Generate Python docs from pytorch/pytorch@37c1f4a

Author: pytorchbot

docs/1.8.0/_modules/index.html

@@ -440,6 +440,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="torch/distributions/kl.html">torch.distributions.kl</a></li>
 <li><a href="torch/distributions/kumaraswamy.html">torch.distributions.kumaraswamy</a></li>
 <li><a href="torch/distributions/laplace.html">torch.distributions.laplace</a></li>
+<li><a href="torch/distributions/lkj_cholesky.html">torch.distributions.lkj_cholesky</a></li>
 <li><a href="torch/distributions/log_normal.html">torch.distributions.log_normal</a></li>
 <li><a href="torch/distributions/lowrank_multivariate_normal.html">torch.distributions.lowrank_multivariate_normal</a></li>
 <li><a href="torch/distributions/mixture_same_family.html">torch.distributions.mixture_same_family</a></li>

docs/1.8.0/_modules/torch/distributions/lkj_cholesky.html

@@ -176,6 +176,15 @@ Probability distributions - torch.distributions
     :undoc-members:
     :show-inheritance:
 
+:hidden:`LKJCholesky`
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. currentmodule:: torch.distributions.lkj_cholesky
+.. autoclass:: LKJCholesky
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 :hidden:`Laplace`
 ~~~~~~~~~~~~~~~~~~~~~~~
 
@@ -337,7 +346,7 @@ Probability distributions - torch.distributions
     :members:
     :undoc-members:
     :show-inheritance:
-    
+
 :hidden:`Weibull`
 ~~~~~~~~~~~~~~~~~~~~~~~
 

docs/1.8.0/_sources/distributions.rst.txt

@@ -957,28 +957,31 @@ if (downloadNote.length >= 1) {
     $(".pytorch-call-to-action-links").hide();
 }
 
-//This code makes the Notes section of the Docs Left Nav collapsible
+//This code handles the Expand/Hide toggle for the Docs/Tutorials left nav items
 
-if ($("p.caption:first").text() == "Notes") {
+var expandMenu = "#pytorch-left-menu p.caption";
 
-    $("p.caption:first").addClass("left-nav-top-caption");
-    $("span.caption-text:first").after("<span class='expand-menu'>[Expand]</span>");
-    $(".expand-menu").after("<span class='hide-menu'>[Hide]</span>");
-    $("p.caption:first").next("ul").hide();
+if ($(expandMenu)) {
+
+    $(expandMenu).addClass("left-nav-top-caption");
+    $("#pytorch-left-menu span.caption-text").after("<span class='expand-menu'>[ + ]</span>");
+    $("#pytorch-left-menu .expand-menu").after("<span class='hide-menu'>[ - ]</span>");
+    $(expandMenu).next("ul").hide();
 
     $(".expand-menu").on("click", function() {
-        $(".hide-menu").toggle();
+        $(this).next(".hide-menu").toggle();
+        $(this).parent().next("ul").toggle();
         toggleList(this);
     });
 
     $(".hide-menu").on("click", function() {
-        $(".expand-menu").toggle();
+        $(this).prev(".expand-menu").toggle();
+        $(this).parent().next("ul").toggle();
         toggleList(this);
     });
 
     function toggleList(menuCommand) {
         $(menuCommand).toggle();
-        $("p.caption:first").next("ul").toggle();
     }
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docs/1.8.0/_static/css/theme.css

@@ -2051,6 +2051,80 @@ <h2><span class="hidden-section">Kumaraswamy</span><a class="headerlink" href="#
 
