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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.evaluation;

import java.util.ArrayList;
import java.util.HashMap;
import moa.cluster.Clustering;
import moa.gui.visualization.DataPoint;


public class MembershipMatrix {

    HashMap  classmap;
    int cluster_class_weights[][];
    int cluster_sums[];
    int class_sums[];
    int total_entries;
    int class_distribution[];
    int total_class_entries;
    int initalBuildTimestamp = -1;

    public MembershipMatrix(Clustering foundClustering, ArrayList points) {
        classmap = Clustering.classValues(points);
//        int lastID  = classmap.size()-1;
//        classmap.put(-1, lastID);
        int numClasses = classmap.size();
        int numCluster = foundClustering.size()+1;

        cluster_class_weights = new int[numCluster][numClasses];
        class_distribution = new int[numClasses];
        cluster_sums = new int[numCluster];
        class_sums = new int[numClasses];
        total_entries = 0;
        total_class_entries = points.size();
        for (int p = 0; p < points.size(); p++) {
            int worklabel = classmap.get((int)points.get(p).classValue());
            //real class distribution
            class_distribution[worklabel]++;
            boolean covered = false;
            for (int c = 0; c < numCluster-1; c++) {
                double prob = foundClustering.get(c).getInclusionProbability(points.get(p));
                if(prob >= 1){
                    cluster_class_weights[c][worklabel]++;
                    class_sums[worklabel]++;
                    cluster_sums[c]++;
                    total_entries++;
                    covered = true;
                }
            }
            if(!covered){
                cluster_class_weights[numCluster-1][worklabel]++;
                class_sums[worklabel]++;
                cluster_sums[numCluster-1]++;
                total_entries++;
            }

        }
        
        initalBuildTimestamp = points.get(0).getTimestamp();
    }

    public int getClusterClassWeight(int i, int j){
        return cluster_class_weights[i][j];
    }

    public int getClusterSum(int i){
        return cluster_sums[i];
    }

    public int getClassSum(int j){
        return class_sums[j];
    }

    public int getClassDistribution(int j){
        return class_distribution[j];
    }

    public int getClusterClassWeightByLabel(int cluster, int classLabel){
        return cluster_class_weights[cluster][classmap.get(classLabel)];
    }

    public int getClassSumByLabel(int classLabel){
        return class_sums[classmap.get(classLabel)];
    }

    public int getClassDistributionByLabel(int classLabel){
        return class_distribution[classmap.get(classLabel)];
    }

    public int getTotalEntries(){
        return total_entries;
    }

    public int getNumClasses(){
        return classmap.size();
    }

    public boolean hasNoiseClass(){
        return classmap.containsKey(-1);
    }

    @Override
    public String toString() {
        StringBuffer sb = new StringBuffer();
        sb.append("Membership Matrix\n");
        for (int i = 0; i < cluster_class_weights.length; i++) {
            for (int j = 0; j < cluster_class_weights[i].length; j++) {
                sb.append(cluster_class_weights[i][j]+"\t ");
            }
            sb.append("| "+cluster_sums[i]+"\n");
        }
        //sb.append("-----------\n");
        for (int i = 0; i < class_sums.length; i++) {
            sb.append(class_sums[i]+"\t ");
        }
        sb.append("| "+total_entries+"\n");


        sb.append("Real class distribution \n");
        for (int i = 0; i < class_distribution.length; i++) {
            sb.append(class_distribution[i]+"\t ");
        }
        sb.append("| "+total_class_entries+"\n");

        return sb.toString();
    }


    public int getInitalBuildTimestamp(){
    	return initalBuildTimestamp;
    }
    

}