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<!DOCTYPE html>
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<title>arrayfire.statistics — ArrayFire Python documentation</title>
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<h1>Source code for arrayfire.statistics</h1><div class="highlight"><pre>
<span></span><span class="c1">#######################################################</span>
<span class="c1"># Copyright (c) 2015, ArrayFire</span>
<span class="c1"># All rights reserved.</span>
<span class="c1">#</span>
<span class="c1"># This file is distributed under 3-clause BSD license.</span>
<span class="c1"># The complete license agreement can be obtained at:</span>
<span class="c1"># http://arrayfire.com/licenses/BSD-3-Clause</span>
<span class="c1">########################################################</span>
<span class="sd">"""</span>
<span class="sd">Statistical algorithms (mean, var, stdev, etc).</span>
<span class="sd">"""</span>
<span class="kn">from</span> <span class="nn">.library</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">.array</span> <span class="kn">import</span> <span class="o">*</span>
<div class="viewcode-block" id="mean"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.mean">[docs]</a><span class="k">def</span> <span class="nf">mean</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate mean along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> weights: optional: af.Array. default: None.</span>
<span class="sd"> Array to calculate the weighted mean. Must match size of the</span>
<span class="sd"> input array.</span>
<span class="sd"> dim: optional: int. default: None.</span>
<span class="sd"> The dimension for which to obtain the mean from input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the mean of the input array along a given</span>
<span class="sd"> dimension.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="k">if</span> <span class="n">weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_mean</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_mean_weighted</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_mean_all</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_mean_all_weighted</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="var"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.var">[docs]</a><span class="k">def</span> <span class="nf">var</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">isbiased</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate variance along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> isbiased: optional: Boolean. default: False.</span>
<span class="sd"> Boolean denoting population variance (false) or sample</span>
<span class="sd"> variance (true).</span>
<span class="sd"> weights: optional: af.Array. default: None.</span>
<span class="sd"> Array to calculate for the weighted mean. Must match size of</span>
<span class="sd"> the input array.</span>
<span class="sd"> dim: optional: int. default: None.</span>
<span class="sd"> The dimension for which to obtain the variance from input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the variance of the input array along a given</span>
<span class="sd"> dimension.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="k">if</span> <span class="n">weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_var</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">isbiased</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_var_weighted</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="k">if</span> <span class="n">weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_var_all</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">isbiased</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_var_all_weighted</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="meanvar"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.meanvar">[docs]</a><span class="k">def</span> <span class="nf">meanvar</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">weights</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="n">VARIANCE</span><span class="o">.</span><span class="n">DEFAULT</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate mean and variance along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> weights: optional: af.Array. default: None.</span>
<span class="sd"> Array to calculate for the weighted mean. Must match size of</span>
<span class="sd"> the input array.</span>
<span class="sd"> bias: optional: af.VARIANCE. default: DEFAULT.</span>
<span class="sd"> population variance(VARIANCE.POPULATION) or</span>
<span class="sd"> sample variance(VARIANCE.SAMPLE).</span>
<span class="sd"> dim: optional: int. default: -1.</span>
<span class="sd"> The dimension for which to obtain the variance from input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> mean: af.Array</span>
<span class="sd"> Array containing the mean of the input array along a given</span>
<span class="sd"> dimension.</span>
<span class="sd"> variance: af.Array</span>
<span class="sd"> Array containing the variance of the input array along a given</span>
<span class="sd"> dimension.</span>
<span class="sd"> """</span>
<span class="n">mean_out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">var_out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="k">if</span> <span class="n">weights</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_meanvar</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">mean_out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">var_out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span>
<span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">weights</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">bias</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">mean_out</span><span class="p">,</span> <span class="n">var_out</span></div>
<div class="viewcode-block" id="stdev"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.stdev">[docs]</a><span class="k">def</span> <span class="nf">stdev</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate standard deviation along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> dim: optional: int. default: None.</span>
<span class="sd"> The dimension for which to obtain the standard deviation from</span>
<span class="sd"> input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the standard deviation of the input array</span>
