Example usage for org.apache.mahout.math Matrix assignRow

List of usage examples for org.apache.mahout.math Matrix assignRow

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Introduction

In this page you can find the example usage for org.apache.mahout.math Matrix assignRow.

Prototype

Matrix assignRow(int row, Vector other);

Source Link

Document

Assign the other vector values to the row of the receiver

Usage

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;
    float alphaI;

    FSDataInputStream in = fs.open(new Path(output, "naiveBayesModel.bin"));
    try {//from w ww .j av  a2s. c  om
        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;
}

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;
    float alphaI;

    System.out.println(modelfile);
    ClassLoader loader = NaiveBayesModel.class.getClassLoader();
    InputStream sin = loader.getResourceAsStream(modelfile);
    DataInputStream in = new DataInputStream(sin);
    try {//  w  ww.ja  va 2s.co m
        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;
}

From source file:com.skp.experiment.math.als.hadoop.ImplicitFeedbackAlternatingLeastSquaresReasonSolver.java

License:Apache License

private Matrix YtransposeY(OpenIntObjectHashMap<Vector> Y) {

    Matrix compactedY = new DenseMatrix(Y.size(), numFeatures);
    IntArrayList indexes = Y.keys();//from  w w w. j  a  va2s  . com
    indexes.quickSort();

    int row = 0;
    for (int index : indexes.elements()) {
        compactedY.assignRow(row++, Y.get(index));
    }

    return compactedY.transpose().times(compactedY);
}

From source file:io.ssc.relationdiscovery.SVD.java

License:Open Source License

public void compute() {

    new LanczosSolver().solve(lanczosState, rank + OVERSHOOT, false);

    Matrix singularVectorCandidates = new DenseMatrix(rank + OVERSHOOT, A.numCols());
    for (int n = 0; n < rank + OVERSHOOT; n++) {
        singularVectorCandidates.assignRow(n, lanczosState.getRightSingularVector(n));
    }/*from  w w w.  j av a  2  s .c  o m*/

    findSingularVectors(singularVectorCandidates);
}

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();
        }/*from   w ww  .jav a2 s .  c  o m*/
    }

    // 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);
}

From source file:org.pigml.classify.naivebayes.NaiveBayesModel.java

License:Apache License

public static NaiveBayesModel materialize(Path modelDir, Configuration conf) throws IOException {
    OpenIntDoubleHashMap weightsPerLabel = new OpenIntDoubleHashMap();
    OpenIntDoubleHashMap weightsPerFeature = new OpenIntDoubleHashMap();

    SequenceFileDirIterable<IntWritable, DoubleWritable> kvs;
    kvs = new SequenceFileDirIterable<IntWritable, DoubleWritable>(new Path(modelDir, "label_weights"),
            PathType.LIST, PathFilters.logsCRCFilter(), conf);
    for (Pair<IntWritable, DoubleWritable> kv : kvs) {
        weightsPerLabel.put(kv.getFirst().get(), kv.getSecond().get());
    }//from w  w w  . j a va 2 s . c o  m

    kvs = new SequenceFileDirIterable<IntWritable, DoubleWritable>(new Path(modelDir, "feature_weights"),
            PathType.LIST, PathFilters.logsCRCFilter(), conf);
    for (Pair<IntWritable, DoubleWritable> kv : kvs) {
        weightsPerFeature.put(kv.getFirst().get(), kv.getSecond().get());
    }

    Matrix weightsPerLabelAndFeature = null;
    SequenceFileDirIterable<IntWritable, VectorWritable> labelVectors = new SequenceFileDirIterable<IntWritable, VectorWritable>(
            new Path(modelDir, "label_feature_weights"), PathType.LIST, PathFilters.logsCRCFilter(), conf);
    for (Pair<IntWritable, VectorWritable> labelVector : labelVectors) {
        int label = labelVector.getFirst().get();
        Vector vector = labelVector.getSecond().get();
        if (weightsPerLabelAndFeature == null) {
            weightsPerLabelAndFeature = new SparseRowMatrix(weightsPerLabel.size(), vector.size());
        }
        weightsPerLabelAndFeature.assignRow(label, vector);
    }

    // TODO alphaI is hard-coded to 1.0
    // TODO perLabelThetaNormalizer is not supported yet
    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,
            1.0f);
    model.validate();
    return model;
}

