Java tutorial
package gr.iti.mklab.visual.quantization; /* * 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 3 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, see <http://www.gnu.org/licenses/>. */ /* * SimpleKMeans.java * Copyright (C) 2000-2012 University of Waikato, Hamilton, New Zealand * */ import java.util.ArrayList; import java.util.Date; import java.util.Enumeration; import java.util.HashMap; import java.util.List; import java.util.Random; import java.util.Vector; import java.util.concurrent.Callable; import java.util.concurrent.ExecutorService; import java.util.concurrent.Executors; import java.util.concurrent.Future; import weka.classifiers.rules.DecisionTableHashKey; import weka.clusterers.NumberOfClustersRequestable; import weka.clusterers.RandomizableClusterer; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.Capabilities.Capability; import weka.core.DenseInstance; import weka.core.DistanceFunction; import weka.core.EuclideanDistance; import weka.core.Instance; import weka.core.Instances; import weka.core.ManhattanDistance; import weka.core.Option; import weka.core.RevisionUtils; import weka.core.TechnicalInformation; import weka.core.TechnicalInformation.Field; import weka.core.TechnicalInformation.Type; import weka.core.TechnicalInformationHandler; import weka.core.Utils; import weka.core.WeightedInstancesHandler; import weka.filters.Filter; import weka.filters.unsupervised.attribute.ReplaceMissingValues; /** * <!-- globalinfo-start --> Cluster data using the k means algorithm. Can use either the Euclidean distance * (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the * component-wise median rather than mean. For more information see:<br/> * <br/> * D. Arthur, S. Vassilvitskii: k-means++: the advantages of carefull seeding. In: Proceedings of the * eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007. * <p/> * <!-- globalinfo-end --> * * <!-- technical-bibtex-start --> BibTeX: * * <pre> * @inproceedings{Arthur2007, * author = {D. Arthur and S. Vassilvitskii}, * booktitle = {Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms}, * pages = {1027-1035}, * title = {k-means++: the advantages of carefull seeding}, * year = {2007} * } * </pre> * <p/> * <!-- technical-bibtex-end --> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -N <num> * number of clusters. * (default 2). * </pre> * * <pre> * -P * Initialize using the k-means++ method. * </pre> * * <pre> * -V * Display std. deviations for centroids. * </pre> * * <pre> * -M * Replace missing values with mean/mode. * </pre> * * <pre> * -A <classname and options> * Distance function to use. * (default: weka.core.EuclideanDistance) * </pre> * * <pre> * -I <num> * Maximum number of iterations. * </pre> * * <pre> * -O * Preserve order of instances. * </pre> * * <pre> * -fast * Enables faster distance calculations, using cut-off values. * Disables the calculation/output of squared errors/distances. * </pre> * * <pre> * -num-slots <num> * Number of execution slots. * (default 1 - i.e. no parallelism) * </pre> * * <pre> * -S <num> * Random number seed. * (default 10) * </pre> * * <!-- options-end --> * * @author Mark Hall (mhall@cs.waikato.ac.nz) * @author Eibe Frank (eibe@cs.waikato.ac.nz) * @version $Revision: 9375 $ * @see RandomizableClusterer */ public class SimpleKMeansWithOutput extends RandomizableClusterer implements NumberOfClustersRequestable, WeightedInstancesHandler, TechnicalInformationHandler { /** for serialization. */ static final long serialVersionUID = -3235809600124455376L; /** * replace missing values in training instances. */ private ReplaceMissingValues m_ReplaceMissingFilter; /** * number of clusters to generate. */ private int m_NumClusters = 2; /** * holds the cluster centroids. */ private Instances m_ClusterCentroids; /** * Holds the standard deviations of the numeric attributes in each cluster. */ private Instances m_ClusterStdDevs; /** * For each cluster, holds the frequency counts for the values of each nominal attribute. */ private int[][][] m_ClusterNominalCounts; private int[][] m_ClusterMissingCounts; /** * Stats on the full data set for comparison purposes. In case the attribute is numeric the value is the * mean if is being used the Euclidian distance or the median if Manhattan distance and if the attribute * is nominal then it's mode is saved. */ private double[] m_FullMeansOrMediansOrModes; private double[] m_FullStdDevs; private int[][] m_FullNominalCounts; private int[] m_FullMissingCounts; /** * Display standard deviations for numeric atts. */ private boolean m_displayStdDevs; /** * Replace missing values globally? */ private boolean m_dontReplaceMissing = false; /** * The number of instances in each cluster. */ private int[] m_ClusterSizes; /** * Maximum number of iterations to be executed. */ private int m_MaxIterations = 500; /** * Keep track of the number of iterations completed before convergence. */ private int m_Iterations = 0; /** * Holds the squared errors for all clusters. */ private double[] m_squaredErrors; /** the distance function used. */ protected DistanceFunction m_DistanceFunction = new EuclideanDistance(); /** * Preserve order of instances. */ private boolean m_PreserveOrder = false; /** * Assignments obtained. */ protected int[] m_Assignments = null; /** whether to use fast calculation of distances (using a cut-off). */ protected boolean m_FastDistanceCalc = false; /** Whether to initialize cluster centers using the k-means++ method */ protected boolean m_initializeWithKMeansPlusPlus = false; protected