BoxUniform

class BoxUniform(low, high, reinterpreted_batch_ndims=1, device=None)

Bases: Independent

Uniform distribution in multiple dimensions.

Parameters:

• low (Tensor | ndarray)

• high (Tensor | ndarray)

• reinterpreted_batch_ndims (int)

• device (str | None)

to(device)

Moves the distribution to the specified device in place.

Parameters:

device (str | device) – Target device (e.g., “cpu”, “cuda”, “mps”).

Return type:

None

Example:

device = "cuda"
prior = BoxUniform(low=torch.zeros(2), high=torch.ones(2))
prior.to(device) #inplace

arg_constraints: dict[str, constraints.Constraint] = {}

property batch_shape: Size

Returns the shape over which parameters are batched.

cdf(value)

Returns the cumulative density/mass function evaluated at value.

Parameters:

value (Tensor)

Return type:

Tensor

entropy()

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

enumerate_support(expand=True)

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-step [[0, 0], [1, 1], …]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use itertools.product(m.enumerate_support()).

Parameters:

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

Returns:

Tensor iterating over dimension 0.

property event_shape: Size

Returns the shape of a single sample (without batching).

expand(batch_shape, _instance=None)

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 __init__.py, when an instance is first created.

Parameters:

• batch_shape (torch.Size) – the desired expanded size.

• _instance – new instance provided by subclasses that need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to batch_size.

property has_enumerate_support: bool

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

property has_rsample: bool

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

icdf(value)

Returns the inverse cumulative density/mass function evaluated at value.

Parameters:

value (Tensor)

Return type:

Tensor

log_prob(value)

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

Parameters:

value (Tensor)

property mean: Tensor

Returns the mean of the distribution.

property mode: Tensor

Returns the mode of the distribution.

perplexity()

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

Return type:

Tensor

rsample(sample_shape=())

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

Parameters:

sample_shape (Size | list[int] | tuple[int, ...])

Return type:

Tensor

sample(sample_shape=())

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

Return type:

Tensor

sample_n(n)

Generates n samples or n batches of samples if the distribution parameters are batched.

Parameters:

n (int)

Return type:

Tensor

static set_default_validate_args(value)

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Parameters:

value (bool) – Whether to enable validation.

Return type:

None

property stddev: Tensor

Returns the standard deviation of the distribution.

property support

Returns a Constraint object representing this distribution’s support.

Parameters:

fn (Callable[[...], Any])

Return type:

_DependentProperty

property variance: Tensor

Returns the variance of the distribution.

base_dist: D