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/* |
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* Copyright (C) 2000-2004 Object Oriented Parallel Simulation Engine (OOPSE) project |
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* |
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* Contact: oopse@oopse.org |
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* |
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* This program is free software; you can redistribute it and/or |
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* modify it under the terms of the GNU Lesser General Public License |
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* as published by the Free Software Foundation; either version 2.1 |
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* of the License, or (at your option) any later version. |
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* All we ask is that proper credit is given for our work, which includes |
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* - but is not limited to - adding the above copyright notice to the beginning |
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* of your source code files, and to any copyright notice that you may distribute |
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* with programs based on this work. |
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* |
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* This program is distributed in the hope that it will be useful, |
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* but WITHOUT ANY WARRANTY; without even the implied warranty of |
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |
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* GNU Lesser General Public License for more details. |
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* |
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* You should have received a copy of the GNU Lesser General Public License |
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* along with this program; if not, write to the Free Software |
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* Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. |
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* Copyright (c) 2005 The University of Notre Dame. All Rights Reserved. |
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* |
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* The University of Notre Dame grants you ("Licensee") a |
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* non-exclusive, royalty free, license to use, modify and |
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* redistribute this software in source and binary code form, provided |
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* that the following conditions are met: |
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* |
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* 1. Acknowledgement of the program authors must be made in any |
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* publication of scientific results based in part on use of the |
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* program. An acceptable form of acknowledgement is citation of |
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* the article in which the program was described (Matthew |
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* A. Meineke, Charles F. Vardeman II, Teng Lin, Christopher |
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* J. Fennell and J. Daniel Gezelter, "OOPSE: An Object-Oriented |
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* Parallel Simulation Engine for Molecular Dynamics," |
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* J. Comput. Chem. 26, pp. 252-271 (2005)) |
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* |
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* 2. Redistributions of source code must retain the above copyright |
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* notice, this list of conditions and the following disclaimer. |
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* |
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* 3. Redistributions in binary form must reproduce the above copyright |
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* notice, this list of conditions and the following disclaimer in the |
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* documentation and/or other materials provided with the |
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* distribution. |
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* |
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* This software is provided "AS IS," without a warranty of any |
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* kind. All express or implied conditions, representations and |
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* warranties, including any implied warranty of merchantability, |
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* fitness for a particular purpose or non-infringement, are hereby |
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* excluded. The University of Notre Dame and its licensors shall not |
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* be liable for any damages suffered by licensee as a result of |
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* using, modifying or distributing the software or its |
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* derivatives. In no event will the University of Notre Dame or its |
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* licensors be liable for any lost revenue, profit or data, or for |
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* direct, indirect, special, consequential, incidental or punitive |
