Java tutorial
/* * RandomRBFGenerator.java * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) * * 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 3 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, see <http://www.gnu.org/licenses/>. * */ package moa.streams.generators; import weka.core.Attribute; import weka.core.DenseInstance; import weka.core.FastVector; import weka.core.Instance; import weka.core.Instances; import java.io.Serializable; import java.util.Random; import moa.core.InstancesHeader; import moa.core.MiscUtils; import moa.core.ObjectRepository; import moa.options.AbstractOptionHandler; import moa.options.IntOption; import moa.streams.InstanceStream; import moa.tasks.TaskMonitor; /** * Stream generator for a random radial basis function stream. * * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz) * @version $Revision: 7 $ */ public class RandomRBFGenerator extends AbstractOptionHandler implements InstanceStream { @Override public String getPurposeString() { return "Generates a random radial basis function stream."; } private static final long serialVersionUID = 1L; public IntOption modelRandomSeedOption = new IntOption("modelRandomSeed", 'r', "Seed for random generation of model.", 1); 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 numCentroidsOption = new IntOption("numCentroids", 'n', "The number of centroids in the model.", 50, 1, Integer.MAX_VALUE); protected static class Centroid implements Serializable { private static final long serialVersionUID = 1L; public double[] centre; public int classLabel; public double stdDev; } protected InstancesHeader streamHeader; protected Centroid[] centroids; protected double[] centroidWeights; protected Random instanceRandom; @Override public void prepareForUseImpl(TaskMonitor monitor, ObjectRepository repository) { monitor.setCurrentActivity("Preparing random RBF...", -1.0); generateHeader(); generateCentroids(); restart(); } @Override public InstancesHeader getHeader() { return this.streamHeader; } @Override public long estimatedRemainingInstances() { return -1; } @Override public boolean hasMoreInstances() { return true; } @Override public boolean isRestartable() { return true; } @Override public void restart() { this.instanceRandom = new Random(this.instanceRandomSeedOption.getValue()); } @Override public Instance nextInstance() { Centroid centroid = this.centroids[MiscUtils.chooseRandomIndexBasedOnWeights(this.centroidWeights, this.instanceRandom)]; int numAtts = this.numAttsOption.getValue(); double[] attVals = new double[numAtts + 1]; for (int i = 0; i < numAtts; i++) { attVals[i] = (this.instanceRandom.nextDouble() * 2.0) - 1.0; } double magnitude = 0.0; for (int i = 0; i < numAtts; i++) { magnitude += attVals[i] * attVals[i]; } magnitude = Math.sqrt(magnitude); double desiredMag = this.instanceRandom.nextGaussian() * centroid.stdDev; double scale = desiredMag / magnitude; for (int i = 0; i < numAtts; i++) { attVals[i] = centroid.centre[i] + attVals[i] * scale; } Instance inst = new DenseInstance(1.0, attVals); inst.setDataset(getHeader()); inst.setClassValue(centroid.classLabel); return inst; } 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); } protected void generateCentroids() { Random modelRand = new Random(this.modelRandomSeedOption.getValue()); this.centroids = new Centroid[this.numCentroidsOption.getValue()]; this.centroidWeights = new double[this.centroids.length]; for (int i = 0; i < this.centroids.length; i++) { this.centroids[i] = new Centroid(); double[] randCentre = new double[this.numAttsOption.getValue()]; for (int j = 0; j < randCentre.length; j++) { randCentre[j] = modelRand.nextDouble(); } this.centroids[i].centre = randCentre; this.centroids[i].classLabel = modelRand.nextInt(this.numClassesOption.getValue()); this.centroids[i].stdDev = modelRand.nextDouble(); this.centroidWeights[i] = modelRand.nextDouble(); } } @Override public void getDescription(StringBuilder sb, int indent) { // TODO Auto-generated method stub } }