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<h1>Source code for torch.distributions.gumbel</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">math</span>
<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span>
<span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.transformed_distribution</span> <span class="kn">import</span> <span class="n">TransformedDistribution</span>
<span class="kn">from</span> <span class="nn">torch.distributions.transforms</span> <span class="kn">import</span> <span class="n">AffineTransform</span><span class="p">,</span> <span class="n">ExpTransform</span>
<span class="kn">from</span> <span class="nn">torch.distributions.uniform</span> <span class="kn">import</span> <span class="n">Uniform</span>
<span class="kn">from</span> <span class="nn">torch.distributions.utils</span> <span class="kn">import</span> <span class="n">broadcast_all</span><span class="p">,</span> <span class="n">euler_constant</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Gumbel"</span><span class="p">]</span>
<div class="viewcode-block" id="Gumbel"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.gumbel.Gumbel">[docs]</a><span class="k">class</span> <span class="nc">Gumbel</span><span class="p">(</span><span class="n">TransformedDistribution</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Samples from a Gumbel Distribution.</span>
<span class="sd"> Examples::</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterministic")</span>
<span class="sd"> >>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))</span>
<span class="sd"> >>> m.sample() # sample from Gumbel distribution with loc=1, scale=2</span>
<span class="sd"> tensor([ 1.0124])</span>
<span class="sd"> Args:</span>
<span class="sd"> loc (float or Tensor): Location parameter of the distribution</span>
<span class="sd"> scale (float or Tensor): Scale parameter of the distribution</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"loc"</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">real</span><span class="p">,</span> <span class="s2">"scale"</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">positive</span><span class="p">}</span>
<span class="n">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">real</span>
<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="p">)</span>
<span class="n">finfo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">Number</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">scale</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
<span class="n">base_dist</span> <span class="o">=</span> <span class="n">Uniform</span><span class="p">(</span><span class="n">finfo</span><span class="o">.</span><span class="n">tiny</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">finfo</span><span class="o">.</span><span class="n">eps</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="n">validate_args</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">base_dist</span> <span class="o">=</span> <span class="n">Uniform</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">,</span> <span class="n">finfo</span><span class="o">.</span><span class="n">tiny</span><span class="p">),</span>
<span class="n">torch</span><span class="o">.</span><span class="n">full_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="p">,</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">finfo</span><span class="o">.</span><span class="n">eps</span><span class="p">),</span>
<span class="n">validate_args</span><span class="o">=</span><span class="n">validate_args</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">transforms</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">ExpTransform</span><span class="p">()</span><span class="o">.</span><span class="n">inv</span><span class="p">,</span>
<span class="n">AffineTransform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=-</span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">)),</span>
<span class="n">ExpTransform</span><span class="p">()</span><span class="o">.</span><span class="n">inv</span><span class="p">,</span>
<span class="n">AffineTransform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</span><span class="o">=-</span><span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="p">),</span>
<span class="p">]</span>
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">base_dist</span><span class="p">,</span> <span class="n">transforms</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="n">validate_args</span><span class="p">)</span>
<div class="viewcode-block" id="Gumbel.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.gumbel.Gumbel.expand">[docs]</a> <span class="k">def</span> <span class="nf">expand</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch_shape</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="n">new</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_checked_instance</span><span class="p">(</span><span class="n">Gumbel</span><span class="p">,</span> <span class="n">_instance</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">loc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">scale</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
<span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">_instance</span><span class="o">=</span><span class="n">new</span><span class="p">)</span></div>
<span class="c1"># Explicitly defining the log probability function for Gumbel due to precision issues</span>
<div class="viewcode-block" id="Gumbel.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.gumbel.Gumbel.log_prob">[docs]</a> <span class="k">def</span> <span class="nf">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_validate_sample</span><span class="p">(</span><span class="n">value</span><span class="p">)</span>
<span class="n">y</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">-</span> <span class="n">value</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
<span class="k">return</span> <span class="p">(</span><span class="n">y</span> <span class="o">-</span> <span class="n">y</span><span class="o">.</span><span class="n">exp</span><span class="p">())</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">log</span><span class="p">()</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span> <span class="o">*</span> <span class="n">euler_constant</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">mode</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">stddev</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="p">(</span><span class="n">math</span><span class="o">.</span><span class="n">pi</span> <span class="o">/</span> <span class="n">math</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">6</span><span class="p">))</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">variance</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">stddev</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<div class="viewcode-block" id="Gumbel.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.gumbel.Gumbel.entropy">[docs]</a> <span class="k">def</span> <span class="nf">entropy</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">log</span><span class="p">()</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">euler_constant</span><span class="p">)</span></div></div>
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