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<h1>Source code for torch.distributions.beta</h1><div class="highlight"><pre>
<span></span><span class="c1"># mypy: allow-untyped-defs</span>
<span class="kn">from</span> <span class="nn">numbers</span> <span class="kn">import</span> <span class="n">Number</span><span class="p">,</span> <span class="n">Real</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.dirichlet</span> <span class="kn">import</span> <span class="n">Dirichlet</span>
<span class="kn">from</span> <span class="nn">torch.distributions.exp_family</span> <span class="kn">import</span> <span class="n">ExponentialFamily</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="kn">from</span> <span class="nn">torch.types</span> <span class="kn">import</span> <span class="n">_size</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Beta"</span><span class="p">]</span>
<div class="viewcode-block" id="Beta"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.beta.Beta">[docs]</a><span class="k">class</span> <span class="nc">Beta</span><span class="p">(</span><span class="n">ExponentialFamily</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Beta distribution parameterized by :attr:`concentration1` and :attr:`concentration0`.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterministic")</span>
<span class="sd"> >>> m = Beta(torch.tensor([0.5]), torch.tensor([0.5]))</span>
<span class="sd"> >>> m.sample() # Beta distributed with concentration concentration1 and concentration0</span>
<span class="sd"> tensor([ 0.1046])</span>
<span class="sd"> Args:</span>
<span class="sd"> concentration1 (float or Tensor): 1st concentration parameter of the distribution</span>
<span class="sd"> (often referred to as alpha)</span>
<span class="sd"> concentration0 (float or Tensor): 2nd concentration parameter of the distribution</span>
<span class="sd"> (often referred to as beta)</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span>
<span class="s2">"concentration1"</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="s2">"concentration0"</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="p">}</span>
<span class="n">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">unit_interval</span>
<span class="n">has_rsample</span> <span class="o">=</span> <span class="kc">True</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">concentration1</span><span class="p">,</span> <span class="n">concentration0</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">concentration1</span><span class="p">,</span> <span class="n">Real</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">concentration0</span><span class="p">,</span> <span class="n">Real</span><span class="p">):</span>
<span class="n">concentration1_concentration0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
<span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">concentration1</span><span class="p">),</span> <span class="nb">float</span><span class="p">(</span><span class="n">concentration0</span><span class="p">)]</span>
<span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">concentration1</span><span class="p">,</span> <span class="n">concentration0</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span>
<span class="n">concentration1</span><span class="p">,</span> <span class="n">concentration0</span>
<span class="p">)</span>
<span class="n">concentration1_concentration0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
<span class="p">[</span><span class="n">concentration1</span><span class="p">,</span> <span class="n">concentration0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span>
<span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span> <span class="o">=</span> <span class="n">Dirichlet</span><span class="p">(</span>
<span class="n">concentration1_concentration0</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="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span><span class="o">.</span><span class="n">_batch_shape</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="Beta.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.beta.Beta.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">Beta</span><span class="p">,</span> <span class="n">_instance</span><span class="p">)</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Size</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">_dirichlet</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</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="nb">super</span><span class="p">(</span><span class="n">Beta</span><span class="p">,</span> <span class="n">new</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">validate_args</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">new</span><span class="o">.</span><span class="n">_validate_args</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_args</span>
<span class="k">return</span> <span class="n">new</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">concentration1</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">concentration1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span><span class="p">)</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">_dirichlet</span><span class="o">.</span><span class="n">mode</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</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="n">total</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration1</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span> <span class="o">/</span> <span class="p">(</span><span class="n">total</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">total</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
<div class="viewcode-block" id="Beta.rsample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.beta.Beta.rsample">[docs]</a> <span class="k">def</span> <span class="nf">rsample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_shape</span><span class="p">:</span> <span class="n">_size</span> <span class="o">=</span> <span class="p">())</span> <span class="o">-></span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span><span class="o">.</span><span class="n">rsample</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span><span class="o">.</span><span class="n">select</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span></div>
<div class="viewcode-block" id="Beta.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.beta.Beta.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">heads_tails</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">value</span><span class="p">,</span> <span class="mf">1.0</span> <span class="o">-</span> <span class="n">value</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">heads_tails</span><span class="p">)</span></div>
<div class="viewcode-block" id="Beta.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.beta.Beta.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">_dirichlet</span><span class="o">.</span><span class="n">entropy</span><span class="p">()</span></div>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">concentration1</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span><span class="o">.</span><span class="n">concentration</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">result</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">result</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">concentration0</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_dirichlet</span><span class="o">.</span><span class="n">concentration</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">Number</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">result</span><span class="p">])</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">result</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">_natural_params</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="bp">self</span><span class="o">.</span><span class="n">concentration1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">_log_normalizer</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">lgamma</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">lgamma</span><span class="p">(</span><span class="n">y</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">lgamma</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">y</span><span class="p">)</span></div>
</pre></div>
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<a href="https://pytorch.org/foundation">PyTorch Foundation</a>
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<a href="https://pytorch.org/governing-board">Governing Board</a>
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