mindspore.nn.probability.distribution.Distribution
- class mindspore.nn.probability.distribution.Distribution(seed, dtype, name, param)[source]
Base class for all mathematical distributions.
- Parameters
seed (int) – The seed is used in sampling. 0 is used if it is None.
dtype (mindspore.dtype) – The type of the event samples.
name (str) – The name of the distribution.
param (dict) – The parameters used to initialize the distribution.
Note
Derived class must override operations such as _mean, _prob, and _log_prob. Required arguments, such as value for _prob, must be passed in through args or kwargs. dist_spec_args which specifies a new distribution are optional.
dist_spec_args is unique for each type of distribution. For example, mean and sd are the dist_spec_args for a Normal distribution, while rate is the dist_spec_args for an Exponential distribution.
For all functions, passing in dist_spec_args, is optional. Function calls with the additional dist_spec_args passed in will evaluate the result with a new distribution specified by the dist_spec_args. However, it will not change the original distribution.
- Supported Platforms:
AscendGPU
- cdf(value, *args, **kwargs)[source]
Evaluate the cumulative distribution function(cdf) at given value.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the cdf of the distribution.
- construct(name, *args, **kwargs)[source]
Override construct in Cell.
Note
Names of supported functions include: 'prob', 'log_prob', 'cdf', 'log_cdf', 'survival_function', 'log_survival', 'var', 'sd', 'mode', 'mean', 'entropy', 'kl_loss', 'cross_entropy', 'sample', 'get_dist_args', and 'get_dist_type'.
- cross_entropy(dist, *args, **kwargs)[source]
Evaluate the cross_entropy between distribution a and b.
- Parameters
Note
dist_spec_args of distribution b must be passed to the function through args or kwargs. Passing in dist_spec_args of distribution a is optional.
- Returns
Tensor, the cross_entropy of two distributions.
- entropy(*args, **kwargs)[source]
Evaluate the entropy.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the entropy of the distribution.
- get_dist_args(*args, **kwargs)[source]
Check the availability and validity of default parameters and dist_spec_args.
- Parameters
Note
dist_spec_args must be passed in through list or dictionary. The order of dist_spec_args should follow the initialization order of default parameters through _add_parameter. If some dist_spec_args is None, the corresponding default parameter is returned.
- Returns
list[Tensor], the list of parameters.
- get_dist_type()[source]
Return the type of the distribution.
- Returns
string, the name of distribution.
- kl_loss(dist, *args, **kwargs)[source]
Evaluate the KL divergence, i.e. KL(a||b).
- Parameters
Note
dist_spec_args of distribution b must be passed to the function through args or kwargs. Passing in dist_spec_args of distribution a is optional.
- Returns
Tensor, the kl loss function of the distribution.
- log_cdf(value, *args, **kwargs)[source]
Evaluate the log the cumulative distribution function(cdf) at given value.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the log cdf of the distribution.
- log_prob(value, *args, **kwargs)[source]
Evaluate the log probability(pdf or pmf) at the given value.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the value of log probability.
- log_survival(value, *args, **kwargs)[source]
Evaluate the log survival function at given value.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the log survival function of the distribution.
- mean(*args, **kwargs)[source]
Evaluate the mean.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the mean of the distribution.
- mode(*args, **kwargs)[source]
Evaluate the mode.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the mode of the distribution.
- prob(value, *args, **kwargs)[source]
Evaluate the probability (pdf or pmf) at given value. For a discrete distribution, it is a probability mass function, while for a continuous distribution, it is probability density function.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the value of probability.
- sample(*args, **kwargs)[source]
Sampling function.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the sample generated from the distribution.
- sd(*args, **kwargs)[source]
Evaluate the standard deviation.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the standard deviation of the distribution.
- survival_function(value, *args, **kwargs)[source]
Evaluate the survival function at given value.
- Parameters
Note
A distribution can be optionally passed to the function by passing its dist_spec_args through args or kwargs.
- Returns
Tensor, the survival function of the distribution.