<span class="sd"> along a given dimension.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_stdev</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_stdev_all</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="cov"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.cov">[docs]</a><span class="k">def</span> <span class="nf">cov</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">isbiased</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate covariance along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> isbiased: optional: Boolean. default: False.</span>
<span class="sd"> Boolean denoting whether biased estimate should be taken.</span>
<span class="sd"> dim: optional: int. default: None.</span>
<span class="sd"> The dimension for which to obtain the covariance from input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the covariance of the input array along a</span>
<span class="sd"> given dimension.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_cov</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">isbiased</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_cov_all</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">isbiased</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="median"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.median">[docs]</a><span class="k">def</span> <span class="nf">median</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate median along a given dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> a: af.Array</span>
<span class="sd"> The input array.</span>
<span class="sd"> dim: optional: int. default: None.</span>
<span class="sd"> The dimension for which to obtain the median from input data.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the median of the input array along a</span>
<span class="sd"> given dimension.</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">out</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_median</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">out</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">)))</span>
<span class="k">return</span> <span class="n">out</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_median_all</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">a</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="corrcoef"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.corrcoef">[docs]</a><span class="k">def</span> <span class="nf">corrcoef</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Calculate the correlation coefficient of the input arrays.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> x: af.Array</span>
<span class="sd"> The first input array.</span>
<span class="sd"> y: af.Array</span>
<span class="sd"> The second input array.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> output: af.Array</span>
<span class="sd"> Array containing the correlation coefficient of the input arrays.</span>
<span class="sd"> """</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">c_double_t</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_corrcoef</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">real</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">imag</span><span class="p">),</span> <span class="n">x</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">y</span><span class="o">.</span><span class="n">arr</span><span class="p">))</span>
<span class="n">real</span> <span class="o">=</span> <span class="n">real</span><span class="o">.</span><span class="n">value</span>
<span class="n">imag</span> <span class="o">=</span> <span class="n">imag</span><span class="o">.</span><span class="n">value</span>
<span class="k">return</span> <span class="n">real</span> <span class="k">if</span> <span class="n">imag</span> <span class="o">==</span> <span class="mi">0</span> <span class="k">else</span> <span class="n">real</span> <span class="o">+</span> <span class="n">imag</span> <span class="o">*</span> <span class="mi">1</span><span class="n">j</span></div>
<div class="viewcode-block" id="topk"><a class="viewcode-back" href="../../arrayfire.statistics.html#arrayfire.statistics.topk">[docs]</a><span class="k">def</span> <span class="nf">topk</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">order</span><span class="o">=</span><span class="n">TOPK</span><span class="o">.</span><span class="n">DEFAULT</span><span class="p">):</span>
<span class="sd">"""</span>
<span class="sd"> Return top k elements along a single dimension.</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> data: af.Array</span>
<span class="sd"> Input array to return k elements from.</span>
<span class="sd"> k: scalar. default: 0</span>
<span class="sd"> The number of elements to return from input array.</span>
<span class="sd"> dim: optional: scalar. default: 0</span>
<span class="sd"> The dimension along which the top k elements are</span>
<span class="sd"> extracted. Note: at the moment, topk() only supports the</span>
<span class="sd"> extraction of values along the first dimension.</span>
<span class="sd"> order: optional: af.TOPK. default: af.TOPK.DEFAULT</span>
<span class="sd"> The ordering of k extracted elements. Defaults to top k max values.</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> values: af.Array</span>
<span class="sd"> Top k elements from input array.</span>
<span class="sd"> indices: af.Array</span>
<span class="sd"> Corresponding index array to top k elements.</span>
<span class="sd"> """</span>
<span class="n">values</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">indices</span> <span class="o">=</span> <span class="n">Array</span><span class="p">()</span>
<span class="n">safe_call</span><span class="p">(</span><span class="n">backend</span><span class="o">.</span><span class="n">get</span><span class="p">()</span><span class="o">.</span><span class="n">af_topk</span><span class="p">(</span><span class="n">c_pointer</span><span class="p">(</span><span class="n">values</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">c_pointer</span><span class="p">(</span><span class="n">indices</span><span class="o">.</span><span class="n">arr</span><span class="p">),</span> <span class="n">data</span><span class="o">.</span><span class="n">arr</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">c_int_t</span><span class="p">(</span><span class="n">dim</span><span class="p">),</span> <span class="n">order</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
<span class="k">return</span> <span class="n">values</span><span class="p">,</span><span class="n">indices</span></div>
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