chainer.distributions.MultivariateNormal

class chainer.distributions.MultivariateNormal(loc, **kwargs)[source]

MultivariateNormal Distribution.

The probability density function of the distribution is expressed as

\[p(x;\mu,V) = \frac{1}{\sqrt{\det(2\pi V)}} \exp\left(-\frac{1}{2}(x-\mu) V^{-1}(x-\mu)\right)\]
Parameters:

Methods

__copy__()[source]
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.