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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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/* |
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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 SquareMatrix3.hpp |
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* @author Teng Lin |
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* @date 10/11/2004 |
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* @version 1.0 |
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*/ |
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#ifndef MATH_SQUAREMATRIX3_HPP |
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#ifndef MATH_SQUAREMATRIX3_HPP |
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#define MATH_SQUAREMATRIX3_HPP |
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|
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#include "Quaternion.hpp" |
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#include "SquareMatrix.hpp" |
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#include "Vector3.hpp" |
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|
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#include "utils/NumericConstant.hpp" |
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namespace oopse { |
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|
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template<typename Real> |
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class SquareMatrix3 : public SquareMatrix<Real, 3> { |
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public: |
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|
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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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SquareMatrix3() : SquareMatrix<Real, 3>() { |
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} |
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|
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/** Constructs and initializes every element of this matrix to a scalar */ |
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SquareMatrix3(Real s) : SquareMatrix<Real,3>(s){ |
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} |
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|
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/** Constructs and initializes from an array */ |
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SquareMatrix3(Real* array) : SquareMatrix<Real,3>(array){ |
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} |
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|
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|
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/** copy constructor */ |
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SquareMatrix3(const SquareMatrix<Real, 3>& m) : SquareMatrix<Real, 3>(m) { |
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} |
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|
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|
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SquareMatrix3( const Vector3<Real>& eulerAngles) { |
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setupRotMat(eulerAngles); |
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} |
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return *this; |
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} |
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|
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|
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SquareMatrix3<Real>& operator =(const Quaternion<Real>& q) { |
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this->setupRotMat(q); |
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return *this; |
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} |
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|
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/** |
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* Sets this matrix to a rotation matrix by three euler angles |
