Java tutorial
package Part2; /* * This program 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; either version 2 of the License, or * (at your option) any later version. * * This program 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 this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ /* * HierarchicalClusterer.java * Copyright (C) 2009 University of Waikato, Hamilton, New Zealand */ import java.io.Serializable; import java.text.DecimalFormat; import java.util.Comparator; import java.util.Enumeration; import java.util.PriorityQueue; import java.util.Vector; import weka.clusterers.AbstractClusterer; import weka.core.Capabilities; import weka.core.CapabilitiesHandler; import weka.core.DistanceFunction; import weka.core.Drawable; import weka.core.EuclideanDistance; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.SelectedTag; import weka.core.Tag; import weka.core.Utils; import weka.core.Capabilities.Capability; /** <!-- globalinfo-start --> * Hierarchical clustering class. * Implements a number of classic hierarchical clustering methods. <!-- globalinfo-end --> * <!-- options-start --> * Valid options are: <p/> * * <pre> -N * number of clusters * </pre> * * * <pre> -L * Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor Joining) * [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMLPETE|NEIGHBOR_JOINING] * </pre> * * <pre> -A * Distance function to use. (default: weka.core.EuclideanDistance) * </pre> * * <pre> -P * Print hierarchy in Newick format, which can be used for display in other programs. * </pre> * * <pre> -D * If set, classifier is run in debug mode and may output additional info to the console. * </pre> * * <pre> -B * \If set, distance is interpreted as branch length, otherwise it is node height. * </pre> * *<!-- options-end --> * * * @author Remco Bouckaert (rrb@xm.co.nz, remco@cs.waikato.ac.nz) * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 6592 $ */ public class HierarchicalClustererEx extends AbstractClusterer implements OptionHandler, CapabilitiesHandler, Drawable { private static final long serialVersionUID = 1L; /** Whether the classifier is run in debug mode. */ protected boolean m_bDebug = false; /** Whether the distance represent node height (if false) or branch length (if true). */ protected boolean m_bDistanceIsBranchLength = false; /** training data **/ Instances m_instances; public HierarchicalClustererEx(DistanceWithLabel dis) { this.m_DistanceFunction = dis; } /** number of clusters desired in clustering **/ int m_nNumClusters = 2; public void setNumClusters(int nClusters) { m_nNumClusters = Math.max(1, nClusters); } public int getNumClusters() { return m_nNumClusters; } /** distance function used for comparing members of a cluster **/ public DistanceWithLabel m_DistanceFunction = null; public DistanceFunction getDistanceFunction() { return m_DistanceFunction; } // public void setDistanceFunction(DistanceFunction distanceFunction) {m_DistanceFunction = distanceFunction;} /** used for priority queue for efficient retrieval of pair of clusters to merge**/ class Tuple { public Tuple(double d, int i, int j, int nSize1, int nSize2) { m_fDist = d; m_iCluster1 = i; m_iCluster2 = j; m_nClusterSize1 = nSize1; m_nClusterSize2 = nSize2; } double m_fDist; int m_iCluster1; int m_iCluster2; int m_nClusterSize1; int m_nClusterSize2; } /** comparator used by priority queue**/ class TupleComparator implements Comparator<Tuple> { public int compare(Tuple o1, Tuple o2) { if (o1.m_fDist < o2.m_fDist) { return -1; } else if (o1.m_fDist == o2.m_fDist) { return 0; } return 1; } } /** the various link types */ final static int SINGLE = 0; final static int COMPLETE = 1; final static int AVERAGE = 2; final static int MEAN = 3; final static int CENTROID = 4; final static int WARD = 5; final static int ADJCOMLPETE = 6; final static int NEIGHBOR_JOINING = 7; public static final Tag[] TAGS_LINK_TYPE = { new Tag(SINGLE, "SINGLE"), new Tag(COMPLETE, "COMPLETE"), new Tag(AVERAGE, "AVERAGE"), new Tag(MEAN, "MEAN"), new Tag(CENTROID, "CENTROID"), new Tag(WARD, "WARD"), new Tag(ADJCOMLPETE, "ADJCOMLPETE"), new