chainer.distributions.Independent¶
- class chainer.distributions.Independent(distribution, reinterpreted_batch_ndims=None)[source]¶
Independent distribution.
- Parameters
distribution (
Distribution
) – The base distribution instance to transform.reinterpreted_batch_ndims (
int
) – Integer number of rightmost batch dims which will be regarded as event dims. WhenNone
all but the first batch axis (batch axis 0) will be transferred to event dimensions.
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
- icdf(x)[source]¶
The inverse cumulative distribution function for multivariate variable.
Cumulative distribution function for multivariate variable is not invertible. This function always raises
RuntimeError
.- Parameters
x (
Variable
or N-dimensional array) – Data points in the codomain of the distribution- Raises
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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
- __eq__(value, /)¶
Return self==value.
- __ne__(value, /)¶
Return self!=value.
- __lt__(value, /)¶
Return self<value.
- __le__(value, /)¶
Return self<=value.
- __gt__(value, /)¶
Return self>value.
- __ge__(value, /)¶
Return self>=value.
Attributes
- batch_shape¶
- covariance¶
The covariance of the independent distribution.
By definition, the covariance of the new distribution becomes block diagonal matrix. Let \(\Sigma_{\mathbf{x}}\) be the covariance matrix of the original random variable \(\mathbf{x} \in \mathbb{R}^d\), and \(\mathbf{x}^{(1)}, \mathbf{x}^{(2)}, \cdots \mathbf{x}^{(m)}\) be the \(m\) i.i.d. random variables, new covariance matrix \(\Sigma_{\mathbf{y}}\) of \(\mathbf{y} = [\mathbf{x}^{(1)}, \mathbf{x}^{(2)}, \cdots, \mathbf{x}^{(m)}] \in \mathbb{R}^{md}\) can be written as
\[\begin{split}\left[\begin{array}{ccc} \Sigma_{\mathbf{x}^{1}} & & 0 \\ & \ddots & \\ 0 & & \Sigma_{\mathbf{x}^{m}} \end{array} \right].\end{split}\]Note that this relationship holds only if the covariance matrix of the original distribution is given analytically.
- Returns
The covariance of the distribution.
- Return type
- distribution¶
- entropy¶
- event_shape¶
- mean¶
- mode¶
- params¶
- reinterpreted_batch_ndims¶
- stddev¶
- support¶
- variance¶
- xp¶