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81 lines
3.0 KiB
C++
81 lines
3.0 KiB
C++
// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
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//
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// Eigen is free software; you can redistribute it and/or
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// modify it under the terms of the GNU Lesser General Public
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// License as published by the Free Software Foundation; either
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// version 3 of the License, or (at your option) any later version.
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//
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// Alternatively, you can redistribute it and/or
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// modify it under the terms of the GNU General Public License as
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// published by the Free Software Foundation; either version 2 of
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// the License, or (at your option) any later version.
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//
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// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
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// GNU 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
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// License and a copy of the GNU General Public License along with
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// Eigen. If not, see <http://www.gnu.org/licenses/>.
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#include "main.h"
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#include <Eigen/LU>
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#include <algorithm>
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template<typename MatrixType> void inverse_permutation_4x4()
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{
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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Vector4i indices(0,1,2,3);
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for(int i = 0; i < 24; ++i)
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{
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MatrixType m = PermutationMatrix<4>(indices);
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MatrixType inv = m.inverse();
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double error = double( (m*inv-MatrixType::Identity()).norm() / NumTraits<Scalar>::epsilon() );
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VERIFY(error == 0.0);
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std::next_permutation(indices.data(),indices.data()+4);
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}
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}
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template<typename MatrixType> void inverse_general_4x4(int repeat)
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{
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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double error_sum = 0., error_max = 0.;
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for(int i = 0; i < repeat; ++i)
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{
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MatrixType m;
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RealScalar absdet;
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do {
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m = MatrixType::Random();
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absdet = ei_abs(m.determinant());
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} while(absdet < NumTraits<Scalar>::epsilon());
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MatrixType inv = m.inverse();
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double error = double( (m*inv-MatrixType::Identity()).norm() * absdet / NumTraits<Scalar>::epsilon() );
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error_sum += error;
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error_max = std::max(error_max, error);
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}
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std::cerr << "inverse_general_4x4, Scalar = " << type_name<Scalar>() << std::endl;
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double error_avg = error_sum / repeat;
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EIGEN_DEBUG_VAR(error_avg);
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EIGEN_DEBUG_VAR(error_max);
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VERIFY(error_avg < (NumTraits<Scalar>::IsComplex ? 8.0 : 1.0));
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VERIFY(error_max < (NumTraits<Scalar>::IsComplex ? 64.0 : 20.0));
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}
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void test_prec_inverse_4x4()
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{
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CALL_SUBTEST_1((inverse_permutation_4x4<Matrix4f>()));
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CALL_SUBTEST_1(( inverse_general_4x4<Matrix4f>(200000 * g_repeat) ));
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CALL_SUBTEST_2((inverse_permutation_4x4<Matrix<double,4,4,RowMajor> >()));
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CALL_SUBTEST_2(( inverse_general_4x4<Matrix<double,4,4,RowMajor> >(200000 * g_repeat) ));
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CALL_SUBTEST_3((inverse_permutation_4x4<Matrix4cf>()));
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CALL_SUBTEST_3((inverse_general_4x4<Matrix4cf>(50000 * g_repeat)));
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}
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