/**
* Assigns a data point to one of the k clusters based on its distance from the means of the
* clusters
*
* @param dp data point to be assigned
*/
public void assignToCluster(kMeansPoint dp) {
int currentCluster = dp.getClusterNumber();
if (dp.getListData().size() == 3) {
double minDistance = kMeansPoint.distance(dp, this.clusters[currentCluster].getMean());
;
for (int i = 0; i < this.k; i++)
if (kMeansPoint.distance(dp, this.clusters[i].getMean()) < minDistance) {
minDistance = kMeansPoint.distance(dp, this.clusters[i].getMean());
currentCluster = i;
}
} else {
double minDistance = kMeansPoint.comPointsDis(dp, this.clusters[currentCluster].getMean());
;
for (int i = 0; i < this.k; i++)
if (kMeansPoint.comPointsDis(dp, this.clusters[i].getMean()) < minDistance) {
minDistance = kMeansPoint.comPointsDis(dp, this.clusters[i].getMean());
currentCluster = i;
}
}
dp.assignToCluster(currentCluster);
} // end of assignToCluster
/**
* Updates the means of all k clusters, and returns if they have changed or not
*
* @return have the updated means of the clusters changed or not
*/
private boolean updateMeans() {
boolean reply = false;
if (((kMeansPoint) this.kMeansPoints.get(0)).getListData().size() == 3) {
double[] x = new double[this.k];
double[] y = new double[this.k];
double[] z = new double[this.k];
int[] size = new int[this.k];
kMeansPoint[] pastMeans = new kMeansPoint[this.k];
for (int i = 0; i < this.k; i++) {
x[i] = 0.0;
y[i] = 0.0;
z[i] = 0.0;
size[i] = 0;
pastMeans[i] = this.clusters[i].getMean();
}
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext()) {
kMeansPoint dp = (kMeansPoint) (i.next());
int currentCluster = dp.getClusterNumber();
x[currentCluster] += dp.getX();
y[currentCluster] += dp.getY();
z[currentCluster] += dp.getZ();
size[currentCluster]++;
}
for (int j = 0; j < this.k; j++)
if (size[j] != 0) {
x[j] /= size[j];
y[j] /= size[j];
z[j] /= size[j];
kMeansPoint temp = new kMeansPoint(x[j], y[j], z[j]);
temp.assignToCluster(j);
this.clusters[j].setMean(temp);
if (kMeansPoint.distance(pastMeans[j], this.clusters[j].getMean()) != 0) reply = true;
}
} else {
double[][] douAVE =
new double[((kMeansPoint) this.kMeansPoints.get(0)).getListData().size()][this.k];
int[] size = new int[this.k];
kMeansPoint[] pastMeans = new kMeansPoint[this.k];
for (int i = 0; i < this.k; i++) {
for (int j = 0; j < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); j++) {
douAVE[j][i] = 0.0;
}
size[i] = 0;
pastMeans[i] = this.clusters[i].getMean();
}
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext()) {
kMeansPoint dp = (kMeansPoint) (i.next());
int currentCluster = dp.getClusterNumber();
for (int j = 0; j < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); j++) {
douAVE[j][currentCluster] += dp.getListData().get(j);
}
size[currentCluster]++;
}
for (int j = 0; j < this.k; j++)
if (size[j] != 0) {
for (int k = 0; k < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); k++) {
douAVE[k][j] /= size[j];
}
List<Double> listKM = new ArrayList<Double>();
for (int k = 0; k < ((kMeansPoint) this.kMeansPoints.get(0)).getListData().size(); k++) {
listKM.add(douAVE[k][j]);
}
kMeansPoint temp = new kMeansPoint(listKM);
temp.assignToCluster(j);
this.clusters[j].setMean(temp);
if (kMeansPoint.comPointsDis(pastMeans[j], this.clusters[j].getMean()) != 0) reply = true;
}
}
return reply;
} // end of updateMeans()