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* @ param euler |
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ctheta = cos(theta); |
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cpsi = cos(psi); |
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|
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data_[0][0] = cpsi * cphi - ctheta * sphi * spsi; |
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data_[0][1] = cpsi * sphi + ctheta * cphi * spsi; |
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data_[0][2] = spsi * stheta; |
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this->data_[0][0] = cpsi * cphi - ctheta * sphi * spsi; |
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this->data_[0][1] = cpsi * sphi + ctheta * cphi * spsi; |
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this->data_[0][2] = spsi * stheta; |
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|
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data_[1][0] = -spsi * ctheta - ctheta * sphi * cpsi; |
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data_[1][1] = -spsi * stheta + ctheta * cphi * cpsi; |
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data_[1][2] = cpsi * stheta; |
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this->data_[1][0] = -spsi * ctheta - ctheta * sphi * cpsi; |
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this->data_[1][1] = -spsi * stheta + ctheta * cphi * cpsi; |
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this->data_[1][2] = cpsi * stheta; |
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|
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data_[2][0] = stheta * sphi; |
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data_[2][1] = -stheta * cphi; |
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data_[2][2] = ctheta; |
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this->data_[2][0] = stheta * sphi; |
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this->data_[2][1] = -stheta * cphi; |
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this->data_[2][2] = ctheta; |
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} |
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|
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|
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Quaternion<Real> q; |
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Real t, s; |
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Real ad1, ad2, ad3; |
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t = data_[0][0] + data_[1][1] + data_[2][2] + 1.0; |
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t = this->data_[0][0] + this->data_[1][1] + this->data_[2][2] + 1.0; |
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|
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if( t > 0.0 ){ |
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if( t > NumericConstant::epsilon ){ |
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|
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s = 0.5 / sqrt( t ); |
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q[0] = 0.25 / s; |
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q[1] = (data_[1][2] - data_[2][1]) * s; |
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q[2] = (data_[2][0] - data_[0][2]) * s; |
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q[3] = (data_[0][1] - data_[1][0]) * s; |
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q[1] = (this->data_[1][2] - this->data_[2][1]) * s; |
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q[2] = (this->data_[2][0] - this->data_[0][2]) * s; |
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q[3] = (this->data_[0][1] - this->data_[1][0]) * s; |
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} else { |
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|
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ad1 = fabs( data_[0][0] ); |
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ad2 = fabs( data_[1][1] ); |
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ad3 = fabs( data_[2][2] ); |
