/**
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.mahout.classifier.naivebayes;
/** Implementation of the Naive Bayes Classifier Algorithm */
public class ComplementaryNaiveBayesClassifier extends AbstractNaiveBayesClassifier {
public ComplementaryNaiveBayesClassifier(NaiveBayesModel model) {
super(model);
}
@Override
public double getScoreForLabelFeature(int label, int feature) {
NaiveBayesModel model = getModel();
double weight = computeWeight(model.featureWeight(feature), model.weight(label, feature),
model.totalWeightSum(), model.labelWeight(label), model.alphaI(), model.numFeatures());
// see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.2, Weight Magnitude Errors
return weight / model.thetaNormalizer(label);
}
// see http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf - Section 3.1, Skewed Data bias
public static double computeWeight(double featureWeight, double featureLabelWeight,
double totalWeight, double labelWeight, double alphaI, double numFeatures) {
double numerator = featureWeight - featureLabelWeight + alphaI;
double denominator = totalWeight - labelWeight + alphaI * numFeatures;
return -Math.log(numerator / denominator);
}
}