standard_normal(self, size=None, chunks='auto', **kwargs)
This docstring was copied from numpy.random.mtrand.RandomState.standard_normal.
Some inconsistencies with the Dask version may exist.
New code should use the standard_normal
method of a default_rng()
instance instead; please see the :None:ref:`random-quick-start`
.
For random samples from $N(\mu, \sigma^2)$ , use one of:
mu + sigma * np.random.standard_normal(size=...) np.random.normal(mu, sigma, size=...)
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.
A floating-point array of shape size
of drawn samples, or a single sample if size
was not specified.
Draw samples from a standard Normal distribution (mean=0, stdev=1).
Generator.standard_normal
which should be used for new code.
normal
Equivalent function with additional loc
and scale
arguments for setting the mean and standard deviation.
>>> np.random.standard_normal() # doctest: +SKIP 2.1923875335537315 #randomThis example is valid syntax, but we were not able to check execution
>>> s = np.random.standard_normal(8000) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... s # doctest: +SKIP array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random -0.38672696, -0.4685006 ]) # random
>>> s.shape # doctest: +SKIP (8000,)This example is valid syntax, but we were not able to check execution
>>> s = np.random.standard_normal(size=(3, 4, 2)) # doctest: +SKIP
... s.shape # doctest: +SKIP (3, 4, 2)
Two-by-four array of samples from $N(3, 6.25)$ :
This example is valid syntax, but we were not able to check execution>>> 3 + 2.5 * np.random.standard_normal(size=(2, 4)) # doctest: +SKIP array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # randomSee :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.random.RandomState.standard_t
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