int m_executionSlots = 1; /** For parallel execution mode */ protected transient ExecutorService m_executorPool; /** * the default constructor. */ public SimpleKMeansWithOutput() { super(); m_SeedDefault = 10; setSeed(m_SeedDefault); } /** * Start the pool of execution threads */ protected void startExecutorPool() { if (m_executorPool != null) { m_executorPool.shutdownNow(); } m_executorPool = Executors.newFixedThreadPool(m_executionSlots); } protected int m_completed; protected int m_failed; public TechnicalInformation getTechnicalInformation() { TechnicalInformation result; result = new TechnicalInformation(Type.INPROCEEDINGS); result.setValue(Field.AUTHOR, "D. Arthur and S. Vassilvitskii"); result.setValue(Field.TITLE, "k-means++: the advantages of carefull seeding"); result.setValue(Field.BOOKTITLE, "Proceedings of the eighteenth annual " + "ACM-SIAM symposium on Discrete algorithms"); result.setValue(Field.YEAR, "2007"); result.setValue(Field.PAGES, "1027-1035"); return result; } /** * Returns a string describing this clusterer. * * @return a description of the evaluator suitable for displaying in the explorer/experimenter gui */ public String globalInfo() { return "Cluster data using the k means algorithm. Can use either " + "the Euclidean distance (default) or the Manhattan distance." + " If the Manhattan distance is used, then centroids are computed " + "as the component-wise median rather than mean." + " For more information see:\n\n" + getTechnicalInformation().toString(); } /** * Returns default capabilities of the clusterer. * * @return the capabilities of this clusterer */ @Override public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); result.disableAll(); result.enable(Capability.NO_CLASS); // attributes result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.MISSING_VALUES); return result; } private class KMeansComputeCentroidTask implements Callable<double[]> { protected Instances m_cluster; protected int m_centroidIndex; public KMeansComputeCentroidTask(int centroidIndex, Instances cluster) { m_cluster = cluster; m_centroidIndex = centroidIndex; } public double[] call() { return moveCentroid(m_centroidIndex, m_cluster, true, false); } } /** * Launch the move centroids tasks * * @param clusters * the cluster centroids * @return the number of empty clusters */ protected int launchMoveCentroids(Instances[] clusters) { int emptyClusterCount = 0; List<Future<double[]>> results = new ArrayList<Future<double[]>>(); for (int i = 0; i < m_NumClusters; i++) { if (clusters[i].numInstances() == 0) { emptyClusterCount++; } else { Future<double[]> futureCentroid = m_executorPool .submit(new KMeansComputeCentroidTask(i, clusters[i])); results.add(futureCentroid); } } try { for (Future<double[]> d : results) { m_ClusterCentroids.add(new DenseInstance(1.0, d.get())); } } catch (Exception ex) { ex.printStackTrace(); } return emptyClusterCount; } private class KMeansClusterTask implements Callable<Boolean> { protected int m_start; protected int m_end; protected Instances m_inst; protected int[] m_clusterAssignments; public KMeansClusterTask(Instances inst, int start, int end, int[] clusterAssignments) { m_start = start; m_end = end; m_inst = inst; m_clusterAssignments = clusterAssignments; } public Boolean call() { boolean converged = true; for (int i = m_start; i < m_end; i++) { Instance toCluster = m_inst.instance(i); int newC = clusterInstance(toCluster); if (newC != m_clusterAssignments[i]) { converged = false; } m_clusterAssignments[i] = newC; } return converged; } protected int clusterInstance(Instance inst) { double minDist = Integer.MAX_VALUE; int bestCluster = 0; for (int i = 0; i < m_NumClusters; i++) { double dist; dist = m_DistanceFunction.distance(inst, m_ClusterCentroids.instance(i), minDist); if (dist < minDist) { minDist = dist; bestCluster = i; } } return bestCluster; } } /** * Launch the tasks that assign instances to clusters * * @param insts * the instances to be clustered * @param clusterAssignments * the array of cluster assignments * @return true if k means has converged * @throws Exception * if a problem occurs */ protected boolean launchAssignToClusters(Instances insts, int[] clusterAssignments) throws Exception { int numPerTask = insts.numInstances() / m_executionSlots; List<Future<Boolean>> results = new ArrayList<Future<Boolean>>(); for (int i = 0; i < m_executionSlots; i++) { int start = i * numPerTask; int end = start + numPerTask; if (i == m_executionSlots - 1) { end = insts.numInstances(); } Future<Boolean> futureKM = m_executorPool .submit(new KMeansClusterTask(insts, start, end, clusterAssignments)); results.add(futureKM); } boolean converged = true; for (Future<Boolean> f : results) { if (!f.get()) { converged = false; } } return converged; } /** * Generates a clusterer. Has to initialize all fields of the clusterer that are not being set via * options. * * @param data * set of instances serving as training data * @throws Exception * if the clusterer has not been generated successfully */ @Override public void buildClusterer(Instances data) throws Exception { // can clusterer handle the data? getCapabilities().testWithFail(data); m_Iterations = 0; m_ReplaceMissingFilter = new ReplaceMissingValues(); Instances instances = new Instances(data); instances.setClassIndex(-1); if (!m_dontReplaceMissing) { m_ReplaceMissingFilter.setInputFormat(instances); instances = Filter.useFilter(instances, m_ReplaceMissingFilter); } m_FullMissingCounts = new int[instances.numAttributes()]; if (m_displayStdDevs) { m_FullStdDevs = new double[instances.numAttributes()]; } m_FullNominalCounts = new int[instances.numAttributes()][0]; m_FullMeansOrMediansOrModes = moveCentroid(0, instances, false, false); for (int i = 0; i < instances.numAttributes(); i++) { m_FullMissingCounts[i] = instances.attributeStats(i).missingCount; if (instances.attribute(i).isNumeric()) { if (m_displayStdDevs) { m_FullStdDevs[i] = Math.sqrt(instances.variance(i)); } if (m_FullMissingCounts[i] == instances.numInstances()) { m_FullMeansOrMediansOrModes[i] = Double.NaN; // mark missing as mean } } else { m_FullNominalCounts[i] = instances.attributeStats(i).nominalCounts; if (m_FullMissingCounts[i] > m_FullNominalCounts[i][Utils.maxIndex(m_FullNominalCounts[i])]) { m_FullMeansOrMediansOrModes[i] = -1; // mark missing as most common // value } } } m_ClusterCentroids = new Instances(instances, m_NumClusters); int[] clusterAssignments = new int[instances.numInstances()]; if (m_PreserveOrder) m_Assignments = clusterAssignments; m_DistanceFunction.setInstances(instances); Random RandomO = new Random(getSeed()); int instIndex; HashMap initC = new HashMap(); DecisionTableHashKey hk = null; Instances initInstances = null; if (m_PreserveOrder) initInstances = new Instances(instances); else initInstances = instances; if (m_initializeWithKMeansPlusPlus) { kMeansPlusPlusInit(initInstances); } else { for (int j = initInstances.numInstances() - 1; j >= 0; j--) { instIndex = RandomO.nextInt(j + 1); hk = new DecisionTableHashKey(initInstances.instance(instIndex), initInstances.numAttributes(), true); if (!initC.containsKey(hk)) { m_ClusterCentroids.add(initInstances.instance(instIndex)); initC.put(hk, null); } initInstances.swap(j, instIndex); if (m_ClusterCentroids.numInstances() == m_NumClusters) { break; } } } m_NumClusters = m_ClusterCentroids.numInstances(); // removing reference initInstances = null; int i; boolean converged = false; int emptyClusterCount; Instances[] tempI = new Instances[m_NumClusters]; m_squaredErrors = new double[m_NumClusters]; m_ClusterNominalCounts = new int[m_NumClusters][instances.numAttributes()][0]; m_ClusterMissingCounts = new int[m_NumClusters][instances.numAttributes()]; startExecutorPool(); long start = System.currentTimeMillis(); while (!converged) { emptyClusterCount = 0; m_Iterations++; converged = true; System.out.print(new Date() + ": " + "Iter " + m_Iterations + " "); if (m_executionSlots <= 1 || instances.numInstances() < 2 * m_executionSlots) { for (i = 0; i < instances.numInstances(); i++) { Instance toCluster = instances.instance(i); int newC = clusterProcessedInstance(toCluster, true, true); if (newC != clusterAssignments[i]) { converged = false; } clusterAssignments[i] = newC; } } else { converged = launchAssignToClusters(instances, clusterAssignments); } // update centroids m_ClusterCentroids = new Instances(instances, m_NumClusters); for (i = 0; i < m_NumClusters; i++) { tempI[i] = new Instances(instances, 0); } for (i = 0; i < instances.numInstances(); i++) { tempI[clusterAssignments[i]].add(instances.instance(i)); } if (m_executionSlots <= 1 || instances.numInstances() < 2 * m_executionSlots) { for (i = 0; i < m_NumClusters; i++) { if (tempI[i].numInstances() == 0) { // empty cluster emptyClusterCount++; } else { moveCentroid(i, tempI[i], true, true); } } } else { emptyClusterCount = launchMoveCentroids(tempI); } if (m_Iterations == m_MaxIterations) converged = true; if (emptyClusterCount > 0) { m_NumClusters -= emptyClusterCount; if (converged) { Instances[] t = new Instances[m_NumClusters]; int index = 0; for (int k = 0; k < tempI.length; k++) { if (tempI[k].numInstances() > 0) { t[index++] = tempI[k]; } } tempI = t; } else { tempI = new Instances[m_NumClusters]; } } if (!converged) { m_ClusterNominalCounts = new int[m_NumClusters][instances.numAttributes()][0]; } System.out.println("Sum of within cluster distances: " + Utils.sum(m_squaredErrors)); // reset erros to zero m_squaredErrors = new double[m_NumClusters]; } long end = System.currentTimeMillis(); System.out.println("\nClustering completed in " + (end - start) + " ms and converged in " + m_Iterations + " iterations"); // calculate errors if (!m_FastDistanceCalc) { for (i = 0; i < instances.numInstances(); i++) { clusterProcessedInstance(instances.instance(i), true, false); } } if (m_displayStdDevs) { m_ClusterStdDevs = new Instances(instances, m_NumClusters); } m_ClusterSizes = new int[m_NumClusters]; for (i = 0; i < m_NumClusters; i++) { if (m_displayStdDevs) { double[] vals2 = new double[instances.numAttributes()]; for (int j = 0; j < instances.numAttributes(); j++) { if (instances.attribute(j).isNumeric()) { vals2[j] = Math.sqrt(tempI[i].variance(j)); } else { vals2[j] = Utils.missingValue(); } } m_ClusterStdDevs.add(new DenseInstance(1.0, vals2)); } m_ClusterSizes[i] = tempI[i].numInstances(); } m_executorPool.shutdown(); } protected void kMeansPlusPlusInit(Instances data) throws Exception { Random randomO = new Random(getSeed()); HashMap<DecisionTableHashKey, String> initC = new HashMap<DecisionTableHashKey, String>(); // choose initial center uniformly at random int index = randomO.nextInt(data.numInstances()); m_ClusterCentroids.add(data.instance(index)); DecisionTableHashKey hk = new DecisionTableHashKey(data.instance(index), data.numAttributes(), true); initC.put(hk, null); int iteration = 0; int remainingInstances = data.numInstances() - 1; if (m_NumClusters > 1) { // proceed with selecting the rest // distances to the initial randomly chose center double[] distances = new