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# Copyright 2020-2024 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
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# ============================================================================
"""math operations, the function docs are adapted from Numpy API."""
from __future__ import absolute_import
from __future__ import division
import operator
import functools
import itertools
import sys
from numpy import dtype as nptype
import mindspore.ops as ops
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.ops import composite as C
from mindspore.ops.primitive import constexpr, _primexpr
from mindspore.common import dtype as mstype
from mindspore.common import Tensor
from mindspore._c_expression import typing
from mindspore.numpy.dtypes import nan, pi, dtype_map, inf
from mindspore.numpy.array_creations import asarray_const, ones, zeros, empty, full, full_like, diag, \
arange, histogram_bin_edges, eye
from mindspore.numpy.array_ops import where as where_
from mindspore.numpy.array_ops import ravel, expand_dims, moveaxis, concatenate, flip, stack, atleast_1d, \
split
from mindspore.numpy.utils_const import _infer_out_shape, _check_axis_valid, _get_device, \
_check_shape_aligned, _raise_type_error, _check_same_type, _check_is_float, \
_raise_value_error, _promote, _check_axis_type, _canonicalize_axis, \
_is_shape_empty, _check_is_int, _expanded_shape, _check_axis_in_range, \
_check_dtype, _list_comprehensions, _tuple_setitem, _add_unit_axes, _seq_prod, \
_make_tensor, _promote_for_trigonometric, _raise_runtime_error, _max, _type_convert, \
_raise_unimplemented_error, _abs, _in, _tuple_slice, _check_is_inf
from mindspore.numpy.utils import _expand, _broadcast_to, _broadcast_to_shape, _check_input_tensor, \
_to_tensor, _to_tensor_origin_dtype, _isnan
from mindspore.ops.composite.multitype_ops._compile_utils import reduce_
ZERO_TENSOR = asarray_const(0)
_mean_keepdims = P.ReduceMean(True)
_matmul = P.MatMul(False, False)
_matmul_t = P.MatMul(False, True)
_reduce_sum_default = P.ReduceSum()
_reduce_sum_keepdims = P.ReduceSum(True)
_reduce_min_default = P.ReduceMin()
_reduce_min_keepdims = P.ReduceMin(True)
_reduce_max_default = P.ReduceMax()
_reduce_max_keepdims = P.ReduceMax(True)
_cumsum_default = P.CumSum()
_concat = P.Concat(-1)
_cumprod_default = P.CumProd()
_round = P.Round()
_rint = P.Rint()
def absolute(x, dtype=None):
"""
Calculates the absolute value element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Currently the backend kernel only supports float calculation, if the input
is not a `float`, then it will be casted to :class:`mstype.float32` and casted back.
Args:
x (Tensor): Tensor to be used for calculation.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Raises:
TypeError: If input arguments have types not specified above.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, -5], np.float32)
>>> output = np.absolute(x)
>>> print(output)
[1. 2. 3. 4. 5.]
"""
original_dtype = x.dtype
allowed_types = None
if _get_device() == "Ascend":
allowed_types = (mstype.float16, mstype.float32)
else:
allowed_types = (mstype.int32, mstype.float16, mstype.float32, mstype.float64)
if original_dtype not in allowed_types and dtype is None:
x = x.astype(mstype.float32)
return _apply_tensor_op(F.absolute, x, dtype=dtype).astype(original_dtype)
return _apply_tensor_op(F.absolute, x, dtype=dtype)
def count_nonzero(x, axis=None, keepdims=False):
"""
Counts the number of non-zero values in the tensor `x`.
Args:
x (Tensor): The tensor for which to count non-zeros.
axis (Union[int,tuple], optional): Axis or tuple of axes along which to
count non-zeros. Default is None, meaning that non-zeros will be counted
along a flattened version of `x`. Default: ``None`` .
keepdims (bool, optional): If this is set to True, the axes that are counted
are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against `x`. Default: ``False`` .
Returns:
Tensor, indicating number of non-zero values in the `x` along a given axis.
Otherwise, the total number of non-zero values in `x` is returned.
Raises:
TypeError: If axis is not int or tuple.
ValueError: If axis is not in range [-x.ndim, x.ndim).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, 0, 3, 2, 0])
>>> output = np.count_nonzero(x)
>>> print(output)
6
"""
if _is_shape_empty(x.shape):
return ZERO_TENSOR
if axis is None:
axis = ()
return ops.count_nonzero(x=x, axis=axis, keep_dims=keepdims)
def clip(x, xmin, xmax, dtype=None):
"""
Clips (limits) the values in an array.
Given an interval, values outside the interval are clipped to the interval edges.
For example, if an interval of :math:`[0, 1]` is specified, values smaller than 0 become 0,
and values larger than 1 become 1.
Args:
x (Tensor): Tensor containing elements to clip.
xmin (Tensor, scalar, None): Minimum value. If None, clipping is not performed
on lower interval edge. Not more than one of `xmin` and `xmax` may be None.
xmax (Tensor, scalar, None): Maximum value. If None, clipping is not performed
on upper interval edge. Not more than one of `xmin` and `xmax` may be None.
If `xmin` or `xmax` are tensors, then the three tensors will be broadcasted
to match their shapes.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor, a tensor with the elements of `x`, but where values
< `xmin` are replaced with `xmin`, and those > `xmax` with `xmax`.
Raises:
TypeError: If inputs have types not specified above.
ValueError: If the shapes of `x1` and `x2` cannot broadcast, or both `xmin` and `xmax` are `None`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, 0, 3, 2, 0])
>>> output = np.clip(x, 0, 2)
>>> print(output)
[1 2 2 0 0 2 2 0]
"""
if xmin is None and xmax is None:
_raise_value_error("One of max or min must be given.")
if xmin is not None:
x = maximum(x, xmin, dtype=dtype)
if xmax is not None:
x = minimum(x, xmax, dtype=dtype)
return x
def deg2rad(x, dtype=None):
"""
Converts angles from degrees to radians.
Args:
x (Tensor): Angles in degrees.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor, the corresponding angle in radians. This is a tensor scalar if `x`
is a tensor scalar.
Raises:
TypeError: If `x` is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, -5])
>>> output = np.deg2rad(x)
>>> print(output)
[ 0.01745329 0.03490658 0.05235988 -0.06981317 -0.08726647]
"""
_check_input_tensor(x)
def convert(a):
return a * pi / 180.0
return _apply_tensor_op(convert, x, dtype=dtype)
def rad2deg(x, dtype=None):
"""
Converts angles from radians to degrees.
Args:
x (Tensor): Angles in radians.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor, the corresponding angle in degrees. This is a tensor scalar if `x`
is a tensor scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, -5])
>>> output = np.rad2deg(x)
>>> print(output)
[ 57.295776 114.59155 171.88733 -229.1831 -286.47888 ]
"""
_check_input_tensor(x)
def convert(a):
return a * 180.0 / pi
return _apply_tensor_op(convert, x, dtype=dtype)
def add(x1, x2, dtype=None):
"""
Adds arguments element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input to be added.
x2 (Tensor): input to be added.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the sum of `x1` and `x2`, element-wise. This is a scalar
if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.add(x1, x2)
>>> print(output)
[[4 6]
[4 6]
[4 6]]
"""
# broadcast is not fully supported in tensor_add on CPU,
# so we use tensor_sub as a substitute solution
if _get_device() == 'CPU':
return subtract(x1, F.neg(_to_tensor(x2)), dtype=dtype)
return _apply_tensor_op(F.tensor_add, x1, x2, dtype=dtype)
def subtract(x1, x2, dtype=None):
"""
Subtracts arguments, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): The input to be subtracted from.
x2 (Tensor): The input to be subtracted by.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the difference of `x1` and `x2`, element-wise. This is a
scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.subtract(x1, x2)
>>> print(output)
[[-2 -2]
[-2 -2]
[-2 -2]]
"""
return _apply_tensor_op(F.tensor_sub, x1, x2, dtype=dtype)
def multiply(x1, x2, dtype=None):
"""
Multiplies arguments element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input tensor to be multiplied.
x2 (Tensor): input tensor to be multiplied.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the product of `x1` and `x2`, element-wise. This is a scalar
if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.multiply(x1, x2)
>>> print(output)
[[3 8]
[3 8]
[3 8]]
"""
if _get_device() == 'CPU':
_check_input_tensor(x1, x2)
# broadcast is not fully supported on CPU backend,
# and explicit broadcasting is performed
shape_out = _infer_out_shape(F.shape(x1), F.shape(x2))
x1 = _broadcast_to_shape(x1, shape_out)
x2 = _broadcast_to_shape(x2, shape_out)
return _apply_tensor_op(F.tensor_mul, x1, x2, dtype=dtype)
def divide(x1, x2, dtype=None):
"""
Returns a true division of the inputs, element-wise.
Instead of the Python traditional "floor division", this returns a true
division.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): the divident.
x2 (Tensor): the divisor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.divide(x1, x2)
>>> print(output)
[[0.33333334 0.5 ]
[0.33333334 0.5 ]
[0.33333334 0.5 ]]
"""
x1, x2 = _to_tensor(x1, x2)
if not _check_is_float(F.dtype(x1)) and not _check_is_float(F.dtype(x2)):
x1 = F.cast(x1, mstype.float32)
x2 = F.cast(x2, mstype.float32)
return _apply_tensor_op(F.tensor_div, x1, x2, dtype=dtype)
def true_divide(x1, x2, dtype=None):
"""
Returns a true division of the inputs, element-wise.
Instead of the Python traditional "floor division", this returns a true
division.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): the dividend.
x2 (Tensor): the divisor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.true_divide(x1, x2)
>>> print(output)
[[0.33333334 0.5 ]
[0.33333334 0.5 ]
[0.33333334 0.5 ]]
"""
return divide(x1, x2, dtype=dtype)
def power(x1, x2, dtype=None):
"""
First array elements raised to powers from second array, element-wise.
Raises each base in `x1` to the positionally-corresponding power in `x2`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
x1 (Tensor): The bases.
x2 (Tensor): The exponents.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the bases in `x1` raised to the exponents in `x2`. This
is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.full((3, 2), [1, 2]).astype('float32')
>>> x2 = np.full((3, 2), [3, 4]).astype('float32')
>>> output = np.power(x1, x2)
>>> print(output)
[[ 1. 16.]
[ 1. 16.]
[ 1. 16.]]
"""
return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
def float_power(x1, x2, dtype=None):
"""
First array elements raised to powers from second array, element-wise.
Raise each base in `x1` to the positionally-corresponding power in `x2`. `x1` and
`x2` must be broadcastable to the same shape. This differs from the power
function in that integers, float16, and float64 are promoted to floats with
a minimum precision of float32 so that the result is always inexact. The
intent is that the function will return a usable result for negative powers
and seldom overflow for positive powers.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Integers and floats are promoted to float32 instead of float64.
Args:
x1 (Tensor): the bases.
x2 (Tensor): the exponents.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the bases in `x1` raised to the exponents in `x2`. This
is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.arange(6)
>>> x2 = np.array(3)
>>> output = np.float_power(x1, x2)
>>> print(output)
[ 0. 1. 8. 27. 64. 125.]
"""
if not _check_same_type(F.dtype(x1), mstype.float32):
x1 = F.cast(x1, mstype.float32)
if not _check_same_type(F.dtype(x2), mstype.float32):
x2 = F.cast(x2, mstype.float32)
return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
def minimum(x1, x2, dtype=None):
"""
Element-wise minimum of tensor elements.
Compares two tensors and returns a new tensor containing the element-wise minima.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On Ascend, input arrays containing inf or NaN are not supported.
Args:
x1 (Tensor): first input tensor to be compared.
x2 (Tensor): second input tensor to be compared.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor, element-wise minimum of `x1` and `x2`.
Raises:
TypeError: If inputs have types not specified above.
ValueError: If the shapes of `x1` and `x2` cannot be broadcast.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.asarray([1, 2])
>>> b = np.asarray([[1, 3],[1, 4]])
>>> print(np.minimum(a, b))
[[1 2]
[1 2]]
"""
if isinstance(x1, (int, float, bool, list, tuple)):
x1 = asarray_const(x1)
elif not isinstance(x1, Tensor):
_raise_type_error("Input x1 is expected to be array_like")
if isinstance(x2, (int, float, bool, list, tuple)):
x2 = asarray_const(x2)
elif not isinstance(x2, Tensor):
_raise_type_error("Input x2 is expected to be array_like")
# if both are scalars, expand x1 to 1d tensor, since cpu kernel doesn't support
# comparisons with 2 scalars
if x1.ndim == 0 and x2.ndim == 0:
x1 = expand_dims(x1, 0)
return _apply_tensor_op(functools.partial(_prop_nan, F.minimum), x1, x2, dtype=dtype).squeeze()
if x1.ndim == 0:
dtype = x2.dtype
elif x2.ndim == 0:
dtype = x1.dtype
return _apply_tensor_op(functools.partial(_prop_nan, F.minimum), x1, x2, dtype=dtype)
def mean(a, axis=None, keepdims=False, dtype=None):
"""
Computes the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken
over the flattened array by default, otherwise over the specified
axis.
Note:
Numpy arguments `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
a (Tensor): input tensor containing numbers whose mean is desired.
If a is not an array, a conversion is attempted.
axis (None or int or tuple of integers, optional): Axis or axes along
which the means are computed. The default is to compute
the mean of the flattened array. If this is a tuple of
ints, a mean is performed over multiple axes.
keepdims (bool, optional): If this is set to ``True`` , the axes which
are reduced are left in the result as dimensions with
size one. With this option, the result will broadcast
correctly against the input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, an array containing the mean values.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(6, dtype='float32')
>>> output = np.mean(a, 0)
>>> print(output)
2.5
"""
return _reduce(a, P.ReduceMean(keepdims), axis=axis, keepdims=keepdims, dtype=dtype)
def inner(a, b):
"""
Returns the inner product of two tensors.
Ordinary inner product of vectors for 1-D tensors (without complex
conjugation), in higher dimensions a sum product over the last
axes.
Note:
Numpy argument `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, and
np.float64.
Args:
a (Tensor): input tensor. If `a` and `b` are nonscalar, their last
dimensions must match.
b (Tensor): input tensor. If `a` and `b` are nonscalar, their last
dimensions must match.
Returns:
Tensor or scalar.
Raises:
ValueError: If ``x1.shape[-1] != x2.shape[-1]``.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.ones((5, 3))
>>> b = np.ones((2, 7, 3))
>>> output = np.inner(a, b)
>>> print(output)
[[[3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3.]]
[[3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3.]]
[[3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3.]]
[[3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3.]]
[[3. 3. 3. 3. 3. 3. 3.]
[3. 3. 3. 3. 3. 3. 3.]]]
"""
if F.rank(a) == 0 or F.rank(b) == 0:
return F.tensor_mul(a, b)
_check_shape_aligned(F.shape(a), F.shape(b))
aligned_shape_a = (F.shape_mul(F.shape(a)[:-1]), F.shape(a)[-1])
aligned_shape_b = (F.shape_mul(F.shape(b)[:-1]), F.shape(a)[-1])
a_aligned = F.reshape(a, aligned_shape_a)
b_aligned = F.reshape(b, aligned_shape_b)
res = _matmul_t(a_aligned, b_aligned)
res = F.reshape(res, F.shape(a)[:-1] + F.shape(b)[:-1])
return res
def dot(a, b):
"""
Returns the dot product of two arrays.
Specifically,
If both `a` and `b` are 1-D arrays, it is inner product of vectors
(without complex conjugation).
If both `a` and `b` are 2-D arrays, it is matrix multiplication.
If either `a` or `b` is 0-D (scalar), it is equivalent to multiply.
If `a` is an `N-D` array and `b` is a 1-D array, it is a sum product
over the last axis of `a` and `b`.
If `a` is an `N-D` array and `b` is an `M-D` array (where ``M>=2``), it is a
sum product over the last axis of `a` and the second-to-last axis of `b`:
``dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])``
Note:
Numpy argument `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, and
np.float64.
Args:
a (Tensor): input tensor
b (Tensor): input tensor
Returns:
Tensor or scalar, the dot product of `a` and `b`. If `a` and `b` are
both scalars or both 1-D arrays then a scalar is returned;
otherwise an array is returned
Raises:
ValueError: If the last dimension of `a` is not the same size
as the second-to-last dimension of `b`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.full((1, 3), 7).astype('float32')
>>> b = np.full((2, 3, 4), 5).astype('float32')
>>> output = np.dot(a, b)
>>> print(output)
[[[105. 105. 105. 105.]
[105. 105. 105. 105.]]]
"""
def _check(dim_a, dim_b):
if dim_a != dim_b:
raise ValueError('shapes are not aligned')
ndim_a, ndim_b = F.rank(a), F.rank(b)
if ndim_a == 0 or ndim_b == 0:
return F.tensor_mul(a, b)
if ndim_a > 0 and ndim_b >= 2:
perm = F.make_range(ndim_b)
perm = perm[:-2] + (perm[-1],) + (perm[-2],)
b = F.transpose(b, perm)
_check(F.shape(a)[-1], F.shape(b)[-1])
a_aligned = F.reshape(a, (-1, F.shape(a)[-1]))
b_aligned = F.reshape(b, (-1, F.shape(b)[-1]))
res = _matmul_t(a_aligned, b_aligned)
res = F.reshape(res, F.shape(a)[:-1] + F.shape(b)[:-1])
return res
def outer(a, b):
"""
Computes the outer product of two vectors.
Given two vectors, ``a = [a0, a1, ..., aM]`` and ``b = [b0, b1, ..., bN]``,
the outer product is:
``[[a0*b0 a0*b1 ... a0*bN ]``
``[a1*b0 . ]``
``[ ... . ]``
``[aM*b0 aM*bN ]]``
Note:
Numpy argument ``out`` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, and
np.float64.
Args:
a (Tensor): first input vector. Input is flattened if not
already 1-dimensional.
b (Tensor): second input vector. Input is flattened if not
already 1-dimensional.
Returns:
Tensor or scalar, ``out[i, j] = a[i] * b[j]``.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.full(7, 2).astype('float32')
>>> b = np.full(4, 3).astype('float32')
>>> output = np.outer(a, b)
>>> print(output)
[[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]]
"""
_check_input_tensor(a, b)
if F.rank(a) != 1:
a = ravel(a)
if F.rank(b) != 1:
b = ravel(b)
a = F.reshape(a, (F.shape(a)[0], 1))
b = _expand(b, 2)
return _matmul(a, b)
def tensordot(a, b, axes=2):
"""
Computes tensor dot product along specified axes.
Given two tensors, `a` and `b`, and an array_like object containing two array_like
objects, `(a_axes, b_axes)`, sum the products of `a`'s and `b`'s elements (components)
over the axes specified by `a_axes` and `b_axes`. The third argument can be a single
non-negative integer_like scalar, `N`; if it is such, then the last `N` dimensions of
`a` and the first `N` dimensions of `b` are summed over.
Three common use cases are:
- ``axes = 0`` : tensor product
- ``axes = 1`` : tensor dot product
- ``axes = 2`` : (default) tensor double contraction
When axes is integer_like, the sequence for evaluation will be: first the `-Nth`
axis in `a` and 0th axis in `b`, and the -1th axis in `a` and `Nth` axis in `b` last.
When there is more than one axis to sum over - and they are not the last (first)
axes of `a` `(b)` - the argument axes should consist of two sequences of the same
length, with the first axis to sum over given first in both sequences, the second
axis second, and so forth.
