Generic R6 class representing distributions
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_args: whether to validate arguments
has_rsample: whether has an rsample
has_enumerate_support: whether has enumerate support
batch_shape: Returns the shape over which parameters are batched.
event_shape: Returns 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.
support: Returns a torch_Constraint object representing this distribution's support.
mean: Returns the mean on of the distribution
variance: Returns the variance of the distribution
stddev: Returns the standard deviation of the distribution TODO: consider different message
new()Initializes a distribution class.
Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)
batch_shape: the shape over which parameters are batched.
event_shape: the shape of a single sample (without batching).
validate_args: whether to validate the arguments or not. Validation can be time consuming so you might want to disable it.
expand()Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in initialize, when an instance is first created.
Distribution$expand(batch_shape, .instance = NULL)
batch_shape: the desired expanded size.
.instance: new 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$sample(sample_shape = NULL)
sample_shape: the shape you want to sample.
rsample()Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.
Distribution$rsample(sample_shape = NULL)
sample_shape: the shape you want to sample.
log_prob()Returns the log of the probability density/mass function evaluated at value.
Distribution$log_prob(value)
value: values to evaluate the density on.
cdf()Returns the cumulative density/mass function evaluated at value.
Distribution$cdf(value)
value: values 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 (whereevent_shape = ()for univariate distributions). Note that this enumerates over allbatched tensors in lock-steplist(c(0, 0), c(1, 1), ...). Withexpand=FALSE, enumeration happens along dim 0, but with the remaining batchdimensions being singleton dimensions,list(c(0), c(1), ...)`.
Distribution$icdf(value)
value: values to evaluate the density on.
enumerate_support()Distribution$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)
deep: Whether to make a deep clone.
Useful links