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ad1 = fabs( this->data_[0][0] ); |
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ad2 = fabs( this->data_[1][1] ); |
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ad3 = fabs( this->data_[2][2] ); |
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|
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if( ad1 >= ad2 && ad1 >= ad3 ){ |
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|
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s = 2.0 * sqrt( 1.0 + data_[0][0] - data_[1][1] - data_[2][2] ); |
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q[0] = (data_[1][2] + data_[2][1]) / s; |
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q[1] = 0.5 / s; |
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q[2] = (data_[0][1] + data_[1][0]) / s; |
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q[3] = (data_[0][2] + data_[2][0]) / s; |
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s = 0.5 / sqrt( 1.0 + this->data_[0][0] - this->data_[1][1] - this->data_[2][2] ); |
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q[0] = (this->data_[1][2] - this->data_[2][1]) * s; |
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q[1] = 0.25 / s; |
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q[2] = (this->data_[0][1] + this->data_[1][0]) * s; |
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q[3] = (this->data_[0][2] + this->data_[2][0]) * s; |
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} else if ( ad2 >= ad1 && ad2 >= ad3 ) { |
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s = sqrt( 1.0 + data_[1][1] - data_[0][0] - data_[2][2] ) * 2.0; |
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q[0] = (data_[0][2] + data_[2][0]) / s; |
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q[1] = (data_[0][1] + data_[1][0]) / s; |
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q[2] = 0.5 / s; |
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q[3] = (data_[1][2] + data_[2][1]) / s; |
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s = 0.5 / sqrt( 1.0 + this->data_[1][1] - this->data_[0][0] - this->data_[2][2] ); |
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q[0] = (this->data_[2][0] - this->data_[0][2] ) * s; |
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q[1] = (this->data_[0][1] + this->data_[1][0]) * s; |
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q[2] = 0.25 / s; |
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q[3] = (this->data_[1][2] + this->data_[2][1]) * s; |
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} else { |
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|
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s = sqrt( 1.0 + data_[2][2] - data_[0][0] - data_[1][1] ) * 2.0; |
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q[0] = (data_[0][1] + data_[1][0]) / s; |
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q[1] = (data_[0][2] + data_[2][0]) / s; |
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q[2] = (data_[1][2] + data_[2][1]) / s; |
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q[3] = 0.5 / s; |
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s = 0.5 / sqrt( 1.0 + this->data_[2][2] - this->data_[0][0] - this->data_[1][1] ); |
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q[0] = (this->data_[0][1] - this->data_[1][0]) * s; |
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q[1] = (this->data_[0][2] + this->data_[2][0]) * s; |
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q[2] = (this->data_[1][2] + this->data_[2][1]) * s; |
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q[3] = 0.25 / s; |
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} |
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} |
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|
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*/ |
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Vector3<Real> toEulerAngles() { |
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Vector3<Real> myEuler; |