The shape of the result consists of the non-contracted axes of the first tensor,
followed by the non-contracted axes of the second.
Note:
On CPU, the supported dypes are np.float16 and np.float32.
On GPU, the supported dypes are np.float16 and np.float32.
Args:
a (Tensor): Tensor to "dot".
b (Tensor): Tensor to "dot".
axes (int or sequence of ints):
integer_like: If an int `N`, sum over the last `N` axes of `a` and the first `N`
axes of `b` in order. The sizes of the corresponding axes must match.
sequence of ints: Or, a list of axes to be summed over, first sequence
applying to `a`, second to `b`. Both elements `array_like` must be of the same
length.
Returns:
Tensor, or list of tensors, the tensor dot product of the input.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.ones((3, 4, 5))
>>> b = np.ones((4, 3, 2))
>>> output = np.tensordot(a, b, axes=([1,0],[0,1]))
>>> print(output.shape)
(5, 2)
"""
if F.rank(a)*F.rank(b) == 0 and axes == 0:
return F.tensor_mul(a, b)
return ops.tensor_dot(a, b, axes)
def std(x, axis=None, ddof=0, keepdims=False):
"""
Computes the standard deviation along the specified axis.
The standard deviation is the square root of the average of the squared deviations
from the mean, i.e., :math:`std = sqrt(mean(abs(x - x.mean())**2))`.
Returns the standard deviation, which is computed for the flattened array by default,
otherwise over the specified axis.
Note:
Numpy arguments `dtype`, `out` and `where` are not supported.
Args:
x (Tensor): A Tensor to be calculated.
axis (Union[None, int, tuple(int)]): Axis or axes along which the standard
deviation is computed. Default: ``None`` .
If ``None`` , compute the standard deviation of the flattened array.
ddof (int): Means Delta Degrees of Freedom. The divisor used in calculations is :math:`N - ddof`,
where :math:`N` represents the number of elements. Default: 0.
keepdims: If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly against the input tensor.
If the default value is passed, then keepdims will not be passed through to the std method of
sub-classes of tensor, however any non-default value will be. If the sub-class’ method does not
implement keepdims any exceptions will be raised. Default: ``False`` .
Returns:
Standard deviation tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> input_x = np.array([1., 2., 3., 4.])
>>> output = np.std(input_x)
>>> print(output)
1.118034
"""
x = _to_tensor(x)
return x.std(axis, ddof, keepdims)
def var(x, axis=None, ddof=0, keepdims=False):
"""
Computes the variance along the specified axis.
The variance is the average of the squared deviations from the mean, i.e.,
:math:`var = mean(abs(x - x.mean())**2)`.
Returns the variance, which is computed for the flattened array by default,
otherwise over the specified axis.
Note:
Numpy arguments `dtype`, `out` and `where` are not supported.
Args:
x (Tensor): A Tensor to be calculated.
axis (Union[None, int, tuple(int)]): Axis or axes along which the variance is computed.
The default is to compute the variance of the flattened array. Default: ``None`` .
ddof (int): Means Delta Degrees of Freedom. Default: ``0`` .
The divisor used in calculations is :math:`N - ddof`, where :math:`N` represents the number of elements.
keepdims (bool): If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly against the input tensor.
If the default value is passed, then keepdims will not be passed through to the var method of
sub-classes of tensor, however any non-default value will be. If the sub-class method does not
implement keepdims any exceptions will be raised. Default: ``False`` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Returns:
Standard deviation tensor.
Examples:
>>> import mindspore.numpy as np
>>> input_x = np.array([1., 2., 3., 4.])
>>> output = np.var(input_x)
>>> print(output)
1.25
"""
x = _to_tensor(x)
return x.var(axis, ddof, keepdims)
def ptp(x, axis=None, keepdims=False):
"""
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for "peak to peak".
Note:
Numpy arguments `dtype` and `out` are not supported.
Args:
x (Tensor): Input tensor.
axis (Union[None, int, tuple(int)]): Axis or axes along which the range is computed.
The default is to compute the variance of the flattened array. Default: ``None``.
keepdims (bool): If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly against the input tensor.
If the default value is passed, then keepdims will not be passed through to the ptp method of
sub-classes of tensor, however any non-default value will be. Default: ``False`` .
Returns:
Tensor.
Raises:
TypeError: If inputs have types not specified above.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([[4.0, 9.0, 2.0, 10.0], [6.0, 9.0, 7.0, 12.0]])
>>> print(np.ptp(x, axis=1))
[8. 6.]
>>> print(np.ptp(x, axis=0))
[2. 0. 5. 2.]
"""
_check_input_tensor(x)
return x.ptp(axis, keepdims)
def average(x, axis=None, weights=None, returned=False):
"""
Computes the weighted average along the specified axis.
Args:
x (Tensor): A Tensor to be averaged.
axis (Union[None, int, tuple(int)]): Axis along which to average `x`. Default: ``None`` .
If the axis is `None`, it will average over all of the elements of the tensor `x`.
If the axis is negative, it counts from the last to the first axis.
weights (Union[None, Tensor]): Weights associated with the values in `x`. Default: ``None`` .
If `weights` is `None`, all the data in `x` are assumed to have a weight equal to one.
If `weights` is 1-D tensor, the length must be the same as the given axis.
Otherwise, `weights` should have the same shape as `x`.
returned (bool): Default: ``False`` .
If `True`, the tuple (average, sum_of_weights) is returned.
If `False`, only the average is returned.
Returns:
Averaged Tensor. If returned is `True`, return tuple.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> input_x = np.array([[1., 2.], [3., 4.]])
>>> output = np.average(input_x, axis=0, weights=input_x, returned=True)
>>> print(output)
(Tensor(shape=[2], dtype=Float32, value= [ 2.50000000e+00, 3.33333325e+00]),
Tensor(shape=[2], dtype=Float32, value= [ 4.00000000e+00, 6.00000000e+00]))
"""
_check_input_tensor(x)
if axis is not None:
_check_axis_type(axis, True, True, False)
axis = _canonicalize_axis(axis, x.ndim)
x_avg = full((), nan, F.dtype(x))
sum_of_weights = None
if weights is None:
x_avg = mean(x, axis)
sum_of_weights = compute_weights_for_mean(x, x_avg, axis)
else:
_check_input_tensor(weights)
if x.shape == weights.shape:
x_avg, sum_of_weights = comput_avg(x, axis, weights)
elif F.rank(weights) == 1:
if not isinstance(axis, int):
_raise_type_error("Axis must be specified when shapes of x and weights differ.")
perm = _expanded_shape(x.ndim, weights.shape[0], axis)
weights = weights.reshape(perm)
x_avg, sum_of_weights = comput_avg(x, axis, weights)
else:
_raise_type_error("Weights should be None, 1-D or the same shape as input x.")
if returned:
if x_avg.shape != sum_of_weights.shape:
sum_of_weights = _broadcast_to(sum_of_weights, sum_of_weights.shape, x_avg.shape, x_avg.ndim)
return (x_avg, sum_of_weights)
return x_avg
def compute_weights_for_mean(x, x_avg, axis):
"""Computes weights for np.average."""
if axis is None:
sum_of_weights = full((), x.size, F.dtype(x))
else:
fill_value = 1
if isinstance(axis, int) or (isinstance(axis, tuple) and F.tuple_len(axis) == 1):
fill_value = x.shape[axis] if isinstance(axis, int) else x.shape[axis[0]]
elif axis is None:
for sh in x.shape:
fill_value *= sh
else:
for ax in axis:
fill_value *= x.shape[ax]
sum_of_weights = full_like(x_avg, fill_value, F.dtype(x))
return sum_of_weights
def comput_avg(x, axis, weights):
"""Computes average value of input x with given parameters."""
axis = () if axis is None else axis
x_mul = F.tensor_mul(x, weights)
x_sum = _reduce_sum_default(x_mul, axis)
sum_of_weights = _reduce_sum_default(weights, axis)
x_avg = F.tensor_div(x_sum, sum_of_weights)
return x_avg, sum_of_weights
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.
x2 (Tensor): Input tensor, scalar not allowed.
dtype (:class:`mindspore.dtype`, optional): Default: ``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.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.arange(2*3*4).reshape(2, 3, 4).astype('float32')
>>> x2 = np.arange(4*5).reshape(4, 5).astype('float32')
>>> output = np.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.]]]
"""
return C.matmul(x1, x2, dtype=dtype)
def square(x, dtype=None):
"""
Returns the element-wise square of the input.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
Args:
x (Tensor): Input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, element-wise ``x*x``, of the same shape and dtype as `x`.
This is a scalar if `x` is a scalar..
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.square(np.arange(6).reshape(2, 3).astype('float32'))
>>> print(x)
[[ 0. 1. 4.]
[ 9. 16. 25.]]
"""
return _apply_tensor_op(F.square, x, dtype=dtype)
def sqrt(x, dtype=None):
"""
Returns the non-negative square-root of an array, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16 and np.float32.
Args:
x (Tensor): The values whose square-roots are required.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, an array of the same shape as `x`, containing the positive
square-root of each element in `x`. For negative elements, nan is returned.
This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(6).reshape(2, 3).astype('float32')
>>> x_squared = np.square(x)
>>> output = np.sqrt(x_squared)
>>> print(output)
[[ 0. 1. 2.]
[ 3. 4. 5.]]
"""
return _apply_tensor_op(F.sqrt, x, dtype=dtype)
def reciprocal(x, dtype=None):
"""
Returns the reciprocal of the argument, element-wise.
Calculates ``1/x``.
Note:
Numpy arguments `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
When `where` is provided, `out` must have a tensor value. `out` is not supported
for storing the result, however it can be used in combination with `where` to set
the value at indices for which `where` is set to False.
Args:
x (Tensor): Input array. For integer arguments with absolute value larger
than 1 the result is always zero because of the way Python handles
integer division. For integer zero the result is an overflow.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(1, 7).reshape(2, 3).astype('float32')
>>> output = np.reciprocal(x)
>>> print(output)
[[1. 0.5 0.33333334]
[0.25 0.2 0.16666667]]
"""
return _apply_tensor_op(lambda x: F.tensor_div(1, x), x, dtype=dtype)
def log(x, dtype=None):
"""
Returns the natural logarithm, element-wise.
The natural logarithm log is the inverse of the exponential function, so that
``log(exp(x)) = x``. The natural logarithm is logarithm in base e.
Note:
Numpy arguments `out`, `where`, `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, np.float32, and np.float64.
Args:
x (Tensor): Input array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the natural logarithm of `x`, element-wise. This is a
scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([2, 3, 4]).astype('float32')
>>> output = np.log(x)
>>> print(output)
[0.69314575 1.09861 1.3862929 ]
"""
return _apply_tensor_op(F.log, x, dtype=dtype)
def _prop_nan(fn, x1, x2):
"""Selects NaN if either element is NaN"""
has_nan = F.logical_or(_isnan(x1), _isnan(x2))
nan_tensor = F.fill(_promote(F.dtype(x1), F.dtype(x2)), F.shape(has_nan), nan)
res = fn(x1, x2)
return F.select(has_nan, nan_tensor, res)
def maximum(x1, x2, dtype=None):
"""
Returns the element-wise maximum of array elements.
Compares two arrays and returns a new array containing the element-wise maxima.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On Ascend, input arrays containing inf or NaN are not supported.
Args:
x1 (Tensor): Input array
x2 (Tensor): The array holding the elements to be compared. If
``x1.shape != x2.shape``, they must be broadcastable to a common shape
(which becomes the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the maximum of `x1` and `x2`, element-wise. This is a scalar
if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.maximum(np.array([2, 3, 4]), np.array([1, 5, 2]))
>>> print(output)
[2 5 4]
"""
if isinstance(x1, (int, float, bool, list, tuple)):
x1 = asarray_const(x1)
elif not isinstance(x1, Tensor):
_raise_type_error("Input x1 is expected to be array_like")
if isinstance(x2, (int, float, bool, list, tuple)):
x2 = asarray_const(x2)
elif not isinstance(x2, Tensor):
_raise_type_error("Input x2 is expected to be array_like")
# F.maximum does not support when both operands are scalar
if x1.ndim == 0 and x2.ndim == 0:
x1 = expand_dims(x1, 0)
return _apply_tensor_op(functools.partial(_prop_nan, F.maximum), x1, x2, dtype=dtype).squeeze()
if x1.ndim == 0:
dtype = x2.dtype
elif x2.ndim == 0:
dtype = x1.dtype
return _apply_tensor_op(functools.partial(_prop_nan, F.maximum), x1, x2, dtype=dtype)
def heaviside(x1, x2, dtype=None):
"""
Computes the Heaviside step function.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input values.
x2 (Tensor): The value of the function when `x1` is 0. If
``x1.shape != x2.shape``, they must be broadcastable to a common shape
(which becomes the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the output array, element-wise Heaviside step function
of `x1`. This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.heaviside(np.array([-1.5, 0, 2.0]), np.array(0.5))
>>> print(output)
[0. 0.5 1. ]
>>> output = np.heaviside(np.array([-1.5, 0, 2.0]), np.array(1))
>>> print(output)
[0. 1. 1.]
"""
def _heaviside(x1, x2):
"""Computes heaviside without passing keyword arguments"""
# performs type promotion
dtype1 = F.dtype(x1)
dtype2 = F.dtype(x2)
dtype_out = _promote(dtype1, dtype2)
if not _check_same_type(dtype1, dtype_out):
x1 = F.cast(x1, dtype_out)
if not _check_same_type(dtype2, dtype_out):
x2 = F.cast(x2, dtype_out)
# performs broadcast
shape_out = _infer_out_shape(F.shape(x1), F.shape(x2))
x1 = _broadcast_to_shape(x1, shape_out)
x2 = _broadcast_to_shape(x2, shape_out)
x2 = F.select(x1 < 0, zeros(shape_out, dtype_out), x2)
x2 = F.select(x1 > 0, ones(shape_out, dtype_out), x2)
return x2
return _apply_tensor_op(_heaviside, x1, x2, dtype=dtype)
def amax(a, axis=None, keepdims=False, initial=None, where=True):
"""
Returns the maximum of an array or maximum along an axis.
Note:
Numpy argument `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
a (Tensor): Input data.
axis (None or int or tuple of integers, optional): Default: ``None`` . Axis or
axes along which to operate. By default, flattened input is used. If
this is a tuple of integers, the maximum is selected over multiple axes,
instead of a single axis or all the axes as before.
keepdims (boolean, optional): Default: ``False`` .
If this is set to True, the axes which are reduced are left in the
result as dimensions with size one. With this option, the result will
broadcast correctly against the input array.
initial (scalar, optional): Default: ``None`` .
The minimum value of an output element. Must be present to allow
computation on empty slice.
where (boolean Tensor, optional): Default: ``True`` .
A boolean array which is broadcasted to match the dimensions of array,
and selects elements to include in the reduction. If non-default value
is passed, initial must also be provided.
Returns:
Tensor or scalar, maximum of `a`. If `axis` is None, the result is a scalar
value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(4).reshape((2,2)).astype('float32')
>>> output = np.amax(a)
>>> print(output)
3.0
>>> output = np.amax(a, axis=0)
>>> print(output)
[2. 3.]
>>> output = np.amax(a, axis=1)
>>> print(output)
[1. 3.]
>>> output = np.amax(a, where=np.array([False, True]), initial=-1, axis=0)
>>> print(output)
[-1. 3.]
"""
return reduce_(a, P.ReduceMax(keepdims), cmp_fn=F.maximum, axis=axis, keepdims=keepdims,
initial=initial, where=where)
def amin(a, axis=None, keepdims=False, initial=None, where=True):
"""
Returns the minimum of an array or minimum along an axis.
Note:
Numpy argument `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
a (Tensor): Input data.
axis (None or int or tuple of integers, optional): Default: ``None`` . Axis or
axes along which to operate. By default, flattened input is used. If
this is a tuple of integers, the minimum is selected over multiple axes,
instead of a single axis or all the axes as before.
keepdims (bool, optional): Default: ``False`` .
If this is set to True, the axes which are reduced are left in the
result as dimensions with size one. With this option, the result will
broadcast correctly against the input array.
initial (Number, optional): Default: ``None`` .
The maximum value of an output element. Must be present to allow
computation on empty slice.
where (bool Tensor, optional): Default: ``True`` .
A boolean array which is broadcasted to match the dimensions of array,
and selects elements to include in the reduction. If non-default value
is passed, initial must also be provided.
Returns:
Tensor or scalar, minimum of `a`. If axis is None, the result is a scalar
value. If `axis` is given, the result is an array of dimension ``a.ndim - 1``.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(4).reshape((2,2)).astype('float32')
>>> output = np.amin(a)
>>> print(output)
0.0
>>> output = np.amin(a, axis=0)
>>> print(output)
[0. 1.]
>>> output = np.amin(a, axis=1)
>>> print(output)
[0. 2.]
>>> output = np.amin(a, where=np.array([False, True]), initial=10, axis=0)
>>> print(output)
[10. 1.]
"""
return reduce_(a, P.ReduceMin(keepdims), cmp_fn=F.minimum, axis=axis, keepdims=keepdims,
initial=initial, where=where)
def hypot(x1, x2, dtype=None):
"""
Given the "legs" of a right triangle, returns its hypotenuse.
Equivalent to ``sqrt(x1**2 + x2**2)``, element-wise. If `x1` or `x2` is scalar_like
(i.e., unambiguously cast-able to a scalar type), it is broadcast for use
with each element of the other argument. (See Examples)
Note:
Numpy arguments `out`, `where`, `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, np.float32, and np.float64.
Args:
x1 (Tensor): Leg of the triangle(s).
x2 (Tensor): Leg of the triangle(s). If ``x1.shape != x2.shape``, they
must be broadcastable to a common shape (which becomes the shape of
the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the hypotenuse of the triangle(s). This is a scalar if
both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.hypot(3*np.ones((3, 3)), 4*np.ones((3, 3)))
>>> print(output)
[[5. 5. 5.]
[5. 5. 5.]
[5. 5. 5.]]
>>> output = np.hypot(3*np.ones((3, 3)), np.array([4.0]))
>>> print(output)
[[5. 5. 5.]
[5. 5. 5.]
[5. 5. 5.]]
"""
def _hypot(x1, x2):
"""Computes hypotenuse without passing keyword arguments"""
if _get_device() == 'CPU':
# broadcast is not fully supported in tensor_add on CPU,
# so we use tensor_sub as a substitute solution
return F.sqrt(F.tensor_sub(F.square(x1), F.neg(F.square(x2))))
return F.sqrt(F.tensor_add(F.square(x1), F.square(x2)))
return _apply_tensor_op(_hypot, x1, x2, dtype=dtype)
def floor(x, dtype=None):
"""
Returns the floor of the input, element-wise.
The floor of the scalar `x` is the largest integer `i`, such that ``i <= x``.
Note:
Numpy arguments `out`, `where`, `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, np.float32, and np.float64.
Args:
x (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the floor of each element in `x`. This is a scalar if `x`
is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.floor(np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]))
>>> print(output)
[-2. -2. -1. 0. 1. 1. 2.]
"""
return _apply_tensor_op(F.floor, x, dtype=dtype)
def floor_divide(x1, x2, dtype=None):
"""
Returns the largest integer smaller or equal to the division of the inputs.
