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<h1>Source code for torch.distributions.multinomial</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</span> <span class="kn">import</span> <span class="n">inf</span>
<span class="kn">from</span> <span class="nn">torch.distributions</span> <span class="kn">import</span> <span class="n">Categorical</span><span class="p">,</span> <span class="n">constraints</span>
<span class="kn">from</span> <span class="nn">torch.distributions.binomial</span> <span class="kn">import</span> <span class="n">Binomial</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="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Multinomial"</span><span class="p">]</span>
<div class="viewcode-block" id="Multinomial"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.multinomial.Multinomial">[docs]</a><span class="k">class</span> <span class="nc">Multinomial</span><span class="p">(</span><span class="n">Distribution</span><span class="p">):</span>
<span class="w"> </span><span class="sa">r</span><span class="sd">"""</span>
<span class="sd"> Creates a Multinomial distribution parameterized by :attr:`total_count` and</span>
<span class="sd"> either :attr:`probs` or :attr:`logits` (but not both). The innermost dimension of</span>
<span class="sd"> :attr:`probs` indexes over categories. All other dimensions index over batches.</span>
<span class="sd"> Note that :attr:`total_count` need not be specified if only :meth:`log_prob` is</span>
<span class="sd"> called (see example below)</span>
<span class="sd"> .. note:: The `probs` argument must be non-negative, finite and have a non-zero sum,</span>
<span class="sd"> and it will be normalized to sum to 1 along the last dimension. :attr:`probs`</span>
<span class="sd"> will return this normalized value.</span>
<span class="sd"> The `logits` argument will be interpreted as unnormalized log probabilities</span>
<span class="sd"> and can therefore be any real number. It will likewise be normalized so that</span>
<span class="sd"> the resulting probabilities sum to 1 along the last dimension. :attr:`logits`</span>
<span class="sd"> will return this normalized value.</span>
<span class="sd"> - :meth:`sample` requires a single shared `total_count` for all</span>
<span class="sd"> parameters and samples.</span>
<span class="sd"> - :meth:`log_prob` allows different `total_count` for each parameter and</span>
<span class="sd"> sample.</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +SKIP("FIXME: found invalid values")</span>
<span class="sd"> >>> m = Multinomial(100, torch.tensor([ 1., 1., 1., 1.]))</span>
<span class="sd"> >>> x = m.sample() # equal probability of 0, 1, 2, 3</span>
<span class="sd"> tensor([ 21., 24., 30., 25.])</span>
<span class="sd"> >>> Multinomial(probs=torch.tensor([1., 1., 1., 1.])).log_prob(x)</span>
<span class="sd"> tensor([-4.1338])</span>
<span class="sd"> Args:</span>
<span class="sd"> total_count (int): number of trials</span>
<span class="sd"> probs (Tensor): event probabilities</span>
<span class="sd"> logits (Tensor): event log probabilities (unnormalized)</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"probs"</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">simplex</span><span class="p">,</span> <span class="s2">"logits"</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">real_vector</span><span class="p">}</span>
<span class="n">total_count</span><span class="p">:</span> <span class="nb">int</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">probs</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">total_count</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="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="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">total_count</span><span class="p">,</span> <span class="nb">int</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 is not supported"</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">total_count</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_categorical</span> <span class="o">=</span> <span class="n">Categorical</span><span class="p">(</span><span class="n">probs</span><span class="o">=</span><span class="n">probs</span><span class="p">,</span> <span class="n">logits</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">_binomial</span> <span class="o">=</span> <span class="n">Binomial</span><span class="p">(</span><span class="n">total_count</span><span class="o">=</span><span class="n">total_count</span><span class="p">,</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="p">)</span>
<span class="n">batch_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorical</span><span class="o">.</span><span class="n">batch_shape</span>
<span class="n">event_shape</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorical</span><span class="o">.</span><span class="n">param_shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</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">event_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="Multinomial.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.multinomial.Multinomial.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">Multinomial</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="n">new</span><span class="o">.</span><span class="n">_categorical</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorical</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">Multinomial</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="bp">self</span><span class="o">.</span><span class="n">event_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">_categorical</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">1</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">multinomial</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">logits</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">_categorical</span><span class="o">.</span><span class="n">logits</span>
