/**
* Constructor. The Gaussian mixture model will be learned from the given data with the EM
* algorithm. The number of components will be selected by BIC.
*
* @param data the training data.
*/
@SuppressWarnings("unchecked")
public GaussianMixture(double[] data) {
if (data.length < 20) throw new IllegalArgumentException("Too few samples.");
ArrayList<Component> mixture = new ArrayList<Component>();
Component c = new Component();
c.priori = 1.0;
c.distribution = new GaussianDistribution(data);
mixture.add(c);
int freedom = 0;
for (int i = 0; i < mixture.size(); i++) freedom += mixture.get(i).distribution.npara();
double bic = 0.0;
for (double x : data) {
double p = c.distribution.p(x);
if (p > 0) bic += Math.log(p);
}
bic -= 0.5 * freedom * Math.log(data.length);
double b = Double.NEGATIVE_INFINITY;
while (bic > b) {
b = bic;
components = (ArrayList<Component>) mixture.clone();
split(mixture);
bic = EM(mixture, data);
freedom = 0;
for (int i = 0; i < mixture.size(); i++) freedom += mixture.get(i).distribution.npara();
bic -= 0.5 * freedom * Math.log(data.length);
}
}