 </dd></dl>
 
+</div>
+<div class="section" id="lkjcholesky">
+<h2><span class="hidden-section">LKJCholesky</span><a class="headerlink" href="#lkjcholesky" title="Permalink to this headline">¶</a></h2>
+<dl class="class">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky">
+<em class="property">class </em><code class="sig-prename descclassname">torch.distributions.lkj_cholesky.</code><code class="sig-name descname">LKJCholesky</code><span class="sig-paren">(</span><em class="sig-param">dim</em>, <em class="sig-param">concentration=1.0</em>, <em class="sig-param">validate_args=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky" title="Permalink to this definition">¶</a></dt>
+<dd><p>Bases: <a class="reference internal" href="#torch.distributions.distribution.Distribution" title="torch.distributions.distribution.Distribution"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.distributions.distribution.Distribution</span></code></a></p>
+<p>LKJ distribution for lower Cholesky factor of correlation matrices.
+The distribution is controlled by <code class="docutils literal notranslate"><span class="pre">concentration</span></code> parameter <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>η</mi></mrow><annotation encoding="application/x-tex">\eta</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="mord mathnormal" style="margin-right:0.03588em;">η</span></span></span></span>
+
+</span>
+to make the probability of the correlation matrix <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>M</mi></mrow><annotation encoding="application/x-tex">M</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.68333em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span></span></span></span>
+
+</span> generated from
+a Cholesky factor propotional to <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>det</mi><mo>⁡</mo><mo stretchy="false">(</mo><mi>M</mi><msup><mo stretchy="false">)</mo><mrow><mi>η</mi><mo>−</mo><mn>1</mn></mrow></msup></mrow><annotation encoding="application/x-tex">\det(M)^{\eta - 1}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.064108em;vertical-align:-0.25em;"></span><span class="mop">det</span><span class="mopen">(</span><span class="mord mathnormal" style="margin-right:0.10903em;">M</span><span class="mclose"><span class="mclose">)</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8141079999999999em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight" style="margin-right:0.03588em;">η</span><span class="mbin mtight">−</span><span class="mord mtight">1</span></span></span></span></span></span></span></span></span></span></span></span>
+
+</span>. Because of that,
+when <code class="docutils literal notranslate"><span class="pre">concentration</span> <span class="pre">==</span> <span class="pre">1</span></code>, we have a uniform distribution over Cholesky
+factors of correlation matrices. Note that this distribution samples the
+Cholesky factor of correlation matrices and not the correlation matrices
+themselves and thereby differs slightly from the derivations in [1] for
+the <cite>LKJCorr</cite> distribution. For sampling, this uses the Onion method from
+[1] Section 3.</p>
+<blockquote>
+<div><p>L ~ LKJCholesky(dim, concentration)
+X = L &#64; L’ ~ LKJCorr(dim, concentration)</p>
+</div></blockquote>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">LKJCholesky</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">l</span><span class="o">.</span><span class="n">sample</span><span class="p">()</span>  <span class="c1"># l @ l.T is a sample of a correlation 3x3 matrix</span>
+<span class="go">tensor([[ 1.0000,  0.0000,  0.0000],</span>
+<span class="go">        [ 0.3516,  0.9361,  0.0000],</span>
+<span class="go">        [-0.1899,  0.4748,  0.8593]])</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters</dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dimension</strong> (<em>dim</em>) – dimension of the matrices</p></li>
+<li><p><strong>concentration</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – concentration/shape parameter of the
+distribution (often referred to as eta)</p></li>
+</ul>
+</dd>
+</dl>
+<p><strong>References</strong></p>
+<p>[1] <cite>Generating random correlation matrices based on vines and extended onion method</cite>,
+Daniel Lewandowski, Dorota Kurowicka, Harry Joe.</p>
+<dl class="attribute">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky.arg_constraints">
+<code class="sig-name descname">arg_constraints</code><em class="property"> = {'concentration': GreaterThan(lower_bound=0.0)}</em><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.arg_constraints" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky.expand">
+<code class="sig-name descname">expand</code><span class="sig-paren">(</span><em class="sig-param">batch_shape</em>, <em class="sig-param">_instance=None</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.expand"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.expand" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky.log_prob">
+<code class="sig-name descname">log_prob</code><span class="sig-paren">(</span><em class="sig-param">value</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.log_prob"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.log_prob" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="method">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky.sample">
+<code class="sig-name descname">sample</code><span class="sig-paren">(</span><em class="sig-param">sample_shape=torch.Size([])</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/distributions/lkj_cholesky.html#LKJCholesky.sample"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.sample" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+<dl class="attribute">
+<dt id="torch.distributions.lkj_cholesky.LKJCholesky.support">
+<code class="sig-name descname">support</code><em class="property"> = CorrCholesky()</em><a class="headerlink" href="#torch.distributions.lkj_cholesky.LKJCholesky.support" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
 </div>
 <div class="section" id="laplace">
 <h2><span class="hidden-section">Laplace</span><a class="headerlink" href="#laplace" title="Permalink to this headline">¶</a></h2>
@@ -4241,6 +4315,7 @@ <h2><span class="hidden-section">Weibull</span><a class="headerlink" href="#weib
 <li><a class="reference internal" href="#halfnormal"><span class="hidden-section">HalfNormal</span></a></li>
 <li><a class="reference internal" href="#independent"><span class="hidden-section">Independent</span></a></li>
 <li><a class="reference internal" href="#kumaraswamy"><span class="hidden-section">Kumaraswamy</span></a></li>
+<li><a class="reference internal" href="#lkjcholesky"><span class="hidden-section">LKJCholesky</span></a></li>
 <li><a class="reference internal" href="#laplace"><span class="hidden-section">Laplace</span></a></li>
 <li><a class="reference internal" href="#lognormal"><span class="hidden-section">LogNormal</span></a></li>
 <li><a class="reference internal" href="#lowrankmultivariatenormal"><span class="hidden-section">LowRankMultivariateNormal</span></a></li>

