# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Operators for linalg function."""
import mindspore.ops as ops
from mindspore.common import dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops.operations import _inner_ops as inner
from mindspore.ops.function.math_func import _check_input_dtype, _check_attr_dtype
from mindspore._c_expression import Tensor as Tensor_
from ..operations import linalg_ops
from .._primitive_cache import _get_cache_prim
__all__ = ['svd', 'pinv']
[文档]def svd(a, full_matrices=False, compute_uv=True):
"""
Computes the singular value decompositions of one or more matrices.
If :math:`A` is a matrix, the svd returns the singular values :math:`S`, the left singular vectors :math:`U`
and the right singular vectors :math:`V`. It meets:
.. math::
A=U*diag(S)*V^{T}
Args:
a (Tensor): Tensor of the matrices to be decomposed. The shape should be :math:`(*, M, N)`,
the supported dtype are float32 and float64..
full_matrices (bool, optional): If true, compute full-sized :math:`U` and :math:`V`. If false, compute
only the leading P singular vectors, with P is the minimum of M and N.
Default: False.
compute_uv (bool, optional): If true, compute the left and right singular vectors.
If false, compute only the singular values. Default: True.
Returns:
- **s** (Tensor) - Singular values. The shape is :math:`(*, P)`.
- **u** (Tensor) - Left singular vectors. If `compute_uv` is False, u will not be returned.
The shape is :math:`(*, M, P)`. If `full_matrices` is True, the shape will be :math:`(*, M, M)`.
- **v** (Tensor) - Right singular vectors. If `compute_uv` is False, v will not be returned.
The shape is :math:`(*, N, P)`. If `full_matrices` is True, the shape will be :math:`(*, N, N)`.
Raises:
TypeError: If `full_matrices` or `compute_uv` is not the type of bool.
TypeError: If the rank of input less than 2.
TypeError: If the type of input is not one of the following dtype: float32, float64.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, set_context
>>> from mindspore import ops
>>> set_context(device_target="CPU")
>>> a = Tensor(np.array([[1, 2], [-4, -5], [2, 1]]).astype(np.float32))
>>> s, u, v = ops.svd(a, full_matrices=True, compute_uv=True)
>>> print(s)
[7.0652843 1.040081 ]
>>> print(u)
[[ 0.30821905 -0.48819482 0.81649697]
[-0.90613353 0.11070572 0.40824813]
[ 0.2896955 0.8656849 0.4082479 ]]
>>> print(v)
[[ 0.63863593 0.769509 ]
[ 0.769509 -0.63863593]]
"""
svd_ = _get_cache_prim(linalg_ops.Svd)(full_matrices=full_matrices, compute_uv=compute_uv)
if compute_uv:
return svd_(a)
s, _, _ = svd_(a)
return s
[文档]def pinv(x, *, atol=None, rtol=None, hermitian=False):
"""
Computes the (Moore-Penrose) pseudo-inverse of a matrix.
Args:
x (Tensor): A matrix to be calculated. The matrix must be at least two dimensions.
Only `float32`, `float64` are supported Tensor dtypes.
Keyword args:
atol (float, Tensor): absolute tolerance value. Default: None.
rtol (float, Tensor): relative tolerance value. Default: None.
hermitian (bool): An optional bool. x is assumed to be symmetric if real. Default: False.
Returns:
Tensor.
Raises:
TypeError: If `hermitian` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If the dimension of `x` is less than 2.
Supported Platforms:
``CPU``
Examples:
>>> x = Tensor([[2., 1.], [1., 2.]], mindspore.float32)
>>> output = ops.pinv(x)
>>> print(output)
[[ 0.6666667 -0.33333334]
[-0.33333334 0.6666667 ]]
"""
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError("The input x must be tensor")
if x.shape == ():
raise TypeError("For pinv, the 0-D input is not supported")
x_shape = F.shape(x)
if len(x_shape) < 2:
raise ValueError("input x should have 2 or more dimensions, " f"but got {len(x_shape)}.")
x_dtype = _get_cache_prim(P.DType)()(x)
_check_input_dtype("x", x_dtype, [mstype.float32, mstype.float64], "pinv")
_check_attr_dtype("hermitian", hermitian, [bool], "pinv")
if atol is not None:
if rtol is None:
rtol = Tensor(0.0)
else:
atol = Tensor(0.0)
if rtol is None:
rtol = max(ops.shape(x)) * ops.Eps()(Tensor(1.0, x.dtype))
if not inner.IsInstance()(rtol, mstype.tensor):
rtol = Tensor(rtol, mstype.float32)
if not inner.IsInstance()(atol, mstype.tensor):
atol = Tensor(atol, mstype.float32)
if not hermitian:
s, u, v = x.svd()
max_singular_val = ops.narrow(s, -1, 0, 1)
threshold = ops.maximum(atol.expand_dims(-1), rtol.expand_dims(-1) * max_singular_val)
condition = s > threshold
reciprocal_s_before = Tensor(ops.Reciprocal()(s)).broadcast_to(condition.shape)
zero = ops.Zeros()(condition.shape, s.dtype)
s_pseudoinv = ops.select(condition, reciprocal_s_before, zero)
output = ops.matmul(v * s_pseudoinv.expand_dims(-2), _nd_transpose(ops.Conj()(u)))
else:
s, u = _compare_eigh(x)
s_abs = s.abs()
max_singular_val = ops.amax(s_abs, -1, True)
threshold = ops.maximum(atol.expand_dims(-1), rtol.expand_dims(-1) * max_singular_val)
condition = s_abs > threshold
reciprocal_s_before = Tensor(ops.Reciprocal()(s))
zero = ops.Zeros()(condition.shape, s.dtype)
s_pseudoinv = ops.select(condition, reciprocal_s_before, zero)
output = ops.matmul(u * s_pseudoinv.expand_dims(-2), _nd_transpose(ops.Conj()(u)))
return output
def _compare_eigh(x):
"""
compare eigh
"""
from mindspore.scipy.ops import Eigh
s, u = Eigh()(x)
return s, u
def _nd_transpose(a):
"""
_nd_transpose
"""
dims = a.ndim
if dims < 2:
raise TypeError("to do _nd_transpose for input a's ndim is not greater or equal to 2d, which is invalid.")
axes = ops.make_range(0, dims)
axes = axes[:-2] + (axes[-1],) + (axes[-2],)
return ops.transpose(a, axes)
__all__.sort()