chainer.distributions.Bernoulli¶

class chainer.distributions.Bernoulli(p=None, logit=None, binary_check=False)[source]¶

Bernoulli Distribution.

The probability mass function of the distribution is expressed as

\[\begin{split}P(x = 1; p) = p \\ P(x = 0; p) = 1 - p\end{split}\]

Parameters:	p (`Variable` or `numpy.ndarray` or `cupy.ndarray`) – Parameter of distribution representing \(p\). Either p or logit (not both) must have a value. logit (`Variable` or `numpy.ndarray` or `cupy.ndarray`) – Parameter of distribution representing \(\log\{p/(1-p)\}\). 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 `numpy.ndarray` or `cupy.ndarray`) – 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 indipendent.
Return type:	tuple

covariance¶

Returns the covariance of the distribution.

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

entropy¶

Returns the entropy of the distribution.

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

event_shape¶

Returns the shape of an event.

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

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

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.