Package org.apache.hadoop.fs

Examples of org.apache.hadoop.fs.FSDataInputStream.readFloat()

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    Matrix weightsPerLabelAndFeature;
    float alphaI;

    FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
    try {
      alphaI = in.readFloat();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = VectorWritable.readVector(in);
      perLabelThetaNormalizer = VectorWritable.readVector(in);

      weightsPerLabelAndFeature = new SparseMatrix(weightsPerLabel.size(), weightsPerFeature.size() );
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    Matrix weightsPerLabelAndFeature;
    float alphaI;

    FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
    try {
      alphaI = in.readFloat();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));

      weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size());
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    float mass = Float.NEGATIVE_INFINITY;
    FileSystem fs = FileSystem.get(getConf());
    for (FileStatus f : fs.listStatus(new Path(outm))) {
      FSDataInputStream fin = fs.open(f.getPath());
      mass = sumLogProbs(mass, fin.readFloat());
      fin.close();
    }

    return mass;
  }
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    System.out.println("Job Finished in " + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds");

    float mass = Float.NEGATIVE_INFINITY;
    for (FileStatus f : fs.listStatus(new Path(outm))) {
      FSDataInputStream fin = fs.open(f.getPath());
      mass = sumLogProbs(mass, fin.readFloat());
      fin.close();
    }

    return mass;
  }
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  }

  public static float readFloat(FileSystem fs, String path) {
    try {
      FSDataInputStream in = fs.open(new Path(path));
      float val = in.readFloat();
      in.close();
      return val;
    } catch (Exception e) {
      throw new RuntimeException("Unable to read property at " + path);
    }
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    Matrix weightsPerLabelAndFeature;
    float alphaI;

    FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
    try {
      alphaI = in.readFloat();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));

      weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), weightsPerFeature.size() );
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    float alphaI;
    boolean isComplementary;

    FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
    try {
      alphaI = in.readFloat();
      isComplementary = in.readBoolean();
      weightsPerFeature = VectorWritable.readVector(in);
      weightsPerLabel = new DenseVector(VectorWritable.readVector(in));
      if (isComplementary){
        perLabelThetaNormalizer = new DenseVector(VectorWritable.readVector(in));
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    // Initialize an array to hold all the doc scores.
    scores = new float[sz];

    // Read each doc length.
    for (int i = 1; i < sz; i++) {
      scores[i] = in.readFloat();
      docs++;

      if (i % 1000000 == 0)
        LOG.info(i + " docscores read");
    }
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