/**
* Samples the number of tables given the number of customers in a dirichlet process distribution.
*
* @param n number of customers
* @param alpha concentration parameter
* @param beta base distribution probability of type
* @return number of tables
*/
public static int sampleDirichletProcessTables(int n, double alpha, double beta) {
if (beta <= 0d) {
return 1;
} else {
double c0 = logGamma(alpha * beta) - logGamma(alpha * beta + (double) n);
double c1 = Math.log(alpha) + Math.log(beta);
double cuSum = 0;
double r = rng.nextDouble();
int m = 0;
for (int i = 1; i <= n; i++) {
cuSum += Math.exp(c0 + lsn.get(n, i) + i * c1);
if (cuSum > r) {
m = i;
break;
}
}
assert m > 0 : "alpha = " + alpha + " beta = " + beta;
return m;
}
}
/**
* Gets the log probability of the given number of tables in the Dirichlet process distribution.
*
* @param m tables
* @param n count
* @param alpha concentration
* @param beta probability of type in base distribution
* @return score
*/
public static double logProbTablesDirichletProcess(int m, int n, double alpha, double beta) {
double c0 = logGamma(alpha * beta) - logGamma(alpha * beta + (double) n);
double c1 = Math.log(alpha) + Math.log(beta);
return c0 + lsn.get(n, m) + m * c1;
}
