package com.yahoo.labs.samoa.moa.evaluation; /* * #%L * SAMOA * %% * Copyright (C) 2010 RWTH Aachen University, Germany * %% * 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. * #L% */ import java.util.ArrayList; import java.util.HashMap; import com.yahoo.labs.samoa.moa.cluster.Clustering; import com.yahoo.labs.samoa.moa.core.DataPoint; public class MembershipMatrix { HashMap<Integer, Integer> 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<DataPoint> 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; } }