org.apache.mahout.classifier.naivebayes.ComplementaryNaiveBayesClassifier.java Source code

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Here is the source code for org.apache.mahout.classifier.naivebayes.ComplementaryNaiveBayesClassifier.java

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/**
 * 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);
    }
}