/usr/lib/python3/dist-packages/arrayfire/statistics.py is in python3-arrayfire 3.3.20160624-2.
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# 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 *
def mean(a, weights=None, dim=None):
if dim is not None:
out = Array()
if weights is None:
safe_call(backend.get().af_mean(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
else:
safe_call(backend.get().af_mean_weighted(ct.pointer(out.arr), a.arr, weights.arr, ct.c_int(dim)))
return out
else:
real = ct.c_double(0)
imag = ct.c_double(0)
if weights is None:
safe_call(backend.get().af_mean_all(ct.pointer(real), ct.pointer(imag), a.arr))
else:
safe_call(backend.get().af_mean_all_weighted(ct.pointer(real), ct.pointer(imag), a.arr, weights.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
def var(a, isbiased=False, weights=None, dim=None):
if dim is not None:
out = Array()
if weights is None:
safe_call(backend.get().af_var(ct.pointer(out.arr), a.arr, isbiased, ct.c_int(dim)))
else:
safe_call(backend.get().af_var_weighted(ct.pointer(out.arr), a.arr, weights.arr, ct.c_int(dim)))
return out
else:
real = ct.c_double(0)
imag = ct.c_double(0)
if weights is None:
safe_call(backend.get().af_var_all(ct.pointer(real), ct.pointer(imag), a.arr, isbiased))
else:
safe_call(backend.get().af_var_all_weighted(ct.pointer(real), ct.pointer(imag), a.arr, weights.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
def stdev(a, dim=None):
if dim is not None:
out = Array()
safe_call(backend.get().af_stdev(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
return out
else:
real = ct.c_double(0)
imag = ct.c_double(0)
safe_call(backend.get().af_stdev_all(ct.pointer(real), ct.pointer(imag), a.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
def cov(a, isbiased=False, dim=None):
if dim is not None:
out = Array()
safe_call(backend.get().af_cov(ct.pointer(out.arr), a.arr, isbiased, ct.c_int(dim)))
return out
else:
real = ct.c_double(0)
imag = ct.c_double(0)
safe_call(backend.get().af_cov_all(ct.pointer(real), ct.pointer(imag), a.arr, isbiased))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
def median(a, dim=None):
if dim is not None:
out = Array()
safe_call(backend.get().af_median(ct.pointer(out.arr), a.arr, ct.c_int(dim)))
return out
else:
real = ct.c_double(0)
imag = ct.c_double(0)
safe_call(backend.get().af_median_all(ct.pointer(real), ct.pointer(imag), a.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
def corrcoef(x, y):
real = ct.c_double(0)
imag = ct.c_double(0)
safe_call(backend.get().af_corrcoef(ct.pointer(real), ct.pointer(imag), x.arr, y.arr))
real = real.value
imag = imag.value
return real if imag == 0 else real + imag * 1j
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