Tag(NEIGHBOR_JOINING, "NEIGHBOR_JOINING") }; /** * Holds the Link type used calculate distance between clusters */ int m_nLinkType = SINGLE; boolean m_bPrintNewick = true;; public boolean getPrintNewick() { return m_bPrintNewick; } public void setPrintNewick(boolean bPrintNewick) { m_bPrintNewick = bPrintNewick; } public void setLinkType(SelectedTag newLinkType) { m_nLinkType = newLinkType.getSelectedTag().getID(); } public SelectedTag getLinkType() { return new SelectedTag(m_nLinkType, TAGS_LINK_TYPE); } /** class representing node in cluster hierarchy **/ class Node implements Serializable { Node m_left; Node m_right; Node m_parent; int m_iLeftInstance; int m_iRightInstance; double m_fLeftLength = 0; double m_fRightLength = 0; double m_fHeight = 0; public String toString(int attIndex) { DecimalFormat myFormatter = new DecimalFormat("#.#####"); if (m_left == null) { if (m_right == null) { return "(" + m_instances.instance(m_iLeftInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_instances.instance(m_iRightInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } else { return "(" + m_instances.instance(m_iLeftInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_right.toString(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } } else { if (m_right == null) { return "(" + m_left.toString(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_instances.instance(m_iRightInstance).stringValue(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } else { return "(" + m_left.toString(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_right.toString(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } } } public String toString2(int attIndex) { DecimalFormat myFormatter = new DecimalFormat("#.#####"); if (m_left == null) { if (m_right == null) { return "(" + m_instances.instance(m_iLeftInstance).value(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_instances.instance(m_iRightInstance).value(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } else { return "(" + m_instances.instance(m_iLeftInstance).value(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_right.toString2(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } } else { if (m_right == null) { return "(" + m_left.toString2(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_instances.instance(m_iRightInstance).value(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } else { return "(" + m_left.toString2(attIndex) + ":" + myFormatter.format(m_fLeftLength) + "," + m_right.toString2(attIndex) + ":" + myFormatter.format(m_fRightLength) + ")"; } } } void setHeight(double fHeight1, double fHeight2) { m_fHeight = fHeight1; if (m_left == null) { m_fLeftLength = fHeight1; } else { m_fLeftLength = fHeight1 - m_left.m_fHeight; } if (m_right == null) { m_fRightLength = fHeight2; } else { m_fRightLength = fHeight2 - m_right.m_fHeight; } } void setLength(double fLength1, double fLength2) { m_fLeftLength = fLength1; m_fRightLength = fLength2; m_fHeight = fLength1; if (m_left != null) { m_fHeight += m_left.m_fHeight; } } } Node[] m_clusters; int[] m_nClusterNr; public Vector<Integer>[] initClusterID = null; public void setInitCluster(Vector<Integer>[] initClusterID) { this.initClusterID = initClusterID; } @Override public void buildClusterer(Instances data) throws Exception { // /System.err.println("Method " + m_nLinkType); m_instances = data; int nInstances = m_instances.numInstances(); if (nInstances == 0) { return; } m_DistanceFunction.setInstances(m_instances); // use array of integer vectors to store cluster indices, // starting with one cluster per instance //Vector<Integer> [] nClusterID = this.initClusterID; //int nClusters = this.initClusterID.length; Vector<Integer>[] nClusterID = new Vector[data.numInstances()]; for (int i = 0; i < data.numInstances(); i++) { nClusterID[i] = new Vector<Integer>(); nClusterID[i].add(i); } // calculate distance matrix int nClusters = data.numInstances(); // used for keeping track of hierarchy Node[] clusterNodes = new Node[nInstances]; doLinkClustering(nClusters, nClusterID, clusterNodes); // move all clusters in