18.0647  12.4616  16.8168  -3.2826  -5.1261
-#>    3.2000 -17.7489 -12.3529   4.3966   3.3123  -0.0723
-#>    5.7643   6.9179  -1.1015   0.8025   1.3063  -1.2388
-#>   -2.1739  -6.4094  -4.2746  -3.4872   1.8942   5.8524
-#>   -0.2877   8.1071  -7.1430  -2.0073  -0.5958  -0.6888
-#>   -2.2713   1.2798 -17.8757  -5.1527   4.8490   2.7732
-#>  -15.9087 -18.2601  -2.4891  -6.5220  -4.8303   1.9908
-#>    6.6733  13.3821   3.4212  -9.9961  -7.5872  -0.9345
-#>   13.2082  -5.6533  -6.0926   4.7916  -0.9141  -4.4621
-#>   -5.1544 -13.5698  -5.2283 -10.9659  -3.0882   5.9742
-#>   12.7492  -2.8567  11.5335  -9.9027  -8.9687  -0.3478
-#>   12.4571  13.0040  -9.6064 -15.1820   0.5775   4.7391
-#>    8.7508   7.6705  18.0860  12.4494  -0.2345  -5.2390
-#>    1.8744  -3.1752  -9.6094   7.4359  -3.3472  -4.7954
-#>   10.9020   9.8056   1.9755  -6.1540   1.8149  -2.7576
-#>    9.7128 -10.5345   5.5649  -0.0743   1.4130  -2.4817
-#>   -0.7271  -1.5640  11.7662   1.4352  -6.7959  -1.4346
-#>    2.9927  -5.6591  -1.1774  -1.0120  -0.9716   9.8057
-#>   -7.2276   4.0850  -0.8801   5.2085  -4.5947   4.2419
-#>    6.9749  18.1678  -2.7030 -12.8955  -7.5763  -2.7017
-#>   -1.0681 -17.7503  -5.8964  -6.9143   6.0632  -0.1159
-#>    1.6390   6.9608  -4.7435   4.3092  -2.7255  -7.1128
-#>    3.2927   4.4731   4.8954  -4.1874   1.2480   2.9463
-#>    8.6057  15.0453   2.8538   2.7735 -10.3994  -0.4426
-#>    4.0538  -6.3148   3.5170   7.2334  -4.1120  -6.4150
-#>   -0.8619  11.2716  -4.3405  -1.9576   5.3423  -2.5746
-#>  -11.5102   4.1975   6.4558 -12.8352  -8.9418   0.9216
-#>   -6.5220   5.2047   4.1922  -4.8530   7.3112  -0.0121
-#>   11.3263   9.6909 -11.5103  -4.5442  -2.5944   4.6614
-#>   -5.3980  -7.5770   7.9005  -7.3473  -2.6545  -0.8043
-#>   -1.5698   1.8889  -9.2245  -2.3570   1.1759   0.8253
-#>    1.0218   1.3002  -6.2019   3.4486   0.1029   2.7112
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    input
    NA input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)
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    NA filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)
    bias
    NA optional bias of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sH, sW). Default: 1
    padding
    NA dilation * (kernel_size - 1) - padding zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      (padH, padW). Default: 0
    output_padding
    NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple (out_padH, out_padW).      Default: 0
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    dilation
    NA the spacing between kernel elements. Can be a single number or      a tuple (dH, dW). Default: 1
    
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    conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -> Tensor 
    
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-composed of several input planes, sometimes also called "deconvolution".
    
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    .. include:: cudnn_deterministic.rst
    
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    Examples
    
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    # \dontrun{
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-# With square kernels and equal stride
-inputs = torch_randn(c(1, 4, 5, 5))
-weights = torch_randn(c(4, 8, 3, 3))
-nnf_conv_transpose2d(inputs, weights, padding=1)
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>   3.1282  4.7949 -5.5622 -1.0866  3.7899
-#>   3.4914  4.8807  4.3209  0.8437  7.1147
-#>  -5.0276  7.5935  2.4507  6.2129  4.9112
-#>   2.9302 -4.0742  1.0907  0.0252  4.4256
-#>   4.1733  6.6849 -0.1333  0.7716  1.2488
-#> 
-#> (1,2,.,.) = 
-#>    4.0475   8.1682   9.4413  -3.4628  -2.3695
-#>   -3.0556  -4.0963   5.6845   2.0032   0.2438
-#>    1.7169  -2.3353  -2.5287  -5.3750  -4.0894
-#>    1.7329  17.4464  -1.9850  -1.2224  -1.0126
-#>   -8.3888   0.5081  -5.4379  -7.7908   1.4902
-#> 
-#> (1,3,.,.) = 
-#>  -4.6889  1.3331 -3.8890 -4.2812 -1.6408
-#>  -5.7474  6.4888 -0.3864 -0.5556  3.3423
-#>  -0.5830 -4.7014  0.4339 -4.4822 -0.9338
-#>  -3.2573 -5.3475 -6.7339 -4.1705  5.4993
-#>   1.4175  5.6303 -1.1562  5.8984  3.9368
-#> 
-#> (1,4,.,.) = 
-#>   2.7974 -1.8220  1.8960 -2.4363  9.3931
-#>  -0.0791  9.0332  2.4753  6.5632 -1.9094
-#>  -0.4198 -4.7226  4.5077 -6.0814  0.9503
-#>  -0.6672  3.4472 -9.0451  1.0115 -4.7566
-#>   6.7951  6.0656  9.2166  3.3023  1.2087
-#> 
-#> (1,5,.,.) = 
-#>   4.9513 -5.1344  0.4485  2.9806  0.6510
-#>   2.6860 -0.6071  5.0654  6.0352 -0.5143
-#>   3.0599  2.8382 -1.2406 -3.1389 -6.3846
-#>  -5.3770  3.7280 -12.7695 -4.8459  3.4087
-#>   1.6425 -3.5262 -2.5308  5.2363  0.8194
-#> 
-#> (1,6,.,.) = 
-#>    1.6877  11.4338   1.1768  -0.3375  -1.5256
-#>   -3.2510  -1.9791   0.4848 -15.0722   2.7618
-#>    0.3499   4.9010  -0.0095  -6.5474  -4.1558
-#>    3.7509   8.8759   8.5394   2.6775  -2.9372
-#>   -6.3247   2.4670  -8.6403  -6.7153  -1.6774
-#> 
-#> (1,7,.,.) = 
-#>   4.5894  2.9587  3.1108  0.5861 -1.6432
-#>   0.2160 -2.0561  2.0616  0.3782  0.4569
-#>  -0.6198 -5.6256 -4.3481 -0.5680 -6.8947
-#>  -1.1064  3.6694  3.1163 -0.8316  5.3151
-#>  -2.6586 -10.2829 -6.7049  1.4970  0.0378
-#> 
-#> (1,8,.,.) = 
-#>  -0.6657 -4.0047 -1.7929 -2.8312 -5.8341
-#>   0.8324  4.9875 -2.9101  4.2271 -0.9717
-#>  -2.1073  2.4393  0.2561 -7.6416 -2.0861
-#>  -0.1300  5.5534 -0.9693  0.1627 -0.2768
-#>   6.7889 -0.3533  3.2501  3.3338 -0.3276
-#> [ CPUFloatType{1,8,5,5} ]
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    bias
    NA optional bias of shape \((\mbox{out\_channels})\). Default: None
    stride
    NA the stride of the convolving kernel. Can be a single number or a      tuple (sT, sH, sW). Default: 1
    padding
    NA dilation * (kernel_size - 1) - padding zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      (padT, padH, padW). Default: 0
    output_padding
    NA additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      (out_padT, out_padH, out_padW). Default: 0
    groups
    NA split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1
    dilation
    NA the spacing between kernel elements. Can be a single number or      a tuple (dT, dH, dW). Default: 1
    
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    .. include:: cudnn_deterministic.rst
    
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-a = torch_randn(c(4))
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    #> torch_tensor 
-#> -0.6755
-#> -0.8212
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    torch_cos(a)
    #> torch_tensor 
-#>  0.7804
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-#> [ CPUFloatType{4} ]
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    #> torch_tensor 
-#> -0.5781
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    torch_cosh(a)
    #> torch_tensor 
-#>  1.1718
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-input1 = torch_randn(c(100, 128))
-input2 = torch_randn(c(100, 128))
-output = torch_cosine_similarity(input1, input2)
-output
    #> torch_tensor 
-#>  0.0628
-#>  0.0734
-#>  0.0535
-#> -0.0163
-#> -0.0151
-#> -0.0383
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-#>  0.0039
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    (Tensor, optional) the output tensor.
    
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-and other.
    
-
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-dim dimension should be 3.
    
-
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-size 3.
    
-
-    
    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4, 3))
-a
    #> torch_tensor 
-#> -1.1643 -0.5561  0.7230
-#> -0.0220  2.0844 -0.3671
-#> -1.4814 -0.9811 -1.6675
-#>  0.4909  0.6285 -0.6379
-#> [ CPUFloatType{4,3} ]
    b = torch_randn(c(4, 3))
-b
    #> torch_tensor 
-#> -1.5896 -1.4300 -0.5647
-#> -1.4339 -1.2857  0.3288
-#> -0.9875  0.1254  0.1272
-#>  0.6789 -0.6983 -0.5560
-#> [ CPUFloatType{4,3} ]
    torch_cross(a, b, dim=2)
    #> torch_tensor 
-#>  1.3479 -1.8068  0.7810
-#>  0.2135  0.5336  3.0171
-#>  0.0842  1.8351 -1.1546
-#> -0.7949 -0.1601 -0.7695
-#> [ CPUFloatType{4,3} ]
    torch_cross(a, b)
    #> torch_tensor 
-#>  1.3479 -1.8068  0.7810
-#>  0.2135  0.5336  3.0171
-#>  0.0842  1.8351 -1.1546
-#> -0.7949 -0.1601 -0.7695
-#> [ CPUFloatType{4,3} ]
    # }
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diff --git a/docs/reference/torch_cummax.html b/docs/reference/torch_cummax.html
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    (int) the dimension to do the operation over
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    (tuple, optional) the result tuple of two output tensors (values, indices)
    
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    cummax(input, dim, out=None) -> (Tensor, LongTensor) 
    
-
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-elements of input in the dimension dim. And indices is the index
-location of each maximum value found in the dimension dim.
    
-
    $$
-    y_i = max(x_1, x_2, x_3, \dots, x_i)
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#>  0.4011
-#>  0.0282
-#>  1.4709
-#> -0.3322
-#> -1.1082
-#> -0.6219
-#> -1.1143
-#>  0.0945
-#> -0.5687
-#> -0.1941
-#> [ CPUFloatType{10} ]
    torch_cummax(a, dim=1)
    #> [[1]]
-#> torch_tensor 
-#>  0.4011
-#>  0.4011
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#>  1.4709
-#> [ CPUFloatType{10} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
-#>  2
-#>  2
-#>  2
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-#> 
    # }
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-elements of input in the dimension dim. And indices is the index
-location of each maximum value found in the dimension dim.
    
-
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-    y_i = min(x_1, x_2, x_3, \dots, x_i)
-$$
    
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#> -1.1300
-#> -0.0916
-#>  1.2476
-#>  1.1859
-#> -0.8123
-#> -1.0110
-#>  0.5914
-#>  1.0707
-#>  1.3137
-#> -0.0139
-#> [ CPUFloatType{10} ]
    torch_cummin(a, dim=1)
    #> [[1]]
-#> torch_tensor 
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> -1.1300
-#> [ CPUFloatType{10} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
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    # }
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-a vector of size N, with elements.
    
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-    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#> -1.1828
-#> -0.2114
-#>  0.2918
-#>  0.5649
-#> -0.9559
-#> -0.9795
-#> -0.7152
-#> -0.2064
-#>  0.5134
-#>  0.5777
-#> [ CPUFloatType{10} ]
    torch_cumprod(a, dim=1)
    #> torch_tensor 
-#> -1.1828
-#>  0.2500
-#>  0.0729
-#>  0.0412
-#> -0.0394
-#>  0.0386
-#> -0.0276
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-#> [ CPUFloatType{10} ]
    # }
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diff --git a/docs/reference/torch_cumsum.html b/docs/reference/torch_cumsum.html
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-dim.
    
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-a vector of size N, with elements.
    
-
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-    y_i = x_1 + x_2 + x_3 + \dots + x_i
-$$
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(10))
-a
    #> torch_tensor 
-#>  0.2718
-#>  0.1169
-#>  0.3449
-#> -1.6346
-#> -0.0393
-#> -0.0197
-#>  0.0704
-#>  0.5245
-#> -1.4307
-#> -0.8103
-#> [ CPUFloatType{10} ]
    torch_cumsum(a, dim=1)
    #> torch_tensor 
-#>  0.2718
-#>  0.3887
-#>  0.7335
-#> -0.9010
-#> -0.9403
-#> -0.9600
-#> -0.8896
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-unstable in when `input` doesn't have distinct singular values. See
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    # \dontrun{
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-A = torch_randn(c(3, 3))
-torch_det(A)
    #> torch_tensor 
-#> -0.485994
-#> [ CPUFloatType{} ]
    A = torch_randn(c(3, 2, 2))
-A
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.0024 -1.0185
-#>   0.3236 -0.2787
-#> 
-#> (2,.,.) = 
-#>   0.4021 -0.3008
-#>  -0.8884  0.5782
-#> 
-#> (3,.,.) = 
-#>  -0.3821  1.3521
-#>  -0.3608  0.3485
-#> [ CPUFloatType{3,2,2} ]
    A$det()
    #> torch_tensor 
-#>  0.3302
-#> -0.0347
-#>  0.3547
-#> [ CPUFloatType{3} ]
    # }
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    Create a Device object
    
-    Source: R/device.R
-    
-    
    
-
-    
    
-    
    A torch_device  is an object representing the device on which a torch_tensor
-is or will be allocated.
    
-    
    
-
-    
    torch_device(type, index = NULL)
    
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    type
    (character) a device type "cuda" or "cpu"
    index
    (integer) optional device ordinal for the device type.  If the device ordinal
-is not present, this object will always represent the current device for the device
-type, even after torch_cuda_set_device() is called; e.g., a torch_tensor constructed
-with device 'cuda' is equivalent to 'cuda:X' where X is the result of
-torch_cuda_current_device().
    
-
    A torch_device can be constructed via a string or via a string and device ordinal
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-# Via string
-torch_device("cuda:1")
    #> torch_device(type='cuda', index=1)
    torch_device("cpu")
    #> torch_device(type='cpu')
    torch_device("cuda") # current cuda device
    #> torch_device(type='cuda')
    
-# Via string and device ordinal
-torch_device("cuda", 0)
    #> torch_device(type='cuda', index=0)
    torch_device("cpu", 0)
    #> torch_device(type='cpu', index=0)
    
-# }
-
    
-  
    
-  
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-
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-
    
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diff --git a/docs/reference/torch_diag.html b/docs/reference/torch_diag.html
deleted file mode 100644
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+++ /dev/null
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    Diag
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Diag
    
-    
    
-
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    Arguments
    
-    
-    
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-      
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    input
    (Tensor) the input tensor.
    diagonal
    (int, optional) the diagonal to consider
    out
    (Tensor, optional) the output tensor.
    
-
-    
    diag(input, diagonal=0, out=None) -> Tensor 
    
-
-    
-
-
      
-
    • If input is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of input as the diagonal.
      • 
-
    • If input is a matrix (2-D tensor), then returns a 1-D tensor with
-the diagonal elements of input.
      • 
-
    
-
-
    The argument diagonal controls which diagonal to consider:
      
-
    • If diagonal = 0, it is the main diagonal.
      • 
-
    • If diagonal > 0, it is above the main diagonal.
      • 
-
    • If diagonal < 0, it is below the main diagonal.
      • 
-
    
-
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-  
    
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    Developed by Daniel Falbel, Javier Luraschi.
    
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diff --git a/docs/reference/torch_diag_embed.html b/docs/reference/torch_diag_embed.html
deleted file mode 100644
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--- a/docs/reference/torch_diag_embed.html
+++ /dev/null
@@ -1,272 +0,0 @@
-
-
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-  
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-Diag_embed — torch_diag_embed • torch
-
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    Diag_embed
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Diag_embed
    
-    
    
-
-
-    
    Arguments
    
-    
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-    
-      
-      
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-    
-      
-      
-    
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-      
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-    
    input
    (Tensor) the input tensor. Must be at least 1-dimensional.
    offset
    (int, optional) which diagonal to consider. Default: 0        (main diagonal).
    dim1
    (int, optional) first dimension with respect to which to        take diagonal. Default: -2.
    dim2
    (int, optional) second dimension with respect to which to        take diagonal. Default: -1.
    
-
-    
    diag_embed(input, offset=0, dim1=-2, dim2=-1) -> Tensor 
    
-
-    
-
-
-
    Creates a tensor whose diagonals of certain 2D planes (specified by
-dim1 and dim2) are filled by input.
-To facilitate creating batched diagonal matrices, the 2D planes formed by
-the last two dimensions of the returned tensor are chosen by default.
    
-
    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
    The size of the new matrix will be calculated to make the specified diagonal
-of the size of the last input dimension.
-Note that for offset other than \(0\), the order of dim1
-and dim2 matters. Exchanging them is equivalent to changing the
-sign of offset.
    
-
    Applying torch_diagonal to the output of this function with
-the same arguments yields a matrix identical to input. However,
-torch_diagonal has different default dimensions, so those
-need to be explicitly specified.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(2, 3))
-torch_diag_embed(a)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -2.3038  0.0000  0.0000
-#>   0.0000  2.0129  0.0000
-#>   0.0000  0.0000 -1.6884
-#> 
-#> (2,.,.) = 
-#>   0.8534  0.0000  0.0000
-#>   0.0000 -0.5520  0.0000
-#>   0.0000  0.0000  2.2299
-#> [ CPUFloatType{2,3,3} ]
    torch_diag_embed(a, offset=1, dim1=1, dim2=3)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0000 -2.3038  0.0000  0.0000
-#>   0.0000  0.8534  0.0000  0.0000
-#> 
-#> (2,.,.) = 
-#>   0.0000  0.0000  2.0129  0.0000
-#>   0.0000  0.0000 -0.5520  0.0000
-#> 
-#> (3,.,.) = 
-#>   0.0000  0.0000  0.0000 -1.6884
-#>   0.0000  0.0000  0.0000  2.2299
-#> 
-#> (4,.,.) = 
-#>   0  0  0  0
-#>   0  0  0  0
-#> [ CPUFloatType{4,2,4} ]
    # }
-
    
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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diff --git a/docs/reference/torch_diagflat.html b/docs/reference/torch_diagflat.html
deleted file mode 100644
index de29c4bb0..000000000
--- a/docs/reference/torch_diagflat.html
+++ /dev/null
@@ -1,253 +0,0 @@
-
-
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-  
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-Diagflat — torch_diagflat • torch
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-    
    
-    
    Diagflat
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Diagflat
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the input tensor.
    offset
    (int, optional) the diagonal to consider. Default: 0 (main        diagonal).
    
-
-    
    diagflat(input, offset=0) -> Tensor 
    
-
-    
-
-
      
-
    • If input is a vector (1-D tensor), then returns a 2-D square tensor
-with the elements of input as the diagonal.
      • 
-
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-2-D tensor with diagonal elements equal to a flattened input.
      • 
-
    
-
-
    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3))
-a
    #> torch_tensor 
-#>  1.2456
-#> -1.0479
-#>  0.2374
-#> [ CPUFloatType{3} ]
    torch_diagflat(a)
    #> torch_tensor 
-#>  1.2456  0.0000  0.0000
-#>  0.0000 -1.0479  0.0000
-#>  0.0000  0.0000  0.2374
-#> [ CPUFloatType{3,3} ]
    torch_diagflat(a, 1)
    #> torch_tensor 
-#>  0.0000  1.2456  0.0000  0.0000
-#>  0.0000  0.0000 -1.0479  0.0000
-#>  0.0000  0.0000  0.0000  0.2374
-#>  0.0000  0.0000  0.0000  0.0000
-#> [ CPUFloatType{4,4} ]
    a = torch_randn(c(2, 2))
-a
    #> torch_tensor 
-#>  0.5628 -0.2248
-#>  0.2077 -2.6745
-#> [ CPUFloatType{2,2} ]
    torch_diagflat(a)
    #> torch_tensor 
-#>  0.5628  0.0000  0.0000  0.0000
-#>  0.0000 -0.2248  0.0000  0.0000
-#>  0.0000  0.0000  0.2077  0.0000
-#>  0.0000  0.0000  0.0000 -2.6745
-#> [ CPUFloatType{4,4} ]
    # }
-
    
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diff --git a/docs/reference/torch_diagonal.html b/docs/reference/torch_diagonal.html
deleted file mode 100644
index 5a4719b5e..000000000
--- a/docs/reference/torch_diagonal.html
+++ /dev/null
@@ -1,268 +0,0 @@
-
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-Diagonal — torch_diagonal • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Diagonal
    
-    
    
-
-
-    
    Arguments
    
-    
-    
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-      
-      
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-    
-      
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    input
    (Tensor) the input tensor. Must be at least 2-dimensional.
    offset
    (int, optional) which diagonal to consider. Default: 0        (main diagonal).
    dim1
    (int, optional) first dimension with respect to which to        take diagonal. Default: 0.
    dim2
    (int, optional) second dimension with respect to which to        take diagonal. Default: 1.
    
-
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    diagonal(input, offset=0, dim1=0, dim2=1) -> Tensor 
    
-
-    
-
-
-
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-with respect to dim1 and dim2 appended as a dimension
-at the end of the shape.
    
-
    The argument offset controls which diagonal to consider:
      
-
    • If offset = 0, it is the main diagonal.
      • 
-
    • If offset > 0, it is above the main diagonal.
      • 
-
    • If offset < 0, it is below the main diagonal.
      • 
-
    
-
-
    Applying torch_diag_embed to the output of this function with
-the same arguments yields a diagonal matrix with the diagonal entries
-of the input. However, torch_diag_embed has different default
-dimensions, so those need to be explicitly specified.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.4461  1.6781  0.7912
-#> -2.0005 -0.9287 -0.1604
-#>  0.6617 -0.6939  1.5567
-#> [ CPUFloatType{3,3} ]
    torch_diagonal(a, offset = 0)
    #> torch_tensor 
-#> -0.4461
-#> -0.9287
-#>  1.5567
-#> [ CPUFloatType{3} ]
    torch_diagonal(a, offset = 1)
    #> torch_tensor 
-#>  1.6781
-#> -0.1604
-#> [ CPUFloatType{2} ]
    x = torch_randn(c(2, 5, 4, 2))
-torch_diagonal(x, offset=-1, dim1=1, dim2=2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.3670
-#>  -1.7063
-#> 
-#> (2,.,.) = 
-#>   0.6795
-#>   1.1359
-#> 
-#> (3,.,.) = 
-#>  -0.5056
-#>  -0.1993
-#> 
-#> (4,.,.) = 
-#>   0.5929
-#>  -0.6632
-#> [ CPUFloatType{4,2,1} ]
    # }
-
    
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diff --git a/docs/reference/torch_digamma.html b/docs/reference/torch_digamma.html
deleted file mode 100644
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-Digamma — torch_digamma • torch
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    Digamma
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Digamma
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the tensor to compute the digamma function on
    
-
-    
    digamma(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Computes the logarithmic derivative of the gamma function on input.
    
-
    $$
-    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_tensor(c(1, 0.5))
-torch_digamma(a)
    #> torch_tensor 
-#> -0.5772
-#> -1.9635
-#> [ CPUFloatType{2} ]
    # }
-
    
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diff --git a/docs/reference/torch_dist.html b/docs/reference/torch_dist.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-
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-    
    Dist
    
-    
    
-
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    Arguments
    
-    
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-      
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    input
    (Tensor) the input tensor.
    other
    (Tensor) the Right-hand-side input tensor
    p
    (float, optional) the norm to be computed
    
-
-    
    dist(input, other, p=2) -> Tensor 
    
-
-    
-
-
-
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-
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-broadcastable .
    
-
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-    
    # \dontrun{
-
-x = torch_randn(c(4))
-x
    #> torch_tensor 
-#>  0.3528
-#> -0.2518
-#> -0.8406
-#> -0.3756
-#> [ CPUFloatType{4} ]
    y = torch_randn(c(4))
-y
    #> torch_tensor 
-#>  0.5376
-#> -1.6162
-#> -0.8764
-#>  0.2081
-#> [ CPUFloatType{4} ]
    torch_dist(x, y, 3.5)
    #> torch_tensor 
-#> 1.38438
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 3)
    #> torch_tensor 
-#> 1.40023
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 0)
    #> torch_tensor 
-#> 4
-#> [ CPUFloatType{} ]
    torch_dist(x, y, 1)
    #> torch_tensor 
-#> 2.16879
-#> [ CPUFloatType{} ]
    # }
-
    
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-other. The resulting tensor is returned.
    
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-$$
-The shapes of input and other must be broadcastable
-. If the torch_dtype of input and
-other differ, the torch_dtype of the result tensor is determined
-following rules described in the type promotion documentation
-. If out is specified, the result must be
-castable  to the torch_dtype of the
-specified output tensor. Integral division by zero leads to undefined behavior.
    
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-perform true division like torch_true_divide.
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-instead.
    
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-out is specified, the result must be castable
-to the torch_dtype of the specified output tensor. Integral division
-by zero leads to undefined behavior.
    
