List of usage examples for weka.core Instances Instances
public Instances(Instances dataset)
From source file:MachinLearningInterface.java
private void jButton7ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton7ActionPerformed Instances data;/*from w w w .ja v a2 s .com*/ try { data = new Instances(new BufferedReader(new FileReader(this.name3 + ".arff"))); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels("rods,punctua,networks"); filter.setAttributeName("target"); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print(newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } URL urlToModel = this.getClass().getResource("/" + "Final.model"); InputStream stream = urlToModel.openStream(); Classifier cls = (Classifier) weka.core.SerializationHelper.read(stream); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); } int p = 0, n = 0, r = 0; //txtarea2.append(Utils.arrayToString(this.target)); for (Object vec1 : vec) { if ("rods".equals(vec1.toString())) { r = r + 1; } if ("punctua".equals(vec1.toString())) { p = p + 1; } if ("networks".equals(vec1.toString())) { n = n + 1; } PrintWriter out = null; try { out = new PrintWriter(this.name3 + "_morphology.txt"); out.println(vec); out.close(); } catch (Exception ex) { ex.printStackTrace(); } //System.out.println(vec.get(i)); } System.out.println("VECTOR-> punctua: " + p + ", rods: " + r + ", networks: " + n); IJ.showMessage( "Your file:" + this.name3 + "arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); } catch (IOException ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } IJ.showMessage("analysing complete "); }
From source file:MachinLearningInterface.java
private void jButton10ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton10ActionPerformed Instances data;//from www . j a v a 2 s. c o m try { data = new Instances(new BufferedReader(new FileReader(this.name3 + ".arff"))); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels(this.liststring); filter.setAttributeName("target"); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print(newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } Classifier cls = (Classifier) weka.core.SerializationHelper.read(this.model); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); //txtarea2.append(Utils.arrayToString(dist)); } URL urlToModel = this.getClass().getResource("/" + "Final.model"); InputStream stream = urlToModel.openStream(); Classifier cls2 = (Classifier) weka.core.SerializationHelper.read(stream); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls2.classifyInstance(newData.instance(i)); double[] dist = cls2.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); } int p = 0, n = 0, r = 0; //txtarea2.append(Utils.arrayToString(this.target)); for (Object vec1 : vec) { if ("rods".equals(vec1.toString())) { r = r + 1; } if ("punctua".equals(vec1.toString())) { p = p + 1; } if ("networks".equals(vec1.toString())) { n = n + 1; } PrintWriter out = null; try { out = new PrintWriter(this.name3 + "_morphology.txt"); out.println(vec); out.close(); } catch (Exception ex) { ex.printStackTrace(); } //System.out.println(vec.get(i)); } System.out.println("VECTOR-> punctua: " + p + ", rods: " + r + ", networks: " + n); IJ.showMessage( "Your file:" + this.name3 + "arff" + "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n); //txtarea2.setText("Your file:" + this.name3 + ".arff" //+ "\nhas been analysed, and it is composed by-> punctua: " + p + ", rods: " + r + ", networks: " + n //+ "\n" //+ "\nAnalyse complete"); //txtarea.setText("Analyse complete"); } catch (IOException ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } IJ.showMessage("analysing complete "); // TODO add your handling code here: // TODO add your handling code here: }
From source file:MPCKMeans.java
License:Open Source License
