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Implement dpnp.cov() though existing dpnp methods (#1396)
* Implement dpnp.cov() though existing dpnp methods * Applied review comments * Clean up the code to get rid of todo * use dpnp.mean()
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| # 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. | ||
| # ***************************************************************************** | ||
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| import dpnp | ||
| from dpnp.dpnp_array import dpnp_array | ||
| from dpnp.dpnp_utils import ( | ||
| get_usm_allocations | ||
| ) | ||
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| import dpctl | ||
| import dpctl.tensor as dpt | ||
| import dpctl.tensor._tensor_impl as ti | ||
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| __all__ = [ | ||
| "dpnp_cov" | ||
| ] | ||
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| 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. | ||
| """ | ||
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| 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. | ||
| """ | ||
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| if x.ndim == 0: | ||
| x = x.reshape((1, 1)) | ||
| elif x.ndim == 1: | ||
| x = x[dpnp.newaxis, :] | ||
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| if not rowvar and x.shape[0] != 1: | ||
| x = x.T | ||
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| if x.dtype != dtype: | ||
| x = dpnp.astype(x, dtype) | ||
| return x | ||
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| # input arrays must follow CFD paradigm | ||
| usm_type, queue = get_usm_allocations((m, ) if y is None else (m, y)) | ||
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| # 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) | ||
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| X = _get_2dmin_array(m, dtype) | ||
| if y is not None: | ||
| y = _get_2dmin_array(y, dtype) | ||
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| # 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") | ||
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| if X.shape[1:] != y.shape[1:]: | ||
| raise ValueError("all the input array dimensions for the concatenation axis must match exactly") | ||
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| 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) | ||
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| # 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]) | ||
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| X = dpnp_array._create_from_usm_ndarray(res_usm) | ||
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| avg = X.mean(axis=1) | ||
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| fact = X.shape[1] - 1 | ||
| X -= avg[:, None] | ||
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| c = dpnp.dot(X, X.T.conj()) | ||
| c *= 1 / fact if fact != 0 else dpnp.nan | ||
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| return dpnp.squeeze(c) |
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