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(5))
-a
    #> torch_tensor 
-#> -1.2469
-#> -0.8301
-#>  0.6777
-#>  0.4991
-#>  2.3110
-#> [ CPUFloatType{5} ]
    torch_div(a, 0.5)
    #> torch_tensor 
-#> -2.4938
-#> -1.6601
-#>  1.3554
-#>  0.9983
-#>  4.6219
-#> [ CPUFloatType{5} ]
    
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-#> -0.1627  0.0346  0.2195  0.4264
-#> -1.3478 -2.4998 -1.5522  0.7143
-#>  0.2875 -1.3762  0.8071 -0.8691
-#>  0.7893 -1.7013 -0.2038 -0.3908
-#> [ CPUFloatType{4,4} ]
    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#> -0.2924
-#>  1.8341
-#>  0.0283
-#> -0.6063
-#> [ CPUFloatType{4} ]
    torch_div(a, b)
    #> torch_tensor 
-#>   0.5566   0.0188   7.7430  -0.7033
-#>   4.6100  -1.3630 -54.7623  -1.1781
-#>  -0.9833  -0.7503  28.4751   1.4334
-#>  -2.6995  -0.9276  -7.1916   0.6445
-#> [ CPUFloatType{4,4} ]
    # }
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    dot(input, tensor) -> Tensor 
    
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    Examples
    
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    # \dontrun{
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-#> 7
-#> [ CPUFloatType{} ]
    # }
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    (Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed
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    (bool) True to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed
    out
    (tuple, optional) the output tensors
    
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    (string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after -> and gives the indices for the output.           If the -> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses ... represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.
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    # \dontrun{
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-x = torch_randn(c(5))
-y = torch_randn(c(4))
-torch_einsum('i,j->ij', list(x, y))  # outer product
    #> torch_tensor 
-#>  0.6481 -0.3038  0.7547  0.3279
-#> -0.7964  0.3732 -0.9273 -0.4029
-#>  0.2729 -0.1279  0.3178  0.1381
-#> -0.4882  0.2288 -0.5684 -0.2470
-#>  2.6219 -1.2288  3.0530  1.3266
-#> [ CPUFloatType{5,4} ]
    A = torch_randn(c(3,5,4))
-l = torch_randn(c(2,5))
-r = torch_randn(c(2,4))
-torch_einsum('bn,anm,bm->ba', list(l, A, r)) # compare torch_nn$functional$bilinear
    #> torch_tensor 
-#>  4.7791 -6.3600  2.0599
-#>  1.2294  0.6515 -3.1033
-#> [ CPUFloatType{2,3} ]
    As = torch_randn(c(3,2,5))
-Bs = torch_randn(c(3,5,4))
-torch_einsum('bij,bjk->bik', list(As, Bs)) # batch matrix multiplication
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.7724  0.1715  0.3778  0.5154
-#>  -0.7152  3.1174  0.5206 -3.0151
-#> 
-#> (2,.,.) = 
-#>  -2.1745  4.5514  0.2279  2.8187
-#>  -0.4776  1.4331 -1.1076  0.3273
-#> 
-#> (3,.,.) = 
-#>   1.0775  0.4867 -2.7086 -0.8871
-#>  -1.4834 -2.0710 -1.2663  0.2878
-#> [ CPUFloatType{3,2,4} ]
    A = torch_randn(c(3, 3))
-torch_einsum('ii->i', list(A)) # diagonal
    #> torch_tensor 
-#>  0.7209
-#>  0.7370
-#>  0.0642
-#> [ CPUFloatType{3} ]
    A = torch_randn(c(4, 3, 3))
-torch_einsum('...ii->...i', list(A)) # batch diagonal
    #> torch_tensor 
-#> -1.1323  2.2000 -0.2178
-#>  1.1550 -0.8415 -1.0067
-#>  2.1543 -0.0441 -1.3181
-#> -0.0296 -2.3285  0.7952
-#> [ CPUFloatType{4,3} ]
    A = torch_randn(c(2, 3, 4, 5))
-torch_einsum('...ij->...ji', list(A))$shape # batch permute
    #> [1] 2 3 5 4
    # }
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
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    (bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: False.
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    # \dontrun{
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    #> torch_tensor 
-#>  0.0000e+00  1.0842e-19 -2.0454e-24
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    (Tensor) the size of input will determine size of the output tensor.
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    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
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    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
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    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
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-#>  1.4057e+14  1.4057e+14  1.4057e+14
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    device
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    requires_grad
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-the tensors, please clone them first.
    
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    Examples
    
-    
    # \dontrun{
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-a = torch_empty_strided(list(2, 3), list(1, 2))
-a
    #> torch_tensor 
-#>  0.0000e+00 -4.2887e-24  2.2101e-10
-#>  1.0842e-19  2.0005e+00  4.5592e+30
-#> [ CPUFloatType{2,3} ]
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    #> [1] 1
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    #> [1] 2
    # }
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    #> torch_tensor 
-#>  1
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-#>  1.0000e+00
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-#>  0.0000
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    Arguments
    
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    input
    (Tensor) the input tensor of at least signal_ndim + 1        dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    
-
-    
    Note
    
-
-    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    fft(input, signal_ndim, normalized=False) -> Tensor 
    
-
-    
-
-
-
    Complex-to-complex Discrete Fourier Transform
    
-
    This method computes the complex-to-complex discrete Fourier transform.
-Ignoring the batch dimensions, it computes the following expression:
    
-
    $$
-    X[\omega_1, \dots, \omega_d] =
-        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = signal_ndim is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).
    
-
    This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
-by signal_ndim. input must be a tensor with last dimension
-of size 2, representing the real and imaginary components of complex
-numbers, and should have at least signal_ndim + 1 dimensions with optionally
-arbitrary number of leading batch dimensions. If normalized is set to
-True, this normalizes the result by dividing it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.
    
-
    Returns the real and the imaginary parts together as one tensor of the same
-shape of input.
    
-
    The inverse of this function is torch_ifft.
    
-    
    Warning
    
-
-    
-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-# unbatched 2D FFT
-x = torch_randn(c(4, 3, 2))
-torch_fft(x, 2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   1.6091  1.1177
-#>   1.7387  0.5245
-#>   1.3022 -7.5020
-#> 
-#> (2,.,.) = 
-#>  -2.2896  1.5947
-#>   2.3894 -3.7256
-#>   2.4421 -6.0534
-#> 
-#> (3,.,.) = 
-#>  -2.6684  0.2216
-#>   4.6351  2.1387
-#>   1.0104  2.1655
-#> 
-#> (4,.,.) = 
-#>   0.6484  1.5756
-#>   3.6644  0.9869
-#>   0.8331  1.2714
-#> [ CPUFloatType{4,3,2} ]
    # batched 1D FFT
-torch_fft(x, 1)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.6751  1.1274
-#>   3.1069 -0.0189
-#>   1.3969 -2.5296
-#> 
-#> (2,.,.) = 
-#>   1.0646 -0.5105
-#>   0.4540 -0.7223
-#>   1.9041 -2.0146
-#> 
-#> (3,.,.) = 
-#>   0.1455 -0.4577
-#>   0.0800  1.3505
-#>  -0.2407 -0.1386
-#> 
-#> (4,.,.) = 
-#>   1.0741  0.9585
-#>  -1.9022 -0.0848
-#>  -1.7582 -2.8191
-#> [ CPUFloatType{4,3,2} ]
    # arbitrary number of batch dimensions, 2D FFT
-x = torch_randn(c(3, 3, 5, 5, 2))
-torch_fft(x, 2)
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>  -5.4725 -1.0036
-#>  -0.4072 -9.9106
-#>  -6.2423 -6.4070
-#>  -1.9919 -0.2080
-#>  -1.5095 -4.7941
-#> 
-#> (2,1,1,.,.) = 
-#>   -1.3392   3.2072
-#>   -2.8398   7.8139
-#>    0.8047  -3.0656
-#>   10.5509  -0.0158
-#>    0.4441   1.4712
-#> 
-#> (3,1,1,.,.) = 
-#>   5.8889 -0.8239
-#>   0.6557 -4.5634
-#>   3.6018 -5.1202
-#>   6.7747 -1.8286
-#>  -6.4471  3.7626
-#> 
-#> (1,2,1,.,.) = 
-#>  -0.2606  1.6037
-#>   1.2933  2.7796
-#>  -1.2262 -8.3113
-#>  -2.9285 -0.8387
-#>  -8.8958  7.9346
-#> 
-#> (2,2,1,.,.) = 
-#>   -5.8665  -5.8427
-#>    3.5695  -0.4600
-#>   10.6353  -4.9917
-#>   -1.8417  -2.2605
-#>    4.0205   3.3620
-#> 
-#> (3,2,1,.,.) = 
-#>  -5.3450  5.1190
-#>  -7.1003  3.1183
-#>   2.1035 -0.7317
-#>  -1.7222  6.8806
-#>   1.1505  7.5300
-#> 
-#> (1,3,1,.,.) = 
-#>  -9.1081  7.4756
-#>   5.1849 -1.2153
-#>  -1.6891 -2.1615
-#>  -4.9926  0.1991
-#>   5.4721 -2.1878
-#> 
-#> (2,3,1,.,.) = 
-#>  -4.9459  0.0120
-#>  -5.7250 -0.8370
-#>   0.7157  1.3166
-#>  -9.2864  4.1198
-#>  -2.5485  6.5091
-#> 
-#> (3,3,1,.,.) = 
-#>   2.9661 -4.1025
-#>   5.0720  3.2858
-#>  -1.6322 -9.3722
-#>  -0.3661 -5.5639
-#>   6.5081 -2.4129
-#> 
-#> (1,1,2,.,.) = 
-#>   -2.3401   1.2643
-#>    5.3967  -7.4990
-#>    0.1779   3.4217
-#>    6.0804   6.1065
-#>   10.1270  -3.5843
-#> 
-#> (2,1,2,.,.) = 
-#>   -9.0572  -1.9909
-#>    4.2537  -0.0385
-#>    5.9323  -1.1033
-#>   -1.4235  11.0225
-#>    0.3750   3.9369
-#> 
-#> (3,1,2,.,.) = 
-#>   7.3848 -1.2410
-#>  -1.4446  1.3323
-#>  -1.4867  6.3887
-#>  -1.3989  4.3918
-#>  -1.6242  0.4001
-#> 
-#> (1,2,2,.,.) = 
-#>   2.2515 -11.7348
-#>  -2.9592 -0.4199
-#>   6.0865 -2.5877
-#>  -3.9066  0.8276
-#>  -6.4707 -0.1932
-#> 
-#> (2,2,2,.,.) = 
-#>   -3.5320 -15.9640
-#>   -3.2020  -4.1973
-#>    3.5907  -1.9048
-#>    4.8187  10.5827
-#>    2.0354   3.6088
-#> 
-#> (3,2,2,.,.) = 
-#>  -1.5620  2.4318
-#>  -4.4488 -3.6563
-#>   1.4412  6.2894
-#>   3.7142 -4.4966
-#>  -1.9539  0.9322
-#> 
-#> (1,3,2,.,.) = 
-#>   -2.5739  11.5350
-#>   -5.6638  -1.9311
-#>   -7.1538   1.8841
-#>    9.9282  -5.0425
-#>    4.0550 -13.0364
-#> 
-#> (2,3,2,.,.) = 
-#>  -4.4776 -3.1815
-#>   5.2017  7.8643
-#>  -1.9960 -3.8433
-#>   1.8730 -3.0428
-#>   0.4528  1.8324
-#> 
-#> (3,3,2,.,.) = 
-#>  -1.6393  3.0116
-#>  -7.4910  4.6567
-#>   3.9892 -1.2984
-#>   2.3925  1.7542
-#>  -0.4821  2.7867
-#> 
-#> (1,1,3,.,.) = 
-#>  -1.9416  6.9300
-#>  -5.9122  4.7269
-#>  -6.1427 -3.6378
-#>   4.8333 -7.6424
-#>  -0.8626 -7.3429
-#> 
-#> (2,1,3,.,.) = 
-#>   0.7169  7.3508
-#>  -2.2415  0.6318
-#>   8.5924 -3.5120
-#>  -1.8660 -2.9021
-#>   2.5242  6.2449
-#> 
-#> (3,1,3,.,.) = 
-#>   3.2523 -9.4747
-#>   6.7858  2.1141
-#>  -3.1627 -8.3128
-#>   8.1738  1.7333
-#>  -12.5794  7.6793
-#> 
-#> (1,2,3,.,.) = 
-#>   2.1388 -0.8053
-#>  -7.5468 -4.9134
-#>  -1.4956  0.7191
-#>   4.5485  1.3213
-#>   1.9775  3.2742
-#> 
-#> (2,2,3,.,.) = 
-#>    2.7705  -3.2113
-#>   -7.1385  10.5362
-#>    2.1267  -3.4305
-#>   -3.4051  -2.5411
-#>   -2.7076   1.7485
-#> 
-#> (3,2,3,.,.) = 
-#>    3.3394  10.9207
-#>   -0.5970  -0.5034
-#>    8.1799   3.0040
-#>   -0.9163  -3.5497
-#>   -3.1114  -5.1809
-#> 
-#> (1,3,3,.,.) = 
-#>  -4.1027 -2.0095
-#>   2.7722 -4.7394
-#>  -3.5039 -1.9563
-#>  -4.3085  8.7966
-#>   2.7449 -4.6416
-#> 
-#> (2,3,3,.,.) = 
-#>  -8.7689 -5.5072
-#>   0.0556 -7.0627
-#>   6.2366  2.7693
-#>  -0.9913  0.2007
-#>   2.4020  2.7007
-#> 
-#> (3,3,3,.,.) = 
-#>    0.1972  -1.1722
-#>    7.4460   3.4332
-#>   -4.7450 -11.9086
-#>   12.5079 -12.2708
-#>   -2.9972  -0.8924
-#> 
-#> (1,1,4,.,.) = 
-#>  -2.6641 -4.3296
-#>   3.0735  1.2215
-#>   0.2079 -4.6184
-#>  -2.3720 -1.2896
-#>  -3.9109 -9.9345
-#> 
-#> (2,1,4,.,.) = 
-#>  -5.0131 -2.2123
-#>   2.7328 -6.4654
-#>  -3.3791 -1.4795
-#>  -3.0048  4.2683
-#>   8.6069  3.8774
-#> 
-#> (3,1,4,.,.) = 
-#>   0.5565 -3.4152
-#>   3.1825 -1.2100
-#>  -5.6883 -4.5265
-#>  -2.6019  6.2445
-#>  -1.2380  0.0733
-#> 
-#> (1,2,4,.,.) = 
-#>  -0.0619 -1.4112
-#>   3.0482 -12.0317
-#>   2.3142 -5.9550
-#>   1.6332 -4.9998
-#>   3.3110  3.2442
-#> 
-#> (2,2,4,.,.) = 
-#>   5.7054 -4.5310
-#>   4.0814  0.5770
-#>  -7.4352  1.0886
-#>  -4.4217 -0.5822
-#>  -2.2274 -2.5406
-#> 
-#> (3,2,4,.,.) = 
-#>   -6.0243   1.6214
-#>    0.9687   2.5785
-#>   -9.2330  12.6507
-#>    0.1245  -3.6675
-#>    2.2133  -0.1646
-#> 
-#> (1,3,4,.,.) = 
-#>  -9.8819  5.0379
-#>  -15.9787 -0.0044
-#>  -3.3972 -4.1292
-#>  -0.2631  3.1780
-#>  -8.3382  2.3260
-#> 
-#> (2,3,4,.,.) = 
-#>   2.6493  5.7085
-#>  -0.8552 -6.9286
-#>   8.8915  1.1790
-#>  -8.0308 -2.3641
-#>  -5.5359 -0.2864
-#> 
-#> (3,3,4,.,.) = 
-#>   6.0509 -4.4619
-#>   2.5174  1.0450
-#>   3.2386 -2.9879
-#>  -4.2332 -11.7932
-#>   8.3415 -11.6524
-#> 
-#> (1,1,5,.,.) = 
-#>  -2.6344  8.4594
-#>  -9.3032 -0.0826
-#>  -0.7539  0.4355
-#>  -5.7352  6.4314
-#>   1.3710  5.0478
-#> 
-#> (2,1,5,.,.) = 
-#>   5.7266 -5.1236
-#>  -3.1017 -0.2088
-#>  -6.6610  5.8354
-#>   3.1587  1.7131
-#>   3.4059  0.6908
-#> 
-#> (3,1,5,.,.) = 
-#>   12.7228   0.6750
-#>   -4.4296   8.4994
-#>    2.5491   1.5131
-#>   -1.2545  -5.1292
-#>    8.7953   3.5627
-#> 
-#> (1,2,5,.,.) = 
-#>  -2.5561 -2.6800
-#>   3.0704  6.9316
-#>  -4.2936  0.3370
-#>  -3.2507  9.0391
-#>   7.8262 -2.8746
-#> 
-#> (2,2,5,.,.) = 
-#>  -4.7915 -2.9852
-#>   0.4093 -1.2396
-#>   1.8851  0.8607
-#>  -3.2552 -4.0731
-#>   7.0195 -3.8280
-#> 
-#> (3,2,5,.,.) = 
-#>  -6.9953 -12.4542
-#>   7.8834  1.2176
-#>   9.6941 -9.1636
-#>  -15.3205  2.1126
-#>   0.3537 -10.4525
-#> 
-#> (1,3,5,.,.) = 
-#>  -10.7714   0.7571
-#>   -0.7565  -0.4820
-#>    5.6134  -8.9826
-#>   -1.1373  -4.2921
-#>    4.1096  -1.6137
-#> 
-#> (2,3,5,.,.) = 
-#>   3.5382  6.4175
-#>  -2.8167 -1.5735
-#>   8.0241 -4.9140
-#>   1.6143  7.2625
-#>  -2.5749  4.8191
-#> 
-#> (3,3,5,.,.) = 
-#>   9.8500 -0.4376
-#>  -4.1255 -2.8707
-#>  -4.6457 -0.7456
-#>  -4.6735  2.8975
-#>   2.7895  9.0651
-#> [ CPUFloatType{3,3,5,5,2} ]
    
-# }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Flatten
    
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    input
    (Tensor) the input tensor.
    start_dim
    (int) the first dim to flatten
    end_dim
    (int) the last dim to flatten
    
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    # \dontrun{
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-t = torch_tensor(matrix(c(1, 2), ncol = 2))
-torch_flatten(t)
    #> torch_tensor 
-#>  1
-#>  2
-#> [ CPUFloatType{2} ]
    torch_flatten(t, start_dim=2)
    #> torch_tensor 
-#>  1  2
-#> [ CPUFloatType{1,2} ]
    # }
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diff --git a/docs/reference/torch_flip.html b/docs/reference/torch_flip.html
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    #> torch_tensor 
-#> (1,.,.) = 
-#>   0  1
-#>   2  3
-#> 
-#> (2,.,.) = 
-#>   4  5
-#>   6  7
-#> [ CPUFloatType{2,2,2} ]
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    #> torch_tensor 
-#> (1,.,.) = 
-#>   6  7
-#>   4  5
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-#> (2,.,.) = 
-#>   2  3
-#>   0  1
-#> [ CPUFloatType{2,2,2} ]
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.4440
-#>  0.1195
-#>  2.0850
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-#> [ CPUFloatType{4} ]
    torch_floor(a)
    #> torch_tensor 
-#>  0
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-$$
    
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    # \dontrun{
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-a = torch_tensor(c(4.0, 3.0))
-b = torch_tensor(c(2.0, 2.0))
-torch_floor_divide(a, b)
    #> torch_tensor 
-#>  2
-#>  1
-#> [ CPUFloatType{2} ]
    torch_floor_divide(a, 1.4)
    #> torch_tensor 
-#>  2
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-#> [ CPUFloatType{2} ]
    # }
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-other must be broadcastable .
    
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    Examples
    
-    
    # \dontrun{
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-torch_fmod(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
    #> torch_tensor 
-#> -1
-#> -0
-#> -1
-#>  1
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-#> [ CPUFloatType{6} ]
    torch_fmod(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
    #> torch_tensor 
-#>  1.0000
-#>  0.5000
-#>  0.0000
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-#> [ CPUFloatType{5} ]
    # }
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    Examples
    
-    
    # \dontrun{
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-torch_frac(torch_tensor(c(1, 2.5, -3.2)))
    #> torch_tensor 
-#>  0.0000
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    device
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-a bool fill_value will return a tensor of torch.bool dtype,
-and an integral fill_value will return a tensor of torch.long dtype.
    
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-    
    # \dontrun{
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-torch_full(list(2, 3), 3.141592)
    #> torch_tensor 
-#>  3.1416  3.1416  3.1416
-#>  3.1416  3.1416  3.1416
-#> [ CPUFloatType{2,3} ]
    # }
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-torch_gather(t, 2, torch_tensor(matrix(c(1,1,2,1), ncol = 2, byrow=TRUE), dtype = torch_int64()))
    #> torch_tensor 
-#>  1  1
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-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
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-# Via string
-generator <- torch_generator()
-generator$current_seed()
    #> Loading required package: bit64
    #> Loading required package: bit
    #> Attaching package bit
    #> package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
    #> creators: bit bitwhich
    #> coercion: as.logical as.integer as.bit as.bitwhich which
    #> operator: ! & | xor != ==
    #> querying: print length any all min max range sum summary
    #> bit access: length<- [ [<- [[ [[<-
    #> for more help type ?bit
    #> 
-#> Attaching package: ‘bit’
    #> The following object is masked from ‘package:base’:
-#> 
-#>     xor
    #> Attaching package bit64
    #> package:bit64 (c) 2011-2012 Jens Oehlschlaegel
    #> creators: integer64 seq :
    #> coercion: as.integer64 as.vector as.logical as.integer as.double as.character as.bin
    #> logical operator: ! & | xor != == < <= >= >
    #> arithmetic operator: + - * / %/% %% ^
    #> math: sign abs sqrt log log2 log10
    #> math: floor ceiling trunc round
    #> querying: is.integer64 is.vector [is.atomic} [length] format print str
    #> values: is.na is.nan is.finite is.infinite
    #> aggregation: any all min max range sum prod
    #> cumulation: diff cummin cummax cumsum cumprod
    #> access: length<- [ [<- [[ [[<-
    #> combine: c rep cbind rbind as.data.frame
    #> WARNING don't use as subscripts
    #> WARNING semantics differ from integer
    #> for more help type ?bit64
    #> 
-#> Attaching package: ‘bit64’
    #> The following object is masked from ‘package:bit’:
-#> 
-#>     still.identical
    #> The following objects are masked from ‘package:base’:
-#> 
-#>     :, %in%, is.double, match, order, rank
    #> integer64
-#> [1] 67280421310721
    generator$set_current_seed(1234567L)
-generator$current_seed()
    #> integer64
-#> [1] 1234567
    
-
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    (Tensor) the input matrix
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    geqrf(input, out=None) -> (Tensor, Tensor) 
    
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-
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-returns a namedtuple (a, tau) as defined in LAPACK documentation for geqrf_ .
    
-
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-
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-\(Q\) and \(R\) as explicit separate matrices.
    
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-which produces a sequence of 'elementary reflectors'.
    
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diff --git a/docs/reference/torch_ger.html b/docs/reference/torch_ger.html
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-Ger — torch_ger • torch
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    Arguments
    
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    (Tensor) 1-D input vector
    vec2
    (Tensor) 1-D input vector
    out
    (Tensor, optional) optional output matrix
    
-
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    Note
    
-
-    
    This function does not broadcast .
    
-    
    ger(input, vec2, out=None) -> Tensor 
    
-
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-
-
-
    Outer product of input and vec2.
-If input is a vector of size \(n\) and vec2 is a vector of
-size \(m\), then out must be a matrix of size \((n \times m)\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-v1 = torch_arange(1., 5.)
-v2 = torch_arange(1., 4.)
-torch_ger(v1, v2)
    #> torch_tensor 
-#>   1   2   3
-#>   2   4   6
-#>   3   6   9
-#>   4   8  12
-#> [ CPUFloatType{4,3} ]
    # }
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diff --git a/docs/reference/torch_gt.html b/docs/reference/torch_gt.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Gt
    
-    
    
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    input
    (Tensor) the tensor to compare
    other
    (Tensor or float) the tensor or value to compare
    out
    (Tensor, optional) the output tensor that must be a BoolTensor
    
-
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    gt(input, other, out=None) -> Tensor 
    
-
-    
-
-
-
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-
    The second argument can be a number or a tensor whose shape is
-broadcastable  with the first argument.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_gt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  1
-#>  0  0
-#> [ CPUBoolType{2,2} ]
    # }
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diff --git a/docs/reference/torch_hamming_window.html b/docs/reference/torch_hamming_window.html
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-Hamming_window — torch_hamming_window • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Hamming_window
    
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    window_length
    (int) the size of returned window
    periodic
    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    alpha
    (float, optional) The coefficient \(\alpha\) in the equation above
    beta
    (float, optional) The coefficient \(\beta\) in the equation above
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-
    This is a generalized version of `torch_hann_window`.
-
    
-
-    
    hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Hamming window function.
    
-
    $$
-    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_hamming_window(L, periodic=True) equal to
-torch_hamming_window(L + 1, periodic=False)[:-1]).
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-
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    Hann_window
    
-    
    
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    Arguments
    
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    window_length
    (int) the size of returned window
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    (bool, optional) If True, returns a window to be used as periodic        function. If False, return a symmetric window.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). Only floating point types are supported.
    layout
    (torch.layout, optional) the desired layout of returned window tensor. Only          torch_strided (dense layout) is supported.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    Note
    
-
-    
-
    If `window_length` \eqn{=1}, the returned window contains a single value 1.
-
    
-
-    
    hann_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Hann window function.
    
-
    $$
-    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
-            \sin^2 \left( \frac{\pi n}{N - 1} \right),
-$$
-where \(N\) is the full window size.
    
