List of usage examples for weka.core Instances setClassIndex
public void setClassIndex(int classIndex)
From source file:anndl.Anndl.java
private static void buildModel(InputStream input) throws Exception { ANNDLLexer lexer = new ANNDLLexer(new ANTLRInputStream(input)); CommonTokenStream tokens = new CommonTokenStream(lexer); ANNDLParser parser = new ANNDLParser(tokens); ParseTree tree = parser.model();/*w ww . ja va 2 s.c o m*/ ModelVisitor visitor = new ModelVisitor(); ModelClassifier themodel = (ModelClassifier) visitor.visit(tree); //themodel.PrintInfo(); themodel.extracthidden(); System.out.println("Membaca File Training..."); DataSource trainingsoure = new DataSource(themodel.filetraining); Instances trainingdata = trainingsoure.getDataSet(); if (trainingdata.classIndex() == -1) { trainingdata.setClassIndex(trainingdata.numAttributes() - 1); } System.out.println("Melakukan konfigurasi ANN ... "); MultilayerPerceptron mlp = new MultilayerPerceptron(); mlp.setLearningRate(themodel.learningrate); mlp.setMomentum(themodel.momentum); mlp.setTrainingTime(themodel.epoch); mlp.setHiddenLayers(themodel.hidden); System.out.println("Melakukan Training data ..."); mlp.buildClassifier(trainingdata); Debug.saveToFile(themodel.namamodel + ".model", mlp); System.out.println("\n~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .."); System.out.println("Model ANN Berhasil Diciptakan dengan nama file : " + themodel.namamodel + ".model"); System.out.println("~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. ~~ .. \n"); }
From source file:ANN_Single.SinglelayerPerceptron.java
public static void main(String[] args) throws Exception { ConverterUtils.DataSource source = new ConverterUtils.DataSource( ("D:\\Program Files\\Weka-3-8\\data\\diabetes.arff")); Instances train = source.getDataSet(); Normalize nm = new Normalize(); nm.setInputFormat(train);/*ww w . ja v a 2 s . c o m*/ train = Filter.useFilter(train, nm); train.setClassIndex(train.numAttributes() - 1); System.out.println(); // System.out.println(i + " "+0.8); SinglelayerPerceptron slp = new SinglelayerPerceptron(train, 0.1, 5000); slp.buildClassifier(train); Evaluation eval = new Evaluation(train); // eval.crossValidateModel(slp, train, 10, new Random(1)); eval.evaluateModel(slp, train); System.out.println(eval.toSummaryString()); System.out.print(eval.toMatrixString()); }
From source file:ANN_single2.MultilayerPerceptron.java
public static void main(String[] args) throws Exception { ConverterUtils.DataSource source = new ConverterUtils.DataSource( ("D:\\Program Files\\Weka-3-8\\data\\Team.arff")); Instances train = source.getDataSet(); Normalize nm = new Normalize(); nm.setInputFormat(train);//from ww w. j ava 2s.c o m train = Filter.useFilter(train, nm); train.setClassIndex(train.numAttributes() - 1); MultilayerPerceptron slp = new MultilayerPerceptron(train, 13, 0.1, 0.5); // slp.buildClassifier(train); Evaluation eval = new Evaluation(train); eval.crossValidateModel(slp, train, 10, new Random(1)); // eval.evaluateModel(slp, train); System.out.println(eval.toSummaryString()); System.out.println(eval.toMatrixString()); }
From source file:ANN_single2.SinglelayerPerceptron.java
public static void main(String[] args) throws Exception { ConverterUtils.DataSource source = new ConverterUtils.DataSource( ("D:\\Program Files\\Weka-3-8\\data\\Team.arff")); Instances train = source.getDataSet(); Normalize nm = new Normalize(); nm.setInputFormat(train);// ww w . ja va 2s .c o m train = Filter.useFilter(train, nm); train.setClassIndex(train.numAttributes() - 1); for (int i = 100; i < 3000; i += 100) { for (double j = 0.01; j < 1; j += 0.01) { System.out.println(i + " " + j); SinglelayerPerceptron slp = new SinglelayerPerceptron(i, j, 0.00); slp.buildClassifier(train); Evaluation eval = new Evaluation(train); // eval.crossValidateModel(slp, train,10, new Random(1)); eval.evaluateModel(slp, train); System.out.println(eval.toSummaryString()); System.out.println(eval.toMatrixString()); } } }
From source file:ap.mavenproject1.HelloWeka.java
