Distribution: Generic R6 class representing distributions

In torch: Tensors and Neural Networks with 'GPU' Acceleration

Generic R6 class representing distributions

Description

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,))

Public fields

.validate_args

whether to validate arguments

has_rsample

whether has an rsample

has_enumerate_support

whether has enumerate support

Active bindings

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

Methods

Public methods

• Distribution$new()

• Distribution$expand()

• Distribution$sample()

• Distribution$rsample()

• Distribution$log_prob()

• Distribution$cdf()

• Distribution$icdf()

• Distribution$enumerate_support()

• Distribution$entropy()

• Distribution$perplexity()

• Distribution$.extended_shape()

• Distribution$.validate_sample()

• Distribution$print()

• Distribution$clone()

---

Method new()

Initializes a distribution class.

Usage

Distribution$new(batch_shape = NULL, event_shape = NULL, validate_args = NULL)

Arguments

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.

---

Method 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.

Usage

Distribution$expand(batch_shape, .instance = NULL)

Arguments

batch_shape

the desired expanded size.

.instance

new instance provided by subclasses that need to override expand.

---

Method sample()

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched.

Usage

Distribution$sample(sample_shape = NULL)

Arguments

sample_shape

the shape you want to sample.

---

Method rsample()

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

Usage

Distribution$rsample(sample_shape = NULL)

Arguments

sample_shape

the shape you want to sample.

---

Method log_prob()

Returns the log of the probability density/mass function evaluated at value.

Usage

Distribution$log_prob(value)

Arguments

value

values to evaluate the density on.

---

Method cdf()

Returns the cumulative density/mass function evaluated at value.

Usage

Distribution$cdf(value)

Arguments

value

values to evaluate the density on.

---

Method 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), ...)'.

Usage

Distribution$icdf(value)

Arguments

value

values to evaluate the density on.

---

Method enumerate_support()

Usage

Distribution$enumerate_support(expand = TRUE)

Arguments

expand

(bool): whether to expand the support over the batch dims to match the distribution's batch_shape.

Returns

Tensor iterating over dimension 0.

---

Method entropy()

Returns entropy of distribution, batched over batch_shape.

Usage

Distribution$entropy()

Returns

Tensor of shape batch_shape.

---

Method perplexity()

Returns perplexity of distribution, batched over batch_shape.

Usage

Distribution$perplexity()

Returns

Tensor of shape batch_shape.

---

Method .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,).

Usage

Distribution$.extended_shape(sample_shape = NULL)

Arguments

sample_shape

(torch_Size): the size of the sample to be drawn.

---

Method .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.

Usage

Distribution$.validate_sample(value)

Arguments

value

(Tensor): the tensor whose log probability is to be computed by the log_prob method.

---

Method print()

Prints the distribution instance.

Usage

Distribution$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Distribution$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

torch documentation built on June 7, 2023, 6:19 p.m.