5272356642

# cython: language_level=3
# distutils: language = c++
# -*- coding: utf-8 -*-
# *****************************************************************************
# Copyright (c) 2023, Intel Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# - Redistributions of source code must retain the above copyright notice,
#   this list of conditions and the following disclaimer.
# - Redistributions in binary form must reproduce the above copyright notice,
#   this list of conditions and the following disclaimer in the documentation
#   and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
# THE POSSIBILITY OF SUCH DAMAGE.
# *****************************************************************************


import dpnp
from dpnp.dpnp_array import dpnp_array
from dpnp.dpnp_utils import (
    get_usm_allocations
)

import dpctl
import dpctl.tensor as dpt
import dpctl.tensor._tensor_impl as ti


__all__ = [
    "dpnp_cov"
]

def dpnp_cov(m, y=None, rowvar=True, dtype=None):
    """
    Estimate a covariance matrix based on passed data.
    No support for given weights is provided now.

    The implementation is done through existing dpnp and dpctl methods
    instead of separate function call of dpnp backend.

    """

    def _get_2dmin_array(x, dtype):
        """
        Transform an input array to a form required for building a covariance matrix.

        If applicable, it reshapes the input array to have 2 dimensions or greater.
        If applicable, it transposes the input array when 'rowvar' is False.
        It casts to another dtype, if the input array differs from requested one.

        """

        if x.ndim == 0:
            x = x.reshape((1, 1))
        elif x.ndim == 1:
            x = x[dpnp.newaxis, :]

        if not rowvar and x.shape[0] != 1:
            x = x.T

        if x.dtype != dtype:
            x = dpnp.astype(x, dtype)
        return x


    # input arrays must follow CFD paradigm
    usm_type, queue = get_usm_allocations((m, ) if y is None else (m, y))

    # calculate a type of result array if not passed explicitly
    if dtype is None:
        dtypes = [m.dtype, dpnp.default_float_type(sycl_queue=queue)]
        if y is not None:
            dtypes.append(y.dtype)
        dtype = dpt.result_type(*dtypes)

    X = _get_2dmin_array(m, dtype)
    if y is not None:
        y = _get_2dmin_array(y, dtype)

        # TODO: replace with dpnp.concatenate((X, y), axis=0) once dpctl implementation is ready
        if X.ndim != y.ndim:
            raise ValueError("all the input arrays must have same number of dimensions")

        if X.shape[1:] != y.shape[1:]:
            raise ValueError("all the input array dimensions for the concatenation axis must match exactly")

        res_shape = tuple(X.shape[i] if i > 0 else (X.shape[i] + y.shape[i]) for i in range(X.ndim))
        res_usm = dpt.empty(res_shape, dtype=dtype, usm_type=usm_type, sycl_queue=queue)

        # concatenate input arrays 'm' and 'y' into single array among 0-axis
        hev1, _ = ti._copy_usm_ndarray_into_usm_ndarray(src=X.get_array(), dst=res_usm[:X.shape[0]], sycl_queue=queue)
        hev2, _ = ti._copy_usm_ndarray_into_usm_ndarray(src=y.get_array(), dst=res_usm[X.shape[0]:], sycl_queue=queue)
        dpctl.SyclEvent.wait_for([hev1, hev2])

        X = dpnp_array._create_from_usm_ndarray(res_usm)

    avg = X.mean(axis=1)

    fact = X.shape[1] - 1
    X -= avg[:, None]

    c = dpnp.dot(X, X.T.conj())
    c *= 1 / fact if fact != 0 else dpnp.nan

    return dpnp.squeeze(c)

