# Source code for mindspore.ops.composite.random_ops

```
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Operations for random number generators."""
from mindspore.ops.primitive import constexpr
from .. import operations as P
from .. import functional as F
from .multitype_ops import _constexpr_utils as const_utils
from ...common import dtype as mstype
from ...common.seed import _get_graph_seed
@constexpr
def _get_seed(op_seed, kernel_name):
"Get the graph-level seed."
return _get_graph_seed(op_seed, kernel_name)
[docs]def normal(shape, mean, stddev, seed=None):
"""
Generates random numbers according to the Normal (or Gaussian) random number distribution.
Args:
shape (tuple): The shape of random tensor to be generated.
The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak,
with data type in [int8, int16, int32, int64, float16, float32].
stddev (Tensor): The deviation σ distribution parameter. It should be greater than 0,
with data type in [int8, int16, int32, int64, float16, float32].
seed (int): Seed is used as entropy source for the Random number engines to generate pseudo-random numbers.
The value must be non-negative. Default: None, which will be treated as 0.
Returns:
Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes
of `mean` and `stddev`.
The dtype is float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> shape = (3, 1, 2)
>>> mean = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
>>> shape = (3, 1, 3)
>>> mean = Tensor(np.array([[3, 4, 3], [3, 5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 3)
>>> shape = (3, 1, 3)
>>> mean = Tensor(np.array([[1, 2, 3], [3, 4, 3], [3, 5, 6]]), mindspore.float32)
>>> stddev = Tensor(1.0, mindspore.float32)
>>> output = ops.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 3, 3)
"""
mean_dtype = F.dtype(mean)
stddev_dtype = F.dtype(stddev)
const_utils.check_type_valid(mean_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal')
const_utils.check_type_valid(stddev_dtype, mstype.int_type + (mstype.float16, mstype.float32), 'normal')
seed1, seed2 = _get_seed(seed, "normal")
stdnormal = P.StandardNormal(seed1, seed2)
random_normal = stdnormal(shape)
value = random_normal * stddev + mean
return value
[docs]def laplace(shape, mean, lambda_param, seed=None):
r"""
Generates random numbers according to the Laplace random number distribution.
It is defined as:
.. math::
\text{f}(x;μ,λ) = \frac{1}{2λ}\exp(-\frac{|x-μ|}{λ}),
Args:
shape (tuple): The shape of random tensor to be generated.
The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
mean (Tensor): The mean μ distribution parameter, which specifies the location of the peak.
With float32 data type.
lambda_param (Tensor): The parameter used for controlling the variance of this random distribution. The
variance of Laplace distribution is equal to twice the square of lambda_param. With float32 data type.
seed (int): Seed is used as entropy source for Random number engines generating pseudo-random numbers.
Default: None, which will be treated as 0.
Returns:
Tensor. The shape should be the broadcasted shape of input `shape` and shapes of `mean` and `lambda_param`.
The dtype is float32.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import ops as ops
>>> shape = (2, 3)
>>> mean = Tensor(1.0, mindspore.float32)
>>> lambda_param = Tensor(1.0, mindspore.float32)
>>> output = ops.laplace(shape, mean, lambda_param, seed=5)
>>> print(output.shape)
(2, 3)
"""
mean_dtype = F.dtype(mean)
lambda_param_dtype = F.dtype(lambda_param)
const_utils.check_tensors_dtype_same(mean_dtype, mstype.float32, "laplace")
const_utils.check_tensors_dtype_same(lambda_param_dtype, mstype.float32, "laplace")
seed1, seed2 = _get_seed(seed, "laplace")
stdlaplace = P.StandardLaplace(seed1, seed2)
rnd = stdlaplace(shape)
value = rnd * lambda_param + mean
return value
[docs]def uniform(shape, minval, maxval, seed=None, dtype=mstype.float32):
"""
Generates random numbers according to the Uniform random number distribution.
Note:
The number in tensor minval should be strictly less than maxval at any position after broadcasting.
Args:
shape (tuple): The shape of random tensor to be generated.
The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions
and the length of :math:`(N,*)` should be less than 8 in broadcast operation.
minval (Tensor): The distribution parameter `a`.
It defines the minimum possible generated value, with int32 or float32 data type.
If dtype is int32, only one number is allowed.
maxval (Tensor): The distribution parameter `b`.
It defines the maximum possible generated value, with int32 or float32 data type.
If dtype is int32, only one number is allowed.
seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers,
must be non-negative. Default: None, which will be treated as 0.
dtype (mindspore.dtype): Type of the Uniform distribution. If it is int32, it generates numbers from discrete
uniform distribution; if it is float32, it generates numbers from continuous uniform distribution. It only
supports these two data types. Default: mindspore.float32.
