nlcpy.random.RandomState.standard_normal

RandomState.standard_normal(self, size=None)

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

Parameters

sizeint or tuple of ints, optional: Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn.

Returns

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

See also

RandomState.normal: Draws random samples from a normal (Gaussian) distribution.

Note

For random samples from $N(\mu, \sigma^2)$ , use one of:

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

Examples

>>> import nlcpy as vp
>>> vp.random.standard_normal()  
array(2.96222821)
>>> s = vp.random.standard_normal(8000)
>>> s   
array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
       -0.38672696, -0.4685006 ])                                # random
>>> s.shape
(8000,)
>>> s = vp.random.standard_normal(size=(3, 4, 2))
>>> s.shape
(3, 4, 2)

Two-by-four array of samples from N(3, 6.25):

>>> 3 + 2.5 * vp.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