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: - loc (
Variable
ornumpy.ndarray
orcupy.ndarray
) – Parameter of distribution representing the location \(\mu\). - scale_tril (
Variable
ornumpy.ndarray
orcupy.ndarray
) – Parameter of distribution representing the scale \(L\) such that \(V=LL^T\).
Methods
-
cdf
(x)[source]¶ Evaluates the cumulative distribution function at the given points.
Parameters: x ( Variable
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: Perplexity function value evaluated at x. Return type: Variable
-
prob
(x)[source]¶ Evaluates probability at the given points.
Parameters: x ( Variable
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
ofint
) – 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
ornumpy.ndarray
orcupy.ndarray
) – Data points in the domain of the distributionReturns: 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
- loc (