It is equivalent to the Python // operator and pairs with the
Python % (remainder), function so that ``a = a % b + b * (a // b)`` up to roundoff.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input array.
x2 (Tensor): Input array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.floor_divide(np.array([1., 2., 3., 4.]), np.array(2.5))
>>> print(output)
[0. 0. 1. 1.]
"""
return _apply_tensor_op(F.tensor_floordiv, x1, x2, dtype=dtype)
def _remainder(x1, x2, c_style=False):
"""Computes remainder without applying keyword arguments."""
dtype = _promote(F.dtype(x1), F.dtype(x2))
if not _check_is_float(dtype):
x1 = F.cast(x1, mstype.float32)
x2 = F.cast(x2, mstype.float32)
quotient = F.tensor_div(x1, x2)
if c_style:
quotient = fix(quotient)
else:
quotient = F.floor(quotient)
prod = F.tensor_mul(x2, quotient)
res = F.tensor_sub(x1, prod)
if _check_is_int(dtype):
zeros_tensor = zeros(F.shape(quotient), F.dtype(quotient))
x2_zeros = F.equal(x2, zeros_tensor)
res = F.select(x2_zeros, zeros_tensor, res)
if not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res
def remainder(x1, x2, dtype=None):
"""
Returns element-wise remainder of division.
Computes the remainder complementary to the floor_divide function. It is
equivalent to the Python modulus operator ``x1 % x2`` and has the same sign
as the divisor `x2`. The MATLAB function equivalent to np.remainder is mod.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input array.
x2 (Tensor): input array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the element-wise remainder of the quotient
``floor_divide(x1, x2)``. This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.remainder(np.array([4, 7]), np.array([2, 3]))
>>> print(output)
[0 1]
>>> output = np.remainder(np.arange(7), np.array(5))
>>> print(output)
[0 1 2 3 4 0 1]
"""
return _apply_tensor_op(_remainder, x1, x2, dtype=dtype)
def fix(x):
"""
Rounds to nearest integer towards zero.
Rounds an array of floats element-wise to nearest integer towards zero. The
rounded values are returned as floats.
Note:
Numpy argument `out` is not supported.
Args:
x (Tensor): An array of floats to be rounded.
Returns:
Tensor.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.fix(np.array([2.1, 2.9, -2.1, -2.9]))
>>> print(output)
[ 2. 2. -2. -2.]
"""
_check_input_tensor(x)
if not _check_is_float(F.dtype(x)):
x = F.cast(x, mstype.float32)
floored = F.floor(x)
# change to F.ceil once supported on CPU.
ceiled = F.neg(F.floor(F.neg(x)))
is_neg = F.tensor_lt(x, zeros(F.shape(x), F.dtype(x)))
return F.select(is_neg, ceiled, floored)
def fmod(x1, x2, dtype=None):
"""
Returns the element-wise remainder of division.
This is the NumPy implementation of the C library function fmod, the remainder
has the same sign as the dividend `x1`. It is equivalent to the Matlab(TM) rem
function and should not be confused with the Python modulus operator ``x1 % x2``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): the first input arrays.
x2 (Tensor): the second input arrays.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the remainder of the division of `x1` by `x2`. This is a
scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.fmod(np.array([-3, -2, -1, 1, 2, 3]), np.array(2))
>>> print(output)
[-1 0 -1 1 0 1]
"""
return _apply_tensor_op(lambda x1, x2: _remainder(x1, x2, c_style=True), x1, x2, dtype=dtype)
def trunc(x, dtype=None):
"""
Returns the truncated value of the input, element-wise.
The truncated value of the scalar `x` is the nearest integer `i` which is closer to zero
than `x` is. In short, the fractional part of the signed number `x` is discarded.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the truncated value of each element in `x`. This is a scalar if `x` is
a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.trunc(np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]))
>>> print(output)
[-1. -1. -0. 0. 1. 1. 2.]
"""
return _apply_tensor_op(fix, x, dtype=dtype)
def exp(x, dtype=None):
"""
Calculates the exponential of all elements in the input array.
Note:
Numpy arguments `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
When `where` is provided, `out` must have a tensor value. `out` is not supported
for storing the result, however it can be used in combination with `where` to set
the value at indices for which `where` is set to False.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, np.float64.
Args:
x (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, element-wise exponential of `x`. This is a scalar if both
`x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.exp(np.arange(5).astype(np.float32))
>>> print(output)
[ 1. 2.718282 7.3890557 20.085537 54.598145 ]
"""
return _apply_tensor_op(F.tensor_exp, x, dtype=dtype)
def expm1(x, dtype=None):
"""
Calculates ``exp(x) - 1`` for all elements in the array.
Note:
Numpy arguments `out`, `where`, `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:
x (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, element-wise exponential minus one, ``out = exp(x) - 1``.
This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.expm1(np.arange(5).astype(np.float32))
>>> print(output)
[ 0. 1.7182819 6.389056 19.085537 53.59815 ]
"""
return _apply_tensor_op(F.tensor_expm1, x, dtype=dtype)
def divmod_(x1, x2, dtype=None):
"""
Returns element-wise quotient and remainder simultaneously.
Args:
x1(Union[Tensor]): Dividend tensor.
x2(Union[Tensor, int, float, bool]): Divisor. If ``x1.shape != x2.shape``,
they must be broadcastable to a common shape.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Element-wise quotient and remainder from floor division, in format of (quotient, remainder)
Raises:
TypeError: If `x1` and `x2` are not Tensor or scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([1, 2, 3, 4, 5])
>>> print(np.divmod(a, 1.5))
(Tensor(shape=[5], dtype=Float32,
value= [ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00, 2.00000000e+00, 3.00000000e+00]),
Tensor(shape=[5], dtype=Float32,
value= [ 1.00000000e+00, 5.00000000e-01, 0.00000000e+00, 1.00000000e+00, 5.00000000e-01]))
"""
q = F.tensor_floordiv(x1, x2)
r = remainder(x1, x2)
if dtype is not None:
q = q.astype(dtype)
r = r.astype(dtype)
return (q, r)
def _handle_prepend_append(combined, tensor, additional_tensor, axis):
"""Concatenates prepend or append to tensor."""
if isinstance(additional_tensor, (int, float, bool)):
additional_tensor = asarray_const(additional_tensor)
elif not isinstance(additional_tensor, Tensor):
_raise_type_error("prepend must be scalar or Tensor, but got ", additional_tensor)
additional_shape = tensor.shape
additional_shape = _tuple_setitem(additional_shape, axis, 1)
additional_tensor = _broadcast_to_shape(additional_tensor, additional_shape)
combined += (additional_tensor,)
return combined
def diff(a, n=1, axis=-1, prepend=None, append=None):
"""
Calculates the n-th discrete difference along the given axis.
The first difference is given by :math:`out[i] = a[i+1] - a[i]` along the given axis,
higher differences are calculated by using `diff` iteratively.
Note:
Since zero-shaped Tensor is not supported in MindSpore, a value error is raised if
an empty Tensor is encountered.
Args:
a (Tensor): Input tensor.
n (int, optional): The number of times values are differenced. If zero,
the input is returned as-is. Default: ``1`` .
axis (int, optional): The axis along which the difference is taken, default
is the last axis. Default: ``-1`` .
prepend/append (Tensor, optional): Values to prepend or append to a along
`axis` prior to performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of `axis` and the shape of the input
array in along all other axes. Otherwise the dimension and shape must
match `a` except along axis. Default: ``None`` .
Returns:
The n-th differences. The shape of the output is the same as a except along
`axis` where the dimension is smaller by `n`. The type of the output is the same
as the type of the difference between any two elements of `a`. This is the same
as the type of `a` in most cases.
Raises:
TypeError: If inputs have types not specified above.
ValueError: If ``n < 0``.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> arr = np.array([1, 3, -1, 0, 4])
>>> print(np.diff(arr, n=2))
[-6 5 3]
"""
# This implementation is inspired by jax.numpy
_check_input_tensor(a)
new_axis = _canonicalize_axis(axis, a.ndim)
if not isinstance(n, int):
_raise_type_error("Input n should be int, but got ", n)
if n < 0:
_raise_value_error("Input n must > 0.")
if n == 0:
return a
combined = ()
if prepend is not None:
combined = _handle_prepend_append(combined, a, prepend, axis)
combined += (a,)
if append is not None:
combined = _handle_prepend_append(combined, a, append, axis)
if combined:
a = concatenate(combined, new_axis)
# if n > maximum length allowed, the tensor is empty, and is not supported
if n >= a.shape[new_axis]:
_raise_value_error("n is bigger then the specified dimension, this will result in an empty tensor.")
original_dtype = a.dtype
# will change once F.tensor_slice supports types other than float32
if not _check_is_float(original_dtype):
a = a.astype(mstype.float32)
a = moveaxis(a, new_axis, -1)
for _ in F.make_range(n):
slice_start = _list_comprehensions(F.rank(a) - 1, 0, True)
slice_size = F.shape(a)[:-1] + (F.shape(a)[-1] - 1,)
minuend = F.tensor_slice(a, slice_start + (1,), slice_size)
subtrahend = F.tensor_slice(a, slice_start + (0,), slice_size)
a = F.tensor_sub(minuend, subtrahend)
if not _check_is_float(original_dtype):
a = a.astype(original_dtype)
return moveaxis(a, -1, new_axis)
def ediff1d(ary, to_end=None, to_begin=None):
"""
The differences between consecutive elements of a tensor.
Args:
ary (Tensor): If necessary, will be flattened before the differences are taken.
to_end (Tensor or scalar, optional): Number(s) to append at the end of the
returned differences.
to_begin (Tensor or scalar, optional): Number(s) to prepend at the beginning
of the returned differences.
Returns:
The differences.
Raises:
TypeError: If inputs have types not specified above.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> arr = np.array([1, 3, -1, 0, 4])
>>> print(np.ediff1d(arr))
[ 2 -4 1 4]
"""
_check_input_tensor(ary)
combined = ()
if to_begin is not None:
if isinstance(to_begin, Tensor):
to_begin = to_begin.ravel()
else:
to_begin = _to_tensor(to_begin).ravel()
to_begin = to_begin.astype(ary.dtype)
combined += (to_begin,)
combined += (diff(ary.ravel()),)
if to_end is not None:
if isinstance(to_end, Tensor):
to_end = to_end.ravel()
else:
to_end = _to_tensor(to_end).ravel()
to_end = to_end.astype(ary.dtype)
combined += (to_end,)
return P.Concat(0)(combined)
def trapz(y, x=None, dx=1.0, axis=-1):
"""
Integrates along the given axis using the composite trapezoidal rule.
Integrates `y` (x) along given axis.
Args:
y (Tensor): Input array to integrate.
x (Union[int, float, bool, list, tuple, Tensor], optional): The sample points
corresponding to the `y` values. If `x` is None, the sample points are
assumed to be evenly spaced `dx` apart. Default: ``None`` .
dx (scalar, optional): The spacing between sample points when `x` is None. Default: ``1.0`` .
axis (int, optional): The axis along which to integrate. Default: ``-1`` .
Returns:
Tensor of float, definite integral as approximated by trapezoidal rule.
Raises:
ValueError: If axis is out of range of ``[-y.ndim, y.ndim)``.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(6).reshape(2, 3)
>>> output = np.trapz(a, x=[-2, 1, 2], axis=1)
>>> print(output)
[ 3. 15.]
>>> output = np.trapz(a, dx=3, axis=0)
>>> print(output)
[ 4.5 7.5 10.5]
"""
y = _to_tensor(y)
ndim = F.rank(y)
_check_axis_in_range(axis, ndim)
axis = axis + ndim if axis < 0 else axis
y_start_axis_left = _list_comprehensions(axis, 0, True)
y_start_axis_right = _list_comprehensions(ndim - axis - 1, 0, True)
shape = F.shape(y)
y_slice_size = _tuple_setitem(shape, axis, shape[axis] - 1)
if x is not None:
x = _to_tensor(x)
dx = diff(x)
else:
dx = _to_tensor(dx)
dx = _expand(dx, ndim - axis, axis=-1)
dx = _broadcast_to_shape(dx, y_slice_size)
if not _check_is_float(F.dtype(y)):
# trapz returns float
y = F.cast(y, mstype.float32)
dx = F.cast(dx, F.dtype(y))
# product of dx and y with the last column removed
y_slice_left = F.tensor_slice(y, y_start_axis_left + (0,) + y_start_axis_right, y_slice_size)
prod_left = F.tensor_mul(y_slice_left, dx)
# product of dx and y with the first column removed
y_slice_right = F.tensor_slice(y, y_start_axis_left + (1,) + y_start_axis_right, y_slice_size)
prod_right = F.tensor_mul(y_slice_right, dx)
prod_sum = F.tensor_div(F.tensor_add(prod_left, prod_right), _to_tensor(2.0).astype(F.dtype(y)))
return F.reduce_sum(prod_sum, axis)
def _gcd(x1, x2):
"""Calculates gcd without applying keyword arguments."""
dtype = _promote(F.dtype(x1), F.dtype(x2))
if not _check_is_float(dtype):
# F.reduce_sum only supports float
x1 = F.cast(x1, mstype.float32)
x2 = F.cast(x2, mstype.float32)
x1 = F.absolute(x1)
x2 = F.absolute(x2)
cond_ge = F.tensor_ge(x1, x2)
a = where_(cond_ge, x1, x2)
b = where_(cond_ge, x2, x1)
b = where_(F.equal(b, ZERO_TENSOR), a, b)
r = _remainder(a, b)
while F.tensor_gt(F.reduce_sum(r), ZERO_TENSOR):
r = _remainder(a, b)
has_terminated = F.equal(r, ZERO_TENSOR)
a = where_(has_terminated, a, b)
b = where_(has_terminated, b, r)
if not _check_same_type(F.dtype(b), dtype):
b = F.cast(b, dtype)
return b
def gcd(x1, x2, dtype=None):
"""
Returns the greatest common divisor of ``|x1|`` and ``|x2|``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input data.
x2 (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the greatest common divisor of the absolute value of the inputs.
This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.gcd(np.arange(6), np.array(20))
>>> print(output)
[20 1 2 1 4 5]
"""
return _apply_tensor_op(_gcd, x1, x2, dtype=dtype)
def lcm(x1, x2, dtype=None):
"""
Returns the lowest common multiple of ``|x1|`` and ``|x2|``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input data.
x2 (Tensor): input data.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the lowest common multiple of the absolute value of the inputs.
This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.lcm(np.arange(6), np.array(20))
>>> print(output)
[ 0 20 20 60 20 20]
"""
def _lcm(x1, x2):
"""Calculates lcm without applying keyword arguments"""
common_divisor = _gcd(x1, x2)
dtype = _promote(F.dtype(x1), F.dtype(x2))
x1 = x1.astype(mstype.float32)
x2 = x2.astype(mstype.float32)
q1 = F.tensor_div(x1, common_divisor)
q2 = F.tensor_div(x2, common_divisor)
res = F.tensor_mul(F.tensor_mul(q1, q2), common_divisor)
has_zero = F.equal(multiply(x1, x2), ZERO_TENSOR)
res = where_(has_zero, ZERO_TENSOR, res)
return F.absolute(res).astype(dtype)
return _apply_tensor_op(_lcm, x1, x2, dtype=dtype)
def convolve(a, v, mode='full'):
"""
Returns the discrete, linear convolution of two one-dimensional sequences.
Note:
If `v` is longer than `a`, the tensors are swapped before computation.
Args:
a (Union[list, tuple, Tensor]): First one-dimensional input tensor.
v (Union[list, tuple, Tensor]): Second one-dimensional input tensor.
mode (str, optional): By default, mode is `\'full\'`. This returns the
convolution at each point of overlap, with an output shape of :math:`(N+M-1,)`.
At the end-points of the convolution, the signals do not overlap completely,
and boundary effects may be seen.
If `mode` is `\'same\'`, it returns output of length :math:`max(M, N)`. Boundary
effects are still visible.
If `mode` is `\'valid\'`, it returns output of length :math:`max(M, N) - min(M, N) + 1`.
The convolution product is only given for points where the signals overlap
completely. Values outside the signal boundary have no effect.
Returns:
Tensor, discrete, linear convolution of a and v.
Raises:
TypeError: If the inputs have types not specified above.
ValueError: If a and v are empty or have wrong dimensions
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.convolve([1., 2., 3., 4., 5.], [2., 3.], mode="valid")
>>> print(output)
[ 7. 12. 17. 22.]
"""
if not isinstance(a, Tensor):
a = asarray_const(a)
if not isinstance(v, Tensor):
v = asarray_const(v)
a_size = F.shape_mul(a.shape)
v_size = F.shape_mul(v.shape)
if a_size == 0 or v_size == 0:
_raise_value_error("Inputs cannot be empty.")
a = _expand(a, 1)
v = _expand(v, 1)
final_dtype = _promote(a.dtype, v.dtype)
a = a.astype("float32")
v = v.astype("float32")
if a.ndim != 1 or v.ndim != 1:
_raise_value_error("a and v must be 1-D tensor.")
if a_size < v_size:
a, v = v, a
a_size, v_size = v_size, a_size
v = v[::-1]
return _compute_1d_conv(a, v, mode).astype(final_dtype)
def _handle_weights(weights, num_samples):
"""Checks fweight and aweight in np.cov."""
weights = asarray_const(weights)
if not _check_is_int(weights.dtype):
_raise_type_error("weights must be integer")
weights = weights.astype("float32")
if weights.ndim > 1:
_raise_runtime_error("cannot handle multidimensional weights")
if weights.shape[0] != num_samples:
_raise_runtime_error("incompatible numbers of samples and weights")
return absolute(weights)
def _handle_inputs(cov_input, rowvar):
"""Checks input arrays for np.cov."""
if not isinstance(cov_input, Tensor):
cov_input = asarray_const(cov_input)
if cov_input.ndim > 2:
_raise_value_error("input array has dimension more than 2.")
cov_input = cov_input.astype("float32")
if cov_input.size == 0:
_raise_value_error("The value of cov_input should not be None, but got {}.".format(cov_input))
cov_input = _expand(cov_input, 2)
if not isinstance(rowvar, bool):
_raise_type_error("input rowvar should be boolean.")
if not rowvar and cov_input.shape[0] != 1:
cov_input = cov_input.T
return cov_input
def _handle_facts(w, m, ddof, aweights):
"""Computes facts for np.cov"""
fact = None
if w is None:
fact = m.shape[1] - ddof
else:
w_sum = _reduce_sum_default(w, -1)
if ddof == 0:
fact = w_sum
elif aweights is None:
fact = w_sum - ddof
else:
fact = w_sum - ddof * F.reduce_sum(w * aweights) / w_sum
return fact
def cov(m, y=None, rowvar=True, bias=False, ddof=None, fweights=None, aweights=None, dtype=None):
"""
Estimates a covariance matrix, given data and weights.
Covariance indicates the level to which two variables vary together. If we examine
N-dimensional samples, :math:`X = [x_1, x_2, .. x_N]^T`, then the covariance matrix
element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`. The element
:math:`C_{ii}` is the variance of :math:`x_i`.
Note:
`fweights` and `aweights` must be all positive, in Numpy if negative values
are detected, a value error will be raised, in MindSpore we converts all values
to positive instead.
