// Sample from a normal distribution with mean 0, stddev 1.

function normal() {

var x = 0, y = 0, rds, c;

do {

x = Math.random() * 2 - 1;

y = Math.random() * 2 - 1;

rds = x * x + y * y;

} while (rds == 0 || rds > 1);

c = Math.sqrt(-2 * Math.log(rds) / rds); // Box-Muller transform

return x * c; // throw away extra sample y * c

}

// Simple 1D Gaussian (normal) distribution

function normal1(mean, deviation) {

return function() {

return mean + deviation * normal();

};

}

// Gaussian Mixture Model (k=3) fit using E-M algorithm

function normal3(dd) {

return function() {

var r = Math.random(),

i = r < dd[0][2] ? 0 : r < dd[0][2] + dd[1][2] ? 1 : 2,

d = dd[i];

return d[0] + Math.sqrt(d[1]) * normal();

}

}