#######################################################
# 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):
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):
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 stdev(a, dim=None):
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):
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):
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