standard_normal

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standard_normal#

class brainstate.random.standard_normal(size=None, key=None, dtype=None)#

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

Parameters:

  • size (int | Sequence[int] | integer | Sequence[integer] | None) – 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.

  • key (int | Array | ndarray | None) – The key for the random number generator. If not given, the default random number generator is used.

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

Examples

Generate a single standard normal sample:

>>> import brainstate
>>> val = brainstate.random.standard_normal()
>>> print(type(val))  # <class 'numpy.float64'>

Generate an array of 8000 standard normal samples:

>>> s = brainstate.random.standard_normal(8000)
>>> print(s.shape)  # (8000,)

Generate a 3x4x2 array of standard normal samples:

>>> s = brainstate.random.standard_normal(size=(3, 4, 2))
>>> print(s.shape)  # (3, 4, 2)

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

>>> samples = 3 + 2.5 * brainstate.random.standard_normal(size=(2, 4))
print(samples.shape)  # (2, 4)
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