/* * HyperplaneGenerator.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.FloatOption; 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; public class HyperplaneGenerator extends AbstractOptionHandler implements InstanceStream { @Override public String getPurposeString() { return "Generates a problem of predicting class of a rotating hyperplane."; } private static final long serialVersionUID = 1L; public IntOption instanceRandomSeedOption = new IntOption( "instanceRandomSeed", 'i', "Seed for random generation of instances.", 1); public IntOption numClassesOption = new IntOption("numClasses", 'c', "The number of classes to generate.", 2, 2, Integer.MAX_VALUE); public IntOption numAttsOption = new IntOption("numAtts", 'a', "The number of attributes to generate.", 10, 0, Integer.MAX_VALUE); public IntOption numDriftAttsOption = new IntOption("numDriftAtts", 'k', "The number of attributes with drift.", 2, 0, Integer.MAX_VALUE); public FloatOption magChangeOption = new FloatOption("magChange", 't', "Magnitude of the change for every example", 0.0, 0.0, 1.0); public IntOption noisePercentageOption = new IntOption("noisePercentage", 'n', "Percentage of noise to add to the data.", 5, 0, 100); public IntOption sigmaPercentageOption = new IntOption("sigmaPercentage", 's', "Percentage of probability that the direction of change is reversed.", 10, 0, 100); protected InstancesHeader streamHeader; protected Random instanceRandom; protected double[] weights; protected int[] sigma; public int numberInstance; @Override protected void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { monitor.setCurrentActivity("Preparing hyperplane...", -1.0); generateHeader(); restart(); } protected void generateHeader() { FastVector attributes = new FastVector(); for (int i = 0; i < this.numAttsOption.getValue(); i++) { attributes.addElement(new Attribute("att" + (i + 1))); } FastVector classLabels = new FastVector(); for (int i = 0; i < this.numClassesOption.getValue(); i++) { classLabels.addElement("class" + (i + 1)); } attributes.addElement(new Attribute("class", classLabels)); this.streamHeader = new InstancesHeader(new Instances( getCLICreationString(InstanceStream.class), attributes, 0)); this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1); } public long estimatedRemainingInstances() { return -1; } public InstancesHeader getHeader() { return this.streamHeader; } public boolean hasMoreInstances() { return true; } public boolean isRestartable() { return true; } public Instance nextInstance() { int numAtts = this.numAttsOption.getValue(); double[] attVals = new double[numAtts + 1]; double sum = 0.0; double sumWeights = 0.0; for (int i = 0; i < numAtts; i++) { attVals[i] = this.instanceRandom.nextDouble(); sum += this.weights[i] * attVals[i]; sumWeights += this.weights[i]; } int classLabel; if (sum >= sumWeights * 0.5) { classLabel = 1; } else { classLabel = 0; } //Add Noise if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption .getValue()) { classLabel = (classLabel == 0 ? 1 : 0); } Instance inst = new DenseInstance(1.0, attVals); inst.setDataset(getHeader()); inst.setClassValue(classLabel); addDrift(); return inst; } private void addDrift() { for (int i = 0; i < this.numDriftAttsOption.getValue(); i++) { this.weights[i] += (double) ((double) sigma[i]) * ((double) this.magChangeOption.getValue()); if (//this.weights[i] >= 1.0 || this.weights[i] <= 0.0 || (1 + (this.instanceRandom.nextInt(100))) <= this.sigmaPercentageOption.getValue()) { this.sigma[i] *= -1; } } } public void restart() { this.instanceRandom = new Random(this.instanceRandomSeedOption .getValue()); this.weights = new double[this.numAttsOption.getValue()]; this.sigma = new int[this.numAttsOption.getValue()]; for (int i = 0; i < this.numAttsOption.getValue(); i++) { this.weights[i] = this.instanceRandom.nextDouble(); this.sigma[i] = (i < this.numDriftAttsOption.getValue() ? 1 : 0); } } public void getDescription(StringBuilder sb, int indent) { // TODO Auto-generated method stub } }