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<div class="section" id="sobolengine">
<h1>SobolEngine<a class="headerlink" href="#sobolengine" title="Permalink to this headline">¶</a></h1>
<dl class="class">
<dt id="torch.quasirandom.SobolEngine">
<em class="property">class </em><code class="sig-prename descclassname">torch.quasirandom.</code><code class="sig-name descname">SobolEngine</code><span class="sig-paren">(</span><em class="sig-param">dimension</em>, <em class="sig-param">scramble=False</em>, <em class="sig-param">seed=None</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine" title="Permalink to this definition">¶</a></dt>
<dd><p>The <a class="reference internal" href="#torch.quasirandom.SobolEngine" title="torch.quasirandom.SobolEngine"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.quasirandom.SobolEngine</span></code></a> is an engine for generating
(scrambled) Sobol sequences. Sobol sequences are an example of low
discrepancy quasi-random sequences.</p>
<p>This implementation of an engine for Sobol sequences is capable of
sampling sequences up to a maximum dimension of 1111. It uses direction
numbers to generate these sequences, and these numbers have been adapted
from <a class="reference external" href="https://web.maths.unsw.edu.au/~fkuo/sobol/joe-kuo-old.1111">here</a>.</p>
<p class="rubric">References</p>
<ul class="simple">
<li><p>Art B. Owen. Scrambling Sobol and Niederreiter-Xing points.
Journal of Complexity, 14(4):466-489, December 1998.</p></li>
<li><p>I. M. Sobol. The distribution of points in a cube and the accurate
evaluation of integrals.
Zh. Vychisl. Mat. i Mat. Phys., 7:784-802, 1967.</p></li>
</ul>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dimension</strong> (<em>Int</em>) – The dimensionality of the sequence to be drawn</p></li>
<li><p><strong>scramble</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – Setting this to <code class="docutils literal notranslate"><span class="pre">True</span></code> will produce
scrambled Sobol sequences. Scrambling is
capable of producing better Sobol
sequences. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>seed</strong> (<em>Int</em><em>, </em><em>optional</em>) – This is the seed for the scrambling. The seed
of the random number generator is set to this,
if specified. Otherwise, it uses a random seed.
Default: <code class="docutils literal notranslate"><span class="pre">None</span></code></p></li>
</ul>
</dd>
</dl>
<p>Examples:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">soboleng</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">quasirandom</span><span class="o">.</span><span class="n">SobolEngine</span><span class="p">(</span><span class="n">dimension</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">soboleng</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go">tensor([[0.5000, 0.5000, 0.5000, 0.5000, 0.5000],</span>
<span class="go"> [0.7500, 0.2500, 0.7500, 0.2500, 0.7500],</span>
<span class="go"> [0.2500, 0.7500, 0.2500, 0.7500, 0.2500]])</span>
</pre></div>
</div>
<dl class="method">
<dt id="torch.quasirandom.SobolEngine.draw">
<code class="sig-name descname">draw</code><span class="sig-paren">(</span><em class="sig-param">n=1</em>, <em class="sig-param">out=None</em>, <em class="sig-param">dtype=torch.float32</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.draw"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.draw" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to draw a sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> points from a Sobol sequence.
Note that the samples are dependent on the previous samples. The size
of the result is <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">(</mo><mi>n</mi><mo separator="true">,</mo><mi>d</mi><mi>i</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">(n, dimension)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">(</span><span class="mord mathnormal">n</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord mathnormal">d</span><span class="mord mathnormal">i</span><span class="mord mathnormal">m</span><span class="mord mathnormal">e</span><span class="mord mathnormal">n</span><span class="mord mathnormal">s</span><span class="mord mathnormal">i</span><span class="mord mathnormal">o</span><span class="mord mathnormal">n</span><span class="mclose">)</span></span></span></span>
</span>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>n</strong> (<em>Int</em><em>, </em><em>optional</em>) – The length of sequence of points to draw.
Default: 1</p></li>
<li><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – The output tensor</p></li>
<li><p><strong>dtype</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code>, optional) – the desired data type of the
returned tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.float32</span></code></p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.quasirandom.SobolEngine.fast_forward">
<code class="sig-name descname">fast_forward</code><span class="sig-paren">(</span><em class="sig-param">n</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.fast_forward"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.fast_forward" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to fast-forward the state of the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> by
<code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> steps. This is equivalent to drawing <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> samples
without using the samples.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>n</strong> (<em>Int</em>) – The number of steps to fast-forward by.</p>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.quasirandom.SobolEngine.reset">
<code class="sig-name descname">reset</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="../_modules/torch/quasirandom.html#SobolEngine.reset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.quasirandom.SobolEngine.reset" title="Permalink to this definition">¶</a></dt>
<dd><p>Function to reset the <code class="docutils literal notranslate"><span class="pre">SobolEngine</span></code> to base state.</p>
</dd></dl>
</dd></dl>
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