kiwi.modules.common.distributions

Module Contents

Classes

TruncatedNormal

Extension of the Distribution class, which applies a sequence of Transforms
class kiwi.modules.common.distributions.TruncatedNormal(loc, scale, lower_bound=0.0, upper_bound=1.0, validate_args=None)

Bases: torch.distributions.TransformedDistribution

Extension of the Distribution class, which applies a sequence of Transforms to a base distribution. Let f be the composition of transforms applied:

X ~ BaseDistribution
Y = f(X) ~ TransformedDistribution(BaseDistribution, f)
log p(Y) = log p(X) + log |det (dX/dY)|

Note that the .event_shape of a TransformedDistribution is the maximum shape of its base distribution and its transforms, since transforms can introduce correlations among events.

An example for the usage of TransformedDistribution would be:

# Building a Logistic Distribution
# X ~ Uniform(0, 1)
# f = a + b * logit(X)
# Y ~ f(X) ~ Logistic(a, b)
base_distribution = Uniform(0, 1)
transforms = [SigmoidTransform().inv, AffineTransform(loc=a, scale=b)]
logistic = TransformedDistribution(base_distribution, transforms)

For more examples, please look at the implementations of Gumbel, HalfCauchy, HalfNormal, LogNormal, Pareto, Weibull, RelaxedBernoulli and RelaxedOneHotCategorical

arg_constraints
support
has_rsample = True
partition_function(self)
property scale(self)
property mean(self)

\(pdf = f(x; \mu, \sigma, a, b) = \frac{\phi(\xi)}{\sigma Z}\)

\(\xi=\frac{x-\mu}{\sigma}\)

\(\alpha=\frac{a-\mu}{\sigma}\)

\(\beta=\frac{b-\mu}{\sigma}\)

\(Z=\Phi(\beta)-\Phi(\alpha)\)

Returns

\(\mu + \frac{\phi(\alpha)-\phi(\beta)}{Z}\sigma\)

property variance(self)

Returns the variance of the distribution.

log_prob(self, value)

Scores the sample by inverting the transform(s) and computing the score using the score of the base distribution and the log abs det jacobian.

cdf(self, value)

Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution.

icdf(self, value)

Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution.

kiwi.modules.common.attention kiwi.modules.common.feedforward