Binomial

class tensorplay.distributions.Binomial(total_count: Tensor | int = 1, probs: Tensor | None = None, logits: Tensor | None = None, validate_args: bool | None = None)[source]

Creates a Binomial distribution parameterized by total_count and either probs or logits (but not both). total_count must be broadcastable with probs/logits.

Example:

>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = Binomial(100, tensorplay.tensor([0 , .2, .8, 1]))
>>> x = m.sample()
tensor([   0.,   22.,   71.,  100.])

>>> m = Binomial(tensorplay.tensor([[5.], [10.]]), tensorplay.tensor([0.5, 0.8]))
>>> x = m.sample()
tensor([[ 4.,  5.],
        [ 7.,  6.]])

Parameters:

• total_count (int or Tensor) – number of Bernoulli trials

• probs (Tensor) – Event probabilities

• logits (Tensor) – Event log-odds

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)

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.