devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

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

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files

154
test/qr_colpivoting.cpp
View File

@@ -15,16 +15,13 @@
template <typename MatrixType>
void cod() {
Index rows = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
Index cols = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
Index cols2 = internal::random<Index>(2, EIGEN_TEST_MAX_SIZE);
Index rank = internal::random<Index>(1, (std::min)(rows, cols) - 1);
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime,
MatrixType::RowsAtCompileTime>
MatrixQType;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;
MatrixType matrix;
createRandomPIMatrixOfRank(rank, rows, cols, matrix);
CompleteOrthogonalDecomposition<MatrixType> cod(matrix);
@@ -42,8 +39,7 @@ void cod() {
MatrixType t;
t.setZero(rows, cols);
t.topLeftCorner(rank, rank) =
cod.matrixT().topLeftCorner(rank, rank).template triangularView<Upper>();
t.topLeftCorner(rank, rank) = cod.matrixT().topLeftCorner(rank, rank).template triangularView<Upper>();
MatrixType c = q * t * z * cod.colsPermutation().inverse();
VERIFY_IS_APPROX(matrix, c);
@@ -64,10 +60,7 @@ void cod() {
template <typename MatrixType, int Cols2>
void cod_fixedsize() {
enum {
Rows = MatrixType::RowsAtCompileTime,
Cols = MatrixType::ColsAtCompileTime
};
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
typedef typename MatrixType::Scalar Scalar;
typedef CompleteOrthogonalDecomposition<Matrix<Scalar, Rows, Cols> > COD;
int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols)) - 1);
@@ -95,18 +88,19 @@ void cod_fixedsize() {
VERIFY_IS_APPROX(cod_solution, pinv * rhs);
}
template<typename MatrixType> void qr()
{
template <typename MatrixType>
void qr() {
using std::sqrt;
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);
Index rank = internal::random<Index>(1, (std::min)(rows, cols)-1);
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);
Index rank = internal::random<Index>(1, (std::min)(rows, cols) - 1);
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> MatrixQType;
MatrixType m1;
createRandomPIMatrixOfRank(rank,rows,cols,m1);
createRandomPIMatrixOfRank(rank, rows, cols, m1);
ColPivHouseholderQR<MatrixType> qr(m1);
VERIFY_IS_EQUAL(rank, qr.rank());
VERIFY_IS_EQUAL(cols - qr.rank(), qr.dimensionOfKernel());
@@ -123,8 +117,7 @@ template<typename MatrixType> void qr()
// Verify that the absolute value of the diagonal elements in R are
// non-increasing until they reach the singularity threshold.
RealScalar threshold =
sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon();
RealScalar threshold = sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon();
for (Index i = 0; i < (std::min)(rows, cols) - 1; ++i) {
RealScalar x = numext::abs(r(i, i));
RealScalar y = numext::abs(r(i + 1, i + 1));
@@ -133,8 +126,7 @@ template<typename MatrixType> void qr()
for (Index j = 0; j < (std::min)(rows, cols); ++j) {
std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl;
}
std::cout << "Failure at i=" << i << ", rank=" << rank
<< ", threshold=" << threshold << std::endl;
std::cout << "Failure at i=" << i << ", rank=" << rank << ", threshold=" << threshold << std::endl;
}
VERIFY_IS_APPROX_OR_LESS_THAN(y, x);
}
@@ -145,43 +137,42 @@ template<typename MatrixType> void qr()
MatrixType m2, m3;
Index size = rows;
do {
m1 = MatrixType::Random(size,size);
m1 = MatrixType::Random(size, size);
qr.compute(m1);
} while(!qr.isInvertible());
} while (!qr.isInvertible());
MatrixType m1_inv = qr.inverse();
m3 = m1 * MatrixType::Random(size,cols2);
m3 = m1 * MatrixType::Random(size, cols2);
m2 = qr.solve(m3);
