Java tutorial
/* * SEAGenerator.java * Copyright (C) 2008 University of Waikato, Hamilton, New Zealand * @author Albert Bifet * * 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. */ package tr.gov.ulakbim.jDenetX.streams.generators; import tr.gov.ulakbim.jDenetX.core.InstancesHeader; import tr.gov.ulakbim.jDenetX.core.ObjectRepository; import tr.gov.ulakbim.jDenetX.options.AbstractOptionHandler; import tr.gov.ulakbim.jDenetX.options.FlagOption; import tr.gov.ulakbim.jDenetX.options.IntOption; import tr.gov.ulakbim.jDenetX.streams.InstanceStream; import tr.gov.ulakbim.jDenetX.tasks.TaskMonitor; import weka.core.*; import java.util.Random; // Generator described in paper: // W. Nick Street and YongSeog Kim // "A streaming ensemble algorithm (SEA) for large-scale classification", // KDD '01: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining // 377-382 2001. // Notes: // The built in functions are based on the paper public class SEAGenerator extends AbstractOptionHandler implements InstanceStream { @Override public String getPurposeString() { return "Generates SEA concepts functions."; } private static final long serialVersionUID = 1L; public IntOption functionOption = new IntOption("function", 'f', "Classification function used, as defined in the original paper.", 1, 1, 4); public IntOption instanceRandomSeedOption = new IntOption("instanceRandomSeed", 'i', "Seed for random generation of instances.", 1); public FlagOption balanceClassesOption = new FlagOption("balanceClasses", 'b', "Balance the number of instances of each class."); public IntOption numInstancesConcept = new IntOption("numInstancesConcept", 'n', "The number of instances for each concept.", 0, 0, Integer.MAX_VALUE); public IntOption noisePercentageOption = new IntOption("noisePercentage", 'n', "Percentage of noise to add to the data.", 10, 0, 100); protected interface ClassFunction { public int determineClass(double attrib1, double attrib2, double attrib3); } protected static ClassFunction[] classificationFunctions = { // function 1 new ClassFunction() { public int determineClass(double attrib1, double attrib2, double attrib3) { return (attrib1 + attrib2 <= 8) ? 0 : 1; } }, // function 2 new ClassFunction() { public int determineClass(double attrib1, double attrib2, double attrib3) { return (attrib1 + attrib2 <= 9) ? 0 : 1; } }, // function 3 new ClassFunction() { public int determineClass(double attrib1, double attrib2, double attrib3) { return (attrib1 + attrib2 <= 7) ? 0 : 1; } }, // function 4 new ClassFunction() { public int determineClass(double attrib1, double attrib2, double attrib3) { return (attrib1 + attrib2 <= 9.5) ? 0 : 1; } } }; protected InstancesHeader streamHeader; protected Random instanceRandom; protected boolean nextClassShouldBeZero; @Override protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { // generate header FastVector attributes = new FastVector(); attributes.addElement(new Attribute("attrib1")); attributes.addElement(new Attribute("attrib2")); attributes.addElement(new Attribute("attrib3")); FastVector classLabels = new FastVector(); classLabels.addElement("groupA"); classLabels.addElement("groupB"); attributes.addElement(new Attribute("class", classLabels)); this.streamHeader = new InstancesHeader( new Instances(getCLICreationString(InstanceStream.class), attributes, 0)); this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1); restart(); } public long estimatedRemainingInstances() { return -1; } public InstancesHeader getHeader() { return this.streamHeader; } public boolean hasMoreInstances() { return true; } public boolean isRestartable() { return true; } public Instance nextInstance() { double attrib1 = 0, attrib2 = 0, attrib3 = 0; int group = 0; boolean desiredClassFound = false; while (!desiredClassFound) { // generate attributes attrib1 = 10 * this.instanceRandom.nextDouble(); attrib2 = 10 * this.instanceRandom.nextDouble(); attrib3 = 10 * this.instanceRandom.nextDouble(); // determine class group = classificationFunctions[this.functionOption.getValue() - 1].determineClass(attrib1, attrib2, attrib3); if (!this.balanceClassesOption.isSet()) { desiredClassFound = true; } else { // balance the classes if ((this.nextClassShouldBeZero && (group == 0)) || (!this.nextClassShouldBeZero && (group == 1))) { desiredClassFound = true; this.nextClassShouldBeZero = !this.nextClassShouldBeZero; } // else keep searching } } //Add Noise if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) { group = (group == 0 ? 1 : 0); } // construct instance InstancesHeader header = getHeader(); Instance inst = new DenseInstance(header.numAttributes()); inst.setValue(0, attrib1); inst.setValue(1, attrib2); inst.setValue(2, attrib3); inst.setDataset(header); inst.setClassValue(group); return inst; } public void restart() { this.instanceRandom = new Random(this.instanceRandomSeedOption.getValue()); this.nextClassShouldBeZero = false; } public void getDescription(StringBuilder sb, int indent) { // TODO Auto-generated method stub } }