List of usage examples for weka.core Instance numAttributes
public int numAttributes();
From source file:org.mcennis.graphrat.algorithm.clustering.WekaClassifierClusterer.java
License:Open Source License
@Override public void execute(Graph g) { ActorByMode mode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); mode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); try {// w w w . ja v a 2 s . c o m Clusterer clusterer = (Clusterer) ((Class) parameter.get("Clusterer").get()).newInstance(); String[] options = ((String) parameter.get("Options").get()).split("\\s+"); ((OptionHandler) clusterer).setOptions(options); Iterator<Actor> actor = AlgorithmMacros.filterActor(parameter, g, mode, null, null); Instances dataSet = null; while (actor.hasNext()) { Actor a = actor.next(); Property property = a.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (!property.getValue().isEmpty()) { Instance value = (Instance) property.getValue().get(0); if ((dataSet == null) && (value.dataset() != null)) { FastVector attributes = new FastVector(); for (int i = 0; i < value.dataset().numAttributes(); ++i) { attributes.addElement(value.dataset().attribute(i)); } dataSet = new Instances("Clustering", attributes, 1000); } else if ((dataSet == null)) { FastVector attributes = new FastVector(); for (int i = 0; i < value.numAttributes(); ++i) { Attribute element = new Attribute(Integer.toString(i)); attributes.addElement(element); } dataSet = new Instances("Clustering", attributes, 1000); } dataSet.add(value); } } clusterer.buildClusterer(dataSet); actor = AlgorithmMacros.filterActor(parameter, g, mode, null, null); HashMap<Integer, Graph> clusters = new HashMap<Integer, Graph>(); while (actor.hasNext()) { Actor a = actor.next(); Property property = a.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (!property.getValue().isEmpty()) { Instance instance = (Instance) property.getValue().get(0); int cluster = -1; try { cluster = clusterer.clusterInstance(instance); if (!clusters.containsKey(cluster)) { Graph graph = GraphFactory.newInstance().create(AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("GraphID").get() + cluster), parameter); clusters.put(cluster, graph); } clusters.get(cluster).add(a); } catch (Exception ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, "ClusterInstance on clusterer failed", ex); } Property clusterProperty = PropertyFactory.newInstance().create("BasicProperty", AlgorithmMacros .getDestID(parameter, g, (String) parameter.get("DestinationProperty").get()), Integer.class); clusterProperty.add(new Integer(cluster)); a.add(clusterProperty); } } Iterator<Graph> graphIt = clusters.values().iterator(); while (graphIt.hasNext()) { LinkQuery query = (LinkQuery) parameter.get("LinkQuery").get(); Graph graph = graphIt.next(); Iterator<Link> link = query.executeIterator(g, graph.getActor(), graph.getActor(), null); while (link.hasNext()) { graph.add(link.next()); } if ((Boolean) parameter.get("AddContext").get()) { TreeSet<Actor> actorSet = new TreeSet<Actor>(); actorSet.addAll(graph.getActor()); link = query.executeIterator(g, actorSet, null, null); while (link.hasNext()) { Link l = link.next(); Actor d = l.getDestination(); if (graph.getActor(d.getMode(), d.getID()) == null) { graph.add(d); } if (graph.getLink(l.getRelation(), l.getSource(), l.getDestination()) == null) { graph.add(l); } } link = query.executeIterator(g, null, actorSet, null); while (link.hasNext()) { Link l = link.next(); Actor d = l.getSource(); if (graph.getActor(d.getMode(), d.getID()) == null) { graph.add(d); } if (graph.getLink(l.getRelation(), l.getSource(), l.getDestination()) == null) { graph.add(l); } } } } } catch (InstantiationException ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } catch (IllegalAccessException ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:org.mcennis.graphrat.algorithm.clustering.WekaProbablisticClusterer.java
License:Open Source License
@Override public void execute(Graph g) { ActorByMode mode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); mode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); try {//from w w w . j av a2 s .co m Clusterer clusterer = (Clusterer) ((Class) parameter.get("Clusterer").get()).newInstance(); String[] options = ((String) parameter.get("Options").get()).split("\\s+"); ((OptionHandler) clusterer).setOptions(options); Iterator<Actor> actor = AlgorithmMacros.filterActor(parameter, g, mode, null, null); Instances dataSet = null; while (actor.hasNext()) { Actor a = actor.next(); Property property = a.