Example usage for org.apache.mahout.math VectorWritable readVector

Introduction

In this page you can find the example usage for org.apache.mahout.math VectorWritable readVector.

Prototype

public static Vector readVector(DataInput in) throws IOException 

Source Link

Usage

From source file:Vectors.java

License:Apache License

public static Vector read(Path path, Configuration conf) throws IOException {
    FileSystem fs = FileSystem.get(path.toUri(), conf);
    FSDataInputStream in = fs.open(path);
    try {//  www.  jav a  2 s. c o  m
        return VectorWritable.readVector(in);
    } finally {
        Closeables.closeQuietly(in);
    }
}

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From source file:at.illecker.hama.rootbeer.examples.matrixmultiplication.compositeinput.util.MatrixRowMessage.java

License:Apache License

@Override
public void readFields(DataInput in) throws IOException {
    rowIndex = in.readInt();//  ww  w .ja va  2s .  c  o m
    rowValues = new VectorWritable(VectorWritable.readVector(in));
}

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From source file:com.cloudera.knittingboar.sgd.ParallelOnlineLogisticRegression.java

License:Apache License

@Override
public void readFields(DataInput in) throws IOException {
    int version = in.readInt();
    if (version == WRITABLE_VERSION) {
        learningRate = in.readDouble();/*w ww.  ja  v  a2 s.c  o m*/
        decayFactor = in.readDouble();
        stepOffset = in.readInt();
        step = in.readInt();
        forgettingExponent = in.readDouble();
        perTermAnnealingOffset = in.readInt();
        numCategories = in.readInt();
        beta = MatrixWritable.readMatrix(in);
        prior = PolymorphicWritable.read(in, PriorFunction.class);

        updateCounts = VectorWritable.readVector(in);
        updateSteps = VectorWritable.readVector(in);
    } else {
        throw new IOException("Incorrect object version, wanted " + WRITABLE_VERSION + " got " + version);
    }

}

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From source file:com.netease.news.classifier.naivebayes.NaiveBayesModel.java

License:Apache License

public static NaiveBayesModel materialize(Path output, Configuration conf) throws IOException {
    FileSystem fs = output.getFileSystem(conf);

    Vector weightsPerLabel = null;
    Vector perLabelThetaNormalizer = null;
    Vector weightsPerFeature = null;
    Matrix weightsPerLabelAndFeature;//  w w w. ja va 2  s.  c o m
    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());
        for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
            weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
        }
    } finally {
        Closeables.close(in, true);
    }
    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,
            perLabelThetaNormalizer, alphaI);
    model.validate();
    return model;
}

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From source file:com.netease.news.classifier.naivebayes.NaiveBayesModel.java

License:Apache License

public static NaiveBayesModel materializeLocal(String modelfile) throws IOException {

    Vector weightsPerLabel = null;
    Vector perLabelThetaNormalizer = null;
    Vector weightsPerFeature = null;
    Matrix weightsPerLabelAndFeature;/*ww w. j  a va 2s. c o  m*/
    float alphaI;

    System.out.println(modelfile);
    ClassLoader loader = NaiveBayesModel.class.getClassLoader();
    InputStream sin = loader.getResourceAsStream(modelfile);
    DataInputStream in = new DataInputStream(sin);
    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());
        for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
            weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
        }
    } finally {
        in.close();
    }
    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,
            perLabelThetaNormalizer, alphaI);
    model.validate();
    return model;
}

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