public static void runFromCommandLine(String[] args) { MPCKMeans mpckmeans = new MPCKMeans(); Instances data = null, clusterData = null; ArrayList labeledPairs = null; try {//from ww w .j a va 2 s. c om String optionString = Utils.getOption('D', args); if (optionString.length() != 0) { FileReader reader = new FileReader(optionString); data = new Instances(reader); System.out.println("Reading dataset: " + data.relationName()); } int classIndex = data.numAttributes() - 1; optionString = Utils.getOption('K', args); if (optionString.length() != 0) { classIndex = Integer.parseInt(optionString); if (classIndex >= 0) { data.setClassIndex(classIndex); // starts with 0 // Remove the class labels before clustering clusterData = new Instances(data); mpckmeans.setNumClusters(clusterData.numClasses()); clusterData.deleteClassAttribute(); System.out.println("Setting classIndex: " + classIndex); } else { clusterData = new Instances(data); } } else { data.setClassIndex(classIndex); // starts with 0 // Remove the class labels before clustering clusterData = new Instances(data); mpckmeans.setNumClusters(clusterData.numClasses()); clusterData.deleteClassAttribute(); System.out.println("Setting classIndex: " + classIndex); } optionString = Utils.getOption('C', args); if (optionString.length() != 0) { labeledPairs = mpckmeans.readConstraints(optionString); System.out.println("Reading constraints from: " + optionString); } else { labeledPairs = new ArrayList(0); } mpckmeans.setTotalTrainWithLabels(data); mpckmeans.setOptions(args); System.out.println(); mpckmeans.buildClusterer(labeledPairs, clusterData, data, mpckmeans.getNumClusters(), data.numInstances()); mpckmeans.printClusterAssignments(); if (mpckmeans.m_TotalTrainWithLabels.classIndex() > -1) { double nCorrect = 0; for (int i = 0; i < mpckmeans.m_TotalTrainWithLabels.numInstances(); i++) { for (int j = i + 1; j < mpckmeans.m_TotalTrainWithLabels.numInstances(); j++) { int cluster_i = mpckmeans.m_ClusterAssignments[i]; int cluster_j = mpckmeans.m_ClusterAssignments[j]; double class_i = (mpckmeans.m_TotalTrainWithLabels.instance(i)).classValue(); double class_j = (mpckmeans.m_TotalTrainWithLabels.instance(j)).classValue(); // System.out.println(cluster_i + "," + cluster_j + ":" + class_i + "," + class_j); if (cluster_i == cluster_j && class_i == class_j || cluster_i != cluster_j && class_i != class_j) { nCorrect++; // System.out.println("nCorrect:" + nCorrect); } } } int numInstances = mpckmeans.m_TotalTrainWithLabels.numInstances(); double RandIndex = 100 * nCorrect / (numInstances * (numInstances - 1) / 2); System.err.println("Acc\t" + RandIndex); } // if (mpckmeans.getTotalTrainWithLabels().classIndex() >= 0) { // SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, // mpckmeans.m_TotalTrainWithLabels.numClasses(), // mpckmeans.m_TotalTrainWithLabels.numClasses()); // eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); // eval.mutualInformation(); // eval.pairwiseFMeasure(); // } } catch (Exception e) { System.out.println("Option not specified"); e.printStackTrace(); } }
From source file:MPCKMeans.java
License:Open Source License
public static void testCase() { try {/*from w w w . jav a 2s. c o m*/ String dataset = new String("lowd"); //String dataset = new String("highd"); if (dataset.equals("lowd")) { //////// Low-D data // String datafile = "/u/ml/data/bio/arffFromPhylo/ecoli_K12-100.arff"; // String datafile = "/u/sugato/weka/data/digits-0.1-389.arff"; String datafile = "/u/sugato/weka/data/iris.arff"; int numPairs = 200, num = 0; // set up the data FileReader reader = new FileReader(datafile); Instances data = new Instances(reader); // Make the last attribute be the class int classIndex = data.numAttributes() - 1; data.setClassIndex(classIndex); // starts with 0 System.out.println("ClassIndex is: " + classIndex); // Remove the class labels before clustering Instances clusterData = new Instances(data); clusterData.deleteClassAttribute(); // create the pairs ArrayList labeledPair = InstancePair.getPairs(data, numPairs); System.out.println("Finished initializing constraint matrix"); MPCKMeans mpckmeans = new MPCKMeans(); mpckmeans.setUseMultipleMetrics(false); System.out.println("\nClustering the data using MPCKmeans...