random

deeptrack.backend.array_api_compat_ext.torch.random.random(size: tuple[int, ...] | None = None) → Tensor

Sample uniform random numbers in [0, 1).

This function mirrors numpy.random.random, which takes the output shape as a tuple. If size is None, a scalar 0D tensor is returned.

Parameters

size: tuple[int, …] | None, optional

Output shape. If None, returns a scalar tensor.

Returns

torch.Tensor

A tensor of shape size (or scalar if size is None) with values sampled uniformly from [0, 1).

Examples

>>> import deeptrack.backend.array_api_compat_ext.torch.random as rnd

>>> rnd.random((2, 3)).shape
torch.Size([2, 3])

Scalar sample:

>>> rnd.random()
tensor(0.1124)