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (!property.getValue().isEmpty()) { Instance value = (Instance) property.getValue().get(0); if ((dataSet == null) && (value.dataset() != null)) { FastVector attributes = new FastVector(); for (int i = 0; i < value.dataset().numAttributes(); ++i) { attributes.addElement(value.dataset().attribute(i)); } dataSet = new Instances("Clustering", attributes, 1000); } else if ((dataSet == null)) { FastVector attributes = new FastVector(); for (int i = 0; i < value.numAttributes(); ++i) { Attribute element = new Attribute(Integer.toString(i)); attributes.addElement(element); } dataSet = new Instances("Clustering", attributes, 1000); } dataSet.add(value); } } clusterer.buildClusterer(dataSet); actor = AlgorithmMacros.filterActor(parameter, g, mode, null, null); HashMap<Integer, Graph> clusters = new HashMap<Integer, Graph>(); while (actor.hasNext()) { Actor a = actor.next(); Property property = a.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (!property.getValue().isEmpty()) { Instance instance = (Instance) property.getValue().get(0); double[] cluster = new double[] {}; try { cluster = clusterer.distributionForInstance(instance); } catch (Exception ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, "ClusterInstance on clusterer failed", ex); } Property clusterProperty = PropertyFactory.newInstance().create("BasicProperty", AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("DestinationProperty").get()), (new double[] {}).getClass()); clusterProperty.add(cluster); a.add(clusterProperty); } } } catch (InstantiationException ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } catch (IllegalAccessException ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(WekaClassifierClusterer.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:org.mcennis.graphrat.algorithm.machinelearning.BuildClassifierPerActor.java
License:Open Source License
public void execute(Graph g) { // construct the queries to be used ActorByMode groundMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); groundMode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); ActorByMode targetMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); targetMode.buildQuery((String) parameter.get("TargetMode").get(), ".*", false); LinkByRelation groundTruth = (LinkByRelation) LinkQueryFactory.newInstance().create("LinkByRelation"); groundTruth.buildQuery((String) parameter.get("Relation").get(), false); // build a list of new artists TreeSet<Actor> artists = new TreeSet<Actor>(); artists.addAll(AlgorithmMacros.filterActor(parameter, g, targetMode.execute(g, artists, null))); // collect the instance variables from the properties to be the for (Actor i : artists) { TreeSet<Actor> artist = new TreeSet<Actor>(); artist.add(i);//from w ww . j a va2 s. c om Classifier classifier = createClassifier(); Iterator<Actor> users = AlgorithmMacros.filterActor(parameter, g, groundMode, null, null); Instances dataSet = null; boolean firstRun = true; while (users.hasNext()) { TreeSet<Actor> user = new TreeSet<Actor>(); user.add(users.next()); Property property = user.first().getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (property.getPropertyClass().getName().contentEquals(Instance.class.getName())) { List values = property.getValue(); if (!values.isEmpty()) { // get the existing instance Instance object = (Instance) values.get(0); if (firstRun == true) { firstRun = false; Instances current = object.dataset(); FastVector attributes = new FastVector(); for (int j = 0; j < current.numAttributes(); ++j) { attributes.addElement(current.attribute(j)); } Attribute classValue = new Attribute(i.getID()); attributes.addElement(classValue); dataSet = new Instances(i.getID(), attributes, 1000); dataSet.setClassIndex(dataSet.numAttributes() - 1); } // for every artist, create a temporary artist classifer double[] content = new double[object.numAttributes() + 1]; for (int j = 0; j < object.numAttributes() + 1; ++j) { content[j] = object.value(j); } Iterator<Link> link = null; if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { link = AlgorithmMacros.filterLink(parameter, g, groundTruth, user, artist, null); } else { link = AlgorithmMacros.filterLink(parameter, g, groundTruth, artist, user, null); } if (link.hasNext()) { content[content.length - 1] = link.next().getStrength(); } else if ((Boolean) parameter.get("AbsenceIsMissing").get()) { content[content.length - 1] = Double.NaN; } else { content[content.length - 1] = 0.0; } Instance base = new Instance(1.0, content); base.setDataset(dataSet); dataSet.add(base); } } } try { classifier.buildClassifier(dataSet); Property classifierProperty = PropertyFactory.newInstance().create( AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("ClassifierProperty").get()), (String) parameter.get("ClassifierProperty").getType(), weka.classifiers.Classifier.class); classifierProperty.add(classifier); i.add(classifierProperty); Property instancesProperty = PropertyFactory.newInstance().create( AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("InstancesProperty").get()), (String) parameter.get("InstancesProperty").getType(), weka.core.Instances.class); instancesProperty.add(classifier); i.add(instancesProperty); } catch (Exception ex) { Logger.getLogger(BuildClassifierPerActor.class.getName()).log(Level.SEVERE, null, ex); } } }