Args:
m (Union[Tensor, list, tuple]): A 1-D or 2-D tensor containing multiple variables
and observations. Each row of `m` represents a variable, and each column
represents a single observation of all those variables. Also see `rowvar` below.
y (Union[Tensor, list, tuple], optional): An additional set of variables
and observations. `y` has the same form as that of `m`, default is ``None``.
rowvar(bool, optional): If `rowvar` is ``True`` (default), then each row represents
a variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows contain
observations.
bias (bool, optional): Default Normalization (``False``) is by :math:`(N - 1)`, where
:math:`N` is the number of observations given (unbiased estimate). If bias is
``True``, then Normalization is by `N`. These values can be overridden by
using the keyword `ddof`.
ddof (int, optional): If not ``None``, the default value implied by `bias` is
overridden. Note that :math:`ddof=1` will return the unbiased estimate, even
if both fweights and aweights are specified, and :math:`ddof=0` will return
the simple average. See the notes for the details. The default value
is ``None``.
fweights (Union[Tensor, list, tuple], optional): 1-D tensor of integer
frequency weights; the number of times each observation vector should
be repeated. The default value is ``None``.
aweights (Union[Tensor, list, tuple], optional): 1-D tensor of observation
vector weights. These relative weights are typically larger for observations
considered more important and smaller for observations considered less
important. If :math:`ddof=0` the tensor of weights can be used to assign probabilities
to observation vectors. The default value is ``None``.
dtype (Union[:class:`mindspore.dtype`, str], optional): Data-type of the
result. By default, the return data-type will have mstype.float32 precision.
Default is ``None``.
Returns:
Tensor, the covariance matrix of the variables.
Raises:
TypeError: If the inputs have types not specified above.
ValueError: If `m` and `y` have wrong dimensions.
RuntimeError: If `aweights` and `fweights` have dimensions > 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.cov([[2., 3., 4., 5.], [0., 2., 3., 4.], [7., 8., 9., 10.]])
>>> print(output)
[[1.6666666 2.1666667 1.6666666]
[2.1666667 2.9166667 2.1666667]
[1.6666666 2.1666667 1.6666666]]
"""
# This implementation was inspired by original numpy implementation.
m = _handle_inputs(m, rowvar)
if m.shape[0] == 0:
return empty((0, 0), dtype="float32")
if y is not None:
y = _handle_inputs(y, rowvar)
m = concatenate((m, y), axis=0)
if ddof is None:
if not bias:
ddof = 1
else:
ddof = 0
# Handle fweights and aweights
w = _handle_weights(fweights, m.shape[1]) if fweights is not None else None
if aweights is not None:
aweights = _handle_weights(aweights, m.shape[1])
w = aweights if w is None else w * aweights
avg = average(m, axis=1, weights=w)
# Determine the Normalization
fact = _handle_facts(w, m, ddof, aweights)
m = m - F.expand_dims(avg, -1)
if w is None:
m_t = m.T
else:
m_t = (m * w).T
res = true_divide(dot(m, m_t), fact).squeeze()
if dtype is not None:
return res.astype(dtype)
return res
@constexpr
def _real_axes(ndim_orig, ndim_out, axes_orig):
"""Returns the real axes to be reduced after performing broadcast"""
_diff = ndim_out - ndim_orig
axes = F.make_range(_diff)
axes_orig = map(functools.partial(operator.add, _diff), axes_orig)
return axes + tuple(axes_orig)
@_primexpr
def _shape_reduced_keepdims(shape, axes):
"""
Reduces dimensions corresponding to argument axes while
keeping the number of dimensions unchanged.
"""
ndim_out = F.tuple_len(shape)
shape_out = [1] * ndim_out
for i in range(ndim_out):
if i not in axes:
shape_out[i] = shape[i]
return tuple(shape_out)
@_primexpr
def _shape_reduced(shape, axes):
"""Removes dimensions corresponding to argument axes"""
ndim_orig = F.tuple_len(shape)
ndim_out = ndim_orig - F.tuple_len(axes)
shape_out = [0]*ndim_out
idx_out = 0
for i in range(ndim_orig):
if i not in axes:
shape_out[idx_out] = shape[i]
idx_out += 1
return tuple(shape_out)
def _reduce(a, reduce_fn, cmp_fn=None, axis=None, keepdims=False, initial=None, where=True, dtype=None):
"""
Applies comparison based on cmp_fn and reduction based on reduce_fn.
If cmp_fn is None, only reduction is performed.
"""
a = _to_tensor(a)
shape = F.shape(a)
ndim = F.rank(a)
if dtype is None:
dtype = F.dtype(a)
axes = _check_axis_valid(axis, ndim)
if initial is not None:
if ((isinstance(initial, Tensor) and F.rank(initial) > 0) or
not isinstance(initial, (int, float, bool, Tensor))):
_raise_type_error('initial should be scalar')
if _is_shape_empty(shape):
if not axes:
return a
if keepdims:
shape_out = _shape_reduced_keepdims(shape, axes)
else:
shape_out = _shape_reduced(shape, axes)
if _is_shape_empty(shape_out):
return empty(shape_out, dtype)
if initial is None:
if cmp_fn is None:
initial = nan
else:
_raise_value_error('initial value must be provided for zero-size arrays')
return full(shape_out, initial, dtype)
if initial is not None:
initial = full(shape, initial, dtype)
a = cmp_fn(a, initial)
if isinstance(where, Tensor):
if initial is None:
_raise_value_error('initial value must be provided for where masks')
ndim_orig = F.rank(a)
a = where_(where, a, initial)
axes = _real_axes(ndim_orig, F.rank(a), axes)
return reduce_fn(a, axes).astype(dtype)
def nanmax(a, axis=None, dtype=None, keepdims=False):
"""
Return the maximum of an array or maximum along an axis, ignoring any NaNs.
Note:
Numpy arguments `out` is not supported.
For all NaN slices, a very small negative number is returned instead of NaN.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers whose maximum
is desired. If `a` is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the maximum is
computed. The default is to compute the maximum of the flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nanmax(a)
>>> print(output)
3.0
>>> output = np.nanmax(a, axis=0)
>>> print(output)
[3. 2.]
"""
a = _to_tensor(a)
if not isinstance(keepdims, int):
_raise_type_error("integer argument expected, got", keepdims)
nan_mask = _isnan(a)
a = F.select(nan_mask, full(F.shape(a), -sys.maxsize - 1, F.dtype(a)), a)
reduce_fn = _reduce_max_keepdims if keepdims else _reduce_max_default
return _reduce(a, reduce_fn, axis=axis, keepdims=keepdims, dtype=dtype)
def nanmin(a, axis=None, dtype=None, keepdims=False):
"""
Returns the minimum of array elements over a given axis, ignoring any NaNs.
Note:
Numpy arguments `out` is not supported.
For all-NaN slices, a very large number is returned instead of NaN.
On Ascend, since checking for NaN is currently not supported, it is not recommended to
use np.nanmin. If the array does not contain NaN, np.min should be used instead.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers whose minimum
is desired. If `a` is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the minimum is
computed. The default is to compute the minimum of the flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nanmin(a)
>>> print(output)
1.0
>>> output = np.nanmin(a, axis=0)
>>> print(output)
[1. 2.]
"""
a = _to_tensor(a)
if not isinstance(keepdims, int):
_raise_type_error("integer argument expected, got", keepdims)
nan_mask = _isnan(a)
a = F.select(nan_mask, full(F.shape(a), sys.maxsize, F.dtype(a)), a)
reduce_fn = _reduce_min_keepdims if keepdims else _reduce_min_default
return _reduce(a, reduce_fn, axis=axis, keepdims=keepdims, dtype=dtype)
def _reduce_nansum(x, axis, keepdims=False):
"""Computes reduce sum treating NaNs as zeros."""
x = F.select(_isnan(x), zeros(F.shape(x), F.dtype(x)), x)
if keepdims:
return _reduce_sum_keepdims(x, axis)
return _reduce_sum_default(x, axis)
def nansum(a, axis=None, dtype=None, keepdims=False):
"""
Returns the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero.
Note:
Numpy arguments `out` is not supported.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers
whose sum is desired. If `a` is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the sum is
computed. The default is to compute the sum of the flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 1], [1, np.nan]])
>>> output = np.nansum(a)
>>> print(output)
3.0
>>> output = np.nansum(a, axis=0)
>>> print(output)
[2. 1.]
"""
a = _to_tensor(a)
nan_mask = _isnan(a)
a = F.select(nan_mask, zeros(F.shape(a), F.dtype(a)), a)
return _reduce(a, functools.partial(_reduce_nansum, keepdims=keepdims), axis=axis,
keepdims=keepdims, dtype=dtype)
def _count_nonnan(a, axis, keepdims=False):
"""Counts the number of elements excluding NaNs."""
nonnan_mask = F.select(_isnan(a), zeros(F.shape(a), F.dtype(a)), ones(F.shape(a), F.dtype(a)))
if keepdims:
return _reduce_sum_keepdims(nonnan_mask, axis)
return _reduce_sum_default(nonnan_mask, axis)
def nanmean(a, axis=None, dtype=None, keepdims=False):
"""
Computes the arithmetic mean along the specified axis, ignoring NaNs.
Returns the average of the array elements. The average is taken over the flattened
array by default, otherwise over the specified axis. float32 intermediate and
return values are used for integer inputs.
Note:
Numpy arguments `out` is not supported.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers
whose mean is desired. If `a` is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the mean is
computed. The default is to compute the mean of the flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, np.nan], [3, 4]])
>>> output = np.nanmean(a)
>>> print(output)
2.6666667
>>> output = np.nanmean(a, axis=0)
>>> print(output)
[2. 4.]
>>> output = np.nanmean(a, axis=1)
>>> print(output)
[1. 3.5]
"""
if dtype is None:
dtype = mstype.float32
a = _to_tensor(a)
axis = _check_axis_valid(axis, F.rank(a))
sum_a = nansum(a, axis=axis, dtype=dtype, keepdims=keepdims)
return F.tensor_div(sum_a, _count_nonnan(a, axis, keepdims))
def _nanvar(a, axis, ddof=0, keepdims=False):
"""Computes nanvar without applying keyword arguments."""
mean_a = nanmean(a, axis=axis, keepdims=True)
pow_a = F.tensor_pow(F.tensor_sub(a, mean_a), 2)
sum_a = _reduce_nansum(pow_a, axis, keepdims)
count = _count_nonnan(a, axis, keepdims)
return divide(sum_a, F.tensor_sub(count, ddof))
def nanvar(a, axis=None, dtype=None, ddof=0, keepdims=False):
"""
Computes the variance along the specified axis, while ignoring NaNs.
Returns the variance of the array elements, a measure of the spread of a distribution. The
variance is computed for the flattened array by default, otherwise over the specified axis.
Note:
Numpy arguments `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers
whose variance is desired. If `a` is not an array, a conversion is attempted.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the variance is
computed. The default is to compute the variance of the flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
ddof (int, optional): "Delta Degrees of Freedom": the divisor used in the calculation is
``N - ddof``, where `N` represents the number of non-NaN elements. By default `ddof`
is zero.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, np.nan], [3, 4]])
>>> output = np.nanvar(a)
>>> print(output)
1.5555557
>>> output = np.nanvar(a, axis=0)
>>> print(output)
[1. 0.]
>>> output = np.nanvar(a, axis=1)
>>> print(output)
[0. 0.25]
"""
if dtype is None:
dtype = mstype.float32
return _reduce(a, functools.partial(_nanvar, ddof=ddof, keepdims=keepdims), axis=axis,
keepdims=keepdims, dtype=dtype)
def nanstd(a, axis=None, dtype=None, ddof=0, keepdims=False):
"""
Computes the standard deviation along the specified axis, while ignoring NaNs.
Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN
array elements. The standard deviation is computed for the flattened array by default,
otherwise over the specified axis.
Note:
Numpy arguments `out` is not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
a (Union[int, float, list, tuple, Tensor]): Calculates the standard deviation of the non-NaN values.
axis (Union[int, tuple of int, None], optional): Axis or axes along which the standard
deviation is computed. The default is to compute the standard deviation of the
flattened array.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
ddof (int, optional): "Delta Degrees of Freedom": the divisor used in the calculation is
``N - ddof``, where `N` represents the number of non-NaN elements. By default `ddof`
is zero.
keepdims (boolean, optional): Default: ``False`` . If this is set to True, the axes which
are reduced are left in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `a`.
Returns:
Tensor.
Raises:
ValueError: If axes are out of the range of ``[-a.ndim, a.ndim)``, or
if the axes contain duplicates.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, np.nan], [3, 4]])
>>> output = np.nanstd(a)
>>> print(output)
1.2472192
>>> output = np.nanstd(a, axis=0)
>>> print(output)
[1. 0.]
>>> output = np.nanstd(a, axis=1)
>>> print(output)
[0. 0.5]
"""
if dtype is None:
dtype = mstype.float32
return _reduce(a, lambda a, axis: F.sqrt(_nanvar(a, axis, ddof=ddof, keepdims=keepdims)),
axis=axis, keepdims=keepdims, dtype=dtype)
def exp2(x, dtype=None):
"""
Calculates ``2**p`` for all p in the input array.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
x (Tensor): input values.
dtype (:class:`mindspore.dtype`, optional): Defaults to ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, element-wise 2 to the power `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([2, 3]).astype(np.float32)
>>> output = np.exp2(x)
>>> print(output)
[4. 8.]
"""
return _apply_tensor_op(lambda x: F.tensor_pow(2, x), x, dtype=dtype)
def kron(a, b):
"""
Kronecker product of two arrays.
Computes the Kronecker product, a composite array made of blocks of the second
array scaled by the first.
Note:
Booleans are not supported.
Args:
a (Union[int, float, list, tuple, Tensor]): input values.
b (Union[int, float, list, tuple, Tensor]): input values.
Returns:
Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.kron([1,10,100], [5,6,7])
>>> print(output)
[ 5 6 7 50 60 70 500 600 700]
>>> output = np.kron([5,6,7], [1,10,100])
>>> print(output)
[ 5 50 500 6 60 600 7 70 700]
>>> output = np.kron(np.eye(2), np.ones((2,2)))
>>> print(output)
[[1. 1. 0. 0.]
[1. 1. 0. 0.]
[0. 0. 1. 1.]
[0. 0. 1. 1.]]
"""
a, b = _to_tensor(a, b)
ndim = _max(F.rank(a), F.rank(b))
if ndim == 0:
return F.tensor_mul(a, b)
a = _expand(a, ndim)
b = _expand(b, ndim)
shape_a = F.shape(a)
shape_b = F.shape(b)
# scales a by the shape of b
kron_shape = _seq_prod(shape_a, shape_b)
a = F.reshape(a, _add_unit_axes(shape_a, 2*ndim, True))
a = F.tile(a, _add_unit_axes(shape_b, 2*ndim, False))
a = moveaxis(a, F.make_range(ndim, 2*ndim), F.make_range(1, 2*ndim, 2))
a = F.reshape(a, kron_shape)
# scales b by the shape of a
b = F.tile(b, shape_a)
return F.tensor_mul(a, b)
def cross(a, b, axisa=- 1, axisb=- 1, axisc=- 1, axis=None):
"""
Returns the cross product of two (arrays of) vectors.
The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular to both
`a` and `b`. If `a` and `b` are arrays of vectors, the vectors are defined by the
last axis of `a` and `b` by default, and these axes can have dimensions 2 or 3.
Where the dimension of either `a` or `b` is 2, the third component of the input
vector is assumed to be zero and the cross product calculated accordingly. In cases
where both input vectors have dimension 2, the z-component of the cross product is
returned.
Args:
a (Union[list, tuple, Tensor]): Components of the first vector(s).
b (Union[list, tuple, Tensor]): Components of the second vector(s).
axisa (int, optional): Axis of `a` that defines the vector(s). By default, the last
axis.
axisb (int, optional): Axis of `b` that defines the vector(s). By default, the last
axis.
axisc (int, optional): Axis of `c` containing the cross product vector(s). Ignored
if both input vectors have dimension 2, as the return is scalar. By default,
the last axis.
axis (int, optional): If defined, the axis of `a`, `b` and `c` that defines the
vector(s) and cross product(s). Overrides `axisa`, `axisb` and `axisc`.
Default: ``None`` .
Returns:
Tensor, vector cross product(s).
Raises:
ValueError: when the dimensions of the vector(s) in `a` and/or `b` does not equal 2
or 3.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([[1,2,3], [4,5,6]])
>>> y = np.array([[4,5,6], [1,2,3]])
>>> output = np.cross(x, y)
>>> print(output)
[[-3 6 -3]
[ 3 -6 3]]
>>> output = np.cross(x, y, axisc=0)
>>> print(output)
[[-3 3]
[ 6 -6]
[-3 3]]
"""
a, b = _to_tensor(a, b)
if axis is not None:
axisa, axisb, axisc = axis, axis, axis
_check_axis_in_range(axisa, F.rank(a))
_check_axis_in_range(axisb, F.rank(b))
a = moveaxis(a, axisa, -1)
b = moveaxis(b, axisb, -1)
shape_a = F.shape(a)
shape_b = F.shape(b)
if F.shape(a)[-1] not in (2, 3) or F.shape(b)[-1] not in (2, 3):
_raise_value_error('incompatible dimensions for cross product (dimension must be 2 or 3)')
a_has_z = shape_a[-1] == 3
b_has_z = shape_b[-1] == 3
shape_out = _infer_out_shape(shape_a[:-1], shape_b[:-1])
if a_has_z or b_has_z:
shape_out += (3,)
_check_axis_in_range(axisc, len(shape_out))
dtype = _promote(F.dtype(a), F.dtype(b))
if _get_device() == 'CPU':
# F.tensor_slice only supports float on CPU
if not _check_is_float(F.dtype(a)):
a = F.cast(a, mstype.float32)
if not _check_is_float(F.dtype(b)):
b = F.cast(b, mstype.float32)
a_slice_start = _list_comprehensions(F.rank(a) - 1, 0, True)
a_slice_size = shape_a[:-1] + (1,)
b_slice_start = _list_comprehensions(F.rank(b) - 1, 0, True)
b_slice_size = shape_b[:-1] + (1,)
def _get_slice_product(idx_a, idx_b):
return multiply(F.tensor_slice(a, a_slice_start + (idx_a,), a_slice_size),
F.tensor_slice(b, b_slice_start + (idx_b,), b_slice_size))
cz = F.tensor_sub(_get_slice_product(0, 1), _get_slice_product(1, 0)) # ax*by - ay*bx
if not a_has_z and not b_has_z:
return F.reshape(cz, shape_out).astype(dtype)
if a_has_z and b_has_z:
cx = F.tensor_sub(_get_slice_product(1, 2), _get_slice_product(2, 1)) # ay*bz - az*by
cy = F.tensor_sub(_get_slice_product(2, 0), _get_slice_product(0, 2)) # az*bx - ax*bz
elif a_has_z:
cx = F.neg(_get_slice_product(2, 1)) # -az*by
cy = _get_slice_product(2, 0) # az*bx
else: # b_has_z
cx = _get_slice_product(1, 2) # ay*bz
cy = F.neg(_get_slice_product(0, 2)) # -ax*bz
res = _concat((cx, cy, cz)).reshape(shape_out)
return moveaxis(res, -1, axisc).astype(dtype)
def ceil(x, dtype=None):
"""
Returns the ceiling of the input, element-wise.
The ceil of the scalar `x` is the smallest integer `i`, such that ``i >= x``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
Args:
x (Tensor): input values.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the floor of each element in `x`. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
>>> output = np.ceil(a)
>>> print(output)
[-1. -1. -0. 1. 2. 2. 2.]
