LogisticNormal

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

Creates a logistic-normal distribution parameterized by loc and scale that define the base Normal distribution transformed with the StickBreakingTransform such that:

X ~ LogisticNormal(loc, scale)
Y = log(X / (1 - X.cumsum(-1)))[..., :-1] ~ Normal(loc, scale)

Parameters:

• loc (float or Tensor) – mean of the base distribution

• scale (float or Tensor) – standard deviation of the base distribution

Example:

>>> # logistic-normal distributed with mean=(0, 0, 0) and stddev=(1, 1, 1)
>>> # of the base Normal distribution
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = LogisticNormal(tensorplay.tensor([0.0] * 3), tensorplay.tensor([1.0] * 3))
>>> m.sample()
tensor([ 0.7653,  0.0341,  0.0579,  0.1427])

property batch_shape: Size

Returns the shape over which parameters are batched.

cdf(value)

Computes the cumulative distribution function by inverting the transform(s) and computing the score of the base distribution.

entropy() → Tensor

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

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).

icdf(value)

Computes the inverse cumulative distribution function using transform(s) and computing the score of the base distribution.

log_prob(value)

Scores the sample by inverting the transform(s) and computing the score using the score of the base distribution and the log abs det jacobian.

property mean: Tensor

Returns the mean of the distribution.

property mode: Tensor

Returns the mode of the distribution.

perplexity() → Tensor

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

rsample(sample_shape: Size | Sequence[int] = ()) → Tensor

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. Samples first from base distribution and applies transform() for every transform in the list.

sample(sample_shape=())

Generates a sample_shape shaped sample or sample_shape shaped batch of samples if the distribution parameters are batched. Samples first from base distribution and applies transform() for every transform in the list.

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.

property stddev: Tensor

Returns the standard deviation of the distribution.

property variance: Tensor

Returns the variance of the distribution.