-
    The input window_length is a positive integer controlling the
-returned window size. periodic flag determines whether the returned
-window trims off the last duplicate value from the symmetric window and is
-ready to be used as a periodic window with functions like
-torch_stft. Therefore, if periodic is true, the \(N\) in
-above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
-torch_hann_window(L, periodic=True) equal to
-torch_hann_window(L + 1, periodic=False)[:-1]).
    
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diff --git a/docs/reference/torch_histc.html b/docs/reference/torch_histc.html
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    Histc
    
-    
    
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    (Tensor) the input tensor.
    bins
    (int) number of histogram bins
    min
    (int) lower end of the range (inclusive)
    max
    (int) upper end of the range (inclusive)
    out
    (Tensor, optional) the output tensor.
    
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-    
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-
-    
-
-
-
    Computes the histogram of a tensor.
    
-
    The elements are sorted into equal width bins between min and
-max. If min and max are both zero, the minimum and
-maximum values of the data are used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_histc(torch_tensor(c(1., 2, 1)), bins=4, min=0, max=3)
    #> torch_tensor 
-#>  0
-#>  2
-#>  1
-#>  0
-#> [ CPUFloatType{4} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor of at least signal_ndim + 1        dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    
-
-    
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-
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-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
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-
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-
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-transform. Ignoring the batch dimensions, it computes the following
-expression:
    
-
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-    X[\omega_1, \dots, \omega_d] =
-        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
-         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
-$$
-where \(d\) = signal_ndim is number of dimensions for the
-signal, and \(N_i\) is the size of signal dimension \(i\).
    
-
    The argument specifications are almost identical with torch_fft.
-However, if normalized is set to True, this instead returns the
-results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
-operator. Therefore, to invert a torch_fft, the normalized
-argument should be set identically for torch_fft.
    
-
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-shape of input.
    
-
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-    
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-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 3, 2))
-x
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.4097 -0.4074
-#>   0.4656  0.3306
-#>  -0.5886 -2.1190
-#> 
-#> (2,.,.) = 
-#>   0.3168 -1.2703
-#>  -0.7706 -0.4545
-#>   0.5461  1.3650
-#> 
-#> (3,.,.) = 
-#>  -0.0778 -0.2976
-#>  -0.1324  0.2705
-#>   1.3860 -0.0941
-#> [ CPUFloatType{3,3,2} ]
    y = torch_fft(x, 2)
-torch_ifft(y, 2)  # recover x
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -0.4097 -0.4074
-#>   0.4656  0.3306
-#>  -0.5886 -2.1190
-#> 
-#> (2,.,.) = 
-#>   0.3168 -1.2703
-#>  -0.7706 -0.4545
-#>   0.5461  1.3650
-#> 
-#> (3,.,.) = 
-#>  -0.0778 -0.2976
-#>  -0.1324  0.2705
-#>   1.3860 -0.0941
-#> [ CPUFloatType{3,3,2} ]
    # }
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    Imag
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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    Imag
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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    imag(input, out=None) -> Tensor 
    
-
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-
-
-
    Returns the imaginary part of the input tensor.
    
-    
    Warning
    
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-
-
    Not yet implemented.
    
-
    $$
-    \mbox{out}_{i} = imag(\mbox{input}_{i})
-$$
    
-
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    Examples
    
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diff --git a/docs/reference/torch_index_select.html b/docs/reference/torch_index_select.html
deleted file mode 100644
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    Index_select
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Index_select
    
-    
    
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    dim
    (int) the dimension in which we index
    index
    (LongTensor) the 1-D tensor containing the indices to index
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
    The returned tensor does not use the same storage as the original
-tensor.  If out has a different shape than expected, we
-silently change it to the correct shape, reallocating the underlying
-storage if necessary.
    
-    
    index_select(input, dim, index, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a new tensor which indexes the input tensor along dimension
-dim using the entries in index which is a LongTensor.
    
-
    The returned tensor has the same number of dimensions as the original tensor
-(input).  The dim\ th dimension has the same size as the length
-of index; other dimensions have the same size as in the original tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 4))
-x
    #> torch_tensor 
-#> -0.6090  0.2050 -0.2528  0.6190
-#>  0.3118  1.3114 -1.0626 -1.2048
-#>  0.3125 -0.2527  0.4871 -0.3394
-#> [ CPUFloatType{3,4} ]
    indices = torch_tensor(c(1, 3), dtype = torch_int64())
-torch_index_select(x, 1, indices)
    #> torch_tensor 
-#> -0.6090  0.2050 -0.2528  0.6190
-#>  0.3125 -0.2527  0.4871 -0.3394
-#> [ CPUFloatType{2,4} ]
    torch_index_select(x, 2, indices)
    #> torch_tensor 
-#> -0.6090 -0.2528
-#>  0.3118 -1.0626
-#>  0.3125  0.4871
-#> [ CPUFloatType{3,2} ]
    # }
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diff --git a/docs/reference/torch_inverse.html b/docs/reference/torch_inverse.html
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-Inverse — torch_inverse • torch
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    Inverse
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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    Inverse
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor of size \((*, n, n)\) where * is zero or more                    batch dimensions
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
-
    Irrespective of the original strides, the returned tensors will be
-transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
-
    
-
-    
    inverse(input, out=None) -> Tensor 
    
-
-    
-
-
-
    Takes the inverse of the square matrix input. input can be batches
-of 2D square tensors, in which case this function would return a tensor composed of
-individual inverses.
    
-
-    
    Examples
    
-    
    
-  
    
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diff --git a/docs/reference/torch_irfft.html b/docs/reference/torch_irfft.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor of at least signal_ndim + 1        dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    onesided
    (bool, optional) controls whether input was halfed to avoid        redundancy, e.g., by torch_rfft(). Default: True
    signal_sizes
    (list or torch.Size, optional) the size of the original        signal (without batch dimension). Default: None
    
-
-    
    Note
    
-
-    
-
    Due to the conjugate symmetry, `input` do not need to contain the full
-complex frequency values. Roughly half of the values will be sufficient, as
-is the case when `input` is given by [`~torch.rfft`] with
-``rfft(signal, onesided=True)``. In such case, set the `onesided`
-argument of this method to ``True``. Moreover, the original signal shape
-information can sometimes be lost, optionally set `signal_sizes` to be
-the size of the original signal (without the batch dimensions if in batched
-mode) to recover it with correct shape.
-
-Therefore, to invert an [torch_rfft()], the `normalized` and
-`onesided` arguments should be set identically for [torch_irfft()],
-and preferably a `signal_sizes` is given to avoid size mismatch. See the
-example below for a case of size mismatch.
-
-See [torch_rfft()] for details on conjugate symmetry.
-
    
-
-
    The inverse of this function is torch_rfft().
    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) -> Tensor 
    
-
-    
-
-
-
    Complex-to-real Inverse Discrete Fourier Transform
    
-
    This method computes the complex-to-real inverse discrete Fourier transform.
-It is mathematically equivalent with torch_ifft with differences only in
-formats of the input and output.
    
-
    The argument specifications are almost identical with torch_ifft.
-Similar to torch_ifft, if normalized is set to True,
-this normalizes the result by multiplying it with
-\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
-\(N_i\) is the size of signal dimension \(i\).
    
-    
    Warning
    
-
-    
-
-
    Generally speaking, input to this function should contain values
-following conjugate symmetry. Note that even if onesided is
-True, often symmetry on some part is still needed. When this
-requirement is not satisfied, the behavior of torch_irfft is
-undefined. Since torch_autograd.gradcheck estimates numerical
-Jacobian with point perturbations, torch_irfft will almost
-certainly fail the check.
    
-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(4, 4))
-torch_rfft(x, 2, onesided=TRUE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -2.6442  0.0000
-#>   3.2951 -1.0012
-#>  -10.0001  0.0000
-#> 
-#> (2,.,.) = 
-#>   1.3956 -4.9153
-#>   3.1204  7.3054
-#>  -1.9820  2.2872
-#> 
-#> (3,.,.) = 
-#>   3.3926  0.0000
-#>   5.2495  2.0603
-#>  -4.0185  0.0000
-#> 
-#> (4,.,.) = 
-#>   1.3956  4.9153
-#>   4.1712  1.2315
-#>  -1.9820 -2.2872
-#> [ CPUFloatType{4,3,2} ]
    x = torch_randn(c(4, 5))
-torch_rfft(x, 2, onesided=TRUE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   6.1729  0.0000
-#>   2.7828 -0.8086
-#>   0.0001 -0.7514
-#> 
-#> (2,.,.) = 
-#>  -0.3550  1.3322
-#>  -1.3598  3.0191
-#>   1.5158 -3.2844
-#> 
-#> (3,.,.) = 
-#>   5.1938  0.0000
-#>  -2.5545  1.6405
-#>   7.3076  5.2401
-#> 
-#> (4,.,.) = 
-#>  -0.3550 -1.3322
-#>   0.9130 -2.5817
-#>  -0.4966 -2.0859
-#> [ CPUFloatType{4,3,2} ]
    y = torch_rfft(x, 2, onesided=TRUE)
-torch_irfft(y, 2, onesided=TRUE, signal_sizes=c(4,5))  # recover x
    #> torch_tensor 
-#>  1.3437 -0.2165  0.6494  0.9663 -0.0787
-#> -0.7215  1.0848 -0.3526 -0.1508 -0.2811
-#>  1.3002 -0.5011  1.6580 -0.1709  0.7330
-#>  0.4254  1.6956 -1.7164 -0.2147  0.7211
-#> [ CPUFloatType{4,5} ]
    # }
-
    
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diff --git a/docs/reference/torch_is_complex.html b/docs/reference/torch_is_complex.html
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    Is_complex
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Is_complex
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the PyTorch tensor to test
    
-
-    
    is_complex(input) -> (bool) 
    
-
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-
-
-
    Returns True if the data type of input is a complex data type i.e.,
-one of torch_complex64, and torch.complex128.
    
-
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diff --git a/docs/reference/torch_is_floating_point.html b/docs/reference/torch_is_floating_point.html
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Is_floating_point
    
-    
    
-
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the PyTorch tensor to test
    
-
-    
    is_floating_point(input) -> (bool) 
    
-
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-
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-
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-one of torch_float64, torch.float32 and torch.float16.
    
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    Isfinite
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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-    
    Isfinite
    
-    
    
-
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    Arguments
    
-    
-    
-    
-      
-      
-    
-    
    tensor
    (Tensor) A tensor to check
    
-
-    
    TEST 
    
-
-    
-
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-
    Returns a new tensor with boolean elements representing if each element is Finite or not.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_isfinite(torch_tensor(c(1, Inf, 2, -Inf, NaN)))
    #> torch_tensor 
-#>  1
-#>  0
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{5} ]
    # }
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    Examples
    
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    # \dontrun{
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    #> torch_tensor 
-#>  0
-#>  1
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    out
    (tuple, optional) the output tuple of (Tensor, LongTensor)                           can be optionally given to be used as output buffers
    
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-
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-are the same size as input, except in the dimension dim where
-they are of size 1. Otherwise, dim is squeezed
-(see torch_squeeze), resulting in both the values and
-indices tensors having 1 fewer dimension than the input tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
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-x = torch_arange(1., 6.)
-x
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{5} ]
    torch_kthvalue(x, 4)
    #> [[1]]
-#> torch_tensor 
-#> 4
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 3
-#> [ CPULongType{} ]
-#> 
    x=torch_arange(1.,7.)$resize_(c(2,3))
-x
    #> torch_tensor 
-#>  1  2  3
-#>  4  5  6
-#> [ CPUFloatType{2,3} ]
    torch_kthvalue(x, 2, 1, TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  4  5  6
-#> [ CPUFloatType{1,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  1  1  1
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-#> 
    # }
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    Examples
    
-    
    # \dontrun{
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-torch_le(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  1  0
-#>  1  1
-#> [ CPUBoolType{2,2} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the tensor with the starting points
    end
    (Tensor) the tensor with the ending points
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-on a scalar or tensor weight and returns the resulting out tensor.
    
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-$$
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-broadcastable . If weight is a tensor, then
-the shapes of weight, start, and end must be broadcastable .
    
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    Examples
    
-    
    # \dontrun{
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-start = torch_arange(1., 5.)
-end = torch_empty(4)$fill_(10)
-start
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
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-#> [ CPUFloatType{4} ]
    end
    #> torch_tensor 
-#>  10
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-#>  10
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    torch_lerp(start, end, 0.5)
    #> torch_tensor 
-#>  5.5000
-#>  6.0000
-#>  6.5000
-#>  7.0000
-#> [ CPUFloatType{4} ]
    torch_lerp(start, end, torch_full_like(start, 0.5))
    #> torch_tensor 
-#>  5.5000
-#>  6.0000
-#>  6.5000
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-#> [ CPUFloatType{4} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Lgamma
    
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    input
    (Tensor) the input tensor.
    out
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-
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-
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-
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    Examples
    
-    
    # \dontrun{
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-torch_lgamma(a)
    #> torch_tensor 
-#>  0.5724
-#>  0.0000
-#> -0.1208
-#> [ CPUFloatType{3} ]
    # }
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    (int) number of points to sample between start        and end. Default: 100.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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    # \dontrun{
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    #> torch_tensor 
-#>   3.0000
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-#> -10
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    torch_linspace(start=-10, end=10, steps=1)
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    # }
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    # \dontrun{
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-a
    #> torch_tensor 
-#>  1.6609
-#> -1.5017
-#>  0.7439
-#> -0.6239
-#>  1.2395
-#> [ CPUFloatType{5} ]
    torch_log(a)
    #> torch_tensor 
-#>  0.5074
-#>     nan
-#> -0.2958
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-#> [ CPUFloatType{5} ]
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-of input.
    
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    Examples
    
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    # \dontrun{
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-#>  0.6619
-#>  0.0908
-#>  0.8331
-#>  0.1240
-#>  0.1908
-#> [ CPUFloatType{5} ]
    torch_log10(a)
    #> torch_tensor 
-#> -0.1792
-#> -1.0419
-#> -0.0793
-#> -0.9067
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-#> [ CPUFloatType{5} ]
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    Note
    
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    This function is more accurate than torch_log for small
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    log1p(input, out=None) -> Tensor 
    
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    # \dontrun{
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-#> -0.7431
-#> -0.9335
-#>  0.2461
-#>  0.9212
-#> -0.0972
-#> [ CPUFloatType{5} ]
    torch_log1p(a)
    #> torch_tensor 
-#> -1.3591
-#> -2.7110
-#>  0.2200
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-#> [ CPUFloatType{5} ]
    # }
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-of input.
    
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-
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    Examples
    
-    
    # \dontrun{
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-a
    #> torch_tensor 
-#>  0.5313
-#>  0.0120
-#>  0.0760
-#>  0.6910
-#>  0.9385
-#> [ CPUFloatType{5} ]
    torch_log2(a)
    #> torch_tensor 
-#> -0.9125
-#> -6.3856
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-is not invertible. In this case, double backward through `logdet` will
-be unstable in when `input` doesn't have distinct singular values. See
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    # \dontrun{
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-torch_det(A)
    #> torch_tensor 
-#> -0.779207
-#> [ CPUFloatType{} ]
    torch_logdet(A)
    #> torch_tensor 
-#> nan
-#> [ CPUFloatType{} ]
    A
    #> torch_tensor 
-#> -0.1953 -0.9165 -0.4366
-#>  0.0674  0.1296 -1.5398
-#> -0.7582 -1.0511 -0.3637
-#> [ CPUFloatType{3,3} ]
    A$det()
    #> torch_tensor 
-#> -0.779207
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-dtype. If the input tensor is not a bool tensor, zeros are treated as False and non-zeros are treated as True.
    
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    Examples
    
-    
    # \dontrun{
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-torch_logical_not(torch_tensor(c(TRUE, FALSE)))
    #> torch_tensor 
-#>  0
-#>  1
-#> [ CPUBoolType{2} ]
    torch_logical_not(torch_tensor(c(0, 1, -10), dtype=torch_int8()))
    #> torch_tensor 
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{3} ]
    torch_logical_not(torch_tensor(c(0., 1.5, -10.), dtype=torch_double()))
    #> torch_tensor 
-#>  1
-#>  0
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    # }
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    # \dontrun{
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    #> torch_tensor 
-#>  0
-#>  0
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-#> [ CPUBoolType{3} ]
    a = torch_tensor(c(0, 1, 10, 0), dtype=torch_int8())
-b = torch_tensor(c(4, 0, 1, 0), dtype=torch_int8())
-torch_logical_xor(a, b)
    #> torch_tensor 
-#>  1
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{4} ]
    torch_logical_xor(a$to(dtype=torch_double()), b$to(dtype=torch_double()))
    #> torch_tensor 
-#>  1
-#>  1
-#>  0
-#>  0
-#> [ CPUBoolType{4} ]
    torch_logical_xor(a$to(dtype=torch_double()), b)
    #> torch_tensor 
-#>  1
-#>  1
-#>  0
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-#> [ CPUBoolType{4} ]
    # }
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    base
    (float) base of the logarithm function. Default: 10.0.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-\({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).
    
-
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-    
    Examples
    
-    
    # \dontrun{
-
-torch_logspace(start=-10, end=10, steps=5)
    #> torch_tensor 
-#>  1.0000e-10
-#>  1.0000e-05
-#>  1.0000e+00
-#>  1.0000e+05
-#>  1.0000e+10
-#> [ CPUFloatType{5} ]
    torch_logspace(start=0.1, end=1.0, steps=5)
    #> torch_tensor 
-#>  1.2589
-#>  2.1135
-#>  3.5481
-#>  5.9566
-#> 10.0000
-#> [ CPUFloatType{5} ]
    torch_logspace(start=0.1, end=1.0, steps=1)
    #> torch_tensor 
-#>  1.2589
-#> [ CPUFloatType{1} ]
    torch_logspace(start=2, end=2, steps=1, base=2)
    #> torch_tensor 
-#>  4
-#> [ CPUFloatType{1} ]
    # }
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diff --git a/docs/reference/torch_logsumexp.html b/docs/reference/torch_logsumexp.html
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    (Tensor) the input tensor.
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    (int or tuple of ints) the dimension or dimensions to reduce.
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    (bool) whether the output tensor has dim retained or not.
    out
    (Tensor, optional) the output tensor.
    
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-
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-tensor in the given dimension dim. The computation is numerically
-stabilized.
    
-
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-
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-        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
-$$
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-torch_logsumexp(a, 1)
    #> torch_tensor 
-#>  1.9705
-#>  0.9976
-#>  1.5809
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_lstsq.html b/docs/reference/torch_lstsq.html
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-size \((m \times k)\).
    
-
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-
    $$
-   \begin{array}{ll}
-   \min_X & \|AX-B\|_2.
-   \end{array}
-$$
-If \(m < n\), torch_lstsq() solves the least-norm problem:
    
-
    $$
-   \begin{array}{llll}
-   \min_X & \|X\|_2 & \mbox{subject to} & AX = B.
-   \end{array}
-$$
-Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
-rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
-for the solution in each column is given by the sum of squares of elements in the
-remaining \(m - n\) rows of that column.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_tensor(rbind(
- c(1,1,1),
- c(2,3,4),
- c(3,5,2),
- c(4,2,5),
- c(5,4,3)
-))
-B = torch_tensor(rbind(
- c(-10, -3),
- c(12, 14),
- c(14, 12),
- c(16, 16),
- c(18, 16)
-))
-out = torch_lstsq(B, A)
-out[[1]]
    #> torch_tensor 
-#>   2.0000   1.0000
-#>   1.0000   1.0000
-#>   1.0000   2.0000
-#>  10.9635   4.8501
-#>   8.9332   5.2418
-#> [ CPUFloatType{5,2} ]
    # }
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    # \dontrun{
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-torch_lt(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(c(1,1,4,4), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  0
-#>  1  0
-#> [ CPUBoolType{2,2} ]
    # }
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diff --git a/docs/reference/torch_lu.html b/docs/reference/torch_lu.html
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-tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
-is set to True.
    
-    
    
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    (Tensor) the tensor to factor of size (, m, n)(,m,n)
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    get_infos
    (bool, optional) – if set to True, returns an info IntTensor. Default: FALSE
    out
    (tuple, optional) – optional output tuple. If get_infos is True, then the elements
-in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
-elements in the tuple are Tensor, IntTensor. Default: NULL
    
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    Examples
    
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    # \dontrun{
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-A = torch_randn(c(2, 3, 3))
-torch_lu(A)
    #> [[1]]
-#> torch_tensor 
-#> (1,.,.) = 
-#>   1.5274 -0.8578 -1.5278
-#>  -0.5446  0.3682 -0.1468
-#>   0.6892  0.4103  1.5956
-#> 
-#> (2,.,.) = 
-#>  -1.3121 -0.1896 -2.3469
-#>  -0.7969  1.0315 -0.9561
-#>   0.7149 -0.0635  1.4525
-#> [ CPUFloatType{2,3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3  2  3
-#>  3  2  3
-#> [ CPUIntType{2,3} ]
-#> 
    
-# }
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    b
    (Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.
    LU_data
    (Tensor) the pivoted LU factorization of A from torch_lu of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.
    LU_pivots
    (IntTensor) the pivots of the LU factorization from torch_lu of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of LU_pivots must be equal to the batch dimensions of                           LU_data.
    out
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-
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-LU factorization of A from torch_lu.
    
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    Examples
    
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    # \dontrun{
-A = torch_randn(c(2, 3, 3))
-b = torch_randn(c(2, 3, 1))
-out = torch_lu(A)
-x = torch_lu_solve(b, out[[1]], out[[2]])
-torch_norm(torch_bmm(A, x) - b)
    #> torch_tensor 
-#> 7.02886e-08
-#> [ CPUFloatType{} ]
    # }
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    (Tensor) the input tensor.
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-x
    #> torch_tensor 
-#>  0.1475 -0.7929 -0.3681  1.0487
-#> -1.1304  1.8525 -0.8021  0.2346
-#>  1.8222  0.7533  0.4753  0.3604
-#> [ CPUFloatType{3,4} ]
    mask = x$ge(0.5)
-mask
    #> torch_tensor 
-#>  0  0  0  1
-#>  0  1  0  0
-#>  1  1  0  0
-#> [ CPUBoolType{3,4} ]
    torch_masked_select(x, mask)
    #> torch_tensor 
-#>  1.0487
-#>  1.8525
-#>  1.8222
-#>  0.7533
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_matmul.html b/docs/reference/torch_matmul.html
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    Matmul
    
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    Arguments
    
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    input
    (Tensor) the first tensor to be multiplied
    other
    (Tensor) the second tensor to be multiplied
    out
    (Tensor, optional) the output tensor.
    
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    Note
    
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    The 1-dimensional dot product version of this function does not support an `out` parameter.
-
    
-
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    matmul(input, other, out=None) -> Tensor 
    
-
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    Matrix product of two tensors.
    