From source file:org.qcri.pca.PCATest.java

License:Apache License

@Test
public void crossTestIterationOfMapReducePPCASequentialPPCA() throws Exception {
    Matrix C_central = PCACommon.randomMatrix(D, d);
    double ss = PCACommon.randSS();
    InitialValues initValSeq = new InitialValues(C_central, ss);
    InitialValues initValMR = new InitialValues(C_central.clone(), ss);

    //1. run sequential
    Matrix Ye_central = new DenseMatrix(N, D);
    int row = 0;/*  w  ww  .j  a  va2  s  .  com*/
    for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(input, PathType.LIST, null,
            conf)) {
        Ye_central.assignRow(row, vw.get());
        row++;
    }
    double bishopSeqErr = ppcaDriver.runSequential(conf, Ye_central, initValSeq, 1);

    //2. run mapreduce
    DistributedRowMatrix Ye = new DistributedRowMatrix(input, tmp, N, D);
    Ye.setConf(conf);
    double bishopMRErr = ppcaDriver.runMapReduce(conf, Ye, initValMR, output, N, D, d, 1, 1, 1, 1);

    Assert.assertEquals("ss value is different in sequential and mapreduce PCA", initValSeq.ss, initValMR.ss,
            EPSILON);
    double seqCTrace = PCACommon.trace(initValSeq.C);
    double mrCTrace = PCACommon.trace(initValMR.C);
    Assert.assertEquals("C value is different in sequential and mapreduce PCA", seqCTrace, mrCTrace, EPSILON);
    Assert.assertEquals("The PPCA error between sequntial and mapreduce methods is too different: "
            + bishopSeqErr + "!= " + bishopMRErr, bishopSeqErr, bishopMRErr, EPSILON);
}

From source file:org.qcri.pca.SPCADriver.java

/***
 * PPCA: sequential PPCA based on the paper from Tipping and Bishop
 * /*from   w ww. j  a  va  2 s  .  c om*/
 * @param conf
 *          the configuration
 * @param input
 *          the path to the input matrix Y
 * @param output
 *          the output path (not used currently)
 * @param nRows
 *          number or rows in Y
 * @param nCols
 *          number of columns in Y
 * @param nPCs
 *          number of desired principal components
 * @return the error
 * @throws Exception
 */
double runSequential(Configuration conf, Path input, Path output, final int nRows, final int nCols,
        final int nPCs) throws Exception {
    Matrix centralY = new DenseMatrix(nRows, nCols);
    FileSystem fs = FileSystem.get(input.toUri(), conf);
    if (fs.listStatus(input).length == 0) {
        System.err.println("No file under " + input);
        return 0;
    }
    int row = 0;
    for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(input, PathType.LIST, null,
            conf)) {
        centralY.assignRow(row, vw.get());
        row++;
    }
    Matrix centralC = PCACommon.randomMatrix(nCols, nPCs);
    double ss = PCACommon.randSS();
    InitialValues initVal = new InitialValues(centralC, ss);
    // Matrix sampledYe = sample(centralY);
    // runSequential(conf, sampledYe, initVal, 100);
    double error = runSequential(conf, centralY, initVal, 100);
    return error;
}

From source file:org.qcri.pca.SPCADriver.java

/**
 * PPCA: sequential PPCA based on the matlab implementation of Jacob Verbeek
 * /*ww  w.  j a  v  a  2  s  .  c o m*/
 * @param conf
 *          the configuration
 * @param input
 *          the path to the input matrix Y
 * @param output
 *          the output path (not used currently)
 * @param nRows
 *          number or rows in Y
 * @param nCols
 *          number of columns in Y
 * @param nPCs
 *          number of desired principal components
 * @return the error
 * @throws Exception
 */
double runSequential_JacobVersion(Configuration conf, Path input, Path output, final int nRows, final int nCols,
        final int nPCs) throws Exception {
    Matrix centralY = new DenseMatrix(nRows, nCols);
    FileSystem fs = FileSystem.get(input.toUri(), conf);
    if (fs.listStatus(input).length == 0) {
        System.err.println("No file under " + input);
        return 0;
    }
    int row = 0;
    for (VectorWritable vw : new SequenceFileDirValueIterable<VectorWritable>(input, PathType.LIST, null,
            conf)) {
        centralY.assignRow(row, vw.get());
        row++;
    }
    Matrix C = PCACommon.randomMatrix(nCols, nPCs);
    double ss = PCACommon.randSS();
    InitialValues initVal = new InitialValues(C, ss);
    double error = runSequential_JacobVersion(conf, centralY, initVal, 100);
    return error;
}