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Clang-format tests, examples, libraries, benchmarks, etc.

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This commit is contained in:
Antonio Sánchez
2023-12-05 21:22:55 +00:00
committed by Rasmus Munk Larsen
parent 3252ecc7a4
commit 46e9cdb7fe
876 changed files with 33453 additions and 37795 deletions
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172
test/eigensolver_generic.cpp
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@@ -12,9 +12,8 @@
#include <limits>
#include <Eigen/Eigenvalues>
template<typename EigType,typename MatType>
void check_eigensolver_for_given_mat(const EigType &eig, const MatType& a)
{
template <typename EigType, typename MatType>
void check_eigensolver_for_given_mat(const EigType& eig, const MatType& a) {
typedef typename NumTraits<typename MatType::Scalar>::Real RealScalar;
typedef Matrix<RealScalar, MatType::RowsAtCompileTime, 1> RealVectorType;
typedef typename std::complex<RealScalar> Complex;
@@ -27,8 +26,8 @@ void check_eigensolver_for_given_mat(const EigType &eig, const MatType& a)
VERIFY_IS_APPROX(a.eigenvalues(), eig.eigenvalues());
}
template<typename MatrixType> void eigensolver(const MatrixType& m)
{
template <typename MatrixType>
void eigensolver(const MatrixType& m) {
/* this test covers the following files:
EigenSolver.h
*/
@@ -39,18 +38,18 @@ template<typename MatrixType> void eigensolver(const MatrixType& m)
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename std::complex<RealScalar> Complex;
MatrixType a = MatrixType::Random(rows,cols);
MatrixType a1 = MatrixType::Random(rows,cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
MatrixType a = MatrixType::Random(rows, cols);
MatrixType a1 = MatrixType::Random(rows, cols);
MatrixType symmA = a.adjoint() * a + a1.adjoint() * a1;
EigenSolver<MatrixType> ei0(symmA);
VERIFY_IS_EQUAL(ei0.info(), Success);
VERIFY_IS_APPROX(symmA * ei0.pseudoEigenvectors(), ei0.pseudoEigenvectors() * ei0.pseudoEigenvalueMatrix());
VERIFY_IS_APPROX((symmA.template cast<Complex>()) * (ei0.pseudoEigenvectors().template cast<Complex>()),
(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
(ei0.pseudoEigenvectors().template cast<Complex>()) * (ei0.eigenvalues().asDiagonal()));
EigenSolver<MatrixType> ei1(a);
CALL_SUBTEST( check_eigensolver_for_given_mat(ei1,a) );
CALL_SUBTEST(check_eigensolver_for_given_mat(ei1, a));
EigenSolver<MatrixType> ei2;
ei2.setMaxIterations(RealSchur<MatrixType>::m_maxIterationsPerRow * rows).compute(a);
@@ -71,10 +70,9 @@ template<typename MatrixType> void eigensolver(const MatrixType& m)
MatrixType id = MatrixType::Identity(rows, cols);
VERIFY_IS_APPROX(id.operatorNorm(), RealScalar(1));
if (rows > 2 && rows < 20)
{
if (rows > 2 && rows < 20) {
// Test matrix with NaN
a(0,0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
a(0, 0) = std::numeric_limits<typename MatrixType::RealScalar>::quiet_NaN();
EigenSolver<MatrixType> eiNaN(a);
VERIFY_IS_NOT_EQUAL(eiNaN.info(), Success);
}
@@ -90,158 +88,146 @@ template<typename MatrixType> void eigensolver(const MatrixType& m)
a.setZero();
EigenSolver<MatrixType> ei3(a);
VERIFY_IS_EQUAL(ei3.info(), Success);
VERIFY_IS_MUCH_SMALLER_THAN(ei3.eigenvalues().norm(),RealScalar(1));
VERIFY((ei3.eigenvectors().transpose()*ei3.eigenvectors().transpose()).eval().isIdentity());
VERIFY_IS_MUCH_SMALLER_THAN(ei3.eigenvalues().norm(), RealScalar(1));
VERIFY((ei3.eigenvectors().transpose() * ei3.eigenvectors().transpose()).eval().isIdentity());
}
}
template<typename MatrixType> void eigensolver_verify_assert(const MatrixType& m)
{
template <typename MatrixType>
void eigensolver_verify_assert(const MatrixType& m) {
EigenSolver<MatrixType> eig;
VERIFY_RAISES_ASSERT(eig.eigenvectors());
VERIFY_RAISES_ASSERT(eig.pseudoEigenvectors());