-
    The behavior depends on the dimensionality of the tensors as follows:
      
-
    • If both tensors are 1-dimensional, the dot product (scalar) is returned.
      • 
-
    • If both arguments are 2-dimensional, the matrix-matrix product is returned.
      • 
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-a 1 is prepended to its dimension for the purpose of the matrix multiply.
-After the matrix multiply, the prepended dimension is removed.
      • 
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    • If the first argument is 2-dimensional and the second argument is 1-dimensional,
-the matrix-vector product is returned.
      • 
-
    • If both arguments are at least 1-dimensional and at least one argument is
-N-dimensional (where N > 2), then a batched matrix multiply is returned.  If the first
-argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
-batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
-1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
-The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
-must be broadcastable).  For example, if input is a
-\((j \times 1 \times n \times m)\) tensor and other is a \((k \times m \times p)\)
-tensor, out will be an \((j \times k \times n \times p)\) tensor.
      • 
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-
-# vector x vector
-tensor1 = torch_randn(c(3))
-tensor2 = torch_randn(c(3))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> -2.01064
-#> [ CPUFloatType{} ]
    # matrix x vector
-tensor1 = torch_randn(c(3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#>  0.8498
-#> -0.1725
-#> -0.2413
-#> [ CPUFloatType{3} ]
    # batched matrix x broadcasted vector
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> -0.3051  0.8335  0.7817
-#> -1.7290 -0.1315 -1.0013
-#> -2.2799 -2.0186 -1.7212
-#> -2.0462  2.6530  2.2152
-#>  1.0726 -1.8564 -0.0805
-#>  2.5083  0.1464 -1.9015
-#>  0.1432  1.3745  0.8548
-#> -0.5524  0.7222  0.1316
-#> -1.5975  0.3861 -2.5685
-#> -3.1016  2.3700  0.1975
-#> [ CPUFloatType{10,3} ]
    # batched matrix x batched matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(10, 4, 5))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   2.7383  3.7776  1.1984 -0.0462  5.9024
-#>   0.1247 -2.9988  0.8401 -1.2394 -2.9948
-#>   3.0933  3.5992  3.5134 -3.3567  6.0899
-#> 
-#> (2,.,.) = 
-#>  -0.9786 -0.3967 -0.3440  0.3824 -1.2635
-#>   0.4944  0.7149  0.5734 -1.1592  0.6260
-#>   1.3268  0.8352  1.9060 -3.7034  0.1600
-#> 
-#> (3,.,.) = 
-#>   0.4321  0.9564  1.6369 -0.1843 -0.3877
-#>  -1.4475 -2.3336 -2.1501 -1.6377  3.0016
-#>  -1.9856  0.2134  1.8436  1.6812  1.5892
-#> 
-#> (4,.,.) = 
-#>   0.7306 -0.1213  0.6505 -2.7818 -0.1171
-#>  -1.4915  3.2909  0.5003  2.5146  1.3735
-#>   0.0837 -0.1117  0.8951  3.6226  1.1665
-#> 
-#> (5,.,.) = 
-#>  -1.9671  0.1609  2.2518  0.7587  2.6455
-#>  -0.5965 -1.3687 -2.4585 -1.6623 -3.6542
-#>   0.0899  1.6690  0.2659 -2.6994  2.8543
-#> 
-#> (6,.,.) = 
-#>  -4.5552  0.7339 -2.3152 -5.9254 -1.6420
-#>  -0.8628  0.0829 -0.6564  0.9928 -0.0438
-#>  -4.1770  0.7927 -2.3129 -4.7196 -0.5457
-#> 
-#> (7,.,.) = 
-#>   1.3550 -0.9541 -1.7768  2.5931  1.6567
-#>   4.4144  1.4763  1.1692 -2.8514 -1.1695
-#>   4.2236  0.0365 -0.3523  1.0364  0.0855
-#> 
-#> (8,.,.) = 
-#>  -3.3315  1.4609 -1.3792 -2.9510 -0.7443
-#>   1.3569 -2.2547  0.7054 -2.1291  5.2801
-#>  -0.0102  1.3483 -0.3866  2.5474 -3.7250
-#> 
-#> (9,.,.) = 
-#>   0.8332  2.8945 -1.7215  1.0412  1.7159
-#>  -1.0112 -5.9806  1.3106  3.5058 -2.5003
-#>   0.3979  0.5793 -0.9296 -2.5669 -3.1548
-#> 
-#> (10,.,.) = 
-#>   1.3155 -0.3605  0.2988 -0.0092 -0.7717
-#>   0.6723  1.2307 -1.8691  0.1663 -1.7657
-#>  -1.2338  0.4575  0.0627  1.7217  2.0104
-#> [ CPUFloatType{10,3,5} ]
    # batched matrix x broadcasted matrix
-tensor1 = torch_randn(c(10, 3, 4))
-tensor2 = torch_randn(c(4, 5))
-torch_matmul(tensor1, tensor2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  -5.1244  2.2971 -1.6275  2.7803 -4.4830
-#>   1.8873 -3.6560  1.6412  0.0507  0.1216
-#>  -0.5433  1.9666 -1.4803 -0.5111  0.0638
-#> 
-#> (2,.,.) = 
-#>   0.0601 -4.0015  2.7077  1.7992 -1.7779
-#>  -1.6433  1.4184 -1.3365  0.5739 -1.9782
-#>  -3.4216  6.9532 -3.3050 -0.2836  0.0183
-#> 
-#> (3,.,.) = 
-#>  -1.8677 -0.3711  0.3040  1.6625 -2.7921
-#>   3.2992 -0.5994 -1.6079 -2.2177 -1.6740
-#>  -3.7891  4.2637  2.9312  2.2233  0.8338
-#> 
-#> (4,.,.) = 
-#>  -2.1277  3.2238  0.0312  0.5080  1.3242
-#>   2.2424 -0.8814  0.2856 -1.2700  1.1283
-#>  -1.0811  2.6808 -0.7240 -0.2231  1.0720
-#> 
-#> (5,.,.) = 
-#>  -3.4333  2.0806 -0.9037  1.4697 -0.1231
-#>   2.3334 -3.4847  1.1505 -0.3664 -0.2367
-#>   1.2297 -2.4053  2.6418  0.6376 -1.1939
-#> 
-#> (6,.,.) = 
-#>  -2.2066  0.7770  1.6664  1.7391 -0.3200
-#>  -2.7109 -1.6160 -1.9334  1.4483  0.3787
-#>  -1.2449 -0.9002 -1.0765  0.8913 -1.9713
-#> 
-#> (7,.,.) = 
-#>   0.2789 -1.0134 -0.5889  0.0808 -1.8249
-#>   0.6334 -0.8853  2.2413  0.6094 -1.5061
-#>  -0.1358  0.5410  0.6620  0.1202  0.3539
-#> 
-#> (8,.,.) = 
-#>   0.5331  0.8902  1.7452 -0.4616  3.8193
-#>   0.4330  2.0916 -0.6593 -0.9295  0.1894
-#>   0.8105 -0.3216  0.7608 -0.4534  2.0104
-#> 
-#> (9,.,.) = 
-#>   2.2947  1.8882  2.8289 -1.2676  2.0636
-#>  -0.6974 -2.0088 -1.8360  0.1831  1.3406
-#>   4.7011 -3.7311  0.0230 -1.9564 -1.7259
-#> 
-#> (10,.,.) = 
-#>   2.7739 -3.8386 -1.8082 -1.4422 -0.0495
-#>   1.3869 -3.8928  0.8670  0.1969  0.0855
-#>  -3.0783  1.1996 -2.8824  0.9550 -0.9141
-#> [ CPUFloatType{10,3,5} ]
    # }
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diff --git a/docs/reference/torch_matrix_power.html b/docs/reference/torch_matrix_power.html
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    Matrix_power
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Matrix_power
    
-    
    
-
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    Arguments
    
-    
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    input
    (Tensor) the input tensor.
    n
    (int) the power to raise the matrix to
    
-
-    
    matrix_power(input, n) -> Tensor 
    
-
-    
-
-
-
    Returns the matrix raised to the power n for square matrices.
-For batch of matrices, each individual matrix is raised to the power n.
    
-
    If n is negative, then the inverse of the matrix (if invertible) is
-raised to the power n.  For a batch of matrices, the batched inverse
-(if invertible) is raised to the power n. If n is 0, then an identity matrix
-is returned.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(2, 2, 2))
-a
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0.0871  0.0197
-#>   0.3185  0.4297
-#> 
-#> (2,.,.) = 
-#>  -0.7818 -0.6402
-#>   0.2400 -0.4367
-#> [ CPUFloatType{2,2,2} ]
    torch_matrix_power(a, 3)
    #> torch_tensor 
-#> (1,.,.) = 
-#>  0.01 *
-#>   0.4446  0.4644
-#>    7.5134  8.5264
-#> 
-#> (2,.,.) = 
-#>  -0.1705 -0.6335
-#>   0.2375  0.1711
-#> [ CPUFloatType{2,2,2} ]
    # }
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diff --git a/docs/reference/torch_matrix_rank.html b/docs/reference/torch_matrix_rank.html
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    (Tensor) the input 2-D tensor
    tol
    (float, optional) the tolerance value. Default: None
    symmetric
    (bool, optional) indicates whether input is symmetric.                               Default: False
    
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-matrix rank is done using SVD by default. If symmetric is True,
-then input is assumed to be symmetric, and the computation of the
-rank is done by obtaining the eigenvalues.
    
-
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-when symmetric is True) are considered to be 0. If tol is not
-specified, tol is set to S.max() * max(S.size()) * eps where S is the
-singular values (or the eigenvalues when symmetric is True), and eps
-is the epsilon value for the datatype of input.
    
-
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    Examples
    
-    
    # \dontrun{
-
-a = torch_eye(10)
-torch_matrix_rank(a)
    #> torch_tensor 
-#> 10
-#> [ CPULongType{} ]
    # }
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diff --git a/docs/reference/torch_max.html b/docs/reference/torch_max.html
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    out
    (tuple, optional) the result tuple of two output tensors (max, max_indices)
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-
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-
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-element of the tensor other and an element-wise maximum is taken.
    
-
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-but they must be broadcastable .
    
-
    $$
-    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.7404  0.4095  0.0815
-#> [ CPUFloatType{1,3} ]
    torch_max(a)
    #> torch_tensor 
-#> 0.409483
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.4374  0.4921  0.5690 -0.5727
-#> -0.7344  1.4273  0.3648  1.0731
-#>  0.4967  0.2687  0.5694 -0.4233
-#>  0.2623  0.0037 -1.2246 -0.3742
-#> [ CPUFloatType{4,4} ]
    torch_max(a, dim = 1)
    #> [[1]]
-#> torch_tensor 
-#>  0.4967
-#>  1.4273
-#>  0.5694
-#>  1.0731
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  2
-#>  3
-#>  2
-#> [ CPULongType{4} ]
-#> 
    
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.0640
-#> -0.2356
-#> -0.5395
-#>  1.8484
-#> [ CPUFloatType{4} ]
    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#>  0.4095
-#> -0.6778
-#> -0.0908
-#> -0.2021
-#> [ CPUFloatType{4} ]
    torch_max(a, other = b)
    #> torch_tensor 
-#>  0.4095
-#> -0.2356
-#> -0.0908
-#>  1.8484
-#> [ CPUFloatType{4} ]
    # }
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-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
-    
    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.0395  1.7826  1.2161
-#> [ CPUFloatType{1,3} ]
    torch_mean(a)
    #> torch_tensor 
-#> 0.986383
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.0216 -1.8383 -0.8037 -1.2803
-#>  1.6988 -1.2344 -0.5559  0.7407
-#> -1.5668  0.8250 -0.0814  0.6922
-#> -0.7400 -0.0428  0.7179 -0.2121
-#> [ CPUFloatType{4,4} ]
    torch_mean(a, 1)
    #> torch_tensor 
-#> -0.1466
-#> -0.5726
-#> -0.1808
-#> -0.0149
-#> [ CPUFloatType{4} ]
    torch_mean(a, 1, TRUE)
    #> torch_tensor 
-#> -0.1466 -0.5726 -0.1808 -0.0149
-#> [ CPUFloatType{1,4} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Median
    
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    Arguments
    
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    (Tensor) the input tensor.
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    (bool) whether the output tensor has dim retained or not.
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    (tuple, optional) the result tuple of two output tensors (max, max_indices)
    
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    median(input) -> Tensor 
    
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-value of each row of the input tensor in the given dimension
-dim. And indices is the index location of each median value found.
    
-
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-
    If keepdim is True, the output tensors are of the same size
-as input except in the dimension dim where they are of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in
-the outputs tensor having 1 fewer dimension than input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.1294  0.8996  0.0937
-#> [ CPUFloatType{1,3} ]
    torch_median(a)
    #> torch_tensor 
-#> 0.0937234
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 5))
-a
    #> torch_tensor 
-#>  0.2865 -0.8226  0.6805  0.3636 -0.6890
-#>  0.8853  0.3427  0.8220 -2.2562 -1.8976
-#> -1.3180  0.3580  1.1346 -0.5496 -0.2493
-#> -1.1359  0.0354 -0.3702 -0.0126  1.0450
-#> [ CPUFloatType{4,5} ]
    torch_median(a, 1)
    #> [[1]]
-#> torch_tensor 
-#> -1.1359
-#>  0.0354
-#>  0.6805
-#> -0.5496
-#> -0.6890
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  3
-#>  0
-#>  2
-#>  0
-#> [ CPULongType{5} ]
-#> 
    # }
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-    Source: R/memory_format.R
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    # \dontrun{
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-x = torch_tensor(c(1, 2, 3))
-y = torch_tensor(c(4, 5, 6))
-out = torch_meshgrid(list(x, y))
-out
    #> [[1]]
-#> torch_tensor 
-#>  1  1  1
-#>  2  2  2
-#>  3  3  3
-#> [ CPUFloatType{3,3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  4  5  6
-#>  4  5  6
-#>  4  5  6
-#> [ CPUFloatType{3,3} ]
-#> 
    # }
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    (Tensor) the input tensor.
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    (bool) whether the output tensor has dim retained or not.
    out
    (tuple, optional) the tuple of two output tensors (min, min_indices)
    other
    (Tensor) the second input tensor
    
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-value of each row of the input tensor in the given dimension
-dim. And indices is the index location of each minimum value found
-(argmin).
    
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    Warning
    
-
-    
-
-
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-minimal value found, unless it is unique.
-The exact implementation details are device-specific.
-Do not expect the same result when run on CPU and GPU in general.
    
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-input except in the dimension dim where they are of size 1.
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-the output tensors having 1 fewer dimension than input.
    
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    min(input, other, out=None) -> Tensor 
    
-
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-
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-element of the tensor other and an element-wise minimum is taken.
-The resulting tensor is returned.
    
-
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-but they must be broadcastable .
    
-
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-    \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
-$$
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -1.0189  1.0439  1.3884
-#> [ CPUFloatType{1,3} ]
    torch_min(a)
    #> torch_tensor 
-#> -1.01891
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.9733  2.4571  1.7912 -1.4290
-#>  0.5607 -0.5847 -0.4779 -0.7823
-#> -0.7391  0.6672 -0.9647  0.1703
-#> -0.5473 -0.2047 -0.1148  1.4254
-#> [ CPUFloatType{4,4} ]
    torch_min(a, dim = 1)
    #> [[1]]
-#> torch_tensor 
-#> -0.7391
-#> -0.5847
-#> -0.9647
-#> -1.4290
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3
-#>  2
-#>  3
-#>  1
-#> [ CPULongType{4} ]
-#> 
    
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.2877
-#> -1.1317
-#> -1.0846
-#>  0.0735
-#> [ CPUFloatType{4} ]
    b = torch_randn(c(4))
-b
    #> torch_tensor 
-#> -1.2118
-#> -0.7290
-#> -0.8948
-#>  0.5896
-#> [ CPUFloatType{4} ]
    torch_min(a, other = b)
    #> torch_tensor 
-#> -1.2118
-#> -1.1317
-#> -1.0846
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-#> [ CPUFloatType{4} ]
    # }
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    (Tensor) the first matrix to be multiplied
    mat2
    (Tensor) the second matrix to be multiplied
    out
    (Tensor, optional) the output tensor.
    
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-
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-    
    # \dontrun{
-
-mat1 = torch_randn(c(2, 3))
-mat2 = torch_randn(c(3, 3))
-torch_mm(mat1, mat2)
    #> torch_tensor 
-#> -4.2557 -0.6099 -0.8782
-#> -3.0933  0.3112 -0.9391
-#> [ CPUFloatType{2,3} ]
    # }
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-in the output tensors having 1 fewer dimension than input.
    
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    # \dontrun{
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-a
    #> torch_tensor 
-#>  18
-#>   0
-#>   7
-#>   4
-#>  19
-#> [ CPUFloatType{5} ]
    torch_mode(a, 1)
    #> [[1]]
-#> torch_tensor 
-#> 0
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 1
-#> [ CPULongType{} ]
-#> 
    # }
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diff --git a/docs/reference/torch_mul.html b/docs/reference/torch_mul.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Mul
    
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    input
    NA
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    (Number) the number to be multiplied to each element of input
    out
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    input
    (Tensor) the first multiplicand tensor
    other
    (Tensor) the second multiplicand tensor
    out
    (Tensor, optional) the output tensor.
    
-
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    mul(input, other, out=None) 
    
-
-    
-
-
-
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-other and returns a new resulting tensor.
    
-
    $$
-    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
-$$
-If input is of type FloatTensor or DoubleTensor, other
-should be a real number, otherwise it should be an integer
    
-
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-
    Each element of the tensor input is multiplied by the corresponding
-element of the Tensor other. The resulting tensor is returned.
    
-
    The shapes of input and other must be
-broadcastable .
    
-
    $$
-    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3))
-a
    #> torch_tensor 
-#>  0.7353
-#>  0.3087
-#>  0.8232
-#> [ CPUFloatType{3} ]
    torch_mul(a, 100)
    #> torch_tensor 
-#>  73.5282
-#>  30.8688
-#>  82.3200
-#> [ CPUFloatType{3} ]
    
-
-a = torch_randn(c(4, 1))
-a
    #> torch_tensor 
-#>  0.1683
-#>  0.6845
-#>  1.4773
-#>  1.1179
-#> [ CPUFloatType{4,1} ]
    b = torch_randn(c(1, 4))
-b
    #> torch_tensor 
-#> -1.4203  0.6324 -0.8087 -0.5061
-#> [ CPUFloatType{1,4} ]
    torch_mul(a, b)
    #> torch_tensor 
-#> -0.2390  0.1064 -0.1361 -0.0852
-#> -0.9722  0.4329 -0.5535 -0.3464
-#> -2.0981  0.9343 -1.1946 -0.7476
-#> -1.5877  0.7070 -0.9040 -0.5657
-#> [ CPUFloatType{4,4} ]
    # }
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diff --git a/docs/reference/torch_multinomial.html b/docs/reference/torch_multinomial.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor containing probabilities
    num_samples
    (int) number of samples to draw
    replacement
    (bool, optional) whether to draw with replacement or not
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
-
    The rows of `input` do not need to sum to one (in which case we use
-the values as weights), but must be non-negative, finite and have
-a non-zero sum.
-
    
-
-
    Indices are ordered from left to right according to when each was sampled
-(first samples are placed in first column).
    
-
    If input is a vector, out is a vector of size num_samples.
    
-
    If input is a matrix with m rows, out is an matrix of shape
-\((m \times \mbox{num\_samples})\).
    
-
    If replacement is True, samples are drawn with replacement.
    
-
    If not, they are drawn without replacement, which means that when a
-sample index is drawn for a row, it cannot be drawn again for that row.
    
-
    When drawn without replacement, `num_samples` must be lower than
-number of non-zero elements in `input` (or the min number of non-zero
-elements in each row of `input` if it is a matrix).
-
    
-
-    
    multinomial(input, num_samples, replacement=False, *, generator=None, out=None) -> LongTensor 
    
-
-    
-
-
-
    Returns a tensor where each row contains num_samples indices sampled
-from the multinomial probability distribution located in the corresponding row
-of tensor input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-weights = torch_tensor(c(0, 10, 3, 0), dtype=torch_float()) # create a tensor of weights
-torch_multinomial(weights, 2)
    #> torch_tensor 
-#>  1
-#>  2
-#> [ CPULongType{2} ]
    torch_multinomial(weights, 4, replacement=TRUE)
    #> torch_tensor 
-#>  1
-#>  2
-#>  1
-#>  2
-#> [ CPULongType{4} ]
    # }
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diff --git a/docs/reference/torch_mv.html b/docs/reference/torch_mv.html
deleted file mode 100644
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-Mv — torch_mv • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Mv
    
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    input
    (Tensor) matrix to be multiplied
    vec
    (Tensor) vector to be multiplied
    out
    (Tensor, optional) the output tensor.
    
-
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    Note
    
-
-    
    This function does not broadcast .
    
-    
    mv(input, vec, out=None) -> Tensor 
    
-
-    
-
-
-
    Performs a matrix-vector product of the matrix input and the vector
-vec.
    
-
    If input is a \((n \times m)\) tensor, vec is a 1-D tensor of
-size \(m\), out will be 1-D of size \(n\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-mat = torch_randn(c(2, 3))
-vec = torch_randn(c(3))
-torch_mv(mat, vec)
    #> torch_tensor 
-#> -0.9277
-#>  1.8568
-#> [ CPUFloatType{2} ]
    # }
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deleted file mode 100644
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-Mvlgamma — torch_mvlgamma • torch
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Mvlgamma
    
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    (Tensor) the tensor to compute the multivariate log-gamma function
    p
    (int) the number of dimensions
    
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    mvlgamma(input, p) -> Tensor 
    
-
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    Computes the multivariate log-gamma function <https://en.wikipedia.org/wiki/Multivariate_gamma_function>_) with dimension
-\(p\) element-wise, given by
    
-
    $$
-    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
-$$
-where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.
    
-
    All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_empty(c(2, 3))$uniform_(1, 2)
-a
    #> torch_tensor 
-#>  1.2019  1.8425  1.1256
-#>  1.9082  1.8734  1.5464
-#> [ CPUFloatType{2,3} ]
    torch_mvlgamma(a, 2)
    #> torch_tensor 
-#>  0.7450  0.3997  0.8720
-#>  0.4162  0.4065  0.4292
-#> [ CPUFloatType{2,3} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Narrow
    
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    Arguments
    
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    (Tensor) the tensor to narrow
    dim
    (int) the dimension along which to narrow
    start
    (int) the starting dimension
    length
    (int) the distance to the ending dimension
    
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    narrow(input, dim, start, length) -> Tensor 
    
-
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-
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-dimension dim is input from start to start + length. The
-returned tensor and input tensor share the same underlying storage.
    
-
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    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(matrix(c(1:9), ncol = 3, byrow= TRUE))
-torch_narrow(x, 1, torch_tensor(0L)$sum(dim = 1), 2)
    #> torch_tensor 
-#>  1  2  3
-#>  4  5  6
-#> [ CPUIntType{2,3} ]
    torch_narrow(x, 2, torch_tensor(1L)$sum(dim = 1), 2)
    #> torch_tensor 
-#>  2  3
-#>  5  6
-#>  8  9
-#> [ CPUIntType{3,2} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the tensor to compare
    other
    (Tensor or float) the tensor or value to compare
    out
    (Tensor, optional) the output tensor that must be a BoolTensor
    
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    ne(input, other, out=None) -> Tensor 
    
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-
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-broadcastable  with the first argument.
    
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    Examples
    
-    
    # \dontrun{
-
-torch_ne(torch_tensor(matrix(1:4, ncol = 2, byrow=TRUE)),
-         torch_tensor(matrix(rep(c(1,4), each = 2), ncol = 2, byrow=TRUE)))
    #> torch_tensor 
-#>  0  1
-#>  1  0
-#> [ CPUBoolType{2,2} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Neg
    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
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-
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-$$
    
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    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5))
-a
    #> torch_tensor 
-#>  0.3160
-#> -0.4731
-#>  0.1641
-#>  0.6355
-#>  0.2480
-#> [ CPUFloatType{5} ]
    torch_neg(a)
    #> torch_tensor 
-#> -0.3160
-#>  0.4731
-#> -0.1641
-#> -0.6355
-#> -0.2480
-#> [ CPUFloatType{5} ]
    # }
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    input
    (Tensor) the input tensor.
    out
    (LongTensor, optional) the output tensor containing indices
    
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    Note
    
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-
    [`torch_nonzero(..., as_tuple=False) &lt;torch.nonzero&gt;`] (default) returns a
-2-D tensor where each row is the index for a nonzero value.
-
-[`torch_nonzero(..., as_tuple=True) &lt;torch.nonzero&gt;`] returns a tuple of 1-D
-index tensors, allowing for advanced indexing, so ``x[x.nonzero(as_tuple=True)]``
-gives all nonzero values of tensor ``x``. Of the returned tuple, each index tensor
-contains nonzero indices for a certain dimension.
-
-See below for more details on the two behaviors.
-
    
-
-    
    nonzero(input, *, out=None, as_tuple=False) -> LongTensor or tuple of LongTensors 
    
-
-    
-
-
-
    When as_tuple is False (default):
    
-
    Returns a tensor containing the indices of all non-zero elements of
-input.  Each row in the result contains the indices of a non-zero
-element in input. The result is sorted lexicographically, with
-the last index changing the fastest (C-style).
    
-
    If input has \(n\) dimensions, then the resulting indices tensor
-out is of size \((z \times n)\), where \(z\) is the total number of
-non-zero elements in the input tensor.
    
-
    When as_tuple is True:
    
-
    Returns a tuple of 1-D tensors, one for each dimension in input,
-each containing the indices (in that dimension) of all non-zero elements of
-input .
    
-
    If input has \(n\) dimensions, then the resulting tuple contains \(n\)
-tensors of size \(z\), where \(z\) is the total number of
-non-zero elements in the input tensor.
    
-
    As a special case, when input has zero dimensions and a nonzero scalar
-value, it is treated as a one-dimensional tensor with one element.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_nonzero(torch_tensor(c(1, 1, 1, 0, 1)))
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  4
-#> [ CPULongType{4,1} ]
    # }
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diff --git a/docs/reference/torch_norm.html b/docs/reference/torch_norm.html
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    (int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: 'fro'        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        None   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is None  sum(abs(x)ord)(1./ord)        =====  ============================  ==========================
    dim
    (int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is None, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.
    keepdim
    (bool, optional) whether the output tensors have dim        retained or not. Ignored if dim = None and        out = None. Default: False
    out
    (Tensor, optional) the output tensor. Ignored if        dim = None and out = None.
    dtype
    (torch.dtype, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: None.
    
-
-    
    TEST 
    
-
-    
-
-
-
    Returns the matrix norm or vector norm of a given tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_arange(0, 9, dtype = torch_float())
-b = a$reshape(list(3, 3))
-torch_norm(a)
    #> torch_tensor 
-#> 14.2829
-#> [ CPUFloatType{} ]
    torch_norm(b)
    #> torch_tensor 
-#> 14.2829
-#> [ CPUFloatType{} ]
    torch_norm(a, Inf)
    #> torch_tensor 
-#> 8
-#> [ CPUFloatType{} ]
    torch_norm(b, Inf)
    #> torch_tensor 
-#> 8
-#> [ CPUFloatType{} ]
    
-# }
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diff --git a/docs/reference/torch_normal.html b/docs/reference/torch_normal.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    mean
    (Tensor) the tensor of per-element means
    std
    (Tensor) the tensor of per-element standard deviations
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    size
    (int...) a sequence of integers defining the shape of the output tensor.
    
-
-    
    Note
    
-
-    
    When the shapes do not match, the shape of mean
-is used as the shape for the returned output tensor
    
-    
    normal(mean, std, *, generator=None, out=None) -> Tensor 
    
-
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-
-
-
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-whose mean and standard deviation are given.
    
-
    The mean is a tensor with the mean of
-each output element's normal distribution
    
-
    The std is a tensor with the standard deviation of
-each output element's normal distribution
    
-
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-total number of elements in each tensor need to be the same.
    
-    
    normal(mean=0.0, std, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the means are shared among all drawn
-elements.
    
-    
    normal(mean, std=1.0, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the standard-deviations are shared among
-all drawn elements.
    
-    
    normal(mean, std, size, *, out=None) -> Tensor 
    
-
-    
-
-
-
    Similar to the function above, but the means and standard deviations are shared
-among all drawn elements. The resulting tensor has size given by size.
    
