WekaStocks.java

package weka;

import weka.classifiers.meta.FilteredClassifier;
import weka.classifiers.trees.J48;
import weka.core.Instances;
import weka.filters.unsupervised.attribute.Remove;

import java.io.BufferedReader;
import java.io.FileReader;

/**
 * Stock analysis using Weka
 *
 * <p/>
 * Copyright 2000-2012 by Mark Watson. All rights reserved.
 * <p/>
 * This software is can be used under either of the following licenses:
 * <p/>
 * 1. LGPL v3<br/>
 * 2. Apache 2
 * <p/>
 */

public class WekaStocks {

	/**
	 * @param args
	 * @throws Exception 
	 */
	public static void main(String[] args) throws Exception {
		Instances training_data = new Instances(
                new BufferedReader(
                      new FileReader("test_data/stock_training_data.arff")));
		training_data.setClassIndex(training_data.numAttributes() - 1);

		Instances testing_data = new Instances(
                new BufferedReader(
                      new FileReader("test_data/stock_testing_data.arff")));
		testing_data.setClassIndex(training_data.numAttributes() - 1);

		String summary = training_data.toSummaryString();
		int number_samples = training_data.numInstances();
		int number_attributes_per_sample = training_data.numAttributes();
        System.out.println("Number of attributes in model = " + number_attributes_per_sample);
        System.out.println("Number of samples = " + number_samples);
        System.out.println("Summary: " + summary);
        System.out.println();
        
        // a classifier for decision trees:
        J48 j48 = new J48();
        
        // filter for removing samples:
        Remove rm = new Remove();
        rm.setAttributeIndices("1");  // remove 1st attribute

        // filtered classifier
        FilteredClassifier fc = new FilteredClassifier();
        fc.setFilter(rm);
        fc.setClassifier(j48);
        // train using stock_training_data.arff:
        fc.buildClassifier(training_data);
        // test using stock_testing_data.arff:
        for (int i = 0; i < testing_data.numInstances(); i++) {
          double pred = fc.classifyInstance(testing_data.instance(i));
          System.out.print("given value: " + testing_data.classAttribute().value((int)testing_data.instance(i).classValue()));
          System.out.println(". predicted value: " + testing_data.classAttribute().value((int) pred));
        }

	}

}