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’.
- Inputs and Outputs of APIs:
The accessible APIs of the transformed distribution are defined in the base class, including:
prob, log_prob, cdf, log_cdf, survival_function, and log_survival
mean
sample
For more details of all APIs, including the inputs and outputs of all APIs of the transformed distribution, please refer to
mindspore.nn.probability.distribution.Distribution
, and examples below.- Supported Platforms:
Ascend
GPU
- Raises
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.
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.