List of usage examples for org.apache.mahout.classifier.naivebayes.training TrainNaiveBayesJob WEIGHTS
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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(); }/*www. ja v a 2s .com*/ } // 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); }