arrayfire.statistics module¶
Statistical algorithms (mean, var, stdev, etc).
arrayfire.statistics.corrcoef(x,y)[source]¶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)[source]¶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 agiven dimension.
arrayfire.statistics.mean(a,weights=None,dim=None)[source]¶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 theinput 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 givendimension.
arrayfire.statistics.meanvar(a,weights=None,bias=<VARIANCE.DEFAULT:0>,dim=-1)[source]¶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 ofthe input array.
- bias: optional: af.VARIANCE. default: DEFAULT.
population variance(VARIANCE.POPULATION) orsample 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 givendimension.
- variance: af.Array
Array containing the variance of the input array along a givendimension.
arrayfire.statistics.median(a,dim=None)[source]¶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 agiven dimension.
arrayfire.statistics.stdev(a,dim=None)[source]¶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 frominput data.
- Returns
- output: af.Array
Array containing the standard deviation of the input arrayalong a given dimension.
arrayfire.statistics.topk(data,k,dim=0,order=<TOPK.DEFAULT:0>)[source]¶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 areextracted. Note: at the moment, topk() only supports theextraction 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)[source]¶Calculate variance along a given dimension.
- Parameters
- a: af.Array
The input array.
- isbiased: optional: Boolean. default: False.
Boolean denoting population variance (false) or samplevariance (true).
- weights: optional: af.Array. default: None.
Array to calculate for the weighted mean. Must match size ofthe 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 givendimension.
