/** * 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_wrappedClusterer.numberOfClusters(); }
/** * 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_Clusterer.numberOfClusters(); } }
/** * Get the number of clusters from the base clusterer * * @return the number of clusters * @throws Exception if a problem occurs */ public int numberOfClusters() throws Exception { return m_model.numberOfClusters(); } }
/** * 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_wrappedClusterer.numberOfClusters(); }
/** * 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_Clusterer.numberOfClusters(); } }
@Override public List<String> getPredictionLabels() throws DistributedWekaException { if (m_predictionLabels == null) { m_predictionLabels = new ArrayList<String>(); try { for (int i = 0; i < m_model.numberOfClusters(); i++) { m_predictionLabels.add("Cluster_" + i); } } catch (Exception ex) { throw new DistributedWekaException(ex); } } return m_predictionLabels; }
private Instances makeClusterDataSetClass(Instances format, weka.clusterers.Clusterer clusterer, String relationNameModifier) throws Exception { weka.filters.unsupervised.attribute.Add addF = new weka.filters.unsupervised.attribute.Add(); addF.setAttributeIndex("last"); String clustererName = clusterer.getClass().getName(); clustererName = clustererName.substring(clustererName.lastIndexOf('.') + 1, clustererName.length()); addF.setAttributeName("assigned_cluster: " + clustererName); // if (format.classAttribute().isNominal()) { String clusterLabels = "0"; /* * Enumeration enu = format.classAttribute().enumerateValues(); * clusterLabels += (String)enu.nextElement(); while (enu.hasMoreElements()) * { clusterLabels += ","+(String)enu.nextElement(); } */ for (int i = 1; i <= clusterer.numberOfClusters() - 1; i++) { clusterLabels += "," + i; } addF.setNominalLabels(clusterLabels); // } addF.setInputFormat(format); Instances newInstances = weka.filters.Filter.useFilter(format, addF); newInstances.setRelationName(format.relationName() + relationNameModifier); return newInstances; }
private Instances makeClusterDataSetClass(Instances format, weka.clusterers.Clusterer clusterer, String relationNameModifier) throws Exception { weka.filters.unsupervised.attribute.Add addF = new weka.filters.unsupervised.attribute.Add(); addF.setAttributeIndex("last"); String clustererName = clusterer.getClass().getName(); clustererName = clustererName.substring(clustererName.lastIndexOf('.') + 1, clustererName.length()); addF.setAttributeName("assigned_cluster: " + clustererName); // if (format.classAttribute().isNominal()) { String clusterLabels = "0"; /* * Enumeration enu = format.classAttribute().enumerateValues(); * clusterLabels += (String)enu.nextElement(); while (enu.hasMoreElements()) * { clusterLabels += ","+(String)enu.nextElement(); } */ for (int i = 1; i <= clusterer.numberOfClusters() - 1; i++) { clusterLabels += "," + i; } addF.setNominalLabels(clusterLabels); // } addF.setInputFormat(format); Instances newInstances = weka.filters.Filter.useFilter(format, addF); newInstances.setRelationName(format.relationName() + relationNameModifier); return newInstances; }
for (int i = 0; i < clusterer.numberOfClusters(); i++) { Add addF = new Add(); addF.setAttributeIndex("last"); addF.setAttributeName("assigned_cluster: " + clustererName); String clusterLabels = "0"; for (int i = 1; i <= clusterer.numberOfClusters() - 1; i++) { clusterLabels += "," + i;
double[] wghts = new double[m_wrappedClusterer.numberOfClusters()]; for (i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) { logprob = 0; for (j = 0; j < inst.numAttributes(); j++) {
for (int i = 0; i < clusterer.numberOfClusters(); i++) { Add addF = new Add(); addF.setAttributeIndex("last"); addF.setAttributeName("assigned_cluster: " + clustererName); String clusterLabels = "0"; for (int i = 1; i <= clusterer.numberOfClusters() - 1; i++) { clusterLabels += "," + i;
double[] wghts = new double[m_wrappedClusterer.numberOfClusters()]; for (i = 0; i < m_wrappedClusterer.numberOfClusters(); i++) { logprob = 0; for (j = 0; j < inst.numAttributes(); j++) {
int cnum; double loglk = 0.0; int cc = clusterer.numberOfClusters(); double[] instanceStats = new double[cc]; int unclusteredInstances = 0;
int cnum; double loglk = 0.0; int cc = clusterer.numberOfClusters(); double[] instanceStats = new double[cc]; int unclusteredInstances = 0;
m_ActualClusterer.numberOfClusters()); for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) { nominal_values.add("cluster" + (i + 1));
m_ActualClusterer.numberOfClusters()); for (int i = 0; i < m_ActualClusterer.numberOfClusters(); i++) { nominal_values.add("cluster" + (i + 1));
private Instances makeClusterDataSetProbabilities(Instances format, weka.clusterers.Clusterer clusterer, String relationNameModifier) throws Exception { Instances newInstances = new Instances(format); for (int i = 0; i < clusterer.numberOfClusters(); i++) { weka.filters.unsupervised.attribute.Add addF = new weka.filters.unsupervised.attribute.Add(); addF.setAttributeIndex("last"); addF.setAttributeName("prob_cluster" + i); addF.setInputFormat(newInstances); newInstances = weka.filters.Filter.useFilter(newInstances, addF); } newInstances.setRelationName(format.relationName() + relationNameModifier); return newInstances; }
new double[m_classifier != null ? m_trainingData.classAttribute() .numValues() : ((weka.clusterers.Clusterer) m_clusterer) .numberOfClusters()]; new double[m_classifier != null ? m_trainingData.classAttribute() .numValues() : ((weka.clusterers.Clusterer) m_clusterer) .numberOfClusters()];
private Instances makeClusterDataSetProbabilities(Instances format, weka.clusterers.Clusterer clusterer, String relationNameModifier) throws Exception { Instances newInstances = new Instances(format); for (int i = 0; i < clusterer.numberOfClusters(); i++) { weka.filters.unsupervised.attribute.Add addF = new weka.filters.unsupervised.attribute.Add(); addF.setAttributeIndex("last"); addF.setAttributeName("prob_cluster" + i); addF.setInputFormat(newInstances); newInstances = weka.filters.Filter.useFilter(newInstances, addF); } newInstances.setRelationName(format.relationName() + relationNameModifier); return newInstances; }
numClusters = clusterer.numberOfClusters();