public void learn(float[][] data, int iteration) {
for (int iter = 0; iter < iteration; iter++) {
if (iter % 10 == 0) {
System.out.print("Iteration " + iter + "\t");
for (int i = 0; i < ksize; i++) System.out.print(clusterDataIndex.get(i).size() + " ");
System.out.println();
System.out.println("mu:");
for (int k = 0; k < ksize; k++) ComUtil.print(p_mu[k], " ", "\n");
System.out.println("lambda:");
for (int k = 0; k < ksize; k++) ComUtil.print(p_lambda[k], " ", "\n");
}
// tranverse data
for (int n = 0; n < data.length; n++) {
double[] probs = new double[ksize];
int currentz = clusterID[n];
// decrease
int toRemoveIndex = clusterDataIndex.get(currentz).indexOf(n);
clusterDataIndex.get(currentz).remove(toRemoveIndex);
// update mu lambda for currentz
comAVGX(data, currentz);
comDateMus(p_mu, p_lambda, data, clusterDataIndex, currentz);
// computeProbs
for (int k = 0; k < ksize; k++) {
probs[k] = (clusterDataIndex.get(k).size() + alpha) / (vector_n + ksize * alpha);
for (int d = 0; d < vector_d; d++)
probs[k] *= MathUtil.phi(data[n][d], p_mu[k][d], Math.sqrt(1 / p_lambda[k][d]));
}
int newz = ComUtil.sample(probs, ksize);
// increase
clusterID[n] = newz;
clusterDataIndex.get(newz).add(n);
// update mu lambda for currentz
comAVGX(data, newz);
comDateMus(p_mu, p_lambda, data, clusterDataIndex, newz);
}
}
}
public double[] LearnProbs(float[][] data, int n) {
double[] probs = new double[ksize];
int currentz = clusterID[n];
// decrease
int toRemoveIndex = clusterDataIndex.get(currentz).indexOf(n);
clusterDataIndex.get(currentz).remove(toRemoveIndex);
// update mu lambda for currentz
comAVGX(data, currentz, n, false);
comDateMus(p_mu, p_lambda, data, clusterDataIndex, currentz);
// computeProbs
for (int k = 0; k < ksize; k++) {
probs[k] = 1;
for (int d = 0; d < vector_d; d++)
probs[k] *= MathUtil.phi(data[n][d], p_mu[k][d], Math.sqrt(1 / p_lambda[k][d]));
if (probs[k] < 1e-300) {
probs[k] = 1e-300;
}
}
return probs;
}
