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42e2578ef9
so let's declare them after and do the respective fixes ;)
166 lines
6.0 KiB
C++
166 lines
6.0 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) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
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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/QR>
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template<typename MatrixType> void qr()
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{
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typedef typename MatrixType::Index Index;
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Index rows = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE), cols2 = internal::random<Index>(2,EIGEN_TEST_MAX_SIZE);
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Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
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typedef typename MatrixType::Scalar Scalar;
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typedef typename MatrixType::RealScalar RealScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
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MatrixType m1;
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createRandomPIMatrixOfRank(rank,rows,cols,m1);
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ColPivHouseholderQR<MatrixType> qr(m1);
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VERIFY(rank == qr.rank());
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VERIFY(cols - qr.rank() == qr.dimensionOfKernel());
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VERIFY(!qr.isInjective());
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VERIFY(!qr.isInvertible());
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VERIFY(!qr.isSurjective());
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MatrixQType q = qr.householderQ();
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VERIFY_IS_UNITARY(q);
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MatrixType r = qr.matrixQR().template triangularView<Upper>();
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MatrixType c = q * r * qr.colsPermutation().inverse();
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VERIFY_IS_APPROX(m1, c);
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MatrixType m2 = MatrixType::Random(cols,cols2);
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MatrixType m3 = m1*m2;
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m2 = MatrixType::Random(cols,cols2);
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m2 = qr.solve(m3);
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VERIFY_IS_APPROX(m3, m1*m2);
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}
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template<typename MatrixType, int Cols2> void qr_fixedsize()
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{
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enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
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typedef typename MatrixType::Scalar Scalar;
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int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols))-1);
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Matrix<Scalar,Rows,Cols> m1;
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createRandomPIMatrixOfRank(rank,Rows,Cols,m1);
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ColPivHouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1);
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VERIFY(rank == qr.rank());
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VERIFY(Cols - qr.rank() == qr.dimensionOfKernel());
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VERIFY(qr.isInjective() == (rank == Rows));
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VERIFY(qr.isSurjective() == (rank == Cols));
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VERIFY(qr.isInvertible() == (qr.isInjective() && qr.isSurjective()));
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Matrix<Scalar,Rows,Cols> r = qr.matrixQR().template triangularView<Upper>();
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Matrix<Scalar,Rows,Cols> c = qr.householderQ() * r * qr.colsPermutation().inverse();
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VERIFY_IS_APPROX(m1, c);
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Matrix<Scalar,Cols,Cols2> m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
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Matrix<Scalar,Rows,Cols2> m3 = m1*m2;
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m2 = Matrix<Scalar,Cols,Cols2>::Random(Cols,Cols2);
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m2 = qr.solve(m3);
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VERIFY_IS_APPROX(m3, m1*m2);
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}
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template<typename MatrixType> void qr_invertible()
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{
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typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
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typedef typename MatrixType::Scalar Scalar;
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int size = internal::random<int>(10,50);
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MatrixType m1(size, size), m2(size, size), m3(size, size);
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m1 = MatrixType::Random(size,size);
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if (internal::is_same<RealScalar,float>::value)
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{
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// let's build a matrix more stable to inverse
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MatrixType a = MatrixType::Random(size,size*2);
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m1 += a * a.adjoint();
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}
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ColPivHouseholderQR<MatrixType> qr(m1);
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m3 = MatrixType::Random(size,size);
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m2 = qr.solve(m3);
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//VERIFY_IS_APPROX(m3, m1*m2);
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// now construct a matrix with prescribed determinant
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m1.setZero();
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for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>();
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RealScalar absdet = internal::abs(m1.diagonal().prod());
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m3 = qr.householderQ(); // get a unitary
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m1 = m3 * m1 * m3;
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qr.compute(m1);
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VERIFY_IS_APPROX(absdet, qr.absDeterminant());
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VERIFY_IS_APPROX(internal::log(absdet), qr.logAbsDeterminant());
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}
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template<typename MatrixType> void qr_verify_assert()
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{
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MatrixType tmp;
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ColPivHouseholderQR<MatrixType> qr;
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VERIFY_RAISES_ASSERT(qr.matrixQR())
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VERIFY_RAISES_ASSERT(qr.solve(tmp))
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VERIFY_RAISES_ASSERT(qr.householderQ())
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VERIFY_RAISES_ASSERT(qr.dimensionOfKernel())
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VERIFY_RAISES_ASSERT(qr.isInjective())
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VERIFY_RAISES_ASSERT(qr.isSurjective())
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VERIFY_RAISES_ASSERT(qr.isInvertible())
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VERIFY_RAISES_ASSERT(qr.inverse())
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VERIFY_RAISES_ASSERT(qr.absDeterminant())
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VERIFY_RAISES_ASSERT(qr.logAbsDeterminant())
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}
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void test_qr_colpivoting()
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{
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( qr<MatrixXf>() );
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CALL_SUBTEST_2( qr<MatrixXd>() );
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CALL_SUBTEST_3( qr<MatrixXcd>() );
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CALL_SUBTEST_4(( qr_fixedsize<Matrix<float,3,5>, 4 >() ));
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CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,6,2>, 3 >() ));
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CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,1,1>, 1 >() ));
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}
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST_1( qr_invertible<MatrixXf>() );
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CALL_SUBTEST_2( qr_invertible<MatrixXd>() );
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CALL_SUBTEST_6( qr_invertible<MatrixXcf>() );
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CALL_SUBTEST_3( qr_invertible<MatrixXcd>() );
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}
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CALL_SUBTEST_7(qr_verify_assert<Matrix3f>());
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CALL_SUBTEST_8(qr_verify_assert<Matrix3d>());
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CALL_SUBTEST_1(qr_verify_assert<MatrixXf>());
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CALL_SUBTEST_2(qr_verify_assert<MatrixXd>());
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CALL_SUBTEST_6(qr_verify_assert<MatrixXcf>());
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CALL_SUBTEST_3(qr_verify_assert<MatrixXcd>());
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// Test problem size constructors
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CALL_SUBTEST_9(ColPivHouseholderQR<MatrixXf>(10, 20));
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}
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