double[data.numInstances()]; double[] cumProbs = new double[data.numInstances()]; for (int i = 0; i < data.numInstances(); i++) { distances[i] = m_DistanceFunction.distance(data.instance(i), m_ClusterCentroids.instance(iteration)); } // now choose the remaining cluster centers for (int i = 1; i < m_NumClusters; i++) { // distances converted to probabilities double[] weights = new double[data.numInstances()]; System.arraycopy(distances, 0, weights, 0, distances.length); Utils.normalize(weights); double sumOfProbs = 0; for (int k = 0; k < data.numInstances(); k++) { sumOfProbs += weights[k]; cumProbs[k] = sumOfProbs; } cumProbs[data.numInstances() - 1] = 1.0; // make sure there are no // rounding issues // choose a random instance double prob = randomO.nextDouble(); for (int k = 0; k < cumProbs.length; k++) { if (prob < cumProbs[k]) { Instance candidateCenter = data.instance(k); hk = new DecisionTableHashKey(candidateCenter, data.numAttributes(), true); if (!initC.containsKey(hk)) { initC.put(hk, null); m_ClusterCentroids.add(candidateCenter); } else { // we shouldn't get here because any instance that is a duplicate // of // an already chosen cluster center should have zero distance (and // hence // zero probability of getting chosen) to that center. System.err.println("We shouldn't get here...."); } remainingInstances--; break; } } iteration++; if (remainingInstances == 0) { break; } // prepare to choose the next cluster center. // check distances against the new cluster center to see if it is closer for (int k = 0; k < data.numInstances(); k++) { if (distances[k] > 0) { double newDist = m_DistanceFunction.distance(data.instance(k), m_ClusterCentroids.instance(iteration)); if (newDist < distances[k]) { distances[k] = newDist; } } } } } } /** * Move the centroid to it's new coordinates. Generate the centroid coordinates based on it's members * (objects assigned to the cluster of the centroid) and the distance function being used. * * @param centroidIndex * index of the centroid which the coordinates will be computed * @param members * the objects that are assigned to the cluster of this centroid * @param updateClusterInfo * if the method is supposed to update the m_Cluster arrays * @param addToCentroidInstances * true if the method is to add the computed coordinates to the Instances holding the centroids * @return the centroid coordinates */ protected double[] moveCentroid(int centroidIndex, Instances members, boolean updateClusterInfo, boolean addToCentroidInstances) { double[] vals = new double[members.numAttributes()]; // used only for Manhattan Distance Instances sortedMembers = null; int middle = 0; boolean dataIsEven = false; if (m_DistanceFunction instanceof ManhattanDistance) { middle = (members.numInstances() - 1) / 2; dataIsEven = ((members.numInstances() % 2) == 0); if (m_PreserveOrder) { sortedMembers = members; } else { sortedMembers = new Instances(members); } } for (int j = 0; j < members.numAttributes(); j++) { // in case of Euclidian distance the centroid is the mean point // in case of Manhattan distance the centroid is the median point // in both cases, if the attribute is nominal, the centroid is the mode if (m_DistanceFunction instanceof EuclideanDistance || members.attribute(j).isNominal()) { vals[j] = members.meanOrMode(j); } else if (m_DistanceFunction instanceof ManhattanDistance) { // singleton special case if (members.numInstances() == 1) { vals[j] = members.instance(0).value(j); } else { vals[j] = sortedMembers.kthSmallestValue(j, middle + 1); if (dataIsEven) { vals[j] = (vals[j] + sortedMembers.kthSmallestValue(j, middle + 2)) / 2; } } } if (updateClusterInfo) { m_ClusterMissingCounts[centroidIndex][j] = members.attributeStats(j).missingCount; m_ClusterNominalCounts[centroidIndex][j] = members.attributeStats(j).nominalCounts; if (members.attribute(j).isNominal()) { if (m_ClusterMissingCounts[centroidIndex][j] > m_ClusterNominalCounts[centroidIndex][j][Utils .maxIndex(m_ClusterNominalCounts[centroidIndex][j])]) { vals[j] = Utils.missingValue(); // mark mode as missing } } else { if (m_ClusterMissingCounts[centroidIndex][j] == members.numInstances()) { vals[j] = Utils.missingValue(); // mark mean as missing } } } } if (addToCentroidInstances) { m_ClusterCentroids.add(new DenseInstance(1.0, vals)); } return vals; } /** * clusters an instance that has been through the filters. * * @param instance * the instance to assign a cluster to * @param updateErrors * if true, update the within clusters sum of errors * @param useFastDistCalc * whether to use the fast distance calculation or not * @return a cluster number */ private int clusterProcessedInstance(Instance instance, boolean updateErrors, boolean useFastDistCalc) { double minDist = Integer.MAX_VALUE; int bestCluster = 0; for (int i = 0; i < m_NumClusters; i++) { double dist; if (useFastDistCalc) dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i), minDist); else dist = m_DistanceFunction.distance(instance, m_ClusterCentroids.instance(i)); if (dist < minDist) { minDist = dist; bestCluster = i; } } if (updateErrors) { if (m_DistanceFunction instanceof EuclideanDistance) { // Euclidean distance to Squared Euclidean distance minDist *= minDist; } m_squaredErrors[bestCluster] += minDist; } return bestCluster; } /** * Classifies a given instance. * * @param instance * the instance to be assigned to a cluster * @return the number of the assigned cluster as an interger if the class is enumerated, otherwise the * predicted value * @throws Exception * if instance could not be classified successfully */ @Override public int clusterInstance(Instance instance) throws Exception { Instance inst = null; if (!m_dontReplaceMissing) { m_ReplaceMissingFilter.input(instance); m_ReplaceMissingFilter.batchFinished(); inst = m_ReplaceMissingFilter.output(); } else { inst = instance; } return clusterProcessedInstance(inst, true, true); } /** * Returns the number of clusters. * * @return the number of clusters generated for a training dataset. * @throws Exception * if number of clusters could not be returned successfully */ @Override public int numberOfClusters() throws Exception { return m_NumClusters; } /** * Returns an enumeration describing the available options. * * @return an enumeration of all the available options. */ @Override public Enumeration listOptions() { Vector result = new Vector(); result.addElement(new Option("\tnumber of clusters.\n" + "\t(default 2).", "N", 1, "-N <num>")); result.addElement(new Option("\tInitialize using the k-means++ method.\n", "P", 0, "-P")); result.addElement(new Option("\tDisplay std. deviations for centroids.\n", "V", 0, "-V")); result.addElement(new Option("\tReplace missing values with mean/mode.\n", "M", 0, "-M")); result.add(new Option("\tDistance function to use.\n" + "\t(default: weka.core.EuclideanDistance)", "A", 1, "-A <classname and options>")); result.add(new Option("\tMaximum number of iterations.\n", "I", 1, "-I <num>")); result.addElement(new Option("\tPreserve order of instances.\n", "O", 0, "-O")); result.addElement(new Option("\tEnables faster distance calculations, using cut-off values.\n" + "\tDisables the calculation/output of squared errors/distances.\n", "fast", 0, "-fast")); result.addElement(new Option("\tNumber of execution slots.\n" + "\t(default 1 - i.e. no parallelism)", "num-slots", 1, "-num-slots <num>")); Enumeration en = super.listOptions(); while (en.hasMoreElements()) result.addElement(en.nextElement()); return result.elements(); } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String numClustersTipText() { return "set number of clusters"; } /** * set the number of clusters to generate. * * @param n * the number of clusters to generate * @throws Exception * if number of clusters is negative */ public void setNumClusters(int n) throws Exception { if (n <= 0) { throw new Exception("Number of clusters must be > 0"); } m_NumClusters = n; } /** * gets the number of clusters to generate. * * @return the number of clusters to generate */ public int getNumClusters() { return m_NumClusters; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String initializeUsingKMeansPlusPlusMethodTipText() { return "Initialize cluster centers using the probabilistic " + " farthest first method of the k-means++ algorithm"; } /** * Set whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm * (rather than the standard random selection of initial cluster centers). * * @param k * true if the k-means++ method is to be used to select initial cluster centers. */ public void setInitializeUsingKMeansPlusPlusMethod(boolean k) { m_initializeWithKMeansPlusPlus = k; } /** * Get whether to initialize using the probabilistic farthest first like method of the k-means++ algorithm * (rather than the standard random selection of initial cluster centers). * * @return true if the k-means++ method is to be used to select initial cluster centers. */ public boolean getInitializeUsingKMeansPlusPlusMethod() { return m_initializeWithKMeansPlusPlus; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String maxIterationsTipText() { return "set maximum number of iterations"; } /** * set the maximum number of iterations to be executed. * * @param n * the maximum number of iterations * @throws Exception * if maximum number of iteration is smaller than 1 */ public void setMaxIterations(int n) throws Exception { if (n <= 0) { throw new Exception("Maximum number of iterations must be > 0"); } m_MaxIterations = n; } /** * gets the number of maximum iterations to be executed. * * @return the number of clusters to generate */ public int getMaxIterations() { return m_MaxIterations; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String displayStdDevsTipText() { return "Display std deviations of numeric attributes " + "and counts of nominal attributes."; } /** * Sets whether standard deviations and nominal count. Should be displayed in the clustering output. * * @param stdD * true if std. devs and counts should be displayed */ public void setDisplayStdDevs(boolean stdD) { m_displayStdDevs = stdD; } /** * Gets whether standard deviations and nominal count. Should be displayed in the clustering output. * * @return true if std. devs and counts should be displayed */ public boolean getDisplayStdDevs() { return m_displayStdDevs; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String dontReplaceMissingValuesTipText() { return "Replace missing values globally with mean/mode."; } /** * Sets whether missing values are to be replaced. * * @param r * true if missing values are to be replaced */ public void setDontReplaceMissingValues(boolean r) { m_dontReplaceMissing = r; } /** * Gets whether missing values are to be replaced. * * @return true if missing values are to be replaced */ public boolean getDontReplaceMissingValues() { return m_dontReplaceMissing; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String distanceFunctionTipText() { return "The distance function to use for instances comparison " + "(default: weka.core.EuclideanDistance). "; } /** * returns the distance function currently in use. * * @return the distance function */ public DistanceFunction getDistanceFunction() { return m_DistanceFunction; } /** * sets the distance function to use for instance comparison. * * @param df * the new distance function to use * @throws Exception * if instances cannot be processed */ public void setDistanceFunction(DistanceFunction df) throws Exception { if (!