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* damages, however caused and regardless of the theory of liability, |
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* arising out of the use of or inability to use software, even if the |
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* University of Notre Dame has been advised of the possibility of |
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* such damages. |
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*/ |
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|
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/** |
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* @file SquareMatrix.hpp |
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* @author Teng Lin |
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#define MATH_SQUAREMATRIX_HPP |
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#include "math/RectMatrix.hpp" |
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#include "utils/NumericConstant.hpp" |
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namespace oopse { |
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|
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/** |
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* @class SquareMatrix SquareMatrix.hpp "math/SquareMatrix.hpp" |
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* @brief A square matrix class |
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* @template Real the element type |
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* @template Dim the dimension of the square matrix |
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*/ |
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template<typename Real, int Dim> |
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class SquareMatrix : public RectMatrix<Real, Dim, Dim> { |
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public: |
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/** |
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* @class SquareMatrix SquareMatrix.hpp "math/SquareMatrix.hpp" |
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* @brief A square matrix class |
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* @template Real the element type |
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* @template Dim the dimension of the square matrix |
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*/ |
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template<typename Real, int Dim> |
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class SquareMatrix : public RectMatrix<Real, Dim, Dim> { |
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public: |
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typedef Real ElemType; |
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typedef Real* ElemPoinerType; |
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|
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/** default constructor */ |
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SquareMatrix() { |
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for (unsigned int i = 0; i < Dim; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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data_[i][j] = 0.0; |
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} |
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/** default constructor */ |
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SquareMatrix() { |
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for (unsigned int i = 0; i < Dim; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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this->data_[i][j] = 0.0; |
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} |
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|
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/** copy constructor */ |
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SquareMatrix(const RectMatrix<Real, Dim, Dim>& m) : RectMatrix<Real, Dim, Dim>(m) { |
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} |
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|
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/** copy assignment operator */ |
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SquareMatrix<Real, Dim>& operator =(const RectMatrix<Real, Dim, Dim>& m) { |
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RectMatrix<Real, Dim, Dim>::operator=(m); |
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return *this; |
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} |
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|
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/** Retunrs an identity matrix*/ |
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/** Constructs and initializes every element of this matrix to a scalar */ |
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SquareMatrix(Real s) : RectMatrix<Real, Dim, Dim>(s){ |
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} |
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|
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static SquareMatrix<Real, Dim> identity() { |
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SquareMatrix<Real, Dim> m; |
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/** Constructs and initializes from an array */ |
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SquareMatrix(Real* array) : RectMatrix<Real, Dim, Dim>(array){ |
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} |
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|
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|
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/** copy constructor */ |
