Kmeans algorithm in JAVA

first the Point class
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package KmeansPractice;

public class Point {

private float X;
private float Y;

public Point()
{
this.X=0;
this.Y=0;
}
public boolean same(Point p1,Point p2)
{
int i1 = Float.compare(p1.getX(),p2.getX());
int i2 = Float.compare(p1.getY(),p2.getY());
if(i1==0 && i2==0)
return true;
else
return false;

}
public Point(float x, float y)
{
System.out.println(x+","+y);
this.X=x;
this.Y=y;
}

public float getX()
{
return X;
}

public float getY()
{
return Y;
}
    public void putX(float x)
    {
    this.X=x;
    }
    public void putY(float y)
    {
    this.Y=y;
    }
   
    public String toString()
    {
      return "("+this.X+","+this.Y+")";
    }
}

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Now the cluster class

---------------------------------------------------------------

package KmeansPractice;

import java.util.*;

import KmeansPractice.Point;

public class Cluster {

private ArrayList<Point> cluster;

Point mean;

Cluster()

{

cluster=new ArrayList<Point>();

mean = new Point();

}

public void setMean(Point p)

{

mean=p;

}

public boolean exists(Point p)

{

if(cluster.contains(p))

 return true;

else return false;

}

    public void add(Point p)

    {

    cluster.add(p);

    }

    public void delete(Point p)

    {

    cluster.remove(p);

    }

public Point getMean()

{

return mean;

}

public Point calMean()

{

float X=0,Y=0;

for(Point p:cluster)

{

X = X+p.getX();

Y = Y+p.getY();

}

X=X/cluster.size();

Y=Y/cluster.size();

mean=new Point(X,Y);

return mean;

}

public String toString()

{

String S="";

for(Point p:cluster)

   {

S+=p.toString();

   }

return S;

}

}

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Now the KMEANS ALGORITHM

--------------------------------------------------------

package KmeansPractice;

import java.util.*;

import KmeansPractice.Point;

import KmeansPractice.Cluster;

public class Kmeans {

ArrayList<Cluster> C = new ArrayList<Cluster>();

ArrayList<Point> P=new ArrayList<Point>();

ArrayList<Point> means=new ArrayList<Point>();

int N;

Kmeans(int N)

{

this.N=N;

}

public void initialize()

{

Random rand=new Random();  

 

  for(int i=0;i<10;i++)

  {

  Point p=new Point((float)(rand.nextInt()%10),(float)(rand.nextInt()%10));

      P.add(p);

      //System.out.println(p.toString());

   } 

  Cluster C1=new Cluster();

  C1.setMean(P.get(0));

        C.add(C1);

  Cluster C2=new Cluster();

  C2.setMean(P.get(1));

  C.add(C2);

  Cluster C3=new Cluster();

  C3.setMean(P.get(2));

  C.add(C3);

}

public boolean AlgoKmeans()

{

System.out.println("entered algokmeans func");

Scanner sc = new Scanner(System.in);

float Mindist,CurrDist;

int index=0;

System.out.println("present clusters:");

means = new ArrayList<Point>();

for(Cluster c:C)

{

System.out.println(c.toString());

means.add(c.getMean());

}

System.out.println("present means:");

for(Cluster c:C)

{

System.out.println(c.getMean());

// means.add(c.getMean());

}

sc.nextInt();

//calculate mean and distance

 for(int i=0;i<10;i++)

 {  Mindist=999;

 for(int j=0;j<N;j++)

   {CurrDist=EDistance(P.get(i),C.get(j).getMean()); 

 

   if(C.get(j).exists(P.get(i)))

    {C.get(j).delete(P.get(i));}

    if(Mindist>CurrDist)

    {

    Mindist=CurrDist;

    index=j;

    }

   }

 C.get(index).add(P.get(i)); 

 

 }

 System.out.println("printing clusters before calculated means");

 for(Cluster c:C)

 {

 System.out.println(c.toString());

 }

 System.out.println("printing calculated means");

 for(int i=0;i<N;i++)

{

C.get(i).calMean();

System.out.println(C.get(i).getMean());

System.out.println("check:"+means.get(i).toString() + C.get(i).getMean().toString());

if(((C.get(i).getMean()).same(means.get(i),C.get(i).getMean())))

{

 

}

else

return false;

}

 System.out.println("printing clusters before calculated means");

 for(Cluster c:C)

 {

 System.out.println(c.toString());

 }

 return true;

}

public void print()

{

System.out.println("entered print func");

for(Cluster c:C)

   {

 System.out.println(c.toString());

   }

}

public float EDistance(Point p1, Point p2)

{  return (float)Math.sqrt((p1.getX()-p2.getX())*(p1.getX()-p2.getX())+(p1.getY()-p2.getY())*(p1.getY()-p2.getY()));

}

public static void main(String args[])

{

/*Point p1 = new Point((float)3.5,(float)4.5);

Point p2 = new Point((float)3.5,(float)4.5);

System.out.println(p1.same(p1,p2));

*/

Kmeans k =new Kmeans(3);

k.initialize();

   while(!k.AlgoKmeans())

  k.AlgoKmeans();

   k.print();

}

}

----------------------------------------------------------------------------------------------------------------------------------End of the code

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