MOA 12.03
Real Time Analytics for Data Streams
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00001 /* 00002 * LEDGenerator.java 00003 * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand 00004 * @author Richard Kirkby ([email protected]) 00005 * 00006 * This program is free software; you can redistribute it and/or modify 00007 * it under the terms of the GNU General Public License as published by 00008 * the Free Software Foundation; either version 3 of the License, or 00009 * (at your option) any later version. 00010 * 00011 * This program is distributed in the hope that it will be useful, 00012 * but WITHOUT ANY WARRANTY; without even the implied warranty of 00013 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 00014 * GNU General Public License for more details. 00015 * 00016 * You should have received a copy of the GNU General Public License 00017 * along with this program. If not, see <http://www.gnu.org/licenses/>. 00018 * 00019 */ 00020 package moa.streams.generators; 00021 00022 import weka.core.Attribute; 00023 import weka.core.DenseInstance; 00024 import weka.core.FastVector; 00025 import weka.core.Instance; 00026 import weka.core.Instances; 00027 00028 import java.util.Random; 00029 00030 import moa.core.InstancesHeader; 00031 import moa.core.ObjectRepository; 00032 import moa.options.AbstractOptionHandler; 00033 import moa.options.FlagOption; 00034 import moa.options.IntOption; 00035 import moa.streams.InstanceStream; 00036 import moa.tasks.TaskMonitor; 00037 00044 public class LEDGenerator extends AbstractOptionHandler implements 00045 InstanceStream { 00046 00047 @Override 00048 public String getPurposeString() { 00049 return "Generates a problem of predicting the digit displayed on a 7-segment LED display."; 00050 } 00051 00052 private static final long serialVersionUID = 1L; 00053 00054 public static final int NUM_IRRELEVANT_ATTRIBUTES = 17; 00055 00056 protected static final int originalInstances[][] = { 00057 {1, 1, 1, 0, 1, 1, 1}, {0, 0, 1, 0, 0, 1, 0}, 00058 {1, 0, 1, 1, 1, 0, 1}, {1, 0, 1, 1, 0, 1, 1}, 00059 {0, 1, 1, 1, 0, 1, 0}, {1, 1, 0, 1, 0, 1, 1}, 00060 {1, 1, 0, 1, 1, 1, 1}, {1, 0, 1, 0, 0, 1, 0}, 00061 {1, 1, 1, 1, 1, 1, 1}, {1, 1, 1, 1, 0, 1, 1}}; 00062 00063 public IntOption instanceRandomSeedOption = new IntOption( 00064 "instanceRandomSeed", 'i', 00065 "Seed for random generation of instances.", 1); 00066 00067 public IntOption noisePercentageOption = new IntOption("noisePercentage", 00068 'n', "Percentage of noise to add to the data.", 10, 0, 100); 00069 00070 public FlagOption suppressIrrelevantAttributesOption = new FlagOption( 00071 "suppressIrrelevantAttributes", 's', 00072 "Reduce the data to only contain 7 relevant binary attributes."); 00073 00074 protected InstancesHeader streamHeader; 00075 00076 protected Random instanceRandom; 00077 00078 @Override 00079 protected void prepareForUseImpl(TaskMonitor monitor, 00080 ObjectRepository repository) { 00081 // generate header 00082 FastVector attributes = new FastVector(); 00083 FastVector binaryLabels = new FastVector(); 00084 binaryLabels.addElement("0"); 00085 binaryLabels.addElement("1"); 00086 int numAtts = 7; 00087 if (!this.suppressIrrelevantAttributesOption.isSet()) { 00088 numAtts += NUM_IRRELEVANT_ATTRIBUTES; 00089 } 00090 for (int i = 0; i < numAtts; i++) { 00091 attributes.addElement(new Attribute("att" + (i + 1), binaryLabels)); 00092 } 00093 FastVector classLabels = new FastVector(); 00094 for (int i = 0; i < 10; i++) { 00095 classLabels.addElement(Integer.toString(i)); 00096 } 00097 attributes.addElement(new Attribute("class", classLabels)); 00098 this.streamHeader = new InstancesHeader(new Instances( 00099 getCLICreationString(InstanceStream.class), attributes, 0)); 00100 this.streamHeader.setClassIndex(this.streamHeader.numAttributes() - 1); 00101 restart(); 00102 } 00103 00104 @Override 00105 public long estimatedRemainingInstances() { 00106 return -1; 00107 } 00108 00109 @Override 00110 public InstancesHeader getHeader() { 00111 return this.streamHeader; 00112 } 00113 00114 @Override 00115 public boolean hasMoreInstances() { 00116 return true; 00117 } 00118 00119 @Override 00120 public boolean isRestartable() { 00121 return true; 00122 } 00123 00124 @Override 00125 public Instance nextInstance() { 00126 InstancesHeader header = getHeader(); 00127 Instance inst = new DenseInstance(header.numAttributes()); 00128 inst.setDataset(header); 00129 int selected = this.instanceRandom.nextInt(10); 00130 for (int i = 0; i < 7; i++) { 00131 if ((1 + (this.instanceRandom.nextInt(100))) <= this.noisePercentageOption.getValue()) { 00132 inst.setValue(i, originalInstances[selected][i] == 0 ? 1 : 0); 00133 } else { 00134 inst.setValue(i, originalInstances[selected][i]); 00135 } 00136 } 00137 if (!this.suppressIrrelevantAttributesOption.isSet()) { 00138 for (int i = 0; i < NUM_IRRELEVANT_ATTRIBUTES; i++) { 00139 inst.setValue(i + 7, this.instanceRandom.nextInt(2)); 00140 } 00141 } 00142 inst.setClassValue(selected); 00143 return inst; 00144 } 00145 00146 @Override 00147 public void restart() { 00148 this.instanceRandom = new Random(this.instanceRandomSeedOption.getValue()); 00149 } 00150 00151 @Override 00152 public void getDescription(StringBuilder sb, int indent) { 00153 // TODO Auto-generated method stub 00154 } 00155 }