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<h1>Source code for torch.distributions.binomial</h1><div class="highlight"><pre>
<span></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.distribution</span> <span class="kn">import</span> <span class="n">Distribution</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">probs_to_logits</span><span class="p">,</span> <span class="n">lazy_property</span><span class="p">,</span> <span class="n">logits_to_probs</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Binomial'</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">_clamp_by_zero</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
<span class="c1"># works like clamp(x, min=0) but has grad at 0 is 0.5</span>
<span class="k">return</span> <span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">+</span> <span class="n">x</span> <span class="o">-</span> <span class="n">x</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
<div class="viewcode-block" id="Binomial"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial">[docs]</a><span class="k">class</span> <span class="nc">Binomial</span><span class="p">(</span><span class="n">Distribution</span><span class="p">):</span>
<span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Creates a Binomial distribution parameterized by :attr:`total_count` and</span>
<span class="sd"> either :attr:`probs` or :attr:`logits` (but not both). :attr:`total_count` must be</span>
<span class="sd"> broadcastable with :attr:`probs`/:attr:`logits`.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterinistic")</span>
<span class="sd"> >>> m = Binomial(100, torch.tensor([0 , .2, .8, 1]))</span>
<span class="sd"> >>> x = m.sample()</span>
<span class="sd"> tensor([ 0., 22., 71., 100.])</span>
<span class="sd"> >>> m = Binomial(torch.tensor([[5.], [10.]]), torch.tensor([0.5, 0.8]))</span>
<span class="sd"> >>> x = m.sample()</span>
<span class="sd"> tensor([[ 4., 5.],</span>
<span class="sd"> [ 7., 6.]])</span>
<span class="sd"> Args:</span>
<span class="sd"> total_count (int or Tensor): number of Bernoulli trials</span>
<span class="sd"> probs (Tensor): Event probabilities</span>
<span class="sd"> logits (Tensor): Event log-odds</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'total_count'</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">nonnegative_integer</span><span class="p">,</span>
<span class="s1">'probs'</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">unit_interval</span><span class="p">,</span>
<span class="s1">'logits'</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="n">has_enumerate_support</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">total_count</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">probs</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">logits</span><span class="o">=</span><span class="kc">None</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="p">(</span><span class="n">probs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">logits</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">):</span>
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Either `probs` or `logits` must be specified, but not both."</span><span class="p">)</span>
<span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">total_count</span><span class="p">,</span> <span class="n">probs</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">total_count</span><span class="p">,</span> <span class="n">logits</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">type_as</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_param</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="k">if</span> <span class="n">probs</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">size</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">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="Binomial.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial.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">Binomial</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">total_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</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">if</span> <span class="s1">'probs'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
<span class="n">new</span><span class="o">.</span><span class="n">probs</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</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">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">probs</span>
<span class="k">if</span> <span class="s1">'logits'</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">:</span>
<span class="n">new</span><span class="o">.</span><span class="n">logits</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</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">_param</span> <span class="o">=</span> <span class="n">new</span><span class="o">.</span><span class="n">logits</span>
<span class="nb">super</span><span class="p">(</span><span class="n">Binomial</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="k">def</span> <span class="nf">_new</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
<span class="nd">@constraints</span><span class="o">.</span><span class="n">dependent_property</span><span class="p">(</span><span class="n">is_discrete</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">event_dim</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">support</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">constraints</span><span class="o">.</span><span class="n">integer_interval</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="p">)</span>
<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">total_count</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</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="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span><span class="o">.</span><span class="n">floor</span><span class="p">()</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="nb">max</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">logits</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">probs_to_logits</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">probs</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="k">return</span> <span class="n">logits_to_probs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">,</span> <span class="n">is_binary</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">param_shape</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">_param</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<div class="viewcode-block" id="Binomial.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial.sample">[docs]</a> <span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_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">shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extended_shape</span><span class="p">(</span><span class="n">sample_shape</span><span class="p">)</span>
<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">binomial</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">shape</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">probs</span><span class="o">.</span><span class="n">expand</span><span class="p">(</span><span class="n">shape</span><span class="p">))</span></div>
<div class="viewcode-block" id="Binomial.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial.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">log_factorial_n</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="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">log_factorial_k</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">value</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">log_factorial_nmk</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="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">-</span> <span class="n">value</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="c1"># k * log(p) + (n - k) * log(1 - p) = k * (log(p) - log(1 - p)) + n * log(1 - p)</span>
<span class="c1"># (case logit < 0) = k * logit - n * log1p(e^logit)</span>
<span class="c1"># (case logit > 0) = k * logit - n * (log(p) - log(1 - p)) + n * log(p)</span>
<span class="c1"># = k * logit - n * logit - n * log1p(e^-logit)</span>
<span class="c1"># (merge two cases) = k * logit - n * max(logit, 0) - n * log1p(e^-|logit|)</span>
<span class="n">normalize_term</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">*</span> <span class="n">_clamp_by_zero</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">)</span>
<span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">log1p</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">)))</span>
<span class="o">-</span> <span class="n">log_factorial_n</span><span class="p">)</span>
<span class="k">return</span> <span class="n">value</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">logits</span> <span class="o">-</span> <span class="n">log_factorial_k</span> <span class="o">-</span> <span class="n">log_factorial_nmk</span> <span class="o">-</span> <span class="n">normalize_term</span></div>
<div class="viewcode-block" id="Binomial.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial.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="n">total_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">==</span> <span class="n">total_count</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Inhomogeneous total count not supported by `entropy`."</span><span class="p">)</span>
<span class="n">log_prob</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">enumerate_support</span><span class="p">(</span><span class="kc">False</span><span class="p">))</span>
<span class="k">return</span> <span class="o">-</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">log_prob</span><span class="p">)</span> <span class="o">*</span> <span class="n">log_prob</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span></div>
<div class="viewcode-block" id="Binomial.enumerate_support"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.binomial.Binomial.enumerate_support">[docs]</a> <span class="k">def</span> <span class="nf">enumerate_support</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">expand</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="n">total_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="o">.</span><span class="n">min</span><span class="p">()</span> <span class="o">==</span> <span class="n">total_count</span><span class="p">:</span>
<span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"Inhomogeneous total count not supported by `enumerate_support`."</span><span class="p">)</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">total_count</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">dtype</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">_param</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span><span class="p">))</span>
<span class="k">if</span> <span class="n">expand</span><span class="p">:</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">expand</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_batch_shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">values</span></div></div>
</pre></div>
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