numpy.random.RandomState.randn#
method
- random.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 likenumpy.zeros
andnumpy.ones
.Note
New code should use the
standard_normal
method of aGenerator
instance instead; please see the 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.- Parameters:
- d0, d1, …, dnint, optional
The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.
- Returns:
- Zndarray or float
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_normal
Similar, but takes a tuple as its argument.
normal
Also accepts mu and sigma arguments.
random.Generator.standard_normal
which should be used for new code.
Notes
For random samples from the normal distribution with mean
mu
and standard deviationsigma
, use:sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn() 2.1923875335537315 # random
Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5:
>>> 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