/**
* Fits the mixture (or mixing) distribution to the data. The data is not pre-clustered for
* computational efficiency.
*
* @param data the data supposedly generated from the mixture
* @param method the method to be used. Refer to the static final variables of this class.
* @return the generated distribution
*/
public DiscreteFunction fitForSingleCluster(DoubleVector data, int method) {
if (data.size() < 2) return new DiscreteFunction(data);
DoubleVector sp = supportPoints(data, 0);
PaceMatrix fi = fittingIntervals(data);
PaceMatrix pm = probabilityMatrix(sp, fi);
PaceMatrix epm = new PaceMatrix(empiricalProbability(data, fi).timesEquals(1. / data.size()));
IntVector pvt = (IntVector) IntVector.seq(0, sp.size() - 1);
DoubleVector weights;
switch (method) {
case NNMMethod:
weights = pm.nnls(epm, pvt);
break;
case PMMethod:
weights = pm.nnlse1(epm, pvt);
break;
default:
throw new IllegalArgumentException("unknown method");
}
DoubleVector sp2 = new DoubleVector(pvt.size());
for (int i = 0; i < sp2.size(); i++) {
sp2.set(i, sp.get(pvt.get(i)));
}
DiscreteFunction d = new DiscreteFunction(sp2, weights);
d.sort();
d.normalize();
return d;
}
/**
* Fits the mixture (or mixing) distribution to the data.
*
* @param data the data supposedly generated from the mixture
* @param method the method to be used. Refer to the static final variables of this class.
*/
public void fit(DoubleVector data, int method) {
DoubleVector data2 = (DoubleVector) data.clone();
if (data2.unsorted()) data2.sort();
int n = data2.size();
int start = 0;
DoubleVector subset;
DiscreteFunction d = new DiscreteFunction();
for (int i = 0; i < n - 1; i++) {
if (separable(data2, start, i, data2.get(i + 1))
&& separable(data2, i + 1, n - 1, data2.get(i))) {
subset = (DoubleVector) data2.subvector(start, i);
d.plusEquals(fitForSingleCluster(subset, method).timesEquals(i - start + 1));
start = i + 1;
}
}
subset = (DoubleVector) data2.subvector(start, n - 1);
d.plusEquals(fitForSingleCluster(subset, method).timesEquals(n - start));
d.sort();
d.normalize();
mixingDistribution = d;
}
