List of usage examples for weka.core Instances setClassIndex
public void setClassIndex(int classIndex)
From source file:eu.linda.analytics.formats.CSVInputFormat.java
@Override public AbstractList importData4weka(String pathToFile, boolean isForRDFOutput, Analytics analytics) { float timeToGetQuery = 0; long startTimeToGetQuery = System.currentTimeMillis(); helpfulFuncions.nicePrintMessage("import CSV file "); System.out.println("Import data from file: " + pathToFile); Instances data = null; try {/* w w w .j a v a 2 s . co m*/ CSVLoader loader = new CSVLoader(); loader.setSource(new File(pathToFile)); if (isForRDFOutput) { loader.setStringAttributes("1,2"); } loader.setFieldSeparator(","); data = loader.getDataSet(); data.setClassIndex(data.numAttributes() - 1); FileInputStream fis = null; try { fis = new FileInputStream(pathToFile); System.out.println("fis.getChannel().size() " + fis.getChannel().size()); analytics.setData_size(analytics.getData_size() + fis.getChannel().size()); } finally { fis.close(); } // Get elapsed time in milliseconds long elapsedTimeToGetQueryMillis = System.currentTimeMillis() - startTimeToGetQuery; // Get elapsed time in seconds timeToGetQuery = elapsedTimeToGetQueryMillis / 1000F; analytics.setTimeToGet_data(analytics.getTimeToGet_data() + timeToGetQuery); System.out.println("timeToGetQuery" + timeToGetQuery); connectionController.updateLindaAnalyticsInputDataPerformanceTime(analytics); } catch (Exception ex) { Logger.getLogger(ArffInputFormat.class.getName()).log(Level.SEVERE, null, ex); } return data; }
From source file:eu.linda.analytics.formats.CSVInputFormat.java
public static void main(String[] args) throws Exception { Instances data = null; String[] options = new String[2]; options[0] = "-S"; // "range" options[1] = "1,2"; CSVLoader loader = new CSVLoader(); try {/*from w w w .j av a2 s . com*/ loader.setSource(new File("/home/eleni/Desktop/mydatasets/NYRandonResearchTotest2.csv")); loader.setStringAttributes("1,2"); loader.setFieldSeparator(","); data = loader.getDataSet(); data.setClassIndex(data.numAttributes() - 1); } catch (IOException ex) { Logger.getLogger(CSVInputFormat.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(CSVInputFormat.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:eu.linda.analytics.formats.RDFInputFormat.java
@Override public AbstractList importData4weka(String query_id, boolean isForRDFOutput, Analytics analytics) { String queryURI = connectionController.getQueryURI(query_id); helpfulFunctions.nicePrintMessage("import data from uri " + queryURI); Instances data = null; try {/*from w w w . j a v a 2s . c om*/ float timeToGetQuery = 0; long startTimeToGetQuery = System.currentTimeMillis(); URL url = new URL(queryURI); if (!helpfulFunctions.isURLResponsive(url)) { return null; } File tmpfile4lindaquery = File.createTempFile("tmpfile4lindaquery" + query_id, ".tmp"); FileUtils.copyURLToFile(url, tmpfile4lindaquery); System.out.println("Downloaded File Query: " + tmpfile4lindaquery); CSVLoader loader = new CSVLoader(); loader.setSource(tmpfile4lindaquery); if (isForRDFOutput) { loader.setStringAttributes("1,2"); } loader.setFieldSeparator(","); data = loader.getDataSet(); data.setClassIndex(data.numAttributes() - 1); FileInputStream fis = null; try { fis = new FileInputStream(tmpfile4lindaquery); System.out.println("fis.getChannel().size() " + fis.getChannel().size()); analytics.setData_size(analytics.getData_size() + fis.getChannel().size()); } finally { fis.close(); } // Get elapsed time in milliseconds long elapsedTimeToGetQueryMillis = System.currentTimeMillis() - startTimeToGetQuery; // Get elapsed time in seconds timeToGetQuery = elapsedTimeToGetQueryMillis / 1000F; analytics.setTimeToGet_data(analytics.getTimeToGet_data() + timeToGetQuery); System.out.println("timeToGetQuery" + timeToGetQuery); connectionController.updateLindaAnalyticsInputDataPerformanceTime(analytics); } catch (Exception ex) { Logger.getLogger(ArffInputFormat.class.getName()).log(Level.SEVERE, null, ex); } return data; }
