Examples of assignRow()

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

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    Configuration conf = getConfiguration();

    int i = 0;
    for (VectorWritable value : new SequenceFileValueIterable<VectorWritable>(rawEigenvectors, conf)) {
      Vector v = value.get();
      eigenVectors.assignRow(i, v);
      i++;
    }
    assertEquals("number of eigenvectors", 7, i);
  }
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Examples of org.apache.mahout.math.DenseMatrix.assignRow()

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    i = 0;
    for (VectorWritable value : new SequenceFileValueIterable<VectorWritable>(cleanEigenvectors2, conf)) {
      NamedVector v = (NamedVector) value.get();
      log.info(v.getName());
      eigenVectors2.assignRow(i, v);
      newEigenValues.add(EigenVector.getEigenValue(v.getName()));
      i++;
    }

    Collection<Integer> oldEigensFound = Lists.newArrayList();
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Examples of org.apache.mahout.math.Matrix.assignRow()

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                    boolean isSymmetric) {
    log.info("Finding {} singular vectors of matrix with {} rows, via Lanczos", desiredRank, corpus.numRows());
    Vector currentVector = getInitialVector(corpus);
    Vector previousVector = new DenseVector(currentVector.size());
    Matrix basis = new SparseRowMatrix(new int[]{desiredRank, corpus.numCols()});
    basis.assignRow(0, currentVector);
    double alpha = 0;
    double beta = 0;
    DoubleMatrix2D triDiag = new DenseDoubleMatrix2D(desiredRank, desiredRank);
    for (int i = 1; i < desiredRank; i++) {
      startTime(TimingSection.ITERATE);
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Examples of org.apache.mahout.math.Matrix.assignRow()

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        log.warn("Lanczos parameters out of range: alpha = {}, beta = {}.  Bailing out early!", alpha, beta);
        break;
      }
      final double b = beta;
      nextVector.assign(new Scale(1 / b));
      basis.assignRow(i, nextVector);
      previousVector = currentVector;
      currentVector = nextVector;
      // save the projections and norms!
      triDiag.set(i - 1, i - 1, alpha);
      if (i < desiredRank - 1) {
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Examples of org.apache.mahout.math.Matrix.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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Examples of org.apache.mahout.math.Matrix.assignRow()

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    // build in-memory data matrix A
    Matrix a = new DenseMatrix(sampleData.size(), sampleDimension);
    int i = 0;
    for (VectorWritable vw : sampleData) {
      a.assignRow(i++, vw.get());
    }
    // extract the eigenvectors into P
    Matrix p = new DenseMatrix(39, desiredRank - 1);
    FileSystem fs = FileSystem.get(cleanEigenvectors.toUri(), conf);
    SequenceFile.Reader reader = new SequenceFile.Reader(fs, cleanEigenvectors, conf);
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Examples of org.apache.mahout.math.Matrix.assignRow()

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    Configuration conf = new Configuration();

    int i = 0;
    for (VectorWritable value : new SequenceFileValueIterable<VectorWritable>(rawEigenvectors, conf)) {
      Vector v = value.get();
      eigenVectors.assignRow(i, v);
      i++;
    }
    assertEquals("number of eigenvectors", 7, i);
  }
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Examples of org.apache.mahout.math.Matrix.assignRow()

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    i = 0;
    for (VectorWritable value : new SequenceFileValueIterable<VectorWritable>(cleanEigenvectors2, conf)) {
      NamedVector v = (NamedVector) value.get();
      log.info(v.getName());
      eigenVectors2.assignRow(i, v);
      newEigenValues.add(EigenVector.getEigenValue(v.getName()));
      i++;
    }

    Collection<Integer> oldEigensFound = Lists.newArrayList();
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Examples of org.apache.mahout.math.Matrix.assignRow()

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      weightsPerLabel = VectorWritable.readVector(in);
      perLabelThetaNormalizer = VectorWritable.readVector(in);

      weightsPerLabelAndFeature = new SparseMatrix(weightsPerLabel.size(), weightsPerFeature.size() );
      for (int label = 0; label < weightsPerLabelAndFeature.numRows(); label++) {
        weightsPerLabelAndFeature.assignRow(label, VectorWritable.readVector(in));
      }
    } finally {
      Closeables.closeQuietly(in);
    }
    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,
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Examples of org.apache.mahout.math.Matrix.assignRow()

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    // build in-memory data matrix A
    Matrix a = new DenseMatrix(sampleData.size(), sampleDimension);
    int i = 0;
    for (VectorWritable vw : sampleData) {
      a.assignRow(i++, vw.get());
    }
    // extract the eigenvectors into P
    Matrix p = new DenseMatrix(39, desiredRank - 1);
    FileSystem fs = FileSystem.get(cleanEigenvectors.toUri(), conf);
   
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