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<h1>Source code for torch.distributions.negative_binomial</h1><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">torch</span>
<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</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">'NegativeBinomial'</span><span class="p">]</span>
<div class="viewcode-block" id="NegativeBinomial"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.negative_binomial.NegativeBinomial">[docs]</a><span class="k">class</span> <span class="nc">NegativeBinomial</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 Negative Binomial distribution, i.e. distribution</span>
<span class="sd"> of the number of successful independent and identical Bernoulli trials</span>
<span class="sd"> before :attr:`total_count` failures are achieved. The probability</span>
<span class="sd"> of success of each Bernoulli trial is :attr:`probs`.</span>
<span class="sd"> Args:</span>
<span class="sd"> total_count (float or Tensor): non-negative number of negative Bernoulli</span>
<span class="sd"> trials to stop, although the distribution is still valid for real</span>
<span class="sd"> valued count</span>
<span class="sd"> probs (Tensor): Event probabilities of success in the half open interval [0, 1)</span>
<span class="sd"> logits (Tensor): Event log-odds for probabilities of success</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">greater_than_eq</span><span class="p">(</span><span class="mi">0</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">half_open_interval</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</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">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">nonnegative_integer</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="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="NegativeBinomial.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.negative_binomial.NegativeBinomial.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">NegativeBinomial</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">NegativeBinomial</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">@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="n">torch</span><span class="o">.</span><span class="n">exp</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="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">logits</span><span class="o">.</span><span class="n">exp</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">min</span><span class="o">=</span><span class="mf">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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">mean</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">logits</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>
<span class="nd">@lazy_property</span>
<span class="k">def</span> <span class="nf">_gamma</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
<span class="c1"># Note we avoid validating because self.total_count can be zero.</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">distributions</span><span class="o">.</span><span class="n">Gamma</span><span class="p">(</span><span class="n">concentration</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="n">rate</span><span class="o">=</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="bp">self</span><span class="o">.</span><span class="n">logits</span><span class="p">),</span>
<span class="n">validate_args</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<div class="viewcode-block" id="NegativeBinomial.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.negative_binomial.NegativeBinomial.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="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="n">rate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_gamma</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">sample_shape</span><span class="o">=</span><span class="n">sample_shape</span><span class="p">)</span>
<span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">poisson</span><span class="p">(</span><span class="n">rate</span><span class="p">)</span></div>
<div class="viewcode-block" id="NegativeBinomial.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.negative_binomial.NegativeBinomial.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_unnormalized_prob</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">F</span><span class="o">.</span><span class="n">logsigmoid</span><span class="p">(</span><span class="o">-</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">value</span> <span class="o">*</span> <span class="n">F</span><span class="o">.</span><span class="n">logsigmoid</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">log_normalization</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">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="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="mf">1.</span> <span class="o">+</span> <span class="n">value</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="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="p">))</span>
<span class="n">log_normalization</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="mf">0.</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.</span>
<span class="k">return</span> <span class="n">log_unnormalized_prob</span> <span class="o">-</span> <span class="n">log_normalization</span></div></div>
</pre></div>
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