# mxnet.np.random.uniform¶

uniform(low=0.0, high=1.0, size=None, dtype=None, device=None, out=None)

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.

Parameters
• low (float, ndarray, optional) – Lower boundary of the output interval. All values generated will be greater than or equal to low. The default value is 0.

• high (float, ndarray, optional) – Upper boundary of the output interval. All values generated will be less than high. The default value is 1.0.

• size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. If size is None (default), a scalar tensor containing a single value is returned if low and high are both scalars. Otherwise, np.broadcast(low, high).size samples are drawn.

• dtype ({'float16', 'float32', 'float64'}, optional) – Data type of output samples. When npx.is_np_default_dtype() returns False, default dtype is float32; When npx.is_np_default_dtype() returns True, default dtype is float64.

• device (Device, optional) – Device context of output. Default is current device.

Returns

out – Drawn samples from the parameterized uniform distribution.

Return type

ndarray

randint()

Discrete uniform distribution, yielding integers.

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).

Notes

The probability density function of the uniform distribution is

$p(x) = \frac{1}{b - a}$

anywhere within the interval [a, b), and zero elsewhere.

When high == low, values of low will be returned. If high < low, the results are officially undefined and may eventually raise an error, i.e. do not rely on this function to behave when passed arguments satisfying that inequality condition.