# Copyright 2020 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.
# ============================================================================
"""math Operations."""
from mindspore.ops import functional as F
from mindspore.ops.function.math_func import cummin as cummin_
def matmul(x1, x2, dtype=None):
"""
Returns the matrix product of two arrays.
Note:
Numpy arguments `out`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
On CPU, the supported dtypes are np.float16 and np.float32.
Args:
x1 (Tensor): Input tensor, scalar not allowed.
The last dimension of `x1` must be the same size as the second last dimension of `x2`.
And the shape of x1 and x2 could be broadcast.
x2 (Tensor): Input tensor, scalar not allowed.
The last dimension of `x1` must be the same size as the second last dimension of `x2`.
And the shape of x1 and x2 could be broadcast.
dtype (:class:`mindspore.dtype`, optional): defaults to None. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the matrix product of the inputs. This is a scalar only
when both `x1`, `x2` are 1-d vectors.
Raises:
ValueError: If the last dimension of `x1` is not the same size as the
second-to-last dimension of `x2`, or if a scalar value is passed in.
ValueError: If the shape of `x1` and `x2` could not broadcast together.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> # case 1 : Reasonable application of broadcast mechanism
>>> x1 = Tensor(np.arange(2*3*4).reshape(2, 3, 4), mindspore.float32)
>>> x2 = Tensor(np.arange(4*5).reshape(4, 5), mindspore.float32)
>>> output = ops.matmul(x1, x2)
>>> print(output)
[[[ 70. 76. 82. 88. 94.]
[ 190. 212. 234. 256. 278.]
[ 310. 348. 386. 424. 462.]]
[[ 430. 484. 538. 592. 646.]
[ 550. 620. 690. 760. 830.]
[ 670. 756. 842. 928. 1014.]]]
>>> print(output.shape)
(2, 3, 5)
>>> # case 2 : the rank of `x1` is 1
>>> x1 = Tensor(np.ones([1, 2]), mindspore.float32)
>>> x2 = Tensor(np.ones([2,]), mindspore.float32)
>>> output = ops.matmul(x1, x2)
>>> print(output)
[2.]
>>> print(output.shape)
(1,)
"""
res = F.matmul(x1, x2)
if dtype is not None:
res = res.astype(dtype)
return res
[文档]def mm(input, mat2):
r"""
Returns the matrix product of two arrays.
If `input` is a :math:`(n \times m)` Tensor, `mat2` is a
:math:`(m \times p)` Tensor, `out` will be a :math:`(n \times p)` Tensor.
Note:
This function cannot support broadcasting.
Refer to :func:`mindspore.ops.matmul` instead if you need a broadcastable function.
Args:
input (Tensor): The first matrix of matrix multiplication.
The last dimension of `input` must be the same size as the first dimension of `mat2`.
mat2 (Tensor): The second matrix of matrix multiplication.
The last dimension of `input` must be the same size as the first dimension of `mat2`.
Returns:
Tensor or scalar, the matrix product of the inputs.
Raises:
ValueError: If the last dimension of `input` is not the same size as the
second-to-last dimension of `mat2`.
ValueError: If `input` or `mat2` is not a matrix.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> import mindspore.ops as ops
>>> import numpy as np
>>> x1 = ms.Tensor(np.random.rand(2, 3))
>>> x2 = ms.Tensor(np.random.rand(3, 4))
>>> out = ops.mm(x1, x2)
>>> print(out.shape)
(2, 4)
"""
if input.ndim != 2 or mat2.ndim != 2:
raise ValueError(f"For mm, the input tensor must be a matrix, "
f"but got mat1.ndim:{input.ndim}, mat2.ndim:{mat2.ndim}")
return matmul(input, mat2)
def cummin(x, axis):
r"""
Returns a tuple (values,indices) where 'values' is the cumulative minimum value of input Tensor `x`
along the dimension `axis`, and `indices` is the index location of each minimum value.
.. math::
\begin{array}{ll} \\
y{i} = min(x{1}, x{2}, ... , x{i})
\end{array}
Args:
x (Tensor): The input Tensor, rank of `x` > 0.
axis (int): The dimension to do the operation over. The value of `axis` must be in the range
`[-x.ndim, x.ndim - 1]`.
Returns:
tuple [Tensor], tuple of 2 Tensors, containing the cumulative minimum of elements and the index,
The shape of each output tensor is the same as input `x`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not an int.
ValueError: If `axis` is out the range of `[-x.ndim, x.ndim - 1]`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> a = Tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220], mindspore.float32)
>>> output = ops.cummin(a, axis=0)
>>> print(output[0])
[-0.2284 -0.6628 -0.6628 -0.6628 -1.3298 -1.3298]
>>> print(output[1])
[0 1 1 1 4 4]
"""
return cummin_(x, axis)