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<title>arrayfire.algorithm module — ArrayFire Python documentation</title>
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<div class="section" id="module-arrayfire.algorithm">
<span id="arrayfire-algorithm-module"></span><h1>arrayfire.algorithm module<a class="headerlink" href="#module-arrayfire.algorithm" title="Permalink to this headline">¶</a></h1>
<p>Vector algorithms (sum, min, sort, etc).</p>
<dl class="py function">
<dt id="arrayfire.algorithm.accum">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">accum</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#accum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.accum" title="Permalink to this definition">¶</a></dt>
<dd><p>Cumulative sum of an array along a specified dimension</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which the cumulative sum is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>array of same size as <cite>a</cite> containing the cumulative sum along <cite>dim</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.allTrueByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">allTrueByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#allTrueByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.allTrueByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate if all elements are true along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the all true check will occur.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of all true check in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>Booleans denoting if all elements are true in <cite>vals</cite> along dimension <cite>dim</cite> according to keys</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.all_true">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">all_true</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#all_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.all_true" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if all the elements along a specified dimension are true.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the product is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>Af.array containing True if all elements in <cite>a</cite> along the dimension are True.
If <cite>dim</cite> is <cite>None</cite>, output is True if <cite>a</cite> does not have any zeros, else False.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.anyTrueByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">anyTrueByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#anyTrueByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.anyTrueByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate if any elements are true along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the any true check will occur.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of any true check in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>Booleans denoting if any elements are true in <cite>vals</cite> along dimension <cite>dim</cite> according to keys.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.any_true">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">any_true</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#any_true"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.any_true" title="Permalink to this definition">¶</a></dt>
<dd><p>Check if any the elements along a specified dimension are true.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the product is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>Af.array containing True if any elements in <cite>a</cite> along the dimension are True.
If <cite>dim</cite> is <cite>None</cite>, output is True if <cite>a</cite> does not have any zeros, else False.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.count">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">count</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#count"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.count" title="Permalink to this definition">¶</a></dt>
<dd><p>Count the number of non zero elements in an array along a specified dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the the non zero elements are to be counted.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>The count of non zero elements in <cite>a</cite> along <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, the total number of non zero elements in <cite>a</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.countByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">countByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#countByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.countByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Counts non-zero elements along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which to count elements.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of count in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>Count of non-zero elements in <cite>vals</cite> along dimension <cite>dim</cite> according to keys.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.diff1">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">diff1</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#diff1"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.diff1" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the first order differences along specified dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which the differences are required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>Array whose length along <cite>dim</cite> is 1 less than that of <cite>a</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.diff2">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">diff2</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#diff2"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.diff2" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the second order differences along specified dimensions</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which the differences are required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>Array whose length along <cite>dim</cite> is 2 less than that of <cite>a</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.imax">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">imax</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#imax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.imax" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the value and location of the maximum value along a specified dimension</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the maximum value is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(val, idx): tuple of af.Array or scalars</dt><dd><p><cite>val</cite> contains the maximum value of <cite>a</cite> along <cite>dim</cite>.
<cite>idx</cite> contains the location of where <cite>val</cite> occurs in <cite>a</cite> along <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, <cite>val</cite> and <cite>idx</cite> value and location of global maximum.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.imin">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">imin</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#imin"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.imin" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the value and location of the minimum value along a specified dimension</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the minimum value is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(val, idx): tuple of af.Array or scalars</dt><dd><p><cite>val</cite> contains the minimum value of <cite>a</cite> along <cite>dim</cite>.
