randn

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randn#

class brainstate.random.randn(*dn, key=None, dtype=None)#

Return a sample (or samples) from the “standard normal” distribution.

If positive int_like arguments are provided, randn generates an array of shape (d0, d1, ..., dn), filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled from the distribution is returned if no argument is provided.

Parameters:

  • d0 (int, optional) – The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

  • d1 (int, optional) – The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

  • ... (int, optional) – The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

  • dn (int, optional) – The dimensions of the returned array, must be non-negative. If no argument is given a single Python float 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:

Z – A (d0, d1, ..., dn)-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied.

Return type:

ndarray or float

See also

standard_normal

Similar, but takes a tuple as its argument.

normal

Also accepts mu and sigma arguments.

Notes

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

sigma * brainstate.random.randn(...) + mu

Examples

Generate a single random number from standard normal distribution:

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

Generate a 2x4 array of standard normal samples:

>>> arr = brainstate.random.randn(2, 4)
>>> print(arr.shape)  # (2, 4)

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

>>> arr = 3 + 2.5 * brainstate.random.randn(2, 4)
>>> print(arr.shape)  # (2, 4)
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