From source file:examples.Pair.java
License:Open Source License
/** * @param args the command line arguments */// w w w.j a va 2s .com public static void main(String[] args) throws Exception { if (args.length != 1) { System.out.println("Requires path to the dataset as the first and only argument"); return; } final String datasetPath = args[0]; // Create classifiers MultiStageCascading msc = new MultiStageCascading(); J48 classifier1 = new J48(); IBk knn = new IBk(3); // Set sequence of classifiers msc.setClassifiers(new Classifier[] { classifier1, new NBTree() }); msc.setDebug(true); // Set a classifier that will classify an instance that is not classified by all other classifiers msc.setLastClassifier(knn); // First classifier will have confidence threshold 0.95 and the second one 0.97 msc.setConfidenceThresholds("0.95,0.97"); // 80% of instances in training set will be randomly selected to train j-th classifier msc.setPercentTrainingInstances(0.8); Instances dataset = DataSource.read(datasetPath); dataset.setClassIndex(dataset.numAttributes() - 1); // Create test and training sets Pair<Instances, Instances> sets = seprateTestAndTrainingSets(dataset, 0.7); Instances trainingSet = sets.getFirst(); Instances testSet = sets.getSecond(); // Build cascade classifier msc.buildClassifier(trainingSet); // Evaluate created classifier Evaluation eval = new Evaluation(trainingSet); eval.evaluateModel(msc, testSet); System.out.println(eval.toSummaryString("\nResults\n\n", false)); }
From source file:examples.TrainerFrame.java
private void jButtonTrainActionPerformed(java.awt.event.ActionEvent evt) {//GEN-FIRST:event_jButtonTrainActionPerformed //This is a temporary fix to make it appear like its finished pBar.setMaximum(7);// w w w .ja v a 2s . com pBar.setValue(0); pBar.repaint(); jLabelTrainerStatus.setText("Extracting Target Features"); //Generate Target Features String featuresTarget = null; new Thread(new TrainerFrame.thread1()).start(); try { featuresTarget = GlobalData.getFeatures(jTextFieldCallDirectory.getText()); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(1); pBar.repaint(); jLabelTrainerStatus.setText("Extracting Other Features"); //Generate Non-targe features Features String featuresOther = null; new Thread(new TrainerFrame.thread1()).start(); try { featuresOther = GlobalData.getFeatures(jTextFieldOtherSoundDirectory.getText()); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(2); pBar.repaint(); jLabelTrainerStatus.setText("Parsing Features"); //Load Target Arrf File BufferedReader readerTarget; Instances dataTarget = null; try { readerTarget = new BufferedReader(new FileReader(featuresTarget)); dataTarget = new Instances(readerTarget); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(3); pBar.repaint(); //Load Other Arrf File BufferedReader readerOther; Instances dataOther = null; try { readerOther = new BufferedReader(new FileReader(featuresOther)); dataOther = new Instances(readerOther); } catch (FileNotFoundException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(4); pBar.repaint(); jLabelTrainerStatus.setText("Training Classifier"); Instances newData = new Instances(dataTarget); FastVector typeList = new FastVector() { }; typeList.add("target"); typeList.add("other"); newData.insertAttributeAt(new Attribute("NewNominal", (java.util.List<String>) typeList), newData.numAttributes()); for (Instance instance : newData) { instance.setValue(newData.numAttributes() - 