I'm currently trying to draw floating numbers from a uniform distribution.
The Numpy provides numpy.random.uniform.
import numpy as np
sample = np.random.uniform (0, 1, size = (N,) + (2,) + (2,) * K)
However, this module generates values over the half-open interval [0, 1).
How can I draw floating numbers with [0, 1] from a uniform distribution?
Thanks.
Use rand to generate 1000 random numbers from the uniform distribution on the interval (0,1). rng('default') % For reproducibility u = rand(1000,1);
To generate random numbers from the Uniform distribution we will use random. uniform() method of random module. In uniform distribution samples are uniformly distributed over the half-open interval [low, high) it includes low but excludes high interval.
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 .
It doesn't matter if you're drawing the uniformly distributed numbers from (0,1) or [0,1] or [0,1) or (0,1]. Because the probability of getting 0 or 1 is zero.
random_integers generates integers on a closed interval. So, if you can recast the actual problem of yours into using integers, you're all set. Otherwise, you may consider if granularity of 1./MAX_INT is sufficient to your problem.
From the standard Python random.uniform documentation :
The end-point value b may or may not be included in the range depending on floating-point rounding in the equation a + (b-a) * random().
So basically, the inclusion of the end point is strictly based on the floating-point rounding scheme used. Therefore, to include 1.0, you need to define the precision required by your operation and round the random number accordingly. If you do not have a defined precision for your problem, you can use numpy.nextafter. Its usage was covered by a previous answer.
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