contiki/tools/cooja/apps/mrm/java/statistics/Gaussian.java

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package statistics;
// Gaussian CDF Taylor approximation
// Code borrowed from http://www.cs.princeton.edu/introcs/21function/Gaussian.java.html 19/9 2006
/*************************************************************************
* Compilation: javac Gaussian.java
* Execution: java Gaussian x mu sigma
*
* Function to compute the Gaussian pdf (probability density function)
* and the Gaussian cdf (cumulative density function)
*
* % java Gaussian 820 1019 209
* 0.17050966869132111
*
* % java Gaussian 1500 1019 209
* 0.9893164837383883
*
* % java Gaussian 1500 1025 231
* 0.9801220907365489
*
*************************************************************************/
public class Gaussian {
// return phi(x) = standard Gaussian pdf
public static double phi(double x) {
return Math.exp(-x*x / 2) / Math.sqrt(2 * Math.PI);
}
// return phi(x) = Gaussian pdf with mean mu and stddev sigma
public static double phi(double x, double mu, double sigma) {
return phi((x - mu) / sigma) / sigma;
}
// return Phi(z) = standard Gaussian cdf using Taylor approximation
public static double Phi(double z) {
if (z < -8.0) return 0.0;
if (z > 8.0) return 1.0;
double sum = 0.0, term = z;
for (int i = 3; sum + term != sum; i += 2) {
sum = sum + term;
term = term * z * z / i;
}
return 0.5 + sum * phi(z);
}
// return Phi(z, mu, sigma) = Gaussian cdf with mean mu and stddev sigma
public static double Phi(double z, double mu, double sigma) {
return Phi((z - mu) / sigma);
}
public static void main(String[] args) {
double z = Double.parseDouble(args[0]);
double mu = Double.parseDouble(args[1]);
double sigma = Double.parseDouble(args[2]);
System.out.println(Phi(z, mu, sigma));
}
}