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digitalmars.D.learn - Normal distributed rand

reply "nobody" <somebody somewhere.com> writes:
Hi,
Does anyone know of a normal/gaussian distributed random number generator 
for D1 + phobos?
Apr 08 2009
parent reply bearophile <bearophileHUGS lycos.com> writes:
nobody:

 Does anyone know of a normal/gaussian distributed random number generator 
 for D1 + phobos? 


I have created this for you, not much (unit)tested yet, adapted from Python
random module, I'll put it in my dlibs. Maybe there are ways to do this in a
faster way (especially if you just need an approximate normal distribution).
Also take a look at Tango source code, it too surely has a similar
function/method.

import std.math: sin, cos, log, sqrt, PI, isnan;
import std.random: rand;

double random() {
    return rand() / cast(double)uint.max;
}

/*************************************
Normal distribution.
mu is the mean, and sigma is the standard deviation. Not thread safe.
*/
double normal(double mu=0.0, double sigma=1.0) {
    // When x and y are two variables from [0, 1), uniformly
    // distributed, then
    //
    //    cos(2*pi*x)*sqrt(-2*log(1-y))
    //    sin(2*pi*x)*sqrt(-2*log(1-y))
    //
    // are two *independent* variables with normal distribution
    // (mu = 0, sigma = 1).
    // (Lambert Meertens)
    static double gauss_next; // nan
    auto z = gauss_next;
    gauss_next = double.init; // nan
    if (isnan(z)) {
        double x2pi = random() * PI * 2;
        double g2rad = sqrt(-2.0 * log(1.0 - random()));
        z = cos(x2pi) * g2rad;
        gauss_next = sin(x2pi) * g2rad;
    }
    return mu + z * sigma;
}


//--------------------------------

import std.string: repeat;
import std.stdio: put = writef, putr = writefln;

void main() {
    auto bins = new int[30];

    for (int i; i < 10000; i++) {
        auto r = cast(int)normal(bins.length / 2, 5);
        if (r < 0)
            r = 0;
        if (r > (bins.length - 1))
            r = bins.length - 1;
        bins[r]++;
    }

    foreach (count; bins)
        putr(">", "*".repeat(count / 20));
}

Bye,
bearophile
Apr 08 2009
parent "nobody" <somebody somewhere.com> writes:
"bearophile" <bearophileHUGS lycos.com> wrote in message 
news:grjgtt$2uti$1 digitalmars.com...

 nobody:

 Does anyone know of a normal/gaussian distributed random number generator
 for D1 + phobos?


 I have created this for you, not much (unit)tested yet, adapted from 
 Python random module, I'll put it in my dlibs. Maybe there are ways to do 
 this in a faster way (especially if you just need an approximate normal 
 distribution).
 Also take a look at Tango source code, it too surely has a similar 
 function/method.

 import std.math: sin, cos, log, sqrt, PI, isnan;
 import std.random: rand;

 double random() {
    return rand() / cast(double)uint.max;
 }

 /*************************************
 Normal distribution.
 mu is the mean, and sigma is the standard deviation. Not thread safe.
 */
 double normal(double mu=0.0, double sigma=1.0) {
    // When x and y are two variables from [0, 1), uniformly
    // distributed, then
    //
    //    cos(2*pi*x)*sqrt(-2*log(1-y))
    //    sin(2*pi*x)*sqrt(-2*log(1-y))
    //
    // are two *independent* variables with normal distribution
    // (mu = 0, sigma = 1).
    // (Lambert Meertens)
    static double gauss_next; // nan
    auto z = gauss_next;
    gauss_next = double.init; // nan
    if (isnan(z)) {
        double x2pi = random() * PI * 2;
        double g2rad = sqrt(-2.0 * log(1.0 - random()));
        z = cos(x2pi) * g2rad;
        gauss_next = sin(x2pi) * g2rad;
    }
    return mu + z * sigma;
 }


 //--------------------------------

 import std.string: repeat;
 import std.stdio: put = writef, putr = writefln;

 void main() {
    auto bins = new int[30];

    for (int i; i < 10000; i++) {
        auto r = cast(int)normal(bins.length / 2, 5);
        if (r < 0)
            r = 0;
        if (r > (bins.length - 1))
            r = bins.length - 1;
        bins[r]++;
    }

    foreach (count; bins)
        putr(">", "*".repeat(count / 20));
 }

 Bye,
 bearophile


Oh wow, thanks!

I think this will suit my purposes just fine.
And the main output is neat :)
Apr 09 2009