nlcpy.random.Generator.standard_normal

Generator.standard_normal(self, size=None, dtype='d', out=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.
dtypestr or dtype, optional: Desired dtype of the result, either ‘d’ (or ‘float64’) or ‘f’ (or ‘float32’). All dtypes are determined by their name. The default value is ‘d’.
outndarray, optional: Alternative output array in which to place the result. If size is not None, it must have the same shape as the provided size and must match the type of the output values.

Returns

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

See also

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

Note

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

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

Examples

>>> import nlcpy as vp
>>> rng = vp.random.default_rng()
>>> rng.standard_normal()    
array(0.07802166) # random

>>> s = rng.standard_normal(8000)
>>> s  
array([-0.66533529, -0.26800564,  0.35053523, ..., -0.77485594,
       -0.31695012, -0.59517798])
>>> s.shape
(8000,)
>>> s = rng.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 * rng.standard_normal(size=(2, 4)) 
array([[2.85310426, 0.13495647, 0.04238584, 4.33929263],
       [3.61694001, 7.61121584, 2.65205908, 2.07678931]])