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;
}
public static void main(String args[]) {
Timers timer = new Timers();
try {
// Get the data set path.
String referenceFile = Utils.getOption('r', args);
String queryFile = Utils.getOption('q', args);
if (referenceFile.length() == 0)
throw new IllegalArgumentException(
"Required option: File containing" + "the reference dataset.");
// Load input dataset.
DataSource source = new DataSource(referenceFile);
Instances referenceData = source.getDataSet();
Instances queryData = null;
if (queryFile.length() != 0) {
source = new DataSource(queryFile);
queryData = source.getDataSet();
}
timer.StartTimer("total_time");
// Get all the parameters.
String leafSize = Utils.getOption('l', args);
String neighbors = Utils.getOption('k', args);
// Validate options.
int k = 0;
if (neighbors.length() == 0) {
throw new IllegalArgumentException(
"Required option: Number of " + "furthest neighbors to find.");
} else {
k = Integer.parseInt(neighbors);
if (k < 1 || k > referenceData.numInstances())
throw new IllegalArgumentException("[Fatal] Invalid k");
}
int l = 20;
if (leafSize.length() != 0) l = Integer.parseInt(leafSize);
// Create KDTree.
KDTree tree = new KDTree();
tree.setMaxInstInLeaf(l);
tree.setInstances(referenceData);
// Perform All K-Nearest-Neighbors.
if (queryFile.length() != 0) {
for (int i = 0; i < queryData.numInstances(); i++) {
Instances out = tree.kNearestNeighbours(queryData.instance(i), k);
}
} else {
for (int i = 0; i < referenceData.numInstances(); i++) {
Instances out = tree.kNearestNeighbours(referenceData.instance(i), k);
}
}
timer.StopTimer("total_time");
timer.PrintTimer("total_time");
} catch (IOException e) {
System.err.println(USAGE);
} catch (Exception e) {
e.printStackTrace();
}
}