1	# cython: language_level=3
2	# distutils: language = c++
3	# -- coding: utf-8 --
4	# *****************************************************************************
5	# Copyright (c) 2023, Intel Corporation
6	# All rights reserved.
7	#
8	# Redistribution and use in source and binary forms, with or without
9	# modification, are permitted provided that the following conditions are met:
10	# - Redistributions of source code must retain the above copyright notice,
11	# this list of conditions and the following disclaimer.
12	# - Redistributions in binary form must reproduce the above copyright notice,
13	# this list of conditions and the following disclaimer in the documentation
14	# and/or other materials provided with the distribution.
15	#
16	# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
17	# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
18	# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
19	# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
20	# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
21	# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
22	# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
23	# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
24	# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
25	# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
26	# THE POSSIBILITY OF SUCH DAMAGE.
27	# *****************************************************************************
28
29
30	import dpnp
31	from dpnp.dpnp_array import dpnp_array
32	from dpnp.dpnp_utils import (
33	get_usm_allocations
34	)
35
36	import dpctl
37	import dpctl.tensor as dpt
38	import dpctl.tensor._tensor_impl as ti
39
40
41	__all__ = [
42	"dpnp_cov"
43	]
44
45	def dpnp_cov(m, y=None, rowvar=True, dtype=None):
46	"""
47	Estimate a covariance matrix based on passed data.
48	No support for given weights is provided now.
49
50	The implementation is done through existing dpnp and dpctl methods
51	instead of separate function call of dpnp backend.
52
53	"""
54
55	def _get_2dmin_array(x, dtype):
56	"""
57	Transform an input array to a form required for building a covariance matrix.
58
59	If applicable, it reshapes the input array to have 2 dimensions or greater.
60	If applicable, it transposes the input array when 'rowvar' is False.
61	It casts to another dtype, if the input array differs from requested one.
62
63	"""
64
65	if x.ndim == 0:	1!
NEW 66	x = x.reshape((1, 1))
67	elif x.ndim == 1:	2✔
68	x = x[dpnp.newaxis, :]
69
70	if not rowvar and x.shape[0] != 1:	2✔
71	x = x.T
72
73	if x.dtype != dtype:	2✔
74	x = dpnp.astype(x, dtype)
75	return x
76
77
78	# input arrays must follow CFD paradigm
79	usm_type, queue = get_usm_allocations((m, ) if y is None else (m, y))
80
81	# calculate a type of result array if not passed explicitly
82	if dtype is None:	2✔
83	dtypes = [m.dtype, dpnp.default_float_type(sycl_queue=queue)]
84	if y is not None:	2✔
85	dtypes.append(y.dtype)
86	dtype = dpt.result_type(*dtypes)
87
88	X = _get_2dmin_array(m, dtype)
89	if y is not None:	2✔
90	y = _get_2dmin_array(y, dtype)
91
92	# TODO: replace with dpnp.concatenate((X, y), axis=0) once dpctl implementation is ready
93	if X.ndim != y.ndim:	2✔
94	raise ValueError("all the input arrays must have same number of dimensions")
95
96	if X.shape[1:] != y.shape[1:]:	1!
NEW 97	raise ValueError("all the input array dimensions for the concatenation axis must match exactly")
98
99	res_shape = tuple(X.shape[i] if i > 0 else (X.shape[i] + y.shape[i]) for i in range(X.ndim))	2✔
100	res_usm = dpt.empty(res_shape, dtype=dtype, usm_type=usm_type, sycl_queue=queue)
101
102	# concatenate input arrays 'm' and 'y' into single array among 0-axis
103	hev1, _ = ti._copy_usm_ndarray_into_usm_ndarray(src=X.get_array(), dst=res_usm[:X.shape[0]], sycl_queue=queue)
104	hev2, _ = ti._copy_usm_ndarray_into_usm_ndarray(src=y.get_array(), dst=res_usm[X.shape[0]:], sycl_queue=queue)
105	dpctl.SyclEvent.wait_for([hev1, hev2])
106
107	X = dpnp_array._create_from_usm_ndarray(res_usm)
108
109	avg = X.mean(axis=1)
110
111	fact = X.shape[1] - 1
112	X -= avg[:, None]
113
114	c = dpnp.dot(X, X.T.conj())
115	c *= 1 / fact if fact != 0 else dpnp.nan
116
117	return dpnp.squeeze(c)

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