mindspore.nn.probability.distribution.TransformedDistribution

class mindspore.nn.probability.distribution.TransformedDistribution(bijector, distribution, seed=None, name='transformed_distribution')[source]

Transformed Distribution. This class contains a bijector and a distribution and transforms the original distribution to a new distribution through the operation defined by the bijector. If \(X\) is an random variable following the underying distribution, and \(g(x)\) is a function represented by the bijector, then \(Y = g(X)\) is a random variable following the transformed distribution.

Parameters

bijector (Bijector) – The transformation to perform.
distribution (Distribution) – The original distribution. Must be a float dtype.
seed (int) – The seed is used in sampling. The global seed is used if it is None. Default: None . If this seed is given when a TransformedDistribution object is initialized, the object’s sampling function will use this seed; elsewise, the underlying distribution’s seed will be used.
name (str) – The name of the transformed distribution. Default: 'transformed_distribution' .

Note

The arguments used to initialize the original distribution cannot be None. For example, mynormal = msd.Normal(dtype=mindspore.float32) cannot be used to initialized a TransformedDistribution since mean and sd are not specified. batch_shape is the batch_shape of the original distribution. broadcast_shape is the broadcast shape between the original distribution and bijector. is_scalar_batch is only true if both the original distribution and the bijector are scalar batches. default_parameters, parameter_names and parameter_type are set to be consistent with the original distribution. Derived class can overwrite default_parameters and parameter_names by calling reset_parameters followed by add_parameter.

Raises

TypeError – When the input bijector is not a Bijector instance.
TypeError – When the input distribution is not a Distribution instance.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import numpy as np
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> import mindspore.nn.probability.bijector as msb
>>> from mindspore import Tensor
>>> class Net(nn.Cell):
...     def __init__(self, shape, dtype=mindspore.float32, seed=0, name='transformed_distribution'):
...         super(Net, self).__init__()
...         # create TransformedDistribution distribution
...         self.exp = msb.Exp()
...         self.normal = msd.Normal(0.0, 1.0, dtype=dtype)
...         self.lognormal = msd.TransformedDistribution(self.exp, self.normal, seed=seed, name=name)
...         self.shape = shape
...
...     def construct(self, value):
...         cdf = self.lognormal.cdf(value)
...         sample = self.lognormal.sample(self.shape)
...         return cdf, sample
>>> shape = (2, 3)
>>> net = Net(shape=shape, name="LogNormal")
>>> x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
>>> tx = Tensor(x, dtype=mindspore.float32)
>>> cdf, sample = net(tx)
>>> print(sample.shape)
(2, 3)

property bijector

Return the bijector.

Returns: Bijector, the bijector.

property distribution

Return the distribution before transformation.

Returns: Distribution, the distribution before transformation.

property dtype

Return the data type of distribution.

Returns: mindspore.dtype, the data type of distribution.

property is_linear_transformation

Return whether the bijector is linear.

Returns: Bool, return True if the bijector is linear, otherwise return False.

cdf(value)

Compute the cumulatuve distribution function(CDF) of the given value.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the value of the cumulatuve distribution function for the given input.

log_cdf(value)

Compute the log value of the cumulatuve distribution function.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the log value of the cumulatuve distribution function.

log_prob(value)

the log value of the probability.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the log value of the probability.

log_survival(value)

Compute the log value of the survival function.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the value of the K-L loss.

mean()

Compute the mean value of the distribution.

Returns: Tensor, the mean of the distribution.

prob(value)

The probability of the given value.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the value of the probability.

sample(shape)

Generate samples.

Parameters

shape (tuple) - the shape of the tensor.

Returns

Tensor, the sample following the distribution.

survival_function(value)

Compute the value of the survival function.

Parameters

value (Tensor) - the value to compute.

Returns

Tensor, the value of the survival function.