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171 lines
6.1 KiB
C++
171 lines
6.1 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 <g.gael@free.fr>
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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(const MatrixType& m)
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{
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/* this test covers the following files: QR.h */
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int rows = m.rows();
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int cols = m.cols();
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typedef typename MatrixType::Scalar Scalar;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime> SquareMatrixType;
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typedef Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> VectorType;
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MatrixType a = MatrixType::Random(rows,cols);
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QR<MatrixType> qrOfA(a);
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VERIFY_IS_APPROX(a, qrOfA.matrixQ() * qrOfA.matrixR());
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VERIFY_IS_NOT_APPROX(a+MatrixType::Identity(rows, cols), qrOfA.matrixQ() * qrOfA.matrixR());
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SquareMatrixType b = a.adjoint() * a;
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// check tridiagonalization
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Tridiagonalization<SquareMatrixType> tridiag(b);
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VERIFY_IS_APPROX(b, tridiag.matrixQ() * tridiag.matrixT() * tridiag.matrixQ().adjoint());
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// check hessenberg decomposition
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HessenbergDecomposition<SquareMatrixType> hess(b);
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VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
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VERIFY_IS_APPROX(tridiag.matrixT(), hess.matrixH());
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b = SquareMatrixType::Random(cols,cols);
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hess.compute(b);
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VERIFY_IS_APPROX(b, hess.matrixQ() * hess.matrixH() * hess.matrixQ().adjoint());
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}
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template<typename MatrixType> void qr_non_invertible()
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{
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/* this test covers the following files: QR.h */
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int rows = ei_random<int>(20,200), cols = ei_random<int>(20,rows), cols2 = ei_random<int>(20,rows);
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int rank = ei_random<int>(1, std::min(rows, cols)-1);
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MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
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createRandomMatrixOfRank(rank, rows, cols, m1);
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QR<MatrixType> lu(m1);
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// typename LU<MatrixType>::KernelResultType m1kernel = lu.kernel();
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// typename LU<MatrixType>::ImageResultType m1image = lu.image();
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std::cerr << rows << "x" << cols << " " << rank << " " << lu.rank() << "\n";
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if (rank != lu.rank())
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std::cerr << lu.matrixR().diagonal().transpose() << "\n";
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VERIFY(rank == lu.rank());
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VERIFY(cols - lu.rank() == lu.dimensionOfKernel());
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VERIFY(!lu.isInjective());
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VERIFY(!lu.isInvertible());
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VERIFY(lu.isSurjective() == (lu.rank() == rows));
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// VERIFY((m1 * m1kernel).isMuchSmallerThan(m1));
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// VERIFY(m1image.lu().rank() == rank);
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// MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
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// sidebyside << m1, m1image;
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// VERIFY(sidebyside.lu().rank() == rank);
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m2 = MatrixType::Random(cols,cols2);
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m3 = m1*m2;
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m2 = MatrixType::Random(cols,cols2);
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lu.solve(m3, &m2);
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VERIFY_IS_APPROX(m3, m1*m2);
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m3 = MatrixType::Random(rows,cols2);
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VERIFY(!lu.solve(m3, &m2));
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}
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template<typename MatrixType> void qr_invertible()
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{
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/* this test covers the following files: QR.h */
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typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
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int size = ei_random<int>(10,200);
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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 (ei_is_same_type<RealScalar,float>::ret)
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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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QR<MatrixType> lu(m1);
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VERIFY(0 == lu.dimensionOfKernel());
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VERIFY(size == lu.rank());
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VERIFY(lu.isInjective());
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VERIFY(lu.isSurjective());
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VERIFY(lu.isInvertible());
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// VERIFY(lu.image().lu().isInvertible());
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m3 = MatrixType::Random(size,size);
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lu.solve(m3, &m2);
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//std::cerr << m3 - m1*m2 << "\n\n";
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VERIFY_IS_APPROX(m3, m1*m2);
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// VERIFY_IS_APPROX(m2, lu.inverse()*m3);
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m3 = MatrixType::Random(size,size);
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VERIFY(lu.solve(m3, &m2));
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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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QR<MatrixType> qr;
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VERIFY_RAISES_ASSERT(qr.isFullRank())
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VERIFY_RAISES_ASSERT(qr.rank())
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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.matrixR())
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VERIFY_RAISES_ASSERT(qr.solve(tmp,&tmp))
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VERIFY_RAISES_ASSERT(qr.matrixQ())
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}
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void test_qr()
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{
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for(int i = 0; i < 1; i++) {
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// CALL_SUBTEST( qr(Matrix2f()) );
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// CALL_SUBTEST( qr(Matrix4d()) );
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// CALL_SUBTEST( qr(MatrixXf(12,8)) );
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// CALL_SUBTEST( qr(MatrixXcd(5,5)) );
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// CALL_SUBTEST( qr(MatrixXcd(7,3)) );
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CALL_SUBTEST( qr(MatrixXf(47,47)) );
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}
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for(int i = 0; i < g_repeat; i++) {
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CALL_SUBTEST( qr_non_invertible<MatrixXf>() );
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CALL_SUBTEST( qr_non_invertible<MatrixXd>() );
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// TODO fix issue with complex
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// CALL_SUBTEST( qr_non_invertible<MatrixXcf>() );
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// CALL_SUBTEST( qr_non_invertible<MatrixXcd>() );
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CALL_SUBTEST( qr_invertible<MatrixXf>() );
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CALL_SUBTEST( qr_invertible<MatrixXd>() );
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// TODO fix issue with complex
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// CALL_SUBTEST( qr_invertible<MatrixXcf>() );
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// CALL_SUBTEST( qr_invertible<MatrixXcd>() );
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}
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CALL_SUBTEST(qr_verify_assert<Matrix3f>());
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CALL_SUBTEST(qr_verify_assert<Matrix3d>());
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CALL_SUBTEST(qr_verify_assert<MatrixXf>());
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CALL_SUBTEST(qr_verify_assert<MatrixXd>());
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CALL_SUBTEST(qr_verify_assert<MatrixXcf>());
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CALL_SUBTEST(qr_verify_assert<MatrixXcd>());
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}
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