From source file:org.mcennis.graphrat.algorithm.machinelearning.BuildClassifierSingleAttribute.java
License:Open Source License
public void execute(Graph g) { // construct the queries to be used ActorByMode groundMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); groundMode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); ActorByMode targetMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); targetMode.buildQuery((String) parameter.get("TargetMode").get(), ".*", false); LinkByRelation groundTruth = (LinkByRelation) LinkQueryFactory.newInstance().create("LinkByRelation"); groundTruth.buildQuery((String) parameter.get("Relation").get(), false); // build a list of new artists TreeSet<Actor> artists = new TreeSet<Actor>(); artists.addAll(AlgorithmMacros.filterActor(parameter, g, targetMode.execute(g, artists, null))); // collect the instance variables from the properties to be the Classifier classifier = createClassifier(); Iterator<Actor> users = AlgorithmMacros.filterActor(parameter, g, groundMode, null, null); Instances dataSet = null;//from w w w .j a v a 2s. c o m boolean firstEntry = true; while (users.hasNext()) { TreeSet<Actor> user = new TreeSet<Actor>(); user.add(users.next()); Property property = user.first().getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (property.getPropertyClass().getName().contentEquals(Instance.class.getName())) { List values = property.getValue(); if (!values.isEmpty()) { // get the existing instance Instance object = (Instance) values.get(0); if (firstEntry) { firstEntry = false; Instances current = object.dataset(); FastVector attributes = new FastVector(); for (int j = 0; j < current.numAttributes(); ++j) { attributes.addElement(current.attribute(j)); } FastVector targetNames = new FastVector(); Iterator<Actor> artistIt = targetMode.executeIterator(g, null, null); while (artistIt.hasNext()) { targetNames.addElement(artistIt.next().getID()); } Attribute classValue = new Attribute("TargetID", targetNames); attributes.addElement(classValue); dataSet = new Instances("Training", attributes, 1000); dataSet.setClassIndex(dataSet.numAttributes() - 1); } // for every artist, create a temporary artist classifer double[] content = new double[object.numAttributes() + 1]; for (int j = 0; j < object.numAttributes() + 1; ++j) { content[j] = object.value(j); } Iterator<Link> link = null; if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { link = AlgorithmMacros.filterLink(parameter, g, groundTruth, user, null, null); } else { link = AlgorithmMacros.filterLink(parameter, g, groundTruth, null, user, null); } if (link.hasNext()) { double strength = Double.NEGATIVE_INFINITY; Actor target = null; while (link.hasNext()) { Link l = link.next(); if (l.getStrength() > strength) { strength = l.getStrength(); if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { target = l.getDestination(); } else { target = l.getSource(); } } } content[content.length - 1] = dataSet.attribute(dataSet.numAttributes() - 1) .indexOfValue(target.getID()); } else { content[content.length - 1] = Double.NaN; } Instance base = new Instance(1.0, content); base.setDataset(dataSet); dataSet.add(base); } } } try { classifier.buildClassifier(dataSet); Property classifierProperty = PropertyFactory.newInstance().create("BasicProperty", AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("ClassifierProperty").get()), weka.classifiers.Classifier.class); classifierProperty.add(classifier); g.add(classifierProperty); Property instancesProperty = PropertyFactory.newInstance().create("BasicProperty", AlgorithmMacros.getDestID(parameter, g, (String) parameter.get("InstancesProperty").get()), weka.core.Instances.class); instancesProperty.add(classifier); g.add(instancesProperty); } catch (Exception ex) { Logger.getLogger(BuildClassifierSingleAttribute.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:org.mcennis.graphrat.algorithm.machinelearning.ClassifyPerActor.java
License:Open Source License
