#######################################################
# Copyright (c) 2015, ArrayFire
# All rights reserved.
#
# This file is distributed under 3-clause BSD license.
# The complete license agreement can be obtained at:
# http://arrayfire.com/licenses/BSD-3-Clause
########################################################
"""
Statistical algorithms (mean, var, stdev, etc).
"""
from .library import *
from .array import *
[docs]def 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.
"""
if dim is not None:
out = Array()
if weights is None:
safe_call(backend.get().af_mean(c_pointer(out.arr), a.arr, c_int_t(dim)))
else:
safe_call(backend.get().af_mean_weighted(c_pointer(out.arr), a.arr, weights.arr, c_int_t(dim)))
return out
else:
real = c_double_t(0)
imag = c_double_t(0)
if weights is None:
safe_call(backend.get().af_mean_all(c_pointer(real), c_pointer(imag), a.arr))
else:
safe_call(backend.get().af_mean_all_weighted(c_pointer(real), c_pointer(imag), a.arr, weights.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
[docs]def 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.
"""
if dim is not None:
out = Array()
if weights is None:
safe_call(backend.get().af_var(c_pointer(out.arr), a.arr, isbiased, c_int_t(dim)))
else:
safe_call(backend.get().af_var_weighted(c_pointer(out.arr), a.arr, weights.arr, c_int_t(dim)))
return out
else:
real = c_double_t(0)
imag = c_double_t(0)
if weights is None:
safe_call(backend.get().af_var_all(c_pointer(real), c_pointer(imag), a.arr, isbiased))
else:
safe_call(backend.get().af_var_all_weighted(c_pointer(real), c_pointer(imag), a.arr, weights.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
[docs]def meanvar(a, weights=None, bias=VARIANCE.DEFAULT, 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.
"""
mean_out = Array()
var_out = Array()
if weights is None:
weights = Array()
safe_call(backend.get().af_meanvar(c_pointer(mean_out.arr), c_pointer(var_out.arr),
a.arr, weights.arr, bias.value, c_int_t(dim)))
return mean_out, var_out
[docs]def 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.
"""
if dim is not None:
out = Array()
safe_call(backend.get().af_stdev(c_pointer(out.arr), a.arr, c_int_t(dim)))
return out
else:
real = c_double_t(0)
imag = c_double_t(0)
safe_call(backend.get().af_stdev_all(c_pointer(real), c_pointer(imag), a.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
[docs]def 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.
"""
if dim is not None:
out = Array()
safe_call(backend.get().af_cov(c_pointer(out.arr), a.arr, isbiased, c_int_t(dim)))
return out
else:
real = c_double_t(0)
imag = c_double_t(0)
safe_call(backend.get().af_cov_all(c_pointer(real), c_pointer(imag), a.arr, isbiased))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
[docs]def 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.
"""
real = c_double_t(0)
imag = c_double_t(0)
safe_call(backend.get().af_corrcoef(c_pointer(real), c_pointer(imag), x.arr, y.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
[docs]def topk(data, k, dim=0, order=TOPK.DEFAULT):
"""
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.
"""
values = Array()
indices = Array()
safe_call(backend.get().af_topk(c_pointer(values.arr), c_pointer(indices.arr), data.arr, k, c_int_t(dim), order.value))
return values,indices
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