-
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    Examples
    
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
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    device
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    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
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    ones(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
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-
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-by the variable argument size.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_ones(c(2, 3))
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{2,3} ]
    torch_ones(c(5))
    #> torch_tensor 
-#>  1
-#>  1
-#>  1
-#>  1
-#>  1
-#> [ CPUFloatType{5} ]
    # }
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diff --git a/docs/reference/torch_ones_like.html b/docs/reference/torch_ones_like.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Ones_like
    
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    (Tensor) the size of input will determine size of the output tensor.
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    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
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-
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-
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-torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
-    
    Warning
    
-
-    
-
-
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-the old torch_ones_like(input, out=output) is equivalent to
-torch_ones(input.size(), out=output).
    
-
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    Examples
    
-    
    # \dontrun{
-
-input = torch_empty(c(2, 3))
-torch_ones_like(input)
    #> torch_tensor 
-#>  1  1  1
-#>  1  1  1
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_orgqr.html b/docs/reference/torch_orgqr.html
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-
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-tuple returned by torch_geqrf.
    
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    input2
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    input3
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-
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-
-
-
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    NA input tensor of shape \(N \times M\).
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    NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).
    
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-if the rows are contiguous.
    
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-equivalent to scipy.spatial.distance.pdist(input, 'hamming') * M.
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    rcond
    (float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15
    
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    This method is implemented using the Singular Value Decomposition.
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    The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
-Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
-However, this method is backprop-able due to the implementation by using SVD results, and
-could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
-See `~torch.svd` for more details.
-
    
-
-    
    pinverse(input, rcond=1e-15) -> Tensor 
    
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    Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
-Please look at Moore-Penrose inverse_ for more details
    
-
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    Examples
    
-    
    # \dontrun{
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-input = torch_randn(c(3, 5))
-input
    #> torch_tensor 
-#>  0.0625  0.0470 -0.6356  1.0166 -0.2998
-#>  0.2736 -0.5027  2.6768  0.3714  0.5533
-#> -0.5951  1.2603  0.2886  0.6099 -1.3339
-#> [ CPUFloatType{3,5} ]
    torch_pinverse(input)
    #> torch_tensor 
-#>  0.1974  0.0598 -0.1754
-#> -0.2446 -0.0864  0.3409
-#> -0.1949  0.3106  0.1489
-#>  0.8721  0.2108 -0.0062
-#>  0.0375  0.0552 -0.3200
-#> [ CPUFloatType{5,3} ]
    # Batched pinverse example
-a = torch_randn(c(2,6,3))
-b = torch_pinverse(a)
-torch_matmul(b, a)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   1.0000e+00  5.2154e-08 -1.0431e-07
-#>   2.9802e-08  1.0000e+00  3.7253e-08
-#>   2.9802e-08 -6.7055e-08  1.0000e+00
-#> 
-#> (2,.,.) = 
-#>   1.0000e+00  2.3283e-08  1.9372e-07
-#>   2.9802e-08  1.0000e+00 -5.9605e-07
-#>   2.9802e-08  1.5087e-07  1.0000e+00
-#> [ CPUFloatType{2,3,3} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Pixel_shuffle
    
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    (Tensor) the input tensor
    upscale_factor
    (int) factor to increase spatial resolution by
    
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    Rearranges elements in a tensor of shape 
    
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    math:(*, C \times r^2, H, W) to a :
    
-
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-tensor of shape \((*, C, H \times r, W \times r)\).
    
-
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-
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    Examples
    
-    
    # \dontrun{
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-input = torch_randn(c(1, 9, 4, 4))
-output = nnf_pixel_shuffle(input, 3)
-print(output$size())
    #> [1]  1  1 12 12
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-element in input i.e.,
    
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-rates = torch_rand(c(4, 4)) * 5  # rate parameter between 0 and 5
-torch_poisson(rates)
    #> torch_tensor 
-#>  1  4  0  4
-#>  6  0  4  2
-#>  1  0  1  0
-#>  1  3  3  4
-#> [ CPUFloatType{4,4} ]
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    n
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    out
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-
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-$$
    
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.6638
-#>  0.2351
-#> -0.1040
-#>  0.7775
-#> [ CPUFloatType{4} ]
    torch_pow(a, 2)
    #> torch_tensor 
-#>  0.4406
-#>  0.0553
-#>  0.0108
-#>  0.6045
-#> [ CPUFloatType{4} ]
    exp = torch_arange(1., 5.)
-a = torch_arange(1., 5.)
-a
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{4} ]
    exp
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{4} ]
    torch_pow(a, exp)
    #> torch_tensor 
-#>    1
-#>    4
-#>   27
-#>  256
-#> [ CPUFloatType{4} ]
    
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-exp = torch_arange(1., 5.)
-base = 2
-torch_pow(base, exp)
    #> torch_tensor 
-#>   2
-#>   4
-#>   8
-#>  16
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    # }
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    (Tensor) the input tensor.
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    (torch.dtype, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to dtype before the operation        is performed. This is useful for preventing data type overflows. Default: None.
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    # \dontrun{
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-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  0.0090  0.8878  1.0236
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    torch_prod(a)
    #> torch_tensor 
-#> 0.00817587
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-a
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-#> -1.1330  0.8404
-#>  2.0557  0.2876
-#>  2.0148  1.2245
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-#> [ CPUFloatType{4,2} ]
    torch_prod(a, 1)
    #> torch_tensor 
-#> -1.9012
-#>  0.0653
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    # }
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-    
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    #> torch_Float
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    #> torch_Long
    # }
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    input
    (Tensor) the input tensor of size \((*, m, n)\) where * is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).
    some
    (bool, optional) Set to True for reduced QR decomposition and False for                complete QR decomposition.
    out
    (tuple, optional) tuple of Q and R tensors                satisfying input = torch.matmul(Q, R).                The dimensions of Q and R are \((*, m, k)\) and \((*, k, n)\)                respectively, where \(k = \min(m, n)\) if some: is True and                \(k = m\) otherwise.
    
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    Note
    
-
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    precision may be lost if the magnitudes of the elements of input
-are large
    
-
    While it should always give you a valid decomposition, it may not
-give you the same one across platforms - it will depend on your
-LAPACK implementation.
    
-    
    qr(input, some=True, out=None) -> (Tensor, Tensor) 
    
-
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-
-
-
    Computes the QR decomposition of a matrix or a batch of matrices input,
-and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
-with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
-\(R\) being an upper triangular matrix or batch of upper triangular matrices.
    
-
    If some is True, then this function returns the thin (reduced) QR factorization.
-Otherwise, if some is False, this function returns the complete QR factorization.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_tensor(matrix(c(12., -51, 4, 6, 167, -68, -4, 24, -41), ncol = 3, byrow = TRUE))
-out = torch_qr(a)
-q = out[[1]]
-r = out[[2]]
-torch_mm(q, r)$round()
    #> torch_tensor 
-#>   12  -51    4
-#>    6  167  -68
-#>   -4   24  -41
-#> [ CPUFloatType{3,3} ]
    torch_mm(q$t(), q)$round()
    #> torch_tensor 
-#>  1  0  0
-#>  0  1 -0
-#>  0 -0  1
-#> [ CPUFloatType{3,3} ]
    # }
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    Creates the corresponding Scheme object
    
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    torch_per_channel_affine()
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diff --git a/docs/reference/torch_quantize_per_channel.html b/docs/reference/torch_quantize_per_channel.html
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    Quantize_per_channel
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Quantize_per_channel
    
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    Arguments
    
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    input
    (Tensor) float tensor to quantize
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    (Tensor) float 1D tensor of scales to use, size should match input.size(axis)
    zero_points
    (int) integer 1D tensor of offset to use, size should match input.size(axis)
    axis
    (int) dimension on which apply per-channel quantization
    dtype
    (torch.dtype) the desired data type of returned tensor.        Has to be one of the quantized dtypes: torch_quint8, torch.qint8, torch.qint32
    
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    quantize_per_channel(input, scales, zero_points, axis, dtype) -> Tensor 
    
-
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-
    Converts a float tensor to per-channel quantized tensor with given scales and zero points.
    
-
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    Examples
    
-    
    # \dontrun{
-x = torch_tensor(matrix(c(-1.0, 0.0, 1.0, 2.0), ncol = 2, byrow = TRUE))
-torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())
    #> torch_tensor 
-#> -1  0
-#>  1  2
-#> [ CPUFloatType{2,2} ]
    torch_quantize_per_channel(x, torch_tensor(c(0.1, 0.01)),
-                           torch_tensor(c(10L, 0L)), 0, torch_quint8())$int_repr()
    #> torch_tensor 
-#>    0   10
-#>  100  200
-#> [ CPUByteType{2,2} ]
    # }
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diff --git a/docs/reference/torch_quantize_per_tensor.html b/docs/reference/torch_quantize_per_tensor.html
deleted file mode 100644
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    Quantize_per_tensor
    
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    input
    (Tensor) float tensor to quantize
    scale
    (float) scale to apply in quantization formula
    zero_point
    (int) offset in integer value that maps to float zero
    dtype
    (torch.dtype) the desired data type of returned tensor.        Has to be one of the quantized dtypes: torch_quint8, torch.qint8, torch.qint32
    
-
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    quantize_per_tensor(input, scale, zero_point, dtype) -> Tensor 
    
-
-    
-
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-
    Converts a float tensor to quantized tensor with given scale and zero point.
    
-
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    Examples
    
-    
    # \dontrun{
-torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())
    #> torch_tensor 
-#> -1
-#>  0
-#>  1
-#>  2
-#> [ CPUFloatType{4} ]
    torch_quantize_per_tensor(torch_tensor(c(-1.0, 0.0, 1.0, 2.0)), 0.1, 10, torch_quint8())$int_repr()
    #> torch_tensor 
-#>   0
-#>  10
-#>  20
-#>  30
-#> [ CPUByteType{4} ]
    # }
-
    
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diff --git a/docs/reference/torch_rand.html b/docs/reference/torch_rand.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Rand
    
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    Arguments
    
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    size
    (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    rand(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
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    Returns a tensor filled with random numbers from a uniform distribution
-on the interval \([0, 1)\)
    
-
    The shape of the tensor is defined by the variable argument size.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_rand(4)
    #> torch_tensor 
-#>  0.8391
-#>  0.5766
-#>  0.5790
-#>  0.0523
-#> [ CPUFloatType{4} ]
    torch_rand(c(2, 3))
    #> torch_tensor 
-#>  0.9712  0.5669  0.1881
-#>  0.4962  0.3052  0.8577
-#> [ CPUFloatType{2,3} ]
    # }
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diff --git a/docs/reference/torch_rand_like.html b/docs/reference/torch_rand_like.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the size of input will determine size of the output tensor.
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    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
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    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
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-
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-
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-random numbers from a uniform distribution on the interval \([0, 1)\).
-torch_rand_like(input) is equivalent to
-torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
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diff --git a/docs/reference/torch_randint.html b/docs/reference/torch_randint.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Randint
    
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    (int, optional) Lowest integer to be drawn from the distribution. Default: 0.
    high
    (int) One above the highest integer to be drawn from the distribution.
    size
    (tuple) a tuple defining the shape of the output tensor.
    generator
    (torch.Generator, optional) a pseudorandom number generator for sampling
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-between low (inclusive) and high (exclusive).
    
-
    The shape of the tensor is defined by the variable argument size.
    
-
    .. note:
-With the global dtype default (torch_float32), this function returns
-a tensor with dtype torch_int64.
    
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    # \dontrun{
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-torch_randint(3, 5, list(3))
    #> torch_tensor 
-#>  4
-#>  3
-#>  3
-#> [ CPUFloatType{3} ]
    torch_randint(0, 10, size = list(2, 2))
    #> torch_tensor 
-#>  0  7
-#>  8  6
-#> [ CPUFloatType{2,2} ]
    torch_randint(3, 10, list(2, 2))
    #> torch_tensor 
-#>  8  8
-#>  3  5
-#> [ CPUFloatType{2,2} ]
    # }
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    low
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    (int) One above the highest integer to be drawn from the distribution.
    dtype
    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
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-high (exclusive).
    
-
    .. note:
-With the global dtype default (torch_float32), this function returns
-a tensor with dtype torch_int64.
    
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diff --git a/docs/reference/torch_randn.html b/docs/reference/torch_randn.html
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    size
    (int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.
    out
    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    randn(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor filled with random numbers from a normal distribution
-with mean 0 and variance 1 (also called the standard normal
-distribution).
    
-
    $$
-    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
-$$
-The shape of the tensor is defined by the variable argument size.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_randn(c(4))
    #> torch_tensor 
-#> -0.5578
-#> -1.6968
-#> -0.0944
-#> -0.7900
-#> [ CPUFloatType{4} ]
    torch_randn(c(2, 3))
    #> torch_tensor 
-#> -2.1279  1.0919  2.2659
-#>  0.1722  0.1719 -1.1738
-#> [ CPUFloatType{2,3} ]
    # }
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    Randn_like
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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    Randn_like
    
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    input
    (Tensor) the size of input will determine size of the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned Tensor.        Default: if None, defaults to the dtype of input.
    layout
    (torch.layout, optional) the desired layout of returned tensor.        Default: if None, defaults to the layout of input.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, defaults to the device of input.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    memory_format
    (torch.memory_format, optional) the desired memory format of        returned Tensor. Default: torch_preserve_format.
    
-
-    
    randn_like(input, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor with the same size as input that is filled with
-random numbers from a normal distribution with mean 0 and variance 1.
-torch_randn_like(input) is equivalent to
-torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).
    
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diff --git a/docs/reference/torch_randperm.html b/docs/reference/torch_randperm.html
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    (Tensor, optional) the output tensor.
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    layout
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    requires_grad
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    randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) -> LongTensor 
    
-
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-
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    Examples
    
-    
    # \dontrun{
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    #> torch_tensor 
-#>  0
-#>  2
-#>  1
-#>  3
-#> [ CPULongType{4} ]
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    (Tensor, optional) the output tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type). If dtype is not given, infer the data type from the other input        arguments. If any of start, end, or stop are floating-point, the        dtype is inferred to be the default dtype, see        ~torch.get_default_dtype. Otherwise, the dtype is inferred to        be torch.int64.
    layout
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    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
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    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
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-
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    Examples
    
-    
    # \dontrun{
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-torch_range(1, 4)
    #> Warning: This function is deprecated in favor of torch_arange.
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{3} ]
    torch_range(1, 4, 0.5)
    #> Warning: This function is deprecated in favor of torch_arange.
    #> torch_tensor 
-#>  1.0000
-#>  1.5000
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diff --git a/docs/reference/torch_reciprocal.html b/docs/reference/torch_reciprocal.html
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.6585
-#>  0.2569
-#>  1.4761
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    torch_reciprocal(a)
    #> torch_tensor 
-#> -1.5185
-#>  3.8925
-#>  0.6775
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-#> [ CPUFloatType{4} ]
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    (Tensor) the dividend
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    (Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend
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    (Tensor, optional) the output tensor.
    
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-    
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-
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-
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-
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-numbers. The remainder has the same sign as the divisor.
    
-
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-other must be broadcastable .
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-torch_remainder(torch_tensor(c(-3., -2, -1, 1, 2, 3)), 2)
    #> torch_tensor 
-#>  1
-#>  0
-#>  1
-#>  1
-#>  0
-#>  1
-#> [ CPUFloatType{6} ]
    torch_remainder(torch_tensor(c(1., 2, 3, 4, 5)), 1.5)
    #> torch_tensor 
-#>  1.0000
-#>  0.5000
-#>  0.0000
-#>  1.0000
-#>  0.5000
-#> [ CPUFloatType{5} ]
    # }
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    (Tensor) the input tensor.
    p
    (float) the power for the norm computation
    dim
    (int) the dimension to slice over to get the sub-tensors
    maxnorm
    (float) the maximum norm to keep each sub-tensor under
    out
    (Tensor, optional) the output tensor.
    
-
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    Note
    
-
-    
    If the norm of a row is lower than maxnorm, the row is unchanged
    
-    
    renorm(input, p, dim, maxnorm, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor where each sub-tensor of input along dimension
-dim is normalized such that the p-norm of the sub-tensor is lower
-than the value maxnorm
    
-
-    
    Examples
    
-    
    # \dontrun{
-x = torch_ones(c(3, 3))
-x[2,]$fill_(2)
    #> torch_tensor 
-#>  2
-#>  2
-#>  2
-#> [ CPUFloatType{3} ]
    x[3,]$fill_(3)
    #> torch_tensor 
-#>  3
-#>  3
-#>  3
-#> [ CPUFloatType{3} ]
    x
    #> torch_tensor 
-#>  1  1  1
-#>  2  2  2
-#>  3  3  3
-#> [ CPUFloatType{3,3} ]
    torch_renorm(x, 1, 1, 5)
    #> torch_tensor 
-#>  1.0000  1.0000  1.0000
-#>  1.6667  1.6667  1.6667
-#>  1.6667  1.6667  1.6667
-#> [ CPUFloatType{3,3} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Repeat_interleave
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    repeats
    (Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.
    dim
    (int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.
    
-
-    
    repeat_interleave(input, repeats, dim=None) -> Tensor 
    
-
-    
-
-
-
    Repeat elements of a tensor.
    
-    
    Warning
    
-
-    
-
    This is different from `torch_Tensor.repeat` but similar to ``numpy.repeat``.
-
    
-
-    
    repeat_interleave(repeats) -> Tensor 
    
-
-    
-
-
-
    If the repeats is tensor([n1, n2, n3, ...]), then the output will be
-tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...]) where 0 appears n1 times,
-1 appears n2 times, 2 appears n3 times, etc.
    
-
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    Examples
    
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    Reshape
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Reshape
    
-    
    
-
-
-    
    Arguments
    
-    
-    
-    
-      
-      
-    
-    
-      
-      
-    
-    
    input
    (Tensor) the tensor to be reshaped
    shape
    (tuple of ints) the new shape
    
-
-    
    reshape(input, shape) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor with the same data and number of elements as input,
-but with the specified shape. When possible, the returned tensor will be a view
-of input. Otherwise, it will be a copy. Contiguous inputs and inputs
-with compatible strides can be reshaped without copying, but you should not
-depend on the copying vs. viewing behavior.
    
-
    See torch_Tensor.view on when it is possible to return a view.
    
-
    A single dimension may be -1, in which case it's inferred from the remaining
-dimensions and the number of elements in input.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_arange(0, 4)
-torch_reshape(a, list(2, 2))
    #> torch_tensor 
-#>  0  1
-#>  2  3
-#> [ CPUFloatType{2,2} ]
    b = torch_tensor(matrix(c(0, 1, 2, 3), ncol = 2, byrow=TRUE))
-torch_reshape(b, list(-1))
    #> torch_tensor 
-#>  0
-#>  1
-#>  2
-#>  3
-#> [ CPUFloatType{4} ]
    # }
-
    
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diff --git a/docs/reference/torch_result_type.html b/docs/reference/torch_result_type.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Result_type
    
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    tensor1
    (Tensor or Number) an input tensor or number
    tensor2
    (Tensor or Number) an input tensor or number
    
-
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    result_type(tensor1, tensor2) -> dtype 
    
-
-    
-
-
-
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-operation on the provided input tensors. See type promotion documentation
-for more information on the type promotion logic.
    
-
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    Examples
    
-    
    # \dontrun{
-
-torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_int()), 1.0)
    #> torch_Float
    # torch_result_type(tensor = torch_tensor(c(1, 2), dtype=torch_uint8()), torch_tensor(1))
-# }
-
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor of at least signal_ndim dimensions
    signal_ndim
    (int) the number of dimensions in each signal.        signal_ndim can only be 1, 2 or 3
    normalized
    (bool, optional) controls whether to return normalized results.        Default: False
    onesided
    (bool, optional) controls whether to return half of results to        avoid redundancy. Default: True
    
-
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    Note
    
-
-    
-
    For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
-repeatedly running FFT methods on tensors of same geometry with same
-configuration. See cufft-plan-cache for more details on how to
-monitor and control the cache.
-
    
-
-    
    rfft(input, signal_ndim, normalized=False, onesided=True) -> Tensor 
    
-
-    
-
-
-
    Real-to-complex Discrete Fourier Transform
    
-
    This method computes the real-to-complex discrete Fourier transform. It is
-mathematically equivalent with torch_fft with differences only in
-formats of the input and output.
    
-
    This method supports 1D, 2D and 3D real-to-complex transforms, indicated
-by signal_ndim. input must be a tensor with at least
-signal_ndim dimensions with optionally arbitrary number of leading batch
-dimensions. If normalized is set to True, this normalizes the result
-by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
-unitary, where \(N_i\) is the size of signal dimension \(i\).
    
-
    The real-to-complex Fourier transform results follow conjugate symmetry:
    
-
    $$
-    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
-$$
-where the index arithmetic is computed modulus the size of the corresponding
-dimension, \(\ ^*\) is the conjugate operator, and
-\(d\) = signal_ndim. onesided flag controls whether to avoid
-redundancy in the output results. If set to True (default), the output will
-not be full complex result of shape \((*, 2)\), where \(*\) is the shape
-of input, but instead the last dimension will be halfed as of size
-\(\lfloor \frac{N_d}{2} \rfloor + 1\).
    
-
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-    
    Warning
    
-
-    
-
-
    For CPU tensors, this method is currently only available with MKL. Use
-torch_backends.mkl.is_available to check if MKL is installed.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(5, 5))
-torch_rfft(x, 2)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   4.9202  0.0000
-#>   2.9093  2.7036
-#>   1.8227 -4.8232
-#> 
-#> (2,.,.) = 
-#>  -2.0160  4.4730
-#>   1.8297 -3.0301
-#>   0.4639  0.6830
-#> 
-#> (3,.,.) = 
-#>  -6.8659  1.3907
-#>  -5.6917 -4.3527
-#>   2.7115  0.4562
-#> 
-#> (4,.,.) = 
-#>  -6.8659 -1.3907
-#>  -3.4394 -1.4155
-#>   4.7454 -1.6532
-#> 
-#> (5,.,.) = 
-#>  -2.0160 -4.4730
-#>  -1.0448  5.6595
-#>   5.7855 -3.2515
-#> [ CPUFloatType{5,3,2} ]
    torch_rfft(x, 2, onesided=FALSE)
    #> torch_tensor 
-#> (1,.,.) = 
-#>   4.9202  0.0000
-#>   2.9093  2.7036
-#>   1.8227 -4.8232
-#>   1.8227  4.8232
-#>   2.9093 -2.7036
-#> 
-#> (2,.,.) = 
-#>  -2.0160  4.4730
-#>   1.8297 -3.0301
-#>   0.4639  0.6830
-#>   5.7855  3.2515
-#>  -1.0448 -5.6595
-#> 
-#> (3,.,.) = 
-#>  -6.8659  1.3907
-#>  -5.6917 -4.3527
-#>   2.7115  0.4562
-#>   4.7454  1.6532
-#>  -3.4394  1.4155
-#> 
-#> (4,.,.) = 
-#>  -6.8659 -1.3907
-#>  -3.4394 -1.4155
-#>   4.7454 -1.6532
-#>   2.7115 -0.4562
-#>  -5.6917  4.3527
-#> 
-#> (5,.,.) = 
-#>  -2.0160 -4.4730
-#>  -1.0448  5.6595
-#>   5.7855 -3.2515
-#>   0.4639 -0.6830
-#>   1.8297  3.0301
-#> [ CPUFloatType{5,5,2} ]
    # }
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diff --git a/docs/reference/torch_roll.html b/docs/reference/torch_roll.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Roll
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    shifts
    (int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value
    dims
    (int or tuple of ints) Axis along which to roll
    
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    roll(input, shifts, dims=None) -> Tensor 
    
-
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-
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-
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-last position are re-introduced at the first position. If a dimension is not
-specified, the tensor will be flattened before rolling and then restored
-to the original shape.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(c(1, 2, 3, 4, 5, 6, 7, 8))$view(c(4, 2))
-x
    #> torch_tensor 
-#>  1  2
-#>  3  4
-#>  5  6
-#>  7  8
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, 1, 1)
    #> torch_tensor 
-#>  7  8
-#>  1  2
-#>  3  4
-#>  5  6
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, -1, 1)
    #> torch_tensor 
-#>  3  4
-#>  5  6
-#>  7  8
-#>  1  2
-#> [ CPUFloatType{4,2} ]
    torch_roll(x, shifts=list(2, 1), dims=list(1, 2))
    #> torch_tensor 
-#>  6  5
-#>  8  7
-#>  2  1
-#>  4  3
-#> [ CPUFloatType{4,2} ]
    # }
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diff --git a/docs/reference/torch_rot90.html b/docs/reference/torch_rot90.html
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    Examples
    
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    # \dontrun{
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    #> torch_tensor 
-#>  0  1
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-#> [ CPUFloatType{2,2} ]
    torch_rot90(x, 1, c(1, 2))
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-#>  1  3
-#>  0  2
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    x = torch_arange(0, 8)$view(c(2, 2, 2))
-x
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-#> (1,.,.) = 
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-#> [ CPUFloatType{2,2,2} ]
    torch_rot90(x, 1, c(1, 2))
    #> torch_tensor 
-#> (1,.,.) = 
-#>   2  3
-#>   6  7
-#> 
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-#>  0.8911
-#> -0.2131
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-#> [ CPUFloatType{4} ]
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    Examples
    
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-#>  0.5826
-#> -0.3344
-#> -0.2779
-#> -1.1775
-#> [ CPUFloatType{4} ]
    torch_rsqrt(a)
    #> torch_tensor 
-#>  1.3101
-#>     nan
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-#>  2.1600
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    Sign
    
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-#>  0.8228
-#> -0.3671
-#> -0.6658
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-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_slogdet.html b/docs/reference/torch_slogdet.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Slogdet
    
-    
    
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    Arguments
    
-    
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-      
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    input
    (Tensor) the input tensor of size (*, n, n) where * is zero or more                batch dimensions.
    
