Examples of cern.colt.matrix.DoubleMatrix2D.rows()

cern.colt.matrix.DoubleMatrix2D.rows()

      TestSolver.verifyAxb(matrix,solver.toDoubleMatrix1D(),vector);
    }
    
    try
    {
      for(int i=0;i<matrix.rows();++i) solver.j_x[i]=0;
      solver.solveUsingColt();
    }
    catch(IllegalArgumentException ex)
    {
      if (ex.getMessage().contains("singular"))

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        throw ex;
    }
    
    if (!singular)
    {
      for(int i=0;i<matrix.rows();++i)
        Assert.assertEquals(solver.j_x[i], vector.getQuick(i),Configuration.fpAccuracy);
      TestSolver.verifyAxb(solver);
    }
  }
}

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  {
    DoubleMatrix2D matrix = solver.toDoubleMatrix2D();
    DoubleMatrix1D b = solver.toDoubleMatrix1D();
    StringBuffer result = new StringBuffer();
    
    for(int row=0;row<matrix.rows();++row)
    {
      int indexRow = findIndexOf(incompatiblePairs, row);
      assert indexRow >= 0;
      PairScore pairRow = getPairScore(indexRow, 0,0);

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    try {
      BufferedWriter writer =
        new BufferedWriter(new FileWriter(filename));
      DoubleMatrix2D matrix = GraphMatrixOperations.<V,E>graphToSparseMatrix(graph,
          mWeightKey);
      for (int i=0;i<matrix.rows();i++) {
        for (int j=0;j<matrix.columns();j++) {
          writer.write(String.format("%4.2f ", matrix.getQuick(i,j)));
        }
        writer.write("\n");
      }

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    public static <V,E> DoubleMatrix2D computeMeanFirstPassageMatrix(Graph<V,E> G,
            Map<E,Number> edgeWeights, DoubleMatrix1D stationaryDistribution)
    {
        DoubleMatrix2D temp = GraphMatrixOperations.graphToSparseMatrix(G,
                edgeWeights);
        for (int i = 0; i < temp.rows(); i++)
        {
            for (int j = 0; j < temp.columns(); j++)
            {
                double value = -1 * temp.get(i, j)
                        + stationaryDistribution.get(j);

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      SingularValueDecomposition sv = new SingularValueDecomposition(M);
      // D contient les valeurs propres sur la diagonale
      DoubleMatrix2D D = sv.getS ();


      for (int i = 0; i < D.rows(); ++i)
         lambda[i] = D.getQuick (i, i);


      // Calculer la racine carree des valeurs propres
      for (int i = 0; i < D.rows(); ++i)
         D.setQuick (i, i, Math.sqrt (lambda[i]));

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      for (int i = 0; i < D.rows(); ++i)
         lambda[i] = D.getQuick (i, i);


      // Calculer la racine carree des valeurs propres
      for (int i = 0; i < D.rows(); ++i)
         D.setQuick (i, i, Math.sqrt (lambda[i]));
      DoubleMatrix2D P = sv.getV();
      // Multiplier par la matrice orthogonale (ici P)
      return P.zMult (D, null);
   }

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      /*
       * Use the diagonal Padé approximant of order [9/9] for exp:
       * See Higham J.H., Functions of matrices, SIAM, 2008.
       */
      DoubleMatrix2D B = A.copy();
      int n = B.rows();
      Algebra alge = new Algebra();
      final double mu = alge.trace(B) / n;
      double x;


      // B <-- B - mu*I

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   public static DoubleMatrix2D expBidiagonal (final DoubleMatrix2D A)  {
      // Use the diagonal Padé approximant of order [9/9] for exp:
      // See Higham J.H., Functions of matrices, SIAM, 2008.


      DoubleMatrix2D B = A.copy();
      final int n = B.rows();
      Algebra alge = new Algebra();
      final double mu = alge.trace(B) / n;
      double x;


      // B <-- B - mu*I

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      // sigma (pour une matrice symetrique et definie-positive seulement).
      SingularValueDecomposition sv =
         new SingularValueDecomposition (new DenseDoubleMatrix2D (sigma));
      DoubleMatrix2D D = sv.getS ();    // diagonal
      // Calculer la racine carree des valeurs propres
      for (int i = 0; i < D.rows(); i++){
         sortedEigenvalues[i] = D.getQuick (i, i);
         D.setQuick (i, i, Math.sqrt (D.getQuick (i, i)));
      }
      DoubleMatrix2D P = sv.getV();   // right factor matrix
      return P.zMult (D, null).toArray ();

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