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https://gitlab.com/libeigen/eigen.git
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Clang-format tests, examples, libraries, benchmarks, etc.
This commit is contained in:
Antonio Sánchez
committed by
Rasmus Munk Larsen
Rasmus Munk Larsen
parent
3252ecc7a4
commit
46e9cdb7fe
@@ -13,8 +13,8 @@
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#include <Eigen/Eigenvalues>
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#include <Eigen/LU>
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template<typename MatrixType> void generalized_eigensolver_real(const MatrixType& m)
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{
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template <typename MatrixType>
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void generalized_eigensolver_real(const MatrixType& m) {
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/* this test covers the following files:
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GeneralizedEigenSolver.h
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*/
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@@ -25,12 +25,12 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType
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typedef std::complex<Scalar> ComplexScalar;
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typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
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MatrixType a = MatrixType::Random(rows,cols);
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MatrixType b = MatrixType::Random(rows,cols);
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MatrixType a1 = MatrixType::Random(rows,cols);
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MatrixType b1 = MatrixType::Random(rows,cols);
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MatrixType spdA = a.adjoint() * a + a1.adjoint() * a1;
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MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1;
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MatrixType a = MatrixType::Random(rows, cols);
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MatrixType b = MatrixType::Random(rows, cols);
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MatrixType a1 = MatrixType::Random(rows, cols);
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MatrixType b1 = MatrixType::Random(rows, cols);
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MatrixType spdA = a.adjoint() * a + a1.adjoint() * a1;
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MatrixType spdB = b.adjoint() * b + b1.adjoint() * b1;
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// lets compare to GeneralizedSelfAdjointEigenSolver
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{
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@@ -40,41 +40,41 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType
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VERIFY_IS_EQUAL(eig.eigenvalues().imag().cwiseAbs().maxCoeff(), 0);
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VectorType realEigenvalues = eig.eigenvalues().real();
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std::sort(realEigenvalues.data(), realEigenvalues.data()+realEigenvalues.size());
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std::sort(realEigenvalues.data(), realEigenvalues.data() + realEigenvalues.size());
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VERIFY_IS_APPROX(realEigenvalues, symmEig.eigenvalues());
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// check eigenvectors
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typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal();
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typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors();
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VERIFY_IS_APPROX(spdA*V, spdB*V*D);
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VERIFY_IS_APPROX(spdA * V, spdB * V * D);
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}
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// non symmetric case:
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{
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GeneralizedEigenSolver<MatrixType> eig(rows);
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// TODO enable full-prealocation of required memory, this probably requires an in-place mode for HessenbergDecomposition
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//Eigen::internal::set_is_malloc_allowed(false);
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eig.compute(a,b);
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//Eigen::internal::set_is_malloc_allowed(true);
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for(Index k=0; k<cols; ++k)
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{
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Matrix<ComplexScalar,Dynamic,Dynamic> tmp = (eig.betas()(k)*a).template cast<ComplexScalar>() - eig.alphas()(k)*b;
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if(tmp.size()>1 && tmp.norm()>(std::numeric_limits<Scalar>::min)())
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tmp /= tmp.norm();
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VERIFY_IS_MUCH_SMALLER_THAN( std::abs(tmp.determinant()), Scalar(1) );
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// TODO enable full-prealocation of required memory, this probably requires an in-place mode for
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// HessenbergDecomposition
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// Eigen::internal::set_is_malloc_allowed(false);
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eig.compute(a, b);
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// Eigen::internal::set_is_malloc_allowed(true);
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for (Index k = 0; k < cols; ++k) {
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Matrix<ComplexScalar, Dynamic, Dynamic> tmp =
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(eig.betas()(k) * a).template cast<ComplexScalar>() - eig.alphas()(k) * b;
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if (tmp.size() > 1 && tmp.norm() > (std::numeric_limits<Scalar>::min)()) tmp /= tmp.norm();
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VERIFY_IS_MUCH_SMALLER_THAN(std::abs(tmp.determinant()), Scalar(1));
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}
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// check eigenvectors
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typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType D = eig.eigenvalues().asDiagonal();
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typename GeneralizedEigenSolver<MatrixType>::EigenvectorsType V = eig.eigenvectors();
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VERIFY_IS_APPROX(a*V, b*V*D);
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VERIFY_IS_APPROX(a * V, b * V * D);
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}
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// regression test for bug 1098
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{
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GeneralizedSelfAdjointEigenSolver<MatrixType> eig1(a.adjoint() * a,b.adjoint() * b);
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eig1.compute(a.adjoint() * a,b.adjoint() * b);
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GeneralizedEigenSolver<MatrixType> eig2(a.adjoint() * a,b.adjoint() * b);
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eig2.compute(a.adjoint() * a,b.adjoint() * b);
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GeneralizedSelfAdjointEigenSolver<MatrixType> eig1(a.adjoint() * a, b.adjoint() * b);
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eig1.compute(a.adjoint() * a, b.adjoint() * b);
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GeneralizedEigenSolver<MatrixType> eig2(a.adjoint() * a, b.adjoint() * b);
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eig2.compute(a.adjoint() * a, b.adjoint() * b);