public static void main(String args[]) { Instances data = null; ArffLoader loader = new ArffLoader(); try {/*from w w w . j a v a 2 s . com*/ loader.setFile(new File("C:\\Users\\USER\\Desktop\\data.arff")); data = loader.getDataSet(); data.setClassIndex(data.numAttributes() - 1); } catch (IOException ex) { Logger.getLogger(HelloWeka.class.getName()).log(Level.SEVERE, null, ex); } Apriori apriori = new Apriori(); try { NumericToNominal numericToNominal = new NumericToNominal(); numericToNominal.setInputFormat(data); Instances nominalData = Filter.useFilter(data, numericToNominal); apriori.buildAssociations(nominalData); FastVector[] allTheRules; allTheRules = apriori.getAllTheRules(); for (int i = 0; i < allTheRules.length; i++) { System.out.println(allTheRules[i]); } // BufferedWriter writer = new BufferedWriter(new FileWriter("./output.arff")); // writer.write(nominalData.toString()); // writer.flush(); // writer.close(); } catch (Exception ex) { Logger.getLogger(HelloWeka.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:asap.CrossValidation.java
/** * * @param dataInput//from w w w . jav a2 s . com * @param classIndex * @param removeIndices * @param cls * @param seed * @param folds * @param modelOutputFile * @return * @throws Exception */ public static String performCrossValidation(String dataInput, String classIndex, String removeIndices, AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception { PerformanceCounters.startTimer("cross-validation ST"); PerformanceCounters.startTimer("cross-validation init ST"); // loads data and set class index Instances data = DataSource.read(dataInput); String clsIndex = classIndex; switch (clsIndex) { case "first": data.setClassIndex(0); break; case "last": data.setClassIndex(data.numAttributes() - 1); break; default: try { data.setClassIndex(Integer.parseInt(clsIndex) - 1); } catch (NumberFormatException e) { data.setClassIndex(data.attribute(clsIndex).index()); } break; } Remove removeFilter = new Remove(); removeFilter.setAttributeIndices(removeIndices); removeFilter.setInputFormat(data); data = Filter.useFilter(data, removeFilter); // randomize data Random rand = new Random(seed); Instances randData = new Instances(data); randData.randomize(rand); if (randData.classAttribute().isNominal()) { randData.stratify(folds); } // perform cross-validation and add predictions Evaluation eval = new Evaluation(randData); Instances trainSets[] = new Instances[folds]; Instances testSets[] = new Instances[folds]; Classifier foldCls[] = new Classifier[folds]; for (int n = 0; n < folds; n++) { trainSets[n] = randData.trainCV(folds, n); testSets[n] = randData.testCV(folds, n); foldCls[n] = AbstractClassifier.makeCopy(cls); } PerformanceCounters.stopTimer("cross-validation init ST"); PerformanceCounters.startTimer("cross-validation folds+train ST"); //paralelize!!:-------------------------------------------------------------- for (int n = 0; n < folds; n++) { Instances train = trainSets[n]; Instances test = testSets[n]; // the above code is used by the StratifiedRemoveFolds filter, the // code below by the Explorer/Experimenter: // Instances train = randData.trainCV(folds, n, rand); // build and evaluate classifier Classifier clsCopy = foldCls[n]; clsCopy.buildClassifier(train); eval.evaluateModel(clsCopy, test); } cls.buildClassifier(data); //until here!----------------------------------------------------------------- PerformanceCounters.stopTimer("cross-validation folds+train ST"); PerformanceCounters.startTimer("cross-validation post ST"); // output evaluation String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " " + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: " + folds + "\n" + "Seed: " + seed + "\n" + "\n" + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n"; if (!modelOutputFile.isEmpty()) { SerializationHelper.write(modelOutputFile, cls); } PerformanceCounters.stopTimer("cross-validation post ST"); PerformanceCounters.stopTimer("cross-validation ST"); return out; }
From source file:asap.CrossValidation.java
/** * * @param dataInput/* w w w. j av a2 s. c o m*/ * @param classIndex * @param removeIndices * @param cls * @param seed * @param folds * @param modelOutputFile * @return * @throws Exception */ public static String performCrossValidationMT(String dataInput, String classIndex, String removeIndices, AbstractClassifier cls, int seed, int folds, String modelOutputFile) throws Exception { PerformanceCounters.startTimer("cross-validation MT"); PerformanceCounters.startTimer("cross-validation init MT"); // loads data and set class index Instances data = DataSource.read(dataInput); String clsIndex = classIndex; switch (clsIndex) { case "first": data.setClassIndex(0); break; case "last": data.setClassIndex(data.numAttributes() - 1); break; default: try { data.setClassIndex(Integer.parseInt(clsIndex) - 1); } catch (NumberFormatException e) { data.setClassIndex(data.attribute(clsIndex).index()); } break; } Remove removeFilter = new Remove(); removeFilter.setAttributeIndices(removeIndices); removeFilter.setInputFormat(data); data = Filter.useFilter(data, removeFilter); // randomize data Random rand = new Random(seed); Instances randData = new Instances(data); randData.randomize(rand); if (randData.classAttribute().isNominal()) { randData.stratify(folds); } // perform cross-validation and add predictions Evaluation eval = new Evaluation(randData); List<Thread> foldThreads = (List<Thread>) Collections.synchronizedList(new LinkedList<Thread>()); List<FoldSet> foldSets = (List<FoldSet>) Collections.synchronizedList(new LinkedList<FoldSet>()); for (int n = 0; n < folds; n++) { foldSets.add(new FoldSet(randData.trainCV(folds, n), randData.testCV(folds, n), AbstractClassifier.makeCopy(cls))); if (n < Config.getNumThreads() - 1) { Thread foldThread = new Thread(new CrossValidationFoldThread(n, foldSets, eval)); foldThreads.add(foldThread); } } PerformanceCounters.stopTimer("cross-validation init MT"); PerformanceCounters.startTimer("cross-validation folds+train MT"); //paralelize!!:-------------------------------------------------------------- if (Config.getNumThreads() > 1) { for (Thread foldThread : foldThreads) { foldThread.start(); } } else { //use the current thread to run the cross-validation instead of using the Thread instance created here: new CrossValidationFoldThread(0, foldSets, eval).run(); } cls.buildClassifier(data); for (Thread foldThread : foldThreads) { foldThread.join(); } //until here!----------------------------------------------------------------- PerformanceCounters.stopTimer("cross-validation folds+train MT"); PerformanceCounters.startTimer("cross-validation post MT"); // evaluation for output: String out = "\n" + "=== Setup ===\n" + "Classifier: " + cls.getClass().getName() + " " + Utils.joinOptions(cls.getOptions()) + "\n" + "Dataset: " + data.relationName() + "\n" + "Folds: " + folds + "\n" + "Seed: " + seed + "\n" + "\n" + eval.toSummaryString("=== " + folds + "-fold Cross-validation ===", false) + "\n"; if (!modelOutputFile.isEmpty()) { SerializationHelper.write(modelOutputFile, cls); } PerformanceCounters.stopTimer("cross-validation post MT"); PerformanceCounters.stopTimer("cross-validation MT"); return out; }
From source file:assign00.ExperimentShell.java
/** * @param args the command line arguments *//*from w w w. java2 s. c o m*/ public static void main(String[] args) throws Exception { DataSource source = new DataSource(file); Instances dataSet = source.getDataSet(); //Set up data dataSet.setClassIndex(dataSet.numAttributes() - 1); dataSet.randomize(new Random(1)); //determine sizes int trainingSize = (int) Math.round(dataSet.numInstances() * .7); int testSize = dataSet.numInstances() - trainingSize; Instances training = new Instances(dataSet, 0, trainingSize); Instances test = new Instances(dataSet, trainingSize, testSize); Standardize standardizedData = new Standardize(); standardizedData.setInputFormat(training); Instances newTest = Filter.useFilter(test, standardizedData); Instances newTraining = Filter.useFilter(training, standardizedData); NeuralNetworkClassifier NWC = new NeuralNetworkClassifier(); NWC.buildClassifier(newTraining); Evaluation eval = new Evaluation(newTraining); eval.evaluateModel(NWC, newTest); System.out.println(eval.toSummaryString("\nResults\n======\n", false)); }
From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java
public Instances loadTrainingData() { try {/*from w w w. j a v a 2 s . c o m*/ //DataSource source = new DataSource("C:\\Users\\David\\Documents\\Datalogi\\TU Wien\\2014W_Advanced Internet Computing\\Labs\\aic_group2_topic1\\Other Stuff\\training_dataset.arff"); DataSource source = new DataSource( "C:\\Users\\David\\Documents\\Datalogi\\TU Wien\\2014W_Advanced Internet Computing\\Labs\\Data sets\\labelled.arff"); // System.out.println("Data Structure pre processing: " + source.getStructure()); Instances data = source.getDataSet(); // Get and save the dataStructure of the dataset dataStructure = source.getStructure(); try { // Save the datastructure to file // serialize dataStructure weka.core.SerializationHelper.write(modelDir + algorithm + ".dataStruct", dataStructure); } catch (Exception ex) { Logger.getLogger(TwitterClassifer.class.getName()).log(Level.SEVERE, null, ex); } // Set class index data.setClassIndex(2); // Giving attributes unique names before converting strings data.renameAttribute(2, "class_attr"); data.renameAttribute(0, "twitter_id"); // Convert String attribute to Words using filter StringToWordVector filter = new StringToWordVector(); filter.setInputFormat(data); Instances filteredData = Filter.useFilter(data, filter); System.out.println("filteredData struct: " + filteredData.attribute(0)); System.out.println("filteredData struct: " + filteredData.attribute(1)); System.out.println("filteredData struct: " + filteredData.attribute(2)); return filteredData; } catch (Exception ex) { System.out.println("Error loading training set: " + ex.toString()); return null; //Logger.getLogger(Trainer.class.getName()).log(Level.SEVERE, null, ex); } }
From source file:at.aictopic1.sentimentanalysis.machinelearning.impl.TwitterClassifer.java
public Integer classify(Tweet[] tweets) { // TEST/* w ww. ja v a2s. c om*/ // Generate two tweet examples Tweet exOne = new Tweet("This is good and fantastic"); exOne.setPreprocessedText("This is good and fantastic"); Tweet exTwo = new Tweet("Horribly, terribly bad and more"); exTwo.setPreprocessedText("Horribly, terribly bad and more"); Tweet exThree = new Tweet( "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh."); exThree.setPreprocessedText( "I want to update lj and read my friends list, but I\\'m groggy and sick and blargh."); Tweet exFour = new Tweet("bad hate worst sick"); exFour.setPreprocessedText("bad hate worst sick"); tweets = new Tweet[] { exOne, exTwo, exThree, exFour }; // TEST // Load model // loadModel(); // Convert Tweet to Instance type // Get String Data // Create attributes for the Instances set Attribute twitter_id = new Attribute("twitter_id"); // Attribute body = new Attribute("body"); FastVector classVal = new FastVector(2); classVal.addElement("pos"); classVal.addElement("neg"); Attribute class_attr = new Attribute("class_attr", classVal); // Add them to a list FastVector attrVector = new FastVector(3); // attrVector.addElement(twitter_id); // attrVector.addElement(new Attribute("body", (FastVector) null)); // attrVector.addElement(class_attr); // Get the number of tweets and then create predictSet int numTweets = tweets.length; Enumeration structAttrs = dataStructure.enumerateAttributes(); // ArrayList<Attribute> attrList = new ArrayList<Attribute>(dataStructure.numAttributes()); while (structAttrs.hasMoreElements()) { attrVector.addElement((Attribute) structAttrs.nextElement()); } Instances predictSet = new Instances("predictInstances", attrVector, numTweets); // Instances predictSet = new Instances(dataStructure); predictSet.setClassIndex(2); // init prediction double prediction = -1; System.out.println("PredictSet matches source structure: " + predictSet.equalHeaders(dataStructure)); System.out.println("PredSet struct: " + predictSet.attribute(0)); System.out.println("PredSet struct: " + predictSet.attribute(1)); System.out.println("PredSet struct: " + predictSet.attribute(2)); // Array to return predictions //double[] tweetsClassified = new double[2][numTweets]; //List<Integer, Double> tweetsClass = new ArrayList<Integer, Double>(numTweets); for (int i = 0; i < numTweets; i++) { String content = (String) tweets[i].getPreprocessedText(); System.out.println("Tweet content: " + content); // attrList Instance tweetInstance = new Instance(predictSet.numAttributes()); tweetInstance.setDataset(predictSet); tweetInstance.setValue(predictSet.attribute(0), i); tweetInstance.setValue(predictSet.attribute(1), content); tweetInstance.setClassMissing(); predictSet.add(tweetInstance); try { // Apply string filter StringToWordVector filter = new StringToWordVector(); filter.setInputFormat(predictSet); Instances filteredPredictSet = Filter.useFilter(predictSet, filter); // Apply model prediction = trainedModel.classifyInstance(filteredPredictSet.instance(i)); filteredPredictSet.instance(i).setClassValue(prediction); System.out.println("Classification: " + filteredPredictSet.instance(i).toString()); System.out.println("Prediction: " + prediction); } catch (Exception ex) { Logger.getLogger(TwitterClassifer.class.getName()).log(Level.SEVERE, null, ex); } } return 0; }