m_nClusterID array // & collect hierarchy int iCurrent = 0; m_clusters = new Node[m_nNumClusters]; m_nClusterNr = new int[nInstances]; for (int i = 0; i < nInstances; i++) { if (nClusterID[i].size() > 0) { for (int j = 0; j < nClusterID[i].size(); j++) { m_nClusterNr[nClusterID[i].elementAt(j)] = iCurrent; } m_clusters[iCurrent] = clusterNodes[i]; iCurrent++; } } } // buildClusterer /** use neighbor joining algorithm for clustering * This is roughly based on the RapidNJ simple implementation and runs at O(n^3) * More efficient implementations exist, see RapidNJ (or my GPU implementation :-)) * @param nClusters * @param nClusterID * @param clusterNodes */ void neighborJoining(int nClusters, Vector<Integer>[] nClusterID, Node[] clusterNodes) { int n = m_instances.numInstances(); double[][] fDist = new double[nClusters][nClusters]; for (int i = 0; i < nClusters; i++) { fDist[i][i] = 0; for (int j = i + 1; j < nClusters; j++) { fDist[i][j] = getDistance0(nClusterID[i], nClusterID[j]); fDist[j][i] = fDist[i][j]; } } double[] fSeparationSums = new double[n]; double[] fSeparations = new double[n]; int[] nNextActive = new int[n]; //calculate initial separation rows for (int i = 0; i < n; i++) { double fSum = 0; for (int j = 0; j < n; j++) { fSum += fDist[i][j]; } fSeparationSums[i] = fSum; fSeparations[i] = fSum / (nClusters - 2); nNextActive[i] = i + 1; } while (nClusters > 2) { // find minimum int iMin1 = -1; int iMin2 = -1; double fMin = Double.MAX_VALUE; if (m_bDebug) { for (int i = 0; i < n; i++) { if (nClusterID[i].size() > 0) { double[] fRow = fDist[i]; double fSep1 = fSeparations[i]; for (int j = 0; j < n; j++) { if (nClusterID[j].size() > 0 && i != j) { double fSep2 = fSeparations[j]; double fVal = fRow[j] - fSep1 - fSep2; if (fVal < fMin) { // new minimum iMin1 = i; iMin2 = j; fMin = fVal; } } } } } } else { int i = 0; while (i < n) { double fSep1 = fSeparations[i]; double[] fRow = fDist[i]; int j = nNextActive[i]; while (j < n) { double fSep2 = fSeparations[j]; double fVal = fRow[j] - fSep1 - fSep2; if (fVal < fMin) { // new minimum iMin1 = i; iMin2 = j; fMin = fVal; } j = nNextActive[j]; } i = nNextActive[i]; } } // record distance double fMinDistance = fDist[iMin1][iMin2]; nClusters--; double fSep1 = fSeparations[iMin1]; double fSep2 = fSeparations[iMin2]; double fDist1 = (0.5 * fMinDistance) + (0.5 * (fSep1 - fSep2)); double fDist2 = (0.5 * fMinDistance) + (0.5 * (fSep2 - fSep1)); if (nClusters > 2) { // update separations & distance double fNewSeparationSum = 0; double fMutualDistance = fDist[iMin1][iMin2]; double[] fRow1 = fDist[iMin1]; double[] fRow2 = fDist[iMin2]; for (int i = 0; i < n; i++) { if (i == iMin1 || i == iMin2 || nClusterID[i].size() == 0) { fRow1[i] = 0; } else { double fVal1 = fRow1[i]; double fVal2 = fRow2[i]; double fDistance = (fVal1 + fVal2 - fMutualDistance) / 2.0; fNewSeparationSum += fDistance; // update the separationsum of cluster i. fSeparationSums[i] += (fDistance - fVal1 - fVal2); fSeparations[i] = fSeparationSums[i] / (nClusters - 2); fRow1[i] = fDistance; fDist[i][iMin1] = fDistance; } } fSeparationSums[iMin1] = fNewSeparationSum; fSeparations[iMin1] = fNewSeparationSum / (nClusters - 2); fSeparationSums[iMin2] = 0; merge(iMin1, iMin2, fDist1, fDist2, nClusterID, clusterNodes); int iPrev = iMin2; // since iMin1 < iMin2 we havenActiveRows[0] >= 0, so the next loop should be save while (nClusterID[iPrev].size() == 0) { iPrev--; } nNextActive[iPrev] = nNextActive[iMin2]; } else { merge(iMin1, iMin2, fDist1, fDist2, nClusterID, clusterNodes); break; } } for (int i = 0; i < n; i++) { if (nClusterID[i].size() > 0) { for (int j = i + 1; j < n; j++) { if (nClusterID[j].size() > 0) { double fDist1 = fDist[i][j]; if (nClusterID[i].size() == 1) { merge(i, j, fDist1, 0, nClusterID, clusterNodes); } else if (nClusterID[j].size() == 1) { merge(i, j, 0, fDist1, nClusterID, clusterNodes); } else { merge(i, j, fDist1 / 2.0, fDist1 / 2.0, nClusterID, clusterNodes); } break; } } } } } // neighborJoining /** Perform clustering using a link method * This implementation uses a priority queue resulting in a O(n^2 