chainer.distributions.Categorical¶

class chainer.distributions.Categorical(p=None, **kwargs)[source]¶

Categorical Distribution.

The probability mass function of the distribution is expressed as

\[P(x = i; p) = p_i\]

Parameters

p (Variable or N-dimensional array) – Parameter of distribution.
logit (Variable or N-dimensional array) – Parameter of distribution representing \(\log\{p\} + C\). Either p or logit (not both) must have a value.

Methods

cdf(x)[source]¶

Evaluates the cumulative distribution function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Cumulative distribution function value evaluated at x.
Return type: Variable

icdf(x)[source]¶

Evaluates the inverse cumulative distribution function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Inverse cumulative distribution function value evaluated at x.
Return type: Variable

log_cdf(x)[source]¶

Evaluates the log of cumulative distribution function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Logarithm of cumulative distribution function value evaluated at x.
Return type: Variable

log_prob(x)[source]¶

Evaluates the logarithm of probability at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Logarithm of probability evaluated at x.
Return type: Variable

log_survival_function(x)[source]¶

Evaluates the logarithm of survival function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Logarithm of survival function value evaluated at x.
Return type: Variable

perplexity(x)[source]¶

Evaluates the perplexity function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Perplexity function value evaluated at x.
Return type: Variable

prob(x)[source]¶

Evaluates probability at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Probability evaluated at x.
Return type: Variable

sample(sample_shape=())[source]¶

Samples random points from the distribution.

This function calls sample_n and reshapes a result of sample_n to sample_shape + batch_shape + event_shape. On implementing sampling code in an inherited ditribution class, it is not recommended to override this function. Instead of doing this, it is preferable to override sample_n.

Parameters: sample_shape (tuple of int) – Sampling shape.
Returns: Sampled random points.
Return type: Variable

sample_n(n)[source]¶

Samples n random points from the distribution.

This function returns sampled points whose shape is (n,) + batch_shape + event_shape. When implementing sampling code in a subclass, it is recommended to override this method.

Parameters: n (int) – Sampling size.
Returns: sampled random points.
Return type: Variable

survival_function(x)[source]¶

Evaluates the survival function at the given points.

Parameters: x (Variable or N-dimensional array) – Data points in the domain of the distribution
Returns: Survival function value evaluated at x.
Return type: Variable

Attributes

batch_shape¶

Returns the shape of a batch.

Returns: The shape of a sample that is not identical and independent.
Return type: tuple

covariance¶

Returns the covariance of the distribution.

Returns: The covariance of the distribution.
Return type: Variable

entropy¶

event_shape¶

Returns the shape of an event.

Returns: The shape of a sample that is not identical and independent.
Return type: tuple

log_p¶

mean¶

Returns the mean of the distribution.

Returns: The mean of the distribution.
Return type: Variable

mode¶

Returns the mode of the distribution.

Returns: The mode of the distribution.
Return type: Variable

p¶

params¶

Returns the parameters of the distribution.

Returns: The parameters of the distribution.
Return type: dict

stddev¶

Returns the standard deviation of the distribution.

Returns: The standard deviation of the distribution.
Return type: Variable

support¶

Returns the support of the distribution.

Returns: String that means support of this distribution.
Return type: str

variance¶

Returns the variance of the distribution.

Returns: The variance of the distribution.
Return type: Variable

xp¶

Array module for the distribution.

Depending on which of CPU/GPU this distribution is on, this property returns numpy or cupy.