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Massive On-line Analysis is an environment for massive data mining. MOA provides a framework for data stream mining and includes tools for evaluation and a collection of machine learning algorithms. Related to the WEKA project, also written in Java, while scaling to more demanding problems.

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/*
 *    Cluster.java
 *    Copyright (C) 2010 RWTH Aachen University, Germany
 *    @author Jansen ([email protected])
 *
 *    Licensed under the Apache License, Version 2.0 (the "License");
 *    you may not use this file except in compliance with the License.
 *    You may obtain a copy of the License at
 *
 *      http://www.apache.org/licenses/LICENSE-2.0
 *
 *    Unless required by applicable law or agreed to in writing, software
 *    distributed under the License is distributed on an "AS IS" BASIS,
 *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *    See the License for the specific language governing permissions and
 *    limitations under the License.
 *    
 *    
 */

package moa.cluster;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.Iterator;
import java.util.Map;
import java.util.Random;
import moa.AbstractMOAObject;
import com.yahoo.labs.samoa.instances.Instance;

public abstract class Cluster extends AbstractMOAObject {

	private static final long serialVersionUID = 1L;

	private double id = -1;
    private double gtLabel = -1;

    private HashMap measure_values;


    public Cluster(){
        this.measure_values = new HashMap();
    }
    /**
     * @return the center of the cluster
     */
    public abstract double[] getCenter();

    /**
     * Returns the weight of this cluster, not neccessarily normalized.
     * It could, for instance, simply return the number of points contined
     * in this cluster.
     * @return the weight
     */
    public abstract double getWeight();

    /**
     * Returns the probability of the given point belonging to
     * this cluster.
     *
     * @param point
     * @return a value between 0 and 1
     */
    public abstract double getInclusionProbability(Instance instance);


    //TODO: for non sphere cluster sample points, find out MIN MAX neighbours within cluster
    //and return the relative distance
    //public abstract double getRelativeHullDistance(Instance instance);
    
    @Override
    public void getDescription(StringBuilder sb, int i) {
        sb.append("Cluster Object");
    }

    public void setId(double id) {
        this.id = id;
    }

    public double getId() {
        return id;
    }

    public boolean isGroundTruth(){
        return gtLabel != -1;
    }

    public void setGroundTruth(double truth){
        gtLabel = truth;
    }

    public double getGroundTruth(){
        return gtLabel;
    }


    /**
     * Samples this cluster by returning a point from inside it.
     * @param random a random number source
     * @return an Instance that lies inside this cluster
     */
    public abstract Instance sample(Random random);


    public void setMeasureValue(String measureKey, String value){
        measure_values.put(measureKey, value);
    }

    public void setMeasureValue(String measureKey, double value){
        measure_values.put(measureKey, Double.toString(value));
    }


    public String getMeasureValue(String measureKey){
        if(measure_values.containsKey(measureKey))
            return measure_values.get(measureKey);
        else
            return "";
    }


    protected void getClusterSpecificInfo(ArrayList infoTitle,ArrayList infoValue){
        infoTitle.add("ClusterID");
        infoValue.add(Integer.toString((int)getId()));

        infoTitle.add("Type");
        infoValue.add(getClass().getSimpleName());

        double c[] = getCenter();
        if(c!=null)
        for (int i = 0; i < c.length; i++) {
            infoTitle.add("Dim"+i);
            infoValue.add(Double.toString(c[i]));
        }

        infoTitle.add("Weight");
        infoValue.add(Double.toString(getWeight()));
        
    }

    public String getInfo() {
        ArrayList infoTitle = new ArrayList();
        ArrayList infoValue = new ArrayList();
        getClusterSpecificInfo(infoTitle, infoValue);

        StringBuffer sb = new StringBuffer();

        //Cluster properties
        sb.append("");
        sb.append("");
        int i = 0;
        while(i < infoTitle.size() && i < infoValue.size()){
            sb.append("");
            i++;
        }
        sb.append(""+infoTitle.get(i)+" "+infoValue.get(i)+"");

        //Evaluation info
        sb.append("
");
        sb.append("Evaluation
");
        sb.append("");
        Iterator miterator = measure_values.entrySet().iterator();
        while(miterator.hasNext()) {
             Map.Entry e = (Map.Entry)miterator.next();
             sb.append("");
        }
        sb.append(""+e.getKey()+" "+e.getValue()+"");
        sb.append("");
        return sb.toString();
    }

}