NumPy参考 >例行程序 >Random sampling (numpy.random) >Legacy Generator (RandomState) > numpy.random.RandomState.randn
method
RandomState.randn(d0, d1, ..., dn)¶Return a sample (or samples) from the “standard normal” distribution.
Note
This is a convenience function for users porting code from Matlab,
and wraps standard_normal. That function takes a
tuple to specify the size of the output, which is consistent with
other NumPy functions like numpy.zeros and numpy.ones.
Note
New code should use the standard_normal method of a default_rng()
instance instead; see random-quick-start.
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.
The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.
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.
See also
standard_normalSimilar, but takes a tuple as its argument.
normalAlso accepts mu and sigma arguments.
Generator.standard_normalwhich should be used for new code.
Notes
For random samples from 
, use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 # random
Two-by-four array of samples from N(3, 6.25):
>>> 3 + 2.5 * np.random.randn(2, 4)
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random