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Real phi,theta,psi,eps; |
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Real ctheta,stheta; |
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Real phi; |
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Real theta; |
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Real psi; |
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Real ctheta; |
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> |
Real stheta; |
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|
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// set the tolerance for Euler angles and rotation elements |
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|
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< |
theta = acos(std::min(1.0, std::max(-1.0,data_[2][2]))); |
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ctheta = data_[2][2]; |
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theta = acos(std::min(1.0, std::max(-1.0,this->data_[2][2]))); |
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ctheta = this->data_[2][2]; |
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stheta = sqrt(1.0 - ctheta * ctheta); |
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|
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// when sin(theta) is close to 0, we need to consider singularity |
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|
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if (fabs(stheta) <= oopse::epsilon){ |
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psi = 0.0; |
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< |
phi = atan2(-data_[1][0], data_[0][0]); |
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> |
phi = atan2(-this->data_[1][0], this->data_[0][0]); |
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} |
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// we only have one unique solution |
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else{ |
260 |
< |
phi = atan2(data_[2][0], -data_[2][1]); |
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< |
psi = atan2(data_[0][2], data_[1][2]); |
260 |
> |
phi = atan2(this->data_[2][0], -this->data_[2][1]); |
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> |
psi = atan2(this->data_[0][2], this->data_[1][2]); |
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} |
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|
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//wrap phi and psi, make sure they are in the range from 0 to 2*Pi |
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Real determinant() const { |
280 |
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Real x,y,z; |
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|
|
282 |
< |
x = data_[0][0] * (data_[1][1] * data_[2][2] - data_[1][2] * data_[2][1]); |
283 |
< |
y = data_[0][1] * (data_[1][2] * data_[2][0] - data_[1][0] * data_[2][2]); |
284 |
< |
z = data_[0][2] * (data_[1][0] * data_[2][1] - data_[1][1] * data_[2][0]); |
282 |
> |
x = this->data_[0][0] * (this->data_[1][1] * this->data_[2][2] - this->data_[1][2] * this->data_[2][1]); |
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> |
y = this->data_[0][1] * (this->data_[1][2] * this->data_[2][0] - this->data_[1][0] * this->data_[2][2]); |
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> |
z = this->data_[0][2] * (this->data_[1][0] * this->data_[2][1] - this->data_[1][1] * this->data_[2][0]); |
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|
|
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return(x + y + z); |
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} |
288 |
+ |
|
289 |
+ |
/** Returns the trace of this matrix. */ |
290 |
+ |
Real trace() const { |
291 |
+ |
return this->data_[0][0] + this->data_[1][1] + this->data_[2][2]; |
292 |
+ |
} |
293 |
|
|
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/** |
295 |
|
* Sets the value of this matrix to the inversion of itself. |
296 |
|
* @note since simple algorithm can be applied to inverse the 3 by 3 matrix, we hide the |
297 |
|
* implementation of inverse in SquareMatrix class |