docs/1.8.0/_static/js/theme.js

@@ -825,6 +825,8 @@ <h2 id="A">A</h2>
         <li><a href="distributions.html#torch.distributions.kumaraswamy.Kumaraswamy.arg_constraints">(torch.distributions.kumaraswamy.Kumaraswamy attribute)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.laplace.Laplace.arg_constraints">(torch.distributions.laplace.Laplace attribute)</a>
+</li>
+        <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky.arg_constraints">(torch.distributions.lkj_cholesky.LKJCholesky attribute)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.arg_constraints">(torch.distributions.log_normal.LogNormal attribute)</a>
 </li>
@@ -2068,14 +2070,14 @@ <h2 id="E">E</h2>
       </ul></li>
       <li><a href="tensors.html#torch.Tensor.erfc_">erfc_() (torch.Tensor method)</a>
 </li>
-  </ul></td>
-  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="generated/torch.erfinv.html#torch.erfinv">erfinv() (in module torch)</a>
 
       <ul>
         <li><a href="tensors.html#torch.Tensor.erfinv">(torch.Tensor method)</a>
 </li>
       </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="tensors.html#torch.Tensor.erfinv_">erfinv_() (torch.Tensor method)</a>
 </li>
       <li><a href="generated/torch.jit.ScriptModule.html#torch.jit.ScriptModule.eval">eval() (torch.jit.ScriptModule method)</a>
@@ -2142,6 +2144,8 @@ <h2 id="E">E</h2>
         <li><a href="distributions.html#torch.distributions.kumaraswamy.Kumaraswamy.expand">(torch.distributions.kumaraswamy.Kumaraswamy method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.laplace.Laplace.expand">(torch.distributions.laplace.Laplace method)</a>
+</li>
+        <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky.expand">(torch.distributions.lkj_cholesky.LKJCholesky method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.expand">(torch.distributions.log_normal.LogNormal method)</a>
 </li>
@@ -3451,6 +3455,8 @@ <h2 id="L">L</h2>
       <li><a href="hub.html#torch.hub.list">list() (in module torch.hub)</a>
 </li>
       <li><a href="cuda.html#torch.cuda.list_gpu_processes">list_gpu_processes() (in module torch.cuda)</a>
+</li>
+      <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky">LKJCholesky (class in torch.distributions.lkj_cholesky)</a>
 </li>
       <li><a href="generated/torch.nn.utils.prune.ln_structured.html#torch.nn.utils.prune.ln_structured">ln_structured() (in module torch.nn.utils.prune)</a>
 </li>
@@ -3572,6 +3578,8 @@ <h2 id="L">L</h2>
         <li><a href="distributions.html#torch.distributions.independent.Independent.log_prob">(torch.distributions.independent.Independent method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.laplace.Laplace.log_prob">(torch.distributions.laplace.Laplace method)</a>
+</li>
+        <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky.log_prob">(torch.distributions.lkj_cholesky.LKJCholesky method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal.log_prob">(torch.distributions.lowrank_multivariate_normal.LowRankMultivariateNormal method)</a>
 </li>
@@ -5146,6 +5154,8 @@ <h2 id="S">S</h2>
         <li><a href="distributions.html#torch.distributions.geometric.Geometric.sample">(torch.distributions.geometric.Geometric method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.independent.Independent.sample">(torch.distributions.independent.Independent method)</a>
+</li>
+        <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky.sample">(torch.distributions.lkj_cholesky.LKJCholesky method)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.mixture_same_family.MixtureSameFamily.sample">(torch.distributions.mixture_same_family.MixtureSameFamily method)</a>
 </li>
@@ -5712,6 +5722,8 @@ <h2 id="S">S</h2>
         <li><a href="distributions.html#torch.distributions.kumaraswamy.Kumaraswamy.support">(torch.distributions.kumaraswamy.Kumaraswamy attribute)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.laplace.Laplace.support">(torch.distributions.laplace.Laplace attribute)</a>
+</li>
+        <li><a href="distributions.html#torch.distributions.lkj_cholesky.LKJCholesky.support">(torch.distributions.lkj_cholesky.LKJCholesky attribute)</a>
 </li>
         <li><a href="distributions.html#torch.distributions.log_normal.LogNormal.support">(torch.distributions.log_normal.LogNormal attribute)</a>
 </li>