List of usage examples for weka.core Debug saveToFile
public static boolean saveToFile(String filename, Object o)
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();//from ww w .j a v a 2 s . c om 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:clasificador.RedNeuronal.java
public void Entrenamiento(String paramNN) { try {/*from www.ja va 2s. co m*/ //aqui va a anetrenar la red neuronal con parametros para la red FileReader trainReader = new FileReader( new File(System.getProperty("user.dir") + "\\src\\clasificador\\archivos\\libro.arff")); //FileReader trainReader = new FileReader("aqui va la ruta"); //intancias //lo que vamoas a hacer en agarrar ese objeto y cargarlo dentro de nuestra clase instancias Instances trainInstance = new Instances(trainReader); trainInstance.setClassIndex(trainInstance.numAttributes() - 1);//esta fijando las etiquetas en el archivo las clases estan en el final es decir el total -1 esto es xk es un ambiento controlado //construccion de la red perceptron multicapa MultilayerPerceptron mlp = new MultilayerPerceptron(); // creo un objeto de perceptron multicapaa mlp.setOptions(Utils.splitOptions(paramNN)); //fijar los parametros de la red perceptron util es para q reciba toda la confiuguracion es proipio de weka mlp.buildClassifier(trainInstance);// la construccion se hace ya basadao en los parametron configurado //Guardar el mlp en un archivo Debug.saveToFile("TrainMLP.train", mlp); //evaluacion del entrenamiento despies solo se ocupa el trainMLp SerializedClassifier sc = new SerializedClassifier(); sc.setModelFile(new File("TrainMLP.train")); Evaluation evaluarEntrenamiento = new Evaluation(trainInstance); evaluarEntrenamiento.evaluateModel(mlp, trainInstance);//evaluando el modelo System.out.println(evaluarEntrenamiento.toSummaryString("resultado", false)); System.out.println(evaluarEntrenamiento.toMatrixString("*****************Matriz de confusion*******")); trainReader.close(); } catch (FileNotFoundException ex) { Logger.getLogger(RedNeuronal.class.getName()).log(Level.SEVERE, null, ex); } catch (IOException ex) { Logger.getLogger(RedNeuronal.class.getName()).log(Level.SEVERE, null, ex); } catch (Exception ex) { Logger.getLogger(RedNeuronal.class.getName()).log(Level.SEVERE, null, ex); } }
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 ww . ja v a2 s. c o m*/ 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"); }