298 |
|
*/ |
299 |
< |
SquareMatrix3<Real> inverse() { |
299 |
> |
SquareMatrix3<Real> inverse() const { |
300 |
|
SquareMatrix3<Real> m; |
301 |
|
double det = determinant(); |
302 |
|
if (fabs(det) <= oopse::epsilon) { |
304 |
|
//"This is a runtime or a programming error in your application."); |
305 |
|
} |
306 |
|
|
307 |
< |
m(0, 0) = data_[1][1]*data_[2][2] - data_[1][2]*data_[2][1]; |
308 |
< |
m(1, 0) = data_[1][2]*data_[2][0] - data_[1][0]*data_[2][2]; |
309 |
< |
m(2, 0) = data_[1][0]*data_[2][1] - data_[1][1]*data_[2][0]; |
310 |
< |
m(0, 1) = data_[2][1]*data_[0][2] - data_[2][2]*data_[0][1]; |
311 |
< |
m(1, 1) = data_[2][2]*data_[0][0] - data_[2][0]*data_[0][2]; |
312 |
< |
m(2, 1) = data_[2][0]*data_[0][1] - data_[2][1]*data_[0][0]; |
313 |
< |
m(0, 2) = data_[0][1]*data_[1][2] - data_[0][2]*data_[1][1]; |
314 |
< |
m(1, 2) = data_[0][2]*data_[1][0] - data_[0][0]*data_[1][2]; |
315 |
< |
m(2, 2) = data_[0][0]*data_[1][1] - data_[0][1]*data_[1][0]; |
307 |
> |
m(0, 0) = this->data_[1][1]*this->data_[2][2] - this->data_[1][2]*this->data_[2][1]; |
308 |
> |
m(1, 0) = this->data_[1][2]*this->data_[2][0] - this->data_[1][0]*this->data_[2][2]; |
309 |
> |
m(2, 0) = this->data_[1][0]*this->data_[2][1] - this->data_[1][1]*this->data_[2][0]; |
310 |
> |
m(0, 1) = this->data_[2][1]*this->data_[0][2] - this->data_[2][2]*this->data_[0][1]; |
311 |
> |
m(1, 1) = this->data_[2][2]*this->data_[0][0] - this->data_[2][0]*this->data_[0][2]; |
312 |
> |
m(2, 1) = this->data_[2][0]*this->data_[0][1] - this->data_[2][1]*this->data_[0][0]; |
313 |
> |
m(0, 2) = this->data_[0][1]*this->data_[1][2] - this->data_[0][2]*this->data_[1][1]; |
314 |
> |
m(1, 2) = this->data_[0][2]*this->data_[1][0] - this->data_[0][0]*this->data_[1][2]; |
315 |
> |
m(2, 2) = this->data_[0][0]*this->data_[1][1] - this->data_[0][1]*this->data_[1][0]; |
316 |
|
|
317 |
|
m /= det; |
318 |
|
return m; |
319 |
|
} |
320 |
+ |
/** |
321 |
+ |
* Extract the eigenvalues and eigenvectors from a 3x3 matrix. |
322 |
+ |
* The eigenvectors (the columns of V) will be normalized. |
323 |
+ |
* The eigenvectors are aligned optimally with the x, y, and z |
324 |
+ |
* axes respectively. |
325 |
+ |
* @param a symmetric matrix whose eigenvectors are to be computed. On return, the matrix is |
326 |
+ |
* overwritten |
327 |
+ |
* @param w will contain the eigenvalues of the matrix On return of this function |
328 |
+ |
* @param v the columns of this matrix will contain the eigenvectors. The eigenvectors are |
329 |
+ |
* normalized and mutually orthogonal. |
330 |
+ |
* @warning a will be overwritten |
331 |
+ |
*/ |
332 |
+ |
static void diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, SquareMatrix3<Real>& v); |
333 |
+ |
}; |
334 |
+ |
/*========================================================================= |
335 |
|
|
336 |
< |
void diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, SquareMatrix3<Real>& v) { |
337 |
< |
int i,j,k,maxI; |
281 |
< |
Real tmp, maxVal; |
282 |
< |
Vector3<Real> v_maxI, v_k, v_j; |
336 |
> |
Program: Visualization Toolkit |
337 |
> |
Module: $RCSfile: SquareMatrix3.hpp,v $ |
338 |
|
|
339 |
< |
// diagonalize using Jacobi |
340 |
< |
jacobi(a, w, v); |
339 |
> |
Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen |
340 |
> |
All rights reserved. |
341 |
> |
See Copyright.txt or http://www.kitware.com/Copyright.htm for details. |
342 |
|
|
343 |
< |
// if all the eigenvalues are the same, return identity matrix |
344 |
< |
if (w[0] == w[1] && w[0] == w[2] ) { |
345 |
< |
v = SquareMatrix3<Real>::identity(); |
346 |
< |
return; |
343 |
> |
This software is distributed WITHOUT ANY WARRANTY; without even |
344 |
> |
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR |
345 |
> |
PURPOSE. See the above copyright notice for more information. |
346 |
> |
|
347 |
> |
=========================================================================*/ |
348 |
> |
template<typename Real> |
349 |
> |
void SquareMatrix3<Real>::diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, |
350 |
> |
SquareMatrix3<Real>& v) { |
351 |
> |
int i,j,k,maxI; |
352 |
> |