Returns:
Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes
of `minval` and `maxval`.
The dtype is designated as the input `dtype`.
Raises:
TypeError: If `shape` is not tuple.
TypeError: If 'minval' or 'maxval' is neither int32 nor float32
and dtype of 'minval' is not the same as 'maxval'.
TypeError: If `seed` is not an int.
TypeError: If 'dtype' is neither int32 nor float32.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> import numpy as np
>>> # For discrete uniform distribution, only one number is allowed for both minval and maxval:
>>> shape = (4, 2)
>>> minval = Tensor(1, mindspore.int32)
>>> maxval = Tensor(2, mindspore.int32)
>>> output = ops.uniform(shape, minval, maxval, seed=5, dtype=mindspore.int32)
>>>
>>> # For continuous uniform distribution, minval and maxval can be multi-dimentional:
>>> shape = (3, 1, 2)
>>> minval = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> maxval = Tensor([8.0, 10.0], mindspore.float32)
>>> output = ops.uniform(shape, minval, maxval, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
"""
minval_dtype = F.dtype(minval)
maxval_dtype = F.dtype(maxval)
const_utils.check_type_valid(dtype, [mstype.int32, mstype.float32], 'uniform')
const_utils.check_tensors_dtype_same(minval_dtype, dtype, "uniform")
const_utils.check_tensors_dtype_same(maxval_dtype, dtype, "uniform")
seed1, seed2 = _get_seed(seed, "uniform")
if const_utils.is_same_type(dtype, mstype.int32):
random_uniform = P.UniformInt(seed1, seed2)
value = random_uniform(shape, minval, maxval)
else:
uniform_real = P.UniformReal(seed1, seed2)
random_uniform = uniform_real(shape)
value = random_uniform * (maxval - minval) + minval
return value
[docs]def gamma(shape, alpha, beta, seed=None):
"""
Generates random numbers according to the Gamma random number distribution.
Args:
shape (tuple): The shape of random tensor to be generated.
The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
alpha (Tensor): The alpha α distribution parameter. It should be greater than 0 with float32 data type.
beta (Tensor): The beta β distribution parameter. It should be greater than 0 with float32 data type.
seed (int): Seed is used as entropy source for the random number engines to generate
pseudo-random numbers, must be non-negative. Default: None, which will be treated as 0.
Returns:
Tensor. The shape should be equal to the broadcasted shape between the input `shape` and shapes
of `alpha` and `beta`.
The dtype is float32.
Raises:
TypeError: If `shape` is not a tuple.
TypeError: If neither `alpha` nor `beta` is a Tensor.
TypeError: If `seed` is not an int.
TypeError: If dtype of `alpha` and `beta` is not float32.
Supported Platforms:
``Ascend``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> # case 1: alpha_shape is (2, 2)
>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> beta = Tensor(np.array([1.0]), mindspore.float32)
>>> output = ops.gamma(shape, alpha, beta, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
>>> # case 2: alpha_shape is (2, 3), so shape is (3, 1, 3)
>>> shape = (3, 1, 3)
>>> alpha = Tensor(np.array([[1, 3, 4], [2, 5, 6]]), mindspore.float32)
>>> beta = Tensor(np.array([1.0]), mindspore.float32)
>>> output = ops.gamma(shape, alpha, beta, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 3)
>>> # case 3: beta_shape is (1, 2), the output is different.
>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> beta = Tensor(np.array([1.0, 2]), mindspore.float32)
>>> output = ops.gamma(shape, alpha, beta, seed=5)
>>> result = output.shape
>>> print(output)
[[[ 2.2132034 5.8855834]]
[ 3.3981476 7.5805717]
[[ 3.3981476 7.5805717]]
[ 3.7190282 19.941492]
[[ 2.9512358 2.5969937]]
[ 3.786061 5.160872 ]]]
>>> # case 4: beta_shape is (2, 1), the output is different.
>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32)
>>> beta = Tensor(np.array([[1.0], [2.0]]), mindspore.float32)
>>> output = ops.gamma(shape, alpha, beta, seed=5)
>>> result = output.shape
>>> print(output)
[[[ 5.6085486 7.8280783]]
[ 15.97684 16.116285]
[[ 1.8347423 1.713663]]
[ 3.2434065 15.667398]
[[ 4.2922077 7.3365674]]
[ 5.3876944 13.159832 ]]]
"""
seed1, seed2 = _get_seed(seed, "gamma")
random_gamma = P.Gamma(seed1, seed2)
value = random_gamma(shape, alpha, beta)
return value
[docs]def poisson(shape, mean, seed=None):
r"""
Generates random numbers according to the Poisson random number distribution.
