List of viewRow() Examples

Examples of viewRow()

cern.colt.matrix.DoubleMatrix2D.viewRow()
Constructs and returns a new slice view representing the columns of the given row. The returned view is backed by this matrix, so changes in the returned view are reflected in this matrix, and vice-versa. To obtain a slice view on subranges, construct a sub-ranging view (viewPart(...)), then apply this method to the sub-range view.
Example:

2 x 3 matrix:
1, 2, 3
4, 5, 6 viewRow(0) ==> Matrix1D of size 3:
1, 2, 3

@param row the row to fix. @return a new slice view. @throws IndexOutOfBoundsException if row < 0 || row>= rows(). @see #viewColumn(int)
cern.colt.matrix.ObjectMatrix2D.viewRow()
Constructs and returns a new slice view representing the columns of the given row. The returned view is backed by this matrix, so changes in the returned view are reflected in this matrix, and vice-versa. To obtain a slice view on subranges, construct a sub-ranging view (viewPart(...)), then apply this method to the sub-range view.
Example:

2 x 3 matrix:
1, 2, 3
4, 5, 6 viewRow(0) ==> Matrix1D of size 3:
1, 2, 3

@param row the row to fix. @return a new slice view. @throws IndexOutOfBoundsException if row < 0 || row>= rows(). @see #viewColumn(int)
org.apache.mahout.math.DenseMatrix.viewRow()
org.apache.mahout.math.Matrix.viewRow()
org.apache.mahout.math.SparseRowMatrix.viewRow()
@param row an int row index @return a shallow view of the Vector at specified row (ie you may mutate the original matrix using this row)
org.apache.mahout.math.matrix.DoubleMatrix2D.viewRow()
Constructs and returns a new slice view representing the columns of the given row. The returned view is backed by this matrix, so changes in the returned view are reflected in this matrix, and vice-versa. To obtain a slice view on subranges, construct a sub-ranging view (viewPart(...)), then apply this method to the sub-range view.
Example:

2 x 3 matrix:
1, 2, 3
4, 5, 6 viewRow(0) ==> Matrix1D of size 3:
1, 2, 3

@param row the row to fix. @return a new slice view. @throws IndexOutOfBoundsException if row < 0 || row>= rows(). @see #viewColumn(int)
stallone.api.doubles.IDoubleArray.viewRow()
Gets an IDoubleArray object which is a view on row i @param i @return

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

      }
    };


    SSVDPrototype mapperSimulation = new SSVDPrototype(seed, kp, 3000);
    for (int i = 0; i < m; i++) {
      mapperSimulation.firstPass(mx.viewRow(i));
    }


    mapperSimulation.finishFirstPass();


    for (int i = 0; i < m; i++) {

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

    }


    mapperSimulation.finishFirstPass();


    for (int i = 0; i < m; i++) {
      mapperSimulation.secondPass(mx.viewRow(i), btEmitter);
    }


    // LocalSSVDTest.assertOrthonormality(mapperSimulation.m_qt.transpose(),
    // false,1e-10);

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

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


  /**

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


    /* Y' (Cu -I) Y by outer products */
    for (Element e : userRatings.nonZeroes()) {
      for (Vector.Element feature : Y.get(e.index()).all()) {
        Vector partial = CuMinusIY.get(e.index()).times(feature.get());
        YtransponseCuMinusIY.viewRow(feature.index()).assign(partial, Functions.PLUS);
      }
    }


    /* Y' (Cu - I) Y + λ I  add lambda on the diagonal */
    for (int feature = 0; feature < numFeatures; feature++) {

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

    Matrix topicTermCounts = new DenseMatrix(numTopics, numTerms);
    Vector topicSums = new DenseVector(numTopics);
    if (random != null) {
      for (int x = 0; x < numTopics; x++) {
        for (int term = 0; term < numTerms; term++) {
          topicTermCounts.viewRow(x).set(term, random.nextDouble());
        }
      }
    }
    for (int x = 0; x < numTopics; x++) {
      topicSums.set(x, random == null ? 1.0 : topicTermCounts.viewRow(x).norm(1));

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

          topicTermCounts.viewRow(x).set(term, random.nextDouble());
        }
      }
    }
    for (int x = 0; x < numTopics; x++) {
      topicSums.set(x, random == null ? 1.0 : topicTermCounts.viewRow(x).norm(1));
    }
    return Pair.of(topicTermCounts, topicSums);
  }


  public static Pair<Matrix, Vector> loadModel(Configuration conf, Path... modelPaths)

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

    }
    numTopics++;
    Matrix model = new DenseMatrix(numTopics, numTerms);
    Vector topicSums = new DenseVector(numTopics);
    for (Pair<Integer, Vector> pair : rows) {
      model.viewRow(pair.getFirst()).assign(pair.getSecond());
      topicSums.set(pair.getFirst(), pair.getSecond().norm(1));
    }
    return Pair.of(model, topicSums);
  }

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


    // Put the centroids into a matrix.
    Matrix x = new DenseMatrix(6, 5);
    int row = 0;
    for (Centroid c : r) {
      x.viewRow(row).assign(c.viewPart(0, 5));
      row++;
    }


    // Verify that each column looks right. Should contain zeros except for a single 6.
    final Vector columnNorms = x.aggregateColumns(new VectorFunction() {

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

   * @return A matrix of scores, one row per row of the input matrix, one column for each but the last category.
   */
  public Matrix classifyFull(Matrix data) {
    Matrix r = new DenseMatrix(data.numRows(), numCategories());
    for (int row = 0; row < data.numRows(); row++) {
      classifyFull(r.viewRow(row), data.viewRow(row));
    }
    return r;
  }


  /**

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

    Matrix topicTermCounts = new DenseMatrix(numTopics, numTerms);
    Vector topicSums = new DenseVector(numTopics);
    if(random != null) {
      for(int x = 0; x < numTopics; x++) {
        for(int term = 0; term < numTerms; term++) {
          topicTermCounts.viewRow(x).set(term, random.nextDouble());
        }
      }
    }
    for(int x = 0; x < numTopics; x++) {
      topicSums.set(x, random == null ? 1.0 : topicTermCounts.viewRow(x).norm(1));

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