Class RNG

imperative/python/megengine/random/rng.py:287–709

r""":class:`RNG` exposes a number of methods for generating random numbers. Args: seed: random seed used to initialize the pseudo-random number generator. Default: None device: the device of generated tensor. Default: None Examples: >>> import megengine.random as r

class RNG:

    r""":class:`RNG` exposes a number of methods for generating random numbers.

    Args:
        seed: random seed used to initialize the pseudo-random number generator. Default: None
        device: the device of generated tensor. Default: None


    Examples:
        >>> import megengine.random as rand
        >>> rng = rand.RNG(seed=100)
        >>> x = rng.uniform(size=(2, 2))
        >>> x.numpy()   # doctest: +SKIP
        array([[0.84811664, 0.6147553 ],
               [0.59429836, 0.64727545]], dtype=float32)
    """

    def __init__(self, seed: int = None, device: str = None):
        self._device = device if device else get_default_device()
        if seed is not None:
            self._seed = seed
            self._handle = _new_rng_handle(self._device, self._seed)
        else:
            self._seed = _get_global_rng_seed
            self._handle = 0
            self._device = None

    def uniform(
        self, low: float = 0, high: float = 1, size: Optional[Iterable[int]] = None
    ):
        r"""Random variable with uniform distribution :math:`U(low, high)`.

        Args:
            low(float): lower range. Default: 0.
            high(float): upper range. Default: 1.
            size(Optional[Iterable[int]]): the size of output tensor. Default: None.

        Returns:
            Return type: tensor. The random variable with uniform distribution.

        Examples:
            >>> import megengine.random as rand
            >>> x = rand.uniform(size=(2, 2))
            >>> x.numpy()   # doctest: +SKIP
            array([[0.28603864, 0.3156649 ],
                   [0.42066026, 0.9805052 ]], dtype=float32)
        """
        _seed = self._seed() if callable(self._seed) else self._seed
        return _uniform(
            low=low,
            high=high,
            size=size,
            seed=_seed,
            device=self._device,
            handle=self._handle,
        )