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<section id="numpy-random-generator-uniform">
<h1>numpy.random.Generator.uniform<a class="headerlink" href="#numpy-random-generator-uniform" title="Link to this heading">#</a></h1>
<p>method</p>
<dl class="py method">
<dt class="sig sig-object py" id="numpy.random.Generator.uniform">
<span class="sig-prename descclassname"><span class="pre">random.Generator.</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.Generator.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.Generator.uniform" title="numpy.random.Generator.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform</span></code></a>.</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>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 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>. high - low must be
non-negative. 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.Generator.integers.html#numpy.random.Generator.integers" title="numpy.random.Generator.integers"><code class="xref py py-obj docutils literal notranslate"><span class="pre">integers</span></code></a></dt><dd><p>Discrete uniform distribution, yielding integers.</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>Floats uniformly distributed over <code class="docutils literal notranslate"><span class="pre">[0,</span> <span class="pre">1)</span></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.</p>
<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">rng</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">default_rng</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">s</span> <span class="o">=</span> <span class="n">rng</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">_</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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