// testFace has been projected to the subspace
static projectedTrainingMatrix[] findKNN(projectedTrainingMatrix[] trainingSet, double[][] testFace, int K, EuclideanDistance metric)
{
int NumOfTrainingSet = trainingSet.Length;
// initialization
projectedTrainingMatrix[] neighbors = new projectedTrainingMatrix[K];
int i;
for (i = 0; i < K; i++)
{
trainingSet[i].distance = metric.getDistance(trainingSet[i].matrix,
testFace);
// System.out.println("index: " + i + " distance: "
// + trainingSet[i].distance);
neighbors[i] = trainingSet[i];
}
// go through the remaining records in the trainingSet to find K nearest
// neighbors
for (i = K; i < NumOfTrainingSet; i++)
{
trainingSet[i].distance = metric.getDistance(trainingSet[i].matrix,
testFace);
// System.out.println("index: " + i + " distance: "
// + trainingSet[i].distance);
int maxIndex = 0;
for (int j = 0; j < K; j++)
{
if (neighbors[j].distance > neighbors[maxIndex].distance)
{
maxIndex = j;
}
}
if (neighbors[maxIndex].distance > trainingSet[i].distance)
{
neighbors[maxIndex] = trainingSet[i];
}
}
return(neighbors);
}
public static String assignLabel(projectedTrainingMatrix[] trainingSet, double[][] testFace, int K, EuclideanDistance metric)
{
projectedTrainingMatrix[] neighbors = findKNN(trainingSet, testFace, K, metric);
return(classify(neighbors));
}