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Difference between functions generating random numbers in numpy

I am trying to understand what is the difference, if any, between these functions:

numpy.random.rand()

numpy.random.random()

numpy.random.uniform()

It seems that they are randomly sampling from a uniform distribution. So, without any parameter in the function, is there any difference?

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2 answers

numpy.random.uniform(low=0.0, high=1.0, size=None) - homogeneous samples from an arbitrary range

Draw patterns from a uniform distribution.
Samples are evenly distributed over a half-open interval [low, high)(includes low, but excludes high). In other words, any value within a given interval is equally likely to be uniform.

numpy.random.random(size=None) - uniform distribution between 0 and 1

[0.0, 1.0).
" " . Unif[a, b), b > a random_sample by (b-a) : (b - a) * random_sample() + a

numpy.random.rand(d0, d1, ..., dn) -

.
[0, 1).

, , numpy.random.uniform, numpy.random.random numpy.random.rand .

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, [0.0,1.0].

numpy.random.rand - , . numpy.random , numpy.zeros numpy.ones , , .. N- ( Python). ( ):

import numpy as np
x = np.random.random_sample((1,2,3)) # a single tuple as parameter
x = np.random.rand(1,2,3) # integers as parameters

numpy.random.random numpy.random.random_sample.

numpy.random.uniform low high [0.0,1.0].

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