List of assignRow() Examples

Examples of assignRow()

org.apache.mahout.math.DenseMatrix.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
org.apache.mahout.math.SparseMatrix.assignRow()
org.apache.mahout.math.SparseRowMatrix.assignRow()

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

        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());
        }

View Full Code Here

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

      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;

View Full Code Here

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

      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;

View Full Code Here

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

    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 = null;
    for (Pair<Text,VectorWritable> entry : new SequenceFileDirIterable<Text,VectorWritable>(
        new Path(base, TrainNaiveBayesJob.THETAS), PathType.LIST, PathFilters.partFilter(), conf)) {

View Full Code Here

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

                    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 beta = 0;
    DoubleMatrix2D triDiag = new DenseDoubleMatrix2D(desiredRank, desiredRank);
    for (int i = 1; i < desiredRank; i++) {
      startTime(TimingSection.ITERATE);
      Vector nextVector = isSymmetric ? corpus.times(currentVector) : corpus.timesSquared(currentVector);

View Full Code Here

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

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

View Full Code Here

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

      Writable key = reader.getKeyClass().asSubclass(Writable.class).newInstance();
      Writable value = reader.getValueClass().asSubclass(Writable.class).newInstance();
      int i = 0;
      while (reader.next(key, value)) {
        Vector v = ((VectorWritable) value).get();
        eigenVectors.assignRow(i, v);
        System.out.println("k=" + key.toString() + " V=" + AbstractCluster.formatVector(v, null));
        value = reader.getValueClass().asSubclass(Writable.class).newInstance();
        i++;
      }
      assertEquals("number of eigenvectors", 9, i);

View Full Code Here

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

      Writable key = reader.getKeyClass().asSubclass(Writable.class).newInstance();
      Writable value = reader.getValueClass().asSubclass(Writable.class).newInstance();
      int i = 0;
      while (reader.next(key, value)) {
        Vector v = ((VectorWritable) value).get();
        eigenVectors.assignRow(i, v);
        System.out.println("k=" + key.toString() + " V=" + AbstractCluster.formatVector(v, null));
        value = reader.getValueClass().asSubclass(Writable.class).newInstance();
        i++;
      }
      assertEquals("number of clean eigenvectors", 4, i);

View Full Code Here

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

   *         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.getRow(row)));
    }
    return r;
  }


  /**

View Full Code Here

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

      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,

View Full Code Here

0 1 2 3 4 5

TOP

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.