mars.tensor.random.randn¶

mars.tensor.random.
randn
(*dn, **kw)[source]¶ Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or intconvertible 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 \(d_i\) 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 (int, optional) – The dimensions of the returned tensor, should be all positive. If no argument is given a single Python float is returned.
d1 (int, optional) – The dimensions of the returned tensor, should be all positive. If no argument is given a single Python float is returned.
.. (int, optional) – The dimensions of the returned tensor, should be all positive. If no argument is given a single Python float is returned.
dn (int, optional) – The dimensions of the returned tensor, should be all positive. If no argument is given a single Python float is returned.
 Returns
Z – A
(d0, d1, ..., dn)
shaped array of floatingpoint samples from the standard normal distribution, or a single such float if no parameters were supplied. Return type
Tensor or float
See also
random.standard_normal
Similar, but takes a tuple as its argument.
Notes
For random samples from \(N(\mu, \sigma^2)\), use:
sigma * mt.random.randn(...) + mu
Examples
>>> import mars.tensor as mt
>>> mt.random.randn().execute() 2.1923875335537315 #random
Twobyfour tensor of samples from N(3, 6.25):
>>> (2.5 * mt.random.randn(2, 4) + 3).execute() array([[4.49401501, 4.00950034, 1.81814867, 7.29718677], #random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random