package tr.gov.ulakbim.jDenetX.cluster;
import tr.gov.ulakbim.jDenetX.AbstractMOAObject;
import tr.gov.ulakbim.jDenetX.core.AutoExpandVector;
import tr.gov.ulakbim.jDenetX.gui.visualization.DataPoint;
import weka.core.Instance;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
/**
* Represents a collection of clusters.
*/
public class Clustering extends AbstractMOAObject {
private AutoExpandVector<Cluster> clusters;
public Clustering() {
this.clusters = new AutoExpandVector<Cluster>();
}
public Clustering(Cluster[] clusters) {
this.clusters = new AutoExpandVector<Cluster>();
for (int i = 0; i < clusters.length; i++) {
this.clusters.add(clusters[i]);
}
}
public Clustering(List<DataPoint> points) {
HashMap<Integer, Integer> labelMap = classValues(points);
int dim = points.get(0).dataset().numAttributes() - 1;
int numClasses = labelMap.size();
ArrayList<Instance>[] sorted_points = (ArrayList<Instance>[]) new ArrayList[numClasses];
for (int i = 0; i < numClasses; i++) {
sorted_points[i] = new ArrayList<Instance>();
}
for (DataPoint point : points) {
int clusterid = (int) point.classValue();
if (clusterid == -1) continue;
sorted_points[labelMap.get(clusterid)].add((Instance) point);
}
this.clusters = new AutoExpandVector<Cluster>();
for (int i = 0; i < numClasses; i++) {
if (sorted_points[i].size() > 0) {
SphereCluster s = new SphereCluster(sorted_points[i], dim);
s.setId(sorted_points[i].get(0).classValue());
s.setGroundTruth(sorted_points[i].get(0).classValue());
clusters.add(s);
}
}
}
public Clustering(ArrayList<DataPoint> points, double overlapThreshold, int initMinPoints) {
HashMap<Integer, Integer> labelMap = Clustering.classValues(points);
int dim = points.get(0).dataset().numAttributes() - 1;
int numClasses = labelMap.size();
int num = 0;
ArrayList<DataPoint>[] sorted_points = (ArrayList<DataPoint>[]) new ArrayList[numClasses];
for (int i = 0; i < numClasses; i++) {
sorted_points[i] = new ArrayList<DataPoint>();
}
for (DataPoint point : points) {
int clusterid = (int) point.classValue();
if (clusterid == -1) continue;
sorted_points[labelMap.get(clusterid)].add(point);
num++;
}
clusters = new AutoExpandVector<Cluster>();
int microID = 0;
for (int i = 0; i < numClasses; i++) {
ArrayList<SphereCluster> microByClass = new ArrayList<SphereCluster>();
ArrayList<DataPoint> pointInCluster = new ArrayList<DataPoint>();
ArrayList<ArrayList<Instance>> pointInMicroClusters = new ArrayList();
pointInCluster.addAll(sorted_points[i]);
while (pointInCluster.size() > 0) {
ArrayList<Instance> micro_points = new ArrayList<Instance>();
for (int j = 0; j < initMinPoints && !pointInCluster.isEmpty(); j++) {
micro_points.add((Instance) pointInCluster.get(0));
pointInCluster.remove(0);
}
if (micro_points.size() > 0) {
SphereCluster s = new SphereCluster(micro_points, dim);
for (int c = 0; c < microByClass.size(); c++) {
if (((SphereCluster) microByClass.get(c)).overlapRadiusDegree(s) > overlapThreshold) {
micro_points.addAll(pointInMicroClusters.get(c));
s = new SphereCluster(micro_points, dim);
pointInMicroClusters.remove(c);
microByClass.remove(c);
//System.out.println("Removing redundant cluster based on radius overlap"+c);
}
}
for (int j = 0; j < pointInCluster.size(); j++) {
Instance instance = pointInCluster.get(j);
if (s.getInclusionProbability(instance) > 0.8) {
pointInCluster.remove(j);
micro_points.add(instance);
}
}
s.setWeight(micro_points.size());
microByClass.add(s);
pointInMicroClusters.add(micro_points);
microID++;
}
}
//
boolean changed = true;
while (changed) {
changed = false;
for (int c = 0; c < microByClass.size(); c++) {
for (int c1 = c + 1; c1 < microByClass.size(); c1++) {
double overlap = microByClass.get(c).overlapRadiusDegree(microByClass.get(c1));
// System.out.println("Overlap C"+(clustering.size()+c)+" ->C"+(clustering.size()+c1)+": "+overlap);
if (overlap > overlapThreshold) {
pointInMicroClusters.get(c).addAll(pointInMicroClusters.get(c1));
SphereCluster s = new SphereCluster(pointInMicroClusters.get(c), dim);
microByClass.set(c, s);
pointInMicroClusters.remove(c1);
microByClass.remove(c1);
changed = true;
break;
}
}
}
}
for (int j = 0; j < microByClass.size(); j++) {
microByClass.get(j).setGroundTruth(sorted_points[i].get(0).classValue());
clusters.add(microByClass.get(j));
}
}
for (int j = 0; j < clusters.size(); j++) {
clusters.get(j).setId(j);
}
}
/**
* @param points
* @return an array with the min and max class label value
*/
public static HashMap<Integer, Integer> classValues(List<DataPoint> points) {
HashMap<Integer, Integer> classes = new HashMap<Integer, Integer>();
int workcluster = 0;
for (int i = 0; i < points.size(); i++) {
int label = (int) points.get(i).classValue();
if (!classes.containsKey(label)) {
classes.put(label, workcluster);
workcluster++;
}
}
return classes;
}
public Clustering(AutoExpandVector<Cluster> clusters) {
this.clusters = clusters;
}
/**
* add a cluster to the clustering
*/
public void add(Cluster cluster) {
clusters.add(cluster);
}
/**
* remove a cluster from the clustering
*/
public void remove(int index) {
if (index < clusters.size()) {
clusters.remove(index);
}
}
/**
* remove a cluster from the clustering
*/
public Cluster get(int index) {
if (index < clusters.size()) {
return clusters.get(index);
}
return null;
}
/**
* @return the <code>Clustering</code> as an AutoExpandVector
*/
public AutoExpandVector<Cluster> getClustering() {
return clusters;
}
/**
* @return A deepcopy of the <code>Clustering</code> as an AutoExpandVector
*/
public AutoExpandVector<Cluster> getClusteringCopy() {
return (AutoExpandVector<Cluster>) clusters.copy();
}
/**
* @return the number of clusters
*/
public int size() {
return clusters.size();
}
/**
* @return the number of dimensions of this clustering
*/
public int dimension() {
assert (clusters.size() != 0);
return clusters.get(0).getCenter().length;
}
@Override
public void getDescription(StringBuilder sb, int i) {
sb.append("Clustering Object");
}
public double getMaxInclusionProbability(Instance point) {
double maxInclusion = 0.0;
for (int i = 0; i < clusters.size(); i++) {
maxInclusion = Math.max(clusters.get(i).getInclusionProbability(point),
maxInclusion);
}
return maxInclusion;
}
}