Java Utililty Methods Gauss

double gauss(double mean, double deviation, double x)
Returns value of Normal/Gaussian distribution for .

double exp = (x - mean) * (x - mean) * (-1) / (2 * deviation * deviation);
double denom = deviation * Math.sqrt(2 * Math.PI);
return Math.exp(exp) / denom;

void gauss(double[] A, int m, int n)
Performs gaussian elimination on the m by n matrix A.

int i = 0;
int j = 0;
while (i < m && j < n) {
 int rowstart = i * n;
 int maxi = i;
 double maxpivot = A[rowstart + j];
 for (int k = i + 1; k < m; k++) {
 double newpivot = A[k * n + j];
...

double gauss(final double mean, final double sigma, final double x)
gauss

return Math.exp(-0.5 * Math.pow((x - mean) / sigma, 2.0)) / (sigma * ROOT2PI);

double[] gauss(int N, long seed)
gauss

int i;
double[] uniftmp = uniform(2 * N, seed);
double[] g = new double[N];
for (i = 0; i < N; i++) {
 g[i] = Math.sqrt(-2 * Math.log(uniftmp[i])) * Math.cos(2 * Math.PI * uniftmp[N + i]);
 if (g[i] == 0.0) {
 g[i] = 1e-99;
return g;

double[] GaussElimination(double a[][])
Implements a Gaussian elimination on the given matrix.

int n = a.length;
return GaussElimination(n, a);

void gaussian(double a[][], int index[])
gaussian

int n = index.length;
double c[] = new double[n];
for (int i = 0; i < n; ++i) {
 index[i] = i;
for (int i = 0; i < n; ++i) {
 double c1 = 0;
 for (int j = 0; j < n; ++j) {
...

double gaussian(double mu, double sigma, double x)
gaussian

return Math.exp(-0.5 * Math.pow((x - mu), 2) / Math.pow(sigma, 2))
 / (Math.sqrt(2 * Math.PI * Math.pow(sigma, 2)));

double gaussian(double mu, double sigma, double x)
Gaussian probabilty density function

return (1 / (sigma * Math.sqrt(2.0 * Math.PI))) * Math.exp(-0.5 * Math.pow(((x - mu) / sigma), 2));

double gaussian(double t)
Satisfies Integral[gaussian(t),t,0,1] == 1D Therefore can distribute a value as a bell curve over the intervel 0 to 1

t = 10D * t - 5D;
return 1D / (Math.sqrt(5D) * Math.sqrt(2D * Math.PI)) * Math.exp(-t * t / 20D);

double gaussian(double x, double sigma)
gaussian

sigma = sigma * sigma;
return (1.0 / (2 * Math.PI * sigma)) * Math.exp(-x * x / (2 * sigma));