1, "target"); } dataOther.insertAttributeAt(new Attribute("NewNominal", (java.util.List<String>) typeList), dataOther.numAttributes()); for (Instance instance : dataOther) { instance.setValue(newData.numAttributes() - 1, "other"); newData.add(instance); } newData.setClassIndex(newData.numAttributes() - 1); pBar.setValue(5); pBar.repaint(); ArffSaver saver = new ArffSaver(); saver.setInstances(newData); try { saver.setFile(new File("AnimalCallTrainingFile.arff")); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } try { saver.writeBatch(); } catch (IOException ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } pBar.setValue(6); pBar.repaint(); //Train a classifier String[] options = new String[1]; options[0] = "-U"; J48 tree = new J48(); try { tree.setOptions(options); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } try { tree.buildClassifier(newData); } catch (Exception ex) { Logger.getLogger(TrainerFrame.class.getName()).log(Level.SEVERE, null, ex); } Debug.saveToFile("Classifiers/" + jTextFieldClassifierName.getText(), tree); System.out.println("classifier saved"); MyClassifier tempClass = new MyClassifier(jTextFieldClassifierName.getText()); GlobalData.classifierList.addElement(tempClass.name); pBar.setValue(7); pBar.repaint(); jLabelTrainerStatus.setText("Finished"); }
From source file:experimentalclassifier.ExperimentalClassifier.java
/** * @param args the command line arguments *//*from w w w . j a va2s .co m*/ public static void main(String[] args) throws Exception { DataSource source = new DataSource("data/iris.csv"); Instances data = source.getDataSet(); if (data.classIndex() == -1) { data.setClassIndex(data.numAttributes() - 1); } data.randomize(new Random()); String[] options = weka.core.Utils.splitOptions("-P 30"); RemovePercentage remove = new RemovePercentage(); remove.setOptions(options); remove.setInputFormat(data); Instances train = Filter.useFilter(data, remove); remove.setInvertSelection(true); remove.setInputFormat(data); Instances test = Filter.useFilter(data, remove); Classifier classifier = new HardCodedClassifier(); classifier.buildClassifier(train);//Currently, this does nothing Evaluation eval = new Evaluation(train); eval.evaluateModel(classifier, test); System.out.println(eval.toSummaryString("\nResults\n======\n", false)); }
From source file:expshell.ExpShell.java
/** * @param args the command line arguments * @throws java.lang.Exception// w ww. j a v a 2 s . c o m */ public static void main(String[] args) throws Exception { String file = "C:\\Users\\YH Jonathan Kwok\\Documents\\NetBeansProjects\\ExpShell\\src\\expshell\\iris.csv"; DataSource source = new DataSource(file); Instances data = source.getDataSet(); if (data.classIndex() == -1) data.setClassIndex(data.numAttributes() - 1); //Randomize it data.randomize(new Random(1)); RemovePercentage rp = new RemovePercentage(); rp.setPercentage(70); rp.setInputFormat(data); Instances training = Filter.useFilter(data, rp); rp.setInvertSelection(true); rp.setInputFormat(data); Instances test = Filter.useFilter(data, rp); //standardize the data Standardize filter = new Standardize(); filter.setInputFormat(training); Instances newTest = Filter.useFilter(test, filter); Instances newTraining = Filter.useFilter(training, filter); //Part 5 - Now it's a knn Classifier knn = new NeuralClassifier(); knn.buildClassifier(newTraining); Evaluation eval = new Evaluation(newTraining); eval.evaluateModel(knn, newTest); System.out.println(eval.toSummaryString("***** Overall results: *****", false)); }
From source file:eyetracker.MLPProcessor.java