log(n)) algorithm * @param nClusters number of clusters * @param nClusterID * @param clusterNodes */ void doLinkClustering(int nClusters, Vector<Integer>[] nClusterID, Node[] clusterNodes) { int nInstances = m_instances.numInstances(); //double [][] fDistance0 = new double[nClusters][nClusters]; double[][] fDistance0 = new double[nInstances][nInstances]; for (int i = 0; i < nClusters; i++) { fDistance0[i][i] = 0; for (int j = i + 1; j < nClusters; j++) { fDistance0[i][j] = getDistance0(nClusterID[i], nClusterID[j]); fDistance0[j][i] = fDistance0[i][j]; } } PriorityQueue<Tuple> queue = null; if (this.initClusterID != null) { // int initNcluster = nInstances; Vector<Integer>[] initClusterID = this.initClusterID; for (int i = 0; i < initClusterID.length; ++i) { if (initClusterID[i].size() > 1) { int base = initClusterID[i].elementAt(0); for (int j = 1; j < initClusterID[i].size(); ++j) { int des = initClusterID[i].elementAt(j); merge(base, des, fDistance0[base][des], fDistance0[base][des], nClusterID, clusterNodes); initNcluster--; } } if (initNcluster <= m_nNumClusters) break; } nClusters = initNcluster; queue = new PriorityQueue<Tuple>(nClusters * nClusters / 2, new TupleComparator()); for (int i = 0; i < nInstances; i++) { for (int j = i + 1; j < nInstances; j++) { if (nClusterID[i].size() != 0 && nClusterID[j].size() != 0) { double fDistance = getDistance(fDistance0, nClusterID[i], nClusterID[j]); queue.add(new Tuple(fDistance, i, j, nClusterID[i].size(), nClusterID[j].size())); } } } } else { queue = new PriorityQueue<Tuple>(nClusters * nClusters / 2, new TupleComparator()); for (int i = 0; i < nClusters; i++) for (int j = i + 1; j < nClusters; j++) queue.add(new Tuple(fDistance0[j][i], i, j, 1, 1)); } while (nClusters > m_nNumClusters) { int iMin1 = -1; int iMin2 = -1; // find closest two clusters // use priority queue to find next best pair to cluster Tuple t; do { t = queue.poll(); } while (t != null && (nClusterID[t.m_iCluster1].size() != t.m_nClusterSize1 || nClusterID[t.m_iCluster2].size() != t.m_nClusterSize2)); iMin1 = t.m_iCluster1; iMin2 = t.m_iCluster2; merge(iMin1, iMin2, t.m_fDist, t.m_fDist, nClusterID, clusterNodes); // merge clusters // update distances & queue for (int i = 0; i < nInstances; i++) { if (i != iMin1 && nClusterID[i].size() != 0) { int i1 = Math.min(iMin1, i); int i2 = Math.max(iMin1, i); double fDistance = getDistance(fDistance0, nClusterID[i1], nClusterID[i2]); queue.add(new Tuple(fDistance, i1, i2, nClusterID[i1].size(), nClusterID[i2].size())); } } nClusters--; } int a = 5; int b = a; } // doLinkClustering void merge(int iMin1, int iMin2, double fDist1, double fDist2, Vector<Integer>[] nClusterID, Node[] clusterNodes) { if (m_bDebug) { System.err.println("Merging " + iMin1 + " " + iMin2 + " " + fDist1 + " " + fDist2); } if (iMin1 > iMin2) { int h = iMin1; iMin1 = iMin2; iMin2 = h; double f = fDist1; fDist1 = fDist2; fDist2 = f; } nClusterID[iMin1].addAll(nClusterID[iMin2]); nClusterID[iMin2].removeAllElements(); // track hierarchy Node node = new Node(); if (clusterNodes[iMin1] == null) { node.m_iLeftInstance = iMin1; } else { node.m_left = clusterNodes[iMin1]; clusterNodes[iMin1].m_parent = node; } if (clusterNodes[iMin2] == null) { node.m_iRightInstance = iMin2; } else { node.m_right = clusterNodes[iMin2]; clusterNodes[iMin2].m_parent = node; } if (m_bDistanceIsBranchLength) { node.setLength(fDist1, fDist2); } else { node.setHeight(fDist1, fDist2); } clusterNodes[iMin1] = node; } // merge /** calculate distance the first time when setting up the distance matrix **/ double getDistance0(Vector<Integer> cluster1, Vector<Integer> cluster2) { double fBestDist = Double.MAX_VALUE; switch (m_nLinkType) { case SINGLE: case NEIGHBOR_JOINING: case CENTROID: case COMPLETE: case ADJCOMLPETE: case AVERAGE: case MEAN: // set up two instances for distance function Instance instance1 = (Instance) m_instances.instance(cluster1.elementAt(0)).copy(); Instance instance2 = (Instance) m_instances.instance(cluster2.elementAt(0)).copy(); fBestDist = m_DistanceFunction.distance(instance1, instance2, cluster1.elementAt(0), cluster2.elementAt(0)); break; case