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<section id="numpy-random-randomstate-randn">
<h1>numpy.random.RandomState.randn<a class="headerlink" href="#numpy-random-randomstate-randn" title="Link to this heading">#</a></h1>
<p>method</p>
<dl class="py method">
<dt class="sig sig-object py" id="numpy.random.RandomState.randn">
<span class="sig-prename descclassname"><span class="pre">random.RandomState.</span></span><span class="sig-name descname"><span class="pre">randn</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">d0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">...</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.RandomState.randn" title="Link to this definition">#</a></dt>
<dd><p>Return a sample (or samples) from the “standard normal” distribution.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This is a convenience function for users porting code from Matlab,
and wraps <a class="reference internal" href="numpy.random.RandomState.standard_normal.html#numpy.random.RandomState.standard_normal" title="numpy.random.RandomState.standard_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standard_normal</span></code></a>. That function takes a
tuple to specify the size of the output, which is consistent with
other NumPy functions like <a class="reference internal" href="../../generated/numpy.zeros.html#numpy.zeros" title="numpy.zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.zeros</span></code></a> and <a class="reference internal" href="../../generated/numpy.ones.html#numpy.ones" title="numpy.ones"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ones</span></code></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>New code should use the
<a class="reference internal" href="numpy.random.Generator.standard_normal.html#numpy.random.Generator.standard_normal" title="numpy.random.Generator.standard_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standard_normal</span></code></a>
method of a <a class="reference internal" href="../generator.html#numpy.random.Generator" title="numpy.random.Generator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Generator</span></code></a> instance instead;
please see the <a class="reference internal" href="../index.html#random-quick-start"><span class="std std-ref">Quick start</span></a>.</p>
</div>
<p>If positive int_like arguments are provided, <a class="reference internal" href="#numpy.random.RandomState.randn" title="numpy.random.RandomState.randn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn</span></code></a> generates an array
of shape <code class="docutils literal notranslate"><span class="pre">(d0,</span> <span class="pre">d1,</span> <span class="pre">...,</span> <span class="pre">dn)</span></code>, filled
with random floats sampled from a univariate “normal” (Gaussian)
distribution of mean 0 and variance 1. A single float randomly sampled
from the distribution is returned if no argument is provided.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>d0, d1, …, dn</strong><span class="classifier">int, optional</span></dt><dd><p>The dimensions of the returned array, must be non-negative.
If no argument is given a single Python float is returned.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>Z</strong><span class="classifier">ndarray or float</span></dt><dd><p>A <code class="docutils literal notranslate"><span class="pre">(d0,</span> <span class="pre">d1,</span> <span class="pre">...,</span> <span class="pre">dn)</span></code>-shaped array of floating-point samples from
the standard normal distribution, or a single such float if
no parameters were supplied.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.random.RandomState.standard_normal.html#numpy.random.RandomState.standard_normal" title="numpy.random.RandomState.standard_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">standard_normal</span></code></a></dt><dd><p>Similar, but takes a tuple as its argument.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.RandomState.normal.html#numpy.random.RandomState.normal" title="numpy.random.RandomState.normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal</span></code></a></dt><dd><p>Also accepts mu and sigma arguments.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.Generator.standard_normal.html#numpy.random.Generator.standard_normal" title="numpy.random.Generator.standard_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random.Generator.standard_normal</span></code></a></dt><dd><p>which should be used for new code.</p>
</dd>
</dl>
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<p class="rubric">Notes</p>
<p>For random samples from the normal distribution with mean <code class="docutils literal notranslate"><span class="pre">mu</span></code> and
standard deviation <code class="docutils literal notranslate"><span class="pre">sigma</span></code>, use:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">sigma</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="o">...</span><span class="p">)</span> <span class="o">+</span> <span class="n">mu</span>
</pre></div>
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<p class="rubric">Examples</p>
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<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">()</span>
<span class="go">2.1923875335537315 # random</span>
</pre></div>
</div>
<p>Two-by-four array of samples from the normal distribution with
mean 3 and standard deviation 2.5:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="mi">3</span> <span class="o">+</span> <span class="mf">2.5</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go">array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random</span>
<span class="go"> [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random</span>
</pre></div>
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