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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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#include "Quaternion.hpp" | 
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            } | 
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            SquareMatrix3(const Quaternion<Real>& q) { | 
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                *this = q.toRotationMatrix3(); | 
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                setupRotMat(q); | 
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            } | 
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            SquareMatrix3(Real w, Real x, Real y, Real z) { | 
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                Quaternion<Real> q(w, x, y, z); | 
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                *this = q.toRotationMatrix3(); | 
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                setupRotMat(w, x, y, z); | 
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            } | 
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            /** copy assignment operator */ | 
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                if (this == &m) | 
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                    return *this; | 
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                 SquareMatrix<Real, 3>::operator=(m); | 
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                 return *this; | 
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            } | 
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            /** | 
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             * @param quat | 
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            */ | 
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            void setupRotMat(const Quaternion<Real>& quat) { | 
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                *this = quat.toRotationMatrix3(); | 
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                setupRotMat(quat.w(), quat.x(), quat.y(), quat.z()); | 
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            } | 
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            /** | 
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             * @param w the first element  | 
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             * @param x the second element | 
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             * @param y the third element | 
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             * @parma z the fourth element | 
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             * @param z the fourth element | 
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            */ | 
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            void setupRotMat(Real w, Real x, Real y, Real z) { | 
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                Quaternion<Real> q(w, x, y, z); | 
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             * z-axis (again).  | 
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            */             | 
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            Vector3<Real> toEulerAngles() { | 
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                Vector<Real> myEuler; | 
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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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                // set the tolerance for Euler angles and rotation elements | 
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                theta = acos(min(1.0,max(-1.0,data_[2][2]))); | 
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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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                stheta = sqrt(1.0 - ctheta * ctheta); | 
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                return myEuler; | 
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            } | 
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             | 
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            /** Returns the determinant of this matrix. */ | 
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            Real determinant() const { | 
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                Real x,y,z; | 
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 | 
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                x = data_[0][0] * (data_[1][1] * data_[2][2] - data_[1][2] * data_[2][1]); | 
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                y = data_[0][1] * (data_[1][2] * data_[2][0] - data_[1][0] * data_[2][2]); | 
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                z = data_[0][2] * (data_[1][0] * data_[2][1] - data_[1][1] * data_[2][0]); | 
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                return(x + y + z); | 
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            }             | 
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             | 
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            /** | 
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             * Sets the value of this matrix to  the inversion of itself.  | 
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             * @note since simple algorithm can be applied to inverse the 3 by 3 matrix, we hide the  | 
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             * implementation of inverse in SquareMatrix class | 
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             */ | 
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            void  inverse() { | 
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            SquareMatrix3<Real>  inverse() { | 
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                SquareMatrix3<Real> m; | 
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                double det = determinant(); | 
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                if (fabs(det) <= oopse::epsilon) { | 
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                //"The method was called on a matrix with |determinant| <= 1e-6.", | 
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                //"This is a runtime or a programming error in your application."); | 
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                } | 
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 | 
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            } | 
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                m(0, 0) = data_[1][1]*data_[2][2] - data_[1][2]*data_[2][1]; | 
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                m(1, 0) = data_[1][2]*data_[2][0] - data_[1][0]*data_[2][2]; | 
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                m(2, 0) = data_[1][0]*data_[2][1] - data_[1][1]*data_[2][0]; | 
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                m(0, 1) = data_[2][1]*data_[0][2] - data_[2][2]*data_[0][1]; | 
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                m(1, 1) = data_[2][2]*data_[0][0] - data_[2][0]*data_[0][2]; | 
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                m(2, 1) = data_[2][0]*data_[0][1] - data_[2][1]*data_[0][0]; | 
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                m(0, 2) = data_[0][1]*data_[1][2] - data_[0][2]*data_[1][1]; | 
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                m(1, 2) = data_[0][2]*data_[1][0] - data_[0][0]*data_[1][2]; | 
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                m(2, 2) = data_[0][0]*data_[1][1] - data_[0][1]*data_[1][0]; | 
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            void diagonalize() { | 
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                m /= det; | 
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                return m; | 
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            } | 
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            /** | 
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             * Extract the eigenvalues and eigenvectors from a 3x3 matrix. | 
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             * The eigenvectors (the columns of V) will be normalized.  | 
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             * The eigenvectors are aligned optimally with the x, y, and z | 
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             * axes respectively. | 
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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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             * @warning a will be overwritten | 
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             */ | 
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            static void diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w, SquareMatrix3<Real>& v);  | 
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    }; | 
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/*========================================================================= | 
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 | 
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  Program:   Visualization Toolkit | 
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  Module:    $RCSfile: SquareMatrix3.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. | 
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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 | 
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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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    template<typename Real> | 
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    void SquareMatrix3<Real>::diagonalize(SquareMatrix3<Real>& a, Vector3<Real>& w,  | 