\n"); WeightedEuclidean metric = new WeightedEuclidean(); WEuclideanLearner metricLearner = new WEuclideanLearner(); // LearnableMetric metric = new WeightedDotP(); // MPCKMeansMetricLearner metricLearner = new DotPGDLearner(); // KL metric = new KL(); // KLGDLearner metricLearner = new KLGDLearner(); // ((KL)metric).setUseIDivergence(true); // BarHillelMetric metric = new BarHillelMetric(); // BarHillelMetricMatlab metric = new BarHillelMetricMatlab(); // XingMetric metric = new XingMetric(); // WeightedMahalanobis metric = new WeightedMahalanobis(); mpckmeans.setMetric(metric); mpckmeans.setMetricLearner(metricLearner); mpckmeans.setVerbose(false); mpckmeans.setRegularize(false); mpckmeans.setTrainable(new SelectedTag(TRAINING_INTERNAL, TAGS_TRAINING)); mpckmeans.setSeedable(true); mpckmeans.buildClusterer(labeledPair, clusterData, data, data.numClasses(), data.numInstances()); mpckmeans.getIndexClusters(); mpckmeans.printIndexClusters(); SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_TotalTrainWithLabels.numClasses(), mpckmeans.m_TotalTrainWithLabels.numClasses()); eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); System.out.println("MI=" + eval.mutualInformation()); System.out.print("FM=" + eval.pairwiseFMeasure()); System.out.print("\tP=" + eval.pairwisePrecision()); System.out.print("\tR=" + eval.pairwiseRecall()); } else if (dataset.equals("highd")) { //////// Newsgroup data String datafile = "/u/ml/users/sugato/groupcode/weka335/data/arffFromCCS/sanitized/different-1000_sanitized.arff"; //String datafile = "/u/ml/users/sugato/groupcode/weka335/data/20newsgroups/small-newsgroup_fromCCS.arff"; //String datafile = "/u/ml/users/sugato/groupcode/weka335/data/20newsgroups/same-100_fromCCS.arff"; // set up the data FileReader reader = new FileReader(datafile); Instances data = new Instances(reader); // Make the last attribute be the class int classIndex = data.numAttributes() - 1; data.setClassIndex(classIndex); // starts with 0 System.out.println("ClassIndex is: " + classIndex); // Remove the class labels before clustering Instances clusterData = new Instances(data); clusterData.deleteClassAttribute(); // create the pairs int numPairs = 0, num = 0; ArrayList labeledPair = new ArrayList(numPairs); Random rand = new Random(42); System.out.println("Initializing constraint matrix:"); while (num < numPairs) { int i = (int) (data.numInstances() * rand.nextFloat()); int j = (int) (data.numInstances() * rand.nextFloat()); int first = (i < j) ? i : j; int second = (i >= j) ? i : j; int linkType = (data.instance(first).classValue() == data.instance(second).classValue()) ? InstancePair.MUST_LINK : InstancePair.CANNOT_LINK; InstancePair pair = new InstancePair(first, second, linkType); if (first != second && !labeledPair.contains(pair)) { labeledPair.add(pair); //System.out.println(num + "th entry is: " + pair); num++; } } System.out.println("Finished initializing constraint matrix"); MPCKMeans mpckmeans = new MPCKMeans(); mpckmeans.setUseMultipleMetrics(false); System.out.println("\nClustering the highd data using MPCKmeans...\n"); LearnableMetric metric = new WeightedDotP(); MPCKMeansMetricLearner metricLearner = new DotPGDLearner(); // KL metric = new KL(); // KLGDLearner metricLearner = new KLGDLearner(); mpckmeans.setMetric(metric); mpckmeans.setMetricLearner(metricLearner); mpckmeans.setVerbose(false); mpckmeans.setRegularize(true); mpckmeans.setTrainable(new SelectedTag(TRAINING_INTERNAL, TAGS_TRAINING)); mpckmeans.setSeedable(true); mpckmeans.buildClusterer(labeledPair, clusterData, data, data.numClasses(), data.numInstances()); mpckmeans.getIndexClusters(); SemiSupClustererEvaluation eval = new SemiSupClustererEvaluation(mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_TotalTrainWithLabels.numClasses(), mpckmeans.m_TotalTrainWithLabels.numClasses()); mpckmeans.getMetric().resetMetric(); // Vital: to reset m_attrWeights to 1 for proper normalization eval.evaluateModel(mpckmeans, mpckmeans.m_TotalTrainWithLabels, mpckmeans.m_Instances); System.out.println("MI=" + eval.mutualInformation()); System.out.print("FM=" + eval.pairwiseFMeasure()); System.out.print("\tP=" + eval.pairwisePrecision()); System.out.print("\tR=" + eval.pairwiseRecall()); } } catch (Exception e) { e.printStackTrace(); } }
From source file:EmClustering.java
License:Open Source License
