public static ArrayList<Integer> getProfiles(Instances inst, List<Integer> marks)
throws Exception {
// Instances inst = Utils.prepareProfileMatcherData(schoolNo, grade, term, subjects);
// ReplaceMissingValues rmv = new ReplaceMissingValues();
// rmv.setInputFormat(inst);
// inst = Filter.useFilter(inst, rmv);
for (int i = 0; i < inst.numAttributes(); i++) {
inst.deleteWithMissing(i);
}
KDTree tree = new KDTree();
tree.setMeasurePerformance(true);
try {
tree.setInstances(inst);
EuclideanDistance df = new EuclideanDistance(inst);
df.setDontNormalize(true);
df.setAttributeIndices("2-last");
tree.setDistanceFunction(df);
} catch (Exception e) {
e.printStackTrace();
}
Instances neighbors = null;
Instances test = CFilter.createInstance(112121, (ArrayList<Integer>) marks);
Instance p = test.firstInstance();
try {
neighbors = tree.kNearestNeighbours(p, 50);
} catch (Exception e) {
e.printStackTrace();
}
// System.out.println(tree.getPerformanceStats().getTotalPointsVisited());
// System.out.println(nn1 + " is the nearest neigbor for " + p);
// System.out.println(nn2 + " is the second nearest neigbor for " + p);
ArrayList<Integer> profiles = new ArrayList<Integer>();
for (int i = 0; i < neighbors.numInstances(); i++) {
System.out.println(neighbors.instance(i));
profiles.add(Integer.valueOf(neighbors.instance(i).toString(0)));
}
// Now we can also easily compute the distances as the KDTree does it
DistanceFunction df = tree.getDistanceFunction();
// System.out.println("The distance between" + nn1 + " and " + p + " is " + df.distance(nn1,
// p));
// System.out.println("The distance between" + nn2 + " and " + p + " is " + df.distance(nn2,
// p));
return profiles;
}
