List of usage examples for weka.clusterers ClusterEvaluation ClusterEvaluation
public ClusterEvaluation()
From source file:soccer.core.models.PlayerModel.java
public static void main(String[] args) throws Exception { PlayerModel pm = new PlayerModel(); Instances data = pm.buildInstance(); SimpleKMeans cluster = new SimpleKMeans(); cluster.setNumClusters(4);//from w w w .j a va 2 s .co m cluster.buildClusterer(data); ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(cluster); eval.evaluateClusterer(data); System.out.println(eval.clusterResultsToString()); }
From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java
License:Open Source License
public Instances clusteredInstances(Instances data) { if (data == null) { throw new NullPointerException("Data is null at clusteredInstances method"); }//w ww . ja v a 2s.c o m Instances sampled_data = data; for (int i = 0; i < sampled_data.numInstances(); i++) { sampled_data.remove(i); } SimpleKMeans sKmeans = new SimpleKMeans(); data.setClassIndex(data.numAttributes() - 1); Remove filter = new Remove(); filter.setAttributeIndices("" + (data.classIndex() + 1)); List assignments = new ArrayList(); try { filter.setInputFormat(data); Instances dataClusterer = Filter.useFilter(data, filter); String[] options = new String[3]; options[0] = "-I"; // max. iterations options[1] = "500"; options[2] = "-O"; sKmeans.setNumClusters(data.numClasses()); sKmeans.setOptions(options); sKmeans.buildClusterer(dataClusterer); System.out.println("Kmeans\n:" + sKmeans); System.out.println(Arrays.toString(sKmeans.getAssignments())); assignments = Arrays.asList(sKmeans.getAssignments()); } catch (Exception e) { e.printStackTrace(); } System.out.println("Assignments\n: " + assignments); ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(sKmeans); try { eval.evaluateClusterer(data); } catch (Exception e) { e.printStackTrace(); } int classesToClustersMap[] = eval.getClassesToClusters(); for (int i = 0; i < classesToClustersMap.length; i++) { if (assignments.get(i).equals(((Integer) classesToClustersMap[(int) data.get(i).classValue()]))) { ((Instances) sampled_data).add(data.get(i)); } } return ((Instances) sampled_data); }
From source file:tr.gov.ulakbim.jDenetX.experiments.wrappers.EvalActiveBoostingID.java
License:Open Source License
public static Instances clusterInstances(Instances data) { XMeans xmeans = new XMeans(); Remove filter = new Remove(); Instances dataClusterer = null;//from www .j av a 2s . co m if (data == null) { throw new NullPointerException("Data is null at clusteredInstances method"); } //Get the attributes from the data for creating the sampled_data object ArrayList<Attribute> attrList = new ArrayList<Attribute>(); Enumeration attributes = data.enumerateAttributes(); while (attributes.hasMoreElements()) { attrList.add((Attribute) attributes.nextElement()); } Instances sampled_data = new Instances(data.relationName(), attrList, 0); data.setClassIndex(data.numAttributes() - 1); sampled_data.setClassIndex(data.numAttributes() - 1); filter.setAttributeIndices("" + (data.classIndex() + 1)); data.remove(0);//In Wavelet Stream of MOA always the first element comes without class try { filter.setInputFormat(data); dataClusterer = Filter.useFilter(data, filter); String[] options = new String[4]; options[0] = "-L"; // max. iterations options[1] = Integer.toString(noOfClassesInPool - 1); if (noOfClassesInPool > 2) { options[1] = Integer.toString(noOfClassesInPool - 1); xmeans.setMinNumClusters(noOfClassesInPool - 1); } else { options[1] = Integer.toString(noOfClassesInPool); xmeans.setMinNumClusters(noOfClassesInPool); } xmeans.setMaxNumClusters(data.numClasses() + 1); System.out.println("No of classes in the pool: " + noOfClassesInPool); xmeans.setUseKDTree(true); //xmeans.setOptions(options); xmeans.buildClusterer(dataClusterer); System.out.println("Xmeans\n:" + xmeans); } catch (Exception e) { e.printStackTrace(); } //System.out.println("Assignments\n: " + assignments); ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(xmeans); try { eval.evaluateClusterer(data); int classesToClustersMap[] = eval.getClassesToClusters(); //check the classes to cluster map int clusterNo = 0; for (int i = 0; i < data.size(); i++) { clusterNo = xmeans.clusterInstance(dataClusterer.get(i)); //Check if the class value of instance and class value of cluster matches if ((int) data.get(i).classValue() == classesToClustersMap[clusterNo]) { sampled_data.add(data.get(i)); } } } catch (Exception e) { e.printStackTrace(); } return ((Instances) sampled_data); }