"""
return _apply_tensor_op(lambda x: F.neg(F.floor(F.neg(x.astype(mstype.float32)))),
x, dtype=dtype)
def _infer_shape_rem(shape1, shape2, ndim1, ndim2, transpose_b):
"""Infers the shape of the last two dimensions after performing matmul."""
shape_rem = ()
if ndim1 >= 2:
shape_rem += (shape1[-2],)
if transpose_b:
if ndim2 >= 2:
shape_rem += (shape2[-2],)
else:
if ndim1 >= 1:
shape_rem += (shape2[-1],)
return shape_rem
def positive(a, dtype=None):
"""
Numerical positive, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
a (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.asarray([1, -1]).astype('float32')
>>> output = np.positive(a)
>>> print(output)
[1. -1.]
"""
_check_input_tensor(a)
neg_tensor = F.neg(a)
return _apply_tensor_op(F.neg, neg_tensor, dtype=dtype)
def negative(a, dtype=None):
"""
Numerical negative, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
a (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.asarray([1, -1]).astype('float32')
>>> output = np.negative(a)
>>> print(output)
[-1. 1.]
"""
return _apply_tensor_op(F.neg, a, dtype=dtype)
def cumsum(a, axis=None, dtype=None):
"""
Returns the cumulative sum of the elements along a given axis.
Note:
If ``a.dtype`` is :class:`int8`, :class:`int16` or :class:`bool`, the result
`dtype` will be elevated to :class:`int32`.
Args:
a (Tensor): Input tensor.
axis (int, optional): Axis along which the cumulative sum is computed. The
default ( ``None`` ) is to compute the cumsum over the flattened array.
dtype (:class:`mindspore.dtype`, optional): If not specified, stay the same as `a`,
unless `a` has an integer dtype with a precision less than that of the
default platform integer. In that case, the default platform integer
is used. Default: ``None`` .
Returns:
Tensor.
Raises:
TypeError: If input arguments have types not specified above.
ValueError: If axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.cumsum(np.ones((3,3)), axis=0)
>>> print(output)
[[1. 1. 1.]
[2. 2. 2.]
[3. 3. 3.]]
"""
_check_input_tensor(a)
return a.cumsum(axis, dtype)
def nancumsum(a, axis=None, dtype=None):
"""
Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs)
as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are
replaced by zeros.
Zeros are returned for slices that are all-NaN or empty.
Note:
If ``a.dtype`` is :class:`int8`, :class:`int16` or :class:`bool`, the result
`dtype` will be elevated to :class:`int32`.
Args:
a (Tensor): Input tensor.
axis (int, optional): Axis along which the cumulative sum is computed. The
default (None) is to compute the cumsum over the flattened array.
dtype (:class:`mindspore.dtype`, optional): If not specified, stay the same as `a`,
unless `a` has an integer dtype with a precision less than that of the
default platform integer. In that case, the default platform integer
is used.
Returns:
Tensor.
Raises:
TypeError: If input arguments have types not specified above.
ValueError: If axis is out of range.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([[1, 2], [3, np.nan]])
>>> output = np.nancumsum(a)
>>> print(output)
[1. 3. 6. 6.]
>>> output = np.nancumsum(a, axis=0)
>>> print(output)
[[1. 2.]
[4. 2.]]
>>> output = np.nancumsum(a, axis=1)
>>> print(output)
[[1. 3.]
[3. 3.]]
"""
a = F.select(_isnan(a), zeros(F.shape(a), F.dtype(a)), a)
return a.cumsum(axis, dtype)
def cbrt(x, dtype=None):
"""
Returns the cube-root of a tensor, element-wise.
Note:
Numpy arguments `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.asarray([1, -1, 3, -8, 64])
>>> output = np.cbrt(a)
>>> print(output)
[ 1. -1. 1.4422495 -2. 4. ]
"""
def _cbrt(x):
compute_type = promote_types(x.dtype, "float32")
x = x.astype(compute_type)
# TODO: use P.Sign() once gpu support is added
abs_x = F.absolute(x)
sign_x = abs_x / x
return sign_x * F.tensor_pow(abs_x, 1. / 3.)
return _apply_tensor_op(_cbrt, x, dtype=dtype)
def log1p(x, dtype=None):
"""
Returns the natural logarithm of one plus the input array, element-wise.
Calculates ``log(1 + x)``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input array.
dtype (:class:`mindspore.dtype`): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([1, 2, 3]).astype('float16')
>>> output = np.log1p(x)
>>> print(output)
[0.6934 1.099 1.387 ]
"""
return _apply_tensor_op(lambda x: F.log(x + 1), x, dtype=dtype)
def logaddexp(x1, x2, dtype=None):
"""
Logarithm of the sum of exponentiations of the inputs. Calculates ``log(exp(x1) + exp(x2))``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input array.
x2 (Tensor): Input array. If ``x1.shape != x2.shape``, they must be broadcastable to
a common shape (which becomes the shape of the output).
dtype (:class:`mindspore.dtype`): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.array([1, 2, 3]).astype('float16')
>>> x2 = np.array(2).astype('float16')
>>> output = np.logaddexp(x1, x2)
>>> print(output)
[2.312 2.693 3.312]
"""
def _logaddexp(x1, x2):
return F.log(F.tensor_add(F.tensor_exp(x1), F.tensor_exp(x2)))
return _apply_tensor_op(_logaddexp, x1, x2, dtype=dtype)
def log2(x, dtype=None):
"""
Base-2 logarithm of `x`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([2, 4, 8]).astype('float16')
>>> output = np.log2(x)
>>> print(output)
[1. 2. 3.]
"""
tensor_2 = _make_tensor(2, x.dtype)
def _log2(x):
return F.log(x) / F.log(tensor_2)
return _apply_tensor_op(_log2, x, dtype=dtype)
def logaddexp2(x1, x2, dtype=None):
"""
Logarithm of the sum of exponentiations of the inputs in base of 2.
Calculates ``log2(2**x1 + 2**x2)``.
This function is useful in machine learning when the calculated probabilities of events
may be so small as to exceed the range of normal floating point numbers.
In such cases the base-2 logarithm of the calculated probability can be used instead.
This function allows adding probabilities stored in such a fashion.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input tensor.
x2 (Tensor): Input tensor. If ``x1.shape != x2.shape``, they must be broadcastable to
a common shape (which becomes the shape of the output).
dtype (:class:`mindspore.dtype`): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.array([2, 4, 8]).astype('float16')
>>> x2 = np.array(2).astype('float16')
>>> output = np.logaddexp2(x1, x2)
>>> print(output)
[3. 4.32 8.02]
"""
_check_input_tensor(x1, x2)
add_exp = F.tensor_add(F.tensor_pow(2, x1), F.tensor_pow(2, x2))
return log2(add_exp, dtype=dtype)
def log10(x, dtype=None):
"""
Base-10 logarithm of `x`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([10, 100, 1000]).astype('float16')
>>> output = np.log10(x)
>>> print(output)
[1. 2. 3.]
"""
tensor_10 = _make_tensor(10, x.dtype)
def _log10(x):
return F.log(x) / F.log(tensor_10)
return _apply_tensor_op(_log10, x, dtype=dtype)
def _cast_type_for_trigonometric(x):
_check_input_tensor(x)
if x.dtype != mstype.float16 or x.dtype != mstype.float32 or x.dtype != mstype.float64:
dtype = _promote_for_trigonometric(x.dtype)
x = F.cast(x, dtype)
return x
def sin(x, dtype=None):
"""
Trigonometric sine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([-5, -1, 0, 2, 4, 100]).astype('float32')
>>> output = np.sin(x)
>>> print(output)
[ 0.9589243 -0.84147096 0. 0.9092974 -0.7568025 -0.50636566]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.sin, x, dtype=dtype)
def cos(x, dtype=None):
"""
Cosine element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(5).astype('float32')
>>> print(np.cos(x))
[ 1. 0.5403023 -0.41614684 -0.9899925 -0.6536436 ]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.cos, x, dtype=dtype)
def tan(x, dtype=None):
"""
Computes tangent element-wise.
Equivalent to :math:`np.sin(x)/np.cos(x)` element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Raises:
TypeError: If the input is not a tensor or is :class:`tensor.dtype` is :class:`mindspore.float64`.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([-5, -1, 0, 2, 4, 100]).astype('float32')
>>> print(np.tan(x))
[ 3.380515 -1.5574077 0. -2.1850398 1.1578213 -0.58721393]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.tan, x, dtype=dtype)
def arcsin(x, dtype=None):
"""
Inverse sine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor. y-coordinate on the unit circle.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Output Tensor.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, -1], np.float32)
>>> output = np.arcsin(x)
>>> print(output)
[ 1.5707964 -1.5707964]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.asin, x, dtype=dtype)
def arccos(input, dtype=None):
"""
Trigonometric inverse cosine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
input (Tensor): Input tensor. x-coordinate on the unit circle.
For real arguments, the domain is :math:`[-1, 1]`.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> input = np.asarray([1, -1], np.float32)
>>> output = np.arccos(input)
>>> print(output)
[0. 3.1415927]
"""
input = _cast_type_for_trigonometric(input)
return _apply_tensor_op(F.acos, input, dtype=dtype)
def arctan(x, dtype=None):
"""
Trigonometric inverse tangent, element-wise.
The inverse of tan, so that if :math:`y = tan(x)` then :math:`x = arctan(y)`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(5).astype('float32')
>>> print(np.arctan(x))
[0. 0.7853982 1.1071488 1.2490457 1.3258177]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.atan, x, dtype=dtype)
def sinh(x, dtype=None):
"""
Hyperbolic sine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(5).astype('float32')
>>> print(np.sinh(x))
[ 0. 1.1752012 3.6268604 10.017875 27.289917 ]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.sinh, x, dtype=dtype)
def cosh(x, dtype=None):
"""
Hyperbolic cosine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(5).astype('float32')
>>> print(np.cosh(x))
[ 1. 1.5430807 3.7621956 10.067662 27.308233 ]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.cosh, x, dtype=dtype)
def tanh(x, dtype=None):
"""
Computes hyperbolic tangent element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(5).astype('float32')
>>> print(np.tanh(x))
[0. 0.7615942 0.9640276 0.9950548 0.9993293]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.tanh, x, dtype=dtype)
def arcsinh(x, dtype=None):
"""
Inverse hyperbolic sine element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([1., 2., 3., 4.], dtype=np.float32)
>>> print(np.arcsinh(x))
[0.8813736 1.4436355 1.8184465 2.0947125]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.asinh, x, dtype=dtype)
def arccosh(x, dtype=None):
"""
Inverse hyperbolic cosine, element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.arange(1, 5).astype('float32')
>>> print(np.arccosh(x))
[0. 1.316958 1.7627472 2.063437 ]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.acosh, x, dtype=dtype)
def arctanh(x, dtype=None):
"""
Inverse hyperbolic tangent element-wise.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Tensor): Input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None``. Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. This is a scalar if `x` is a scalar.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([-0.99, -0.75, -0.5, 0, 0.5]).astype('float32')
>>> print(np.arctanh(x))
[-2.646653 -0.97295505 -0.54930615 0. 0.54930615]
"""
x = _cast_type_for_trigonometric(x)
return _apply_tensor_op(F.atanh, x, dtype=dtype)
def arctan2(x1, x2, dtype=None):
"""
Element-wise arc tangent of :math:`x1/x2` choosing the quadrant correctly.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): input tensor.
x2 (Tensor): input tensor.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, the sum of `x1` and `x2`, element-wise. This is a scalar
if both `x1` and `x2` are scalars.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.array([-1, +1, +1, -1])
>>> x2 = np.array([-1, -1, +1, +1])
>>> output = np.arctan2(x1, x2)
>>> print(output)
[-2.3561945 2.3561945 0.78539819 -0.78539819]
"""
x1 = _cast_type_for_trigonometric(x1)
x2 = _cast_type_for_trigonometric(x2)
return _apply_tensor_op(F.atan2, x1, x2, dtype=dtype)
def promote_types(type1, type2):
"""
Returns the data type with the smallest size and smallest scalar kind.
Note:
The promotion rule is slightly different from original Numpy, but more like
jax, due to the preference on ``32-bit`` over ``64-bit`` data types.
Args:
type1 (Union[:class:`mindspore.dtype`, str]): First data type.
type2 (Union[:class:`mindspore.dtype`, str]): Second data type.
Returns:
The promoted data type.
Raises:
TypeError: If the input are not valid :class:`mindspore.dtype` input.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.promote_types(np.float32, np.float64)
>>> print(output)
Float64
"""
type1 = _check_dtype(type1)
type2 = _check_dtype(type2)
return _promote(type1, type2)
def corrcoef(x, y=None, rowvar=True, dtype=None):
r"""
Returns Pearson product-moment correlation coefficients.
Please refer to the documentation for cov for more detail. The relationship
between the correlation coefficient matrix, R, and the covariance matrix, C, is
:math:`R_{ij} = \frac{ C_{ij} } { \sqrt{ C_{ii} * C_{jj} } }`
The values of R are between -1 and 1, inclusive.
Note:
Currently, complex numbers are not supported.
Args:
x (Union[int, float, bool, tuple, list, Tensor]): A 1-D or 2-D array containing
multiple variables and observations. Each row of `x` represents a variable,
and each column a single observation of all those variables. Also see rowvar below.
y (Union[int, float, bool, tuple, list, Tensor], optional): An additional set
of variables and observations. Default: ``None`` .
rowvar (bool, optional): If rowvar is ``True`` (default), then each row represents
a variable, with observations in the columns. Otherwise, the relationship
is transposed: each column represents a variable, while the rows contain observations.
Default: ``True`` .
dtype (:class:`mindspore.dtype`, optional): Data-type of the result. By default,
the return data-type will have at least float32 precision. Default: ``None`` .
Returns:
Tensor, The correlation coefficient matrix of the variables.
Raises:
TypeError: If the inputs have types not specified above.
ValueError: If `x` and `y` have wrong dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.corrcoef([[2., 3., 4., 5.], [0., 2., 3., 4.], [7., 8., 9., 10.]])
>>> print(output)
[[1. 0.9827076 1. ]
[0.9827077 0.99999994 0.9827077 ]
[1. 0.9827076 1. ]]
"""
# This implementation was adapted from original Numpy.
c = cov(x, y, rowvar)
if not c.shape:
return F.tensor_div(c, c)
d = diag(c)
stddev = sqrt(d)
c /= F.expand_dims(stddev, -1)
c /= F.expand_dims(stddev, 0)
c = clip(c, -1, 1)
if dtype is not None:
return c.astype(dtype)
return c
def _slice_along_axis(f, axis, slice_start, slice_end):
"""
Slice a tensor along a given axis, a helper function for gradient
Args:
f (Tensor): Input Tensor.
axis (int): Specified axis.
slice_start (int): The start of the slice.
slice_end (int): The end of the int.
Returns:
Sliced tensor.
"""
slice_size = slice_end - slice_start
index_start = (0,) * f.ndim
index_end = f.shape
index_start = _tuple_setitem(index_start, axis, slice_start)
index_end = _tuple_setitem(index_end, axis, slice_size)
return F.tensor_slice(f, index_start, index_end)
def _gradient_along_axis(f, h, axis):
"""compute the gradients of `f` along a given axis, a helper function of gradient."""
end = f.shape[axis]
upper_edge = _slice_along_axis(f, axis, 1, 2) - _slice_along_axis(f, axis, 0, 1)
lower_edge = _slice_along_axis(f, axis, end-1, end) - _slice_along_axis(f, axis, end-2, end-1)
if end <= 2:
a_grad = concatenate((upper_edge, lower_edge), axis)
else:
middle = (_slice_along_axis(f, axis, 2, end) - _slice_along_axis(f, axis, 0, end-2)) * 0.5
a_grad = concatenate((upper_edge, middle, lower_edge), axis)
return a_grad / h
def check_gradient_arguments(f, axis, edge_order):
"""check arguments for gradient"""
if edge_order != 1:
_raise_unimplemented_error("edge_order != 1 not implemented")
if not isinstance(f, Tensor):
f = asarray_const(f)
if f.dtype != mstype.float64:
f = f.astype(mstype.float32)
if axis is None:
axis = F.make_range(f.ndim)
else:
_check_axis_type(axis, True, True, True)
axis = _canonicalize_axis(axis, f.ndim)
axis = (axis,) if isinstance(axis, int) else axis
return f, axis, edge_order
def gradient(f, *varargs, axis=None, edge_order=1):
"""
Returns the gradient of a N-dimensional array.
The gradient is computed using second order accurate central differences
in the interior points and either first or second order accurate one-sides
(forward or backwards) differences at the boundaries.
The returned gradient hence has the same shape as the input array.
Note:
Currently we only support `edge_order` =1 and uniform spacing of `varargs`.
Args:
f (Union[tuple, list, Tensor]): An N-dimensional array containing samples of
a scalar function.
varargs (Union[tuple[number], tuple[tensor scalar]], optional)
Spacing between f values. Default unitary spacing for all dimensions.
Spacing can be specified using:
1. single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
axis (Union[None, int, tuple(int), list(int)], optional): Gradient is calculated
only along the given axis or axes. The default :class:`(axis = None)` is to calculate
the gradient for all the axes of the input tensor. `axis` may be negative,
in which case it counts from the last to the first `axis`.
edge_order (int): Gradient is calculated using N-th order accurate differences
at the boundaries. Default: ``1`` .
Returns:
gradient, a list of tensors (or a single tensor if there is only one dimension
to be calculated). Each derivative has the same shape as f.
Raises:
TypeError: If the inputs have types not specified above.
ValueError: If `axis` values out of bounds, or shape of `f` has entries < 1.
NotImplementedError: If `edge_order` != 1, or `varargs` contains non-scalar entries.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.gradient([[1, 2, 6], [3, 4, 5]], axis=-1)
>>> print(output)
[[1. 2.5 4. ]
[1. 1. 1. ]]
"""
# This implementation was adapted from Numpy and jax.numpy
f, axis, edge_order = check_gradient_arguments(f, axis, edge_order)
len_axes = len(axis)
n = len(varargs)
dx = None
# check varargs and make varags the same length as axis
if n == 0 or varargs is None:
# no spacing
dx = (1,) * len_axes
elif n == 1:
# single value for all axes
dx = varargs * len_axes
elif n == len_axes:
dx = varargs
else:
_raise_type_error("Invalid number of arguments")
a_grad = []
for idx in F.make_range(len_axes):
h = dx[idx]
ax = axis[idx]
if f.shape[ax] < 2:
_raise_value_error("Shape of array too small to calculate a numerical gradient, "
"at least 2 elements are required.")
# if h is not scalar
if not (isinstance(h, (int, float, bool)) or (isinstance(h, Tensor) and h.ndim == 0)):
_raise_unimplemented_error("Non-constant spacing not implemented")
a_grad.append(_gradient_along_axis(f, h, ax))
if len(axis) == 1:
return a_grad[0]
return a_grad
def sum_(a, axis=None, dtype=None, keepdims=False, initial=None):
"""
Returns sum of array elements over a given axis.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and
`extobj` are not supported.
Args:
x (Union[int, float, bool, list, tuple, Tensor]): Elements to sum.
axis (Union[None, int, tuple(int)]): Axis or axes along which a sum is performed. Default: `None`.
If `None`, sum all of the elements of the input array.
