brainpy.math.random.standard_normal

brainpy.math.random.standard_normal(size=None, key=None)

Draw samples from a standard Normal distribution (mean=0, stdev=1).

Parameters:

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. Default is None, in which case a single value is returned.

Returns:

out – A floating-point array of shape size of drawn samples, or a single sample if size was not specified.

Return type:

float or ndarray

See also

normal

Equivalent function with additional loc and scale arguments for setting the mean and standard deviation.

Notes

For random samples from the normal distribution with mean mu and standard deviation sigma, use one of:

mu + sigma * bm.random.standard_normal(size=...)
bm.random.normal(mu, sigma, size=...)

Examples

>>> bm.random.standard_normal()
2.1923875335537315 #random

>>> s = bm.random.standard_normal(8000)
>>> s
array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
       -0.38672696, -0.4685006 ])                                # random
>>> s.shape
(8000,)
>>> s = bm.random.standard_normal(size=(3, 4, 2))
>>> s.shape
(3, 4, 2)

Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5:

>>> 3 + 2.5 * bm.random.standard_normal(size=(2, 4))
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random