Java Utililty Methods Gauss

List of utility methods to do Gauss

  1. HOME
  2. Java
  3. G
  4. Gauss

Description

The list of methods to do Gauss are organized into topic(s).

Method

doublegauss(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;
voidgauss(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];
...
doublegauss(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);
voidgaussian(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) {
...
doublegaussian(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)));
doublegaussian(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));
doublegaussian(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);
doublegaussian(double x, double sigma)
gaussian 
sigma = sigma * sigma;
return (1.0 / (2 * Math.PI * sigma)) * Math.exp(-x * x / (2 * sigma));