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The Waikato Environment for Knowledge Analysis (WEKA), a machine
learning workbench. This version represents the developer version, the
"bleeding edge" of development, you could say. New functionality gets added
to this version.
/*
* 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 theRules;
/** The confidence for C45-type pruning. */
private double CF = 0.25f;
/** Minimum number of objects */
private final int minNumObj;
/** The model selection method. */
private final ModelSelection toSelectModeL;
/**
* How many subsets of equal size? One used for pruning, the rest for
* training.
*/
private int numSetS = 3;
/** Use reduced error pruning? */
private boolean reducedErrorPruning = false;
/** Generated unpruned list? */
private boolean unpruned = false;
/** The seed for random number generation. */
private int m_seed = 1;
/**
* Constructor for unpruned dec list.
*/
public MakeDecList(ModelSelection toSelectLocModel, int minNum) {
toSelectModeL = toSelectLocModel;
reducedErrorPruning = false;
unpruned = true;
minNumObj = minNum;
}
/**
* Constructor for dec list pruned using C4.5 pruning.
*/
public MakeDecList(ModelSelection toSelectLocModel, double cf, int minNum) {
toSelectModeL = toSelectLocModel;
CF = cf;
reducedErrorPruning = false;
unpruned = false;
minNumObj = minNum;
}
/**
* Constructor for dec list pruned using hold-out pruning.
*/
public MakeDecList(ModelSelection toSelectLocModel, int num, int minNum,
int seed) {
toSelectModeL = toSelectLocModel;
numSetS = num;
reducedErrorPruning = true;
unpruned = false;
minNumObj = minNum;
m_seed = seed;
}
/**
* Builds dec list.
*
* @exception Exception if dec list can't be built successfully
*/
public void buildClassifier(Instances data) throws Exception {
ClassifierDecList currentRule;
double currentWeight;
Instances oldGrowData, newGrowData, oldPruneData, newPruneData;
theRules = new Vector();
if ((reducedErrorPruning) && !(unpruned)) {
Random random = new Random(m_seed);
data.randomize(random);
data.stratify(numSetS);
oldGrowData = data.trainCV(numSetS, numSetS - 1, random);
oldPruneData = data.testCV(numSetS, numSetS - 1);
} else {
oldGrowData = data;
oldPruneData = null;
}
while (Utils.gr(oldGrowData.numInstances(), 0)) {
// Create rule
if (unpruned) {
currentRule = new ClassifierDecList(toSelectModeL, minNumObj);
currentRule.buildRule(oldGrowData);
} else if (reducedErrorPruning) {
currentRule = new PruneableDecList(toSelectModeL, minNumObj);
((PruneableDecList) currentRule).buildRule(oldGrowData, oldPruneData);
} else {
currentRule = new C45PruneableDecList(toSelectModeL, CF, minNumObj);
((C45PruneableDecList) currentRule).buildRule(oldGrowData);
}
// Remove instances from growing data
newGrowData = new Instances(oldGrowData, oldGrowData.numInstances());
Enumeration enu = oldGrowData.enumerateInstances();
while (enu.hasMoreElements()) {
Instance instance = enu.nextElement();
currentWeight = currentRule.weight(instance);
if (Utils.sm(currentWeight, 1)) {
instance.setWeight(instance.weight() * (1 - currentWeight));
newGrowData.add(instance);
}
}
newGrowData.compactify();
oldGrowData = newGrowData;
// Remove instances from pruning data
if ((reducedErrorPruning) && !(unpruned)) {
newPruneData = new Instances(oldPruneData, oldPruneData.numInstances());
enu = oldPruneData.enumerateInstances();
while (enu.hasMoreElements()) {
Instance instance = enu.nextElement();
currentWeight = currentRule.weight(instance);
if (Utils.sm(currentWeight, 1)) {
instance.setWeight(instance.weight() * (1 - currentWeight));
newPruneData.add(instance);
}
}
newPruneData.compactify();
oldPruneData = newPruneData;
}
theRules.addElement(currentRule);
}
}
/**
* Outputs the classifier into a string.
*/
@Override
public String toString() {
StringBuffer text = new StringBuffer();
for (int i = 0; i < theRules.size(); i++) {
text.append(theRules.elementAt(i) + "\n");
}
text.append("Number of Rules : \t" + theRules.size() + "\n");
return text.toString();
}
/**
* Classifies an instance.
*
* @exception Exception if instance can't be classified
*/
public double classifyInstance(Instance instance) throws Exception {
double maxProb = -1;
double[] sumProbs;
int maxIndex = 0;
sumProbs = distributionForInstance(instance);
for (int j = 0; j < sumProbs.length; j++) {
if (Utils.gr(sumProbs[j], maxProb)) {
maxIndex = j;
maxProb = sumProbs[j];
}
}
return maxIndex;
}
/**
* Returns the class distribution for an instance.
*
* @exception Exception if distribution can't be computed
*/
public double[] distributionForInstance(Instance instance) throws Exception {
double[] currentProbs = null;
double[] sumProbs;
double currentWeight, weight = 1;
int i, j;
// Get probabilities.
sumProbs = new double[instance.numClasses()];
i = 0;
while ((Utils.gr(weight, 0)) && (i < theRules.size())) {
currentWeight = theRules.elementAt(i).weight(instance);
if (Utils.gr(currentWeight, 0)) {
currentProbs = theRules.elementAt(i).distributionForInstance(instance);
for (j = 0; j < sumProbs.length; j++) {
sumProbs[j] += weight * currentProbs[j];
}
weight = weight * (1 - currentWeight);
}
i++;
}
return sumProbs;
}
/**
* Outputs the number of rules in the classifier.
*/
public int numRules() {
return theRules.size();
}
/**
* Returns the revision string.
*
* @return the revision
*/
@Override
public String getRevision() {
return RevisionUtils.extract("$Revision: 14511 $");
}
}