# 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 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 $$d_i$$ 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 floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied.

Return type

Tensor or float

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


Two-by-four 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