torchsig.utils.random.UniformDistribution

class torchsig.utils.random.UniformDistribution(params: float, **kwargs)

Bases: Distribution

A class for handling uniform random variables.

Methods

add_parent	Add parent Seedable object and set up RNGs accordingly.
get_distribution	Create distribution function with proper seeding.
get_second_seed	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.
get_value	Samples a random value from the uniform distribution.
seed	Seed number generators with given seed.
setup_rngs	Initialize torch and numpy number generators, and update its children.
update_from_parent	Update numpy and torch number generators with parent seed.

__init__(params: float, **kwargs)

Initialize uniform distribution with given parameters.

Parameters:

• params – Upper bound for the uniform distribution.

• **kwargs – Additional keyword arguments.

get_value() → Any

Samples a random value from the uniform distribution.

Returns:

Random value between 0 and high.

__call__(size: int | None = None) → Any | ndarray

Distribution can return single value or np array of values sampled.

Parameters:

size – Number of values to return. Defaults to None.

Returns:

Value(s) sampled from distribution.

__repr__() → str

Printable representation with params, seed, and parent.

Returns:

String representation of the object.

add_parent(parent: Seedable, register: bool = True) → None

Add parent Seedable object and set up RNGs accordingly.

Parameters:

• parent – Parent Seedable object to add.

• register – If True (default), add self to parent.children so that future seed propagation reaches this object. Pass False for transient objects (e.g. per-sample Signal instances) that only need the parent link for metadata/RNG access during their lifetime but must not accumulate in the parent’s child list, which would otherwise cause unbounded memory growth.

get_distribution(params: list | tuple | float, scaling: str = 'linear') → Distribution

Create distribution function with proper seeding.

Parameters:

• params – Parameters for distribution.

• scaling – Scaling param for distribution. Defaults to ‘linear’.

Returns:

Distribution function, seeded.

Return type:

Distribution

get_second_seed(seed: int) → int

Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.

Parameters:

seed – Seed to use.

Returns:

New seed.

seed(seed: int) → None

Seed number generators with given seed.

Parameters:

seed – Seed to use.

setup_rngs() → None

Initialize torch and numpy number generators, and update its children.

update_from_parent() → None

Update numpy and torch number generators with parent seed.