arrayfire.statistics module

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

arrayfire.statistics.corrcoef(x, y)

Calculate the correlation coefficient of the input arrays.

Parameters

x: af.Array

The first input array.

y: af.Array

The second input array.

Returns

output: af.Array

Array containing the correlation coefficient of the input arrays.

arrayfire.statistics.cov(a, isbiased=False, dim=None)

Calculate covariance along a given dimension.

Parameters

a: af.Array

The input array.

isbiased: optional: Boolean. default: False.

Boolean denoting whether biased estimate should be taken.

dim: optional: int. default: None.

The dimension for which to obtain the covariance from input data.

Returns

output: af.Array

Array containing the covariance of the input array along a given dimension.

arrayfire.statistics.mean(a, weights=None, dim=None)

Calculate mean along a given dimension.

Parameters

a: af.Array

The input array.

weights: optional: af.Array. default: None.

Array to calculate the weighted mean. Must match size of the input array.

dim: optional: int. default: None.

The dimension for which to obtain the mean from input data.

Returns

output: af.Array

Array containing the mean of the input array along a given dimension.

arrayfire.statistics.meanvar(a, weights=None, bias=<VARIANCE.DEFAULT: 0>, dim=-1)

Calculate mean and variance along a given dimension.

Parameters

a: af.Array

The input array.

weights: optional: af.Array. default: None.

Array to calculate for the weighted mean. Must match size of the input array.

bias: optional: af.VARIANCE. default: DEFAULT.

population variance(VARIANCE.POPULATION) or sample variance(VARIANCE.SAMPLE).

dim: optional: int. default: -1.

The dimension for which to obtain the variance from input data.

Returns

mean: af.Array

Array containing the mean of the input array along a given dimension.

variance: af.Array

Array containing the variance of the input array along a given dimension.

arrayfire.statistics.median(a, dim=None)

Calculate median along a given dimension.

Parameters

a: af.Array

The input array.

dim: optional: int. default: None.

The dimension for which to obtain the median from input data.

Returns

output: af.Array

Array containing the median of the input array along a given dimension.

arrayfire.statistics.stdev(a, dim=None)

Calculate standard deviation along a given dimension.

Parameters

a: af.Array

The input array.

dim: optional: int. default: None.

The dimension for which to obtain the standard deviation from input data.

Returns

output: af.Array

Array containing the standard deviation of the input array along a given dimension.

arrayfire.statistics.topk(data, k, dim=0, order=<TOPK.DEFAULT: 0>)

Return top k elements along a single dimension.

Parameters

data: af.Array

Input array to return k elements from.

k: scalar. default: 0

The number of elements to return from input array.

dim: optional: scalar. default: 0

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.

order: optional: af.TOPK. default: af.TOPK.DEFAULT

The ordering of k extracted elements. Defaults to top k max values.

Returns

values: af.Array

Top k elements from input array.

indices: af.Array

Corresponding index array to top k elements.

arrayfire.statistics.var(a, isbiased=False, weights=None, dim=None)

Calculate variance along a given dimension.

Parameters

a: af.Array

The input array.

isbiased: optional: Boolean. default: False.

Boolean denoting population variance (false) or sample variance (true).

weights: optional: af.Array. default: None.

Array to calculate for the weighted mean. Must match size of the input array.

dim: optional: int. default: None.

The dimension for which to obtain the variance from input data.

Returns

output: af.Array

Array containing the variance of the input array along a given dimension.