com.netease.news.classifier.naivebayes.AbstractThetaTrainer.java Source code

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Here is the source code for com.netease.news.classifier.naivebayes.AbstractThetaTrainer.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 com.netease.news.classifier.naivebayes;

import com.google.common.base.Preconditions;
import org.apache.mahout.math.Vector;

public abstract class AbstractThetaTrainer {

    private final Vector weightsPerFeature;
    private final Vector weightsPerLabel;
    private final Vector perLabelThetaNormalizer;
    private final double alphaI;
    private final double totalWeightSum;
    private final double numFeatures;

    protected AbstractThetaTrainer(Vector weightsPerFeature, Vector weightsPerLabel, double alphaI) {
        Preconditions.checkNotNull(weightsPerFeature);
        Preconditions.checkNotNull(weightsPerLabel);
        this.weightsPerFeature = weightsPerFeature;
        this.weightsPerLabel = weightsPerLabel;
        this.alphaI = alphaI;
        perLabelThetaNormalizer = weightsPerLabel.like();
        totalWeightSum = weightsPerLabel.zSum();
        numFeatures = weightsPerFeature.getNumNondefaultElements();
    }

    public abstract void train(int label, Vector instance);

    protected double alphaI() {
        return alphaI;
    }

    protected double numFeatures() {
        return numFeatures;
    }

    protected double labelWeight(int label) {
        return weightsPerLabel.get(label);
    }

    protected double totalWeightSum() {
        return totalWeightSum;
    }

    protected double featureWeight(int feature) {
        return weightsPerFeature.get(feature);
    }

    protected void updatePerLabelThetaNormalizer(int label, double weight) {
        perLabelThetaNormalizer.set(label, perLabelThetaNormalizer.get(label) + weight);
    }

    public Vector retrievePerLabelThetaNormalizer() {
        return perLabelThetaNormalizer.clone();
    }
}