If axis is negative it counts from the last to the first axis.
If axis is a tuple of integers, a sum is performed on all of the axes specified in the tuple
instead of a single axis or all the axes as before.
dtype (:class:`mindspore.dtype`, optional): Defaults to `None`. Overrides the dtype of the
output Tensor.
keepdims (bool): If this is set to True, the axes which are reduced are left in the result as
dimensions with size one. With this option, the result will broadcast correctly against the input array.
If the default value is passed, then keepdims will not be passed through to the sum method of
sub-classes of ndarray, however any non-default value will be. If the sub-class method does not
implement keepdims any exceptions will be raised. Default: `False`.
initial (scalar): Starting value for the sum, if `None`, which refers to the first element of the reduction.
Default: `None`.
Returns:
Tensor. An array with the same shape as a, with the specified axis removed.
If a is a 0-d array, or if axis is `None`, a scalar is returned.
If an output array is specified, a reference to out is returned.
Raises:
TypeError: If input is not array_like or `axis` is not int or tuple of integers or
`keepdims` is not integer or `initial` is not scalar.
ValueError: If any axis is out of range or duplicate axes exist.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.sum([0.5, 1.5]))
2.0
>>> x = np.arange(10).reshape(2, 5).astype('float32')
>>> print(np.sum(x, axis=1))
[10. 35.]
"""
a = _to_tensor(a)
return a.sum(axis, dtype, keepdims, initial)
@constexpr
def _min_cost_chain_matmul(dims):
"""
Returns indices of splits that has the minimal cost for matmul.
s[i, j] holds the index of the split with minimal cost for arrays[i, i + 1, ... j]
"""
dims = tuple(dims)
n = len(dims) - 1
m = [[0]*n for _ in range(n)]
s = [[0]*n for _ in range(n)]
for pos in range(1, n):
for i in range(n - pos):
j = i + pos
m[i][j] = sys.maxsize
for k in range(i, j):
cost = m[i][k] + m[k + 1][j] + dims[i]*dims[k + 1]*dims[j + 1]
if cost < m[i][j]:
m[i][j] = cost
s[i][j] = k
return s
@_primexpr
def _get_dims(shapes):
"""
Returns the chain of the dimensions in arrays.
dims[i] == arrays[i - 1].shape[1] == arrays[i].shape[0]
"""
shapes = tuple(shapes)
if any(len(shape) != 2 for shape in shapes):
raise ValueError('Array must be 2 dimensional')
dims = tuple(map(operator.itemgetter(0), shapes))
if any(shape[1] != dim for shape, dim in zip(shapes[:-1], dims[1:])):
raise ValueError('Shapes{} are not aligned'.format(str(shapes)))
return dims + (shapes[-1][1],)
def _multi_dot(arrays, i, j, order):
"""Computes multi dot recursively using minimal cost."""
if i == j:
return arrays[i]
return dot(_multi_dot(arrays, i, order[i][j], order),
_multi_dot(arrays, order[i][j] + 1, j, order))
def multi_dot(arrays):
"""
Computes the dot product of two or more arrays in a single function call, while automatically
selecting the fastest evaluation order.
multi_dot chains numpy.dot and uses optimal parenthesization of the matrices. For more
information, refer to the `wiki page <https://en.wikipedia.org/wiki/Matrix_chain_multiplication>`_.
Depending on the shapes of the matrices, this can speed up the multiplication a lot.
If the first argument is 1-D, it is treated as a row vector. If the last argument is 1-D, it
is treated as a column vector. The other arguments must be 2-D.
Note:
Numpy argument `out` is not supported.
Args:
arrays (sequence of array_like): If the first argument is 1-D, it is treated as row
vector. If the last argument is 1-D, it is treated as column vector. The other
arguments must be 2-D.
Returns:
Tensor, the dot product of the supplied arrays.
Raises:
ValueError: arrays are not 2-D.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> A = np.ones((10000, 100))
>>> B = np.ones((100, 1000))
>>> C = np.ones((1000, 5))
>>> D = np.ones((5, 333))
>>> output = np.multi_dot([A, B, C, D])
>>> print(output)
[[500000. 500000. 500000. ... 500000. 500000. 500000.]
[500000. 500000. 500000. ... 500000. 500000. 500000.]
[500000. 500000. 500000. ... 500000. 500000. 500000.]
...
[500000. 500000. 500000. ... 500000. 500000. 500000.]
[500000. 500000. 500000. ... 500000. 500000. 500000.]
[500000. 500000. 500000. ... 500000. 500000. 500000.]]
"""
if len(arrays) < 2:
_raise_value_error('Expecting at least 2 arrays')
if isinstance(arrays, (tuple, list)):
arrays = _to_tensor(*arrays)
else:
arrays = _to_tensor(arrays)
num = len(arrays)
arrays = F.reshape(arrays, (-1,) + _tuple_slice(F.shape(arrays), 2, None))
arrays = split(arrays, num)
if len(arrays) == 2:
return dot(*arrays)
shape_out = ()
arrs = []
for arr in arrays:
arrs.append(arr)
if F.rank(arrs[0]) == 1:
arrs[0] = F.reshape(arrs[0], (1, arrs[0].size))
else:
shape_out += (F.shape(arrs[0])[0],)
if F.rank(arrs[-1]) == 1:
arrs[-1] = F.reshape(arrs[-1], (arrs[-1].size, 1))
else:
shape_out += (F.shape(arrs[-1])[1],)
shapes = []
for arr in arrs:
shapes.append(F.shape(arr))
dims = _get_dims(shapes)
order = _min_cost_chain_matmul(dims)
res = _multi_dot(arrs, 0, len(arrs) - 1, order)
return F.reshape(res, shape_out)
def argmax(a, axis=None):
"""
Returns the indices of the maximum values along an axis.
Note:
Numpy argument `out` is not supported.
On Ascend, in case of multiple occurrences of the maximum values, the return
indices may not necessarily correspond to the first occurrence.
Args:
a (Union[int, float, bool, list, tuple, Tensor]): Input array.
axis (int, optional): By default, the index is into
the flattened array, otherwise along the specified axis.
Default: ``None`` .
Returns:
Tensor, array of indices into the array. It has the same
shape as a.shape with the dimension along axis removed.
Raises:
ValueError: If axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmax(a))
5
>>> print(np.argmax(a, axis=0))
[1 1 1]
>>> print(np.argmax(a, axis=1))
[2 2]
"""
a = _to_tensor(a)
if a.dtype == mstype.bool_:
a = a.astype(mstype.int32)
return a.argmax(axis)
def argmin(a, axis=None):
"""
Returns the indices of the minimum values along an axis.
Note:
Numpy argument `out` is not supported.
Args:
a (Union[int, float, bool, list, tuple, Tensor]): Input array.
axis (int, optional): By default, the index is into
the flattened array, otherwise along the specified axis.
Default: ``None`` .
Returns:
Tensor, array of indices into the array. It has the same
shape as a.shape with the dimension along axis removed.
Raises:
ValueError: If axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.arange(10, 16).reshape(2, 3)
>>> print(np.argmin(a))
0
>>> print(np.argmin(a, axis=0))
[0 0 0]
>>> print(np.argmin(a, axis=1))
[0 0]
"""
a = _to_tensor(a)
return a.argmin(axis)
@constexpr
def _get_sort_range(size):
"""Returns the range for number of searches (log2(size)) on a sorted array with the given size."""
return tuple(range(ceil(log2(_to_tensor(size + 1).astype(mstype.float32))).astype(mstype.int32)))
def searchsorted(a, v, side='left', sorter=None):
"""
Finds indices where elements should be inserted to maintain order.
Finds the indices into a sorted array `a` such that, if the corresponding elements
in `v` were inserted before the indices, the order of `a` would be preserved.
Args:
a (Union[list, tuple, Tensor]): 1-D input array. If `sorter` is
None, then it must be sorted in ascending order, otherwise `sorter` must be
an array of indices that sort it.
v (Union[int, float, bool, list, tuple, Tensor]): Values to insert into `a`.
side ('left', 'right', optional): If ``'left'`` , the index of the first suitable
location found is given. If ``'right'`` , return the last such index. If there is
no suitable index, return either 0 or N (where N is the length of `a`).
sorter (Union[int, float, bool, list, tuple, Tensor]): 1-D optional array of
integer indices that sort array `a` into ascending order. They are typically
the result of argsort.
Returns:
Tensor, array of insertion points with the same shape as `v`.
Raises:
ValueError: If argument for `side` or `sorter` is invalid.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import numpy as np
>>> print(np.searchsorted([1,2,3,4,5], 3))
2
>>> print(np.searchsorted([1,2,3,4,5], 3, side='right'))
3
>>> print(np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3]))
[0 5 1 2]
"""
if side not in ('left', 'right'):
_raise_value_error('invalid value for keyword "side"')
a = _to_tensor(a).astype(mstype.float32)
if F.rank(a) != 1:
_raise_value_error('`a` should be 1-D array')
v = _to_tensor(v)
shape = F.shape(v)
if sorter is not None:
if F.rank(sorter) != 1 or sorter.size != a.size:
_raise_value_error('sorter must be 1-D array with the same size as `a`')
sorter = _to_tensor(sorter)
sorter = F.expand_dims(sorter, -1)
a = F.gather_nd(a, sorter)
less_op = F.tensor_le if side == 'left' else F.tensor_lt
i = F.fill(mstype.int32, shape, 0)
j = F.fill(mstype.int32, shape, a.size)
two = F.fill(mstype.int32, shape, 2)
for _ in _get_sort_range(a.size):
mid = floor_divide(add(i, j), two)
mask = less_op(v, F.gather_nd(a, F.expand_dims(mid, -1)))
i = F.select(mask, i, mid)
j = F.select(mask, mid, j)
return j
def interp(x, xp, fp, left=None, right=None):
"""
One-dimensional linear interpolation for monotonically increasing sample points.
Returns the one-dimensional piecewise linear interpolant to a function with given
discrete data points `(xp, fp)`, evaluated at `x`.
Note:
Numpy argument `period` is not supported.
Complex values are not supported.
Args:
x (Union[int, float, bool, list, tuple, Tensor]): The x-coordinates at which
to evaluate the interpolated values.
xp (Union[int, float, bool, list, tuple, Tensor]): 1-D sequence of floats, the
x-coordinates of the data points, must be increasing.
fp (Union[int, float, bool, list, tuple, Tensor]): 1-D sequence of floats, the
y-coordinates of the data points, same length as `xp`.
left (float, optional): Value to return for ``x < xp[0]``, default is ``fp[0]``
once obtained.
right (float, optional): Value to return for ``x > xp[-1]``, default is ``fp[-1]``
once obtained.
Returns:
Tensor, the interpolated values, same shape as `x`.
Raises:
ValueError: If `xp` or `fp` is not one-dimensional, or if `xp` and `fp` do not have
the same length.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> xp = [1, 2, 3]
>>> fp = [3, 2, 0]
>>> print(np.interp([0, 1, 1.5, 2.72, 3.14], xp, fp))
[3. 3. 2.5 0.55999994 0. ]
>>> UNDEF = -99.0
>>> print(np.interp(3.14, xp, fp, right=UNDEF))
-99.0
"""
# implement period once sort is supported
x, xp, fp = _to_tensor(x, xp, fp)
if F.rank(xp) != 1 or F.rank(fp) != 1:
_raise_value_error('xp and fp must be 1-d sequences')
size = xp.size
if fp.size != size:
_raise_value_error('the y-coordinates must have the same length as `xp`')
xp = xp.astype(mstype.float32)
fp = fp.astype(mstype.float32)
indices_1 = clip(searchsorted(xp, x), 0, size - 1)
indices_0 = clip(indices_1 - _to_tensor(1), 0, size - 1)
indices_0 = F.expand_dims(indices_0, -1)
indices_1 = F.expand_dims(indices_1, -1)
x_0 = F.gather_nd(xp, indices_0)
x_1 = F.gather_nd(xp, indices_1)
y_0 = F.gather_nd(fp, indices_0)
y_1 = F.gather_nd(fp, indices_1)
res = (y_0*(x_1 - x) + y_1*(x - x_0))/(x_1 - x_0)
res = F.select(F.equal(x_0, x_1), y_0, res)
idx_0 = _to_tensor([0])
idx_last = _to_tensor([size - 1])
if left is None:
left = F.gather_nd(fp, idx_0)
left = full(F.shape(x), left, mstype.float32)
if right is None:
right = F.gather_nd(fp, idx_last)
right = full(F.shape(x), right, mstype.float32)
res = F.select(F.tensor_lt(x, F.gather_nd(xp, idx_0)), left, res)
res = F.select(F.tensor_gt(x, F.gather_nd(xp, idx_last)), right, res)
return res
def _apply_tensor_op(fn, *args, dtype=None):
"""Applies tensor operations based on fn"""
args = _to_tensor(*args)
if isinstance(args, Tensor):
res = fn(args)
else:
res = fn(*args)
if dtype is not None and not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res
def sign(x, dtype=None):
"""
Returns an element-wise indication of the sign of a number.
The sign function returns `-1 if x < 0, 0 if x == 0, 1 if x > 0`. nan is returned for nan inputs.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Complex inputs are not supported now.
On Ascend, integer inputs are not supported.
Args:
x (Union[int, float, list, tuple, Tensor]): Input values.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
The sign of x. This is a tensor or a scalar when x is a scalar.
Raises:
TypeError: If dtype of the input is not in the given types or
the input can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.sign(np.array([-1., 0., 1., 1.2]))
>>> print(output)
[-1. 0. 1. 1.]
"""
if not isinstance(x, (int, float, list, tuple, Tensor)):
_raise_type_error('integer, float, list, tuple or Tensor are expected, but got', x)
x = _to_tensor(x)
if _check_same_type(F.dtype(x), mstype.bool_):
_raise_type_error("sign does not accept dtype bool.")
_non_zero_sign = x / absolute(x)
_zero = _broadcast_to_shape(_make_tensor(0, x.dtype), x.shape)
is_zero = F.equal(x, 0)
res = F.select(is_zero, _zero, _non_zero_sign)
if dtype is not None and not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res
def copysign(x1, x2, dtype=None):
"""
Changes the sign of `x1` to that of `x2`, element-wise.
If `x2` is a scalar, its sign will be copied to all elements of `x1`.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Complex inputs are not supported now.
Args:
x1 (Union[int, float, list, tuple, Tensor]): Values to change the sign of.
x2 (Union[int, float, list, tuple, Tensor]): The sign of x2 is copied to x1. If `x1.shape != x2.shape`,
they must be broadcastable to a common shape (which becomes the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar. The values of `x1` with the sign of `x2`. This is a scalar if both `x1` and `x2` are scalars.
Raises:
TypeError: If dtype of the input is not in the given types or
the input can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> output = np.copysign(np.array([1, -1, -1]), np.array([-1, 1, -1]))
>>> print(output)
[-1 1 -1]
"""
if not isinstance(x1, (int, float, list, tuple, Tensor)):
_raise_type_error('integer, float, list, tuple or Tensor are expected, but got', x1)
if not isinstance(x2, (int, float, list, tuple, Tensor)):
_raise_type_error('integer, float, list, tuple or Tensor are expected, but got', x2)
x1, x2 = _to_tensor(x1, x2)
shape_out = _infer_out_shape(F.shape(x1), F.shape(x2))
x1 = _broadcast_to_shape(x1, shape_out)
x2 = _broadcast_to_shape(x2, shape_out)
if _check_same_type(F.dtype(x1), mstype.bool_) or _check_same_type(F.dtype(x2), mstype.bool_):
_raise_type_error("sign does not accept dtype bool.")
original_dtype = x1.dtype
if not _check_is_float(original_dtype):
pos_tensor = F.absolute(x1.astype('float32')).astype(original_dtype)
else:
pos_tensor = F.absolute(x1)
neg_tensor = F.neg(pos_tensor)
less_zero = F.less(x2, 0)
res = F.select(less_zero, neg_tensor, pos_tensor)
if dtype is not None and not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res
def digitize(x, bins, right=False):
"""
Returns the indices of the bins to which each value in input array belongs.
If values in `x` are beyond the bounds of `bins`, 0 or ``len(bins)`` is returned
as appropriate.
Args:
x (Union[int, float, bool, list, tuple, Tensor]): Input array to be binned.
bins (Union[list, tuple, Tensor]): Array of bins. It has to
be 1-dimensional and monotonic.
right (boolean, optional): Indicating whether the intervals include the right
or the left bin edge. Default behavior is ``(right==False)`` indicating
that the interval does not include the right edge. The left bin end is
open in this case, i.e., ``bins[i-1] <= x < bins[i]`` is the default
behavior for monotonically increasing bins.
Returns:
Tensor of ints, output array of indices, of same shape as `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = np.array([0, 5, 10, 15, 20])
>>> inds = np.digitize(x, bins)
>>> print(inds)
[1 3 3 4 5]
"""
x, bins = _to_tensor(x, bins)
if F.rank(bins) != 1:
_raise_value_error('bins should be 1-dimensional')
if x.size == 0:
return x
if bins.size == 0:
return zeros(F.shape(x), mstype.int32)
side = 'left' if right else 'right'
first_bin = bins[0]
last_bin = bins[_type_convert(int, bins.size) - 1]
cond = first_bin <= last_bin
incr = searchsorted(bins, x, side)
decr = _to_tensor(bins.size) - searchsorted(flip(bins), x, side)
return where_(cond, incr, decr)
def bincount(x, weights=None, minlength=0, length=None):
"""
Count number of occurrences of each value in array of non-negative ints.
The number of bins (of size 1) is one larger than the largest value in `x`.
If `minlength` is specified, there will be at least this number of bins in the
output array (though it will be longer if necessary, depending on the contents
of `x`). Each bin gives the number of occurrences of its index value in `x`. If
`weights` is specified the input array is weighted by it, i.e. if a value `n`
is found at position `i`, ``out[n] += weight[i]`` instead of ``out[n] += 1``.
Note:
The additional argument `length` specifies the number of bins (overriding
``x.max() + 1``), which must be provided in graph mode.
If `x` contains negative values, no error will be raised, and negative values
are treated as zeros instead.
Args:
x (Union[list, tuple, Tensor]): 1-d input array.
weights (Union[int, float, bool, list, tuple, Tensor], optional): Weights,
array of the same shape as `x`. Default: ``None`` .
minlength (int, optional): A minimum number of bins for the output array.
Default: ``0`` .
length (int, optional): Number of bins. Default: ``None`` .
Returns:
Tensor, the result of binning the input array. The length of out is equal to
``np.amax(x)+1``.
Raises:
ValueError: If `x` is not one-dimensional, or if `x` and `weights` do not have
the same shape.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.bincount(np.arange(5)))
[1. 1. 1. 1. 1.]
>>> print(np.bincount(np.array([0, 1, 1, 3, 2, 1, 7])))
[1. 3. 1. 1. 0. 0. 0. 1.]
