/**
* 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.training;
import org.apache.mahout.math.Vector;
import java.util.Iterator;
public class ComplementaryThetaTrainer extends AbstractThetaTrainer {
public ComplementaryThetaTrainer(Vector weightsPerFeature, Vector weightsPerLabel, double alphaI) {
super(weightsPerFeature, weightsPerLabel, alphaI);
}
@Override
public void train(int label, Vector instance) {
double sigmaK = labelWeight(label);
Iterator<Vector.Element> it = instance.iterateNonZero();
while (it.hasNext()) {
Vector.Element e = it.next();
double numerator = featureWeight(e.index()) - e.get() + alphaI();
double denominator = totalWeightSum() - sigmaK + alphaI() * numFeatures();
double weight = Math.log(numerator / denominator);
updatePerLabelThetaNormalizer(label, weight);
}
}
}