Distribution is the abstract base class for probability distributions. Note: in Python, adding torch.Size objects works as concatenation Try for example: torch.Size((2, 1)) + torch.Size((1,))
.validate_argswhether to validate arguments
has_rsamplewhether has an rsample
has_enumerate_supportwhether has enumerate support
batch_shapeReturns the shape over which parameters are batched.
event_shapeReturns the shape of a single sample (without batching).
Returns a dictionary from argument names to
torch_Constraint objects that
should be satisfied by each argument of this distribution. Args that
are not tensors need not appear in this dict.
supportReturns a torch_Constraint object representing this distribution's
support.
meanReturns the mean on of the distribution
varianceReturns the variance of the distribution
stddevReturns the standard deviation of the distribution TODO: consider different message
new()Initializes a distribution class.
Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)
batch_shapethe shape over which parameters are batched.
event_shapethe shape of a single sample (without batching).
validate_argswhether to validate the arguments or not. Validation can be time consuming so you might want to disable it.
expand()Returns a new distribution instance (or populates an existing instance
provided by a derived class) with batch dimensions expanded to batch_shape.
This method calls expand on the distribution<U+2019>s parameters. As such, this
does not allocate new memory for the expanded distribution instance.
Additionally, this does not repeat any args checking or parameter
broadcasting in initialize, when an instance is first created.
Distribution$expand(batch_shape, .instance = NULL)
batch_shapethe desired expanded size.
.instancenew instance provided by subclasses that need to
override expand.
sample()Generates a sample_shape shaped sample or sample_shape shaped batch of
samples if the distribution parameters are batched.
Distribution$sample(sample_shape = NULL)
sample_shapethe shape you want to sample.
rsample()Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
Distribution$rsample(sample_shape = NULL)
sample_shapethe shape you want to sample.
log_prob()Returns the log of the probability density/mass function evaluated at
value.
Distribution$log_prob(value)
valuevalues to evaluate the density on.
cdf()Returns the cumulative density/mass function evaluated at
value.
Distribution$cdf(value)
valuevalues to evaluate the density on.
icdf()Returns the inverse cumulative density/mass function evaluated at
value.
@description
Returns tensor containing all values supported by a discrete
distribution. The result will enumerate over dimension 0, so the shape
of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = ()for univariate distributions). Note that this enumerates over all batched tensors in lock-steplist(c(0, 0), c(1, 1), ...). With expand=FALSE, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, list(c(0), c(1), ...)`.
Distribution$icdf(value)
valuevalues to evaluate the density on.
enumerate_support()Distribution$enumerate_support(expand = TRUE)
expand(bool): whether to expand the support over the
batch dims to match the distribution's batch_shape.
Tensor iterating over dimension 0.
entropy()Returns entropy of distribution, batched over batch_shape.
Distribution$entropy()
Tensor of shape batch_shape.
perplexity()Returns perplexity of distribution, batched over batch_shape.
Distribution$perplexity()
Tensor of shape batch_shape.
.extended_shape()Returns the size of the sample returned by the distribution, given
a sample_shape. Note, that the batch and event shapes of a distribution
instance are fixed at the time of construction. If this is empty, the
returned shape is upcast to (1,).
Distribution$.extended_shape(sample_shape = NULL)
sample_shape(torch_Size): the size of the sample to be drawn.
.validate_sample()Argument validation for distribution methods such as log_prob,
cdf and icdf. The rightmost dimensions of a value to be
scored via these methods must agree with the distribution's batch
and event shapes.
Distribution$.validate_sample(value)
value(Tensor): the tensor whose log probability is to be
computed by the log_prob method.
print()Prints the distribution instance.
Distribution$print()
clone()The objects of this class are cloneable with this method.
Distribution$clone(deep = FALSE)
deepWhether to make a deep clone.