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                                                                           SquareMatrix3<Real>& v) { | 
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        int i,j,k,maxI; | 
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        Real tmp, maxVal; | 
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        Vector3<Real> v_maxI, v_k, v_j; | 
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 | 
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        // diagonalize using Jacobi | 
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        jacobi(a, w, v); | 
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        // if all the eigenvalues are the same, return identity matrix | 
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        if (w[0] == w[1] && w[0] == w[2] ) { | 
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              v = SquareMatrix3<Real>::identity(); | 
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              return; | 
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        } | 
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        // transpose temporarily, it makes it easier to sort the eigenvectors | 
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        v = v.transpose();  | 
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        // if two eigenvalues are the same, re-orthogonalize to optimally line | 
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        // up the eigenvectors with the x, y, and z axes | 
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        for (i = 0; i < 3; i++) { | 
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            if (w((i+1)%3) == w((i+2)%3)) {// two eigenvalues are the same | 
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            // find maximum element of the independant eigenvector | 
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            maxVal = fabs(v(i, 0)); | 
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            maxI = 0; | 
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            for (j = 1; j < 3; j++) { | 
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                if (maxVal < (tmp = fabs(v(i, j)))){ | 
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                    maxVal = tmp; | 
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                    maxI = j; | 
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                } | 
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            } | 
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            // swap the eigenvector into its proper position | 
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            if (maxI != i) { | 
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                tmp = w(maxI); | 
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                w(maxI) = w(i); | 
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                w(i) = tmp; | 
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                v.swapRow(i, maxI); | 
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            } | 
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            // maximum element of eigenvector should be positive | 
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            if (v(maxI, maxI) < 0) { | 
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                v(maxI, 0) = -v(maxI, 0); | 
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                v(maxI, 1) = -v(maxI, 1); | 
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                v(maxI, 2) = -v(maxI, 2); | 
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            } | 
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            // re-orthogonalize the other two eigenvectors | 
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            j = (maxI+1)%3; | 
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            k = (maxI+2)%3; | 
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            v(j, 0) = 0.0;  | 
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            v(j, 1) = 0.0;  | 
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            v(j, 2) = 0.0; | 
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            v(j, j) = 1.0; | 
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 | 
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            /** @todo */ | 
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            v_maxI = v.getRow(maxI); | 
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            v_j = v.getRow(j); | 
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            v_k = cross(v_maxI, v_j); | 
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            v_k.normalize(); | 
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            v_j = cross(v_k, v_maxI); | 
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            v.setRow(j, v_j); | 
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            v.setRow(k, v_k); | 
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            // transpose vectors back to columns | 
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            v = v.transpose(); | 
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            return; | 
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            } | 
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        } | 
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 | 
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        // the three eigenvalues are different, just sort the eigenvectors | 
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        // to align them with the x, y, and z axes | 
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 | 
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        // find the vector with the largest x element, make that vector | 
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        // the first vector | 
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        maxVal = fabs(v(0, 0)); | 
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        maxI = 0; | 
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        for (i = 1; i < 3; i++) { | 
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            if (maxVal < (tmp = fabs(v(i, 0)))) { | 
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                maxVal = tmp; | 
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                maxI = i; | 
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            } | 
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        } | 
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 | 
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        // swap eigenvalue and eigenvector | 
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        if (maxI != 0) { | 
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            tmp = w(maxI); | 
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            w(maxI) = w(0); | 
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            w(0) = tmp; | 
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            v.swapRow(maxI, 0); | 
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        } | 
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        // do the same for the y element | 
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        if (fabs(v(1, 1)) < fabs(v(2, 1))) { | 
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            tmp = w(2); | 
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            w(2) = w(1); | 
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            w(1) = tmp; | 
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            v.swapRow(2, 1); | 
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        } | 
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 | 
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        // ensure that the sign of the eigenvectors is correct | 
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        for (i = 0; i < 2; i++) { | 
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            if (v(i, i) < 0) { | 
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                v(i, 0) = -v(i, 0); | 
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                v(i, 1) = -v(i, 1); | 
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                v(i, 2) = -v(i, 2); | 
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            } | 
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        } | 
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 | 
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        // set sign of final eigenvector to ensure that determinant is positive | 
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        if (v.determinant() < 0) { | 
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            v(2, 0) = -v(2, 0); | 
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            v(2, 1) = -v(2, 1); | 
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            v(2, 2) = -v(2, 2); | 
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        } | 
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 | 
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        // transpose the eigenvectors back again | 
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        v = v.transpose(); | 
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        return ; | 
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    } | 
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    typedef SquareMatrix3<double> Mat3x3d; | 
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    typedef SquareMatrix3<double> RotMat3x3d; | 
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 | 
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} //namespace oopse | 
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#endif // MATH_SQUAREMATRIX_HPP | 
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