numpy.random.randn(d0, d1, ..., dn)
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 (d0, d1, ..., dn)
, filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1 (if any of the 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: |
d0, d1, …, dn : int, optional The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned. |
---|---|
Returns: |
Z : ndarray or float A |
See also
random.standard_normal
For random samples from , use:
sigma * np.random.randn(...) + mu
>>> 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
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https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.random.randn.html