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SquareMatrix(const RectMatrix<Real, Dim, Dim>& m) : RectMatrix<Real, Dim, Dim>(m) { |
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} |
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|
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for (unsigned int i = 0; i < Dim; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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if (i == j) |
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m(i, j) = 1.0; |
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else |
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m(i, j) = 0.0; |
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/** copy assignment operator */ |
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SquareMatrix<Real, Dim>& operator =(const RectMatrix<Real, Dim, Dim>& m) { |
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RectMatrix<Real, Dim, Dim>::operator=(m); |
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return *this; |
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} |
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|
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/** Retunrs an identity matrix*/ |
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|
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return m; |
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} |
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static SquareMatrix<Real, Dim> identity() { |
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SquareMatrix<Real, Dim> m; |
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|
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for (unsigned int i = 0; i < Dim; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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if (i == j) |
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m(i, j) = 1.0; |
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else |
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m(i, j) = 0.0; |
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|
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/** |
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* Retunrs the inversion of this matrix. |
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* @todo |
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*/ |
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SquareMatrix<Real, Dim> inverse() { |
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SquareMatrix<Real, Dim> result; |
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return m; |
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} |
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|
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return result; |
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} |
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/** |
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* Retunrs the inversion of this matrix. |
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* @todo need implementation |
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*/ |
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SquareMatrix<Real, Dim> inverse() { |
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SquareMatrix<Real, Dim> result; |
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|
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/** |
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* Returns the determinant of this matrix. |
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* @todo |
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*/ |
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double determinant() const { |
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double det; |
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return det; |
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} |
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return result; |
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} |
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|
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/** Returns the trace of this matrix. */ |
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double trace() const { |
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double tmp = 0; |
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|
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for (unsigned int i = 0; i < Dim ; i++) |
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tmp += data_[i][i]; |
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/** |
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* Returns the determinant of this matrix. |
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* @todo need implementation |
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*/ |
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Real determinant() const { |
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Real det; |
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return det; |
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} |
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return tmp; |
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} |
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/** Returns the trace of this matrix. */ |
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Real trace() const { |
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Real tmp = 0; |
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|
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for (unsigned int i = 0; i < Dim ; i++) |
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tmp += this->data_[i][i]; |
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|
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/** Tests if this matrix is symmetrix. */ |
