# chainer.distributions.Dirichlet¶

class chainer.distributions.Dirichlet(alpha)[source]

Dirichlet Distribution.

The probability density function of the distribution is expressed as

$p(x) = \frac{\Gamma(\sum_{i=1}^{K} \alpha_i)} {\prod_{i=1}^{K} \Gamma (\alpha_i)} \prod_{i=1}^{K} {x_i}^{\alpha_i-1}$
Parameters

alpha (Variable or N-dimensional array) – Parameter of distribution.

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 distribution class, it is not recommended that you 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 that you 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

__eq__()

Return self==value.

__ne__()

Return self!=value.

__lt__()

Return self<value.

__le__()

Return self<=value.

__gt__()

Return self>value.

__ge__()

Return self>=value.

Attributes

alpha
alpha0
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

mean
mode

Returns the mode of the distribution.

Returns

The mode of the distribution.

Return type

Variable

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
xp

Array module for the distribution.

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