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<!DOCTYPE html>

<html>

<head>

<title>arrayfire.statistics module — ArrayFire Python documentation</title>

</head><body>

<h1>arrayfire.statistics module<a class="headerlink" href="#module-arrayfire.statistics" title="Permalink to this headline">¶</a></h1>

Statistical algorithms (mean, var, stdev, etc).

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">corrcoef</code>(x, y)<a class="reference internal" href="_modules/arrayfire/statistics.html#corrcoef">[source]</a><a class="headerlink" href="#arrayfire.statistics.corrcoef" title="Permalink to this definition">¶</a></dt>

<dd>Calculate the correlation coefficient of the input arrays.

<dt class="field-odd">Parameters</dt>

<dt>x: af.Array</dt><dd>The first input array.

</dd>

<dt>y: af.Array</dt><dd>The second input array.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the correlation coefficient of the input arrays.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">cov</code>(a, isbiased=False, dim=None)<a class="reference internal" href="_modules/arrayfire/statistics.html#cov">[source]</a><a class="headerlink" href="#arrayfire.statistics.cov" title="Permalink to this definition">¶</a></dt>

<dd>Calculate covariance along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>isbiased: optional: Boolean. default: False.</dt><dd>Boolean denoting whether biased estimate should be taken.

</dd>

<dt>dim: optional: int. default: None.</dt><dd>The dimension for which to obtain the covariance from input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the covariance of the input array along a

given dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">mean</code>(a, weights=None, dim=None)<a class="reference internal" href="_modules/arrayfire/statistics.html#mean">[source]</a><a class="headerlink" href="#arrayfire.statistics.mean" title="Permalink to this definition">¶</a></dt>

<dd>Calculate mean along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>weights: optional: af.Array. default: None.</dt><dd>Array to calculate the weighted mean. Must match size of the

input array.

</dd>

<dt>dim: optional: int. default: None.</dt><dd>The dimension for which to obtain the mean from input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the mean of the input array along a given

dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">meanvar</code>(a, weights=None, bias=<VARIANCE.DEFAULT: 0>, dim=-1)<a class="reference internal" href="_modules/arrayfire/statistics.html#meanvar">[source]</a><a class="headerlink" href="#arrayfire.statistics.meanvar" title="Permalink to this definition">¶</a></dt>

<dd>Calculate mean and variance along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>weights: optional: af.Array. default: None.</dt><dd>Array to calculate for the weighted mean. Must match size of

the input array.

</dd>

<dt>bias: optional: af.VARIANCE. default: DEFAULT.</dt><dd>population variance(VARIANCE.POPULATION) or

sample variance(VARIANCE.SAMPLE).

</dd>

<dt>dim: optional: int. default: -1.</dt><dd>The dimension for which to obtain the variance from input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>mean: af.Array</dt><dd>Array containing the mean of the input array along a given

dimension.

</dd>

<dt>variance: af.Array</dt><dd>Array containing the variance of the input array along a given

dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">median</code>(a, dim=None)<a class="reference internal" href="_modules/arrayfire/statistics.html#median">[source]</a><a class="headerlink" href="#arrayfire.statistics.median" title="Permalink to this definition">¶</a></dt>

<dd>Calculate median along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>dim: optional: int. default: None.</dt><dd>The dimension for which to obtain the median from input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the median of the input array along a

given dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">stdev</code>(a, dim=None)<a class="reference internal" href="_modules/arrayfire/statistics.html#stdev">[source]</a><a class="headerlink" href="#arrayfire.statistics.stdev" title="Permalink to this definition">¶</a></dt>

<dd>Calculate standard deviation along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>dim: optional: int. default: None.</dt><dd>The dimension for which to obtain the standard deviation from

input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the standard deviation of the input array

along a given dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">topk</code>(data, k, dim=0, order=<TOPK.DEFAULT: 0>)<a class="reference internal" href="_modules/arrayfire/statistics.html#topk">[source]</a><a class="headerlink" href="#arrayfire.statistics.topk" title="Permalink to this definition">¶</a></dt>

<dd>Return top k elements along a single dimension.

<dt class="field-odd">Parameters</dt>

<dt>data: af.Array</dt><dd>Input array to return k elements from.

</dd>

<dt>k: scalar. default: 0</dt><dd>The number of elements to return from input array.

</dd>

<dt>dim: optional: scalar. default: 0</dt><dd>The dimension along which the top k elements are

extracted. Note: at the moment, topk() only supports the

extraction of values along the first dimension.

</dd>

<dt>order: optional: af.TOPK. default: af.TOPK.DEFAULT</dt><dd>The ordering of k extracted elements. Defaults to top k max values.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>values: af.Array</dt><dd>Top k elements from input array.

</dd>

<dt>indices: af.Array</dt><dd>Corresponding index array to top k elements.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

<code class="sig-prename descclassname">arrayfire.statistics.</code><code class="sig-name descname">var</code>(a, isbiased=False, weights=None, dim=None)<a class="reference internal" href="_modules/arrayfire/statistics.html#var">[source]</a><a class="headerlink" href="#arrayfire.statistics.var" title="Permalink to this definition">¶</a></dt>

<dd>Calculate variance along a given dimension.

<dt class="field-odd">Parameters</dt>

<dt>a: af.Array</dt><dd>The input array.

</dd>

<dt>isbiased: optional: Boolean. default: False.</dt><dd>Boolean denoting population variance (false) or sample

variance (true).

</dd>

<dt>weights: optional: af.Array. default: None.</dt><dd>Array to calculate for the weighted mean. Must match size of

the input array.

</dd>

<dt>dim: optional: int. default: None.</dt><dd>The dimension for which to obtain the variance from input data.

</dd>

</dl>

</dd>

<dt class="field-even">Returns</dt>

<dt>output: af.Array</dt><dd>Array containing the variance of the input array along a given

dimension.

</dd>

</dl>

</dd>

</dl>

</dd></dl>

</div>

</a>

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