-
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    Note
    
-
-    
-
    If ``input`` has zero determinant, this returns ``(0, -inf)``.
-
    
-
-
    Backward through `slogdet` internally uses SVD results when `input`
-is not invertible. In this case, double backward through `slogdet`
-will be unstable in when `input` doesn't have distinct singular values.
-See `~torch.svd` for details.
-
    
-
-    
    slogdet(input) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_randn(c(3, 3))
-A
    #> torch_tensor 
-#>  0.0461 -1.3909  0.9825
-#>  0.5340  0.3877 -0.2309
-#>  0.3683  1.6290  0.3208
-#> [ CPUFloatType{3,3} ]
    torch_det(A)
    #> torch_tensor 
-#> 1.094
-#> [ CPUFloatType{} ]
    torch_logdet(A)
    #> torch_tensor 
-#> 0.0898445
-#> [ CPUFloatType{} ]
    torch_slogdet(A)
    #> [[1]]
-#> torch_tensor 
-#> 1
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.0898445
-#> [ CPUFloatType{} ]
-#> 
    # }
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diff --git a/docs/reference/torch_solve.html b/docs/reference/torch_solve.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.
    A
    (Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.
    out
    ((Tensor, Tensor) optional output tuple.
    
-
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-`solution` and `LU` will be transposed, i.e. with strides like
-`B.contiguous().transpose(-1, -2).stride()` and
-`A.contiguous().transpose(-1, -2).stride()` respectively.
-
    
-
-    
    torch.solve(input, A, out=None) -> (Tensor, Tensor) 
    
-
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-
-
-
    This function returns the solution to the system of linear
-equations represented by \(AX = B\) and the LU factorization of
-A, in order as a namedtuple solution, LU.
    
-
    LU contains L and U factors for LU factorization of A.
    
-
    torch_solve(B, A) can take in 2D inputs B, A or inputs that are
-batches of 2D matrices. If the inputs are batches, then returns
-batched outputs solution, LU.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-A = torch_tensor(rbind(c(6.80, -2.11,  5.66,  5.97,  8.23),
-                      c(-6.05, -3.30,  5.36, -4.44,  1.08),
-                      c(-0.45,  2.58, -2.70,  0.27,  9.04),
-                      c(8.32,  2.71,  4.35,  -7.17,  2.14),
-                      c(-9.67, -5.14, -7.26,  6.08, -6.87)))$t()
-B = torch_tensor(rbind(c(4.02,  6.19, -8.22, -7.57, -3.03),
-                      c(-1.56,  4.00, -8.67,  1.75,  2.86),
-                      c(9.81, -4.09, -4.57, -8.61,  8.99)))$t()
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, torch_mm(A, X))
    #> torch_tensor 
-#> 7.09771e-06
-#> [ CPUFloatType{} ]
    # Batched solver example
-A = torch_randn(c(2, 3, 1, 4, 4))
-B = torch_randn(c(2, 3, 1, 4, 6))
-out = torch_solve(B, A)
-X = out[[1]]
-LU = out[[2]]
-torch_dist(B, A$matmul(X))
    #> torch_tensor 
-#> 6.14486e-06
-#> [ CPUFloatType{} ]
    # }
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diff --git a/docs/reference/torch_sort.html b/docs/reference/torch_sort.html
deleted file mode 100644
index e628801de..000000000
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Sort
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    dim
    (int, optional) the dimension to sort along
    descending
    (bool, optional) controls the sorting order (ascending or descending)
    out
    (tuple, optional) the output tuple of (Tensor, LongTensor) that can        be optionally given to be used as output buffers
    
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    sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor) 
    
-
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    Sorts the elements of the input tensor along a given dimension
-in ascending order by value.
    
-
    If dim is not given, the last dimension of the input is chosen.
    
-
    If descending is True then the elements are sorted in descending
-order by value.
    
-
    A namedtuple of (values, indices) is returned, where the values are the
-sorted values and indices are the indices of the elements in the original
-input tensor.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(3, 4))
-out = torch_sort(x)
-out
    #> [[1]]
-#> torch_tensor 
-#> -0.9253 -0.3520 -0.0946  1.1472
-#> -0.6827  0.1674  0.7136  1.4204
-#> -0.4754 -0.0864  0.5561  1.1917
-#> [ CPUFloatType{3,4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  3  0  1  2
-#>  0  1  2  3
-#>  2  0  3  1
-#> [ CPULongType{3,4} ]
-#> 
    out = torch_sort(x, 1)
-out
    #> [[1]]
-#> torch_tensor 
-#> -0.6827 -0.0946 -0.4754 -0.9253
-#> -0.3520  0.1674  0.7136  0.5561
-#> -0.0864  1.1917  1.1472  1.4204
-#> [ CPUFloatType{3,4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  1  0  2  0
-#>  0  1  1  2
-#>  2  2  0  1
-#> [ CPULongType{3,4} ]
-#> 
    # }
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diff --git a/docs/reference/torch_sparse_coo_tensor.html b/docs/reference/torch_sparse_coo_tensor.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Arguments
    
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    (array_like) Initial data for the tensor. Can be a list, tuple,        NumPy ndarray, scalar, and other types. Will be cast to a torch_LongTensor        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.
    values
    (array_like) Initial values for the tensor. Can be a list, tuple,        NumPy ndarray, scalar, and other types.
    size
    (list, tuple, or torch.Size, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, infers data type from values.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
    (bool, optional) If autograd should record operations on the        returned tensor. Default: False.
    
-
-    
    sparse_coo_tensor(indices, values, size=None, dtype=None, device=None, requires_grad=False) -> Tensor 
    
-
-    
-
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-
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-with the given values. A sparse tensor can be uncoalesced, in that case, there are duplicate
-coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
-torch_sparse_.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-i = torch_tensor(matrix(c(1, 2, 2, 3, 1, 3), ncol = 3, byrow = TRUE), dtype=torch_int64())
-v = torch_tensor(c(3, 4, 5), dtype=torch_float32())
-torch_sparse_coo_tensor(i, v)
    #> torch_tensor 
-#> [ SparseCPUFloatType{}
-#> indices:
-#>  0  1  1
-#>  2  0  2
-#> [ CPULongType{2,3} ]
-#> values:
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{3} ]
-#> size:
-#> [2, 3]
-#> ]
    torch_sparse_coo_tensor(i, v, c(2, 4))
    #> torch_tensor 
-#> [ SparseCPUFloatType{}
-#> indices:
-#>  0  1  1
-#>  2  0  2
-#> [ CPULongType{2,3} ]
-#> values:
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{3} ]
-#> size:
-#> [2, 4]
-#> ]
    
-# create empty sparse tensors
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()),
-  torch_tensor(numeric(), dtype = torch_float32()),
-  c(1)
-)
-S = torch_sparse_coo_tensor(
-  torch_empty(c(1, 0), dtype = torch_int64()),
-  torch_empty(c(0, 2)),
-  c(1, 2)
-)
-# }
    
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diff --git a/docs/reference/torch_split.html b/docs/reference/torch_split.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Split
    
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    Arguments
    
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    tensor
    (Tensor) tensor to split.
    split_size_or_sections
    (int) size of a single chunk or        list of sizes for each chunk
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    (int) dimension along which to split the tensor.
    
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    If `split_size_or_sections` is an integer type, then `tensor` will
-be split into equally sized chunks (if possible). Last chunk will be smaller if
-the tensor size along the given dimension `dim` is not divisible by
-`split_size`.
-
-If `split_size_or_sections` is a list, then `tensor` will be split
-into ``len(split_size_or_sections)`` chunks with sizes in `dim` according
-to `split_size_or_sections`.
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diff --git a/docs/reference/torch_sqrt.html b/docs/reference/torch_sqrt.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    out
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    Examples
    
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#>  0.4471
-#>  1.6376
-#>  0.0918
-#>  0.6598
-#> [ CPUFloatType{4} ]
    torch_sqrt(a)
    #> torch_tensor 
-#>  0.6686
-#>  1.2797
-#>  0.3031
-#>  0.8123
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_square.html b/docs/reference/torch_square.html
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    (Tensor) the input tensor.
    out
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    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.3828
-#>  0.0233
-#> -1.1883
-#>  1.0369
-#> [ CPUFloatType{4} ]
    torch_square(a)
    #> torch_tensor 
-#>  0.1466
-#>  0.0005
-#>  1.4121
-#>  1.0752
-#> [ CPUFloatType{4} ]
    # }
-
    
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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-    
    
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    Squeeze
    
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    input
    (Tensor) the input tensor.
    dim
    (int, optional) if given, the input will be squeezed only in           this dimension
    out
    (Tensor, optional) the output tensor.
    
-
-    
    Note
    
-
-    
    The returned tensor shares the storage with the input tensor,
-so changing the contents of one will change the contents of the other.
    
-    
    squeeze(input, dim=None, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns a tensor with all the dimensions of input of size 1 removed.
    
-
    For example, if input is of shape:
-\((A \times 1 \times B \times C \times 1 \times D)\) then the out tensor
-will be of shape: \((A \times B \times C \times D)\).
    
-
    When dim is given, a squeeze operation is done only in the given
-dimension. If input is of shape: \((A \times 1 \times B)\),
-squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1)
-will squeeze the tensor to the shape \((A \times B)\).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_zeros(c(2, 1, 2, 1, 2))
-x
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,1,.,.) = 
-#>   0  0
-#> 
-#> (1,1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,1,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,1,2,1,2} ]
    y = torch_squeeze(x)
-y
    #> torch_tensor 
-#> (1,.,.) = 
-#>   0  0
-#>   0  0
-#> 
-#> (2,.,.) = 
-#>   0  0
-#>   0  0
-#> [ CPUFloatType{2,2,2} ]
    y = torch_squeeze(x, 1)
-y
    #> torch_tensor 
-#> (1,1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,1,.,.) = 
-#>   0  0
-#> 
-#> (1,1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,1,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,1,2,1,2} ]
    y = torch_squeeze(x, 2)
-y
    #> torch_tensor 
-#> (1,1,.,.) = 
-#>   0  0
-#> 
-#> (2,1,.,.) = 
-#>   0  0
-#> 
-#> (1,2,.,.) = 
-#>   0  0
-#> 
-#> (2,2,.,.) = 
-#>   0  0
-#> [ CPUFloatType{2,2,1,2} ]
    # }
-
    
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diff --git a/docs/reference/torch_stack.html b/docs/reference/torch_stack.html
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+++ /dev/null
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    Stack
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Stack
    
-    
    
-
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    Arguments
    
-    
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    tensors
    (sequence of Tensors) sequence of tensors to concatenate
    dim
    (int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)
    out
    (Tensor, optional) the output tensor.
    
-
-    
    stack(tensors, dim=0, out=None) -> Tensor 
    
-
-    
-
-
-
    Concatenates sequence of tensors along a new dimension.
    
-
    All tensors need to be of the same size.
    
-
-  
    
-  
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diff --git a/docs/reference/torch_std.html b/docs/reference/torch_std.html
deleted file mode 100644
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--- a/docs/reference/torch_std.html
+++ /dev/null
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-Std — torch_std • torch
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-    
    Std
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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-    
    Std
    
-    
    
-
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    Arguments
    
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    input
    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    out
    (Tensor, optional) the output tensor.
    
-
-    
    std(input, unbiased=True) -> Tensor 
    
-
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-
-
-
    Returns the standard-deviation of all elements in the input tensor.
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    std(input, dim, unbiased=True, keepdim=False, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns the standard-deviation of each row of the input tensor in the
-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.3162  0.4255  0.1976
-#> [ CPUFloatType{1,3} ]
    torch_std(a)
    #> torch_tensor 
-#> 0.379947
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  1.2036 -2.0630 -2.1182 -0.6214
-#> -1.6360  0.5014 -0.1266 -1.7918
-#>  0.2972 -0.5018  1.3086  1.4842
-#>  0.6903 -0.5105  0.5911  0.7067
-#> [ CPUFloatType{4,4} ]
    torch_std(a, dim=1)
    #> torch_tensor 
-#>  1.2400
-#>  1.0589
-#>  1.4759
-#>  1.4476
-#> [ CPUFloatType{4} ]
    # }
-
    
-  
    
-  
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-  
    Developed by Daniel Falbel, Javier Luraschi.
    
-
    
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-  
    Site built with pkgdown 1.5.1.
    
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diff --git a/docs/reference/torch_std_mean.html b/docs/reference/torch_std_mean.html
deleted file mode 100644
index 7940b4467..000000000
--- a/docs/reference/torch_std_mean.html
+++ /dev/null
@@ -1,278 +0,0 @@
-
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-  
-  
-
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-Std_mean — torch_std_mean • torch
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-    
    
-    
    Std_mean
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Std_mean
    
-    
    
-
-
-    
    Arguments
    
-    
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-    
-      
-      
-    
-    
-      
-      
-    
-    
-      
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-    
-    
-      
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-    
    input
    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
-
-    
    std_mean(input, unbiased=True) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Returns the standard-deviation and mean of all elements in the input tensor.
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    std_mean(input, dim, unbiased=True, keepdim=False) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    Returns the standard-deviation and mean of each row of the input tensor in the
-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the standard-deviation will be calculated
-via the biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.8958  2.0764  0.0589
-#> [ CPUFloatType{1,3} ]
    torch_std_mean(a)
    #> [[1]]
-#> torch_tensor 
-#> 1.51748
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.413156
-#> [ CPUFloatType{} ]
-#> 
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.2699 -0.5021 -0.4712  1.0471
-#> -2.2339  0.6823 -0.9388  0.5461
-#> -0.4050  0.9288  0.8127  1.5763
-#>  1.1732 -0.2562  0.5626  0.8124
-#> [ CPUFloatType{4,4} ]
    torch_std_mean(a, 1)
    #> [[1]]
-#> torch_tensor 
-#>  1.4429
-#>  0.6986
-#>  0.8327
-#>  0.4380
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -0.2989
-#>  0.2132
-#> -0.0087
-#>  0.9955
-#> [ CPUFloatType{4} ]
-#> 
    # }
-
    
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diff --git a/docs/reference/torch_stft.html b/docs/reference/torch_stft.html
deleted file mode 100644
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+++ /dev/null
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
-    
    
-    
    Stft
    
-    
    
-
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    input
    (Tensor) the input tensor
    n_fft
    (int) size of Fourier transform
    hop_length
    (int, optional) the distance between neighboring sliding window        frames. Default: None (treated as equal to floor(n_fft / 4))
    win_length
    (int, optional) the size of window frame and STFT filter.        Default: None  (treated as equal to n_fft)
    window
    (Tensor, optional) the optional window function.        Default: None (treated as window of all \(1\) s)
    center
    (bool, optional) whether to pad input on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: True
    pad_mode
    (string, optional) controls the padding method used when        center is True. Default: "reflect"
    normalized
    (bool, optional) controls whether to return the normalized STFT results         Default: False
    onesided
    (bool, optional) controls whether to return half of results to        avoid redundancy Default: True
    
-
-    
    Short-time Fourier transform (STFT). 
    
-
-    
-
-
-
    Short-time Fourier transform (STFT).
    Ignoring the optional batch dimension, this method computes the following
-expression:
-
    
-
-
    $$
-        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
-                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
-                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
-$$
-where \(m\) is the index of the sliding window, and \(\omega\) is
-the frequency that \(0 \leq \omega < \mbox{n\_fft}\). When
-onesided is the default value True,
    * `input` must be either a 1-D time sequence or a 2-D batch of time
-  sequences.
-
-* If `hop_length` is ``None`` (default), it is treated as equal to
-  ``floor(n_fft / 4)``.
-
-* If `win_length` is ``None`` (default), it is treated as equal to
-  `n_fft`.
-
-* `window` can be a 1-D tensor of size `win_length`, e.g., from
-  `torch_hann_window`. If `window` is ``None`` (default), it is
-  treated as if having \eqn{1} everywhere in the window. If
-  \eqn{\mbox{win\_length} &lt; \mbox{n\_fft}}, `window` will be padded on
-  both sides to length `n_fft` before being applied.
-
-* If `center` is ``True`` (default), `input` will be padded on
-  both sides so that the \eqn{t}-th frame is centered at time
-  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
-  begins at time  \eqn{t \times \mbox{hop\_length}}.
-
-* `pad_mode` determines the padding method used on `input` when
-  `center` is ``True``. See `torch_nn.functional.pad` for
-  all available options. Default is ``"reflect"``.
-
-* If `onesided` is ``True`` (default), only values for \eqn{\omega}
-  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
-  are returned because the real-to-complex Fourier transform satisfies the
-  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
-
-* If `normalized` is ``True`` (default is ``False``), the function
-  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
-
-Returns the real and the imaginary parts together as one tensor of size
-\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
-batch size of `input`, \eqn{N} is the number of frequencies where
-STFT is applied, \eqn{T} is the total number of frames used, and each pair
-in the last dimension represents a complex number as the real part and the
-imaginary part.
-
-.. warning::
-  This function changed signature at version 0.4.1. Calling with the
-  previous signature may cause error or return incorrect result.
-
    
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diff --git a/docs/reference/torch_sum.html b/docs/reference/torch_sum.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to dtype before the operation        is performed. This is useful for preventing data type overflows. Default: None.
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
-
-    
    sum(input, dtype=None) -> Tensor 
    
-
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-
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-
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-    
    sum(input, dim, keepdim=False, dtype=None) -> Tensor 
    
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-dimension dim. If dim is a list of dimensions,
-reduce over all of them.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#> -0.6977 -0.5155 -1.9107
-#> [ CPUFloatType{1,3} ]
    torch_sum(a)
    #> torch_tensor 
-#> -3.12391
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#> -0.4190  0.0463 -0.7716  0.4229
-#> -1.2665  0.4791 -0.5515 -1.0623
-#>  1.1148 -1.2247  0.0682  1.1490
-#> -0.7653 -1.3195  0.4248 -0.6928
-#> [ CPUFloatType{4,4} ]
    torch_sum(a, 1)
    #> torch_tensor 
-#> -1.3360
-#> -2.0188
-#> -0.8302
-#> -0.1831
-#> [ CPUFloatType{4} ]
    b = torch_arange(0, 4 * 5 * 6)$view(c(4, 5, 6))
-torch_sum(b, list(2, 1))
    #> torch_tensor 
-#>   435
-#>  1335
-#>  2235
-#>  3135
-#> [ CPUFloatType{4} ]
    # }
-
    
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diff --git a/docs/reference/torch_svd.html b/docs/reference/torch_svd.html
deleted file mode 100644
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    Svd
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
-
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    Svd
    
-    
    
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    input
    (Tensor) the input tensor of size \((*, m, n)\) where * is zero or more                    batch dimensions consisting of \(m \times n\) matrices.
    some
    (bool, optional) controls the shape of returned U and V
    compute_uv
    (bool, optional) option whether to compute U and V or not
    out
    (tuple, optional) the output tuple of tensors
    
-
-    
    Note
    
-
-    
    The singular values are returned in descending order. If input is a batch of matrices,
-then the singular values of each matrix in the batch is returned in descending order.
    
-
    The implementation of SVD on CPU uses the LAPACK routine ?gesdd (a divide-and-conquer
-algorithm) instead of ?gesvd for speed. Analogously, the SVD on GPU uses the MAGMA routine
-gesdd as well.
    
-
    Irrespective of the original strides, the returned matrix U
-will be transposed, i.e. with strides U.contiguous().transpose(-2, -1).stride()
    
-
    Extra care needs to be taken when backward through U and V
-outputs. Such operation is really only stable when input is
-full rank with all distinct singular values. Otherwise, NaN can
-appear as the gradients are not properly defined. Also, notice that
-double backward will usually do an additional backward through U and
-V even if the original backward is only on S.
    
-
    When some = False, the gradients on U[..., :, min(m, n):]
-and V[..., :, min(m, n):] will be ignored in backward as those vectors
-can be arbitrary bases of the subspaces.
    
-
    When compute_uv = False, backward cannot be performed since U and V
-from the forward pass is required for the backward operation.
    
-    
    svd(input, some=True, compute_uv=True, out=None) -> (Tensor, Tensor, Tensor) 
    
-
-    
-
-
-
    This function returns a namedtuple (U, S, V) which is the singular value
-decomposition of a input real matrix or batches of real matrices input such that
-\(input = U \times diag(S) \times V^T\).
    
-
    If some is True (default), the method returns the reduced singular value decomposition
-i.e., if the last two dimensions of input are m and n, then the returned
-U and V matrices will contain only \(min(n, m)\) orthonormal columns.
    
-
    If compute_uv is False, the returned U and V matrices will be zero matrices
-of shape \((m \times m)\) and \((n \times n)\) respectively. some will be ignored here.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5, 3))
-a
    #> torch_tensor 
-#> -0.7888 -0.7696  0.3986
-#> -0.7711  0.2121 -1.5684
-#>  0.3715 -0.0456 -1.0608
-#>  1.1539 -0.8844 -0.7384
-#> -1.3783 -0.2028  1.6554
-#> [ CPUFloatType{5,3} ]
    out = torch_svd(a)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a, torch_mm(torch_mm(u, torch_diag(s)), v$t()))
    #> torch_tensor 
-#> 8.51333e-07
-#> [ CPUFloatType{} ]
    a_big = torch_randn(c(7, 5, 3))
-out = torch_svd(a_big)
-u = out[[1]]
-s = out[[2]]
-v = out[[3]]
-torch_dist(a_big, torch_matmul(torch_matmul(u, torch_diag_embed(s)), v$transpose(-2, -1)))
    #> torch_tensor 
-#> 2.71036e-06
-#> [ CPUFloatType{} ]
    # }
-
    
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diff --git a/docs/reference/torch_symeig.html b/docs/reference/torch_symeig.html
deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
-    
    
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    Symeig
    
-    
    
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    Arguments
    
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    input
    (Tensor) the input tensor of size \((*, n, n)\) where * is zero or more                    batch dimensions consisting of symmetric matrices.
    eigenvectors
    (boolean, optional) controls whether eigenvectors have to be computed
    upper
    (boolean, optional) controls whether to consider upper-triangular or lower-triangular region
    out
    (tuple, optional) the output tuple of (Tensor, Tensor)
    
-
-    
    Note
    
-
-    
    The eigenvalues are returned in ascending order. If input is a batch of matrices,
-then the eigenvalues of each matrix in the batch is returned in ascending order.
    
-
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-be transposed, i.e. with strides V.contiguous().transpose(-1, -2).stride().
    
-
    Extra care needs to be taken when backward through outputs. Such
-operation is really only stable when all eigenvalues are distinct.
-Otherwise, NaN can appear as the gradients are not properly defined.
    
-    
    symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor) 
    
-
-    
-
-
-
    This function returns eigenvalues and eigenvectors
-of a real symmetric matrix input or a batch of real symmetric matrices,
-represented by a namedtuple (eigenvalues, eigenvectors).
    
-
    This function calculates all eigenvalues (and vectors) of input
-such that \(\mbox{input} = V \mbox{diag}(e) V^T\).
    
-
    The boolean argument eigenvectors defines computation of
-both eigenvectors and eigenvalues or eigenvalues only.
    
-
    If it is False, only eigenvalues are computed. If it is True,
-both eigenvalues and eigenvectors are computed.
    
-
    Since the input matrix input is supposed to be symmetric,
-only the upper triangular portion is used by default.
    
-
    If upper is False, then lower triangular portion is used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(5, 5))
-a = a + a$t()  # To make a symmetric
-a
    #> torch_tensor 
-#>  2.1703 -0.5663 -0.5122 -0.2134 -0.0549
-#> -0.5663  0.9832 -0.9685  0.1017  0.9142
-#> -0.5122 -0.9685 -0.8703  0.7874 -0.6067
-#> -0.2134  0.1017  0.7874  2.8112 -0.1549
-#> -0.0549  0.9142 -0.6067 -0.1549 -0.4494
-#> [ CPUFloatType{5,5} ]
    o = torch_symeig(a, eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-e
    #> torch_tensor 
-#> -1.5747
-#> -0.9141
-#>  1.6122
-#>  2.4168
-#>  3.1047
-#> [ CPUFloatType{5} ]
    v
    #> torch_tensor 
-#>  0.1632 -0.0736  0.4932  0.7860 -0.3270
-#>  0.3015 -0.4689  0.6486 -0.5181 -0.0109
-#>  0.8988 -0.0414 -0.3445  0.1244  0.2372
-#> -0.1524  0.0538  0.3046  0.2248  0.9114
-#>  0.2266  0.8776  0.3530 -0.2188 -0.0780
-#> [ CPUFloatType{5,5} ]
    a_big = torch_randn(c(5, 2, 2))
-a_big = a_big + a_big$transpose(-2, -1)  # To make a_big symmetric
-o = a_big$symeig(eigenvectors=TRUE)
-e = o[[1]]
-v = o[[2]]
-torch_allclose(torch_matmul(v, torch_matmul(e$diag_embed(), v$transpose(-2, -1))), a_big)
    #> [1] TRUE
    # }
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diff --git a/docs/reference/torch_t.html b/docs/reference/torch_t.html
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor.
    
-
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    t(input) -> Tensor 
    
-
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    Expects input to be <= 2-D tensor and transposes dimensions 0
-and 1.
    
