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smile.stat.distribution.BetaDistribution Maven / Gradle / Ivy
/******************************************************************************
* Confidential Proprietary *
* (c) Copyright Haifeng Li 2011, All Rights Reserved *
******************************************************************************/
package smile.stat.distribution;
import smile.math.special.Beta;
import smile.math.special.Gamma;
import smile.math.Math;
/**
* The beta distribution is defined on the interval [0, 1] parameterized by
* two positive shape parameters, typically denoted by α and β.
* It is the special case of the Dirichlet distribution with only two parameters.
* The beta distribution is used as a prior distribution for binomial
* proportions in Bayesian analysis. In Bayesian statistics, it can be seen as
* the posterior distribution of the parameter α of a binomial distribution
* after observing α - 1 independent events with probability α and β - 1 with
* probability 1 - α, if the prior distribution of α was uniform.
* If α = 1 and β =1, the Beta distribution is the uniform [0, 1] distribution.
* The probability density function of the beta distribution is
* f(x;α,β) = xα-1 (1-x)β-1 / B(α,β)
* where B(α,β) is the beta function.
* @author Haifeng Li
*/
public class BetaDistribution extends AbstractDistribution implements ExponentialFamily {
private double alpha;
private double beta;
private double mean;
private double var;
private double entropy;
private RejectionLogLogistic rng;
/**
* Constructor.
* @param alpha shape parameter.
* @param beta shape parameter.
*/
public BetaDistribution(double alpha, double beta) {
if (alpha <= 0) {
throw new IllegalArgumentException("Invalid alpha: " + alpha);
}
if (beta <= 0) {
throw new IllegalArgumentException("Invalid beta: " + beta);
}
this.alpha = alpha;
this.beta = beta;
mean = alpha / (alpha + beta);
var = alpha * beta / ((alpha + beta) * (alpha + beta) * (alpha + beta + 1));
entropy = Math.log(Beta.beta(alpha, beta)) - (alpha - 1) * Gamma.digamma(alpha) - (beta - 1) * Gamma.digamma(beta) + (alpha + beta - 2) * Gamma.digamma(alpha + beta);
}
/**
* Construct an Beta from the given samples. Parameter
* will be estimated from the data by the moment method.
*/
public BetaDistribution(double[] data) {
for (int i = 0; i < data.length; i++) {
if (data[i] < 0 || data[i] > 1) {
throw new IllegalArgumentException("Samples are not in range [0, 1].");
}
}
mean = Math.mean(data);
var = Math.var(data);
alpha = mean * (mean * (1 - mean) / var - 1);
beta = (1 - mean) * (mean * (1 - mean) / var - 1);
if (alpha <= 0 || beta <= 0) {
throw new IllegalArgumentException("Samples don't follow Beta Distribution.");
}
mean = alpha / (alpha + beta);
var = alpha * beta / ((alpha + beta) * (alpha + beta) * (alpha + beta + 1));
entropy = Math.log(Beta.beta(alpha, beta)) - (alpha - 1) * Gamma.digamma(alpha) - (beta - 1) * Gamma.digamma(beta) + (alpha + beta - 2) * Gamma.digamma(alpha + beta);
}
/**
* Returns the shape parameter alpha.
* @return the shape parameter alpha
*/
public double getAlpha() {
return alpha;
}
/**
* Returns the shape parameter beta.
* @return the shape parameter beta
*/
public double getBeta() {
return beta;
}
@Override
public int npara() {
return 2;
}
@Override
public double mean() {
return mean;
}
@Override
public double var() {
return var;
}
@Override
public double sd() {
return Math.sqrt(var);
}
@Override
public double entropy() {
return entropy;
}
@Override
public String toString() {
return String.format("Beta Distribution(%.4f, %.4f)", alpha, beta);
}
@Override
public double p(double x) {
if (x < 0 || x > 1) {
return 0.0;
} else {
return Math.pow(x, alpha - 1) * Math.pow(1 - x, beta - 1) / Beta.beta(alpha, beta);
}
}
@Override
public double logp(double x) {
if (x < 0 || x > 1) {
return Double.NEGATIVE_INFINITY;
} else {
return (alpha - 1) * Math.log(x) + (beta - 1) * Math.log(1 - x) - Math.log(Beta.beta(alpha, beta));
}
}
@Override
public double cdf(double x) {
if (x <= 0) {
return 0.0;
} else if (x >= 1) {
return 1.0;
} else {
return Beta.regularizedIncompleteBetaFunction(alpha, beta, x);
}
}
@Override
public double quantile(double p) {
if (p < 0.0 || p > 1.0) {
throw new IllegalArgumentException("Invalid p: " + p);
}
return Beta.inverseRegularizedIncompleteBetaFunction(alpha, beta, p);
}
@Override
public Mixture.Component M(double[] x, double[] posteriori) {
double weight = 0.0;
double mu = 0.0;
double v = 0.0;
for (int i = 0; i < x.length; i++) {
weight += posteriori[i];
mu += x[i] * posteriori[i];
}
mu /= weight;
for (int i = 0; i < x.length; i++) {
double d = x[i] - mu;
v += d * d * posteriori[i];
}
v = v / weight;
double a = mu * (mu * (1 - mu) / v - 1);
double b = (1 - mu) * (mu * (1 - mu) / v - 1);
Mixture.Component c = new Mixture.Component();
c.priori = weight;
c.distribution = new BetaDistribution(a, b);
return c;
}
@Override
public double rand() {
if (rng == null) {
rng = new RejectionLogLogistic();
}
return rng.rand();
}
/**
* Implements Beta random variate generators using
* the rejection method with log-logistic envelopes.
