@Override
protected void doTrain(Map<ClassDescriptor, List<double[]>> data, int dimension) {
this.dimension = dimension;
for (ClassDescriptor c : data.keySet()) {
/*
* Estimate covariance using ML.
*/
double[] mean = MaximumLikelihoodEstimation.getMean(data.get(c), dimension);
double[][] cov = MaximumLikelihoodEstimation.getCovariance(data.get(c), mean);
Kernel kernel = type.getKernel(cov, radius);
distributions.put(c, new ParzenDistribution(kernel, data.get(c)));
}
}
/**
* Reconstruct a parzen window classifier from serialized data.
*
* @param model
* @param classes
* @return
*/
@Import(ModelType.CLASSIFIER)
public static ParzenWindowClassifier newInstance(
Map<String, Object> model, Map<ClassDescriptor, Map<String, Object>> classes) {
ParzenWindowClassifier classifier;
double radius = Double.parseDouble((String) model.get("radius"));
int dimension = Integer.parseInt((String) model.get("dimension"));
KernelType type;
if (((String) model.get("kernel")).equals("normal")) {
type = KernelType.NORMAL;
} else {
type = KernelType.UNIFORM;
}
classifier = new ParzenWindowClassifier(radius, type);
for (ClassDescriptor c : classes.keySet()) {
double[][] a = (double[][]) classes.get(c).get("vectors");
List<double[]> l = new ArrayList<double[]>(a.length);
for (int i = 0; i < a.length; i++) {
l.add(a[i]);
}
// reconstruct covariance matrix
double[] mean = MaximumLikelihoodEstimation.getMean(l, dimension);
double[][] cov = MaximumLikelihoodEstimation.getCovariance(l, mean);
Kernel kernel = type.getKernel(cov, radius);
classifier.distributions.put(c, new ParzenDistribution(kernel, l));
}
return classifier;
}