Geometric

class tensorplay.distributions.Geometric(probs: Tensor | bool | int | float | None = None, logits: Tensor | bool | int | float | None = None, validate_args: bool | None = None)[source]

Creates a Geometric distribution parameterized by probs, where probs is the probability of success of Bernoulli trials.

$$P(X=k) = (1-p)^{k} p, k = 0, 1, ...$$

Note

tensorplay.distributions.geometric.Geometric() $(k+1)$-th trial is the first success hence draws samples in $\{0, 1, \ldots\}$, whereas tensorplay.Tensor.geometric_() k-th trial is the first success hence draws samples in $\{1, 2, \ldots\}$.

Example:

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Geometric(tensorplay.tensor([0.3]))
>>> m.sample()  # underlying Bernoulli has 30% chance 1; 70% chance 0
tensor([ 2.])

Parameters:

• probs (Number, Tensor) – the probability of sampling 1. Must be in range (0, 1]

• logits (Number, Tensor) – the log-odds of sampling 1.

property batch_shape: Size

Returns the shape over which parameters are batched.

cdf(value: Tensor) → Tensor

Returns the cumulative density/mass function evaluated at value.

Parameters:

value (Tensor)

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: Tensor) → Tensor

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

Parameters:

value (Tensor)

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.

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.