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)
, thenm * n * k
samples are drawn. If size isNone
(default), a scalar tensor containing a single value is returned iflow
andhigh
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
See also
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 oflow
will be returned. Ifhigh
<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.