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bool isSymmetric() const { |
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for (unsigned int i = 0; i < Dim - 1; i++) |
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for (unsigned int j = i; j < Dim; j++) |
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if (fabs(data_[i][j] - data_[j][i]) > oopse::epsilon) |
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return false; |
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|
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return true; |
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} |
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return tmp; |
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} |
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|
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/** Tests if this matrix is orthogonal. */ |
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bool isOrthogonal() { |
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SquareMatrix<Real, Dim> tmp; |
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/** Tests if this matrix is symmetrix. */ |
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bool isSymmetric() const { |
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for (unsigned int i = 0; i < Dim - 1; i++) |
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for (unsigned int j = i; j < Dim; j++) |
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if (fabs(this->data_[i][j] - this->data_[j][i]) > epsilon) |
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return false; |
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|
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return true; |
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} |
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tmp = *this * transpose(); |
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/** Tests if this matrix is orthogonal. */ |
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bool isOrthogonal() { |
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SquareMatrix<Real, Dim> tmp; |
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return tmp.isDiagonal(); |
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} |
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tmp = *this * transpose(); |
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|
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/** Tests if this matrix is diagonal. */ |
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bool isDiagonal() const { |
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for (unsigned int i = 0; i < Dim ; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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if (i !=j && fabs(data_[i][j]) > oopse::epsilon) |
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return false; |
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return tmp.isDiagonal(); |
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} |
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|
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/** Tests if this matrix is diagonal. */ |
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bool isDiagonal() const { |
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for (unsigned int i = 0; i < Dim ; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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if (i !=j && fabs(this->data_[i][j]) > epsilon) |
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return false; |
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|
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return true; |
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} |
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|
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/** Tests if this matrix is the unit matrix. */ |
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bool isUnitMatrix() const { |
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if (!isDiagonal()) |
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return false; |
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|
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for (unsigned int i = 0; i < Dim ; i++) |
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if (fabs(this->data_[i][i] - 1) > epsilon) |
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return false; |
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|
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return true; |
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} |
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return true; |
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} |
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|
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/** Tests if this matrix is the unit matrix. */ |
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bool isUnitMatrix() const { |
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if (!isDiagonal()) |
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return false; |
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|
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for (unsigned int i = 0; i < Dim ; i++) |
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if (fabs(data_[i][i] - 1) > oopse::epsilon) |
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return false; |
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/** Return the transpose of this matrix */ |
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SquareMatrix<Real, Dim> transpose() const{ |
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SquareMatrix<Real, Dim> result; |
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|