<span class="nd">@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="bp">self</span><span class="o">.</span><span class="n">_categorical</span><span class="o">.</span><span class="n">probs</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">_categorical</span><span class="o">.</span><span class="n">param_shape</span>
<div class="viewcode-block" id="Multinomial.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.multinomial.Multinomial.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">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">sample_shape</span><span class="p">)</span>
<span class="n">samples</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorical</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span>
<span class="n">torch</span><span class="o">.</span><span class="n">Size</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="o">+</span> <span class="n">sample_shape</span>
<span class="p">)</span>
<span class="c1"># samples.shape is (total_count, sample_shape, batch_shape), need to change it to</span>
<span class="c1"># (sample_shape, batch_shape, total_count)</span>
<span class="n">shifted_idx</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">samples</span><span class="o">.</span><span class="n">dim</span><span class="p">()))</span>
<span class="n">shifted_idx</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">shifted_idx</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
<span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="o">*</span><span class="n">shifted_idx</span><span class="p">)</span>
<span class="n">counts</span> <span class="o">=</span> <span class="n">samples</span><span class="o">.</span><span class="n">new</span><span class="p">(</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="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="n">counts</span><span class="o">.</span><span class="n">scatter_add_</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">samples</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">samples</span><span class="p">))</span>
<span class="k">return</span> <span class="n">counts</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></div>
<div class="viewcode-block" id="Multinomial.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.multinomial.Multinomial.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">n</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="bp">self</span><span class="o">.</span><span class="n">total_count</span><span class="p">)</span>
<span class="n">cat_entropy</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_categorical</span><span class="o">.</span><span class="n">entropy</span><span class="p">()</span>
<span class="n">term1</span> <span class="o">=</span> <span class="n">n</span> <span class="o">*</span> <span class="n">cat_entropy</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">n</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">support</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_binomial</span><span class="o">.</span><span class="n">enumerate_support</span><span class="p">(</span><span class="n">expand</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]</span>
<span class="n">binomial_probs</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">_binomial</span><span class="o">.</span><span class="n">log_prob</span><span class="p">(</span><span class="n">support</span><span class="p">))</span>
<span class="n">weights</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">support</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">term2</span> <span class="o">=</span> <span class="p">(</span><span class="n">binomial_probs</span> <span class="o">*</span> <span class="n">weights</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> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
<span class="k">return</span> <span class="n">term1</span> <span class="o">+</span> <span class="n">term2</span></div>
<div class="viewcode-block" id="Multinomial.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.multinomial.Multinomial.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">logits</span><span class="p">,</span> <span class="n">value</span> <span class="o">=</span> <span class="n">broadcast_all</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">value</span><span class="p">)</span>
<span class="n">logits</span> <span class="o">=</span> <span class="n">logits</span><span class="o">.</span><span class="n">clone</span><span class="p">(</span><span class="n">memory_format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">contiguous_format</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="n">value</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">log_factorial_xs</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="o">.</span><span class="n">sum</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">logits</span><span class="p">[(</span><span class="n">value</span> <span class="o">==</span> <span class="mi">0</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">logits</span> <span class="o">==</span> <span class="o">-</span><span class="n">inf</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">log_powers</span> <span class="o">=</span> <span class="p">(</span><span class="n">logits</span> <span class="o">*</span> <span class="n">value</span><span class="p">)</span><span class="o">.</span><span class="n">sum</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="n">log_factorial_n</span> <span class="o">-</span> <span class="n">log_factorial_xs</span> <span class="o">+</span> <span class="n">log_powers</span></div></div>
</pre></div>
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