applications.FoodSpectrograms.java Source code

Introduction

Here is the source code for applications.FoodSpectrograms.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 applications;

import utilities.ClassifierTools;
import utilities.ThreadedClassifierExperiment;
import weka.classifiers.Classifier;
import weka.classifiers.functions.SMO;
import weka.classifiers.functions.supportVector.PolyKernel;
import weka.classifiers.lazy.DTW_kNN;
import weka.classifiers.lazy.IBk;
import weka.classifiers.lazy.kNN;
import weka.core.Instances;
import weka.core.shapelet.QualityMeasures;
import weka.filters.NormalizeCase;
import weka.filters.timeseries.shapelet_transforms.FullShapeletTransform;
import weka.filters.timeseries.shapelet_transforms.ShapeletTransformDistCaching;

/**
 *
 * @author ajb
 */
public class FoodSpectrograms {
    public static Instances[] train, test;
    public static Instances strawberryTrain, strawberryTest;
    public static Instances wineTrain, wineTest;
    public static Instances hamTrain, hamTest;
    public static String path = "C:\\Users\\ajb\\Dropbox\\TSC Problems\\";
    public static String[] names = { "Meat", "Wine", "Ham", "Strawberry" };

    public static void loadData() {
        train = new Instances[4];
        test = new Instances[4];
        train[0] = ClassifierTools.loadData(path + "Meat\\Meat_TRAIN");
        test[0] = ClassifierTools.loadData(path + "Meat\\Meat_TEST");
        train[1] = ClassifierTools.loadData(path + "Wine\\Wine_TRAIN");
        test[1] = ClassifierTools.loadData(path + "Wine\\Wine_TEST");
        train[2] = ClassifierTools.loadData(path + "Ham\\Ham_TRAIN");
        test[2] = ClassifierTools.loadData(path + "Ham\\Ham_TEST");
        train[3] = ClassifierTools.loadData(path + "Strawberry\\Strawberry_TRAIN");
        test[3] = ClassifierTools.loadData(path + "Strawberry\\Strawberry_TEST");

        NormalizeCase nc = new NormalizeCase();
        for (int i = 0; i < test.length; i++) {
            try {
                train[i] = nc.process(train[i]);
                test[i] = nc.process(test[i]);
            } catch (Exception e) {
                System.out.println(" Errereere");
                System.exit(0);

            }
        }
    }

    public static void shapeletClassifier() {
        int nosExp = 3;
        ThreadedClassifierExperiment[] runs = new ThreadedClassifierExperiment[nosExp];
        Thread[] threads = new Thread[nosExp];
        for (int i = 0; i < nosExp; i++) {
            Classifier c = new SMO();
            PolyKernel kernel = new PolyKernel();
            kernel.setExponent(1);
            ((SMO) c).setKernel(kernel);

            FullShapeletTransform s = new FullShapeletTransform();
            s.setDebug(false);
            s.setNumberOfShapelets(train[i].numAttributes() / 2);
            int minLength = 5;
            int maxLength = train[i].numAttributes() / 4;
            //       int maxLength=(train.numAttributes()-1)/10;
            s.setShapeletMinAndMax(minLength, maxLength);
            s.setQualityMeasure(QualityMeasures.ShapeletQualityChoice.F_STAT);
            s.turnOffLog();
            runs[i] = new ThreadedClassifierExperiment(train[i], test[i], c);
            runs[i].setTransform(s);
            threads[i] = new Thread(runs[i]);
        }
        for (int i = 0; i < nosExp; i++)
            threads[i].start();

        try {
            for (int i = 0; i < nosExp; i++)
                threads[i].join();
        } catch (InterruptedException e) {
            System.out.println(" Interrupted!!");
        }
        for (int i = 0; i < nosExp; i++)
            System.out.println(" ED Accuracy for " + names[i] + " is " + runs[i].getTestAccuracy());

    }

    public static void baselineClassifier() {
        int nosExp = 4;
        ThreadedClassifierExperiment[] runs = new ThreadedClassifierExperiment[nosExp];
        Thread[] threads = new Thread[nosExp];

        for (int i = 0; i < nosExp; i++) {
            DTW_kNN c = new DTW_kNN();
            c.setMaxR(1);
            c.optimiseWindow(true);

            runs[i] = new ThreadedClassifierExperiment(train[i], test[i], c);
            threads[i] = new Thread(runs[i]);
        }
        for (int i = 0; i < nosExp; i++)
            threads[i].start();

        try {
            for (int i = 0; i < nosExp; i++)
                threads[i].join();
        } catch (InterruptedException e) {
            System.out.println(" Interrupted!!");
        }
        for (int i = 0; i < nosExp; i++)
            System.out.println(" DTWCV Accuracy for " + names[i] + " is " + runs[i].getTestAccuracy());

    }

    public static void main(String[] args) {
        loadData();
        //       baselineClassifier();
        shapeletClassifier();
    }
}