public MLPProcessor() { try {/* w ww. java 2 s .c om*/ FileReader fr = new FileReader("trainingData.arff"); Instances training = new Instances(fr); training.setClassIndex(training.numAttributes() - 1); mlp = new MultilayerPerceptron(); mlp.setOptions(Utils.splitOptions("-L 0.3 -M 0.2 -N 500 -V 0 -S 0 -E 20 -H 5")); mlp.buildClassifier(training); FileReader tr = new FileReader("trainingData.arff"); Instances testdata = new Instances(tr); inst = testdata; testdata.setClassIndex(testdata.numAttributes() - 1); Evaluation eval = new Evaluation(training); eval.evaluateModel(mlp, testdata); System.out.println(eval.toSummaryString("\nResults\n*******\n", false)); tr.close(); fr.close(); } catch (FileNotFoundException e) { e.printStackTrace(); } catch (IOException e) { e.printStackTrace(); } catch (Exception e) { e.printStackTrace(); } }
From source file:fantail.algorithms.RankingByPairwiseComparison.java
License:Open Source License
@Override public void buildRanker(Instances data) throws Exception { m_Classifiers = new ArrayList<weka.classifiers.AbstractClassifier>(); m_AlgoPairs = new ArrayList<String>(); m_NumLabels = Tools.getNumberTargets(data); // build pb datasets for (int a = 0; a < m_NumLabels; a++) { for (int b = 0; b < m_NumLabels; b++) { String pairStr = a + "|" + b; if (!hasPair(m_AlgoPairs, pairStr) && a != b) { m_AlgoPairs.add(pairStr); Instances d = new Instances(data); d.setClassIndex(-1); d.deleteAttributeAt(d.numAttributes() - 1); weka.filters.unsupervised.attribute.Add add = new weka.filters.unsupervised.attribute.Add(); add.setInputFormat(d);//w w w. java2s . c o m add.setOptions(weka.core.Utils .splitOptions("-T NOM -N class -L " + ((int) a) + "," + ((int) b) + " -C last")); d = Filter.useFilter(d, add); d.setClassIndex(d.numAttributes() - 1); for (int i = 0; i < d.numInstances(); i++) { Instance metaInst = (Instance) data.instance(i); Instance inst = d.instance(i); double[] rankVector = Tools.getTargetVector(metaInst); double rank_a = rankVector[a]; double rank_b = rankVector[b]; if (rank_a < rank_b) { inst.setClassValue(0.0); } else { inst.setClassValue(1.0); } } //weka.classifiers.functions.SMO cls = new weka.classifiers.functions.SMO(); //String ops = "weka.classifiers.functions.SMO -C 1.0 -L 0.001 -P 1.0E-12 -N 0 -V -1 -W 1 -K \"weka.classifiers.functions.supportVector.RBFKernel -C 250007 -G 0.01\""; //cls.setOptions(weka.core.Utils.splitOptions(ops)); //cls.buildClassifier(d); //weka.classifiers.functions.Logistic cls = new weka.classifiers.functions.Logistic(); //weka.classifiers.trees.J48 cls = new weka.classifiers.trees.J48(); //weka.classifiers.rules.ZeroR cls = new weka.classifiers.rules.ZeroR(); weka.classifiers.trees.DecisionStump cls = new weka.classifiers.trees.DecisionStump(); cls.buildClassifier(d); m_Classifiers.add(cls); m_BaseClassifierName = cls.getClass().getSimpleName(); m_Add = add; } } } }
From source file:fantail.algorithms.RankingByPairwiseComparison.java
License:Open Source License
@Override public double[] recommendRanking(Instance testInst) throws Exception { Instances tempData = new Instances(testInst.dataset(), 0); tempData.add((Instance) testInst.copy()); // remove the relation att tempData.setClassIndex(-1); tempData.deleteAttributeAt(tempData.numAttributes() - 1); tempData = Filter.useFilter(tempData, m_Add); tempData.setClassIndex(tempData.numAttributes() - 1); double predRanking[] = new double[m_NumLabels]; for (int i = 0; i < predRanking.length; i++) { predRanking[i] = m_NumLabels - 1; }/* w ww . j a v a 2 s .c o m*/ for (int i = 0; i < m_Classifiers.size(); i++) { double predIndex = m_Classifiers.get(i).classifyInstance(tempData.instance(0)); String algoPair = m_AlgoPairs.get(i); String[] parts = algoPair.split("\\|"); int trueIndex = Integer.parseInt(parts[(int) predIndex]); predRanking[trueIndex] -= 1; } predRanking = Tools.doubleArrayToRanking(predRanking); return predRanking; }