dpnp.random.RandomState.uniform¶

method

RandomState.uniform(low=0.0, high=1.0, size=None, dtype=None, usm_type='device')[source]¶

Draw samples from a uniform distribution.

Samples are uniformly distributed over the half-open interval [low, high) (includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.

For full documentation refer to numpy.random.RandomState.uniform.

Parameters:

usm_type{"device", "shared", "host"}, optional

The type of SYCL USM allocation for the output array.

Default: "device".

Returns:

outdpnp.ndarray: Drawn samples from the parameterized uniform distribution. Output array data type is the same as input dtype. If dtype is None (the default), dpnp.float64 type will be used if device supports it, or dpnp.float32 otherwise.

Limitations

Parameters low and high are supported as a scalar. Otherwise, numpy.random.RandomState.uniform(low, high, size) samples are drawn. Parameter dtype is supported only as dpnp.int32, dpnp.float32, dpnp.float64 or None.

See also

dpnp.random.RandomState.randint: Discrete uniform distribution, yielding integers.
dpnp.random.RandomState.random_integers: Discrete uniform distribution over the closed interval [low, high].
dpnp.random.RandomState.random_sample: Floats uniformly distributed over [0, 1).
dpnp.random.RandomState.random: Alias for dpnp.random.RandomState.random_sample.
dpnp.random.RandomState.rand: Convenience function that accepts dimensions as input, e.g., rand(2, 2) would generate a 2-by-2 array of floats, uniformly distributed over [0, 1).

Examples

>>> low, high = 1.23, 10.54    # low and high
>>> s = dpnp.random.RandomState().uniform(low, high, 5)
>>> print(s)
[2.48093112 6.52928804 9.1196081  8.6990877  8.34074171]