-
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-is equivalent to transpose(input, 0, 1).
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_randn(c(2,3))
-x
    #> torch_tensor 
-#> -1.4813  0.4524 -0.0294
-#>  2.6447 -0.7806  0.5434
-#> [ CPUFloatType{2,3} ]
    torch_t(x)
    #> torch_tensor 
-#> -1.4813  2.6447
-#>  0.4524 -0.7806
-#> -0.0294  0.5434
-#> [ CPUFloatType{3,2} ]
    x = torch_randn(c(3))
-x
    #> torch_tensor 
-#> -0.0126
-#> -0.5420
-#>  0.5410
-#> [ CPUFloatType{3} ]
    torch_t(x)
    #> torch_tensor 
-#> -0.0126
-#> -0.5420
-#>  0.5410
-#> [ CPUFloatType{3} ]
    x = torch_randn(c(2, 3))
-x
    #> torch_tensor 
-#> -1.9746 -0.2671  0.6073
-#> -0.1863  0.6615  1.5133
-#> [ CPUFloatType{2,3} ]
    torch_t(x)
    #> torch_tensor 
-#> -1.9746 -0.1863
-#> -0.2671  0.6615
-#>  0.6073  1.5133
-#> [ CPUFloatType{3,2} ]
    # }
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diff --git a/docs/reference/torch_take.html b/docs/reference/torch_take.html
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    Take
    
-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    Take
    
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    Arguments
    
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    input
    (Tensor) the input tensor.
    indices
    (LongTensor) the indices into tensor
    
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    take(input, index) -> Tensor 
    
-
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-
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    Returns a new tensor with the elements of input at the given indices.
-The input tensor is treated as if it were viewed as a 1-D tensor. The result
-takes the same shape as the indices.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-src = torch_tensor(matrix(c(4,3,5,6,7,8), ncol = 3, byrow = TRUE))
-torch_take(src, torch_tensor(c(0, 2, 5), dtype = torch_int64()))
    #> torch_tensor 
-#>  8
-#>  3
-#>  7
-#> [ CPUFloatType{3} ]
    # }
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diff --git a/docs/reference/torch_tan.html b/docs/reference/torch_tan.html
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    Tan
    
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    Arguments
    
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    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
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    tan(input, out=None) -> Tensor 
    
-
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    Returns a new tensor with the tangent of the elements of input.
    
-
    $$
-    \mbox{out}_{i} = \tan(\mbox{input}_{i})
-$$
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -1.1158
-#>  0.0583
-#>  0.2334
-#> -0.9159
-#> [ CPUFloatType{4} ]
    torch_tan(a)
    #> torch_tensor 
-#> -2.0440
-#>  0.0583
-#>  0.2377
-#> -1.3021
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_tanh.html b/docs/reference/torch_tanh.html
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    Tanh
    
-    
    
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    Arguments
    
-    
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-      
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-    
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    input
    (Tensor) the input tensor.
    out
    (Tensor, optional) the output tensor.
    
-
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    tanh(input, out=None) -> Tensor 
    
-
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-
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    Returns a new tensor with the hyperbolic tangent of the elements
-of input.
    
-
    $$
-    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(4))
-a
    #> torch_tensor 
-#> -0.5525
-#> -0.3071
-#>  0.8392
-#> -0.6511
-#> [ CPUFloatType{4} ]
    torch_tanh(a)
    #> torch_tensor 
-#> -0.5024
-#> -0.2978
-#>  0.6854
-#> -0.5724
-#> [ CPUFloatType{4} ]
    # }
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diff --git a/docs/reference/torch_tensor.html b/docs/reference/torch_tensor.html
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    Examples
    
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    # \dontrun{
-torch_tensor(c(1,2,3,4))
    #> torch_tensor 
-#>  1
-#>  2
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-#> [ CPUFloatType{4} ]
    torch_tensor(c(1,2,3,4), dtype = torch_int())
    #> torch_tensor 
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    (int) the k in "top-k"
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    (tuple, optional) the output tuple of (Tensor, LongTensor) that can be        optionally given to be used as output buffers
    
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-a given dimension.
    
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-of the elements in the original input tensor.
    
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-k elements are themselves sorted
    
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    Examples
    
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    # \dontrun{
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-x = torch_arange(1., 6.)
-x
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#>  5
-#> [ CPUFloatType{5} ]
    torch_topk(x, 3)
    #> [[1]]
-#> torch_tensor 
-#>  5
-#>  4
-#>  3
-#> [ CPUFloatType{3} ]
-#> 
-#> [[2]]
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-#>  4
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-#>  0.3114 -0.1716  0.4504
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-#>  0.3114 -0.4723
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-#>  0.0190  1.0024  1.9078
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-torch_trapz(y, x = x)
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-#>  2.4765
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    transpose
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    (bool, optional) whether \(A\) is unit triangular.        If True, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: False.
    
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-#> -0.3460  0.1356
-#>  0.0000  1.5035
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-#> -0.4014 -0.1958  0.0379
-#> -1.3143 -0.0766 -0.3524
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    #> [[1]]
-#> torch_tensor 
-#>  0.8174  0.5459 -0.2014
-#> -0.8742 -0.0509 -0.2344
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-#> torch_tensor 
-#> -0.3460  0.1356
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    (Tensor) the input tensor.
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-
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-input, the other elements of the result tensor out are set to 0.
    
-
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-below the diagonal.
    
-
    The argument diagonal controls which diagonal to consider. If
-diagonal = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.
    
-
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    Examples
    
-    
    # \dontrun{
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-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.6849  0.8749 -0.7531
-#> -0.5497 -1.0238  0.3323
-#> -0.0980  0.0908 -0.4867
-#> [ CPUFloatType{3,3} ]
    torch_tril(a)
    #> torch_tensor 
-#> -0.6849  0.0000  0.0000
-#> -0.5497 -1.0238  0.0000
-#> -0.0980  0.0908 -0.4867
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(4, 6))
-b
    #> torch_tensor 
-#> -0.7116 -0.9359 -1.5487  1.6909  0.9290 -1.8224
-#>  2.1791 -0.8098  1.4367 -0.5204  1.0782 -0.4998
-#>  1.3149 -1.0202 -0.4302 -0.5773  0.0928 -1.0440
-#> -1.7950  0.6438 -0.7581  0.0569 -1.0737  1.3707
-#> [ CPUFloatType{4,6} ]
    torch_tril(b, diagonal=1)
    #> torch_tensor 
-#> -0.7116 -0.9359  0.0000  0.0000  0.0000  0.0000
-#>  2.1791 -0.8098  1.4367  0.0000  0.0000  0.0000
-#>  1.3149 -1.0202 -0.4302 -0.5773  0.0000  0.0000
-#> -1.7950  0.6438 -0.7581  0.0569 -1.0737  0.0000
-#> [ CPUFloatType{4,6} ]
    torch_tril(b, diagonal=-1)
    #> torch_tensor 
-#>  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
-#>  2.1791  0.0000  0.0000  0.0000  0.0000  0.0000
-#>  1.3149 -1.0202  0.0000  0.0000  0.0000  0.0000
-#> -1.7950  0.6438 -0.7581  0.0000  0.0000  0.0000
-#> [ CPUFloatType{4,6} ]
    # }
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    (int) number of rows in the 2-D matrix.
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    (int) diagonal offset from the main diagonal.        Default: if not provided, 0.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, torch_long.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    layout
    (torch.layout, optional) currently only support torch_strided.
    
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-prevent overflow during calculation.
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-
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    tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor 
    
-
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-
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-col matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.
    
-
    The lower triangular part of the matrix is defined as the elements on and
-below the diagonal.
    
-
    The argument offset controls which diagonal to consider. If
-offset = 0, all elements on and below the main diagonal are
-retained. A positive value includes just as many diagonals above the main
-diagonal, and similarly a negative value excludes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
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    input
    (Tensor) the input tensor.
    diagonal
    (int, optional) the diagonal to consider
    out
    (Tensor, optional) the output tensor.
    
-
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    triu(input, diagonal=0, out=None) -> Tensor 
    
-
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-
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-input, the other elements of the result tensor out are set to 0.
    
-
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-above the diagonal.
    
-
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-diagonal = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
-\(d_{1}, d_{2}\) are the dimensions of the matrix.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(3, 3))
-a
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#> -0.3459 -0.2626 -0.7964
-#>  0.5357 -0.4833 -0.2087
-#> [ CPUFloatType{3,3} ]
    torch_triu(a)
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#>  0.0000 -0.2626 -0.7964
-#>  0.0000  0.0000 -0.2087
-#> [ CPUFloatType{3,3} ]
    torch_triu(a, diagonal=1)
    #> torch_tensor 
-#> 0.01 *
-#>  0.0000 -18.6760  8.7416
-#>   0.0000  0.0000 -79.6368
-#>   0.0000  0.0000  0.0000
-#> [ CPUFloatType{3,3} ]
    torch_triu(a, diagonal=-1)
    #> torch_tensor 
-#> -0.3956 -0.1868  0.0874
-#> -0.3459 -0.2626 -0.7964
-#>  0.0000 -0.4833 -0.2087
-#> [ CPUFloatType{3,3} ]
    b = torch_randn(c(4, 6))
-b
    #> torch_tensor 
-#> -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#>  0.9661  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#>  0.0011  0.5442  0.5278 -0.5429  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    torch_triu(b, diagonal=1)
    #> torch_tensor 
-#>  0.0000  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.0000  0.0000 -0.6427  0.3658 -0.2912  0.3553
-#>  0.0000  0.0000  0.0000 -0.1047 -1.6832 -0.0952
-#>  0.0000  0.0000  0.0000  0.0000  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    torch_triu(b, diagonal=-1)
    #> torch_tensor 
-#> -0.1247  0.3568  1.5481  0.9310  0.2551 -1.8148
-#>  0.7493  0.8313 -0.6427  0.3658 -0.2912  0.3553
-#>  0.0000  2.0171  0.9854 -0.1047 -1.6832 -0.0952
-#>  0.0000  0.0000  0.5278 -0.5429  0.4507 -0.8038
-#> [ CPUFloatType{4,6} ]
    # }
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    (int) number of rows in the 2-D matrix.
    col
    (int) number of columns in the 2-D matrix.
    offset
    (int) diagonal offset from the main diagonal.        Default: if not provided, 0.
    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, torch_long.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    layout
    (torch.layout, optional) currently only support torch_strided.
    
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-
    When running on CUDA, ``row * col`` must be less than \eqn{2^{59}} to
-prevent overflow during calculation.
-
    
-
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    triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -> Tensor 
    
-
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-
-
-
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-col matrix in a 2-by-N Tensor, where the first row contains row
-coordinates of all indices and the second row contains column coordinates.
-Indices are ordered based on rows and then columns.
    
-
    The upper triangular part of the matrix is defined as the elements on and
-above the diagonal.
    
-
    The argument offset controls which diagonal to consider. If
-offset = 0, all elements on and above the main diagonal are
-retained. A positive value excludes just as many diagonals above the main
-diagonal, and similarly a negative value includes just as many diagonals below
-the main diagonal. The main diagonal are the set of indices
-\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
-where \(d_{1}, d_{2}\) are the dimensions of the matrix.
    
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-    
    
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    Arguments
    
-    
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-      
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-      
-      
-    
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    (Tensor) the dividend
    divisor
    (Tensor or Scalar) the divisor
    
-
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    true_divide(dividend, divisor) -> Tensor 
    
-
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-
-
-
    Performs "true division" that always computes the division
-in floating point. Analogous to division in Python 3 and equivalent to
-torch_div except when both inputs have bool or integer scalar types,
-in which case they are cast to the default (floating) scalar type before the division.
    
-
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-    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
-$$
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-dividend = torch_tensor(c(5, 3), dtype=torch_int())
-divisor = torch_tensor(c(3, 2), dtype=torch_int())
-torch_true_divide(dividend, divisor)
    #> torch_tensor 
-#>  1.6667
-#>  1.5000
-#> [ CPUFloatType{2} ]
    torch_true_divide(dividend, 2)
    #> torch_tensor 
-#>  2.5000
-#>  1.5000
-#> [ CPUFloatType{2} ]
    # }
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    # \dontrun{
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-a
    #> torch_tensor 
-#> -0.4632
-#> -1.3494
-#>  0.0517
-#> -1.1300
-#> [ CPUFloatType{4} ]
    torch_trunc(a)
    #> torch_tensor 
-#> -0
-#> -1
-#>  0
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-#> [ CPUFloatType{4} ]
    # }
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    # \dontrun{
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-torch_unbind(torch_tensor(matrix(1:9, ncol = 3, byrow=TRUE)))
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#> [ CPUIntType{3} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  4
-#>  5
-#>  6
-#> [ CPUIntType{3} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#>  7
-#>  8
-#>  9
-#> [ CPUIntType{3} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (Tensor) the input tensor
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    (bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.
    return_counts
    (bool) Whether to also return the counts for each unique        element.
    dim
    (int) the dimension to apply unique. If None, the unique of the        flattened input is returned. default: None
    
-
-    
    TEST 
    
-
-    
-
-
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    Eliminates all but the first element from every consecutive group of equivalent elements.
    .. note:: This function is different from [`torch_unique`] in the sense that this function
-    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
-    in C++.
-
    
-
-
-    
    Examples
    
-    
    # \dontrun{
-x = torch_tensor(c(1, 1, 2, 2, 3, 1, 1, 2))
-output = torch_unique_consecutive(x)
-output
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
    torch_unique_consecutive(x, return_inverse=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#>  0
-#>  0
-#>  1
-#>  1
-#>  2
-#>  3
-#>  3
-#>  4
-#> [ CPULongType{8} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
    torch_unique_consecutive(x, return_counts=TRUE)
    #> [[1]]
-#> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  1
-#>  2
-#> [ CPUFloatType{5} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> [ CPULongType{0} ]
-#> 
-#> [[3]]
-#> torch_tensor 
-#>  2
-#>  2
-#>  1
-#>  2
-#>  1
-#> [ CPULongType{5} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    Unsqueeze
    
-    
    
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    Arguments
    
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    (Tensor) the input tensor.
    dim
    (int) the index at which to insert the singleton dimension
    
-
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    unsqueeze(input, dim) -> Tensor 
    
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    Returns a new tensor with a dimension of size one inserted at the
-specified position.
    
-
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-
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-can be used. Negative dim will correspond to unsqueeze
-applied at dim = dim + input.dim() + 1.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-x = torch_tensor(c(1, 2, 3, 4))
-torch_unsqueeze(x, 1)
    #> torch_tensor 
-#>  1  2  3  4
-#> [ CPUFloatType{1,4} ]
    torch_unsqueeze(x, 2)
    #> torch_tensor 
-#>  1
-#>  2
-#>  3
-#>  4
-#> [ CPUFloatType{4,1} ]
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
-    
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    (Tensor) the input tensor.
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    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    out
    (Tensor, optional) the output tensor.
    
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    var(input, unbiased=True) -> Tensor 
    
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-
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-biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    var(input, dim, keepdim=False, unbiased=True, out=None) -> Tensor 
    
-
-    
-
-
-
    Returns the variance of each row of the input tensor in the given
-dimension dim.
    
-
    If keepdim is True, the output tensor is of the same size
-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  0.9097 -1.4605 -0.9481
-#> [ CPUFloatType{1,3} ]
    torch_var(a)
    #> torch_tensor 
-#> 1.55533
-#> [ CPUFloatType{} ]
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.9322 -0.2661 -1.1364 -0.1596
-#> -0.8385  0.0366 -0.8830 -0.5310
-#>  1.6003  0.1409 -0.4186  2.4136
-#> -0.7193 -0.5766  0.0958 -0.3928
-#> [ CPUFloatType{4,4} ]
    torch_var(a, 1)
    #> torch_tensor 
-#>  1.4709
-#>  0.1046
-#>  0.2947
-#>  1.9483
-#> [ CPUFloatType{4} ]
    # }
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deleted file mode 100644
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    input
    (Tensor) the input tensor.
    unbiased
    (bool) whether to use the unbiased estimation or not
    dim
    (int or tuple of ints) the dimension or dimensions to reduce.
    keepdim
    (bool) whether the output tensor has dim retained or not.
    
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    var_mean(input, unbiased=True) -> (Tensor, Tensor) 
    
-
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-
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-biased estimator. Otherwise, Bessel's correction will be used.
    
-    
    var_mean(input, dim, keepdim=False, unbiased=True) -> (Tensor, Tensor) 
    
-
-    
-
-
-
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-dimension dim.
    
-
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-as input except in the dimension(s) dim where it is of size 1.
-Otherwise, dim is squeezed (see torch_squeeze), resulting in the
-output tensor having 1 (or len(dim)) fewer dimension(s).
    
-
    If unbiased is False, then the variance will be calculated via the
-biased estimator. Otherwise, Bessel's correction will be used.
    
-
-    
    Examples
    
-    
    # \dontrun{
-
-a = torch_randn(c(1, 3))
-a
    #> torch_tensor 
-#>  1.4242 -0.2759  0.7106
-#> [ CPUFloatType{1,3} ]
    torch_var_mean(a)
    #> [[1]]
-#> torch_tensor 
-#> 0.728761
-#> [ CPUFloatType{} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> 0.61961
-#> [ CPUFloatType{} ]
-#> 
    
-
-a = torch_randn(c(4, 4))
-a
    #> torch_tensor 
-#>  0.0532 -0.3304 -0.5824  1.8389
-#> -2.0310 -0.6095 -0.1087  0.3034
-#> -1.3414  1.7987 -0.3098  0.8658
-#>  0.0799 -0.7031 -0.5875 -1.0066
-#> [ CPUFloatType{4,4} ]
    torch_var_mean(a, 1)
    #> [[1]]
-#> torch_tensor 
-#>  1.1034
-#>  1.4015
-#>  0.0538
-#>  1.4116
-#> [ CPUFloatType{4} ]
-#> 
-#> [[2]]
-#> torch_tensor 
-#> -0.8098
-#>  0.0389
-#> -0.3971
-#>  0.5004
-#> [ CPUFloatType{4} ]
-#> 
    # }
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-    Source: R/gen-namespace-docs.R, R/gen-namespace-examples.R
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    (BoolTensor) When True (nonzero), yield x, otherwise yield y
    x
    (Tensor) values selected at indices where condition is True
    y
    (Tensor) values selected at indices where condition is False
    
-
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-
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-
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-
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-    \mbox{out}_i = \left\{ \begin{array}{ll}
-        \mbox{x}_i & \mbox{if } \mbox{condition}_i \\
-        \mbox{y}_i & \mbox{otherwise} \\
-    \end{array}
-    \right.
-$$
    
-    
    where(condition) -> tuple of LongTensor 
    
-
-    
-
-
-
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-torch_nonzero(condition, as_tuple=True).
    
-
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diff --git a/docs/reference/torch_zeros.html b/docs/reference/torch_zeros.html
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    out
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    dtype
    (torch.dtype, optional) the desired data type of returned tensor.        Default: if None, uses a global default (see torch_set_default_tensor_type).
    layout
    (torch.layout, optional) the desired layout of returned Tensor.        Default: torch_strided.
    device
    (torch.device, optional) the desired device of returned tensor.        Default: if None, uses the current device for the default tensor type        (see torch_set_default_tensor_type). device will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.
    requires_grad
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-    
    # \dontrun{
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    #> torch_tensor 
-#>  0  0  0
-#>  0  0  0
-#> [ CPUFloatType{2,3} ]
    torch_zeros(c(5))
    #> torch_tensor 
-#>  0
-#>  0
-#>  0
-#>  0
-#>  0
-#> [ CPUFloatType{5} ]
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-the old torch_zeros_like(input, out=output) is equivalent to
-torch_zeros(input.size(), out=output).
    
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    Examples
    
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    # \dontrun{
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-input = torch_empty(c(2, 3))
-torch_zeros_like(input)
    #> torch_tensor 
-#>  0  0  0
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diff --git a/docs/reference/utils_dataset.html b/docs/reference/utils_dataset.html
deleted file mode 100644
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diff --git a/docs/reference/utils_dataset_tensor.html b/docs/reference/utils_dataset_tensor.html
deleted file mode 100644
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-})
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-#> -0.1943
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-# }
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-    
-    
    



    
-          
    All functions
    
-          
    
-        
    
-          
    AutogradContext 
    
-        
    Class representing the context.
    
-          
    as_array() 
    
-        
    Converts to array
    
-          
    autograd_backward() 
    
-        
    Computes the sum of gradients of given tensors w.r.t. graph leaves.
    
-          
    autograd_function() 
    
-        
    Records operation history and defines formulas for differentiating ops.
    
-          
    autograd_grad() 
    
-        
    Computes and returns the sum of gradients of outputs w.r.t. the inputs.
    
-          
    autograd_set_grad_mode() 
    
-        
    Set grad mode
    
-          
    cuda_current_device() 
    
-        
    Returns the index of a currently selected device.
    
-          
    cuda_device_count() 
    
-        
    Returns the number of GPUs available.
    
-          
    cuda_is_available() 
    
-        
    Returns a bool indicating if CUDA is currently available.
    
-          
    dataloader() 
    
-        
    Data loader. Combines a dataset and a sampler, and provides
-single- or multi-process iterators over the dataset.
    
-          
    dataloader_make_iter() 
    
-        
    Creates an iterator from a DataLoader
    
-          
    dataloader_next() 
    
-        
    Get the next element of a dataloader iterator
    
-          
    dataset() 
    
-        
    An abstract class representing a Dataset.
    
-          
    torch_set_default_dtype() torch_get_default_dtype() 
    
-        
    Gets and sets the default floating point dtype.
    
-          
    enumerate() 
    
-        
    Enumerate an iterator
    
-          
    enumerate(<dataloader>) 
    
-        
    Enumerate an iterator
    
-          
    install_torch() 
    
-        
    Install Torch
    
-          
    is_dataloader() 
    
-        
    Checks if the object is a dataloader
    
-          
    is_torch_dtype() 
    
-        
    Check if object is a torch data type
    
-          
    is_torch_layout() 
    
-        
    Check if an object is a torch layout.
    
-          
    is_torch_memory_format() 
    
-        
    Check if an object is a memory format
    
-          
    is_torch_qscheme() 
    
-        
    Checks if an object is a QScheme
    
-          
    kmnist_dataset() 
    
-        
    Kuzushiji-MNIST
    
-          
    mnist_dataset() 
    
-        
    MNIST dataset
    
-          
    nn_adaptive_log_softmax_with_loss() 
    
-        
    AdaptiveLogSoftmaxWithLoss module
    
-          
    nn_batch_norm1d() 
    
-        
    BatchNorm1D module
    
-          
    nn_batch_norm2d() 
    
-        
    BatchNorm2D
    
-          
    nn_bce_loss() 
    
-        
    Binary cross entropy loss
    
-          
    nn_bilinear() 
    
-        
    Bilinear module
    
-          
    nn_celu() 
    
-        
    CELU module
    
-          
    nn_conv1d() 
    
-        
    Conv1D module
    
-          
    nn_conv2d() 
    
-        
    Conv2D module
    
-          
    nn_conv3d() 
    
-        
    Conv3D module
    
-          
    nn_conv_transpose1d() 
    
-        
    ConvTranspose1D
    
-          
    nn_conv_transpose2d() 
    
-        
    ConvTranpose2D module
    
-          
    nn_conv_transpose3d() 
    
-        
    ConvTranpose3D module
    
-          
    nn_cross_entropy_loss() 
    
-        
    CrossEntropyLoss module
    
-          
    nn_dropout() 
    
-        
    Dropout module
    
-          
    nn_dropout2d() 
    
-        
    Dropout2D module
    
-          
    nn_dropout3d() 
    
-        
    Dropout3D module
    
-          
    nn_elu() 
    
-        
    ELU module
    
-          
    nn_embedding() 
    
-        
    Embedding module
    
-          
    nn_gelu() 
    
-        
    GELU module
    
-          
    nn_glu() 
    
-        
    GLU module
    
-          
    nn_hardshrink() 
    
-        
    Hardshwink module
    
-          
    nn_hardsigmoid() 
    
-        
    Hardsigmoid module
    
-          
    nn_hardswish() 
    
-        
    Hardswish module
    
-          
    nn_hardtanh() 
    
-        
    Hardtanh module
    
-          
    nn_identity() 
    
-        
    Identity module
    
-          
    nn_init_calculate_gain() 
    
-        
    Calculate gain
    
-          
    nn_init_constant_() 
    
-        
    Constant initialization
    
-          
    nn_init_dirac_() 
    
-        
    Dirac initialization
    
-          
    nn_init_eye_() 
    
-        
    Eye initialization
    
-          
    nn_init_kaiming_normal_() 
    
-        
    Kaiming normal initialization
    
-          
    nn_init_kaiming_uniform_() 
    
-        
    Kaiming uniform initialization
    
-          
    nn_init_normal_() 
    
-        
    Normal initialization
    
-          
    nn_init_ones_() 
    
-        
    Ones initialization
    
-          
    nn_init_orthogonal_() 
    
-        
    Orthogonal initialization
    
-          
    nn_init_sparse_() 
    
-        
    Sparse initialization
    
-          
    nn_init_trunc_normal_() 
    
-        
    Truncated normal initialization
    
-          
    nn_init_uniform_() 
    
-        
    Uniform initialization
    
-          
    nn_init_xavier_normal_() 
    
-        
    Xavier normal initialization
    
-          
    nn_init_xavier_uniform_() 
    
-        
    Xavier uniform initialization
    
-          
    nn_init_zeros_() 
    
-        
    Zeros initialization
    
-          
    nn_leaky_relu() 
    
-        
    LeakyReLU module
    
-          
    nn_linear() 
    
-        
    Linear module
    
-          
    nn_log_sigmoid() 
    
-        
    LogSigmoid module
    
-          
    nn_log_softmax() 
    
-        
    LogSoftmax module
    
-          
    nn_max_pool1d() 
    
-        
    MaxPool1D module
    
-          
    nn_max_pool2d() 
    
-        
    MaxPool2D module
    
-          
    nn_module() 
    
-        
    Base class for all neural network modules.
    
-          
    nn_module_list() 
    
-        
    Holds submodules in a list.
    
