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<section id="numpy-random-randomstate-uniform">
<h1>numpy.random.RandomState.uniform<a class="headerlink" href="#numpy-random-randomstate-uniform" title="Link to this heading">#</a></h1>
<p>method</p>
<dl class="py method">
<dt class="sig sig-object py" id="numpy.random.RandomState.uniform">
<span class="sig-prename descclassname"><span class="pre">random.RandomState.</span></span><span class="sig-name descname"><span class="pre">uniform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">low</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">high</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.RandomState.uniform" title="Link to this definition">#</a></dt>
<dd><p>Draw samples from a uniform distribution.</p>
<p>Samples are uniformly distributed over the half-open interval
<code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high)</span></code> (includes low, but excludes high). In other words,
any value within the given interval is equally likely to be drawn
by <a class="reference internal" href="#numpy.random.RandomState.uniform" title="numpy.random.RandomState.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform</span></code></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>New code should use the <a class="reference internal" href="numpy.random.Generator.uniform.html#numpy.random.Generator.uniform" title="numpy.random.Generator.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform</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>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>low</strong><span class="classifier">float or array_like of floats, optional</span></dt><dd><p>Lower boundary of the output interval. All values generated will be
greater than or equal to low. The default value is 0.</p>
</dd>
<dt><strong>high</strong><span class="classifier">float or array_like of floats</span></dt><dd><p>Upper boundary of the output interval. All values generated will be
less than or equal to high. The high limit may be included in the
returned array of floats due to floating-point rounding in the
equation <code class="docutils literal notranslate"><span class="pre">low</span> <span class="pre">+</span> <span class="pre">(high-low)</span> <span class="pre">*</span> <span class="pre">random_sample()</span></code>. The default value
is 1.0.</p>
</dd>
<dt><strong>size</strong><span class="classifier">int or tuple of ints, optional</span></dt><dd><p>Output shape. If the given shape is, e.g., <code class="docutils literal notranslate"><span class="pre">(m,</span> <span class="pre">n,</span> <span class="pre">k)</span></code>, then
<code class="docutils literal notranslate"><span class="pre">m</span> <span class="pre">*</span> <span class="pre">n</span> <span class="pre">*</span> <span class="pre">k</span></code> samples are drawn. If size is <code class="docutils literal notranslate"><span class="pre">None</span></code> (default),
a single value is returned if <code class="docutils literal notranslate"><span class="pre">low</span></code> and <code class="docutils literal notranslate"><span class="pre">high</span></code> are both scalars.
Otherwise, <code class="docutils literal notranslate"><span class="pre">np.broadcast(low,</span> <span class="pre">high).size</span></code> samples are drawn.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">ndarray or scalar</span></dt><dd><p>Drawn samples from the parameterized uniform distribution.</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.randint.html#numpy.random.RandomState.randint" title="numpy.random.RandomState.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a></dt><dd><p>Discrete uniform distribution, yielding integers.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.RandomState.random_integers.html#numpy.random.RandomState.random_integers" title="numpy.random.RandomState.random_integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_integers</span></code></a></dt><dd><p>Discrete uniform distribution over the closed interval <code class="docutils literal notranslate"><span class="pre">[low,</span> <span class="pre">high]</span></code>.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.RandomState.random_sample.html#numpy.random.RandomState.random_sample" title="numpy.random.RandomState.random_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sample</span></code></a></dt><dd><p>Floats uniformly distributed over <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1)</span></code>.</p>
</dd>
<dt><a class="reference internal" href="../index.html#module-numpy.random" title="numpy.random"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a></dt><dd><p>Alias for <a class="reference internal" href="numpy.random.RandomState.random_sample.html#numpy.random.RandomState.random_sample" title="numpy.random.RandomState.random_sample"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_sample</span></code></a>.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.RandomState.rand.html#numpy.random.RandomState.rand" title="numpy.random.RandomState.rand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand</span></code></a></dt><dd><p>Convenience function that accepts dimensions as input, e.g., <code class="docutils literal notranslate"><span class="pre">rand(2,2)</span></code> would generate a 2-by-2 array of floats, uniformly distributed over <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1)</span></code>.</p>
</dd>
<dt><a class="reference internal" href="numpy.random.Generator.uniform.html#numpy.random.Generator.uniform" title="numpy.random.Generator.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random.Generator.uniform</span></code></a></dt><dd><p>which should be used for new code.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>The probability density function of the uniform distribution is</p>
<div class="math notranslate nohighlight">
\[p(x) = \frac{1}{b - a}\]</div>
<p>anywhere within the interval <code class="docutils literal notranslate"><span class="pre">[a,</span> <span class="pre">b)</span></code>, and zero elsewhere.</p>
<p>When <code class="docutils literal notranslate"><span class="pre">high</span></code> == <code class="docutils literal notranslate"><span class="pre">low</span></code>, values of <code class="docutils literal notranslate"><span class="pre">low</span></code> will be returned.
If <code class="docutils literal notranslate"><span class="pre">high</span></code> < <code class="docutils literal notranslate"><span class="pre">low</span></code>, the results are officially undefined
and may eventually raise an error, i.e. do not rely on this
function to behave when passed arguments satisfying that
inequality condition. The <code class="docutils literal notranslate"><span class="pre">high</span></code> limit may be included in the
returned array of floats due to floating-point rounding in the
equation <code class="docutils literal notranslate"><span class="pre">low</span> <span class="pre">+</span> <span class="pre">(high-low)</span> <span class="pre">*</span> <span class="pre">random_sample()</span></code>. For example:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">(</span><span class="mi">5</span><span class="o">*</span><span class="mf">0.99999999</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">np.float32(5.0)</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
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<p>Draw samples from the distribution:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">s</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">uniform</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1000</span><span class="p">)</span>
</pre></div>
</div>
<p>All values are within the given interval:</p>
<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">all</span><span class="p">(</span><span class="n">s</span> <span class="o">>=</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">np</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">s</span> <span class="o"><</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">True</span>
</pre></div>
</div>
<p>Display the histogram of the samples, along with the
probability density function:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span><span class="w"> </span><span class="nn">matplotlib.pyplot</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">plt</span>
<span class="gp">>>> </span><span class="n">count</span><span class="p">,</span> <span class="n">bins</span><span class="p">,</span> <span class="n">ignored</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">s</span><span class="p">,</span> <span class="mi">15</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">bins</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">bins</span><span class="p">),</span> <span class="n">linewidth</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">'r'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
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