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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 Instances value
* @exception Exception if an error occurs
*/
public final void buildClassifier(Instances instances) throws Exception {
m_nb = new NaiveBayesUpdateable();
m_disc = new Discretize();
m_disc.setInputFormat(instances);
Instances temp = Filter.useFilter(instances, m_disc);
m_nb.buildClassifier(temp);
if (temp.numInstances() >= 5) {
m_errors = crossValidate(m_nb, temp, new Random(1));
}
m_numSubsets = 1;
}
/**
* Return the errors made by the naive bayes model at this node
*
* @return the number of errors made
*/
public double getErrors() {
return m_errors;
}
/**
* Return the discretizer used at this node
*
* @return a Discretize value
*/
public Discretize getDiscretizer() {
return m_disc;
}
/**
* Get the naive bayes model at this node
*
* @return a NaiveBayesUpdateable value
*/
public NaiveBayesUpdateable getNaiveBayesModel() {
return m_nb;
}
/**
* Always returns 0 because only there is only one subset.
*/
public final int whichSubset(Instance instance){
return 0;
}
/**
* Always returns null because there is only one subset.
*/
public final double [] weights(Instance instance){
return null;
}
/**
* Does nothing because no condition has to be satisfied.
*/
public final String leftSide(Instances instances){
return "";
}
/**
* Does nothing because no condition has to be satisfied.
*/
public final String rightSide(int index, Instances instances){
return "";
}
/**
* Returns a string containing java source code equivalent to the test
* made at this node. The instance being tested is called "i".
*
* @param index index of the nominal value tested
* @param data the data containing instance structure info
* @return a value of type 'String'
*/
public final String sourceExpression(int index, Instances data) {
return "true"; // or should this be false??
}
/**
* Return the probability for a class value
*
* @param classIndex the index of the class value
* @param instance the instance to generate a probability for
* @param theSubset the subset to consider
* @return a probability
* @exception Exception if an error occurs
*/
public double classProb(int classIndex, Instance instance, int theSubset)
throws Exception {
m_disc.input(instance);
Instance temp = m_disc.output();
return m_nb.distributionForInstance(temp)[classIndex];
}
/**
* Return a textual description of the node
*
* @return a String value
*/
public String toString() {
return m_nb.toString();
}
/**
* Utility method for fast 5-fold cross validation of a naive bayes
* model
*
* @param fullModel a NaiveBayesUpdateable value
* @param trainingSet an Instances value
* @param r a Random value
* @return a double value
* @exception Exception if an error occurs
*/
public static double crossValidate(NaiveBayesUpdateable fullModel,
Instances trainingSet,
Random r) throws Exception {
// make some copies for fast evaluation of 5-fold xval
Classifier [] copies = AbstractClassifier.makeCopies(fullModel, 5);
Evaluation eval = new Evaluation(trainingSet);
// make some splits
for (int j = 0; j < 5; j++) {
Instances test = trainingSet.testCV(5, j);
// unlearn these test instances
for (int k = 0; k < test.numInstances(); k++) {
test.instance(k).setWeight(-test.instance(k).weight());
((NaiveBayesUpdateable)copies[j]).updateClassifier(test.instance(k));
// reset the weight back to its original value
test.instance(k).setWeight(-test.instance(k).weight());
}
eval.evaluateModel(copies[j], test);
}
return eval.incorrect();
}
/**
* Returns the revision string.
*
* @return the revision
*/
public String getRevision() {
return RevisionUtils.extract("$Revision: 10531 $");
}
}