Real tmp, maxVal; |
353 |
> |
Vector3<Real> v_maxI, v_k, v_j; |
354 |
> |
|
355 |
> |
// diagonalize using Jacobi |
356 |
> |
jacobi(a, w, v); |
357 |
> |
// if all the eigenvalues are the same, return identity matrix |
358 |
> |
if (w[0] == w[1] && w[0] == w[2] ) { |
359 |
> |
v = SquareMatrix3<Real>::identity(); |
360 |
> |
return; |
361 |
> |
} |
362 |
> |
|
363 |
> |
// transpose temporarily, it makes it easier to sort the eigenvectors |
364 |
> |
v = v.transpose(); |
365 |
> |
|
366 |
> |
// if two eigenvalues are the same, re-orthogonalize to optimally line |
367 |
> |
// up the eigenvectors with the x, y, and z axes |
368 |
> |
for (i = 0; i < 3; i++) { |
369 |
> |
if (w((i+1)%3) == w((i+2)%3)) {// two eigenvalues are the same |
370 |
> |
// find maximum element of the independant eigenvector |
371 |
> |
maxVal = fabs(v(i, 0)); |
372 |
> |
maxI = 0; |
373 |
> |
for (j = 1; j < 3; j++) { |
374 |
> |
if (maxVal < (tmp = fabs(v(i, j)))){ |
375 |
> |
maxVal = tmp; |
376 |
> |
maxI = j; |
377 |
|
} |
378 |
+ |
} |
379 |
+ |
|
380 |
+ |
// swap the eigenvector into its proper position |
381 |
+ |
if (maxI != i) { |
382 |
+ |
tmp = w(maxI); |
383 |
+ |
w(maxI) = w(i); |
384 |
+ |
w(i) = tmp; |
385 |
|
|
386 |
< |
// transpose temporarily, it makes it easier to sort the eigenvectors |
387 |
< |
v = v.transpose(); |
388 |
< |
|
389 |
< |
// if two eigenvalues are the same, re-orthogonalize to optimally line |
390 |
< |
// up the eigenvectors with the x, y, and z axes |
391 |
< |
for (i = 0; i < 3; i++) { |
392 |
< |
if (w((i+1)%3) == w((i+2)%3)) {// two eigenvalues are the same |
393 |
< |
// find maximum element of the independant eigenvector |
301 |
< |
maxVal = fabs(v(i, 0)); |
302 |
< |
maxI = 0; |
303 |
< |
for (j = 1; j < 3; j++) { |
304 |
< |
if (maxVal < (tmp = fabs(v(i, j)))){ |
305 |
< |
maxVal = tmp; |
306 |
< |
maxI = j; |
307 |
< |
} |
308 |
< |
} |
309 |
< |
|
310 |
< |
// swap the eigenvector into its proper position |
311 |
< |
if (maxI != i) { |
312 |
< |
tmp = w(maxI); |
313 |
< |
w(maxI) = w(i); |
314 |
< |
w(i) = tmp; |
386 |
> |
v.swapRow(i, maxI); |
387 |
> |
} |
388 |
> |
// maximum element of eigenvector should be positive |
389 |
> |
if (v(maxI, maxI) < 0) { |
390 |
> |
v(maxI, 0) = -v(maxI, 0); |
391 |
> |
v(maxI, 1) = -v(maxI, 1); |
392 |
> |
v(maxI, 2) = -v(maxI, 2); |
393 |
> |
} |
394 |
|
|
395 |
< |
v.swapRow(i, maxI); |
396 |
< |
} |
397 |
< |
// maximum element of eigenvector should be positive |
319 |
< |
if (v(maxI, maxI) < 0) { |
320 |
< |
v(maxI, 0) = -v(maxI, 0); |
321 |
< |
v(maxI, 1) = -v(maxI, 1); |
322 |
< |
v(maxI, 2) = -v(maxI, 2); |
323 |
< |
} |
395 |
> |
// re-orthogonalize the other two eigenvectors |
396 |
> |
j = (maxI+1)%3; |
397 |
> |
k = (maxI+2)%3; |
398 |
|
|
399 |
< |
// re-orthogonalize the other two eigenvectors |
400 |
< |
j = (maxI+1)%3; |
401 |
< |
k = (maxI+2)%3; |
399 |
> |
v(j, 0) = 0.0; |
400 |
> |
v(j, 1) = 0.0; |
401 |
> |
v(j, 2) = 0.0; |
402 |
> |
v(j, j) = 1.0; |
403 |
|
|
404 |
< |
v(j, 0) = 0.0; |
405 |
< |
v(j, 1) = 0.0; |
406 |
< |
v(j, 2) = 0.0; |
407 |
< |
v(j, j) = 1.0; |
404 |
> |
/** @todo */ |
405 |
> |
v_maxI = v.getRow(maxI); |
406 |
> |
v_j = v.getRow(j); |
407 |
> |
v_k = cross(v_maxI, v_j); |
408 |
> |
v_k.normalize(); |
409 |
> |
v_j = cross(v_k, v_maxI); |
410 |
> |
v.setRow(j, v_j); |
411 |
> |
v.setRow(k, v_k); |
412 |
|
|
334 |
– |
/** @todo */ |
335 |
– |
v_maxI = v.getRow(maxI); |
336 |
– |
v_j = v.getRow(j); |
337 |
– |
v_k = cross(v_maxI, v_j); |
338 |
– |
v_k.normalize(); |
339 |
– |
v_j = cross(v_k, v_maxI); |
340 |
– |
v.setRow(j, v_j); |
341 |
– |
v.setRow(k, v_k); |
413 |
|
|
414 |
+ |
// transpose vectors back to columns |
415 |
+ |
v = v.transpose(); |
416 |
+ |
return; |
417 |
+ |
} |
418 |
+ |
} |
419 |
|
|
420 |
< |
// transpose vectors back to columns |
421 |
< |
v = v.transpose(); |
346 |
< |
return; |
347 |
< |
} |
348 |
< |
} |
420 |
> |
// the three eigenvalues are different, just sort the eigenvectors |
421 |
> |
// to align them with the x, y, and z axes |
422 |
|
|
423 |
< |
// the three eigenvalues are different, just sort the eigenvectors |
424 |
< |
