flopscope.

flopscope.numpy.random.Generator.standard_normal

fnp.random.Generator.standard_normal(self, size=None, dtype=<class 'numpy.float64'>, out=None)

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

Adapted from NumPy docs np.random.Generator.standard_normal

Arearandom
Typecounted
NumPy Refnp.random.Generator.standard_normal
Cost
numel(output)\text{numel}(\text{output})
Flopscope Context

Standard normal; cost = numel(output).

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.

dtype:dtype, optional

Desired dtype of the result, only float64 and float32 are supported. Byteorder must be native. The default value is flops.float64.

out:ndarray, 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

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

  • 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 * rng.standard_normal(size=...)
rng.normal(mu, sigma, size=...)

Examples

>>> rng = flops.random.default_rng()
>>> rng.standard_normal()
2.1923875335537315 # random
>>> s = rng.standard_normal(8000)
>>> s
array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
       -0.38672696, -0.4685006 ])                                # random
>>> s.shape
(8000,)
>>> s = rng.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 * rng.standard_normal(size=(2, 4))
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random