Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or int-convertible arguments are provided,
randn generates an array of shape (d1, ..., dn), filled
with random floats sampled from a univariate “normal” (Gaussian)
distribution of mean 0 and variance 1 (if any of the
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are
floats, they are first converted to integers by truncation). A single
float randomly sampled from the distribution is returned if no
argument is provided.
This is a convenience function. If you want an interface that takes a
tuple as the first argument, use numpy.random.standard_normal instead.
| Parameters : | d1, ..., dn : n ints, optional
The dimensions of the returned array, should be all positive.
|
|---|
| Returns : | Z : ndarray or float
A (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
- random.standard_normal
- Similar, but takes a tuple as its argument.
Notes
For random samples from
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, use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #random
Two-by-four array of samples from N(3, 6.25):
>>> 2.5 * np.random.randn(2, 4) + 3
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
