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<div class="section" id="torch-bincount">
<h1>torch.bincount<a class="headerlink" href="#torch-bincount" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.bincount">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">bincount</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">weights=None</em>, <em class="sig-param">minlength=0</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.bincount" title="Permalink to this definition">¶</a></dt>
<dd><p>Count the frequency of each value in an array of non-negative ints.</p>
<p>The number of bins (size 1) is one larger than the largest value in
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> unless <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is empty, in which case the result is a
tensor of size 0. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">minlength</span></code> is specified, the number of bins is at least
<code class="xref py py-attr docutils literal notranslate"><span class="pre">minlength</span></code> and if <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is empty, then the result is tensor of size
<code class="xref py py-attr docutils literal notranslate"><span class="pre">minlength</span></code> filled with zeros. If <code class="docutils literal notranslate"><span class="pre">n</span></code> is the value at position <code class="docutils literal notranslate"><span class="pre">i</span></code>,
<code class="docutils literal notranslate"><span class="pre">out[n]</span> <span class="pre">+=</span> <span class="pre">weights[i]</span></code> if <code class="xref py py-attr docutils literal notranslate"><span class="pre">weights</span></code> is specified else
<code class="docutils literal notranslate"><span class="pre">out[n]</span> <span class="pre">+=</span> <span class="pre">1</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>In some circumstances when using the CUDA backend with CuDNN, this operator
may select a nondeterministic algorithm to increase performance. If this is
undesirable, you can try to make the operation deterministic (potentially at
a performance cost) by setting <code class="docutils literal notranslate"><span class="pre">torch.backends.cudnn.deterministic</span> <span class="pre">=</span>
<span class="pre">True</span></code>.
Please see the notes on <a class="reference internal" href="../notes/randomness.html"><span class="doc">Reproducibility</span></a> for background.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – 1-d int tensor</p></li>
<li><p><strong>weights</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – optional, weight for each value in the input tensor.
Should be of same size as input tensor.</p></li>
<li><p><strong>minlength</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.8)"><em>int</em></a>) – optional, minimum number of bins. Should be non-negative.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>a tensor of shape <code class="docutils literal notranslate"><span class="pre">Size([max(input)</span> <span class="pre">+</span> <span class="pre">1])</span></code> if
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is non-empty, else <code class="docutils literal notranslate"><span class="pre">Size(0)</span></code></p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p>output (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor">Tensor</a>)</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">weights</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">steps</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="nb">input</span><span class="p">,</span> <span class="n">weights</span>
<span class="go">(tensor([4, 3, 6, 3, 4]),</span>
<span class="go"> tensor([ 0.0000, 0.2500, 0.5000, 0.7500, 1.0000])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([0, 0, 0, 2, 2, 0, 1])</span>
<span class="gp">>>> </span><span class="nb">input</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="go">tensor([0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.5000])</span>
</pre></div>
</div>
</dd></dl>
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