# Java Program to Implement Lloyd’s Algorithm

This is a Java Program to implement Lloyd’s Algorithm. The LBG-algorithm or Lloyd’s algorithm allows clustering of vectors of any dimension. This is helpful for example for image classification when using the SIFT or SURF algorithms. It might be also useful if you want to cluster a large amount of points on a map.

Here is the source code of the Java Program to Implement Lloyd’s Algorithm. The Java program is successfully compiled and run on a Windows system. The program output is also shown below.

//This is a java program to implement Lloyd’s Algorithm
import java.util.ArrayList;

public class GenLloyd
{
protected double[][] samplePoints;
protected double[][] clusterPoints;

int[] pointApproxIndices;
int pointDimension = 0;
protected double epsilon = 0.0005;
protected double avgDistortion = 0.0;

/**
* Create Generalized Lloyd object with an array of sample points
*/
public GenLloyd(double[][] samplePoints)
{
this.setSamplePoints(samplePoints);
}

/**
* Return epsilon parameter (accuracy)
*/
public double getEpsilon()
{
return epsilon;
}

/**
* Set epsilon parameter (accuracy). Should be a small number 0.0 < epsilon
* < 0.1
*/
public void setEpsilon(double epsilon)
{
this.epsilon = epsilon;
}

/**
* Set array of sample points
*/
public void setSamplePoints(double[][] samplePoints)
{
if (samplePoints.length > 0)
{
this.samplePoints = samplePoints;
this.pointDimension = samplePoints[0].length;
}
}

/**
* Get array of sample points
*/
public double[][] getSamplePoints()
{
return samplePoints;
}

/**
* Get calculated cluster points. <numClusters> cluster points will be
* calculated and returned
*/
public double[][] getClusterPoints(int numClusters)
{
this.calcClusters(numClusters);

return clusterPoints;
}

protected void calcClusters(int numClusters)
{
// initialize with first cluster
clusterPoints = new double[1][pointDimension];

double[] newClusterPoint = initializeClusterPoint(samplePoints);
clusterPoints[0] = newClusterPoint;

if (numClusters > 1)
{
// calculate initial average distortion
avgDistortion = 0.0;
for (double[] samplePoint : samplePoints)
{
avgDistortion += calcDist(samplePoint, newClusterPoint);
}

avgDistortion /= (double) (samplePoints.length * pointDimension);

// set up array of point approximization indices
pointApproxIndices = new int[samplePoints.length];

// split the clusters
int i = 1;
do
{
i = splitClusters();
} while (i < numClusters);
}
}

protected int splitClusters()
{
int newClusterPointSize = 2;
if (clusterPoints.length != 1)
{
newClusterPointSize = clusterPoints.length * 2;
}

// split clusters
double[][] newClusterPoints = new double[newClusterPointSize][pointDimension];
int newClusterPointIdx = 0;
for (double[] clusterPoint : clusterPoints)
{
newClusterPoints[newClusterPointIdx] = createNewClusterPoint(
clusterPoint, -1);
newClusterPoints[newClusterPointIdx + 1] = createNewClusterPoint(
clusterPoint, +1);

newClusterPointIdx += 2;
}

clusterPoints = newClusterPoints;

// iterate to approximate cluster points
// int iteration = 0;
double curAvgDistortion = 0.0;
do
{
curAvgDistortion = avgDistortion;

// find the min values
for (int pointIdx = 0; pointIdx < samplePoints.length; pointIdx++)
{
double minDist = Double.MAX_VALUE;
for (int clusterPointIdx = 0; clusterPointIdx < clusterPoints.length; clusterPointIdx++)
{
double newMinDist = calcDist(samplePoints[pointIdx],
clusterPoints[clusterPointIdx]);
if (newMinDist < minDist)
{
minDist = newMinDist;
pointApproxIndices[pointIdx] = clusterPointIdx;
}
}
}

// update codebook
for (int clusterPointIdx = 0; clusterPointIdx < clusterPoints.length; clusterPointIdx++)
{
double[] newClusterPoint = new double[pointDimension];
int num = 0;
for (int pointIdx = 0; pointIdx < samplePoints.length; pointIdx++)
{
if (pointApproxIndices[pointIdx] == clusterPointIdx)
{
num++;
}
}

if (num > 0)
{
multiplyPointValues(newClusterPoint, 1.0 / (double) num);
clusterPoints[clusterPointIdx] = newClusterPoint;
}
}

// update average distortion
avgDistortion = 0.0;
for (int pointIdx = 0; pointIdx < samplePoints.length; pointIdx++)
{
avgDistortion += calcDist(samplePoints[pointIdx],
clusterPoints[pointApproxIndices[pointIdx]]);
}

avgDistortion /= (double) (samplePoints.length * pointDimension);

} while (((curAvgDistortion - avgDistortion) / curAvgDistortion) > epsilon);

return clusterPoints.length;
}

protected double[] initializeClusterPoint(double[][] pointsInCluster)
{
// calculate point sum
double[] clusterPoint = new double[pointDimension];
for (int numPoint = 0; numPoint < pointsInCluster.length; numPoint++)
{
}

// calculate average
multiplyPointValues(clusterPoint, 1.0 / (double) pointsInCluster.length);

return clusterPoint;
}

protected double[] createNewClusterPoint(double[] clusterPoint,
int epsilonFactor)
{
double[] newClusterPoint = new double[pointDimension];
multiplyPointValues(newClusterPoint, 1.0 + (double) epsilonFactor
* epsilon);

return newClusterPoint;
}

protected double calcDist(double[] v1, double[] v2)
{
double distSum = 0.0;
for (int pointIdx = 0; pointIdx < v1.length; pointIdx++)
{
double absDist = Math.abs(v1[pointIdx] - v2[pointIdx]);
distSum += absDist * absDist;
}

return distSum;
}

protected void addPointValues(double[] v1, double[] v2)
{
for (int pointIdx = 0; pointIdx < v1.length; pointIdx++)
{
v1[pointIdx] += v2[pointIdx];
}
}

protected void multiplyPointValues(double[] v1, double f)
{
for (int pointIdx = 0; pointIdx < v1.length; pointIdx++)
{
v1[pointIdx] *= f;
}
}

public static void main(String[] args)
{
ArrayList<double[]> points = new ArrayList<double[]>();

GenLloyd gl = new GenLloyd(points.toArray(new double[points.size()][2]));

double[][] results = gl.getClusterPoints(4);
for (double[] point : results)
{
System.out.println("Cluster " + point[0] + ", " + point[1] + ", "
+ point[2]);
}
}

private static double[] arrayOf(double x, double y, double z)
{
double[] a = new double[3];
a[0] = x;
a[1] = y;
a[2] = z;

return a;
}
}


Output:

$javac GenLloyd.java$ java GenLloyd

Cluster -2.0, -2.0, 0.0
Cluster 1.0, -2.5, -2.833333333333333
Cluster 1.25, 1.25, 1.6
Cluster -0.25, 2.0, 5.3