rand

deeptrack.backend.array_api_compat_ext.torch.random.rand(*size: int) → Tensor

Sample uniform random numbers in [0, 1) with a given shape.

This function mirrors numpy.random.rand, i.e., it takes the output shape as positional integer arguments.

Parameters

*size: int

Output shape given as positional integers. If empty, returns a scalar 0D tensor.

Returns

torch.Tensor

A tensor of shape size (or scalar if size is empty) with values sampled uniformly from [0, 1).

Examples

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

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

Scalar sample:

>>> rnd.rand()
tensor(0.1735)