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return true; |
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} |
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for (unsigned int i = 0; i < Dim; i++) |
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for (unsigned int j = 0; j < Dim; j++) |
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result(j, i) = this->data_[i][j]; |
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|
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/** @todo need implement */ |
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void diagonalize() { |
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//jacobi(m, eigenValues, ortMat); |
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} |
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|
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/** |
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* Finds the eigenvalues and eigenvectors of a symmetric matrix |
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* @param eigenvals a reference to a vector3 where the |
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* eigenvalues will be stored. The eigenvalues are ordered so |
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* that eigenvals[0] <= eigenvals[1] <= eigenvals[2]. |
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* @return an orthogonal matrix whose ith column is an |
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* eigenvector for the eigenvalue eigenvals[i] |
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*/ |
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SquareMatrix<Real, Dim> findEigenvectors(Vector<Real, Dim>& eigenValues) { |
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SquareMatrix<Real, Dim> ortMat; |
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return result; |
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} |
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|
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if ( !isSymmetric()){ |
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throw(); |
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} |
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|
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SquareMatrix<Real, Dim> m(*this); |
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jacobi(m, eigenValues, ortMat); |
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/** @todo need implementation */ |
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void diagonalize() { |
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//jacobi(m, eigenValues, ortMat); |
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} |
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|
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return ortMat; |
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} |
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/** |
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* Jacobi iteration routines for computing eigenvalues/eigenvectors of |
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* real symmetric matrix |
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* |
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* @return true if success, otherwise return false |
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* @param a source matrix |
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* @param w output eigenvalues |
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* @param v output eigenvectors |
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*/ |
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bool jacobi(const SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
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SquareMatrix<Real, Dim>& v); |
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};//end SquareMatrix |
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/** |
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* Jacobi iteration routines for computing eigenvalues/eigenvectors of |
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* real symmetric matrix |
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* |
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* @return true if success, otherwise return false |
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* @param a symmetric matrix whose eigenvectors are to be computed. On return, the matrix is |
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* overwritten |
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* @param w will contain the eigenvalues of the matrix On return of this function |
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* @param v the columns of this matrix will contain the eigenvectors. The eigenvectors are |
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* normalized and mutually orthogonal. |
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*/ |
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|
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static int jacobi(SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& d, |
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SquareMatrix<Real, Dim>& v); |
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};//end SquareMatrix |
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|
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|
|
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#define ROT(a,i,j,k,l) g=a(i, j);h=a(k, l);a(i, j)=g-s*(h+g*tau);a(k, l)=h+s*(g-h*tau) |
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#define MAX_ROTATIONS 60 |
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/*========================================================================= |
213 |
|
|
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template<typename Real, int Dim> |
