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Tests to compare performance of various methods to generate random numbers according to a Normal Distribution.
Each of the tested function generate a pair of random numbers. In an actual implementation, the second number would actually be cached to be returned for the next successive call.
To apply mean and standard deviation, apply the following to each returned value r: x = mean + stdDev * r
function randBoxMuller() {
var u, v, a, b, x, y;
u = Math.random();
v = Math.random();
a = Math.sqrt(-2 * Math.log(u));
b = 2 * Math.PI * v;
x = a * Math.cos(b);
y = a * Math.sin(b);
return [x, y];
}
function randMarsaglia() {
var u, v, s, a, x, y;
do {
u = Math.random() * 2 - 1;
v = Math.random() * 2 - 1;
s = u * u + v * v;
} while (s >= 1 || s == 0)
a = Math.sqrt(-2.0 * Math.log(s) / s);
x = u * a;
y = v * a;
return [x, y];
}
function randApproximation() {
var x, y;
// this method doesn't actually need to generate a pair of randoms
// it is doing so in order to compare to the other methods
x = (Math.random() + Math.random() + Math.random() + Math.random() + Math.random() + Math.random()) / 6;
y = (Math.random() + Math.random() + Math.random() + Math.random() + Math.random() + Math.random()) / 6;
return [x, y]
}
function randRatioOfUniforms() {
var u, v, x, y, q;
do {
u = Math.random();
v = 1.7156 * (Math.random() - 0.5);
x = u - 0.449871;
y = Math.abs(v) + 0.386595;
q = x*x + y*(0.19600*y - 0.25472*x);
} while (q > 0.27597 && (q > 0.27846 || v*v > -4.0 * Math.log(u)*u*u));
return [v/u, 0];
}Ready to run.
| Test | Ops/sec | |
|---|---|---|
| Box-Muller Method | | ready |
| Marsaglia Polar Method | | ready |
| Approximation Method | | ready |
| Ratio of Uniforms | | ready |
You can edit these tests or add more tests to this page by appending /edit to the URL.