Normal

class tensorplay.distributions.Normal(loc: Tensor | float, scale: Tensor | float, validate_args: bool | None = None)[source]

Creates a normal (also called Gaussian) distribution parameterized by loc and scale.

Example:

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Normal(tensorplay.tensor([0.0]), tensorplay.tensor([1.0]))
>>> m.sample()  # normally distributed with loc=0 and scale=1
tensor([ 0.1046])

Parameters:

• loc (float or Tensor) – mean of the distribution (often referred to as mu)

• scale (float or Tensor) – standard deviation of the distribution (often referred to as sigma)

property batch_shape: Size

Returns the shape over which parameters are batched.

enumerate_support(expand: bool = True) → Tensor

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).

perplexity() → Tensor

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

sample_n(n: int) → Tensor

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

static set_default_validate_args(value: bool) → None

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.