public void execute(Graph g) { // construct the queries to be used ActorByMode groundMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); groundMode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); ActorByMode targetMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); targetMode.buildQuery((String) parameter.get("TargetMode").get(), ".*", false); // build a list of new artists TreeSet<Actor> artists = new TreeSet<Actor>(); artists.addAll(AlgorithmMacros.filterActor(parameter, g, targetMode.execute(g, artists, null))); // collect the instance variables from the properties to be the for (Actor i : artists) { Property classifierProperty = i.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("ClassifierProperty").get())); if (!classifierProperty.getValue().isEmpty()) { Classifier classifier = (Classifier) classifierProperty.getValue().get(0); TreeSet<Actor> artist = new TreeSet<Actor>(); artist.add(i);// ww w . jav a 2 s . co m Iterator<Actor> users = AlgorithmMacros.filterActor(parameter, g, groundMode, null, null); Instances dataSet = null; boolean firstRun = true; while (users.hasNext()) { TreeSet<Actor> user = new TreeSet<Actor>(); user.add(users.next()); Property property = user.first().getProperty(AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (property.getPropertyClass().getName().contentEquals(Instance.class.getName())) { List values = property.getValue(); if (!values.isEmpty()) { // get the existing instance Instance object = (Instance) values.get(0); if (firstRun) { firstRun = false; Instances current = object.dataset(); FastVector attributes = new FastVector(); for (int j = 0; j < current.numAttributes(); ++j) { attributes.addElement(current.attribute(j)); } Attribute classValue = new Attribute(i.getID()); attributes.addElement(classValue); dataSet = new Instances(i.getID(), attributes, 1000); dataSet.setClassIndex(dataSet.numAttributes() - 1); } // for every artist, create a temporary artist classifer double[] content = new double[object.numAttributes() + 1]; for (int j = 0; j < object.numAttributes() + 1; ++j) { content[j] = object.value(j); } Instance base = new Instance(1.0, content); try { double strength = classifier.classifyInstance(base); if ((!Double.isNaN(strength)) && (strength != 0.0)) { Link link = LinkFactory.newInstance() .create((String) parameter.get("Relation").get()); if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { link.set(user.first(), strength, artist.first()); } else { link.set(artist.first(), strength, user.first()); } g.add(link); } } catch (Exception ex) { Logger.getLogger(ClassifyPerActor.class.getName()).log(Level.SEVERE, null, ex); } } } } } } }
From source file:org.mcennis.graphrat.algorithm.machinelearning.ClassifySingleAttribute.java
License:Open Source License
public void execute(Graph g) { // construct the queries to be used ActorByMode groundMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); groundMode.buildQuery((String) parameter.get("GroundMode").get(), ".*", false); ActorByMode targetMode = (ActorByMode) ActorQueryFactory.newInstance().create("ActorByMode"); targetMode.buildQuery((String) parameter.get("TargetMode").get(), ".*", false); LinkByRelation groundTruth = (LinkByRelation) LinkQueryFactory.newInstance().create("LinkByRelation"); groundTruth.buildQuery((String) parameter.get("Relation").get(), false); // build a list of new artists TreeSet<Actor> artists = new TreeSet<Actor>(); artists.addAll(AlgorithmMacros.filterActor(parameter, g, targetMode.execute(g, artists, null))); // collect the instance variables from the properties to be the Property classifierProperty = g.getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("ClassifierProperty").get())); if (!classifierProperty.getValue().isEmpty()) { Classifier classifier = (Classifier) classifierProperty.getValue().get(0); Iterator<Actor> users = AlgorithmMacros.filterActor(parameter, g, groundMode, null, null); Instances dataSet = null;/*from w w w . ja va 2 s.c o m*/ boolean firstEntry = true; while (users.hasNext()) { TreeSet<Actor> user = new TreeSet<Actor>(); user.add(users.next()); Property property = user.first().getProperty( AlgorithmMacros.getSourceID(parameter, g, (String) parameter.get("SourceProperty").get())); if (property.getPropertyClass().getName().contentEquals(Instance.class.getName())) { List values = property.getValue(); if (!values.isEmpty()) { // get the existing instance Instance object = (Instance) values.get(0); if (firstEntry) { firstEntry = false; Instances current = object.dataset(); FastVector attributes = new FastVector(); for (int j = 0; j < current.numAttributes(); ++j) { attributes.addElement(current.attribute(j)); } FastVector targetNames = new FastVector(); Iterator<Actor> artistIt = targetMode.executeIterator(g, null, null); while (artistIt.hasNext()) { targetNames.addElement(artistIt.next().getID()); } Attribute classValue = new Attribute("TargetID", targetNames); attributes.addElement(classValue); dataSet = new Instances("Training", attributes, 1000); dataSet.setClassIndex(dataSet.numAttributes() - 1); } // for every artist, create a temporary artist classifer double[] content = new double[object.numAttributes() + 1]; for (int j = 0; j < object.numAttributes() + 1; ++j) { content[j] = object.value(j); } Instance base = new Instance(1.0, content); try { double strength = classifier.classifyInstance(base); if (!Double.isNaN(strength)) { String id = dataSet.classAttribute().value((int) strength); Actor target = g.getActor((String) parameter.get("TargetMode").get(), id); Link link = LinkFactory.newInstance() .create((String) parameter.get("Relation").get()); if ((LinkEnd) parameter.get("LinkEnd").get() == LinkEnd.SOURCE) { link.set(user.first(), strength, target); } else { link.set(target, strength, user.first()); } g.add(link); } } catch (Exception ex) { Logger.getLogger(ClassifyPerActor.class.getName()).log(Level.SEVERE, null, ex); } } } } } }
