predictor.Predictor.java Source code

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Here is the source code for predictor.Predictor.java

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package predictor;

import conf.Conf;
import java.io.*;

import weka.core.*;
import weka.core.Instances;
import weka.classifiers.Evaluation;
import weka.classifiers.*;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.core.converters.ConverterUtils.DataSource;

/**
 *
 * @author francisco
 */
public class Predictor {
    private static Conf configuration;

    static {
        configuration = Conf.getConfiguration();
    }

    public static void multilayerPerceptron() throws Exception {

        DataSource train = new DataSource(configuration.getWorkspace() + "train_common.arff");
        DataSource test = new DataSource(configuration.getWorkspace() + "test_common.arff");

        Instances trainInstances = train.getDataSet();
        Instances testInstances = test.getDataSet();

        //last attribute classify
        trainInstances.setClassIndex(trainInstances.numAttributes() - 1);
        testInstances.setClassIndex(testInstances.numAttributes() - 1);
        //        
        //        Classifier cModel = (Classifier)new MultilayerPerceptron();  
        //        cModel.buildClassifier(trainInstances);  
        //
        //        weka.core.SerializationHelper.write("/some/where/nBayes.model", cModel);
        //
        //        Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/nBayes.model");
        //
        //        // Test the model
        //        Evaluation eTest = new Evaluation(trainInstances);
        //        eTest.evaluateModel(cls, testInstances);

        MultilayerPerceptron mlp = new MultilayerPerceptron();
        mlp.buildClassifier(trainInstances);
        mlp.setHiddenLayers(configuration.getHiddenLayers());
        mlp.setLearningRate(configuration.getLearningRate());
        mlp.setTrainingTime(configuration.getEpocs());
        mlp.setMomentum(configuration.getMomentum());

        // train classifier
        Classifier cls = new MultilayerPerceptron();
        cls.buildClassifier(trainInstances);

        // evaluate classifier and print some statistics
        Evaluation eval = new Evaluation(trainInstances);
        eval.evaluateModel(cls, testInstances);

        System.out.println(eval.toSummaryString());
    }

    public static void main(String[] args) throws Exception {

        multilayerPerceptron();
        //        System.out.println("TestPredictor");
        //        DataSource train = new DataSource("/home/francisco/Dropbox/TFG/data/wekafiles/train_borr.arff");
        //        DataSource test = new DataSource("/home/francisco/Dropbox/TFG/data/wekafiles/test_borr.arff");
        //
        //
        //
        //        Instances trainInstances = train.getDataSet();
        //        Instances testInstances = test.getDataSet();
        //
        //        //last attribute classify
        //        trainInstances.setClassIndex(trainInstances.numAttributes() - 1);
        //        testInstances.setClassIndex(testInstances.numAttributes() - 1);
        ////        
        ////        Classifier cModel = (Classifier)new MultilayerPerceptron();  
        ////        cModel.buildClassifier(trainInstances);  
        ////
        ////        weka.core.SerializationHelper.write("/some/where/nBayes.model", cModel);
        ////
        ////        Classifier cls = (Classifier) weka.core.SerializationHelper.read("/some/where/nBayes.model");
        ////
        ////        // Test the model
        ////        Evaluation eTest = new Evaluation(trainInstances);
        ////        eTest.evaluateModel(cls, testInstances);
        //
        //
        //
        //        MultilayerPerceptron mlp = new MultilayerPerceptron();
        //        mlp.buildClassifier(trainInstances);
        //        mlp.setHiddenLayers("a");
        //        mlp.setLearningRate(0.3);    
        //        mlp.setTrainingTime(500);
        //        mlp.setMomentum(0.2);
        //
        //        // train classifier
        //        Classifier cls = new MultilayerPerceptron();  
        //        cls.buildClassifier(trainInstances);   
        //       
        //
        //        // evaluate classifier and print some statistics
        //        Evaluation eval = new Evaluation(trainInstances);
        //        eval.evaluateModel(cls, testInstances);        
        //        
        //        System.out.println(eval.toSummaryString());
        //
    }
}