<cite>idx</cite> contains the location of where <cite>val</cite> occurs in <cite>a</cite> along <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, <cite>val</cite> and <cite>idx</cite> value and location of global minimum.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.max">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">max</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#max"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.max" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the maximum value of all the elements along a specified dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the maximum value is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>The maximum value of all elements in <cite>a</cite> along dimension <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, maximum value of the entire Array is returned.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.maxByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">maxByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#maxByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.maxByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the max of elements along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the max will occur.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of all elements in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>The max of all elements in <cite>vals</cite> along dimension <cite>dim</cite> according to keys.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.min">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">min</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</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="reference internal" href="_modules/arrayfire/algorithm.html#min"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.min" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the minimum value of all the elements along a specified dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the minimum value is required.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>The minimum value of all elements in <cite>a</cite> along dimension <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, minimum value of the entire Array is returned.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.minByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">minByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#minByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.minByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the min of elements along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the min will occur.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of all elements in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>The min of all elements in <cite>vals</cite> along dimension <cite>dim</cite> according to keys</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.product">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">product</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_val</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="reference internal" href="_modules/arrayfire/algorithm.html#product"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.product" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the product of all the elements along a specified dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the product is required.</p>
</dd>
<dt><strong>nan_val: optional: scalar. default: None</strong></dt><dd><p>The value that replaces NaN in the array</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>The product of all elements in <cite>a</cite> along dimension <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, product of the entire Array is returned.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.productByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">productByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_val</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="reference internal" href="_modules/arrayfire/algorithm.html#productByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.productByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the product of elements along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the product will occur.</p>
</dd>
<dt><strong>nan_val: optional: scalar. default: None</strong></dt><dd><p>The value that replaces NaN in the array</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of all elements in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>The product of all elements in <cite>vals</cite> along dimension <cite>dim</cite> according to keys</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.scan">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">scan</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">a</span></em>, <em class="sig-param"><span class="pre">dim=0</span></em>, <em class="sig-param"><span class="pre">op=<BINARYOP.ADD:</span> <span class="pre">0></span></em>, <em class="sig-param"><span class="pre">inclusive_scan=True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#scan"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.scan" title="Permalink to this definition">¶</a></dt>
<dd><p>Generalized scan of an array.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim</strong><span class="classifier">optional: int. default: 0</span></dt><dd><p>Dimension along which the scan is performed.</p>
</dd>
<dt><strong>op</strong><span class="classifier">optional: af.BINARYOP. default: af.BINARYOP.ADD.</span></dt><dd><p>Binary option the scan algorithm uses. Can be one of:
- af.BINARYOP.ADD
- af.BINARYOP.MUL
- af.BINARYOP.MIN
- af.BINARYOP.MAX</p>
</dd>
<dt><strong>inclusive_scan: optional: bool. default: True</strong></dt><dd><p>Specifies if the scan is inclusive</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>will contain scan of input.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.scan_by_key">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">scan_by_key</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="pre">key</span></em>, <em class="sig-param"><span class="pre">a</span></em>, <em class="sig-param"><span class="pre">dim=0</span></em>, <em class="sig-param"><span class="pre">op=<BINARYOP.ADD:</span> <span class="pre">0></span></em>, <em class="sig-param"><span class="pre">inclusive_scan=True</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#scan_by_key"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.scan_by_key" title="Permalink to this definition">¶</a></dt>
<dd><p>Generalized scan by key of an array.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>key</strong><span class="classifier">af.Array</span></dt><dd><p>key array.</p>
</dd>
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim</strong><span class="classifier">optional: int. default: 0</span></dt><dd><p>Dimension along which the scan is performed.</p>
</dd>
<dt><strong>op</strong><span class="classifier">optional: af.BINARYOP. default: af.BINARYOP.ADD.</span></dt><dd><p>Binary option the scan algorithm uses. Can be one of:
- af.BINARYOP.ADD
- af.BINARYOP.MUL
- af.BINARYOP.MIN
- af.BINARYOP.MAX</p>
</dd>
<dt><strong>inclusive_scan: optional: bool. default: True</strong></dt><dd><p>Specifies if the scan is inclusive</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">af.Array</span></dt><dd><ul class="simple">
<li><p>will contain scan of input.</p></li>
</ul>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.set_intersect">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">set_intersect</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_unique</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#set_intersect"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.set_intersect" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the intersect of two arrays.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>A 1D arrayfire array.</p>
</dd>
<dt><strong>b</strong><span class="classifier">af.Array</span></dt><dd><p>A 1D arrayfire array.</p>
</dd>