-          
    nn_multihead_attention() 
    
-        
    MultiHead attention
    
-          
    nn_prelu() 
    
-        
    PReLU module
    
-          
    nn_relu() 
    
-        
    ReLU module
    
-          
    nn_relu6() 
    
-        
    ReLu6 module
    
-          
    nn_rnn() 
    
-        
    RNN module
    
-          
    nn_rrelu() 
    
-        
    RReLU module
    
-          
    nn_selu() 
    
-        
    SELU module
    
-          
    nn_sequential() 
    
-        
    A sequential container
    
-          
    nn_sigmoid() 
    
-        
    Sigmoid module
    
-          
    nn_softmax() 
    
-        
    Softmax module
    
-          
    nn_softmax2d() 
    
-        
    Softmax2d module
    
-          
    nn_softmin() 
    
-        
    Softmin
    
-          
    nn_softplus() 
    
-        
    Softplus module
    
-          
    nn_softshrink() 
    
-        
    Softshrink module
    
-          
    nn_softsign() 
    
-        
    Softsign module
    
-          
    nn_tanh() 
    
-        
    Tanh module
    
-          
    nn_tanhshrink() 
    
-        
    Tanhshrink module
    
-          
    nn_threshold() 
    
-        
    Threshoold module
    
-          
    nn_utils_rnn_pack_padded_sequence() 
    
-        
    Packs a Tensor containing padded sequences of variable length.
    
-          
    nn_utils_rnn_pack_sequence() 
    
-        
    Packs a list of variable length Tensors
    
-          
    nn_utils_rnn_pad_packed_sequence() 
    
-        
    Pads a packed batch of variable length sequences.
    
-          
    nn_utils_rnn_pad_sequence() 
    
-        
    Pad a list of variable length Tensors with padding_value
    
-          
    nnf_adaptive_avg_pool1d() 
    
-        
    Adaptive_avg_pool1d
    
-          
    nnf_adaptive_avg_pool2d() 
    
-        
    Adaptive_avg_pool2d
    
-          
    nnf_adaptive_avg_pool3d() 
    
-        
    Adaptive_avg_pool3d
    
-          
    nnf_adaptive_max_pool1d() 
    
-        
    Adaptive_max_pool1d
    
-          
    nnf_adaptive_max_pool2d() 
    
-        
    Adaptive_max_pool2d
    
-          
    nnf_adaptive_max_pool3d() 
    
-        
    Adaptive_max_pool3d
    
-          
    nnf_affine_grid() 
    
-        
    Affine_grid
    
-          
    nnf_alpha_dropout() 
    
-        
    Alpha_dropout
    
-          
    nnf_avg_pool1d() 
    
-        
    Avg_pool1d
    
-          
    nnf_avg_pool2d() 
    
-        
    Avg_pool2d
    
-          
    nnf_avg_pool3d() 
    
-        
    Avg_pool3d
    
-          
    nnf_batch_norm() 
    
-        
    Batch_norm
    
-          
    nnf_bilinear() 
    
-        
    Bilinear
    
-          
    nnf_binary_cross_entropy() 
    
-        
    Binary_cross_entropy
    
-          
    nnf_binary_cross_entropy_with_logits() 
    
-        
    Binary_cross_entropy_with_logits
    
-          
    nnf_celu() nnf_celu_() 
    
-        
    Celu
    
-          
    nnf_conv1d() 
    
-        
    Conv1d
    
-          
    nnf_conv2d() 
    
-        
    Conv2d
    
-          
    nnf_conv3d() 
    
-        
    Conv3d
    
-          
    nnf_conv_tbc() 
    
-        
    Conv_tbc
    
-          
    nnf_conv_transpose1d() 
    
-        
    Conv_transpose1d
    
-          
    nnf_conv_transpose2d() 
    
-        
    Conv_transpose2d
    
-          
    nnf_conv_transpose3d() 
    
-        
    Conv_transpose3d
    
-          
    nnf_cosine_embedding_loss() 
    
-        
    Cosine_embedding_loss
    
-          
    nnf_cosine_similarity() 
    
-        
    Cosine_similarity
    
-          
    nnf_cross_entropy() 
    
-        
    Cross_entropy
    
-          
    nnf_ctc_loss() 
    
-        
    Ctc_loss
    
-          
    nnf_dropout() 
    
-        
    Dropout
    
-          
    nnf_dropout2d() 
    
-        
    Dropout2d
    
-          
    nnf_dropout3d() 
    
-        
    Dropout3d
    
-          
    nnf_elu() nnf_elu_() 
    
-        
    Elu
    
-          
    nnf_embedding() 
    
-        
    Embedding
    
-          
    nnf_embedding_bag() 
    
-        
    Embedding_bag
    
-          
    nnf_fold() 
    
-        
    Fold
    
-          
    nnf_fractional_max_pool2d() 
    
-        
    Fractional_max_pool2d
    
-          
    nnf_fractional_max_pool3d() 
    
-        
    Fractional_max_pool3d
    
-          
    nnf_gelu() 
    
-        
    Gelu
    
-          
    nnf_glu() 
    
-        
    Glu
    
-          
    nnf_grid_sample() 
    
-        
    Grid_sample
    
-          
    nnf_group_norm() 
    
-        
    Group_norm
    
-          
    nnf_gumbel_softmax() 
    
-        
    Gumbel_softmax
    
-          
    nnf_hardshrink() 
    
-        
    Hardshrink
    
-          
    nnf_hardsigmoid() 
    
-        
    Hardsigmoid
    
-          
    nnf_hardswish() 
    
-        
    Hardswish
    
-          
    nnf_hardtanh() nnf_hardtanh_() 
    
-        
    Hardtanh
    
-          
    nnf_hinge_embedding_loss() 
    
-        
    Hinge_embedding_loss
    
-          
    nnf_instance_norm() 
    
-        
    Instance_norm
    
-          
    nnf_interpolate() 
    
-        
    Interpolate
    
-          
    nnf_kl_div() 
    
-        
    Kl_div
    
-          
    nnf_l1_loss() 
    
-        
    L1_loss
    
-          
    nnf_layer_norm() 
    
-        
    Layer_norm
    
-          
    nnf_leaky_relu() 
    
-        
    Leaky_relu
    
-          
    nnf_linear() 
    
-        
    Linear
    
-          
    nnf_local_response_norm() 
    
-        
    Local_response_norm
    
-          
    nnf_log_softmax() 
    
-        
    Log_softmax
    
-          
    nnf_logsigmoid() 
    
-        
    Logsigmoid
    
-          
    nnf_lp_pool1d() 
    
-        
    Lp_pool1d
    
-          
    nnf_lp_pool2d() 
    
-        
    Lp_pool2d
    
-          
    nnf_margin_ranking_loss() 
    
-        
    Margin_ranking_loss
    
-          
    nnf_max_pool1d() 
    
-        
    Max_pool1d
    
-          
    nnf_max_pool2d() 
    
-        
    Max_pool2d
    
-          
    nnf_max_pool3d() 
    
-        
    Max_pool3d
    
-          
    nnf_max_unpool1d() 
    
-        
    Max_unpool1d
    
-          
    nnf_max_unpool2d() 
    
-        
    Max_unpool2d
    
-          
    nnf_max_unpool3d() 
    
-        
    Max_unpool3d
    
-          
    nnf_mse_loss() 
    
-        
    Mse_loss
    
-          
    nnf_multi_head_attention_forward() 
    
-        
    Multi head attention forward
    
-          
    nnf_multi_margin_loss() 
    
-        
    Multi_margin_loss
    
-          
    nnf_multilabel_margin_loss() 
    
-        
    Multilabel_margin_loss
    
-          
    nnf_multilabel_soft_margin_loss() 
    
-        
    Multilabel_soft_margin_loss
    
-          
    nnf_nll_loss() 
    
-        
    Nll_loss
    
-          
    nnf_normalize() 
    
-        
    Normalize
    
-          
    nnf_one_hot() 
    
-        
    One_hot
    
-          
    nnf_pad() 
    
-        
    Pad
    
-          
    nnf_pairwise_distance() 
    
-        
    Pairwise_distance
    
-          
    nnf_pdist() 
    
-        
    Pdist
    
-          
    nnf_pixel_shuffle() 
    
-        
    Pixel_shuffle
    
-          
    nnf_poisson_nll_loss() 
    
-        
    Poisson_nll_loss
    
-          
    nnf_prelu() 
    
-        
    Prelu
    
-          
    nnf_relu() nnf_relu_() 
    
-        
    Relu
    
-          
    nnf_relu6() 
    
-        
    Relu6
    
-          
    nnf_rrelu() nnf_rrelu_() 
    
-        
    Rrelu
    
-          
    nnf_selu() nnf_selu_() 
    
-        
    Selu
    
-          
    nnf_smooth_l1_loss() 
    
-        
    Smooth_l1_loss
    
-          
    nnf_soft_margin_loss() 
    
-        
    Soft_margin_loss
    
-          
    nnf_softmax() 
    
-        
    Softmax
    
-          
    nnf_softmin() 
    
-        
    Softmin
    
-          
    nnf_softplus() 
    
-        
    Softplus
    
-          
    nnf_softshrink() 
    
-        
    Softshrink
    
-          
    nnf_softsign() 
    
-        
    Softsign
    
-          
    nnf_tanhshrink() 
    
-        
    Tanhshrink
    
-          
    nnf_threshold() nnf_threshold_() 
    
-        
    Threshold
    
-          
    nnf_triplet_margin_loss() 
    
-        
    Triplet_margin_loss
    
-          
    nnf_unfold() 
    
-        
    Unfold
    
-          
    optim_adam() 
    
-        
    Implements Adam algorithm.
    
-          
    optim_required() 
    
-        
    Dummy value indicating a required value.
    
-          
    optim_sgd() 
    
-        
    SGD optimizer
    
-          
    tensor_dataset() 
    
-        
    Dataset wrapping tensors.
    
-          
    torch_abs 
    
-        
    Abs
    
-          
    torch_acos 
    
-        
    Acos
    
-          
    torch_adaptive_avg_pool1d 
    
-        
    Adaptive_avg_pool1d
    
-          
    torch_add 
    
-        
    Add
    
-          
    torch_addbmm 
    
-        
    Addbmm
    
-          
    torch_addcdiv 
    
-        
    Addcdiv
    
-          
    torch_addcmul 
    
-        
    Addcmul
    
-          
    torch_addmm 
    
-        
    Addmm
    
-          
    torch_addmv 
    
-        
    Addmv
    
-          
    torch_addr 
    
-        
    Addr
    
-          
    torch_allclose 
    
-        
    Allclose
    
-          
    torch_angle 
    
-        
    Angle
    
-          
    torch_arange 
    
-        
    Arange
    
-          
    torch_argmax 
    
-        
    Argmax
    
-          
    torch_argmin 
    
-        
    Argmin
    
-          
    torch_argsort 
    
-        
    Argsort
    
-          
    torch_as_strided 
    
-        
    As_strided
    
-          
    torch_asin 
    
-        
    Asin
    
-          
    torch_atan 
    
-        
    Atan
    
-          
    torch_atan2 
    
-        
    Atan2
    
-          
    torch_avg_pool1d 
    
-        
    Avg_pool1d
    
-          
    torch_baddbmm 
    
-        
    Baddbmm
    
-          
    torch_bartlett_window 
    
-        
    Bartlett_window
    
-          
    torch_bernoulli 
    
-        
    Bernoulli
    
-          
    torch_bincount 
    
-        
    Bincount
    
-          
    torch_bitwise_and 
    
-        
    Bitwise_and
    
-          
    torch_bitwise_not 
    
-        
    Bitwise_not
    
-          
    torch_bitwise_or 
    
-        
    Bitwise_or
    
-          
    torch_bitwise_xor 
    
-        
    Bitwise_xor
    
-          
    torch_blackman_window 
    
-        
    Blackman_window
    
-          
    torch_bmm 
    
-        
    Bmm
    
-          
    torch_broadcast_tensors 
    
-        
    Broadcast_tensors
    
-          
    torch_can_cast 
    
-        
    Can_cast
    
-          
    torch_cartesian_prod 
    
-        
    Cartesian_prod
    
-          
    torch_cat 
    
-        
    Cat
    
-          
    torch_cdist 
    
-        
    Cdist
    
-          
    torch_ceil 
    
-        
    Ceil
    
-          
    torch_celu_ 
    
-        
    Celu_
    
-          
    torch_chain_matmul 
    
-        
    Chain_matmul
    
-          
    torch_cholesky 
    
-        
    Cholesky
    
-          
    torch_cholesky_inverse 
    
-        
    Cholesky_inverse
    
-          
    torch_cholesky_solve 
    
-        
    Cholesky_solve
    
-          
    torch_chunk 
    
-        
    Chunk
    
-          
    torch_clamp 
    
-        
    Clamp
    
-          
    torch_combinations 
    
-        
    Combinations
    
-          
    torch_conj 
    
-        
    Conj
    
-          
    torch_conv1d 
    
-        
    Conv1d
    
-          
    torch_conv2d 
    
-        
    Conv2d
    
-          
    torch_conv3d 
    
-        
    Conv3d
    
-          
    torch_conv_tbc 
    
-        
    Conv_tbc
    
-          
    torch_conv_transpose1d 
    
-        
    Conv_transpose1d
    
-          
    torch_conv_transpose2d 
    
-        
    Conv_transpose2d
    
-          
    torch_conv_transpose3d 
    
-        
    Conv_transpose3d
    
-          
    torch_cos 
    
-        
    Cos
    
-          
    torch_cosh 
    
-        
    Cosh
    
-          
    torch_cosine_similarity 
    
-        
    Cosine_similarity
    
-          
    torch_cross 
    
-        
    Cross
    
-          
    torch_cummax 
    
-        
    Cummax
    
-          
    torch_cummin 
    
-        
    Cummin
    
-          
    torch_cumprod 
    
-        
    Cumprod
    
-          
    torch_cumsum 
    
-        
    Cumsum
    
-          
    torch_det 
    
-        
    Det
    
-          
    torch_device() 
    
-        
    Create a Device object
    
-          
    torch_diag 
    
-        
    Diag
    
-          
    torch_diag_embed 
    
-        
    Diag_embed
    
-          
    torch_diagflat 
    
-        
    Diagflat
    
-          
    torch_diagonal 
    
-        
    Diagonal
    
-          
    torch_digamma 
    
-        
    Digamma
    
-          
    torch_dist 
    
-        
    Dist
    
-          
    torch_div 
    
-        
    Div
    
-          
    torch_dot 
    
-        
    Dot
    
-          
    torch_float32() torch_float() torch_float64() torch_double() torch_float16() torch_half() torch_uint8() torch_int8() torch_int16() torch_short() torch_int32() torch_int() torch_int64() torch_long() torch_bool() torch_quint8() torch_qint8() torch_qint32() 
    
-        
    Torch data types
    
-          
    torch_eig 
    
-        
    Eig
    
-          
    torch_einsum 
    
-        
    Einsum
    
-          
    torch_empty 
    
-        
    Empty
    
-          
    torch_empty_like 
    
-        
    Empty_like
    
-          
    torch_empty_strided 
    
-        
    Empty_strided
    
-          
    torch_eq 
    
-        
    Eq
    
-          
    torch_equal 
    
-        
    Equal
    
-          
    torch_erf 
    
-        
    Erf
    
-          
    torch_erfc 
    
-        
    Erfc
    
-          
    torch_erfinv 
    
-        
    Erfinv
    
-          
    torch_exp 
    
-        
    Exp
    
-          
    torch_expm1 
    
-        
    Expm1
    
-          
    torch_eye 
    
-        
    Eye
    
-          
    torch_fft 
    
-        
    Fft
    
-          
    torch_flatten 
    
-        
    Flatten
    
-          
    torch_flip 
    
-        
    Flip
    
-          
    torch_floor 
    
-        
    Floor
    
-          
    torch_floor_divide 
    
-        
    Floor_divide
    
-          
    torch_fmod 
    
-        
    Fmod
    
-          
    torch_frac 
    
-        
    Frac
    
-          
    torch_full 
    
-        
    Full
    
-          
    torch_full_like 
    
-        
    Full_like
    
-          
    torch_gather 
    
-        
    Gather
    
-          
    torch_ge 
    
-        
    Ge
    
-          
    torch_generator() 
    
-        
    Create a Generator object
    
-          
    torch_geqrf 
    
-        
    Geqrf
    
-          
    torch_ger 
    
-        
    Ger
    
-          
    torch_gt 
    
-        
    Gt
    
-          
    torch_hamming_window 
    
-        
    Hamming_window
    
-          
    torch_hann_window 
    
-        
    Hann_window
    
-          
    torch_histc 
    
-        
    Histc
    
-          
    torch_ifft 
    
-        
    Ifft
    
-          
    torch_imag 
    
-        
    Imag
    
-          
    torch_index_select 
    
-        
    Index_select
    
-          
    torch_inverse 
    
-        
    Inverse
    
-          
    torch_irfft 
    
-        
    Irfft
    
-          
    torch_is_complex 
    
-        
    Is_complex
    
-          
    torch_is_floating_point 
    
-        
    Is_floating_point
    
-          
    torch_isfinite 
    
-        
    Isfinite
    
-          
    torch_isinf 
    
-        
    Isinf
    
-          
    torch_isnan 
    
-        
    Isnan
    
-          
    torch_kthvalue 
    
-        
    Kthvalue
    
-          
    torch_strided() torch_sparse_coo() 
    
-        
    Creates the corresponding layout
    
-          
    torch_le 
    
-        
    Le
    
-          
    torch_lerp 
    
-        
    Lerp
    
-          
    torch_lgamma 
    
-        
    Lgamma
    
-          
    torch_linspace 
    
-        
    Linspace
    
-          
    torch_load() 
    
-        
    Loads a saved object
    
-          
    torch_log 
    
-        
    Log
    
-          
    torch_log10 
    
-        
    Log10
    
-          
    torch_log1p 
    
-        
    Log1p
    
-          
    torch_log2 
    
-        
    Log2
    
-          
    torch_logdet 
    
-        
    Logdet
    
-          
    torch_logical_and 
    
-        
    Logical_and
    
-          
    torch_logical_not 
    
-        
    Logical_not
    
-          
    torch_logical_or 
    
-        
    Logical_or
    
-          
    torch_logical_xor 
    
-        
    Logical_xor
    
-          
    torch_logspace 
    
-        
    Logspace
    
-          
    torch_logsumexp 
    
-        
    Logsumexp
    
-          
    torch_lstsq 
    
-        
    Lstsq
    
-          
    torch_lt 
    
-        
    Lt
    
-          
    torch_lu() 
    
-        
    LU
    
-          
    torch_lu_solve 
    
-        
    Lu_solve
    
-          
    torch_masked_select 
    
-        
    Masked_select
    
-          
    torch_matmul 
    
-        
    Matmul
    
-          
    torch_matrix_power 
    
-        
    Matrix_power
    
-          
    torch_matrix_rank 
    
-        
    Matrix_rank
    
-          
    torch_max 
    
-        
    Max
    
-          
    torch_mean 
    
-        
    Mean
    
-          
    torch_median 
    
-        
    Median
    
-          
    torch_contiguous_format() torch_preserve_format() torch_channels_last_format() 
    
-        
    Memory format
    
-          
    torch_meshgrid 
    
-        
    Meshgrid
    
-          
    torch_min 
    
-        
    Min
    
-          
    torch_mm 
    
-        
    Mm
    
-          
    torch_mode 
    
-        
    Mode
    
-          
    torch_mul 
    
-        
    Mul
    
-          
    torch_multinomial 
    
-        
    Multinomial
    
-          
    torch_mv 
    
-        
    Mv
    
-          
    torch_mvlgamma 
    
-        
    Mvlgamma
    
-          
    torch_narrow 
    
-        
    Narrow
    
-          
    torch_ne 
    
-        
    Ne
    
-          
    torch_neg 
    
-        
    Neg
    
-          
    torch_nonzero 
    
-        
    Nonzero
    
-          
    torch_norm 
    
-        
    Norm
    
-          
    torch_normal 
    
-        
    Normal
    
-          
    torch_ones 
    
-        
    Ones
    
-          
    torch_ones_like 
    
-        
    Ones_like
    
-          
    torch_orgqr 
    
-        
    Orgqr
    
-          
    torch_ormqr 
    
-        
    Ormqr
    
-          
    torch_pdist 
    
-        
    Pdist
    
-          
    torch_pinverse 
    
-        
    Pinverse
    
-          
    torch_pixel_shuffle 
    
-        
    Pixel_shuffle
    
-          
    torch_poisson 
    
-        
    Poisson
    
-          
    torch_polygamma 
    
-        
    Polygamma
    
-          
    torch_pow 
    
-        
    Pow
    
-          
    torch_prod 
    
-        
    Prod
    
-          
    torch_promote_types 
    
-        
    Promote_types
    
-          
    torch_qr 
    
-        
    Qr
    
-          
    torch_per_channel_affine() torch_per_tensor_affine() torch_per_channel_symmetric() torch_per_tensor_symmetric() 
    
-        
    Creates the corresponding Scheme object
    
-          
    torch_quantize_per_channel 
    
-        
    Quantize_per_channel
    
-          
    torch_quantize_per_tensor 
    
-        
    Quantize_per_tensor
    
-          
    torch_rand 
    
-        
    Rand
    
-          
    torch_rand_like 
    
-        
    Rand_like
    
-          
    torch_randint 
    
-        
    Randint
    
-          
    torch_randint_like 
    
-        
    Randint_like
    
-          
    torch_randn 
    
-        
    Randn
    
-          
    torch_randn_like 
    
-        
    Randn_like
    
-          
    torch_randperm 
    
-        
    Randperm
    
-          
    torch_range 
    
-        
    Range
    
-          
    torch_real 
    
-        
    Real
    
-          
    torch_reciprocal 
    
-        
    Reciprocal
    
-          
    torch_reduction_sum() torch_reduction_mean() torch_reduction_none() 
    
-        
    Creates the reduction objet
    
-          
    torch_relu_ 
    
-        
    Relu_
    
-          
    torch_remainder 
    
-        
    Remainder
    
-          
    torch_renorm 
    
-        
    Renorm
    
-          
    torch_repeat_interleave 
    
-        
    Repeat_interleave
    
-          
    torch_reshape 
    
-        
    Reshape
    
-          
    torch_result_type 
    
-        
    Result_type
    
-          
    torch_rfft 
    
-        
    Rfft
    
-          
    torch_roll 
    
-        
    Roll
    
-          
    torch_rot90 
    
-        
    Rot90
    
-          
    torch_round 
    
-        
    Round
    
-          
    torch_rrelu_ 
    
-        
    Rrelu_
    
-          
    torch_rsqrt 
    
-        
    Rsqrt
    
-          
    torch_save() 
    
-        
    Saves an object to a disk file.
    
-          
    torch_selu_ 
    
-        
    Selu_
    
-          
    torch_sigmoid 
    
-        
    Sigmoid
    
-          
    torch_sign 
    
-        
    Sign
    
-          
    torch_sin 
    
-        
    Sin
    
-          
    torch_sinh 
    
-        
    Sinh
    
-          
    torch_slogdet 
    
-        
    Slogdet
    
-          
    torch_solve 
    
-        
    Solve
    
-          
    torch_sort 
    
-        
    Sort
    
-          
    torch_sparse_coo_tensor 
    
-        
    Sparse_coo_tensor
    
-          
    torch_split 
    
-        
    Split
    
-          
    torch_sqrt 
    
-        
    Sqrt
    
-          
    torch_square 
    
-        
    Square
    
-          
    torch_squeeze 
    
-        
    Squeeze
    
-          
    torch_stack 
    
-        
    Stack
    
-          
    torch_std 
    
-        
    Std
    
-          
    torch_std_mean 
    
-        
    Std_mean
    
-          
    torch_stft 
    
-        
    Stft
    
-          
    torch_sum 
    
-        
    Sum
    
-          
    torch_svd 
    
-        
    Svd
    
-          
    torch_symeig 
    
-        
    Symeig
    
-          
    torch_t 
    
-        
    T
    
-          
    torch_take 
    
-        
    Take
    
-          
    torch_tan 
    
-        
    Tan
    
-          
    torch_tanh 
    
-        
    Tanh
    
-          
    torch_tensor() 
    
-        
    Converts R objects to a torch tensor
    
-          
    torch_tensordot 
    
-        
    Tensordot
    
-          
    torch_threshold_ 
    
-        
    Threshold_
    
-          
    torch_topk 
    
-        
    Topk
    
-          
    torch_trace 
    
-        
    Trace
    
-          
    torch_transpose 
    
-        
    Transpose
    
-          
    torch_trapz 
    
-        
    Trapz
    
-          
    torch_triangular_solve 
    
-        
    Triangular_solve
    
-          
    torch_tril 
    
-        
    Tril
    
-          
    torch_tril_indices 
    
-        
    Tril_indices
    
-          
    torch_triu 
    
-        
    Triu
    
-          
    torch_triu_indices 
    
-        
    Triu_indices
    
-          
    torch_true_divide 
    
-        
    True_divide
    
-          
    torch_trunc 
    
-        
    Trunc
    
-          
    torch_unbind 
    
-        
    Unbind
    
-          
    torch_unique_consecutive 
    
-        
    Unique_consecutive
    
-          
    torch_unsqueeze 
    
-        
    Unsqueeze
    
-          
    torch_var 
    
-        
    Var
    
-          
    torch_var_mean 
    
-        
    Var_mean
    
-          
    torch_where 
    
-        
    Where
    
-          
    torch_zeros 
    
-        
    Zeros
    
-          
    torch_zeros_like 
    
-        
    Zeros_like
    
-          
    vision_make_grid() 
    
-        
    A simplified version of torchvision.utils.make_grid.
    
-          
    with_enable_grad() 
    
-        
    Enable grad
    
-          
    with_no_grad() 
    
-        
    Temporarily modify gradient recording.
    
-  
    
-
-  
-
    
-
-
-      
-