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}
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// check without eigenvectors
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@@ -85,56 +85,55 @@ template<typename MatrixType> void generalized_eigensolver_real(const MatrixType
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}
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}
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template<typename MatrixType>
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template <typename MatrixType>
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void generalized_eigensolver_assert() {
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GeneralizedEigenSolver<MatrixType> eig;
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// all raise assert if uninitialized
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VERIFY_RAISES_ASSERT(eig.info());
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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VERIFY_RAISES_ASSERT(eig.eigenvalues());
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VERIFY_RAISES_ASSERT(eig.alphas());
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VERIFY_RAISES_ASSERT(eig.betas());
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GeneralizedEigenSolver<MatrixType> eig;
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// all raise assert if uninitialized
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VERIFY_RAISES_ASSERT(eig.info());
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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VERIFY_RAISES_ASSERT(eig.eigenvalues());
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VERIFY_RAISES_ASSERT(eig.alphas());
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VERIFY_RAISES_ASSERT(eig.betas());
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// none raise assert after compute called
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20));
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VERIFY(eig.info() == Success);
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eig.eigenvectors();
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eig.eigenvalues();
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eig.alphas();
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eig.betas();
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// none raise assert after compute called
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20));
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VERIFY(eig.info() == Success);
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eig.eigenvectors();
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eig.eigenvalues();
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eig.alphas();
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eig.betas();
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// eigenvectors() raises assert, if eigenvectors were not requested
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20), false);
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VERIFY(eig.info() == Success);
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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eig.eigenvalues();
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eig.alphas();
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eig.betas();
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// eigenvectors() raises assert, if eigenvectors were not requested
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20), false);
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VERIFY(eig.info() == Success);
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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eig.eigenvalues();
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eig.alphas();
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eig.betas();
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// all except info raise assert if realQZ did not converge
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eig.setMaxIterations(0); // force real QZ to fail.
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20));
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VERIFY(eig.info() == NoConvergence);
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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VERIFY_RAISES_ASSERT(eig.eigenvalues());
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VERIFY_RAISES_ASSERT(eig.alphas());
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VERIFY_RAISES_ASSERT(eig.betas());
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// all except info raise assert if realQZ did not converge
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eig.setMaxIterations(0); // force real QZ to fail.
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eig.compute(MatrixType::Random(20, 20), MatrixType::Random(20, 20));
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VERIFY(eig.info() == NoConvergence);
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VERIFY_RAISES_ASSERT(eig.eigenvectors());
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VERIFY_RAISES_ASSERT(eig.eigenvalues());
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VERIFY_RAISES_ASSERT(eig.alphas());
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VERIFY_RAISES_ASSERT(eig.betas());
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}
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EIGEN_DECLARE_TEST(eigensolver_generalized_real)
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{
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for(int i = 0; i < g_repeat; i++) {
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EIGEN_DECLARE_TEST(eigensolver_generalized_real) {
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for (int i = 0; i < g_repeat; i++) {
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int s = 0;
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CALL_SUBTEST_1( generalized_eigensolver_real(Matrix4f()) );
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s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
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CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(s,s)) );
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CALL_SUBTEST_1(generalized_eigensolver_real(Matrix4f()));
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s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 4);
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CALL_SUBTEST_2(generalized_eigensolver_real(MatrixXd(s, s)));
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// some trivial but implementation-wise special cases
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CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(1,1)) );
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CALL_SUBTEST_2( generalized_eigensolver_real(MatrixXd(2,2)) );
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CALL_SUBTEST_3( generalized_eigensolver_real(Matrix<double,1,1>()) );
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CALL_SUBTEST_4( generalized_eigensolver_real(Matrix2d()) );
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CALL_SUBTEST_5( generalized_eigensolver_assert<MatrixXd>() );
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CALL_SUBTEST_2(generalized_eigensolver_real(MatrixXd(1, 1)));
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CALL_SUBTEST_2(generalized_eigensolver_real(MatrixXd(2, 2)));
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CALL_SUBTEST_3(generalized_eigensolver_real(Matrix<double, 1, 1>()));
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CALL_SUBTEST_4(generalized_eigensolver_real(Matrix2d()));
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CALL_SUBTEST_5(generalized_eigensolver_assert<MatrixXd>());
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TEST_SET_BUT_UNUSED_VARIABLE(s)
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}
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}
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