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  <h1>Source code for torch.distributions.poisson</h1><div class="highlight"><pre>
<span></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.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="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Poisson&quot;</span><span class="p">]</span>


<div class="viewcode-block" id="Poisson"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.poisson.Poisson">[docs]</a><span class="k">class</span> <span class="nc">Poisson</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">&quot;&quot;&quot;</span>
<span class="sd">    Creates a Poisson distribution parameterized by :attr:`rate`, the rate parameter.</span>

<span class="sd">    Samples are nonnegative integers, with a pmf given by</span>

<span class="sd">    .. math::</span>
<span class="sd">      \mathrm{rate}^k \frac{e^{-\mathrm{rate}}}{k!}</span>

<span class="sd">    Example::</span>

<span class="sd">        &gt;&gt;&gt; # xdoctest: +SKIP(&quot;poisson_cpu not implemented for &#39;Long&#39;&quot;)</span>
<span class="sd">        &gt;&gt;&gt; m = Poisson(torch.tensor([4]))</span>
<span class="sd">        &gt;&gt;&gt; m.sample()</span>
<span class="sd">        tensor([ 3.])</span>

<span class="sd">    Args:</span>
<span class="sd">        rate (Number, Tensor): the rate parameter</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;rate&quot;</span><span class="p">:</span> <span class="n">constraints</span><span class="o">.</span><span class="n">nonnegative</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="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">rate</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">rate</span><span class="o">.</span><span class="n">floor</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">rate</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">rate</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="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rate</span><span class="p">,)</span> <span class="o">=</span> <span class="n">broadcast_all</span><span class="p">(</span><span class="n">rate</span><span class="p">)</span>
        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rate</span><span class="p">,</span> <span class="n">Number</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="k">else</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">rate</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="Poisson.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.poisson.Poisson.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">Poisson</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">rate</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rate</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">Poisson</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>

<div class="viewcode-block" id="Poisson.sample"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.poisson.Poisson.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">poisson</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rate</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="Poisson.log_prob"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.poisson.Poisson.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">rate</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">rate</span><span class="p">,</span> <span class="n">value</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">value</span><span class="o">.</span><span class="n">xlogy</span><span class="p">(</span><span class="n">rate</span><span class="p">)</span> <span class="o">-</span> <span class="n">rate</span> <span class="o">-</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">lgamma</span><span class="p">()</span></div>

    <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="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rate</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="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">x</span><span class="p">)</span></div>
</pre></div>

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