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 produce a random sample from a uniform distribution. So, without any parameter in the function, is there any difference?
numpy.random.uniform(low=0.0, high=1.0, size=None) - uniform samples from arbitrary range
Draw samples from a uniform distribution.
Samples are uniformly distributed over the half-open interval[low, high)(includes low, but excludes high). In other words, any value within the given interval is equally likely to be drawn by uniform.
numpy.random.random(size=None) - uniform distribution between 0 and 1
Return random floats in the half-open interval
[0.0, 1.0).
Results are from the “continuous uniform” distribution over the stated interval. To sampleUnif[a, b),b > amultiply the output ofrandom_sample by(b-a) and add a:
(b - a) * random_sample() + a
numpy.random.rand(d0, d1, ..., dn) - Samples from a uniform distribution to populate an array of a given shape
Random values in a given shape.
Create an array of the given shape and propagate it with random samples from a uniform distribution over[0, 1).
To answer your other question, given all default parameters all of the functions numpy.random.uniform, numpy.random.random, and numpy.random.rand are identical.
numpy.random populate an array rather than create a single value (if no shape is specified, they return a single value). The main difference between np.random.rand and np.random.random is in how the shape of the array is specified. data = np.random.random([10, 10]) instead of data = np.random.rand(10, 10).Without parameters, the three functions are equivalent, producing a random float in the range [0.0,1.0).
numpy.random.rand is a convenience function that accepts an arbitrary number of parameters as dimensions. It's different from the other numpy.random functions, numpy.zeros, and numpy.ones also, in that all of the others accept shapes, i.e. N-tuples (specified as Python lists or tuples). The following two lines produce identical results (the random seed notwithstanding):
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 is an alias for numpy.random.random_sample.
numpy.random.uniform allows you to specify the limits of the distribution, with the low and high keyword parameters, instead of using the default [0.0,1.0).
numpy.random.rand was designed to mimic MATLAB's rand function which, incidentally, now also accepts shapes as arguments.