// to align them with the x, y, and z axes |
423 |
> |
// find the vector with the largest x element, make that vector |
424 |
> |
// the first vector |
425 |
> |
maxVal = fabs(v(0, 0)); |
426 |
> |
maxI = 0; |
427 |
> |
for (i = 1; i < 3; i++) { |
428 |
> |
if (maxVal < (tmp = fabs(v(i, 0)))) { |
429 |
> |
maxVal = tmp; |
430 |
> |
maxI = i; |
431 |
> |
} |
432 |
> |
} |
433 |
|
|
434 |
< |
// find the vector with the largest x element, make that vector |
435 |
< |
// the first vector |
436 |
< |
maxVal = fabs(v(0, 0)); |
437 |
< |
maxI = 0; |
438 |
< |
for (i = 1; i < 3; i++) { |
439 |
< |
if (maxVal < (tmp = fabs(v(i, 0)))) { |
440 |
< |
maxVal = tmp; |
441 |
< |
maxI = i; |
442 |
< |
} |
443 |
< |
} |
434 |
> |
// swap eigenvalue and eigenvector |
435 |
> |
if (maxI != 0) { |
436 |
> |
tmp = w(maxI); |
437 |
> |
w(maxI) = w(0); |
438 |
> |
w(0) = tmp; |
439 |
> |
v.swapRow(maxI, 0); |
440 |
> |
} |
441 |
> |
// do the same for the y element |
442 |
> |
if (fabs(v(1, 1)) < fabs(v(2, 1))) { |
443 |
> |
tmp = w(2); |
444 |
> |
w(2) = w(1); |
445 |
> |
w(1) = tmp; |
446 |
> |
v.swapRow(2, 1); |
447 |
> |
} |
448 |
|
|
449 |
< |
// swap eigenvalue and eigenvector |
450 |
< |
if (maxI != 0) { |
451 |
< |
tmp = w(maxI); |
452 |
< |
w(maxI) = w(0); |
453 |
< |
w(0) = tmp; |
454 |
< |
v.swapRow(maxI, 0); |
455 |
< |
} |
456 |
< |
// do the same for the y element |
372 |
< |
if (fabs(v(1, 1)) < fabs(v(2, 1))) { |
373 |
< |
tmp = w(2); |
374 |
< |
w(2) = w(1); |
375 |
< |
w(1) = tmp; |
376 |
< |
v.swapRow(2, 1); |
377 |
< |
} |
449 |
> |
// ensure that the sign of the eigenvectors is correct |
450 |
> |
for (i = 0; i < 2; i++) { |
451 |
> |
if (v(i, i) < 0) { |
452 |
> |
v(i, 0) = -v(i, 0); |
453 |
> |
v(i, 1) = -v(i, 1); |
454 |
> |
v(i, 2) = -v(i, 2); |
455 |
> |
} |
456 |
> |
} |
457 |
|
|
458 |
< |
// ensure that the sign of the eigenvectors is correct |
459 |
< |
for (i = 0; i < 2; i++) { |
460 |
< |
if (v(i, i) < 0) { |
461 |
< |
v(i, 0) = -v(i, 0); |
462 |
< |
v(i, 1) = -v(i, 1); |
463 |
< |
v(i, 2) = -v(i, 2); |
385 |
< |
} |
386 |
< |
} |
458 |
> |
// set sign of final eigenvector to ensure that determinant is positive |
459 |
> |
if (v.determinant() < 0) { |
460 |
> |
v(2, 0) = -v(2, 0); |
461 |
> |
v(2, 1) = -v(2, 1); |
462 |
> |
v(2, 2) = -v(2, 2); |
463 |
> |
} |
464 |
|
|
465 |
< |
// set sign of final eigenvector to ensure that determinant is positive |
466 |
< |
if (v.determinant() < 0) { |
467 |
< |
v(2, 0) = -v(2, 0); |
468 |
< |
v(2, 1) = -v(2, 1); |
392 |
< |
v(2, 2) = -v(2, 2); |
393 |
< |
} |
465 |
> |
// transpose the eigenvectors back again |
466 |
> |
v = v.transpose(); |
467 |
> |
return ; |
468 |
> |
} |
469 |
|
|
470 |
< |
// transpose the eigenvectors back again |
471 |
< |
v = v.transpose(); |
472 |
< |
return ; |
470 |
> |
/** |
471 |
> |
* Return the multiplication of two matrixes (m1 * m2). |
472 |
> |
* @return the multiplication of two matrixes |
473 |
> |
* @param m1 the first matrix |
474 |
> |
* @param m2 the second matrix |
475 |
> |
*/ |
476 |
> |
template<typename Real> |
477 |
> |
inline SquareMatrix3<Real> operator *(const SquareMatrix3<Real>& m1, const SquareMatrix3<Real>& m2) { |
478 |
> |
SquareMatrix3<Real> result; |
479 |
> |
|
480 |
> |
for (unsigned int i = 0; i < 3; i++) |
481 |
> |
for (unsigned int j = 0; j < 3; j++) |
482 |
> |
for (unsigned int k = 0; k < 3; k++) |
483 |
> |
result(i, j) += m1(i, k) * m2(k, j); |
484 |
> |
|
485 |
> |
return result; |
486 |
> |
} |
487 |
> |
|
488 |
> |
template<typename Real> |
489 |
> |
inline SquareMatrix3<Real> outProduct(const Vector3<Real>& v1, const Vector3<Real>& v2) { |
490 |
> |
SquareMatrix3<Real> result; |
491 |
> |
|
492 |
> |
for (unsigned int i = 0; i < 3; i++) { |
493 |
> |
for (unsigned int j = 0; j < 3; j++) { |
494 |
> |
result(i, j) = v1[i] * v2[j]; |
495 |
> |
} |
496 |
|
} |
497 |
< |
}; |
497 |
> |
|
498 |
> |
return result; |
499 |
> |
} |
500 |
|
|
501 |
+ |
|
502 |
|
typedef SquareMatrix3<double> Mat3x3d; |
503 |
|
typedef SquareMatrix3<double> RotMat3x3d; |
504 |
|
|
505 |
|
} //namespace oopse |
506 |
|
#endif // MATH_SQUAREMATRIX_HPP |
507 |
+ |
|