Java tutorial
/******************************************************************************* * Copyright (C) 2014 Anonymized * Contributors: * Anonymized * * This file is part of ICDM2014SUBMISSION. * This is a program related to the paper "Dynamic Time Warping Averaging of * Time Series allows more Accurate and Faster Classification" submitted to the * 2014 Int. Conf. on Data Mining. * * ICDM2014SUBMISSION is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation, version 3 of the License. * * ICDM2014SUBMISSION is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with ICDM2014SUBMISSION. If not, see <http://www.gnu.org/licenses/>. ******************************************************************************/ package classif.fuzzycmeans; import items.MonoDoubleItemSet; import items.Sequence; import java.util.ArrayList; import java.util.Arrays; import java.util.List; import org.apache.commons.math3.random.RandomDataGenerator; import classif.kmeans.EUCKMeansSymbolicSequence; public class EUCFUZZYCMEANSSymbolicSequence { public int nbClusters; public ArrayList<Sequence> data; public RandomDataGenerator randGen; protected Sequence[] centroidsPerCluster = null; protected double[] sigmasPerCluster = null; protected int dataAttributes; private static final double minObj = 1; private static final double sqrt2Pi = Math.sqrt(2 * Math.PI); private double[] nck = null; private int sumnck; public EUCFUZZYCMEANSSymbolicSequence(int nbClusters, ArrayList<Sequence> data, int dataAttributes) { if (data.size() < nbClusters) { this.nbClusters = data.size(); } else { this.nbClusters = nbClusters; } this.data = data; this.dataAttributes = dataAttributes; this.randGen = new RandomDataGenerator(); } public void cluster() { // init boolean isBig; ArrayList<Sequence>[] affectation = new ArrayList[nbClusters]; int runtime = 0; do { if (runtime > 10) nbClusters -= 1; isBig = true; EUCKMeansSymbolicSequence kmeans = new EUCKMeansSymbolicSequence(nbClusters, data); kmeans.cluster(); centroidsPerCluster = kmeans.centers; affectation = kmeans.affectation; for (ArrayList<Sequence> Eachaffectation : affectation) { if (Eachaffectation.size() < minObj) { isBig = false; break; } } runtime++; } while (isBig == false); sigmasPerCluster = new double[nbClusters]; nck = new double[nbClusters]; sumnck = data.size(); for (int k = 0; k < nbClusters; k++) { if (centroidsPerCluster[k] != null) { // ~ if empty cluster // find the center nck[k] = affectation[k].size(); // compute sigma double sumOfSquares = centroidsPerCluster[k].EUCsumOfSquares(affectation[k]); sigmasPerCluster[k] = Math.sqrt(sumOfSquares / nck[k]); } else System.err.println("ERROR"); } // double sumoflog = 0.0; // double prevsumoflog = -(Math.exp(308)); // computing initial likelihood // sumoflog = loglikelihood(centroidsPerCluster, sigmasPerCluster, nck); /*while (Math.abs(sumoflog - prevsumoflog) > threshold) { prevsumoflog = sumoflog; sumoflog = gmmprocess(); }*/ for (int i = 0; i < 10; i++) { gmmprocess(); } } private double gmmprocess() { double[][] gammak = new double[sumnck][nbClusters]; double[] sumofgammak = new double[sumnck]; double[][] gamma = new double[sumnck][nbClusters]; // p(i,k) // get p(i,k) ArrayList<Sequence> sequencesForClass = data; // for each data point computer gamma for (int i = 0; i < sequencesForClass.size(); i++) { Sequence s = sequencesForClass.get(i); // for each p(k) for (int k = 0; k < centroidsPerCluster.length; k++) { double dist = s.distanceEuc(centroidsPerCluster[k]); double p = computeProbaForQueryAndCluster(sigmasPerCluster[k], dist); gammak[i][k] = p * (nck[k] / sumnck); } // sum of p(k) for (int k = 0; k < gammak[i].length; k++) { sumofgammak[i] += gammak[i][k]; } // p(i,k) for (int k = 0; k < centroidsPerCluster.length; k++) { gamma[i][k] = gammak[i][k] / sumofgammak[i]; } } // Nk = sum of gamma for (int k = 0; k < centroidsPerCluster.length; k++) { // for each cluster double