The standard random number generator you’ve got (rand() in C after a simple transformation, equivalents in many languages) is a fairly good approximation to a uniform distribution over the range [0,1]. If that’s what you need, you’re done. It’s also trivial to convert that to a random number generated over a somewhat larger integer range. … Read more
Update for the latest versions of matplotlib (3.3.4) and seaborn (0.11.1): the kdeplot with shade=True now doesn’t create a line object anymore. To get the same outcome as before, setting shade=False will still create the line object. The curve can then be filled with ax.fill_between(). The code below is changed accordingly. (Use the revision history … Read more
One simple trick is to select each dimension from a gaussian distribution, then normalize: from random import gauss def make_rand_vector(dims): vec = [gauss(0, 1) for i in range(dims)] mag = sum(x**2 for x in vec) ** .5 return [x/mag for x in vec] For example, if you want a 7-dimensional random vector, select 7 random … Read more
My guess is that you want to estimate the shape parameter and the scale of the Weibull distribution while keeping the location fixed. Fixing loc assumes that the values of your data and of the distribution are positive with lower bound at zero. floc=0 keeps the location fixed at zero, f0=1 keeps the first shape … Read more
Distribution objects are lightweight. Simply construct a new distribution when you need a random number. I use this approach in a game engine, and, after benchmarking, it’s comparable to using good old rand(). Also, I’ve asked how to vary the range of distribution on GoingNative 2013 live stream, and Stephen T. Lavavej, a member of … Read more
Since this is the first Google result for “js gaussian random” in my experience, I feel an obligation to give an actual answer to that query. The Box-Muller transform converts two independent uniform variates on (0, 1) into two standard Gaussian variates (mean 0, variance 1). This probably isn’t very performant because of the sqrt, … Read more
Using the Statistics Toolbox The randsample function can do that directly: result = randsample(supp_epsilon, n, true, pr_mass_epsilon); Without using toolboxes Manual approach: Generate n samples of a uniform random variable in the interval (0,1). Compare each sample with the distribution function (cumulative sum of mass function). See in which interval of the distribution function each … Read more
It’s not a bug. It’s not an incorrect result either. Probability density function’s value at some specific point does not give you probability; it is a measure of how dense the distribution is around that value. For continuous random variables, the probability at a given point is equal to zero. Instead of p(X = x), … Read more
I don’t think there is a better way. Unless the DLLs are explicitly loaded by path name, you would just need to make sure they are in the system path, so if they lived right next to the JVM executables it should also work. Windows does implicitly include the directory the program was started from … Read more
Visualizing all scipy.stats distributions Based on the list of scipy.stats distributions, plotted below are the histograms and PDFs of each continuous random variable. The code used to generate each distribution is at the bottom. Note: The shape constants were taken from the examples on the scipy.stats distribution documentation pages. alpha(a=3.57, loc=0.00, scale=1.00) anglit(loc=0.00, scale=1.00) arcsine(loc=0.00, … Read more