Example usage for org.apache.mahout.classifier.naivebayes.training TrainNaiveBayesJob WEIGHTS_PER_FEATURE

Introduction

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

Prototype

String WEIGHTS_PER_FEATURE

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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();
        }// w w w  .j a v a 2 s . c om
    }

    // 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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