VERIFY_RAISES_ASSERT(eig.pseudoEigenvalueMatrix());
VERIFY_RAISES_ASSERT(eig.eigenvalues());
MatrixType a = MatrixType::Random(m.rows(),m.cols());
MatrixType a = MatrixType::Random(m.rows(), m.cols());
eig.compute(a, false);
VERIFY_RAISES_ASSERT(eig.eigenvectors());
VERIFY_RAISES_ASSERT(eig.pseudoEigenvectors());
}
template<typename CoeffType>
Matrix<typename CoeffType::Scalar,Dynamic,Dynamic>
make_companion(const CoeffType& coeffs)
{
Index n = coeffs.size()-1;
Matrix<typename CoeffType::Scalar,Dynamic,Dynamic> res(n,n);
template <typename CoeffType>
Matrix<typename CoeffType::Scalar, Dynamic, Dynamic> make_companion(const CoeffType& coeffs) {
Index n = coeffs.size() - 1;
Matrix<typename CoeffType::Scalar, Dynamic, Dynamic> res(n, n);
res.setZero();
res.row(0) = -coeffs.tail(n) / coeffs(0);
res.diagonal(-1).setOnes();
res.row(0) = -coeffs.tail(n) / coeffs(0);
res.diagonal(-1).setOnes();
return res;
}
template<int>
void eigensolver_generic_extra()
{
template <int>
void eigensolver_generic_extra() {
{
// regression test for bug 793
MatrixXd a(3,3);
a << 0, 0, 1,
1, 1, 1,
1, 1e+200, 1;
MatrixXd a(3, 3);
a << 0, 0, 1, 1, 1, 1, 1, 1e+200, 1;
Eigen::EigenSolver<MatrixXd> eig(a);
double scale = 1e-200; // scale to avoid overflow during the comparisons
VERIFY_IS_APPROX(a * eig.pseudoEigenvectors()*scale, eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix()*scale);
VERIFY_IS_APPROX(a * eig.eigenvectors()*scale, eig.eigenvectors() * eig.eigenvalues().asDiagonal()*scale);
double scale = 1e-200; // scale to avoid overflow during the comparisons
VERIFY_IS_APPROX(a * eig.pseudoEigenvectors() * scale,
eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix() * scale);
VERIFY_IS_APPROX(a * eig.eigenvectors() * scale, eig.eigenvectors() * eig.eigenvalues().asDiagonal() * scale);
}
{
// check a case where all eigenvalues are null.
MatrixXd a(2,2);
a << 1, 1,
-1, -1;
MatrixXd a(2, 2);
a << 1, 1, -1, -1;
Eigen::EigenSolver<MatrixXd> eig(a);
VERIFY_IS_APPROX(eig.pseudoEigenvectors().squaredNorm(), 2.);
VERIFY_IS_APPROX((a * eig.pseudoEigenvectors()).norm()+1., 1.);
VERIFY_IS_APPROX((eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix()).norm()+1., 1.);
VERIFY_IS_APPROX((a * eig.eigenvectors()).norm()+1., 1.);
VERIFY_IS_APPROX((eig.eigenvectors() * eig.eigenvalues().asDiagonal()).norm()+1., 1.);
VERIFY_IS_APPROX((a * eig.pseudoEigenvectors()).norm() + 1., 1.);
VERIFY_IS_APPROX((eig.pseudoEigenvectors() * eig.pseudoEigenvalueMatrix()).norm() + 1., 1.);
VERIFY_IS_APPROX((a * eig.eigenvectors()).norm() + 1., 1.);
VERIFY_IS_APPROX((eig.eigenvectors() * eig.eigenvalues().asDiagonal()).norm() + 1., 1.);
}
// regression test for bug 933
{
{
VectorXd coeffs(5); coeffs << 1, -3, -175, -225, 2250;
VectorXd coeffs(5);
coeffs << 1, -3, -175, -225, 2250;
MatrixXd C = make_companion(coeffs);
EigenSolver<MatrixXd> eig(C);
CALL_SUBTEST( check_eigensolver_for_given_mat(eig,C) );
CALL_SUBTEST(check_eigensolver_for_given_mat(eig, C));
}
{
// this test is tricky because it requires high accuracy in smallest eigenvalues
VectorXd coeffs(5); coeffs << 6.154671e-15, -1.003870e-10, -9.819570e-01, 3.995715e+03, 2.211511e+08;
VectorXd coeffs(5);
coeffs << 6.154671e-15, -1.003870e-10, -9.819570e-01, 3.995715e+03, 2.211511e+08;
MatrixXd C = make_companion(coeffs);
EigenSolver<MatrixXd> eig(C);
CALL_SUBTEST( check_eigensolver_for_given_mat(eig,C) );
CALL_SUBTEST(check_eigensolver_for_given_mat(eig, C));
Index n = C.rows();
for(Index i=0;i<n;++i)
{
for (Index i = 0; i < n; ++i) {
typedef std::complex<double> Complex;
MatrixXcd ac = C.cast<Complex>();
ac.diagonal().array() -= eig.eigenvalues()(i);
VectorXd sv = ac.jacobiSvd().singularValues();
// comparing to sv(0) is not enough here to catch the "bug",
// the hard-coded 1.0 is important!