sumofgammai = 0; for (int i = 0; i < sumnck; i++) { sumofgammai += gamma[i][k]; } // System.out.println(sumofgammai); nck[k] = sumofgammai; if (nck[k] <= minObj) { delcluster(k); double log = gmmprocess(); return log; } } // centroidsPerClass MonoDoubleItemSet[] sequence = new MonoDoubleItemSet[dataAttributes]; for (int k = 0; k < centroidsPerCluster.length; k++) { MonoDoubleItemSet[] sequencetmp = new MonoDoubleItemSet[dataAttributes]; MonoDoubleItemSet[] sumofSTmp = new MonoDoubleItemSet[dataAttributes]; // new MonoDoubleItemSet for (int t = 0; t < sequence.length; t++) { sequencetmp[t] = new MonoDoubleItemSet(0.0); sumofSTmp[t] = new MonoDoubleItemSet(0.0); } for (int i = 0; i < sumnck; i++) { sequence = (MonoDoubleItemSet[]) data.get(i).getSequence(); // gamma(i)*x(i)[t] for (int t = 0; t < sequence.length; t++) { // System.out.println("gamma"+gamma[i][k]); sequencetmp[t] = new MonoDoubleItemSet(gamma[i][k] * sequence[t].getValue()); } // sum of gamma(i)*x(i) for (int t = 0; t < sequence.length; t++) { sumofSTmp[t] = new MonoDoubleItemSet(sequencetmp[t].getValue() + sumofSTmp[t].getValue()); } } // (sum of gamma*x) / (sum of gamma) for (int t = 0; t < sequence.length; t++) { sumofSTmp[t] = new MonoDoubleItemSet(sumofSTmp[t].getValue() / nck[k]); } centroidsPerCluster[k] = new Sequence(sumofSTmp); } // sigma for (int k = 0; k < centroidsPerCluster.length; k++) { sigmasPerCluster[k] = 0; double sumOfSquares = 0.0; for (int i = 0; i < sumnck; i++) { double dist = data.get(i).distanceEuc(centroidsPerCluster[k]); sumOfSquares += (gamma[i][k] * dist * dist); } // sigmasPerCluster[k] = Math.sqrt(sumOfSquares / (nck[k] - 1)); sigmasPerCluster[k] = Math.sqrt(sumOfSquares / nck[k]); } // computer log-likelihood double log = loglikelihood(centroidsPerCluster, sigmasPerCluster, nck); return log; } private double loglikelihood(Sequence[] mu, double[] sigma, double[] nbObjects) { double loglikelihood = 0.0; for (Sequence ss : data) { double prob = 0.0; for (int k = 0; k < mu.length; k++) { double dist = ss.distanceEuc(centroidsPerCluster[k]); double p = computeProbaForQueryAndCluster(sigma[k], dist); prob += p * (nbObjects[k] / sumnck);// probability of every point generated by each cluster } loglikelihood += Math.log(prob); } return loglikelihood; } private double computeProbaForQueryAndCluster(double sigma, double d) { double pqk = 0.0; if (sigma == 0) { if (d == 0) { pqk = 1; } else pqk = 0; } else pqk = Math.exp(-(d * d) / (2 * sigma * sigma)) / (sigma * sqrt2Pi); return pqk; } private void delcluster(int k) { centroidsPerCluster[k] = null; sigmasPerCluster[k] = Double.NaN; Sequence[] newcenter = new Sequence[nbClusters - 1]; double[] newsigma = new double[nbClusters - 1]; nbClusters = nbClusters - 1; int flag = 0; for (int i = 0; i < centroidsPerCluster.length; i++) { if (centroidsPerCluster[i] != null) { newcenter[i - flag] = centroidsPerCluster[i]; newsigma[i - flag] = sigmasPerCluster[i]; } else flag++; } centroidsPerCluster = newcenter; sigmasPerCluster = newsigma; ArrayList<Sequence>[] affectation = new ArrayList[nbClusters]; int[] clusterMap = new int[data.size()]; for (int i = 0; i < affectation.length; i++) { affectation[i] = new ArrayList<Sequence>(); } for (int j = 0; j < data.size(); j++) { double minDist = Double.MAX_VALUE; // for each cluster k for (int i = 0; i < centroidsPerCluster.length; i++) { // distance between cluster k and data point j if (centroidsPerCluster[i] != null) { double currentDist = centroidsPerCluster[i].distanceEuc(data.get(j)); if (currentDist < minDist) { clusterMap[j] = i; minDist = currentDist; } } } // affect data point j to cluster affected to j affectation[clusterMap[j]].add(data.get(j)); } for (int i = 0; i < nbClusters; i++) { nck[i] = affectation[i].size(); } } public Sequence[] getMus() { return centroidsPerCluster; } public double[] getSigmas() { return sigmasPerCluster; } public double[] getNck() { return nck; } }