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Merged
shssf merged 4 commits into from
Sep 7, 2020
Merged

Statistics funcs cov #2

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9af254e
naive numpy.cov implementation
Alexander-Makaryev Sep 7, 2020
250e1c9
del statistics_tests
Alexander-Makaryev Sep 7, 2020
f5a3a47
add statics_tests
Alexander-Makaryev Sep 7, 2020
5dca4ce
throw errors in np.cov
Alexander-Makaryev Sep 7, 2020
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naive numpy.cov implementation
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@Alexander-Makaryev
Alexander-Makaryev committed Sep 7, 2020
commit 9af254efb855a8e6098b711ee41f3824fab6617c
24 changes: 24 additions & 0 deletions dpnp/backend_statistics.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -38,10 +38,34 @@ from dpnp.dpnp_utils cimport checker_throw_type_error, normalize_axis


__all__ += [
"dpnp_cov",
"dpnp_mean"
]


cpdef dparray dpnp_cov(dparray array1):
cdef dparray mean = dparray(array1.shape[0], dtype=array1.dtype)
cdef dparray X = dparray(array1.shape, dtype=array1.dtype)

# mean(array1, axis=1) #################################
for i in range(array1.shape[0]):
sum = 0.0
for j in range(array1.shape[1]):
sum += array1[i,j]
mean[i] = sum/array1.shape[1]
########################################################
#X = array1 - mean[:, None]
#X = array1 - mean[:, numpy.newaxis]
#X = array1 - mean.reshape((array1.shape[0], 1))
for i in range(array1.shape[0]):
for j in range(array1.shape[1]):
X[i,j] = array1[i,j] - mean[i]
########################################################
Y = X.transpose()
res = dpnp_matmul(X,Y)
return res/(array1.shape[1]-1)


cpdef dparray dpnp_mean(dparray a, axis):
cdef dparray_shape_type shape_a = a.shape
cdef long size_a = a.size
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14 changes: 14 additions & 0 deletions dpnp/dpnp_iface_statistics.py
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Expand Up @@ -47,10 +47,24 @@
from dpnp.dpnp_utils import checker_throw_value_error, use_origin_backend

__all__ = [
'cov',
'mean'
]


def cov(in_array1):
"""
Estimate a covariance matrix, given data and weights.
"""

is_dparray1 = isinstance(in_array1, dparray)

if (not use_origin_backend(in_array1) and is_dparray1):

@shssf shssf Sep 7, 2020

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👍

return dpnp_cov(in_array1)

return numpy.cov(in_array1)


def mean(a, axis=None):
"""
Compute the arithmetic mean along the specified axis.
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