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<section id="random">
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<span id="id1"></span><h1>Random<a class="headerlink" href="#random" title="Permalink to this heading"></a></h1>
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<p>Random sampling functions in MLX use an implicit global PRNG state by default.
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However, all function take an optional <code class="docutils literal notranslate"><span class="pre">key</span></code> keyword argument for when more
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fine-grained control or explicit state management is needed.</p>
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<p>For example, you can generate random numbers with:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">())</span>
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</pre></div>
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</div>
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<p>which will print a sequence of unique pseudo random numbers. Alternatively you
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can explicitly set the key:</p>
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<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">key</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">key</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">):</span>
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<span class="nb">print</span><span class="p">(</span><span class="n">mx</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="n">key</span><span class="o">=</span><span class="n">key</span><span class="p">))</span>
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</pre></div>
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</div>
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<p>which will yield the same pseudo random number at each iteration.</p>
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<p>Following <a class="reference external" href="https://jax.readthedocs.io/en/latest/jep/263-prng.html">JAX’s PRNG design</a>
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we use a splittable version of Threefry, which is a counter-based PRNG.</p>
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<table class="autosummary longtable docutils align-default">
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<tbody>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.seed.html#mlx.core.random.seed" title="mlx.core.random.seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code></a>(seed)</p></td>
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<td><p>Seed the global PRNG.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.key.html#mlx.core.random.key" title="mlx.core.random.key"><code class="xref py py-obj docutils literal notranslate"><span class="pre">key</span></code></a>(seed)</p></td>
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<td><p>Get a PRNG key from a seed.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.split.html#mlx.core.random.split" title="mlx.core.random.split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split</span></code></a>(key[, num, stream])</p></td>
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<td><p>Split a PRNG key into sub keys.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.bernoulli.html#mlx.core.random.bernoulli" title="mlx.core.random.bernoulli"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bernoulli</span></code></a>([p, shape, key, stream])</p></td>
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<td><p>Generate Bernoulli random values.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.categorical.html#mlx.core.random.categorical" title="mlx.core.random.categorical"><code class="xref py py-obj docutils literal notranslate"><span class="pre">categorical</span></code></a>(logits[, axis, shape, ...])</p></td>
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<td><p>Sample from a categorical distribution.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.gumbel.html#mlx.core.random.gumbel" title="mlx.core.random.gumbel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gumbel</span></code></a>([shape, dtype, stream, key])</p></td>
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<td><p>Sample from the standard Gumbel distribution.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.normal.html#mlx.core.random.normal" title="mlx.core.random.normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal</span></code></a>([shape, dtype, key, stream])</p></td>
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<td><p>Generate normally distributed random numbers.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.randint.html#mlx.core.random.randint" title="mlx.core.random.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a>(low, high[, shape, dtype, key, stream])</p></td>
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<td><p>Generate random integers from the given interval.</p></td>
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</tr>
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<tr class="row-odd"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.uniform.html#mlx.core.random.uniform" title="mlx.core.random.uniform"><code class="xref py py-obj docutils literal notranslate"><span class="pre">uniform</span></code></a>([low, high, shape, dtype, key, stream])</p></td>
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<td><p>Generate uniformly distributed random numbers.</p></td>
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</tr>
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<tr class="row-even"><td><p><a class="reference internal" href="_autosummary/mlx.core.random.truncated_normal.html#mlx.core.random.truncated_normal" title="mlx.core.random.truncated_normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">truncated_normal</span></code></a>(lower, upper[, shape, ...])</p></td>
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<td><p>Generate values from a truncated normal distribution.</p></td>
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</tr>
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</tbody>
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