.. math::
\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}
Args:
shape (tuple): The shape of random tensor to be generated.
The format is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
mean (Tensor): The mean μ distribution parameter. It should be greater than 0 with float32 data type.
seed (int): Seed is used as entropy source for the random number engines to generate pseudo-random numbers
and must be non-negative. Default: None, which will be treated as 0.
Returns:
Tensor. The shape should be equal to the broadcasted shape between the input "shape" and shapes of `mean`.
The dtype is float32.
Raises:
TypeError: If `shape` is not a tuple.
TypeError: If `mean` is not a Tensor whose dtype is not float32.
TypeError: If `seed` is not an int.
Supported Platforms:
``Ascend``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> # case 1: It can be broadcast.
>>> shape = (4, 1)
>>> mean = Tensor(np.array([5.0, 10.0]), mindspore.float32)
>>> output = ops.poisson(shape, mean, seed=5)
>>> result = output.shape
>>> print(result)
(4, 2)
>>> # case 2: It can not be broadcast. It is recommended to use the same shape.
>>> shape = (2, 2)
>>> mean = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), mindspore.float32)
>>> output = ops.poisson(shape, mean, seed=5)
>>> result = output.shape
>>> print(result)
(2, 2)
"""
seed1, seed2 = _get_seed(seed, "poisson")
random_poisson = P.Poisson(seed1, seed2)
value = random_poisson(shape, mean)
return value
[docs]def multinomial(inputs, num_sample, replacement=True, seed=None):
r"""
Returns a tensor sampled from the multinomial probability distribution located in the corresponding
row of the input tensor.
Note:
The rows of input do not need to sum to one (in which case we use the values as weights),
but must be non-negative, finite and have a non-zero sum.
Args:
inputs (Tensor): The input tensor containing probabilities, must be 1 or 2 dimensions, with
float32 data type.
num_sample (int): Number of samples to draw.
replacement (bool, optional): Whether to draw with replacement or not, default True.
seed (int, optional): Seed is used as entropy source for the random number engines to generate
pseudo-random numbers, must be non-negative. Default: None.
Outputs:
Tensor, has the same rows with input. The number of sampled indices of each row is `num_samples`.
The dtype is float32.
Raises:
TypeError: If `x` is not a Tensor whose dtype is not float32.
TypeError: If `num_sample` is not an int.
TypeError: If `seed` is neither an int nor an optional.
Supported Platforms:
``GPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> # case 1: The output is random, and the length of the output is the same as num_sample.
>>> x = Tensor([0, 9, 4, 0], mindspore.float32)
>>> output = ops.multinomial(x, 2)
>>> # print(output)
>>> # [1 2] or [2 1]
>>> # the case where the result is [2 1] in multiple times.
>>> # This is because the value corresponding to the index 1 is larger than the value of the index 2.
>>> print(len(output))
2
>>> # case 2: The output is random, and the length of the output is the same as num_sample.
>>> # replacement is False(Default).
>>> # If the extracted value is 0, the index value of 1 will be returned.
>>> x = Tensor([0, 9, 4, 0], mstype.float32)
>>> output = ops.multinomial(x, 4)
>>> print(output)
[1 1 2 1]
>>> # case 3: The output is random, num_sample == x_length = 4, and replacement is True, Can extract the same elements。
>>> x = Tensor([0, 9, 4, 0], mstype.float32)
>>> output = ops.multinomial(x, 4, True)
>>> print(output)
[1 1 2 2]
"""
shape = P.Shape()
reshape = P.Reshape()
const_utils.check_valid_dim(len(shape(inputs)), "multinomial")
seed1, seed2 = _get_seed(seed, "multinomial")
if not replacement:
if shape(inputs)[-1] < num_sample:
const_utils.raise_value_error("For 'multinomial', the 'num_sample' must be less than "
"the last dimension of input without 'replacement', "
"but got 'num_sample': {} and "
"'replacement': {}".format(num_sample, replacement))
n_dist = 1
if len(shape(inputs)) > 1:
n_dist = shape(inputs)[-2]
random_uniform = P.UniformReal(seed1, seed2)((n_dist * shape(inputs)[-1],))
if n_dist != 1:
random_uniform = reshape(random_uniform, (n_dist, shape(inputs)[-1]))
vals = P.RealDiv()(P.Log()(random_uniform), inputs + 1e-6)
_, indices = P.TopK()(vals, num_sample)
return indices
return P.Multinomial(seed1, seed2)(inputs, num_sample)
```