/**
* 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;
}