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bool SquareMatrix<Real, Dim>::jacobi(const SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
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SquareMatrix<Real, Dim>& v) { |
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const int N = Dim; |
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int i, j, k, iq, ip; |
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double tresh, theta, tau, t, sm, s, h, g, c; |
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double tmp; |
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Vector<Real, Dim> b, z; |
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Program: Visualization Toolkit |
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Module: $RCSfile: SquareMatrix.hpp,v $ |
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|
|
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Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen |
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All rights reserved. |
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See Copyright.txt or http://www.kitware.com/Copyright.htm for details. |
220 |
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|
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This software is distributed WITHOUT ANY WARRANTY; without even |
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the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
223 |
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PURPOSE. See the above copyright notice for more information. |
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|
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=========================================================================*/ |
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|
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#define VTK_ROTATE(a,i,j,k,l) g=a(i, j);h=a(k, l);a(i, j)=g-s*(h+g*tau); \ |
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a(k, l)=h+s*(g-h*tau) |
229 |
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|
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#define VTK_MAX_ROTATIONS 20 |
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|
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// Jacobi iteration for the solution of eigenvectors/eigenvalues of a nxn |
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// real symmetric matrix. Square nxn matrix a; size of matrix in n; |
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// output eigenvalues in w; and output eigenvectors in v. Resulting |
235 |
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// eigenvalues/vectors are sorted in decreasing order; eigenvectors are |
236 |
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// normalized. |
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template<typename Real, int Dim> |
238 |
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int SquareMatrix<Real, Dim>::jacobi(SquareMatrix<Real, Dim>& a, Vector<Real, Dim>& w, |
239 |
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SquareMatrix<Real, Dim>& v) { |
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+ |
const int n = Dim; |
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+ |
int i, j, k, iq, ip, numPos; |
242 |
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Real tresh, theta, tau, t, sm, s, h, g, c, tmp; |
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+ |
Real bspace[4], zspace[4]; |
244 |
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Real *b = bspace; |
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+ |
Real *z = zspace; |
246 |
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|
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// only allocate memory if the matrix is large |
248 |
+ |
if (n > 4) { |
249 |
+ |
b = new Real[n]; |
250 |
+ |
z = new Real[n]; |
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+ |
} |
252 |
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|
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|
// initialize |
254 |
< |
for (ip=0; ip<N; ip++) { |
255 |
< |
for (iq=0; iq<N; iq++) |
256 |
< |
v(ip, iq) = 0.0; |
257 |
< |
v(ip, ip) = 1.0; |
254 |
> |
for (ip=0; ip<n; ip++) { |
255 |
> |
for (iq=0; iq<n; iq++) { |
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> |
v(ip, iq) = 0.0; |
257 |
> |
} |
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> |
v(ip, ip) = 1.0; |
259 |
|
} |
260 |
< |
|
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< |
for (ip=0; ip<N; ip++) { |
262 |
< |
b(ip) = w(ip) = a(ip, ip); |
211 |
< |
z(ip) = 0.0; |
260 |
> |
for (ip=0; ip<n; ip++) { |
261 |
> |
b[ip] = w[ip] = a(ip, ip); |
262 |
> |
z[ip] = 0.0; |
263 |
|
} |
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|
|
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// begin rotation sequence |
266 |
< |
for (i=0; i<MAX_ROTATIONS; i++) { |
267 |
< |
sm = 0.0; |
268 |
< |
for (ip=0; ip<2; ip++) { |
269 |
< |
for (iq=ip+1; iq<N; iq++) |
270 |
< |
sm += fabs(a(ip, iq)); |
271 |
< |
} |
272 |
< |
|
273 |
< |
if (sm == 0.0) |
274 |
< |
break; |
266 |
> |
for (i=0; i<VTK_MAX_ROTATIONS; i++) { |
267 |
> |
sm = 0.0; |
268 |
> |
for (ip=0; ip<n-1; ip++) { |
269 |
> |
for (iq=ip+1; iq<n; iq++) { |
270 |
> |
sm += fabs(a(ip, iq)); |
271 |
> |
} |
272 |
> |
} |
273 |
> |
if (sm == 0.0) { |
274 |
> |
break; |
275 |
> |
} |
276 |
|
|
277 |
< |
if (i < 4) |
278 |
< |
tresh = 0.2*sm/(9); |
279 |
< |
else |
280 |
< |
tresh = 0.0; |
277 |
> |
if (i < 3) { // first 3 sweeps |
278 |
> |
tresh = 0.2*sm/(n*n); |
279 |
> |
} else { |
280 |
> |
tresh = 0.0; |
281 |
> |
} |
282 |
|
|
283 |
< |
for (ip=0; ip<2; ip++) { |
284 |
< |
for (iq=ip+1; iq<N; iq++) { |
285 |
< |
g = 100.0*fabs(a(ip, iq)); |
233 |
< |
