predictforex.ANN.java Source code

Introduction

Here is the source code for predictforex.ANN.java

Source

/*
 * To change this license header, choose License Headers in Project Properties.
 * To change this template file, choose Tools | Templates
 * and open the template in the editor.
 */

package predictforex;

/**
 *
 * @author Melvin
 */
import java.util.Random;
import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.functions.MultilayerPerceptron;
import weka.classifiers.meta.FilteredClassifier;
import weka.core.Instances;
import weka.core.SerializationHelper;
import weka.core.converters.ConverterUtils.DataSink;
import weka.core.converters.ConverterUtils.DataSource;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

public class ANN {
    private static Instances dataset;
    private static Classifier clasifier;
    private static Evaluation eval;
    private static String[] args;

    static void ReadDataset(String FilePath) throws Exception {
        dataset = DataSource.read(FilePath);
        dataset.setClassIndex(dataset.numAttributes() - 1);
    }

    /* Multilayer Perceptron */
    static void TenFoldTrain_aNN() throws Exception {
        eval = new Evaluation(dataset);
        MultilayerPerceptron tree = new MultilayerPerceptron();
        eval.crossValidateModel(tree, dataset, 10, new Random(1));
        System.out.println(eval.toSummaryString("Results ANN tenfold \n", false));
        //        System.out.println(eval.toClassDetailsString());
        //        System.out.println(eval.fMeasure(1) + " "+eval.precision(1)+" "+eval.recall(1));
        //        System.out.println(eval.toMatrixString());
    }

    static void FullTraining_aNN() throws Exception {
        Classifier cls = new MultilayerPerceptron();
        cls.buildClassifier(dataset);
        eval = new Evaluation(dataset);
        eval.evaluateModel(cls, dataset);
        System.out.println(eval.toSummaryString("Results ANN full training \n", false));
        System.out.println(eval.toClassDetailsString());
        System.out.println(eval.fMeasure(1) + " " + eval.precision(1) + " " + eval.recall(1));
        System.out.println(eval.toMatrixString());
    }

    static void SaveModel(Classifier cls) throws Exception {
        clasifier = cls;
        clasifier.buildClassifier(dataset);
        SerializationHelper.write(cls.getClass().getSimpleName() + ".model", cls);
    }

    static void ReadModel(String Filepath) throws Exception {
        clasifier = (Classifier) SerializationHelper.read(Filepath);
    }

    static void Classify(String FilePath) throws Exception {
        String[] options = new String[2];
        options[0] = "-R"; // "range"
        options[1] = "1"; // first attribute
        Remove remove = new Remove(); // new instance of filter

        Instances unlabeled = DataSource.read(FilePath);
        Instances labeled = new Instances(unlabeled);
        remove.setOptions(options); // set options
        remove.setInputFormat(unlabeled); // inform filter about dataset **AFTER** setting options
        unlabeled = Filter.useFilter(unlabeled, remove); // apply filter
        unlabeled.setClassIndex(unlabeled.numAttributes() - 1);
        labeled.setClassIndex(unlabeled.numAttributes());
        for (int i = 0; i < unlabeled.numInstances(); i++) {
            double clsLabel = clasifier.classifyInstance(unlabeled.instance(i));
            labeled.instance(i).setClassValue(clsLabel);
            //            System.out.print("timestamp: " + labeled.instance(i).stringValue(0));
            //            System.out.print(", open: " + labeled.instance(i).value(1));
            //            System.out.print(", high: " + labeled.instance(i).value(2));
            //            System.out.print(", low: " + labeled.instance(i).value(3));
            //            System.out.print(", close: " + labeled.instance(i).value(4));
            //            System.out.println(", predicted close: " + labeled.instance(i).value(5));
        }
        DataSink.write("arff_files/" + "Labeled_" + ForexPredictor.unlabeledFilename + ".arff", labeled);

    }

    /**
     * @param args the command line arguments
     * @throws java.lang.Exception
     */
    //    public static void main(String[] args) throws Exception {
    //        // membaca dataset awal
    //        ReadDataset("forexPrice.arff");
    //        
    //        // membuat model dan menyimpannya
    //        SaveModel(new MultilayerPerceptron());
    //
    //        /* aNN */
    //        TenFoldTrain_aNN();
    //        FullTraining_aNN();
    //        
    //        // membaca model yang telah disimpan pada file eksternal
    //        ReadModel("MultilayerPerceptron.model");
    //        
    //        // melakukan klasifikasi terhadap suatu dataset yang belum terlabel berdasarkan model yang telah diload
    //        Classify("forexPrice-unlabeled.arff");
    //        
    //    }

}