>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
>>> x = np.array([0, 1, 1, 2, 2, 2])
>>> print(np.bincount(x, weights=w))
[0.3 0.7 1.1]
"""
x = _to_tensor(x)
if F.rank(x) != 1:
_raise_value_error('`x` should be one-dimensional')
if not _check_is_int(F.dtype(x)):
_raise_type_error('`x` should be an array of ints')
x = clip(x, 0, None)
if length is None:
if F.isconstant(x):
length = int(maximum(F.reduce_max(x.astype(mstype.float32)), minlength - 1).asnumpy()) + 1
else:
_raise_value_error('argument `length` must be provided in graph mode')
idx = arange(length).reshape(length, 1)
idx_mapping = F.equal(x, idx)
if weights is not None:
weights = _to_tensor(weights)
if F.shape(x) != F.shape(weights):
_raise_value_error('`x` and `weights` must have the same length')
idx_mapping *= weights
return F.reduce_sum(idx_mapping.astype(mstype.float32), 1).ravel()
def histogram(a, bins=10, range=None, weights=None, density=False): # pylint: disable=redefined-builtin
"""
Computes the histogram of a dataset.
Note:
String values for `bins` is not supported.
Deprecated numpy argument `normed` is not supported.
Args:
a (Union[int, float, bool, list, tuple, Tensor]): Input data. The histogram
is computed over the flattened array.
bins (Union[int, tuple, list, Tensor], optional): If `bins` is an int, it
defines the number of equal-width bins in the given range (10, by
default). If `bins` is a sequence, it defines the bin edges, including
the rightmost edge, allowing for non-uniform bin widths.
range((float, float), optional): The lower and upper range of the bins. If
not provided, `range` is simply ``(a.min(), a.max())``. Values outside
the range are ignored. The first element of the range must be less than
or equal to the second.
weights (Union[int, float, bool, list, tuple, Tensor], optional): An array
of weights, of the same shape as `a`. If density is True, the weights
are normalized, so that the integral of the density over the range
remains 1.
density (boolean, optional): If False, the result will contain the number of
samples in each bin. If True, the result is the value of the probability
density function at the bin, normalized such that the integral over the
range is 1. Note that the sum of the histogram values will not be equal
to 1 unless bins of unity width are chosen; it is not a probability mass
function.
Returns:
(Tensor, Tensor), the values of the histogram and the bin edges.
Raises:
ValueError: If `x` and `weights` do not have the same size.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import numpy as np
>>> print(np.histogram([1, 2, 1], bins=[0, 1, 2, 3]))
(Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 2.00000000e+00, 1.00000000e+00]),
Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))
>>> print(np.histogram(np.arange(4), bins=np.arange(5), density=True))
(Tensor(shape=[4], dtype=Float32, value=
[ 2.50000000e-01, 2.50000000e-01, 2.50000000e-01, 2.50000000e-01]),
Tensor(shape=[5], dtype=Int32, value= [0, 1, 2, 3, 4]))
>>> print(np.histogram([[1, 2, 1], [1, 0, 1]], bins=[0,1,2,3]))
(Tensor(shape=[3], dtype=Float32, value= [ 1.00000000e+00, 4.00000000e+00, 1.00000000e+00]),
Tensor(shape=[4], dtype=Int32, value= [0, 1, 2, 3]))
"""
a = _to_tensor(a)
if weights is not None:
weights = _to_tensor(weights)
if F.shape(a) != F.shape(weights):
_raise_value_error('weights should have the same shape as a')
weights = weights.ravel()
a = a.ravel()
bin_edges = histogram_bin_edges(a, bins, range, weights)
data_to_bins = searchsorted(bin_edges, a, 'right')
bin_size = _type_convert(int, bin_edges.size)
data_to_bins = where_(a == bin_edges[-1], _to_tensor(bin_size - 1), data_to_bins)
count = bincount(data_to_bins, weights, length=bin_size)[1:]
if count.size == 0:
return count, bin_edges
if density:
count = F.cast(count, mstype.float32)
count = count/diff(bin_edges)/F.reduce_sum(count)
return count, bin_edges
@constexpr
def _factor_flattened_hist(nbin):
"""Returns the factor that will be applied to the histogram to be flattened."""
factor = list((itertools.accumulate(nbin[1:][::-1], operator.mul)))[::-1]
factor.append(1)
return factor
def _get_histogramdd_count(ndim, bin_edges, sample, weights):
"""Returns count for histogramdd."""
data_indices = []
nbin = ()
flattened_bin_size = 1
for i in F.make_range(ndim):
data_to_bins = searchsorted(bin_edges[i], sample[:, i], 'right')
bin_size = _type_convert(int, bin_edges[i].size)
data_to_bins = where_(sample[:, i] == bin_edges[i][-1], _to_tensor(bin_size - 1), data_to_bins)
data_indices.append(data_to_bins)
nbin += (bin_size + 1,)
flattened_bin_size *= (bin_size + 1)
factor = F.reshape(_to_tensor(_factor_flattened_hist(nbin)), (ndim, 1))
stacked_indices = stack(data_indices) * factor
if _get_device() == 'Ascend':
stacked_indices = F.cast(stacked_indices, mstype.float32)
flattened_hist = F.reduce_sum(stacked_indices.astype(mstype.float32), 0)
count = bincount(flattened_hist.astype(mstype.int32), weights, length=flattened_bin_size)
count = F.reshape(count, nbin)
slices = _list_comprehensions(ndim, F.make_slice(1, -1, 1), True)
count = count[slices]
return count
def histogramdd(sample, bins=10, range=None, weights=None, density=False): # pylint: disable=redefined-builtin
"""
Computes the multidimensional histogram of some data.
Note:
Deprecated numpy argument `normed` is not supported.
Args:
sample (Union[list, tuple, Tensor]): The data to be histogrammed, either `(N, D)`
array, or `(D, N)` array_like. Note the unusual interpretation of sample
when an array_like:
When an array, each row is a coordinate in a `D-dimensional` space, such as
``histogramdd(np.array([p1, p2, p3]))``.
When an array_like, each element is the list of values for single coordinate,
such as ``histogramdd((X, Y, Z))``.
The first form should be preferred.
bins (Union[int, tuple, list], optional): The bin specification:
A sequence of arrays describing the monotonically increasing bin edges along
each dimension.
The number of bins for each dimension ``(nx, ny, … =bins)``
The number of bins for all dimensions ``(nx=ny=…=bins)``.
range(Union[list, tuple], optional): A sequence of length `D`, each an optional
``(lower, upper)`` tuple giving the outer bin edges to be used if the edges
are not given explicitly in bins. An entry of None in the sequence results in
the minimum and maximum values being used for the corresponding dimension.
The default, None, is equivalent to passing a tuple of `D` None values.
weights (Union[list, tuple, Tensor], optional): An array with shape `(N,)` of values
`w_i` weighing each sample ``(x_i, y_i, z_i, …)``.
density (boolean, optional): If False, the default, returns the number of samples
in each bin. If True, returns the probability density function at the bin,
``bin_count / sample_count / bin_volume``.
Returns:
(Tensor, list of Tensor), the values of the histogram and the bin edges.
Raises:
ValueError: If `range` does not have the same size as the number of samples.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import numpy as np
>>> sample = np.arange(15).reshape(5, 3)
>>> print(sample)
[[ 0 1 2]
[ 3 4 5]
[ 6 7 8]
[ 9 10 11]
[12 13 14]]
>>> print(np.histogramdd(sample, bins=(2, 3, 4)))
(Tensor(shape=[2, 3, 4], dtype=Float32, value=
[[[ 1.00000000e+00, 1.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]],
[[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 1.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.00000000e+00]]]),
[Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 6.00000000e+00, 1.20000000e+01]),
Tensor(shape=[4], dtype=Float32, value=
[ 1.00000000e+00, 5.00000000e+00, 9.00000000e+00, 1.30000000e+01]),
Tensor(shape=[5], dtype=Float32, value=
[ 2.00000000e+00, 5.00000000e+00, 8.00000000e+00, 1.10000000e+01, 1.40000000e+01])])
"""
if isinstance(sample, (tuple, list)):
sample = _to_tensor(*sample)
sample = stack(sample, -1)
elif not isinstance(sample, Tensor):
_raise_type_error('sample should be (N, D) array, or (D, N) array_like')
if F.rank(sample) != 2:
_raise_value_error('when an array, sample should be 2-dimensional')
ndim = F.shape(sample)[1]
if isinstance(bins, int):
bins = _list_comprehensions(ndim, bins)
if isinstance(bins, (tuple, list, Tensor)):
if len(bins) != ndim:
_raise_value_error('The dimension of bins must be equal to the dimension of the sample')
else:
_raise_type_error('bins should be int or sequence')
if range is None:
range = _list_comprehensions(ndim, None, False, True)
else:
if len(range) != ndim:
_raise_value_error('range argument must have one entry per dimension')
bin_edges = []
dedges = []
for i in F.make_range(ndim):
edges = histogram_bin_edges(sample[:, i], bins[i], range[i], weights)
bin_edges.append(edges)
dedges.append(diff(edges))
count = _get_histogramdd_count(ndim, bin_edges, sample, weights)
if density:
s = F.reduce_sum(count.astype(mstype.float32))
for i in F.make_range(ndim):
shape = _expanded_shape(ndim, dedges[i].size, i)
count /= _to_tensor(dedges[i]).reshape(shape)
count /= s
return count, bin_edges
def histogram2d(x, y, bins=10, range=None, weights=None, density=False): # pylint: disable=redefined-builtin
"""
Computes the multidimensional histogram of some data.
Note:
Deprecated numpy argument `normed` is not supported.
Args:
x (Union[list, tuple, Tensor]): An array with shape `(N,)` containing the x
coordinates of the points to be histogrammed.
y (Union[list, tuple, Tensor]): An array with shape `(N,)` containing the y
coordinates of the points to be histogrammed.
bins (Union[int, tuple, list], optional): The bin specification:
If int, the number of bins for the two dimensions ``(nx=ny=bins)``.
If array_like, the bin edges for the two dimensions ``(x_edges=y_edges=bins)``.
If [int, int], the number of bins in each dimension ``(nx, ny = bins)``.
If [array, array], the bin edges in each dimension ``(x_edges, y_edges = bins)``.
A combination [int, array] or [array, int], where int is the number of bins and
array is the bin edges.
range(Union[list, tuple], optional): has shape (2, 2), the leftmost and rightmost
edges of the bins along each dimension (if not specified explicitly in the bins
parameters): ``[[xmin, xmax], [ymin, ymax]]``. All values outside of this range
will be considered outliers and not tallied in the histogram.
weights (Union[list, tuple, Tensor], optional): An array with shape `(N,)` of values
`w_i` weighing each sample `(x_i, y_i)`.
density (boolean, optional): If False, the default, returns the number of samples
in each bin. If True, returns the probability density function at the bin,
``bin_count / sample_count / bin_volume``.
Returns:
(Tensor, Tensor, Tensor), the values of the bi-directional histogram and the bin edges
along the first and second dimensions.
Raises:
ValueError: If `range` does not have the same size as the number of samples.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import numpy as np
>>> x = np.arange(5)
>>> y = np.arange(2, 7)
>>> print(np.histogram2d(x, y, bins=(2, 3)))
(Tensor(shape=[2, 3], dtype=Float32, value=
[[ 2.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 0.00000000e+00, 1.00000000e+00, 2.00000000e+00]]),
Tensor(shape=[3], dtype=Float32, value= [ 0.00000000e+00, 2.00000000e+00, 4.00000000e+00]),
Tensor(shape=[4], dtype=Float32, value=
[ 2.00000000e+00, 3.33333349e+00, 4.66666698e+00, 6.00000000e+00]))
"""
count, bin_edges = histogramdd((x, y), bins=bins, range=range, weights=weights, density=density)
return count, bin_edges[0], bin_edges[1]
def matrix_power(a, n):
"""
Raises a square matrix to the (integer) power `n`.
For positive integers `n`, the power is computed by repeated matrix squarings and
matrix multiplications.
If :math:`n == 0`, the identity matrix of the same shape as `M` is returned.
Note:
Stacks of object matrices are not currently supported and
:math:`n < 0` is not supported.
Args:
a (Union[int, float, bool, list, tuple, Tensor]): Input matrix.
n (int): The exponent can be any integer or long integer, positive or zero.
Returns:
Tensor.
Raises:
TypeError: If the input can not be converted to a tensor or
the exponent is not integer.
ValueError: If the input includes less than 2 dimensions or
the last 2 dimensions are not square.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import numpy as np
>>> a = np.arange(16).reshape(4, 4).astype('float32')
>>> print(np.matrix_power(a, 2))
[[ 56. 62. 68. 74.]
[152. 174. 196. 218.]
[248. 286. 324. 362.]
[344. 398. 452. 506.]]
"""
a = _to_tensor(a)
if not isinstance(n, int):
_raise_type_error("exponent must be an integer")
if a.ndim < 2:
_raise_value_error("Array must be at least two-dimensional")
if a.shape[-2] != a.shape[-1]:
_raise_value_error("Last 2 dimensions of the array must be square")
if n < 0:
_raise_value_error("n < 0 is not supported now.")
if n == 0:
return _broadcast_to_shape(eye(a.shape[-1], a.shape[-1], dtype=a.dtype), a.shape)
if n == 1:
return a
res = a
while n > 1:
res = C.matmul(res, a)
n = n - 1
return res
def around(a, decimals=0):
"""
Evenly round to the given number of decimals.
Note:
Numpy argument `out` is not supported.
Complex numbers are not supported.
Args:
a (Union[int, float, list, tuple, Tensor]): Input data.
decimals (int): Number of decimal places to round to. Default: ``0`` .
Returns:
Tensor. A tensor of the same type as a, containing the rounded values.
The result of rounding a float is a float.
Raises:
TypeError: If the input can not be converted to a tensor or
the `decimals` argument is not integer.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> a = np.array([-1.3, 0.0, 0.5, 1.5, 2.5])
>>> print(np.around(a))
[-1. 0. 0. 2. 2.]
"""
a = _to_tensor_origin_dtype(a)
if not isinstance(decimals, int):
_raise_type_error("decimals must be an integer")
if decimals < 0:
_raise_value_error("decimals < 0 is not supported now.")
if decimals == 0:
return _round(a)
return F.tensor_div(_round(a * 10**decimals), 10**decimals)
def _to_poly1d(x):
x = atleast_1d(_to_tensor(x))
if F.rank(x) > 1:
_raise_value_error('input array must be scalar or 1-d sequence')
return x
def polyadd(a1, a2):
"""
Finds the sum of two polynomials.
Returns the polynomial resulting from the sum of two input polynomials.
Note:
Numpy object poly1d is currently not supported.
Args:
a1 (Union[int, float, list, tuple, Tensor): Input polynomial.
a2 (Union[int, float, list, tuple, Tensor): Input polynomial.
Returns:
Tensor, the sum of the inputs.
Raises:
ValueError: If the input array has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polyadd([1, 2], [9, 5, 4]))
[9 6 6]
"""
a1 = _to_poly1d(a1)
a2 = _to_poly1d(a2)
diff_size = a1.size - a2.size
if diff_size == 0:
return add(a1, a2)
if diff_size > 0:
return concatenate((a1[:diff_size], add(a1[diff_size:], a2)))
return concatenate((a2[:-diff_size], add(a1, a2[-diff_size:])))
def polysub(a1, a2):
"""
Difference (subtraction) of two polynomials.
Given two polynomials `a1` and `a2`, returns ``a1 - a2``.
Note:
Numpy object poly1d is currently not supported.
Args:
a1 (Union[int, float, list, tuple, Tensor): Minuend polynomial.
a2 (Union[int, float, list, tuple, Tensor): Subtrahend polynomial.
Returns:
Tensor, the difference of the inputs.
Raises:
ValueError: If the input array has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polysub([2, 10, -2], [3, 10, -4]))
[-1 0 2]
"""
return polyadd(a1, F.neg(_to_tensor(a2)))
def polyval(p, x):
"""
Evaluates a polynomial at specific values.
If `p` is of length `N`, this function returns the value:
``p[0]*x**(N-1) + p[1]*x**(N-2) + ... + p[N-2]*x + p[N-1]``
If `x` is a sequence, then ``p(x)`` is returned for each element of `x`. If `x`
is another polynomial then the composite polynomial ``p(x(t))`` is returned.
Note:
Numpy object poly1d is currently not supported.
Args:
p (Union[int, float, bool, list, tuple, Tensor): 1D array of polynomial
coefficients (including coefficients equal to zero) from highest
degree to the constant term.
x (Union[int, float, bool, list, tuple, Tensor): A number, an array of
numbers, at which to evaluate `p`.
Returns:
Tensor.
Raises:
ValueError: If `p` has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polyval([3.,0.,1.], 5.))
76.0
"""
p = _to_poly1d(p)
x = _to_tensor(x)
shape = F.shape(x)
exp_p = arange(_type_convert(int, p.size) - 1, -1, -1).astype(mstype.float32)
var_p = (x.reshape(shape + (1,)))**exp_p
return F.reduce_sum(p*var_p, -1)
def polyder(p, m=1):
"""
Returns the derivative of the specified order of a polynomial.
Note:
Numpy object poly1d is currently not supported.
Args:
p (Union[int, float, bool, list, tuple, Tensor): Polynomial to differentiate.
A sequence is interpreted as polynomial coefficients.
m (int, optional): Default: ``1`` , order of differentiation.
Returns:
Tensor, a new polynomial representing the derivative.
Raises:
ValueError: If `p` has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polyder([1, 1, 1, 1]))
[3 2 1]
"""
p = _to_poly1d(p)
if m < 0:
_raise_value_error('Order of derivative must be positive')
if m >= p.size:
return _to_tensor([])
for _ in range(m):
coeff = _to_tensor(F.make_range(_type_convert(int, p.size) - 1, 0, -1))
p = p[:-1] * coeff
return p
def polymul(a1, a2):
"""
Finds the product of two polynomials.
Note:
Numpy object poly1d is currently not supported.
Args:
a1 (Union[int, float, bool, list, tuple, Tensor): Input polynomial.
a2 (Union[int, float, bool, list, tuple, Tensor): Input polynomial.
Returns:
Tensor, a new polynomial representing the derivative.
Raises:
ValueError: If the input array has more than 1 dimensions.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polymul([3, 1, 2], [2, 5]))
[ 6 17 9 10]
"""
a1 = _to_poly1d(a1)
a2 = _to_poly1d(a2)
return convolve(a1, a2)
def polyint(p, m=1, k=None):
"""
Returns an antiderivative (indefinite integral) of a polynomial.
Note:
Numpy object poly1d is currently not supported.
Args:
p (Union[int, float, bool, list, tuple, Tensor): Polynomial to integrate. A
sequence is interpreted as polynomial coefficients.
m (int, optional): Defaults to 1, Order of the antiderivative.
k (Union[int, list of int]y, optinoal): Integration constants. They are given
in the order of integration: those corresponding to highest-order terms
come first. If None (default), all constants are assumed to be zero. If
``m = 1``, a single scalar can be given instead of a list.
Returns:
Tensor, a new polynomial representing the antiderivative.
Raises:
ValueError: If `p` has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.polyint([1, 1, 1]))
[0.33333334 0.5 1. 0. ]
"""
p = _to_poly1d(p)
if m < 0:
_raise_value_error('Order of derivative must be positive')
if m == 0:
return p
if k is None:
k = zeros(m, F.dtype(p))
k = atleast_1d(_to_tensor(k))
if k.size == 1:
k = F.tile(k, (m,))
k = F.expand_dims(k, -1)
for i in range(m):
coeff = _to_tensor(F.make_range(_type_convert(int, p.size), 0, -1))
p = concatenate((true_divide(p, coeff), k[i]))
return p
@constexpr
def _get_dtype(x):
"""Returns the dtype of x."""
if isinstance(x, bool):
return mstype.bool_
if isinstance(x, int):
return mstype.int32
if isinstance(x, float):
return mstype.float32
if isinstance(x, typing.Number):
return x
if isinstance(x, str):
t = dtype_map.get(x, None)
if t is None:
t = dtype_map.get(str(nptype(x)))
return t
raise TypeError('data type not understood')
def result_type(*arrays_and_dtypes):
"""
Returns the type that results from applying the type promotion rules to the arguments.