From source file:view.centerPanels.ClusteringPredictPnlCenter.java
private void btnStartActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_btnStartActionPerformed Instances test = new Instances(Data.getInstance().getInstances()); test.delete();// w w w .ja v a2 s.co m //proverava da li su dobro unete vrednosti //ako nesto nije doro uneseno nekaa iskoci JoptionPane //sta je lose uneseno, naziv aributa recimo for (int i = 0; i < fields.size(); i++) { String text = fields.get(i).getText().trim(); //prekace prazna pollja jer za klasterizaciju znaci da se ona ignorisu //to za klasifikaciju nije slucaj if (!text.equals("")) { if (test.attribute(i).isNominal()) { boolean correct = false; for (int j = 0; j < test.attribute(i).numValues(); j++) { if (text.equals(test.attribute(i).value(j))) { correct = true; } } if (!correct) { JOptionPane.showMessageDialog(this, "Incorrect format for attribute " + test.attribute(i).name()); break; } } if (test.attribute(i).isNumeric()) { try { double value = Double.parseDouble(text); } catch (Exception e) { JOptionPane.showMessageDialog(this, "Incorrect format for attribute " + test.attribute(i).name()); break; } } } } int numAttributes = test.numAttributes(); Instance instance = new Instance(numAttributes); //ovaj remove je potreban samo zaklasterizaciju String remove = ""; boolean hasRemove = false; for (int i = 0; i < fields.size(); i++) { String text = fields.get(i).getText().trim(); //vama ne sme da se pojavi prazan string if (text.equals("")) { remove = remove + (i + 1) + ","; hasRemove = true; } else { try { double value = Double.parseDouble(text); instance.setValue(i, value); } catch (Exception e) { instance.setValue(i, text); } } } if (hasRemove) { remove = remove.substring(0, remove.length() - 1); } //meni se InstanceS zove test a vama instances, ovako se dodaje ta jedna instanca test.add(instance); //sad radite vasu evaluaciju ovo je klaserizacija ostalo Remove removeFilter = new Remove(); removeFilter.setAttributeIndices(remove); FilteredClusterer filteredClusterer = new FilteredClusterer(); try { filteredClusterer.setClusterer(kMeans); filteredClusterer.setFilter(removeFilter); filteredClusterer.buildClusterer(Data.getInstance().getInstances()); } catch (Exception e) { } ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(filteredClusterer); try { eval.evaluateClusterer(test); } catch (Exception ex) { Logger.getLogger(ClusteringPredictPnlCenter.class.getName()).log(Level.SEVERE, null, ex); } String[] results = eval.clusterResultsToString().split("\n"); String cluster = results[results.length - 1].split(" ")[0]; textAreaResult.setText("This instance belongs to \ncluster number: " + cluster + ".\n\n" + "Take a look on visualization \nfor better feeleing about \nthis instance"); test.delete(); }
From source file:wekimini.InputGenerator.java
public void selectEmClusters() throws Exception { String[] options = new String[2]; options[0] = "-I"; options[1] = "100"; buildDataset();// www . j a v a2s. c o m EM clusterer = new EM(); clusterer.setOptions(options); clusterer.buildClusterer(dataset); ClusterEvaluation eval = new ClusterEvaluation(); eval.setClusterer(clusterer); eval.evaluateClusterer(dataset); System.out.println(eval.clusterResultsToString()); double[][][] clusterAtts = clusterer.getClusterModelsNumericAtts(); System.out.println(Arrays.deepToString(clusterAtts)); numClusters = clusterer.numberOfClusters(); System.out.println(numClusters); addEmClustersToTraining(clusterAtts); }