Package org.apache.mahout.math

Examples of org.apache.mahout.math.SparseMatrix.assignRow()

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    // read the class matrix
    reader = new SequenceFile.Reader(fs, classVectorPath, conf);
    IntWritable label = new IntWritable();
    Matrix matrix = new SparseMatrix(new int[] {labelCount, featureCount});
    while (reader.next(label, value)) {
      matrix.assignRow(label.get(), value.get());
    }
    reader.close();
   
    model.setWeightMatrix(matrix);
  
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    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 = null;
    for (Pair<Text,VectorWritable> entry : new SequenceFileDirIterable<Text,VectorWritable>(
        new Path(base, TrainNaiveBayesJob.THETAS), PathType.LIST, PathFilters.partFilter(), conf)) {
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    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)) {
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    Matrix matrix = new SparseMatrix(new int[] {labelCount, featureCount});
    for (Pair<IntWritable,VectorWritable> record
         : new SequenceFileIterable<IntWritable,VectorWritable>(classVectorPath, true, conf)) {
      IntWritable label = record.getFirst();
      VectorWritable value = record.getSecond();
      matrix.assignRow(label.get(), value.get());
    }
   
    model.setWeightMatrix(matrix);

    // read theta normalizer
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    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)) {
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    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());
    }
   
    // perLabelThetaNormalizer is only used by the complementary model, we do not instantiate it for the standard model
    Vector perLabelThetaNormalizer = null;
    if (isComplementary) {
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