WARD: { // finds the distance of the change in caused by merging the cluster. // The information of a cluster is calculated as the error sum of squares of the // centroids of the cluster and its members. double ESS1 = calcESS(cluster1); double ESS2 = calcESS(cluster2); Vector<Integer> merged = new Vector<Integer>(); merged.addAll(cluster1); merged.addAll(cluster2); double ESS = calcESS(merged); fBestDist = ESS * merged.size() - ESS1 * cluster1.size() - ESS2 * cluster2.size(); } break; } return fBestDist; } // getDistance0 /** calculate the distance between two clusters * @param cluster1 list of indices of instances in the first cluster * @param cluster2 dito for second cluster * @return distance between clusters based on link type */ double getDistance(double[][] fDistance, Vector<Integer> cluster1, Vector<Integer> cluster2) { double fBestDist = Double.MAX_VALUE; //double SemiDist = m_DistanceFunction.Semi_distance(fDistance, cluster1, cluster2); switch (m_nLinkType) { case SINGLE: // find single link distance aka minimum link, which is the closest distance between // any item in cluster1 and any item in cluster2 fBestDist = Double.MAX_VALUE; for (int i = 0; i < cluster1.size(); i++) { int i1 = cluster1.elementAt(i); for (int j = 0; j < cluster2.size(); j++) { int i2 = cluster2.elementAt(j); double fDist = fDistance[i1][i2]; if (fBestDist > fDist) { fBestDist = fDist; } } } break; case COMPLETE: case ADJCOMLPETE: // find complete link distance aka maximum link, which is the largest distance between // any item in cluster1 and any item in cluster2 fBestDist = 0; for (int i = 0; i < cluster1.size(); i++) { int i1 = cluster1.elementAt(i); for (int j = 0; j < cluster2.size(); j++) { int i2 = cluster2.elementAt(j); double fDist = fDistance[i1][i2]; if (fBestDist < fDist) { fBestDist = fDist; } } } if (m_nLinkType == COMPLETE) { break; } // calculate adjustment, which is the largest within cluster distance double fMaxDist = 0; for (int i = 0; i < cluster1.size(); i++) { int i1 = cluster1.elementAt(i); for (int j = i + 1; j < cluster1.size(); j++) { int i2 = cluster1.elementAt(j); double fDist = fDistance[i1][i2]; if (fMaxDist < fDist) { fMaxDist = fDist; } } } for (int i = 0; i < cluster2.size(); i++) { int i1 = cluster2.elementAt(i); for (int j = i + 1; j < cluster2.size(); j++) { int i2 = cluster2.elementAt(j); double fDist = fDistance[i1][i2]; if (fMaxDist < fDist) { fMaxDist = fDist; } } } fBestDist -= fMaxDist; break; case AVERAGE: // finds average distance between the elements of the two clusters fBestDist = 0; for (int i = 0; i < cluster1.size(); i++) { int i1 = cluster1.elementAt(i); for (int j = 0; j < cluster2.size(); j++) { int i2 = cluster2.elementAt(j); fBestDist += fDistance[i1][i2]; } } fBestDist /= (cluster1.size() * cluster2.size()); break; case MEAN: { // calculates the mean distance of a merged cluster (akak Group-average agglomerative clustering) Vector<Integer> merged = new Vector<Integer>(); merged.addAll(cluster1); merged.addAll(cluster2); fBestDist = 0; for (int i = 0; i < merged.size(); i++) { int i1 = merged.elementAt(i); for (int j = i + 1; j < merged.size(); j++) { int i2 = merged.elementAt(j); fBestDist += fDistance[i1][i2]; } } int n = merged.size(); fBestDist /= (n * (n - 1.0) / 2.0); } break; case CENTROID: // finds the distance of the centroids of the clusters double[] fValues1 = new double[m_instances.numAttributes()]; for (int i = 0; i < cluster1.size(); i++) { Instance instance = m_instances.instance(cluster1.elementAt(i)); for (int j = 0; j < m_instances.numAttributes(); j++) { fValues1[j] += instance.value(j); } } double[] fValues2 = new double[m_instances.numAttributes()]; for (int i = 0; i < cluster2.size(); i++) { Instance instance = m_instances.instance(cluster2.elementAt(i)); for (int j = 0; j < m_instances.numAttributes(); j++) { fValues2[j] += instance.value(j); } } for (int j = 0; j < m_instances.numAttributes(); j++) { fValues1[j] /= cluster1.size(); fValues2[j] /= cluster2.size(); } // set up two instances for distance function Instance instance1 = (Instance) m_instances.instance(0).copy(); Instance instance2 = (Instance) m_instances.instance(0).copy(); for (int j = 0; j < m_instances.numAttributes(); j++) { instance1.setValue(j, fValues1[j]); instance2.setValue(j, fValues2[j]); } fBestDist = m_DistanceFunction.distance(instance1, instance2); break; case WARD: { // finds the distance of the change in caused by merging the cluster. // The information of a cluster is calculated as the error sum of squares of the // centroids of the cluster and its members. double ESS1 = calcESS(cluster1); double ESS2 = calcESS(cluster2); Vector<Integer> merged = new Vector<Integer>(); merged.addAll(cluster1); merged.addAll(cluster2); double ESS = calcESS(merged); fBestDist = ESS * merged.size() - ESS1 * cluster1.size() - ESS2 * cluster2.size(); } break; } double alpha = 0.5; //return alpha*SemiDist + (1-alpha)*fBestDist; return fBestDist; } // getDistance /** calculated error sum-of-squares for instances wrt centroid **/ double calcESS(Vector<Integer> cluster) { double[] fValues1 = new double[m_instances.numAttributes()]; for (int i = 0; i < cluster.size(); i++) { Instance instance = m_instances.instance(cluster.elementAt(i)); for (int j = 0; j < m_instances.numAttributes(); j++) { fValues1[j] += instance.value(j); } } for (int j = 0; j < m_instances.numAttributes(); j++) { fValues1[j] /= cluster.size(); } // set up two instances for distance function Instance centroid = (Instance) m_instances.instance(cluster.elementAt(0)).copy(); for (int j = 0; j < m_instances.numAttributes(); j++) { centroid.setValue(j, fValues1[j]); } double fESS = 0; for (int i = 0; i < cluster.size(); i++) { Instance instance = m_instances.instance(cluster.elementAt(i)); fESS += m_DistanceFunction.distance(centroid, instance); } return fESS / cluster.size(); } // calcESS @Override /** instances are assigned a cluster by finding the instance in the training data * with the closest distance to the instance to be clustered. The cluster index of * the training data point is taken as the cluster index. */ public int clusterInstance(Instance instance) throws Exception { if (m_instances.numInstances() == 0) { return 0; } double fBestDist = Double.MAX_VALUE; int iBestInstance = -1; for (int i = 0; i < m_instances.numInstances(); i++) { double fDist = m_DistanceFunction.distance(instance, m_instances.instance(i)); if (fDist < fBestDist) { fBestDist = fDist; iBestInstance = i; } } return m_nClusterNr[iBestInstance]; } @Override /** create distribution with all clusters having zero probability, except the * cluster the instance is assigned to. */ public double[] distributionForInstance(Instance instance) throws Exception { if (numberOfClusters() == 0) { double[] p = new double[1]; p[0] = 1; return p; } double[] p = new double[numberOfClusters()]; p[clusterInstance(instance)] = 1.0; return p; } @Override public Capabilities getCapabilities() { Capabilities result = new Capabilities(this); result.disableAll(); result.enable(Capability.NO_CLASS); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.DATE_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); result.enable(Capability.STRING_ATTRIBUTES); // other result.setMinimumNumberInstances(0); return result; } @Override public int numberOfClusters() throws Exception { return Math.min(m_nNumClusters, m_instances.numInstances()); } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ public Enumeration listOptions() { Vector newVector = new Vector(8); newVector.addElement(new Option( "\tIf set, classifier is run in debug mode and\n" + "\tmay output additional info to the console", "D", 0, "-D")); newVector.addElement(new Option( "\tIf set, distance is interpreted as branch length\n" + "\totherwise it is node height.", "B", 0, "-B")); newVector.addElement(new Option("\tnumber of clusters", "N", 1, "-N <Nr Of Clusters>")); newVector.addElement( new Option("\tFlag to indicate the cluster should be printed in Newick format.", "P", 0, "-P")); newVector.addElement(new Option( "Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor joining)", "L", 1, "-L [SINGLE|COMPLETE|AVERAGE|MEAN|CENTROID|WARD|ADJCOMLPETE|NEIGHBOR_JOINING]")); newVector.add(new Option("\tDistance function to use.