<dt><strong>is_unique: optional: bool. default: False</strong></dt><dd><p>Specifies if the both inputs contain unique elements.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>an array values after performing the intersect of <cite>a</cite> and <cite>b</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.set_union">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">set_union</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">b</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_unique</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#set_union"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.set_union" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the union of two arrays.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>A 1D arrayfire array.</p>
</dd>
<dt><strong>b</strong><span class="classifier">af.Array</span></dt><dd><p>A 1D arrayfire array.</p>
</dd>
<dt><strong>is_unique: optional: bool. default: False</strong></dt><dd><p>Specifies if the both inputs contain unique elements.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>an array values after performing the union of <cite>a</cite> and <cite>b</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.set_unique">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">set_unique</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_sorted</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#set_unique"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.set_unique" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the unique elements of an array.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>A 1D arrayfire array.</p>
</dd>
<dt><strong>is_sorted: optional: bool. default: False</strong></dt><dd><p>Specifies if the input is pre-sorted.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>an array containing the unique values from <cite>a</cite></p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.sort">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">sort</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_ascending</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#sort"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.sort" title="Permalink to this definition">¶</a></dt>
<dd><p>Sort the array along a specified dimension</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which sort is to be performed.</p>
</dd>
<dt><strong>is_ascending: optional: bool. default: True</strong></dt><dd><p>Specifies the direction of the sort</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array</dt><dd><p>array containing the sorted values</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.sort_by_key">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">sort_by_key</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ik</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">iv</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_ascending</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#sort_by_key"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.sort_by_key" title="Permalink to this definition">¶</a></dt>
<dd><p>Sort an array based on specified keys</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>ik</strong><span class="classifier">af.Array</span></dt><dd><p>An Array containing the keys</p>
</dd>
<dt><strong>iv</strong><span class="classifier">af.Array</span></dt><dd><p>An Array containing the values</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which sort is to be performed.</p>
</dd>
<dt><strong>is_ascending: optional: bool. default: True</strong></dt><dd><p>Specifies the direction of the sort</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(ok, ov): tuple of af.Array</dt><dd><p><cite>ok</cite> contains the values from <cite>ik</cite> in sorted order
<cite>ov</cite> contains the values from <cite>iv</cite> after sorting them based on <cite>ik</cite></p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.sort_index">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">sort_index</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">is_ascending</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#sort_index"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.sort_index" title="Permalink to this definition">¶</a></dt>
<dd><p>Sort the array along a specified dimension and get the indices.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: 0</strong></dt><dd><p>Dimension along which sort is to be performed.</p>
</dd>
<dt><strong>is_ascending: optional: bool. default: True</strong></dt><dd><p>Specifies the direction of the sort</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>(val, idx): tuple of af.Array</dt><dd><p><cite>val</cite> is an af.Array containing the sorted values.
<cite>idx</cite> is an af.Array containing the original indices of <cite>val</cite> in <cite>a</cite>.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.sum">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">sum</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_val</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="reference internal" href="_modules/arrayfire/algorithm.html#sum"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.sum" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the sum of all the elements along a specified dimension.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
<dt><strong>dim: optional: int. default: None</strong></dt><dd><p>Dimension along which the sum is required.</p>
</dd>
<dt><strong>nan_val: optional: scalar. default: None</strong></dt><dd><p>The value that replaces NaN in the array</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>out: af.Array or scalar number</dt><dd><p>The sum of all elements in <cite>a</cite> along dimension <cite>dim</cite>.
If <cite>dim</cite> is <cite>None</cite>, sum of the entire Array is returned.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.sumByKey">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">sumByKey</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">keys</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vals</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nan_val</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="reference internal" href="_modules/arrayfire/algorithm.html#sumByKey"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.sumByKey" title="Permalink to this definition">¶</a></dt>
<dd><p>Calculate the sum of elements along a specified dimension according to a key.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>keys</strong><span class="classifier">af.Array</span></dt><dd><p>One dimensional arrayfire array with reduction keys.</p>
</dd>
<dt><strong>vals</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array that will be reduced.</p>
</dd>
<dt><strong>dim: optional: int. default: -1</strong></dt><dd><p>Dimension along which the sum will occur.</p>
</dd>
<dt><strong>nan_val: optional: scalar. default: None</strong></dt><dd><p>The value that replaces NaN in the array</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>keys: af.Array or scalar number</dt><dd><p>The reduced keys of all elements in <cite>vals</cite> along dimension <cite>dim</cite>.</p>
</dd>
<dt>values: af.Array or scalar number</dt><dd><p>The sum of all elements in <cite>vals</cite> along dimension <cite>dim</cite> according to keys</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
<dl class="py function">
<dt id="arrayfire.algorithm.where">
<code class="sig-prename descclassname"><span class="pre">arrayfire.algorithm.</span></code><code class="sig-name descname"><span class="pre">where</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">a</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/arrayfire/algorithm.html#where"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arrayfire.algorithm.where" title="Permalink to this definition">¶</a></dt>
<dd><p>Find the indices of non zero elements</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>a</strong><span class="classifier">af.Array</span></dt><dd><p>Multi dimensional arrayfire array.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt>idx: af.Array</dt><dd><p>Linear indices for non zero elements.</p>
</dd>
</dl>
</dd>
</dl>
</dd></dl>
</div>
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