(df instanceof EuclideanDistance) && !(df instanceof ManhattanDistance)) { throw new Exception("SimpleKMeans currently only supports the Euclidean and Manhattan distances."); } m_DistanceFunction = df; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String preserveInstancesOrderTipText() { return "Preserve order of instances."; } /** * Sets whether order of instances must be preserved. * * @param r * true if missing values are to be replaced */ public void setPreserveInstancesOrder(boolean r) { m_PreserveOrder = r; } /** * Gets whether order of instances must be preserved. * * @return true if missing values are to be replaced */ public boolean getPreserveInstancesOrder() { return m_PreserveOrder; } /** * Returns the tip text for this property. * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String fastDistanceCalcTipText() { return "Uses cut-off values for speeding up distance calculation, but " + "suppresses also the calculation and output of the within cluster sum " + "of squared errors/sum of distances."; } /** * Sets whether to use faster distance calculation. * * @param value * true if faster calculation to be used */ public void setFastDistanceCalc(boolean value) { m_FastDistanceCalc = value; } /** * Gets whether to use faster distance calculation. * * @return true if faster calculation is used */ public boolean getFastDistanceCalc() { return m_FastDistanceCalc; } /** * Returns the tip text for this property * * @return tip text for this property suitable for displaying in the explorer/experimenter gui */ public String numExecutionSlotsTipText() { return "The number of execution slots (threads) to use. " + "Set equal to the number of available cpu/cores"; } /** * Set the degree of parallelism to use. * * @param slots * the number of tasks to run in parallel when computing the nearest neighbors and evaluating * different values of k between the lower and upper bounds */ public void setNumExecutionSlots(int slots) { m_executionSlots = slots; } /** * Get the degree of parallelism to use. * * @return the number of tasks to run in parallel when computing the nearest neighbors and evaluating * different values of k between the lower and upper bounds */ public int getNumExecutionSlots() { return m_executionSlots; } /** * Parses a given list of options. * <p/> * * <!-- options-start --> Valid options are: * <p/> * * <pre> * -N <num> * number of clusters. * (default 2). * </pre> * * <pre> * -P * Initialize using the k-means++ method. * </pre> * * <pre> * -V * Display std. deviations for centroids. * </pre> * * <pre> * -M * Replace missing values with mean/mode. * </pre> * * <pre> * -A <classname and options> * Distance function to use. * (default: weka.core.EuclideanDistance) * </pre> * * <pre> * -I <num> * Maximum number of iterations. * </pre> * * <pre> * -O * Preserve order of instances. * </pre> * * <pre> * -fast * Enables faster distance calculations, using cut-off values. * Disables the calculation/output of squared errors/distances. * </pre> * * <pre> * -num-slots <num> * Number of execution slots. * (default 1 - i.e. no parallelism) * </pre> * * <pre> * -S <num> * Random number seed. * (default 10) * </pre> * * <!-- options-end --> * * @param options * the list of options as an array of strings * @throws Exception * if an option is not supported */ @Override public void setOptions(String[] options) throws Exception { m_displayStdDevs = Utils.getFlag("V", options); m_dontReplaceMissing = Utils.getFlag("M", options); m_initializeWithKMeansPlusPlus = Utils.getFlag('P', options); String optionString = Utils.getOption('N', options); if (optionString.length() != 0) { setNumClusters(Integer.parseInt(optionString)); } optionString = Utils.getOption("I", options); if (optionString.length() != 0) { setMaxIterations(Integer.parseInt(optionString)); } String distFunctionClass = Utils.getOption('A', options); if (distFunctionClass.length() != 0) { String distFunctionClassSpec[] = Utils.splitOptions(distFunctionClass); if (distFunctionClassSpec.length == 0) { throw new Exception("Invalid DistanceFunction specification string."); } String className = distFunctionClassSpec[0]; distFunctionClassSpec[0] = ""; setDistanceFunction( (DistanceFunction) Utils.forName(DistanceFunction.class, className, distFunctionClassSpec)); } else { setDistanceFunction(new EuclideanDistance()); } m_PreserveOrder = Utils.getFlag("O", options); m_FastDistanceCalc = Utils.getFlag("fast", options); String slotsS = Utils.getOption("num-slots", options); if (slotsS.length() > 0) { setNumExecutionSlots(Integer.parseInt(slotsS)); } super.setOptions(options); } /** * Gets the current settings of SimpleKMeans. * * @return an array of strings suitable for passing to setOptions() */ @Override public String[] getOptions() { int i; Vector result; String[] options; result = new Vector(); if (m_initializeWithKMeansPlusPlus) { result.add("-P"); } if (m_displayStdDevs) { result.add("-V"); } if (m_dontReplaceMissing) { result.add("-M"); } result.add("-N"); result.add("" + getNumClusters()); result.add("-A"); result.add( (m_DistanceFunction.getClass().getName() + " " + Utils.joinOptions(m_DistanceFunction.getOptions())) .trim()); result.add("-I"); result.add("" + getMaxIterations()); if (m_PreserveOrder) { result.add("-O"); } if (m_FastDistanceCalc) { result.add("-fast"); } result.add("-num-slots"); result.add("" + getNumExecutionSlots()); options = super.getOptions(); for (i = 0; i < options.length; i++) result.add(options[i]); return (String[]) result.toArray(new String[result.size()]); } /** * return a string describing this clusterer. * * @return a description of the clusterer as a string */ @Override public String toString() { if (m_ClusterCentroids == null) { return "No clusterer built yet!"; } int maxWidth = 0; int maxAttWidth = 0; boolean containsNumeric = false; for (int i = 0; i < m_NumClusters; i++) { for (int j = 0; j < m_ClusterCentroids.numAttributes(); j++) { if (m_ClusterCentroids.attribute(j).name().length() > maxAttWidth) { maxAttWidth = m_ClusterCentroids.attribute(j).name().length(); } if (m_ClusterCentroids.attribute(j).isNumeric()) { containsNumeric = true; double width = Math.log(Math.abs(m_ClusterCentroids.instance(i).value(j))) / Math.log(10.0); // System.err.println(m_ClusterCentroids.instance(i).value(j)+" "+width); if (width < 0) { width = 1; } // decimal + # decimal places + 1 width += 6.0; if ((int) width > maxWidth) { maxWidth = (int) width; } } } } for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) { if (m_ClusterCentroids.attribute(i).isNominal()) { Attribute a = m_ClusterCentroids.attribute(i); for (int j = 0; j < m_ClusterCentroids.numInstances(); j++) { String val = a.value((int) m_ClusterCentroids.instance(j).value(i)); if (val.length() > maxWidth) { maxWidth = val.length(); } } for (int j = 0; j < a.numValues(); j++) { String val = a.value(j) + " "; if (val.length() > maxAttWidth) { maxAttWidth = val.length(); } } } } if (m_displayStdDevs) { // check for maximum width of maximum frequency count for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) { if (m_ClusterCentroids.attribute(i).isNominal()) { int maxV = Utils.maxIndex(m_FullNominalCounts[i]); /* * int percent = (int)((double)m_FullNominalCounts[i][maxV] / Utils.sum(m_ClusterSizes) * * 100.0); */ int percent = 6; // max percent width (100%) String nomV = "" + m_FullNominalCounts[i][maxV]; // + " (" + percent + "%)"; if (nomV.length() + percent > maxWidth) { maxWidth = nomV.length() + 1; } } } } // check for size of cluster sizes for (int i = 0; i < m_ClusterSizes.length; i++) { String size = "(" + m_ClusterSizes[i] + ")"; if (size.length() > maxWidth) { maxWidth = size.length(); } } if (m_displayStdDevs && maxAttWidth < "missing".length()) { maxAttWidth = "missing".length(); } String plusMinus = "+/-"; maxAttWidth += 2; if (m_displayStdDevs && containsNumeric) { maxWidth += plusMinus.length(); } if (maxAttWidth < "Attribute".length() + 2) { maxAttWidth = "Attribute".length() + 2; } if (maxWidth < "Full Data".length()) { maxWidth = "Full Data".length() + 1; } if (maxWidth < "missing".length()) { maxWidth = "missing".length() + 1; } StringBuffer temp = new StringBuffer(); temp.append("\nkMeans\n======\n"); temp.append("\nNumber of iterations: " + m_Iterations); if (!m_FastDistanceCalc) { temp.append("\n"); if (m_DistanceFunction instanceof EuclideanDistance) { temp.append("Within cluster sum of squared errors: " + Utils.sum(m_squaredErrors)); } else { temp.append("Sum of within cluster distances: " + Utils.sum(m_squaredErrors)); } } if (!m_dontReplaceMissing) { temp.append("\nMissing values globally replaced with mean/mode"); } temp.append("\n\nCluster centroids:\n"); temp.append(pad("Cluster#", " ", (maxAttWidth + (maxWidth * 2 + 2)) - "Cluster#".length(), true)); temp.append("\n"); temp.append(pad("Attribute", " ", maxAttWidth - "Attribute".length(), false)); temp.append(pad("Full Data", " ", maxWidth + 1 - "Full Data".length(), true)); // cluster numbers for (int i = 0; i < m_NumClusters; i++) { String clustNum = "" + i; temp.append(pad(clustNum, " ", maxWidth + 1 - clustNum.length(), true)); } temp.append("\n"); // cluster sizes String cSize = "(" + Utils.sum(m_ClusterSizes) + ")"; temp.append(pad(cSize, " ", maxAttWidth + maxWidth + 1 - cSize.length(), true)); for (int i = 0; i < m_NumClusters; i++) { cSize = "(" + m_ClusterSizes[i] + ")"; temp.append(pad(cSize, " ", maxWidth + 1 - cSize.length(), true)); } temp.append("\n"); temp.append(pad("", "=", maxAttWidth + (maxWidth * (m_ClusterCentroids.numInstances() + 1) + m_ClusterCentroids.numInstances() + 1), true)); temp.append("\n"); for (int i = 0; i < m_ClusterCentroids.numAttributes(); i++) { String attName = m_ClusterCentroids.attribute(i).name(); temp.append(attName); for (int j = 0; j < maxAttWidth - attName.length(); j++) { temp.append(" "); } String strVal; String valMeanMode; // full data if (m_ClusterCentroids.attribute(i).isNominal()) { if (m_FullMeansOrMediansOrModes[i] == -1) { // missing valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true); } else { valMeanMode = pad( (strVal = m_ClusterCentroids.attribute(i).value((int) m_FullMeansOrMediansOrModes[i])), " ", maxWidth + 1 - strVal.length(), true); } } else { if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) { valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true); } else { valMeanMode = pad( (strVal = Utils.doubleToString(m_FullMeansOrMediansOrModes[i], maxWidth, 4).trim()), " ", maxWidth + 1 - strVal.length(), true); } } temp.append(valMeanMode); for (int j = 0; j < m_NumClusters; j++) { if (m_ClusterCentroids.attribute(i).isNominal()) { if (m_ClusterCentroids.instance(j).isMissing(i)) { valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true); } else { valMeanMode = pad( (strVal = m_ClusterCentroids.attribute(i) .value((int) m_ClusterCentroids.instance(j).value(i))), " ", maxWidth + 1 - strVal.length(), true); } } else { if (m_ClusterCentroids.instance(j).isMissing(i)) { valMeanMode = pad("missing", " ", maxWidth + 1 - "missing".length(), true); } else { valMeanMode = pad((strVal = Utils .doubleToString(m_ClusterCentroids.instance(j).value(i), maxWidth, 4).trim()), " ", maxWidth + 1 - strVal.length(), true); } } temp.append(valMeanMode); } temp.append("\n"); if (m_displayStdDevs) { // Std devs/max nominal String stdDevVal = ""; if (m_ClusterCentroids.attribute(i).isNominal()) { // Do the values of the nominal attribute Attribute a = m_ClusterCentroids.attribute(i); for (int j = 0; j < a.numValues(); j++) { // full data String val = " " + a.value(j); temp.append(pad(val, " ", maxAttWidth + 1 - val.length(), false)); int count = m_FullNominalCounts[i][j]; int percent = (int) ((double) m_FullNominalCounts[i][j] / Utils.sum(m_ClusterSizes) * 100.0); String percentS = "" + percent + "%)"; percentS = pad(percentS, " ", 5 - percentS.length(), true); stdDevVal = "" + count + " (" + percentS; stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true); temp.append(stdDevVal); // Clusters for (int k = 0; k < m_NumClusters; k++) { count = m_ClusterNominalCounts[k][i][j]; percent = (int) ((double) m_ClusterNominalCounts[k][i][j] / m_ClusterSizes[k] * 100.0); percentS = "" + percent + "%)"; percentS = pad(percentS, " ", 5 - percentS.length(), true); stdDevVal = "" + count + " (" + percentS; stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true); temp.append(stdDevVal); } temp.append("\n"); } // missing (if any) if (m_FullMissingCounts[i] > 0) { // Full data temp.append(pad(" missing", " ", maxAttWidth + 1 - " missing".length(), false)); int count = m_FullMissingCounts[i]; int percent = (int) ((double) m_FullMissingCounts[i] / Utils.sum(m_ClusterSizes) * 100.0); String percentS = "" + percent + "%)"; percentS = pad(percentS, " ", 5 - percentS.length(), true); stdDevVal = "" + count + " (" + percentS; stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true); temp.append(stdDevVal); // Clusters for (int k = 0; k < m_NumClusters; k++) { count = m_ClusterMissingCounts[k][i]; percent = (int) ((double) m_ClusterMissingCounts[k][i] / m_ClusterSizes[k] * 100.0); percentS = "" + percent + "%)"; percentS = pad(percentS, " ", 5 - percentS.length(), true); stdDevVal = "" + count + " (" + percentS; stdDevVal = pad(stdDevVal, " ", maxWidth + 1 - stdDevVal.length(), true); temp.append(stdDevVal); } temp.append("\n"); } temp.append("\n"); } else { // Full data if (Double.isNaN(m_FullMeansOrMediansOrModes[i])) { stdDevVal = pad("--", " ", maxAttWidth + maxWidth + 1 - 2, true); } else { stdDevVal = pad( (strVal = plusMinus + Utils.doubleToString(m_FullStdDevs[i], maxWidth, 4).trim()), " ", maxWidth + maxAttWidth + 1 - strVal.length(), true); } temp.append(stdDevVal); // Clusters for (int j = 0; j < m_NumClusters; j++) { if (m_ClusterCentroids.instance(j).isMissing(i)) { stdDevVal = pad("--", " ", maxWidth + 1 - 2, true); } else { stdDevVal = pad((strVal = plusMinus + Utils .doubleToString(m_ClusterStdDevs.instance(j).value(i), maxWidth, 4).trim()), " ", maxWidth + 1 - strVal.length(), true); } temp.append(stdDevVal); } temp.append("\n\n"); } } } temp.append("\n\n"); return temp.toString(); } private String pad(String source, String padChar, int length, boolean leftPad) { StringBuffer temp = new StringBuffer(); if (leftPad) { for (int i = 0; i < length; i++) { temp.append(padChar); } temp.append(source); } else { temp.append(source); for (int i = 0; i < length; i++) { temp.append(padChar); } } return temp.toString(); } /** * Gets the the cluster centroids. * * @return the cluster centroids */ public Instances getClusterCentroids() { return m_ClusterCentroids; } /** * Gets the standard deviations of the numeric attributes in each cluster. * * @return the standard deviations of the numeric attributes in each cluster */ public Instances getClusterStandardDevs() { return m_ClusterStdDevs; } /** * Returns for each cluster the frequency counts for the values of each nominal attribute. * * @return the counts */ public int[][][] getClusterNominalCounts() { return m_ClusterNominalCounts; } /** * Gets the squared error for all clusters. * * @return the squared error, NaN if fast distance calculation is used * @see #m_FastDistanceCalc */ public double getSquaredError() { if (m_FastDistanceCalc) return Double.NaN; else return Utils.sum(m_squaredErrors); } /** * Gets the number of instances in each cluster. * * @return The number of instances in each cluster */ public int[] getClusterSizes() { return m_ClusterSizes; } /** * Gets the assignments for each instance. * * @return Array of indexes of the centroid assigned to each instance * @throws Exception * if order of instances wasn't preserved or no assignments were made */ public int[] getAssignments() throws Exception { if (!m_PreserveOrder) { throw new Exception("The assignments are only available when order of instances is preserved (-O)"); } if (m_Assignments == null) { throw new Exception("No assignments made."); } return m_Assignments; } /** * Returns the revision string. * * @return the revision */ @Override public String getRevision() { return RevisionUtils.extract("$Revision: 9375 $"); } /** * Main method for executing this class. * * @param args * use -h to list all parameters */ public static void main(String[] args) { runClusterer(new SimpleKMeansWithOutput(), args); } }