* The method draws the first two uniforms from the main stream
* and uses the auxiliary stream for the remaining uniforms,
* when more than two are needed (i.e., when rejection occurs).
*
*/
class RejectionLogLogistic {
private static final int BB = 0;
private static final int BC = 1;
private int method;
private double am;
private double bm;
private double al;
private double alnam;
private double be;
private double ga;
private double si;
private double rk1;
private double rk2;
/**
* Creates a beta random variate generator.
*/
public RejectionLogLogistic() {
if (alpha > 1.0 && beta > 1.0) {
method = BB;
am = (alpha < beta) ? alpha : beta;
bm = (alpha > beta) ? alpha : beta;
al = am + bm;
be = Math.sqrt((al - 2.0) / (2.0 * alpha * beta - al));
ga = am + 1.0 / be;
} else {
method = BC;
am = (alpha > beta) ? alpha : beta;
bm = (alpha < beta) ? alpha : beta;
al = am + bm;
alnam = al * Math.log(al / am) - 1.386294361;
be = 1.0 / bm;
si = 1.0 + am - bm;
rk1 = si * (0.013888889 + 0.041666667 * bm) / (am * be - 0.77777778);
rk2 = 0.25 + (0.5 + 0.25 / si) * bm;
}
}
public double rand() {
double X = 0.0;
double u1, u2, v, w, y, z, r, s, t;
switch (method) {
case BB:
/* -X- generator code -X- */
while (true) {
/* Step 1 */
u1 = Math.random();
u2 = Math.random();
v = be * Math.log(u1 / (1.0 - u1));
w = am * Math.exp(v);
z = u1 * u1 * u2;
r = ga * v - 1.386294361;
s = am + r - w;
/* Step 2 */
if (s + 2.609437912 < 5.0 * z) {
/* Step 3 */
t = Math.log(z);
if (s < t) /* Step 4 */ {
if (r + al * Math.log(al / (bm + w)) < t) {
continue;
}
}
}
/* Step 5 */
X = Math.equals(am, alpha) ? w / (bm + w) : bm / (bm + w);
break;
}
/* -X- end of generator code -X- */
break;
case BC:
while (true) {
/* Step 1 */
u1 = Math.random();
u2 = Math.random();
if (u1 < 0.5) {
/* Step 2 */
y = u1 * u2;
z = u1 * y;
if ((0.25 * u2 - y + z) >= rk1) {
continue; /* goto 1 */
}
/* Step 5 */
v = be * Math.log(u1 / (1.0 - u1));
if (v > 80.0) {
if (alnam < Math.log(z)) {
continue;
}
X = Math.equals(am, alpha) ? 1.0 : 0.0;
break;
} else {
w = am * Math.exp(v);
if ((al * (Math.log(al / (bm + w)) + v) - 1.386294361) <
Math.log(z)) {
continue; /* goto 1 */
}
/* Step 6_a */
X = !Math.equals(am, alpha) ? bm / (bm + w) : w / (bm + w);
break;
}
} else {
/* Step 3 */
z = u1 * u1 * u2;
if (z < 0.25) {
/* Step 5 */
v = be * Math.log(u1 / (1.0 - u1));
if (v > 80.0) {
X = Math.equals(am, alpha) ? 1.0 : 0.0;
break;
}
w = am * Math.exp(v);
X = !Math.equals(am, alpha) ? bm / (bm + w) : w / (bm + w);
break;
} else {
if (z >= rk2) {
continue;
}
v = be * Math.log(u1 / (1.0 - u1));
if (v > 80.0) {
if (alnam < Math.log(z)) {
continue;
}
X = Math.equals(am, alpha) ? 1.0 : 0.0;
break;
}
w = am * Math.exp(v);
if ((al * (Math.log(al / (bm + w)) + v) - 1.386294361) <
Math.log(z)) {
continue; /* goto 1 */
}
/* Step 6_b */
X = !Math.equals(am, alpha) ? bm / (bm + w) : w / (bm + w);
break;
}
}
}
break;
default:
throw new IllegalStateException();
}
return X;
}
}
}
