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package util;
import java.util.Random;
public class MathUtil {
public static final boolean closeToOne(double number){
return Math.abs(number-1) < 1.E-10;
}
public static final boolean closeToZero(double number){
return Math.abs(number) < 1.E-5;
}
/**
* Return a ramdom multinominal distribution.
*
* @param size
* @return
*/
public static final double[] randomVector(int size, Random r){
double[] random = new double[size];
double sum=0;
for(int i = 0; i < size; i++){
double number = r.nextDouble();
random[i] = number;
sum+=number;
}
for(int i = 0; i < size; i++){
random[i] = random[i]/sum;
}
return random;
}
public static double sum(double[] ds) {
double res = 0;
for (int i = 0; i < ds.length; i++) {
res+=ds[i];
}
return res;
}
public static double max(double[] ds) {
double res = Double.NEGATIVE_INFINITY;
for (int i = 0; i < ds.length; i++) {
res = Math.max(res, ds[i]);
}
return res;
}
public static double min(double[] ds) {
double res = Double.POSITIVE_INFINITY;
for (int i = 0; i < ds.length; i++) {
res = Math.min(res, ds[i]);
}
return res;
}
public static double KLDistance(double[] p, double[] q) {
int len = p.length;
double kl = 0;
for (int j = 0; j < len; j++) {
if (p[j] == 0 || q[j] == 0) {
continue;
} else {
kl += q[j] * Math.log(q[j] / p[j]);
}
}
return kl;
}
public static double L2Distance(double[] p, double[] q) {
int len = p.length;
double l2 = 0;
for (int j = 0; j < len; j++) {
if (p[j] == 0 || q[j] == 0) {
continue;
} else {
l2 += (q[j] - p[j])*(q[j] - p[j]);
}
}
return Math.sqrt(l2);
}
public static double L1Distance(double[] p, double[] q) {
int len = p.length;
double l1 = 0;
for (int j = 0; j < len; j++) {
if (p[j] == 0 || q[j] == 0) {
continue;
} else {
l1 += Math.abs(q[j] - p[j]);
}
}
return l1;
}
public static double dot(double[] ds, double[] ds2) {
double res = 0;
for (int i = 0; i < ds2.length; i++) {
res+= ds[i]*ds2[i];
}
return res;
}
public static double expDigamma(double number){
return Math.exp(digamma(number));
}
public static double digamma(double number){
if(number > 7){
return digammApprox(number-0.5);
}else{
return digamma(number+1) - 1.0/number;
}
}
private static double digammApprox(double value){
return Math.log(value) + 0.04167*Math.pow(value, -2) - 0.00729*Math.pow(value, -4)
+ 0.00384*Math.pow(value, -6) - 0.00413*Math.pow(value, -8);
}
public static double eulerGamma = 0.57721566490152386060651209008240243;
// FIXME -- so far just the initialization from Minka's paper "Estimating a Dirichlet distribution".
public static double invDigamma(double y) {
if (y>= -2.22) return Math.exp(y)+0.5;
return -1.0/(y+eulerGamma);
}
public static void main(String[] args) {
for(double i = 0; i < 10 ; i+=0.1){
System.out.println(i+"\t"+expDigamma(i)+"\t"+(i-0.5));
}
// double gammaValue = (expDigamma(3)/expDigamma(10) + expDigamma(3)/expDigamma(10) + expDigamma(4)/expDigamma(10));
// double normalValue = 3/10+3/4+10/10;
// System.out.println("Gamma " + gammaValue + " normal " + normalValue);
}
}
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