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MALLET is a Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text.
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/* Copyright (C) 2002 Univ. of Massachusetts Amherst, Computer Science Dept.
This file is part of "MALLET" (MAchine Learning for LanguagE Toolkit).
http://www.cs.umass.edu/~mccallum/mallet
This software is provided under the terms of the Common Public License,
version 1.0, as published by http://www.opensource.org. For further
information, see the file `LICENSE' included with this distribution. */
/**
@author Andrew McCallum [email protected]
*/
package cc.mallet.util;
import java.util.*;
public class Randoms extends java.util.Random {
public static final double ONE_OVER_E = Math.exp(-1);
public Randoms (int seed) {
super(seed);
}
public Randoms () {
super();
}
/** Return random integer from Poission with parameter lambda.
* The mean of this distribution is lambda. The variance is lambda. */
public synchronized int nextPoisson(double lambda) {
int i,j,v=-1;
double l=Math.exp(-lambda),p;
p=1.0;
while (p>=l) {
p*=nextUniform();
v++;
}
return v;
}
/** Return nextPoisson(1). */
public synchronized int nextPoisson() {
return nextPoisson(1);
}
/** Return a random boolean, equally likely to be true or false. */
public synchronized boolean nextBoolean() {
return (next(32) & 1 << 15) != 0;
}
/** Return a random boolean, with probability p of being true. */
public synchronized boolean nextBoolean(double p) {
double u=nextUniform();
if(u < p) return true;
return false;
}
/** Return a random BitSet with "size" bits, each having probability p of being true. */
public synchronized BitSet nextBitSet (int size, double p)
{
BitSet bs = new BitSet (size);
for (int i = 0; i < size; i++)
if (nextBoolean (p)) {
bs.set (i);
}
return bs;
}
/** Return a random double in the range 0 to 1, inclusive, uniformly sampled from that range.
* The mean of this distribution is 0.5. The variance is 1/12. */
public synchronized double nextUniform() {
long l = ((long)(next(26)) << 27) + next(27);
return l / (double)(1L << 53);
}
/** Return a random double in the range a to b, inclusive, uniformly sampled from that range.
* The mean of this distribution is (b-a)/2. The variance is (b-a)^2/12 */
public synchronized double nextUniform(double a,double b) {
return a + (b-a)*nextUniform();
}
/** Draw a single sample from multinomial "a". */
public synchronized int nextDiscrete (double[] a) {
double b = 0, r = nextUniform();
for (int i = 0; i < a.length; i++) {
b += a[i];
if (b > r) {
return i;
}
}
return a.length-1;
}
/** draw a single sample from (unnormalized) multinomial "a", with normalizing factor "sum". */
public synchronized int nextDiscrete (double[] a, double sum) {
double b = 0, r = nextUniform() * sum;
for (int i = 0; i < a.length; i++) {
b += a[i];
if (b > r) {
return i;
}
}
return a.length-1;
}
private double nextGaussian;
private boolean haveNextGaussian = false;
/** Return a random double drawn from a Gaussian distribution with mean 0 and variance 1. */
public synchronized double nextGaussian() {
if (!haveNextGaussian) {
double v1=nextUniform(),v2=nextUniform();
double x1,x2;
x1=Math.sqrt(-2*Math.log(v1))*Math.cos(2*Math.PI*v2);
x2=Math.sqrt(-2*Math.log(v1))*Math.sin(2*Math.PI*v2);
nextGaussian=x2;
haveNextGaussian=true;
return x1;
}
else {
haveNextGaussian=false;
return nextGaussian;
}
}
/** Return a random double drawn from a Gaussian distribution with mean m and variance s2. */
public synchronized double nextGaussian(double m,double s2) {
return nextGaussian()*Math.sqrt(s2)+m;
}
// generate Gamma(1,1)
// E(X)=1 ; Var(X)=1
/** Return a random double drawn from a Gamma distribution with mean 1.0 and variance 1.0. */
