Example usage for org.apache.mahout.classifier.naivebayes NaiveBayesModel NaiveBayesModel

Introduction

In this page you can find the example usage for org.apache.mahout.classifier.naivebayes NaiveBayesModel NaiveBayesModel.

Prototype

public NaiveBayesModel(Matrix weightMatrix, Vector weightsPerFeature, Vector weightsPerLabel,
            Vector thetaNormalizer, float alphaI, boolean isComplementary) 

Source Link

Usage

From source file:mlbench.bayes.BayesUtils.java

License:Apache License

public static NaiveBayesModel readModelFromDir(Path base, Configuration conf) {

    float alphaI = conf.getFloat(ThetaMapper.ALPHA_I, 1.0f);

    // read feature sums and label sums
    Vector scoresPerLabel = null;
    Vector scoresPerFeature = null;
    for (Pair<Text, VectorWritable> record : new SequenceFileDirIterable<Text, VectorWritable>(
            new Path(base, TrainNaiveBayesJob.WEIGHTS), PathType.LIST, PathFilters.partFilter(), conf)) {
        String key = record.getFirst().toString();
        VectorWritable value = record.getSecond();
        if (key.equals(TrainNaiveBayesJob.WEIGHTS_PER_FEATURE)) {
            scoresPerFeature = value.get();
        } else if (key.equals(TrainNaiveBayesJob.WEIGHTS_PER_LABEL)) {
            scoresPerLabel = value.get();
        }/*from w w  w .j ava 2  s .  co  m*/
    }

    // Preconditions.checkNotNull(scoresPerFeature);
    // Preconditions.checkNotNull(scoresPerLabel);

    Matrix scoresPerLabelAndFeature = new SparseMatrix(scoresPerLabel.size(), scoresPerFeature.size());
    for (Pair<IntWritable, VectorWritable> entry : new SequenceFileDirIterable<IntWritable, VectorWritable>(
            new Path(base, TrainNaiveBayesJob.SUMMED_OBSERVATIONS), PathType.LIST, PathFilters.partFilter(),
            conf)) {
        scoresPerLabelAndFeature.assignRow(entry.getFirst().get(), entry.getSecond().get());
    }

    Vector perlabelThetaNormalizer = scoresPerLabel.like();
    /*
     * for (Pair<Text,VectorWritable> entry : new
     * SequenceFileDirIterable<Text,VectorWritable>( new Path(base,
     * TrainNaiveBayesJob.THETAS), PathType.LIST, PathFilters.partFilter(),
     * conf)) { if (entry.getFirst().toString().equals(TrainNaiveBayesJob.
     * LABEL_THETA_NORMALIZER)) { perlabelThetaNormalizer =
     * entry.getSecond().get(); } }
     * 
     * Preconditions.checkNotNull(perlabelThetaNormalizer);
     */
    return new NaiveBayesModel(scoresPerLabelAndFeature, scoresPerFeature, scoresPerLabel,
            perlabelThetaNormalizer, alphaI, false);
}

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