Java examples for Machine Learning AI:weka
A little demo java program for using WEKA
/*//from w w w . java2 s . c o m * This program is free software; you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Free Software Foundation; either version 2 of the License, or * (at your option) any later version. * * This program is distributed in the hope that it will be useful, * but WITHOUT ANY WARRANTY; without even the implied warranty of * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * GNU General Public License for more details. * * You should have received a copy of the GNU General Public License * along with this program; if not, write to the Free Software * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. */ import weka.classifiers.Classifier; import weka.classifiers.Evaluation; import weka.core.Instances; import weka.core.OptionHandler; import weka.core.Utils; import weka.filters.Filter; import java.io.FileReader; import java.io.BufferedReader; import java.util.Vector; /** * A little demo java program for using WEKA.<br/> * Check out the Evaluation class for more details. * * @author FracPete (fracpete at waikato dot ac dot nz) * @see Evaluation */ public class WekaDemo { /** the classifier used internally */ protected Classifier m_Classifier = null; /** the filter to use */ protected Filter m_Filter = null; /** the training file */ protected String m_TrainingFile = null; /** the training instances */ protected Instances m_Training = null; /** for evaluating the classifier */ protected Evaluation m_Evaluation = null; /** * initializes the demo */ public WekaDemo() { super(); } /** * sets the classifier to use * @param name the classname of the classifier * @param options the options for the classifier */ public void setClassifier(String name, String[] options) throws Exception { m_Classifier = Classifier.forName(name, options); } /** * sets the filter to use * @param name the classname of the filter * @param options the options for the filter */ public void setFilter(String name, String[] options) throws Exception { m_Filter = (Filter) Class.forName(name).newInstance(); if (m_Filter instanceof OptionHandler) ((OptionHandler) m_Filter).setOptions(options); } /** * sets the file to use for training */ public void setTraining(String name) throws Exception { m_TrainingFile = name; m_Training = new Instances(new BufferedReader(new FileReader( m_TrainingFile))); m_Training.setClassIndex(m_Training.numAttributes() - 1); } /** * runs 10fold CV over the training file */ public void execute() throws Exception { // run filter m_Filter.setInputFormat(m_Training); Instances filtered = Filter.useFilter(m_Training, m_Filter); // train classifier on complete file for tree m_Classifier.buildClassifier(filtered); // 10fold CV with seed=1 m_Evaluation = new Evaluation(filtered); m_Evaluation.crossValidateModel(m_Classifier, filtered, 10, m_Training.getRandomNumberGenerator(1)); } /** * outputs some data about the classifier */ public String toString() { StringBuffer result; result = new StringBuffer(); result.append("Weka - Demo\n===========\n\n"); result.append("Classifier...: " + m_Classifier.getClass().getName() + " " + Utils.joinOptions(m_Classifier.getOptions()) + "\n"); if (m_Filter instanceof OptionHandler) result.append("Filter.......: " + m_Filter.getClass().getName() + " " + Utils.joinOptions(((OptionHandler) m_Filter) .getOptions()) + "\n"); else result.append("Filter.......: " + m_Filter.getClass().getName() + "\n"); result.append("Training file: " + m_TrainingFile + "\n"); result.append("\n"); result.append(m_Classifier.toString() + "\n"); result.append(m_Evaluation.toSummaryString() + "\n"); try { result.append(m_Evaluation.toMatrixString() + "\n"); } catch (Exception e) { e.printStackTrace(); } try { result.append(m_Evaluation.toClassDetailsString() + "\n"); } catch (Exception e) { e.printStackTrace(); } return result.toString(); } /** * returns the usage of the class */ public static String usage() { return "\nusage:\n " + WekaDemo.class.getName() + " CLASSIFIER <classname> [options] \n" + " FILTER <classname> [options]\n" + " DATASET <trainingfile>\n\n" + "e.g., \n" + " java -classpath \".:weka.jar\" WekaDemo \n" + " CLASSIFIER weka.classifiers.trees.J48 -U \n" + " FILTER weka.filters.unsupervised.instance.Randomize \n" + " DATASET iris.arff\n"; } /** * runs the program, the command line looks like this:<br/> * WekaDemo CLASSIFIER classname [options] * FILTER classname [options] * DATASET filename * <br/> * e.g., <br/> * java -classpath ".:weka.jar" WekaDemo \<br/> * CLASSIFIER weka.classifiers.trees.J48 -U \<br/> * FILTER weka.filters.unsupervised.instance.Randomize \<br/> * DATASET iris.arff<br/> */ public static void main(String[] args) throws Exception { WekaDemo demo; if (args.length < 6) { System.out.println(WekaDemo.usage()); System.exit(1); } // parse command line String classifier = ""; String filter = ""; String dataset = ""; Vector classifierOptions = new Vector(); Vector filterOptions = new Vector(); int i = 0; String current = ""; boolean newPart = false; do { // determine part of command line if (args[i].equals("CLASSIFIER")) { current = args[i]; i++; newPart = true; } else if (args[i].equals("FILTER")) { current = args[i]; i++; newPart = true; } else if (args[i].equals("DATASET")) { current = args[i]; i++; newPart = true; } if (current.equals("CLASSIFIER")) { if (newPart) classifier = args[i]; else classifierOptions.add(args[i]); } else if (current.equals("FILTER")) { if (newPart) filter = args[i]; else filterOptions.add(args[i]); } else if (current.equals("DATASET")) { if (newPart) dataset = args[i]; } // next parameter i++; newPart = false; } while (i < args.length); // everything provided? if (classifier.equals("") || filter.equals("") || dataset.equals("")) { System.out.println("Not all parameters provided!"); System.out.println(WekaDemo.usage()); System.exit(2); } // run demo = new WekaDemo(); demo.setClassifier(classifier, (String[]) classifierOptions .toArray(new String[classifierOptions.size()])); demo.setFilter(filter, (String[]) filterOptions .toArray(new String[filterOptions.size()])); demo.setTraining(dataset); demo.execute(); System.out.println(demo.toString()); } }