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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="torch-combinations">
<h1>torch.combinations<a class="headerlink" href="#torch-combinations" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.combinations">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">combinations</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">r=2</em>, <em class="sig-param">with_replacement=False</em><span class="sig-paren">)</span> → seq<a class="headerlink" href="#torch.combinations" title="Permalink to this definition">¶</a></dt>
<dd><p>Compute combinations of length <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>r</mi></mrow><annotation encoding="application/x-tex">r</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.43056em;vertical-align:0em;"></span><span class="mord mathnormal" style="margin-right:0.02778em;">r</span></span></span></span>
</span> of the given tensor. The behavior is similar to
python’s <cite>itertools.combinations</cite> when <cite>with_replacement</cite> is set to <cite>False</cite>, and
<cite>itertools.combinations_with_replacement</cite> when <cite>with_replacement</cite> is set to <cite>True</cite>.</p>
<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>) – 1D vector.</p></li>
<li><p><strong>r</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a><em>, </em><em>optional</em>) – number of elements to combine</p></li>
<li><p><strong>with_replacement</strong> (<em>boolean</em><em>, </em><em>optional</em>) – whether to allow duplication in combination</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>A tensor equivalent to converting all the input tensors into lists, do
<cite>itertools.combinations</cite> or <cite>itertools.combinations_with_replacement</cite> on these
lists, and finally convert the resulting list into tensor.</p>
</dd>
<dt class="field-odd">Return type</dt>
<dd class="field-odd"><p><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="n">a</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="go">[(1, 2), (1, 3), (2, 3)]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="mi">3</span><span class="p">))</span>
<span class="go">[(1, 2, 3)]</span>
<span class="gp">>>> </span><span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">combinations_with_replacement</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span>
<span class="go">[(1, 1), (1, 2), (1, 3), (2, 2), (2, 3), (3, 3)]</span>
<span class="gp">>>> </span><span class="n">tensor_a</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="n">a</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">tensor_a</span><span class="p">)</span>
<span class="go">tensor([[1, 2],</span>
<span class="go"> [1, 3],</span>
<span class="go"> [2, 3]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">tensor_a</span><span class="p">,</span> <span class="n">r</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="go">tensor([[1, 2, 3]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">combinations</span><span class="p">(</span><span class="n">tensor_a</span><span class="p">,</span> <span class="n">with_replacement</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">tensor([[1, 1],</span>
<span class="go"> [1, 2],</span>
<span class="go"> [1, 3],</span>
<span class="go"> [2, 2],</span>
<span class="go"> [2, 3],</span>
<span class="go"> [3, 3]])</span>
</pre></div>
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