flopscope.

flopscope.numpy.random.standard_normal

fnp.random.standard_normal(size=None)[flopscope source]

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

Adapted from NumPy docs np.random.standard_normal

Arearandom
Typecustom
NumPy Refnp.random.standard_normal
Cost
16×per-operation
Flopscope Context

Sampling; cost = numel(output).

Note.

New code should use the standard_normal method of a Generator instance instead; please see the random-quick-start.

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:float or ndarray

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

See also

Notes

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

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

Examples

>>> flops.random.standard_normal()
2.1923875335537315 #random
>>> s = flops.random.standard_normal(8000)
>>> s
array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
       -0.38672696, -0.4685006 ])                                # random
>>> s.shape
(8000,)
>>> s = flops.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 * flops.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