mindspore.ops.random_poisson
- mindspore.ops.random_poisson(shape, rate, seed=None, dtype=mstype.float32)[source]
Generates random number Tensor with shape shape according to a Poisson distribution with mean rate.
\[\text{P}(i|μ) = \frac{\exp(-μ)μ^{i}}{i!}\]- Parameters
shape (Tensor) – The shape of random tensor to be sampled from each poisson distribution, 1-D Tensor whose dtype is mstype.int32 or mstype.int64.
rate (Tensor) – The \(μ\) parameter the distribution is constructed with. It represents the mean of the distribution and also the variance of the distribution. It should be a Tensor whose dtype is mstype.int64, mstype.int32, mstype.float64, mstype.float32 or mstype.float16.
seed (int, optional) – 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.dtype (mindspore.dtype) – The data type of output:
mstype.int64
,mstype.int32
,mstype.float64
,mstype.float32
ormstype.float16
. Default:mstype.float32
.
- Returns
A Tensor whose shape is mindspore.concat(['shape', mindspore.shape('rate')], axis=0) and data type is equal to argument dtype.
- Raises
TypeError – If shape is not a Tensor.
TypeError – If datatype of shape is not mstype.int64 nor mstype.int32.
ValueError – If shape of shape is not 1-D.
TypeError – If rate is not a Tensor nor a scalar.
TypeError – If datatype of rate is not in [mstype.int64, mstype.int32, mstype.float64, mstype.float32 or mstype.float16].
TypeError – If seed is not a non-negtive int.
TypeError – If dtype is not in [mstype.int64, mstype.int32, mstype.float64, mstype.float32 nor mstype.float16].
ValueError – If any element of input shape tensor is not positive.
- Supported Platforms:
GPU
CPU
Examples
>>> import mindspore >>> import numpy as np >>> from mindspore import Tensor, ops >>> # case 1: 1-D shape, 2-D rate, float64 output >>> shape = Tensor(np.array([2, 2]), mindspore.int64) >>> rate = Tensor(np.array([[5.0, 10.0], [5.0, 1.0]]), mindspore.float32) >>> output = ops.random_poisson(shape, rate, seed=5, dtype=mindspore.float64) >>> print(output.shape, output.dtype) (2, 2, 2, 2) Float64 >>> # case 2: 1-D shape, scalar rate, int64 output >>> shape = Tensor(np.array([2, 2]), mindspore.int64) >>> rate = Tensor(5.0, mindspore.float64) >>> output = ops.random_poisson(shape, rate, seed=5, dtype=mindspore.int64) >>> print(output.shape, output.dtype) (2, 2) Int64