VERIFY_IS_MUCH_SMALLER_THAN(sv(n-1), 1.0);
VERIFY_IS_MUCH_SMALLER_THAN(sv(n - 1), 1.0);
}
}
}
// regression test for bug 1557
{
// this test is interesting because it contains zeros on the diagonal.
MatrixXd A_bug1557(3,3);
MatrixXd A_bug1557(3, 3);
A_bug1557 << 0, 0, 0, 1, 0, 0.5887907064808635127, 0, 1, 0;
EigenSolver<MatrixXd> eig(A_bug1557);
CALL_SUBTEST( check_eigensolver_for_given_mat(eig,A_bug1557) );
CALL_SUBTEST(check_eigensolver_for_given_mat(eig, A_bug1557));
}
// regression test for bug 1174
{
Index n = 12;
MatrixXf A_bug1174(n,n);
A_bug1174 << 262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432,
262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432,
262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432,
262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0;
MatrixXf A_bug1174(n, n);
A_bug1174 << 262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432, 262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0,
786432, 262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0, 786432, 262144, 0, 0, 262144, 786432, 0, 0, 0, 0, 0, 0,
786432, 0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0, 0, 262144, 262144, 0, 0,
262144, 262144, 262144, 262144, 262144, 262144, 0, 0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144,
262144, 262144, 0, 0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0, 0, 262144,
262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0, 0, 262144, 262144, 0, 0, 262144, 262144,
262144, 262144, 262144, 262144, 0, 0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0,
0, 262144, 262144, 0, 0, 262144, 262144, 262144, 262144, 262144, 262144, 0;
EigenSolver<MatrixXf> eig(A_bug1174);
CALL_SUBTEST( check_eigensolver_for_given_mat(eig,A_bug1174) );
CALL_SUBTEST(check_eigensolver_for_given_mat(eig, A_bug1174));
}
}
EIGEN_DECLARE_TEST(eigensolver_generic)
{
EIGEN_DECLARE_TEST(eigensolver_generic) {
int s = 0;
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( eigensolver(Matrix4f()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_2( eigensolver(MatrixXd(s,s)) );
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(eigensolver(Matrix4f()));
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 4);
CALL_SUBTEST_2(eigensolver(MatrixXd(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
// some trivial but implementation-wise tricky cases
CALL_SUBTEST_2( eigensolver(MatrixXd(1,1)) );
CALL_SUBTEST_2( eigensolver(MatrixXd(2,2)) );
CALL_SUBTEST_3( eigensolver(Matrix<double,1,1>()) );
CALL_SUBTEST_4( eigensolver(Matrix2d()) );
CALL_SUBTEST_2(eigensolver(MatrixXd(1, 1)));
CALL_SUBTEST_2(eigensolver(MatrixXd(2, 2)));
CALL_SUBTEST_3(eigensolver(Matrix<double, 1, 1>()));
CALL_SUBTEST_4(eigensolver(Matrix2d()));
}
CALL_SUBTEST_1( eigensolver_verify_assert(Matrix4f()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/4);
CALL_SUBTEST_2( eigensolver_verify_assert(MatrixXd(s,s)) );
CALL_SUBTEST_3( eigensolver_verify_assert(Matrix<double,1,1>()) );
CALL_SUBTEST_4( eigensolver_verify_assert(Matrix2d()) );
CALL_SUBTEST_1(eigensolver_verify_assert(Matrix4f()));
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 4);
CALL_SUBTEST_2(eigensolver_verify_assert(MatrixXd(s, s)));
CALL_SUBTEST_3(eigensolver_verify_assert(Matrix<double, 1, 1>()));
CALL_SUBTEST_4(eigensolver_verify_assert(Matrix2d()));
// Test problem size constructors
CALL_SUBTEST_5(EigenSolver<MatrixXf> tmp(s));
// regression test for bug 410
CALL_SUBTEST_2(
{
MatrixXd A(1,1);
A(0,0) = std::sqrt(-1.); // is Not-a-Number
Eigen::EigenSolver<MatrixXd> solver(A);
VERIFY_IS_EQUAL(solver.info(), NumericalIssue);
}
);
CALL_SUBTEST_2( eigensolver_generic_extra<0>() );
CALL_SUBTEST_2({
MatrixXd A(1, 1);
A(0, 0) = std::sqrt(-1.); // is Not-a-Number
Eigen::EigenSolver<MatrixXd> solver(A);
VERIFY_IS_EQUAL(solver.info(), NumericalIssue);
});
CALL_SUBTEST_2(eigensolver_generic_extra<0>());
TEST_SET_BUT_UNUSED_VARIABLE(s)
}