public synchronized double nextGamma() {
return nextGamma(1,1,0);
}
/** Return a random double drawn from a Gamma distribution with mean alpha and variance 1.0. */
public synchronized double nextGamma(double alpha) {
return nextGamma(alpha,1,0);
}
/* Return a sample from the Gamma distribution, with parameter IA */
/* From Numerical "Recipes in C", page 292 */
public synchronized double oldNextGamma (int ia)
{
int j;
double am, e, s, v1, v2, x, y;
assert (ia >= 1) ;
if (ia < 6)
{
x = 1.0;
for (j = 1; j <= ia; j++)
x *= nextUniform ();
x = - Math.log (x);
}
else
{
do
{
do
{
do
{
v1 = 2.0 * nextUniform () - 1.0;
v2 = 2.0 * nextUniform () - 1.0;
}
while (v1 * v1 + v2 * v2 > 1.0);
y = v2 / v1;
am = ia - 1;
s = Math.sqrt (2.0 * am + 1.0);
x = s * y + am;
}
while (x <= 0.0);
e = (1.0 + y * y) * Math.exp (am * Math.log (x/am) - s * y);
}
while (nextUniform () > e);
}
return x;
}
/** Return a random double drawn from a Gamma distribution with mean alpha*beta and variance alpha*beta^2. */
public synchronized double nextGamma(double alpha, double beta) {
return nextGamma(alpha,beta,0);
}
/** Return a random double drawn from a Gamma distribution
* with mean alpha*beta+lamba and variance alpha*beta^2.
* Note that this means the pdf is:
* frac{ x^{alpha-1} exp(-x/beta) }{ beta^alpha Gamma(alpha) }
* in other words, beta is a "scale" parameter. An alternative
* parameterization would use 1/beta, the "rate" parameter.
*/
public synchronized double nextGamma(double alpha, double beta, double lambda) {
double gamma=0;
if (alpha <= 0 || beta <= 0) {
throw new IllegalArgumentException ("alpha and beta must be strictly positive.");
}
if (alpha < 1) {
double b,p;
boolean flag = false;
b = 1 + alpha * ONE_OVER_E;
while (!flag) {
p = b * nextUniform();
if (p > 1) {
gamma = -Math.log((b - p) / alpha);
if (nextUniform() <= Math.pow(gamma, alpha - 1)) {
flag = true;
}
}
else {
gamma = Math.pow(p, 1.0/alpha);
if (nextUniform() <= Math.exp(-gamma)) {
flag = true;
}
}
}
}
else if (alpha == 1) {
// Gamma(1) is equivalent to Exponential(1). We can
// sample from an exponential by inverting the CDF:
gamma = -Math.log (nextUniform ());
// There is no known closed form for Gamma(alpha != 1)...
}
else {
// This is Best's algorithm: see pg 410 of
// Luc Devroye's "non-uniform random variate generation"
// This algorithm is constant time for alpha > 1.
double b = alpha - 1;
double c = 3 * alpha - 0.75;
double u, v;
double w, y, z;
boolean accept = false;
while (! accept) {
u = nextUniform();
v = nextUniform();
w = u * (1 - u);
y = Math.sqrt( c / w ) * (u - 0.5);
gamma = b + y;
if (gamma >= 0.0) {
z = 64 * w * w * w * v * v; // ie: 64 * w^3 v^2
accept = z <= 1.0 - ((2 * y * y) / gamma);
if (! accept) {
accept = (Math.log(z) <=
2 * (b * Math.log(gamma / b) - y));
}
}
}
/* // Old version, uses time linear in alpha
double y = -Math.log (nextUniform ());
while (nextUniform () > Math.pow (y * Math.exp (1 - y), alpha - 1))
y = -Math.log (nextUniform ());
gamma = alpha * y;
*/
}
return beta*gamma+lambda;
}
/** Return a random double drawn from an Exponential distribution with mean 1 and variance 1. */
public synchronized double nextExp() {
return nextGamma(1,1,0);
}
/** Return a random double drawn from an Exponential distribution with mean beta and variance beta^2. */
public synchronized double nextExp(double beta) {
return nextGamma(1,beta,0);
}
/** Return a random double drawn from an Exponential distribution with mean beta+lambda and variance beta^2. */
public synchronized double nextExp(double beta,double lambda) {
return nextGamma(1,beta,lambda);
}
/** Return a random double drawn from an Chi-squarted distribution with mean 1 and variance 2.