public void createArff(int orgsNum, String[] orgsIds, int totalGenesNumber) { //create an arff file for the graph representation try {//from w w w .j a va 2 s . c o m //create EM files directory String emDirStr = GlobalInitializer.genDataDirStr + "/EM_Files"; boolean success = (new File(emDirStr)).mkdir(); if (success) { System.out.println("Directory: " + emDirStr + " was created succesfully."); } FileWriter arffFstream = new FileWriter(GlobalInitializer.genDataDirStr + "/EM_Files/graph.arff"); BufferedWriter arffOut = new BufferedWriter(arffFstream); arffOut.write("@RELATION P_matrix_data\n\n"); for (int i = 0; i < orgsNum; i++) { arffOut.write("@ATTRIBUTE " + orgsIds[i] + "_homology" + "\tNUMERIC\n"); } arffOut.write("\n@DATA\n"); for (int i = 0; i < totalGenesNumber; i++) { String geneStr = MainClass.getGeneNameByIdxInP(i, orgsNum); for (int j = 0; j < orgsNum; j++) { arffOut.write(Double.toString(MainClass.P[i][j])); if (j < orgsNum - 1) arffOut.write(","); } if (i != (totalGenesNumber - 1)) arffOut.write("\r"); } arffOut.close(); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } try { BufferedReader arffReader = new BufferedReader( new FileReader(GlobalInitializer.genDataDirStr + "/EM_Files/graph.arff")); graphData = new Instances(arffReader); arffReader.close(); } catch (Exception e) { System.err.println("Error: " + e.getMessage()); } }
From source file:WekaClassify.java
License:Open Source License
/** * sets the file to use for training/*from w w w . ja v a 2s. com*/ */ public void setTraining(String name) throws Exception { m_TrainingFile = name; m_Training = new Instances(new DataSource(m_TrainingFile).getDataSet()); m_Training.setClassIndex(m_Training.numAttributes() - 1); }
From source file:PCADetector.java
License:Apache License
public boolean runPCA(ArrayList<Double> newData, int slidewdSz, double cAlpha, int nAttrs) { try {//from w w w . jav a 2 s .com if (m_nDims == 0) { m_nDims = nAttrs; for (int i = 0; i < this.m_nDims; i++) { m_oriDataMatrix.add(new ArrayList<Double>()); // one list for each attribute } } verifyData(newData); this.c_alpha = cAlpha; if (false == prepareData(newData, slidewdSz)) return false; Instances oriDataInsts = getInstances(); if (oriDataInsts != null) { // standardization + PCA covariance matrix m_scaledInstances = new Instances(oriDataInsts); Standardize filter = new Standardize(); filter.setInputFormat(m_scaledInstances); m_scaledInstances = Standardize.useFilter(m_scaledInstances, filter); // standardization PrincipalComponents PCA = new PrincipalComponents(); PCA.setVarianceCovered(1.0); // means 100% PCA.setMaximumAttributeNames(-1); PCA.setCenterData(true); Ranker ranker = new Ranker(); AttributeSelection selector = new AttributeSelection(); selector.setSearch(ranker); selector.setEvaluator(PCA); selector.SelectAttributes(m_scaledInstances); // Instances transformedData = selector.reduceDimensionality(m_scaledInstances); // get sorted eigens double[] eigenValues = PCA.getEigenValues(); // eigenVectors[i][j] i: rows; j: cols double[][] eigenVectors = PCA.getUnsortedEigenVectors(); Sort(eigenValues, eigenVectors); setEigens(eigenValues); // get residual start dimension int residualStartDimension = -1; double sum = 0; double major = 0; for (int ss = 0; ss < eigenValues.length; ss++) { sum += eigenValues[ss]; } for (int ss = 0; ss < eigenValues.length; ss++) { major += eigenValues[ss]; if ((residualStartDimension < 0) && (major / sum > 0.95)) { residualStartDimension = ss + 1; break; } } // System.out.println("residualStartDim: "+residualStartDimension); m_threshold = computeThreshold(eigenValues, residualStartDimension); // check new data abnormal or not boolean bAbnormal = checkSPE(eigenVectors, residualStartDimension, newData); computeProjPCs(eigenVectors, residualStartDimension, newData); // only for demo if (bAbnormal) { // anomaly, now to diagnosis // check original space using all the lists diagnosis(eigenVectors, residualStartDimension, newData); } } } catch (Exception exc) { } return true; }
From source file:A_AdvanceMachineLearning.java
private void jButton10ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton10ActionPerformed UIManager.put("OptionPane.yesButtonText", "Confirm"); UIManager.put("OptionPane.noButtonText", "Cancel"); int dialogButton = JOptionPane.YES_NO_OPTION; int dialogResult = JOptionPane.showConfirmDialog(this, "The labels must be the same used in the weka model", "Advance Machine learning", dialogButton, JOptionPane.WARNING_MESSAGE); if (dialogResult == 0) { this.list.clear(); //txtcodigo1.setText("hola"); this.valor = txtcodigo1.getText(); this.valor1 = txtcodigo2.getText(); this.valor2 = txtcodigo3.getText(); this.valor3 = txtcodigo4.getText(); this.valor4 = txtcodigo5.getText(); this.valor5 = txtcodigo6.getText(); //IJ.showMessage("your label 1 is = "+valor+", "+valor1+", "+valor2+", "+valor3+", "+valor4); // Array list this.list.add(this.valor); this.list.add(this.valor1); this.list.add(this.valor2); this.list.add(this.valor3); this.list.add(this.valor4); this.list.add(this.valor5); this.list.removeAll(Arrays.asList("", null)); System.out.println(this.list); this.liststring = ""; for (String s : this.list) { this.liststring += s + ","; }//from www .ja v a 2s.c om txtlabel.setText(this.liststring); System.out.println(this.liststring); txtarea.setText("Your labels are = " + this.list + "\nThe labels had been saved"); //txtarea.setText("The labels had been saved"); System.out.println(label); } else { System.out.println("No Option"); } Instances data; try { System.out.println(this.file2 + "arff"); FileReader reader = new FileReader(this.file2 + ".arff"); BufferedReader br = new BufferedReader(reader); data = new Instances(br); System.out.println(data); Instances newData = null; Add filter; newData = new Instances(data); filter = new Add(); filter.setAttributeIndex("last"); filter.setNominalLabels(this.liststring); filter.setAttributeName(txtcodigo7.getText()); filter.setInputFormat(newData); newData = Filter.useFilter(newData, filter); System.out.print("hola" + newData); Vector vec = new Vector(); newData.setClassIndex(newData.numAttributes() - 1); if (!newData.equalHeaders(newData)) { throw new IllegalArgumentException("Train and test are not compatible!"); } Classifier cls = (Classifier) weka.core.SerializationHelper.read(this.model); System.out.println("PROVANT MODEL.classifyInstance"); for (int i = 0; i < newData.numInstances(); i++) { double pred = cls.classifyInstance(newData.instance(i)); double[] dist = cls.distributionForInstance(newData.instance(i)); System.out.print((i + 1) + " - "); System.out.print(newData.classAttribute().value((int) pred) + " - "); //txtarea2.setText(Utils.arrayToString(dist)); System.out.println(Utils.arrayToString(dist)); vec.add(newData.classAttribute().value((int) pred)); classif.add(newData.classAttribute().value((int) pred)); } classif.removeAll(Arrays.asList("", null)); System.out.println(classif); String vecstring = ""; for (Object s : classif) { vecstring += s + ","; } Map<String, Integer> seussCount = new HashMap<String, Integer>(); for (String t : classif) { Integer i = seussCount.get(t); if (i == null) { i = 0; } seussCount.put(t, i + 1); } String s = vecstring; String in = vecstring; int i = 0; Pattern p = Pattern.compile(this.valor1); Matcher m = p.matcher(in); while (m.find()) { i++; } System.out.println("hola " + i); // Prints 2 System.out.println(seussCount); txtarea2.append("Your file:" + this.file2 + "arff" + "\n is composed by" + seussCount); IJ.showMessage("Your file:" + this.file2 + "arff" + "\n is composed by" + seussCount); } catch (Exception ex) { Logger.getLogger(MachinLearningInterface.class.getName()).log(Level.SEVERE, null, ex); } //IJ.showMessage("analysing complete ");// TODO add your handling code here: }
From source file:A_AdvanceMachineLearning.java
private void jButton1ActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButton1ActionPerformed try {//from ww w.j a va2 s. c o m /*URL urlToTraining = this.getClass().getResourceAsStream("/" + "train.arff"); InputStream stream = urlToTraining.openStream();*/ InputStream stream = this.getClass().getResourceAsStream("/" + "train.arff"); //BufferedReader reader = new BufferedReader(new InputStreamReader(stream)); Instances data = new Instances(new BufferedReader(new InputStreamReader(stream))); String data1 = data.toString(); txtarea2.setText(data1); txtarea.setText("You have choose to display an example training file: "); } catch (IOException ex) { Logger.getLogger(A_AdvanceMachineLearning.class.getName()).log(Level.SEVERE, null, ex); } //GEN-LAST:event_jButton1ActionPerformed }
From source file:MeansClassifier.java
public static void main(String[] args) throws Exception { Instances read = null;//from w ww .j a v a 2 s.c om String str = "\\\\ueahome4\\stusci4\\ysj13kxu\\data\\Documents\\Sheet2_Train.arff"; FileReader r; try { r = new FileReader(str); read = new Instances(r); read.setClassIndex(read.numAttributes() - 1); } catch (Exception e) { System.out.println(" Exception caught =" + e); } MeansClassifier m = new MeansClassifier(); m.buildClassifier(read); }