ml_project.ML_Project.java Source code

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Here is the source code for ml_project.ML_Project.java

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/*
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package ml_project;

import weka.core.*;
import weka.core.FastVector;
import weka.classifiers.meta.FilteredClassifier;
import java.util.List;
import java.util.ArrayList;
import java.io.*;

/**
 *
 * @author madhumita
 */

/**
 * @param args the command line arguments
 */

public class ML_Project {

    String testInput;
    Instances instances;
    FilteredClassifier classifier;

    public String classify(String testFileName, String modelFileName) throws Exception {
        BufferedReader reader = new BufferedReader(new FileReader(testFileName));
        String str;
        testInput = "";
        while ((str = reader.readLine()) != null) {
            testInput = testInput + " " + str;
        }
        reader.close();

        ObjectInputStream in = new ObjectInputStream(new FileInputStream(modelFileName));
        Object tmp = in.readObject();
        classifier = (FilteredClassifier) tmp;
        in.close();

        Attribute testInputAttr = new Attribute("testInput", (FastVector) null);

        FastVector classVals = new FastVector(6);
        classVals.addElement("Course");
        classVals.addElement("Department");
        classVals.addElement("Faculty");
        classVals.addElement("Project");
        classVals.addElement("Staff");
        classVals.addElement("Student");
        Attribute classAttr = new Attribute("class", classVals);

        FastVector attrs = new FastVector(2);
        attrs.addElement(testInputAttr);
        attrs.addElement(classAttr);

        instances = new Instances("Test relation", attrs, 1);
        instances.setClassIndex(1);
        Instance instance = new Instance(2);
        instance.setValue(testInputAttr, testInput);
        instances.add(instance);

        double pred = classifier.classifyInstance(instances.instance(0));
        return instances.classAttribute().value((int) pred);
    }

    public static void main(String[] args) throws Exception {
        ML_Project c = new ML_Project();
        if (args.length < 2) {
            System.out.println("plaese enter <test file path> <model path>");
        } else {
            // System.out.println("Class Predicted: "+c.classify("F:\\http_^^www.cs.utexas.edu^users^almstrum^classes^cs336^fall96^","F:\\smo3.model"));
            System.out.println("Class Predicted: " + c.classify(args[0], args[1]));
        }
    }
}