if (i > 4 && (fabs(w(ip))+g) == fabs(w(ip)) |
234 |
< |
&& (fabs(w(iq))+g) == fabs(w(iq))) { |
235 |
< |
a(ip, iq) = 0.0; |
236 |
< |
} else if (fabs(a(ip, iq)) > tresh) { |
237 |
< |
h = w(iq) - w(ip); |
238 |
< |
if ( (fabs(h)+g) == fabs(h)) { |
239 |
< |
t = (a(ip, iq)) / h; |
240 |
< |
} else { |
241 |
< |
theta = 0.5*h / (a(ip, iq)); |
242 |
< |
t = 1.0 / (fabs(theta)+sqrt(1.0+theta*theta)); |
283 |
> |
for (ip=0; ip<n-1; ip++) { |
284 |
> |
for (iq=ip+1; iq<n; iq++) { |
285 |
> |
g = 100.0*fabs(a(ip, iq)); |
286 |
|
|
287 |
< |
if (theta < 0.0) |
288 |
< |
t = -t; |
289 |
< |
} |
287 |
> |
// after 4 sweeps |
288 |
> |
if (i > 3 && (fabs(w[ip])+g) == fabs(w[ip]) |
289 |
> |
&& (fabs(w[iq])+g) == fabs(w[iq])) { |
290 |
> |
a(ip, iq) = 0.0; |
291 |
> |
} else if (fabs(a(ip, iq)) > tresh) { |
292 |
> |
h = w[iq] - w[ip]; |
293 |
> |
if ( (fabs(h)+g) == fabs(h)) { |
294 |
> |
t = (a(ip, iq)) / h; |
295 |
> |
} else { |
296 |
> |
theta = 0.5*h / (a(ip, iq)); |
297 |
> |
t = 1.0 / (fabs(theta)+sqrt(1.0+theta*theta)); |
298 |
> |
if (theta < 0.0) { |
299 |
> |
t = -t; |
300 |
> |
} |
301 |
> |
} |
302 |
> |
c = 1.0 / sqrt(1+t*t); |
303 |
> |
s = t*c; |
304 |
> |
tau = s/(1.0+c); |
305 |
> |
h = t*a(ip, iq); |
306 |
> |
z[ip] -= h; |
307 |
> |
z[iq] += h; |
308 |
> |
w[ip] -= h; |
309 |
> |
w[iq] += h; |
310 |
> |
a(ip, iq)=0.0; |
311 |
|
|
312 |
< |
c = 1.0 / sqrt(1+t*t); |
313 |
< |
s = t*c; |
314 |
< |
tau = s/(1.0+c); |
315 |
< |
h = t*a(ip, iq); |
316 |
< |
z(ip) -= h; |
317 |
< |
z(iq) += h; |
318 |
< |
w(ip) -= h; |
319 |
< |
w(iq) += h; |
320 |
< |
a(ip, iq)=0.0; |
321 |
< |
|
322 |
< |
for (j=0;j<ip-1;j++) |
323 |
< |
ROT(a,j,ip,j,iq); |
312 |
> |
// ip already shifted left by 1 unit |
313 |
> |
for (j = 0;j <= ip-1;j++) { |
314 |
> |
VTK_ROTATE(a,j,ip,j,iq); |
315 |
> |
} |
316 |
> |
// ip and iq already shifted left by 1 unit |
317 |
> |
for (j = ip+1;j <= iq-1;j++) { |
318 |
> |
VTK_ROTATE(a,ip,j,j,iq); |
319 |
> |
} |
320 |
> |
// iq already shifted left by 1 unit |
321 |
> |
for (j=iq+1; j<n; j++) { |
322 |
> |
VTK_ROTATE(a,ip,j,iq,j); |
323 |
> |
} |
324 |
> |
for (j=0; j<n; j++) { |
325 |
> |
VTK_ROTATE(v,j,ip,j,iq); |
326 |
> |
} |
327 |
> |
} |
328 |
> |
} |
329 |
> |
} |
330 |
|
|
331 |
< |
for (j=ip+1;j<iq-1;j++) |
332 |
< |
ROT(a,ip,j,j,iq); |
331 |
> |
for (ip=0; ip<n; ip++) { |
332 |
> |
b[ip] += z[ip]; |
333 |
> |
w[ip] = b[ip]; |
334 |
> |
z[ip] = 0.0; |
335 |
> |
} |
336 |
> |
} |
337 |
|
|
338 |
< |
for (j=iq+1; j<N; j++) |
339 |
< |
ROT(a,ip,j,iq,j); |
340 |
< |
|
341 |
< |
for (j=0; j<N; j++) |
342 |
< |
ROT(v,j,ip,j,iq); |
269 |
< |
} |
270 |
< |
} |
271 |
< |
}//for (ip=0; ip<2; ip++) |
338 |
> |
//// this is NEVER called |
339 |
> |
if ( i >= VTK_MAX_ROTATIONS ) { |
340 |
> |
std::cout << "vtkMath::Jacobi: Error extracting eigenfunctions" << std::endl; |
341 |
> |
return 0; |
342 |
> |
} |
343 |
|
|
344 |
< |
for (ip=0; ip<N; ip++) { |
345 |
< |
b(ip) += z(ip); |
346 |
< |
w(ip) = b(ip); |
347 |
< |
z(ip) = 0.0; |
348 |
< |
} |
349 |
< |
|
350 |
< |
} // end for (i=0; i<MAX_ROTATIONS; i++) |
351 |
< |
|
352 |
< |
if ( i >= MAX_ROTATIONS ) |
353 |
< |
return false; |
354 |
< |
|
355 |
< |
// sort eigenfunctions |
356 |
< |
for (j=0; j<N; j++) { |
357 |
< |
k = j; |
358 |
< |
tmp = w(k); |
359 |
< |
for (i=j; i<N; i++) { |
360 |
< |
if (w(i) >= tmp) { |
361 |
< |
k = i; |
362 |
< |
tmp = w(k); |
292 |
< |
} |
293 |
< |
} |
294 |
< |
|
295 |
< |
if (k != j) { |
296 |
< |
w(k) = w(j); |
297 |
< |
w(j) = tmp; |
298 |
< |
for (i=0; i<N; i++) { |
299 |
< |
tmp = v(i, j); |
300 |
< |
v(i, j) = v(i, k); |
301 |
< |
v(i, k) = tmp; |
302 |
< |
} |
303 |
< |
} |
344 |
> |
// sort eigenfunctions these changes do not affect accuracy |
345 |
> |
for (j=0; j<n-1; j++) { // boundary incorrect |
346 |
> |
k = j; |
347 |
> |
tmp = w[k]; |
348 |
> |
for (i=j+1; i<n; i++) { // boundary incorrect, shifted already |
349 |
> |
if (w[i] >= tmp) { // why exchage if same? |
350 |
> |
k = i; |
351 |
> |
tmp = w[k]; |
352 |
> |
} |
353 |
> |
} |
354 |
> |
if (k != j) { |
355 |
> |
w[k] = w[j]; |
356 |
> |
w[j] = tmp; |
357 |
> |
for (i=0; i<n; i++) { |
358 |
> |
tmp = v(i, j); |
359 |
> |
v(i, j) = v(i, k); |
360 |
> |
v(i, k) = tmp; |
361 |
> |
} |
362 |
> |
} |
363 |
|
} |
364 |
+ |
// insure eigenvector consistency (i.e., Jacobi can compute vectors that |
365 |
+ |
// are negative of one another (.707,.707,0) and (-.707,-.707,0). This can |
366 |
+ |
// reek havoc in hyperstreamline/other stuff. We will select the most |
367 |
+ |
// positive eigenvector. |
368 |
+ |
int ceil_half_n = (n >> 1) + (n & 1); |
369 |
+ |
for (j=0; j<n; j++) { |
370 |
+ |
for (numPos=0, i=0; i<n; i++) { |
371 |
+ |
if ( v(i, j) >= 0.0 ) { |
372 |
+ |
numPos++; |
373 |
+ |
} |
374 |
+ |
} |
375 |
+ |
// if ( numPos < ceil(double(n)/double(2.0)) ) |
376 |
+ |
if ( numPos < ceil_half_n) { |
377 |
+ |
for (i=0; i<n; i++) { |
378 |
+ |
v(i, j) *= -1.0; |
379 |
+ |
} |
380 |
+ |
} |
381 |
+ |
} |
382 |
|
|
383 |
< |
// insure eigenvector consistency (i.e., Jacobi can compute |
384 |
< |
// vectors that are negative of one another (.707,.707,0) and |
385 |
< |
// (-.707,-.707,0). This can reek havoc in |
309 |
< |
// hyperstreamline/other stuff. We will select the most |
310 |
< |
// positive eigenvector. |
311 |
< |
int numPos; |
312 |
< |
for (j=0; j<N; j++) { |
313 |
< |
for (numPos=0, i=0; i<N; i++) if ( v(i, j) >= 0.0 ) numPos++; |
314 |
< |
if ( numPos < 2 ) for(i=0; i<N; i++) v(i, j) *= -1.0; |
383 |
> |
if (n > 4) { |
384 |
> |
delete [] b; |
385 |
> |
delete [] z; |
386 |
|
} |
387 |
+ |
return 1; |
388 |
+ |
} |
389 |
|
|
317 |
– |
return true; |
318 |
– |
} |
390 |
|
|
391 |
< |
#undef ROT |
321 |
< |
#undef MAX_ROTATIONS |
322 |
< |
|
391 |
> |
typedef SquareMatrix<double, 6> Mat6x6d; |
392 |
|
} |
324 |
– |
|
393 |
|
#endif //MATH_SQUAREMATRIX_HPP |
394 |
+ |
|