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  <h1>Source code for torch.distributions.kumaraswamy</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">nan</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.transformed_distribution</span> <span class="kn">import</span> <span class="n">TransformedDistribution</span>
<span class="kn">from</span> <span class="nn">torch.distributions.transforms</span> <span class="kn">import</span> <span class="n">AffineTransform</span><span class="p">,</span> <span class="n">PowerTransform</span>
<span class="kn">from</span> <span class="nn">torch.distributions.uniform</span> <span class="kn">import</span> <span class="n">Uniform</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">euler_constant</span>

<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;Kumaraswamy&quot;</span><span class="p">]</span>


<span class="k">def</span> <span class="nf">_moments</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Computes nth moment of Kumaraswamy using using torch.lgamma</span>
<span class="sd">    &quot;&quot;&quot;</span>
    <span class="n">arg1</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">+</span> <span class="n">n</span> <span class="o">/</span> <span class="n">a</span>
    <span class="n">log_value</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">arg1</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">b</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">arg1</span> <span class="o">+</span> <span class="n">b</span><span class="p">)</span>
    <span class="k">return</span> <span class="n">b</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="n">log_value</span><span class="p">)</span>


<div class="viewcode-block" id="Kumaraswamy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.kumaraswamy.Kumaraswamy">[docs]</a><span class="k">class</span> <span class="nc">Kumaraswamy</span><span class="p">(</span><span class="n">TransformedDistribution</span><span class="p">):</span>
<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Samples from a Kumaraswamy distribution.</span>

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

<span class="sd">        &gt;&gt;&gt; # xdoctest: +IGNORE_WANT(&quot;non-deterministic&quot;)</span>
<span class="sd">        &gt;&gt;&gt; m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))</span>
<span class="sd">        &gt;&gt;&gt; m.sample()  # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1</span>
<span class="sd">        tensor([ 0.1729])</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">    &quot;&quot;&quot;</span>
    <span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span>
        <span class="s2">&quot;concentration1&quot;</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">&quot;concentration0&quot;</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="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="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">finfo</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
        <span class="n">base_dist</span> <span class="o">=</span> <span class="n">Uniform</span><span class="p">(</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">full_like</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="mi">0</span><span class="p">),</span>
            <span class="n">torch</span><span class="o">.</span><span class="n">full_like</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="mi">1</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="p">)</span>
        <span class="n">transforms</span> <span class="o">=</span> <span class="p">[</span>
            <span class="n">PowerTransform</span><span class="p">(</span><span class="n">exponent</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="n">reciprocal</span><span class="p">()),</span>
            <span class="n">AffineTransform</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">scale</span><span class="o">=-</span><span class="mf">1.0</span><span class="p">),</span>
            <span class="n">PowerTransform</span><span class="p">(</span><span class="n">exponent</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="n">reciprocal</span><span class="p">()),</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">base_dist</span><span class="p">,</span> <span class="n">transforms</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="Kumaraswamy.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.kumaraswamy.Kumaraswamy.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">Kumaraswamy</span><span class="p">,</span> <span class="n">_instance</span><span class="p">)</span>
        <span class="n">new</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">concentration1</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">concentration0</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="n">expand</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
        <span class="k">return</span> <span class="nb">super</span><span class="p">()</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">_instance</span><span class="o">=</span><span class="n">new</span><span class="p">)</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="n">_moments</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="mi">1</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="c1"># Evaluate in log-space for numerical stability.</span>
        <span class="n">log_mode</span> <span class="o">=</span> <span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span><span class="o">.</span><span class="n">reciprocal</span><span class="p">()</span> <span class="o">*</span> <span class="p">(</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="o">.</span><span class="n">log1p</span><span class="p">()</span>
            <span class="o">-</span> <span class="p">(</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="bp">self</span><span class="o">.</span><span class="n">concentration1</span><span class="p">)</span><span class="o">.</span><span class="n">log1p</span><span class="p">()</span>
        <span class="p">)</span>
        <span class="n">log_mode</span><span class="p">[(</span><span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">)</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">&lt;</span> <span class="mi">1</span><span class="p">)]</span> <span class="o">=</span> <span class="n">nan</span>
        <span class="k">return</span> <span class="n">log_mode</span><span class="o">.</span><span class="n">exp</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="n">_moments</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="mi">2</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">mean</span><span class="p">,</span> <span class="mi">2</span>
        <span class="p">)</span>

<div class="viewcode-block" id="Kumaraswamy.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.kumaraswamy.Kumaraswamy.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">t1</span> <span class="o">=</span> <span class="mi">1</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="n">reciprocal</span><span class="p">()</span>
        <span class="n">t0</span> <span class="o">=</span> <span class="mi">1</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="n">reciprocal</span><span class="p">()</span>
        <span class="n">H0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">digamma</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">concentration0</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="n">euler_constant</span>
        <span class="k">return</span> <span class="p">(</span>
            <span class="n">t0</span>
            <span class="o">+</span> <span class="n">t1</span> <span class="o">*</span> <span class="n">H0</span>
            <span class="o">-</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">concentration1</span><span class="p">)</span>
            <span class="o">-</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">concentration0</span><span class="p">)</span>
        <span class="p">)</span></div></div>
</pre></div>

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