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<h1>Source code for torch.distributions.log_normal</h1><div class="highlight"><pre>
<span></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.normal</span> <span class="kn">import</span> <span class="n">Normal</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">ExpTransform</span>
<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"LogNormal"</span><span class="p">]</span>
<div class="viewcode-block" id="LogNormal"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.log_normal.LogNormal">[docs]</a><span class="k">class</span> <span class="nc">LogNormal</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">"""</span>
<span class="sd"> Creates a log-normal distribution parameterized by</span>
<span class="sd"> :attr:`loc` and :attr:`scale` where::</span>
<span class="sd"> X ~ Normal(loc, scale)</span>
<span class="sd"> Y = exp(X) ~ LogNormal(loc, scale)</span>
<span class="sd"> Example::</span>
<span class="sd"> >>> # xdoctest: +IGNORE_WANT("non-deterministic")</span>
<span class="sd"> >>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))</span>
<span class="sd"> >>> m.sample() # log-normal distributed with mean=0 and stddev=1</span>
<span class="sd"> tensor([ 0.1046])</span>
<span class="sd"> Args:</span>
<span class="sd"> loc (float or Tensor): mean of log of distribution</span>
<span class="sd"> scale (float or Tensor): standard deviation of log of the distribution</span>
<span class="sd"> """</span>
<span class="n">arg_constraints</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"loc"</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="s2">"scale"</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="n">support</span> <span class="o">=</span> <span class="n">constraints</span><span class="o">.</span><span class="n">positive</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">loc</span><span class="p">,</span> <span class="n">scale</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="n">base_dist</span> <span class="o">=</span> <span class="n">Normal</span><span class="p">(</span><span class="n">loc</span><span class="p">,</span> <span class="n">scale</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="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">ExpTransform</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="LogNormal.expand"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.log_normal.LogNormal.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">LogNormal</span><span class="p">,</span> <span class="n">_instance</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">loc</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">base_dist</span><span class="o">.</span><span class="n">loc</span>
<span class="nd">@property</span>
<span class="k">def</span> <span class="nf">scale</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">base_dist</span><span class="o">.</span><span class="n">scale</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="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</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">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">loc</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">square</span><span class="p">())</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="n">scale_sq</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="k">return</span> <span class="n">scale_sq</span><span class="o">.</span><span class="n">expm1</span><span class="p">()</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span> <span class="o">+</span> <span class="n">scale_sq</span><span class="p">)</span><span class="o">.</span><span class="n">exp</span><span class="p">()</span>
<div class="viewcode-block" id="LogNormal.entropy"><a class="viewcode-back" href="../../../distributions.html#torch.distributions.log_normal.LogNormal.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="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">base_dist</span><span class="o">.</span><span class="n">entropy</span><span class="p">()</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">loc</span></div></div>
</pre></div>
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