\n" + "\t(default: weka.core.EuclideanDistance)", "A", 1, "-A <classname and options>")); return newVector.elements(); } /** * Parses a given list of options. <p/> * <!-- options-start --> * Valid options are: <p/> * <!-- options-end --> * * @param options the list of options as an array of strings * @throws Exception if an option is not supported */ public void setOptions(String[] options) throws Exception { m_bPrintNewick = Utils.getFlag('P', options); String optionString = Utils.getOption('N', options); if (optionString.length() != 0) { Integer temp = new Integer(optionString); setNumClusters(temp); } else { setNumClusters(2); } setDebug(Utils.getFlag('D', options)); setDistanceIsBranchLength(Utils.getFlag('B', options)); String sLinkType = Utils.getOption('L', options); if (sLinkType.compareTo("SINGLE") == 0) { setLinkType(new SelectedTag(SINGLE, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("COMPLETE") == 0) { setLinkType(new SelectedTag(COMPLETE, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("AVERAGE") == 0) { setLinkType(new SelectedTag(AVERAGE, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("MEAN") == 0) { setLinkType(new SelectedTag(MEAN, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("CENTROID") == 0) { setLinkType(new SelectedTag(CENTROID, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("WARD") == 0) { setLinkType(new SelectedTag(WARD, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("ADJCOMLPETE") == 0) { setLinkType(new SelectedTag(ADJCOMLPETE, TAGS_LINK_TYPE)); } if (sLinkType.compareTo("NEIGHBOR_JOINING") == 0) { setLinkType(new SelectedTag(NEIGHBOR_JOINING, TAGS_LINK_TYPE)); } String nnSearchClass = Utils.getOption('A', options); if (nnSearchClass.length() != 0) { String nnSearchClassSpec[] = Utils.splitOptions(nnSearchClass); if (nnSearchClassSpec.length == 0) { throw new Exception("Invalid DistanceFunction specification string."); } String className = nnSearchClassSpec[0]; nnSearchClassSpec[0] = ""; } Utils.checkForRemainingOptions(options); } /** * Gets the current settings of the clusterer. * * @return an array of strings suitable for passing to setOptions() */ public String[] getOptions() { String[] options = new String[14]; int current = 0; options[current++] = "-N"; options[current++] = "" + getNumClusters(); options[current++] = "-L"; switch (m_nLinkType) { case (SINGLE): options[current++] = "SINGLE"; break; case (COMPLETE): options[current++] = "COMPLETE"; break; case (AVERAGE): options[current++] = "AVERAGE"; break; case (MEAN): options[current++] = "MEAN"; break; case (CENTROID): options[current++] = "CENTROID"; break; case (WARD): options[current++] = "WARD"; break; case (ADJCOMLPETE): options[current++] = "ADJCOMLPETE"; break; case (NEIGHBOR_JOINING): options[current++] = "NEIGHBOR_JOINING"; break; } if (m_bPrintNewick) { options[current++] = "-P"; } if (getDebug()) { options[current++] = "-D"; } if (getDistanceIsBranchLength()) { options[current++] = "-B"; } options[current++] = "-A"; options[current++] = (m_DistanceFunction.getClass().getName() + " " + Utils.joinOptions(m_DistanceFunction.getOptions())).trim(); while (current < options.length) { options[current++] = ""; } return options; } public String toString() { StringBuffer buf = new StringBuffer(); int attIndex = m_instances.classIndex(); if (attIndex < 0) { // try find a string, or last attribute otherwise attIndex = 0; while (attIndex < m_instances.numAttributes() - 1) { if (m_instances.attribute(attIndex).isString()) { break; } attIndex++; } } try { if (m_bPrintNewick && (numberOfClusters() > 0)) { for (int i = 0; i < m_clusters.length; i++) { if (m_clusters[i] != null) { buf.append("Cluster " + i + "\n"); if (m_instances.attribute(attIndex).isString()) { buf.append(m_clusters[i].toString(attIndex)); } else { buf.append(m_clusters[i].toString2(attIndex)); } buf.append("\n\n"); } } } } catch (Exception e) { e.printStackTrace(); } return buf.toString(); } /** * Set debugging mode. * * @param debug true if debug output should be printed */ public void setDebug(boolean debug) { m_bDebug = debug; } /** * Get whether debugging is turned on. * * @return true if debugging output is on */ public boolean getDebug() { return m_bDebug; } public boolean getDistanceIsBranchLength() { return m_bDistanceIsBranchLength; } public void setDistanceIsBranchLength(boolean bDistanceIsHeight) { m_bDistanceIsBranchLength = bDistanceIsHeight; } public String distanceIsBranchLengthTipText() { return "If set to false, the distance between clusters is interpreted " + "as the height of the node linking the clusters. This is appropriate for " + "example for single link clustering. However, for neighbor joining, the " + "distance is better interpreted as branch length. Set this flag to " + "get the latter interpretation."; } /** * Returns the tip text for this property * @return tip text for this property suitable for * displaying in the explorer/experimenter gui */ public String debugTipText() { return "If set to true, classifier may output additional info to " + "the console."; } /** * @return a string to describe the NumClusters */ public String numClustersTipText() { return "Sets the number of clusters. " + "If a single hierarchy is desired, set this to 1."; } /** * @return a string to describe the print Newick flag */ public String printNewickTipText() { return "Flag to indicate whether the cluster should be print in Newick format." + " This can be useful for display in other programs. However, for large datasets" + " a lot of text may be produced, which may not be a nuisance when the Newick format" + " is not required"; } /** * @return a string to describe the distance function */ public String distanceFunctionTipText() { return "Sets the distance function, which measures the distance between two individual. " + "instances (or possibly the distance between an instance and the centroid of a cluster" + "depending on the Link type)."; } /** * @return a string to describe the Link type */ public String linkTypeTipText() { return "Sets the method used to measure the distance between two clusters.\n" + "SINGLE:\n" + " find single link distance aka minimum link, which is the closest distance between" + " any item in cluster1 and any item in cluster2\n" + "COMPLETE:\n" + " find complete link distance aka maximum link, which is the largest distance between" + " any item in cluster1 and any item in cluster2\n" + "ADJCOMLPETE:\n" + " as COMPLETE, but with adjustment, which is the largest within cluster distance\n" + "AVERAGE:\n" + " finds average distance between the elements of the two clusters\n" + "MEAN: \n" + " calculates the mean distance of a merged cluster (akak Group-average agglomerative clustering)\n" + "CENTROID:\n" + " finds the distance of the centroids of the clusters\n" + "WARD:\n" + " finds the distance of the change in caused by merging the cluster." + " The information of a cluster is calculated as the error sum of squares of the" + " centroids of the cluster and its members.\n" + "NEIGHBOR_JOINING\n" + " use neighbor joining algorithm."; } /** * This will return a string describing the clusterer. * @return The string. */ public String globalInfo() { return "Hierarchical clustering class.\n" + "Implements a number of classic agglomorative (i.e. bottom up) hierarchical clustering methods" + "based on ."; } public String graph() throws Exception { if (numberOfClusters() == 0) { return "Newick:(no,clusters)"; } int attIndex = m_instances.classIndex(); if (attIndex < 0) { // try find a string, or last attribute otherwise attIndex = 0; while (attIndex < m_instances.numAttributes() - 1) { if (m_instances.attribute(attIndex).isString()) { break; } attIndex++; } } String sNewick = null; if (m_instances.attribute(attIndex).isString()) { sNewick = m_clusters[0].toString(attIndex); } else { sNewick = m_clusters[0].toString2(attIndex); } return "Newick:" + sNewick; } public int graphType() { return Drawable.Newick; } /** * Returns the revision string. * * @return the revision */ public String getRevision() { return RevisionUtils.extract("$Revision: 6592 $"); } } // class HierarchicalClusterer