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

org.apache.mahout.math.Matrix.assignRow()
Assign the other vector values to the row of the receiver @param row the int row to assign @param other a Vector @return the modified receiver @throws CardinalityException if the cardinalities differ

    
    // 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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      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.closeQuietly(in);
    }
    NaiveBayesModel model = new NaiveBayesModel(weightsPerLabelAndFeature, weightsPerFeature, weightsPerLabel,

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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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    IntArrayList indexes = Y.keys();
    indexes.quickSort();


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


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

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   *         last category.
   */
  public Matrix classify(Matrix data) {
    Matrix r = new DenseMatrix(data.numRows(), numCategories() - 1);
    for (int row = 0; row < data.numRows(); row++) {
      r.assignRow(row, classify(data.viewRow(row)));
    }
    return r;
  }


  /**

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        double val = r.nextGaussian();
        v.set(col, val * entryMean);
      }
      int c = r.nextInt(numRows);
      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.viewRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }

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      if (r.nextBoolean() || numRows == nonNullRows) {
        m.assignRow(numRows == nonNullRows ? i : c, v);
      } else {
        Vector other = m.viewRow(r.nextInt(numRows));
        if (other != null && other.getLengthSquared() > 0) {
          m.assignRow(c, other.clone());
        }
      }
      //n += m.getRow(c).getLengthSquared();
    }
    return m;

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      for (int col = 0; col < numCols; col++) {
        double val = r.nextGaussian();
        v.set(col, val);
      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if (symmetric) {
      return matrix.times(matrix.transpose());
    }
    return matrix;

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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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    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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