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MASON is a fast discrete-event multiagent simulation library core in Java, designed to be the foundation for large custom-purpose Java simulations, and also to provide more than enough functionality for many lightweight simulation needs. MASON contains both a model library and an optional suite of visualization tools in 2D and 3D.

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/*
  Copyright 2006 by Sean Luke and George Mason University
  Licensed under the Academic Free License version 3.0
  See the file "LICENSE" for more information
*/

package sim.app.cto;

import sim.util.Double2D;
import sim.engine.*;

public /*strictfp*/ class KMeansEngine implements Steppable
    {
    private static final long serialVersionUID = 1;

    final static double ALFA = 0.25;

    Double2D[] clusterPoints;// = new Double2D[ CooperativeObservation.NUM_AGENTS ];
    boolean[] usable; // = new boolean[ CooperativeObservation.NUM_AGENTS ];
    Double2D[] means;// = new Double2D[ CooperativeObservation.NUM_AGENTS ];
    int[] labels;// = new int[ CooperativeObservation.NUM_TARGETS ];
    int[] n;// = new int[ CooperativeObservation.NUM_AGENTS ];
    double[] weight;// = new double[ CooperativeObservation.NUM_AGENTS ];

    CooperativeObservation co;

    public KMeansEngine( CooperativeObservation co )
        {
        this.co = co;

        clusterPoints = new Double2D[ CooperativeObservation.NUM_AGENTS ];
        usable = new boolean[ CooperativeObservation.NUM_AGENTS ];
        means = new Double2D[ CooperativeObservation.NUM_AGENTS ];
        for( int i = 0 ; i < CooperativeObservation.NUM_AGENTS ; i++ )
            {
            clusterPoints[i] = new Double2D();
            means[i] = new Double2D();
            }
        labels = new int[ CooperativeObservation.NUM_TARGETS ];
        n = new int[ CooperativeObservation.NUM_AGENTS ];
        weight = new double[ CooperativeObservation.NUM_AGENTS ];
        }

    public Double2D getGoalPosition( int id )
        {
        if( usable[id] )
            return clusterPoints[id];
        else
            return null; // for exploration purposes
        }

    public final double distanceBetweenPointsSQR( double x1, double y1, double x2, double y2 )
        {
        return (x1-x2)*(x1-x2) + (y1-y2)*(y1-y2);
        }

    public void step( final SimState state )
        {
        for( int i = 0 ; i < CooperativeObservation.NUM_AGENTS ; i++ )
            {
            weight[i] = 0;
            if( means[i].x == 0 && means[i].y == 0 )
                {
                clusterPoints[i] = means[i];
                }
            means[i] = co.agentPos[i];
            }
        for( int i = 0 ; i < CooperativeObservation.NUM_TARGETS ; i++ )
            {
            int min = -1;
            double distance = -1;
            for( int j = 0 ; j < CooperativeObservation.NUM_AGENTS ; j++ )
                {
                double currDist = distanceBetweenPointsSQR( co.targetPos[i].x, co.targetPos[i].y,
                    co.agentPos[j].x, co.agentPos[j].y );
                if( distance == -1 || distance > currDist )
                    {
                    min = j;
                    distance = currDist;
                    }
                }
            labels[i] = min;
            }
        for( int i = 0 ; i < CooperativeObservation.NUM_AGENTS ; i++ )
            {
            means[i] = new Double2D( 0.0, 0.0 );
            n[i] = 0;
            }
        for( int i = 0 ; i < CooperativeObservation.NUM_TARGETS ; i++ )
            {
            if( labels[i] != -1 )
                {
                means[labels[i]] = new Double2D( means[labels[i]].x + co.targetPos[i].x,
                    means[labels[i]].y + co.targetPos[i].y );
                n[labels[i]]++;
                }
            }
        for( int i = 0 ; i < CooperativeObservation.NUM_AGENTS ; i++ )
            {
            if( n[i] != 0 )
                {
                means[i] = new Double2D( means[i].x/n[i], means[i].y/n[i] );
                clusterPoints[i] = new Double2D( (1-ALFA)*clusterPoints[i].x + ALFA*means[i].x,
                    (1-ALFA)*clusterPoints[i].y + ALFA*means[i].y );
                usable[i] = true; 
                }
            else
                {
                usable[i] = false;
                }
            }
        }

    }