# 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
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# Unless required by applicable law or agreed to in writing, software
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# See the License for the specific language governing permissions and
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# ============================================================================
"""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.
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.
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:
>>> shape = (3, 1, 2)
>>> mean = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> stddev = Tensor(1.0, mstype.float32)
>>> output = C.normal(shape, mean, stddev, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
"""
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.
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:
>>> from mindspore import Tensor
>>> from mindspore.ops import composite as C
>>> import mindspore.common.dtype as mstype
>>> shape = (2, 3)
>>> mean = Tensor(1.0, mstype.float32)
>>> lambda_param = Tensor(1.0, mstype.float32)
>>> output = C.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 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.
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:
>>> shape = (3, 1, 2)
>>> alpha = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> beta = Tensor(np.array([1.0]), mstype.float32)
>>> output = C.gamma(shape, alpha, beta, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)
"""
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.
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:
>>> shape = (4, 1)
>>> mean = Tensor(np.array([5.0, 10.0]), mstype.float32)
>>> output = C.poisson(shape, mean, seed=5)
>>> result = output.shape
>>> print(result)
(4, 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: 0.
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 `inputs` 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 a optional.
Supported Platforms:
``GPU``
Examples:
>>> input = Tensor([0, 9, 4, 0], mstype.float32)
>>> output = C.multinomial(input, 2, True)
>>> print(output)
[1 1]
"""
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("num_sample must be less than shape(input)[-1] without 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)