* Equivalent to nextChiSq(1) */
public synchronized double nextChiSq() {
return nextGamma(0.5,2,0);
}
/** Return a random double drawn from an Chi-squared distribution with mean df and variance 2*df. */
public synchronized double nextChiSq(int df) {
return nextGamma(0.5*(double)df,2,0);
}
/** Return a random double drawn from an Chi-squared distribution with mean df+lambda and variance 2*df. */
public synchronized double nextChiSq(int df,double lambda) {
return nextGamma(0.5*(double)df,2,lambda);
}
/** Return a random double drawn from a Beta distribution with mean a/(a+b) and variance ab/((a+b+1)(a+b)^2). */
public synchronized double nextBeta(double alpha,double beta) {
if (alpha <= 0 || beta <= 0) {
throw new IllegalArgumentException ("alpha and beta must be strictly positive.");
}
if (alpha == 1 && beta == 1) {
return nextUniform ();
} else if (alpha >= 1 && beta >= 1) {
double A = alpha - 1,
B = beta - 1,
C = A + B,
L = C * Math.log (C),
mu = A / C,
sigma = 0.5 / Math.sqrt (C);
double y = nextGaussian (), x = sigma * y + mu;
while (x < 0 || x > 1) {
y = nextGaussian ();
x = sigma * y + mu;
}
double u = nextUniform ();
while (Math.log (u) >= A * Math.log (x / A) + B * Math.log ((1 - x) / B) + L + 0.5 * y * y) {
y = nextGaussian ();
x = sigma * y + mu;
while (x < 0 || x > 1) {
y = nextGaussian ();
x = sigma * y + mu;
}
u = nextUniform ();
}
return x;
} else {
double v1 = Math.pow (nextUniform (), 1 / alpha),
v2 = Math.pow (nextUniform (), 1 / beta);
while (v1 + v2 > 1) {
v1 = Math.pow (nextUniform (), 1 / alpha);
v2 = Math.pow (nextUniform (), 1 / beta);
}
return v1 / (v1 + v2);
}
}
/** Wrap this instance as a java random, so it can be passed to legacy methods.
* All methods of the returned java.util.Random object will affect the state of
* this object, as well. */
@Deprecated // This should no longer be necessary, since Randoms is now a subclass of java.util.random anyway
public java.util.Random asJavaRandom ()
{
return new java.util.Random () {
protected int next (int bits)
{
return cc.mallet.util.Randoms.this.next (bits);
}
};
}
public static void main (String[] args)
{
// Prints the nextGamma() and oldNextGamma() distributions to
// System.out for testing/comparison.
Randoms r = new Randoms();
final int resolution = 60;
int[] histogram1 = new int[resolution];
int[] histogram2 = new int[resolution];
int scale = 10;
for (int i = 0; i < 10000; i++) {
double x = 4;
int index1 = ((int)(r.nextGamma(x)/scale * resolution)) % resolution;
int index2 = ((int)(r.oldNextGamma((int)x)/scale * resolution)) % resolution;
histogram1[index1]++;
histogram2[index2]++;
}
for (int i = 0; i < resolution; i++) {
for (int y = 0; y < histogram1[i]/scale; y++)
System.out.print("*");
System.out.print("\n");
for (int y = 0; y < histogram2[i]/scale; y++)
System.out.print("-");
System.out.print("\n");
}
}
}
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