NumpyBernoulliDistribution

class probly.representation.distribution.numpy_bernoulli.NumpyBernoulliDistribution(*args, **kwargs)

Bases: BernoulliDistribution, NumpyCategoricalDistribution, ABC

A Bernoulli distribution represented as a categorical distribution with 2 classes.

property T: Self

The transposed version of the underlying array.

abstract property array: ndarray

Get the underlying array representing the categorical distribution.

astype(dtype: DTypeLike, order: Order = 'K', casting: Literal['no', 'equiv', 'safe', 'same_kind', 'unsafe'] = 'unsafe', subok: bool = True, copy: bool = True) → Self

Cast each protected field using ndarray.astype’s casting and layout options.

copy(order: Order = 'C') → Self

Return a copy of the array.

property device: str

Device of the array.

property dtype: dtype

Data type of the array.

entropy() → ArrayLike

Compute entropy.

property flags: ArrayFlagsLike

The flags of the array.

property log_probabilities: ndarray

Get the log probabilities of the categorical distribution.

property logits: ndarray

Get logits of the categorical distribution.

property mT: Self

The transposed version of the underlying array.

property ndim: int

Number of dimensions.

property num_classes: int

Get the fixed number of Bernoulli classes.

permitted_functions = {}

permitted_ufuncs = {}

classmethod primary_protected_name() → str

Return the first protected field (dict order).

property probabilities: ndarray

Get the probabilities of the categorical distribution.

protected_axes = {}

property protected_shape: tuple[int, ...]

Protected trailing shape of the primary field.

protected_value() → NumpyProtectedValue

Return the primary protected value.

protected_values(func: Callable | None = None, method: str | None = None) → dict[str, NumpyProtectedValue] | None

Return all protected field values.

The values are preserved as-is and are not coerced to np.ndarray. Optionally takes the function that triggered the call for context. This can be used to conditionally modify the returned values or prevent them from being accessed.

reshape(*shape: int | tuple[int, ...], order: str = 'C', copy: bool | None = None) → Self

Return a copy with reshaped protected values.

sample(num_samples: int = 1, rng: Generator | None = None) → NumpySample[ndarray]

Sample from the categorical distribution (NumPy backend).

property shape: tuple[int, ...]

Shape of the array.

property size: int

Number of elements in the array.

abstractmethod to_categorical() → CategoricalDistribution

Convert to a two-class categorical distribution.

to_device(device: Any, /, *, stream: Any = None) → Self

Move the array to a device.

transpose(*axes: int | None) → Self

Return a transposed version of the NumpySample.

This method implicitly also provides full axis tracking support for - np.moveaxis - np.rollaxis Those functions call out to transpose methods for custom array types.

Parameters:

axes – The axes to transpose.

Returns:

A transposed version of the NumpySample.

type: Literal['bernoulli'] = 'bernoulli'

property unnormalized_probabilities: ndarray

Get unnormalized probabilities of the categorical distribution.

with_protected_values(values: dict[str, Any], func: Callable | None = None) → NumpyAxisProtected[np.ndarray]

Return a copy with a replaced primary protected value.