From source file:org.mcennis.graphrat.algorithm.reusablecores.InstanceManipulation.java
License:Open Source License
/** * Takes the contents of the Instance array and creates a new Instance object * whose attributes are the attributes of the Instance objects in sequence * backed by a new Dataset relfecting the new set of attributes. * //from w ww.j a v a2 s . c o m * If there is any conflict of attribute names between the Instance objects, * they are duplicated in the Instance object (which may cause difficulties * for some machine learning algorithms.) If this a problem, utilize * normalizeFieldNames instead of concatenation. * * If either array is null or if the length of the data and meta array do not * match, a new Instance object without attributes is created and returned. * * @param data array of Instance objects * @param meta array of Instances backing the data array * @return new Instance containing all the given data */ static public Instance instanceConcatenation(Instance[] data, Instances[] meta) { FastVector attributeVector = new FastVector(); LinkedList<Double> values = new LinkedList<Double>(); Instance ret = new Instance(0); ret.setDataset(new Instances("Default", new FastVector(), 0)); if ((data != null) && (meta != null) && (data.length == meta.length) && (data.length > 0)) { for (int i = 0; i < data.length; ++i) { for (int j = 0; j < meta[i].numAttributes(); ++j) { attributeVector.addElement(meta[i].attribute(j)); values.add(data[i].value(j)); } } ret = new Instance(values.size()); Iterator<Double> it = values.iterator(); for (int i = 0; i < ret.numAttributes(); ++i) { ret.setValue(i, it.next()); } ret.setDataset(new Instances(meta[0].relationName() + " Concatenated", attributeVector, 1)); } return ret; }
From source file:org.opentox.jaqpot3.qsar.predictor.FastRbfNnPredictor.java
License:Open Source License
private static double squaredNormDifference(Instance a, Instance b) { int numAttributes = a.numAttributes(); if (numAttributes != b.numAttributes()) { throw new IllegalArgumentException("Provided instances of different length! " + "Squared Norm of the difference cannot be calculated!"); }// w w w. j ava 2 s . c om double sum = 0; for (int i = 0; i < numAttributes; i++) { sum += Math.pow(a.value(i) - b.value(i), 2); } return sum; }
From source file:org.ssase.debt.classification.OnlineMultilayerPerceptron.java
License:Open Source License
public Instances getInstances(Instance inst) { Instances insts;/* ww w . j a v a 2 s . com*/ FastVector atts = new FastVector(); for (int i = 0; i < inst.numAttributes(); i++) { atts.addElement(inst.attribute(i)); } insts = new Instances("CurrentTrain", atts, 0); insts.add(inst); insts.setClassIndex(inst.numAttributes() - 1); return insts; }
From source file:pk.lums.edu.sma.processing.ml.DBSCAN.EuclideanDataObject.java
License:Open Source License
/** * Compares two DataObjects in respect to their attribute-values * //from ww w . j a v a 2 s .com * @param dataObject * The DataObject, that is compared with this.dataObject; now * assumed to be of the same type and with the same structure * @return Returns true, if the DataObjects correspond in each value, else * returns false */ public boolean equals(DataObject dataObject) { if (this == dataObject) return true; Instance firstInstance = getInstance(); Instance secondInstance = dataObject.getInstance(); int firstNumValues = firstInstance.numValues(); int secondNumValues = secondInstance.numValues(); int numAttributes = firstInstance.numAttributes(); int firstI, secondI; for (int p1 = 0, p2 = 0; p1 < firstNumValues || p2 < secondNumValues;) { if (p1 >= firstNumValues) { firstI = numAttributes; } else { firstI = firstInstance.index(p1); } if (p2 >= secondNumValues) { secondI = numAttributes; } else { secondI = secondInstance.index(p2); } if (firstI == secondI) { if (firstInstance.valueSparse(p1) != secondInstance.valueSparse(p2)) { return false; } p1++; p2++; } else if (firstI > secondI) { if (0 != secondInstance.valueSparse(p2)) { return false; } p2++; } else { if (0 != firstInstance.valueSparse(p1)) { return false; } p1++; } } return true; }