/**
* Method for building this classifier.
*
* @param training the data to train with
* @throws Exception if the training fails
*/
public void buildClassifier(Instances training) throws Exception {
m_Random = new Random(m_Seed);
super.buildClassifier(training);
m_Random = null;
}
/**
* Parses a given list of options.
*
* <p>
* <!-- options-start -->
* Valid options are:
*
* <p>
*
* <pre> -C <num>
* Number of sub-committees. (Default 3)</pre>
*
* <pre> -P <num>
* Percentage of weight mass to base training on.
* (default 100, reduce to around 90 speed up)</pre>
*
* <pre> -Q
* Use resampling for boosting.</pre>
*
* <pre> -S <num>
* Random number seed.
* (default 1)</pre>
*
* <pre> -I <num>
* Number of iterations.
* (default 10)</pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <pre> -W
* Full name of base classifier.
* (default: weka.classifiers.trees.DecisionStump)</pre>
*
* <pre>
* Options specific to classifier weka.classifiers.trees.DecisionStump:
* </pre>
*
* <pre> -D
* If set, classifier is run in debug mode and
* may output additional info to the console</pre>
*
* <!-- options-end -->
* Options after -- are passed to the designated classifier.
*
* <p>
*
* @param options the list of options as an array of strings
* @throws Exception if an option is not supported
*/
public void setOptions(String[] options) throws Exception {
String subcmtyString = Utils.getOption('C', options);
if (subcmtyString.length() != 0) {
setNumSubCmtys(Integer.parseInt(subcmtyString));
} else {
setNumSubCmtys(3);
}
super.setOptions(options);
}
/**
* Sets the weights for the next iteration.
*
* @param training the data to train with
* @param reweight the reweighting factor
* @throws Exception in case of an error
*/
protected void setWeights(Instances training, double reweight) throws Exception {
int subCmtySize = m_Classifiers.length / m_NumSubCmtys;
if ((m_NumIterationsPerformed + 1) % subCmtySize == 0) {
if (getDebug()) System.err.println(m_NumIterationsPerformed + " " + subCmtySize);
double oldSumOfWeights = training.sumOfWeights();
// Randomly set the weights of the training instances to the poisson distributon
for (int i = 0; i < training.numInstances(); i++) {
training.instance(i).setWeight(-Math.log((m_Random.nextDouble() * 9999) / 10000));
}
// Renormailise weights
double sumProbs = training.sumOfWeights();
for (int i = 0; i < training.numInstances(); i++) {
training.instance(i).setWeight(training.instance(i).weight() * oldSumOfWeights / sumProbs);
}
} else {
super.setWeights(training, reweight);
}
}
