public double GetTestErrorNN() {
DataSet trainSet = new DataSet();
trainSet.LoadMatrixes(S, Y, 0, numTrainInstances);
DataSet testSet = new DataSet();
testSet.LoadMatrixes(S, Y, numTrainInstances, numTotalInstances);
NearestNeighbour nn = new NearestNeighbour("euclidean");
return nn.Classify(trainSet, testSet);
}
// build the kernel datasets
public void BuildKernels() {
int n = trainSet.instances.size();
int m = testSet.instances.size();
trainKernel = new DataSet();
trainKernel.name = trainSet.name;
trainKernel.numFeatures = n;
testKernel = new DataSet();
testKernel.name = testSet.name;
testKernel.numFeatures = n;
for (int i = 0; i < n; i++) {
DataInstance ins = new DataInstance();
DataInstance train_i = trainSet.instances.get(i);
for (int j = 0; j < n; j++) {
DataInstance train_j = trainSet.instances.get(j);
double k_ij = EuclideanDistance.getInstance().CalculateDistance(train_i, train_j);
// double k_ij = DTW.getInstance().CalculateDistance(train_i, train_j);
ins.features.add(new FeaturePoint(k_ij));
}
ins.target = train_i.target;
trainKernel.instances.add(ins);
}
for (int i = 0; i < m; i++) {
DataInstance ins = new DataInstance();
DataInstance test_i = testSet.instances.get(i);
for (int j = 0; j < n; j++) {
DataInstance train_j = trainSet.instances.get(j);
double k_ij = EuclideanDistance.getInstance().CalculateDistance(test_i, train_j);
// double k_ij = DTW.getInstance().CalculateDistance(test_i, train_j);
ins.features.add(new FeaturePoint(k_ij));
}
ins.target = test_i.target;
testKernel.instances.add(ins);
}
}