Note:
The promotion rule is slightly different from original Numpy, but more like
jax, due to the preference on ``32-bit`` over ``64-bit`` data types.
Complex dtypes are not supported.
Args:
*arrays_and_dtypes (Union[int, float, bool, list, tuple, Tensor, :class:`mindspore.dtype`, str]):
The operands of some operation whose result type is needed.
Returns:
:class:`mindspore.dtype`, the result type.
Raises:
TypeError: If the input is not a valid data type.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.result_type('i2', np.float32, True))
Float32
"""
def get_dtype(x):
if isinstance(x, Tensor):
return F.dtype(_to_tensor(x))
return _get_dtype(x)
dtype_out = get_dtype(arrays_and_dtypes[0])
for i in arrays_and_dtypes[1:]:
dtype_out = _promote(dtype_out, get_dtype(i))
return dtype_out
def unwrap(p, discont=3.141592653589793, axis=-1):
"""
Unwraps by changing deltas between values to ``2*pi`` complement.
Unwraps radian phase `p` by changing absolute jumps greater than `discont` to their
``2*pi`` complement along the given axis.
Note:
For absolute jumps that are within a very close range to pi, unwrapping may be done
differently than numpy due to differences in round-off.
Args:
p (Union[int, float, bool, list, tuple, Tensor): Input array.
discont (float, optional): Maximum discontinuity between values, default: ``pi`` .
axis (int, optional): Axis along which unwrap will operate, default: ``-1`` .
Returns:
Tensor.
Raises:
ValueError: If the axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> phase = np.add(np.linspace(0, np.pi, num=5), [0, 0, 0, np.pi, np.pi])
>>> print(phase)
[0. 0.7853982 1.5707964 5.4977875 6.2831855]
>>> print(np.unwrap(phase))
[ 0.0000000e+00 7.8539819e-01 1.5707964e+00 -7.8539848e-01 -4.7683716e-07]
"""
if not isinstance(discont, (int, float)):
_raise_type_error('discont should be a float')
p = _to_tensor(p)
ndim = F.rank(p)
axis = _check_axis_in_range(axis, ndim)
dd = diff(p, axis=axis)
ddmod = remainder(add(dd, pi), 2*pi) - pi
ddmod = F.masked_fill(ddmod, F.logical_and(ddmod == -pi, dd > 0), F.cast(pi, ddmod.dtype))
ph_correct = ddmod - dd
ph_correct = F.masked_fill(ph_correct, absolute(dd) < discont, F.cast(0, ph_correct.dtype))
slice_all = _list_comprehensions(F.rank(p), F.make_slice(None, None, None), True)
slice0 = _tuple_setitem(slice_all, axis, F.make_slice(0, 1, None))
slice1 = _tuple_setitem(slice_all, axis, F.make_slice(1, None, None))
head = p[slice0]
tail = add(p[slice1], cumsum(ph_correct, axis))
return concatenate((head, tail), axis=axis)
def cumprod(a, axis=None, dtype=None):
"""
Returns the cumulative product of elements along a given axis.
Note:
Numpy argument `out` is not supported.
Args:
a (Union[int, float, bool, list, tuple, Tensor]): Input tensor.
axis (int, optional): Axis along which the cumulative product is computed.
By default the input is flattened. Default: ``None`` .
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor.
Raises:
TypeError: If the input can not be converted to tensor or `axis` is not integer.
ValueError: If axis is out of range.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([1, 2, 3])
>>> print(np.cumprod(x))
[1 2 6]
"""
a = _to_tensor_origin_dtype(a)
original_dtype = F.dtype(a)
if axis is not None and not isinstance(axis, int):
_raise_type_error("integer axis is expected, but got", axis)
if axis is None:
a = a.ravel()
axis = 0
_check_axis_in_range(axis, a.ndim)
a = a.astype('float32') if original_dtype != mstype.float64 else a
if dtype is None:
if original_dtype in [mstype.int8, mstype.int16, mstype.bool_]:
dtype = mstype.int32
elif original_dtype in [mstype.uint8, mstype.uint16]:
dtype = mstype.uint32
else:
dtype = original_dtype
return _cumprod_default(a, axis).astype(dtype, copy=False)
def _process_index(index, dims, mode='raise'):
"""Generates index (Tensor) according to different modes."""
if mode == "raise":
_raise_unimplemented_error("'raise' mode is not implemented")
if mode not in ['clip', 'wrap']:
_raise_value_error("invalid mode. Expected 'wrap' or 'clip'")
ori_shape = index.shape
tup = ()
for i, idx in enumerate(index):
d = dims[i]
if mode == "clip":
idx = clip(idx, 0, d - 1)
elif mode == "wrap":
idx = remainder(idx, d)
idx = F.expand_dims(idx, 0) if idx.ndim < 1 else idx
tup += (idx,)
return P.Concat(0)(tup).reshape(ori_shape)
def _get_strides(dims, order='C'):
"""Generates strides (1-D tensor) according to `dims` (1-D tensor)."""
if order not in ['C', 'F']:
_raise_value_error("invalid order. Expected 'C' or 'F'")
tup = (_to_tensor([1]),)
dims = dims[1:][::-1] if order == 'C' else dims[:-1]
for d in dims:
tensor = tup[-1] * d
if tensor.ndim < 1:
tensor = F.expand_dims(tensor, 0)
tup += (tensor,)
tup = tup[::-1] if order == 'C' else tup
return P.Concat(0)(tup)
def ravel_multi_index(multi_index, dims, mode='clip', order='C'):
"""
Converts a tuple of index arrays into an array of flat indices,
applying boundary modes to the multi-index.
Note:
`raise` mode is not supported. Default mode is `clip`.
Args:
multi_index (tuple of array_like):
A tuple of integer arrays, one array for each dimension.
dims (Union[int, tuple of integers]): The shape of array into which the indices from multi_index apply.
mode ({`wrap`, `clip`}): Specifies how out-of-bounds indices are handled. Default: ``clip''``.
- `wrap`: wrap around
- `clip`: clip to the range
In `clip` mode, a negative index which would normally wrap will clip to 0 instead.
order ({`C`, `F`}): Determines whether the multi-index should be viewed as indexing in
row-major (C-style) or column-major (Fortran-style) order.
Returns:
Raveled_indices array. An array of indices into the flattened version of an array of dimensions dims.
Raises:
TypeError: If `multi_index` or `dims` can not be converted to tensor or
`dims` is not a sequence of integer values.
ValueError: If the length of `multi_index` and that of `dims` are not equal.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore.numpy as np
>>> arr = np.array([[3, 6, 6], [4, 5, 1]])
>>> output = np.ravel_multi_index(arr, (7, 6))
>>> print(output)
[22. 41. 37.]
>>> output = np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))
>>> print(output)
1621.0
"""
if isinstance(dims, int):
dims = (dims,)
dims = _to_tensor(dims)
if dims.ndim > 1 or dims.dtype in (mstype.float16, mstype.float32, mstype.float64, mstype.bool_):
_raise_type_error("only 1-D integer arrays are accepted.")
multi_index = _to_tensor(multi_index)
if len(multi_index) != len(dims):
_raise_value_error("parameter multi_index must be a sequence of length ", len(dims))
if multi_index.dtype in (mstype.float16, mstype.float32, mstype.float64):
_raise_type_error("only int indices permitted")
multi_index = _process_index(multi_index, dims, mode)
strides = _get_strides(dims, order)
s_shape = strides.shape + _list_comprehensions(multi_index.ndim - 1, 1, True)
strides = _broadcast_to_shape(strides.reshape(s_shape), multi_index.shape)
return sum_((multi_index * strides).astype('float32'), axis=0)
def _vector_norm(x, _ord, axis, keepdims):
"""Returns norm of a vector."""
if _in(_ord, ('fro', 'nuc')):
_raise_value_error('Frobenius norm and nuclear norm are only defined for vectors')
if _ord is None:
_ord = 2
if _ord == inf:
res = P.ReduceMax(keepdims)(absolute(x), axis)
elif _ord == -inf:
res = P.ReduceMin(keepdims)(absolute(x), axis)
elif _ord == 0:
res = P.ReduceSum(keepdims)(F.not_equal(x, 0).astype(mstype.float32), axis)
else:
res = power(P.ReduceSum(keepdims)(power(absolute(x), _ord), axis), 1./_ord)
return res
def _matrix_norm(x, _ord, axis, keepdims):
"""Returns norm of a matrix."""
if _ord == 0:
_raise_value_error('for 0 axis, norm is defined only for 2-D matrices')
if _ord == 'nuc':
_raise_unimplemented_error('nuclear norm is not implemented')
if _in(_ord, (2, -2)):
_raise_unimplemented_error('2-norm is not implemented for matrices')
if _in(_ord, (None, 'fro')):
return F.sqrt(P.ReduceSum(keepdims)(F.square(x), axis))
axis0, axis1 = axis
if not keepdims:
if _check_is_inf(_abs(_ord)) and axis0 > axis1:
axis0 -= 1
elif _abs(_ord) == 1 and axis1 > axis0:
axis1 -= 1
if _check_is_inf(_ord):
return P.ReduceMax(keepdims)(P.ReduceSum(keepdims)(absolute(x), axis1), axis0)
if _check_is_inf(_ord, True):
return P.ReduceMin(keepdims)(P.ReduceSum(keepdims)(absolute(x), axis1), axis0)
if _ord == 1:
return P.ReduceMax(keepdims)(P.ReduceSum(keepdims)(absolute(x), axis0), axis1)
if _ord == -1:
return P.ReduceMin(keepdims)(P.ReduceSum(keepdims)(absolute(x), axis0), axis1)
return _raise_value_error('invalid norm order for matrices')
def norm(x, ord=None, axis=None, keepdims=False): # pylint: disable=redefined-builtin
"""
Matrix or vector norm.
This function is able to return one of eight different matrix norms, or one of an
infinite number of vector norms (described below), depending on the value of the
ord parameter.
Note:
Nuclear norm and 2-norm are not supported for matrices.
Args:
x (Union[int, float, bool, list, tuple, Tensor]): Input array. If `axis` is None,
`x` must be 1-D or 2-D, unless `ord` is None. If both `axis` and `ord` are None,
the 2-norm of ``x.ravel`` will be returned.
ord (Union[None, 'fro', 'nuc', inf, -inf, int, float], optional): Order of the norm.
inf means numpy’s inf object. Default: ``None`` .
axis (Union[None, int, 2-tuple of integers], optional): If `axis` is an integer, it
specifies the axis of `x` along which to compute the vector norms. If `axis` is
a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of
these matrices are computed. If `axis` is None then either a vector norm (when x
is 1-D) or a matrix norm (when `x` is 2-D) is returned. The default is None.
keepdims (boolean, optional): If this is set to True, the axes which are normed over
are left in the result as dimensions with size one. With this option the result
will broadcast correctly against the original `x`.
Returns:
Tensor, norm of the matrix or vector(s).
Raises:
ValueError: If the norm order is not defined.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.norm(np.arange(9).astype(np.float32)))
14.282857
"""
if not isinstance(ord, (int, float)) and not _in(ord, (None, 'fro', 'nuc', inf, -inf)):
_raise_value_error('invalid value for `ord`')
x = _to_tensor(x)
ndim = F.rank(x)
if axis is None:
if ord is None:
x = x.ravel()
if F.rank(x) not in (1, 2):
_raise_value_error('for None axis, array must a vector or a 2-D matrix')
axis = F.make_range(F.rank(x))
axis = _check_axis_valid(axis, F.rank(x))
if len(axis) == 1:
res = _vector_norm(x, ord, axis, keepdims)
elif len(axis) == 2:
res = _matrix_norm(x, ord, axis, keepdims)
else:
return _raise_value_error('invalid number of dimensions to norm')
if keepdims and ndim > F.rank(res):
res = _expand(res, ndim)
return res
def bitwise_and(x1, x2, dtype=None):
"""
Computes the bit-wise AND of two arrays element-wise.
Computes the bit-wise AND of the underlying binary representation of the integers in
the input arrays. This ufunc implements the C/Python operator &.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input array.
x2 (Tensor): Input array. Only integer and boolean types are handled. If
``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes
the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if both x1 and x2 are scalars.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.bitwise_and(13, 17))
1
"""
return _apply_tensor_op(F.bitwise_and, x1, x2, dtype=dtype)
def bitwise_or(x1, x2, dtype=None):
r"""
Computes the bit-wise OR of two arrays element-wise.
Computes the bit-wise OR of the underlying binary representation of the integers in
the input arrays. This ufunc implements the C/Python operator \|.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input array.
x2 (Tensor): Input array. Only integer and boolean types are handled. If
``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes
the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if both x1 and x2 are scalars.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.bitwise_or(13, 16))
29
"""
return _apply_tensor_op(F.bitwise_or, x1, x2, dtype=dtype)
def bitwise_xor(x1, x2, dtype=None):
"""
Computes the bit-wise XOR of two arrays element-wise.
Computes the bit-wise XOR of the underlying binary representation of the integers in
the input arrays. This ufunc implements the C/Python operator ^.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x1 (Tensor): Input array.
x2 (Tensor): Input array. Only integer and boolean types are handled. If
``x1.shape != x2.shape``, they must be broadcastable to a common shape (which becomes
the shape of the output).
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar, this is a scalar if both x1 and x2 are scalars.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> print(np.bitwise_xor(13, 17))
28
"""
return _apply_tensor_op(F.bitwise_xor, x1, x2, dtype=dtype)
def invert(x, dtype=None):
"""
Computes bit-wise inversion, or bit-wise NOT, element-wise.
Computes the bit-wise NOT of the underlying binary representation of the integers in
the input arrays. This ufunc implements the C/Python operator ~.
For signed integer inputs, the two's complement is returned. In a two's-complement system
negative numbers are represented by the two's complement of the absolute value. This is
the most common method of representing signed integers on computers
`[1] <https://en.wikipedia.org/wiki/Two's_complement>`_. A N-bit two's-complement system
can represent every integer in the range ``-2^{N-1}`` to ``+2^{N-1}-1``.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Supported dtypes on Ascend: np.int16, np.uint16.
Args:
x (Tensor): Only integer and boolean types are handled.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor or scalar.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore.numpy as np
>>> print(np.invert(np.array(13, dtype=np.uint16)))
65522
"""
return _apply_tensor_op(F.invert, x, dtype=dtype)
def rint(x, dtype=None):
"""
Rounds elements of the array to the nearest integer.
Note:
Numpy arguments `out`, `where`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
Args:
x (Union[float, list, tuple, Tensor]): Input tensor of any dimension.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Output tensor is same shape and type as x. This is a scalar if x is a scalar.
Raises:
TypeError: If `x` can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.array([-1.7, -1.5, 0.2, 1.5, 1.7, 2.0])
>>> print(np.rint(x))
[-2. -2. 0. 2. 2. 2.]
"""
x = _to_tensor_origin_dtype(x)
res = _rint(x)
if dtype is not None and not _check_same_type(F.dtype(res), dtype):
res = F.cast(res, dtype)
return res
def correlate(a, v, mode='valid'):
r"""
Cross-correlation of two 1-dimensional sequences.
This function computes the correlation as generally defined in signal processing texts:
:math:`c_{av}[k] = \sum_{n}{a[n+k] * conj(v[n])}`
with `a` and `v` sequences being zero-padded where necessary and conj being the conjugate.
Note:
- `correlate` is currently only used in `mindscience` scientific computing scenarios and
dose not support other usage scenarios.
- `correlate` is not supported on Windows platform yet.
Args:
a (Union[list, tuple, Tensor]): First input sequence.
v (Union[list, tuple, Tensor]): Second input sequence.
mode (str, optional): Specifies padding mode. The optional values are
``"same"`` , ``"valid"`` and ``"full"`` . Default: ``"valid"`` .
- ``"same"``: it returns output of length :math:`max(M, N)`. Boundary
effects are still visible.
- ``"valid"``: it returns output of length :math:`max(M, N) - min(M, N) + 1`.
The convolution product is only given for points where the signals overlap
completely. Values outside the signal boundary have no effect.
- ``"full"``: it returns the convolution at each point of overlap, with
an output shape of :math:`(N + M - 1,)`.At the end-points of the convolution,
the signals do not overlap completely, and boundary effects may be seen.
Returns:
Tensor, Discrete cross-correlation of `a` and `v`.
Raises:
TypeError: If `a` or `v` is not a tensor.
TypeError: If `a` and `v` is of different dtype.
ValueError: If `a` and `v` are empty or have wrong dimensions
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as mnp
>>> from mindspore import Tensor
>>> output = mnp.correlate(Tensor([1., 2., 3.]), Tensor([0., 1., 0.5]))
>>> print(output)
[3.5]
>>> output = mnp.correlate(Tensor([1., 2., 3.]), Tensor([0., 1., 0.5]), mode="same")
>>> print(output)
[2. 3.5 3. ]
>>> output = mnp.correlate(Tensor([1., 2., 3., 4., 5.]), Tensor([1., 2.]), mode="full")
>>> print(output)
[ 2. 5. 8. 11. 14. 5.]
"""
if isinstance(a, list):
a = ops.auto_generate.list_to_tuple(a)
if isinstance(a, tuple):
a = ops.auto_generate.tuple_to_tensor(a)
if isinstance(v, list):
v = ops.auto_generate.list_to_tuple(v)
if isinstance(v, tuple):
v = ops.auto_generate.tuple_to_tensor(v)
return ops.auto_generate.correlate(a, v, mode)
def _compute_1d_conv(a, v, mode):
"""Returns a 1-D sequence which is the cross-correlate of two 1-D sequences (`a` and `v`)."""
v_size = F.shape_mul(v.shape)
if mode not in ('same', 'full', 'valid'):
_raise_value_error("mode must be one of ['full', 'same', 'valid']")
if v_size > 1:
if mode == 'same':
pad_left = _to_tensor(_list_comprehensions(v_size // 2, 0.0, True))
pad_right = _to_tensor(_list_comprehensions(v_size - v_size // 2 - 1, 0.0, True))
a = P.Concat(0)((pad_left, a, pad_right))
elif mode == 'full':
pad = _to_tensor(_list_comprehensions(v_size - 1, 0.0, True))
a = P.Concat(0)((pad, a, pad))
a = a.reshape(1, 1, 1, a.size)
v = v.reshape(1, 1, 1, v.size)
_conv = P.Conv2D(1, (1, v.size))
return _conv(a, v).reshape(-1)
def radians(x, dtype=None):
"""
Converts angles from degrees to radians.
Args:
x (Tensor): Angles in degrees.
dtype (:class:`mindspore.dtype`, optional): Default: ``None`` . Overrides the dtype of the
output Tensor.
Returns:
Tensor, the corresponding radian values. This is a tensor scalar if `x`
is a tensor scalar.
Raises:
TypeError: If `x` is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, -5])
>>> output = np.radians(x)
>>> print(output)
[ 0.01745329 0.03490658 0.05235988 -0.06981317 -0.08726647]
"""
return deg2rad(x, dtype=dtype)
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