VERIFY_IS_APPROX(m2, m1_inv*m3);
VERIFY_IS_APPROX(m2, m1_inv * m3);
}
}
template<typename MatrixType, int Cols2> void qr_fixedsize()
{
using std::sqrt;
template <typename MatrixType, int Cols2>
void qr_fixedsize() {
using std::abs;
using std::sqrt;
enum { Rows = MatrixType::RowsAtCompileTime, Cols = MatrixType::ColsAtCompileTime };
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols))-1);
Matrix<Scalar,Rows,Cols> m1;
createRandomPIMatrixOfRank(rank,Rows,Cols,m1);
ColPivHouseholderQR<Matrix<Scalar,Rows,Cols> > qr(m1);
int rank = internal::random<int>(1, (std::min)(int(Rows), int(Cols)) - 1);
Matrix<Scalar, Rows, Cols> m1;
createRandomPIMatrixOfRank(rank, Rows, Cols, m1);
ColPivHouseholderQR<Matrix<Scalar, Rows, Cols> > qr(m1);
VERIFY_IS_EQUAL(rank, qr.rank());
VERIFY_IS_EQUAL(Cols - qr.rank(), qr.dimensionOfKernel());
VERIFY_IS_EQUAL(qr.isInjective(), (rank == Rows));
VERIFY_IS_EQUAL(qr.isSurjective(), (rank == Cols));
VERIFY_IS_EQUAL(qr.isInvertible(), (qr.isInjective() && qr.isSurjective()));
Matrix<Scalar,Rows,Cols> r = qr.matrixQR().template triangularView<Upper>();
Matrix<Scalar,Rows,Cols> c = qr.householderQ() * r * qr.colsPermutation().inverse();
Matrix<Scalar, Rows, Cols> r = qr.matrixQR().template triangularView<Upper>();
Matrix<Scalar, Rows, Cols> c = qr.householderQ() * r * qr.colsPermutation().inverse();
VERIFY_IS_APPROX(m1, c);
check_solverbase<Matrix<Scalar,Cols,Cols2>, Matrix<Scalar,Rows,Cols2> >(m1, qr, Rows, Cols, Cols2);
check_solverbase<Matrix<Scalar, Cols, Cols2>, Matrix<Scalar, Rows, Cols2> >(m1, qr, Rows, Cols, Cols2);
// Verify that the absolute value of the diagonal elements in R are
// non-increasing until they reache the singularity threshold.
RealScalar threshold =
sqrt(RealScalar(Rows)) * (std::abs)(r(0, 0)) * NumTraits<Scalar>::epsilon();
RealScalar threshold = sqrt(RealScalar(Rows)) * (std::abs)(r(0, 0)) * NumTraits<Scalar>::epsilon();
for (Index i = 0; i < (std::min)(int(Rows), int(Cols)) - 1; ++i) {
RealScalar x = numext::abs(r(i, i));
RealScalar y = numext::abs(r(i + 1, i + 1));
@@ -190,8 +181,7 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
for (Index j = 0; j < (std::min)(int(Rows), int(Cols)); ++j) {
std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl;
}
std::cout << "Failure at i=" << i << ", rank=" << rank
<< ", threshold=" << threshold << std::endl;
std::cout << "Failure at i=" << i << ", rank=" << rank << ", threshold=" << threshold << std::endl;
}
VERIFY_IS_APPROX_OR_LESS_THAN(y, x);
}
@@ -204,20 +194,20 @@ template<typename MatrixType, int Cols2> void qr_fixedsize()
// for rank-revealing QR. See
// http://www.netlib.org/lapack/lawnspdf/lawn176.pdf
// page 3 for more detail.
template<typename MatrixType> void qr_kahan_matrix()
{
using std::sqrt;
template <typename MatrixType>
void qr_kahan_matrix() {
using std::abs;
using std::sqrt;
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
Index rows = 300, cols = rows;
MatrixType m1;
m1.setZero(rows,cols);
m1.setZero(rows, cols);
RealScalar s = std::pow(NumTraits<RealScalar>::epsilon(), 1.0 / rows);
RealScalar c = std::sqrt(1 - s*s);
RealScalar pow_s_i(1.0); // pow(s,i)
RealScalar c = std::sqrt(1 - s * s);
RealScalar pow_s_i(1.0); // pow(s,i)
for (Index i = 0; i < rows; ++i) {
m1(i, i) = pow_s_i;
m1.row(i).tail(rows - i - 1) = -pow_s_i * c * MatrixType::Ones(1, rows - i - 1);
@@ -227,8 +217,7 @@ template<typename MatrixType> void qr_kahan_matrix()
ColPivHouseholderQR<MatrixType> qr(m1);
MatrixType r = qr.matrixQR().template triangularView<Upper>();
RealScalar threshold =
std::sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon();
RealScalar threshold = std::sqrt(RealScalar(rows)) * numext::abs(r(0, 0)) * NumTraits<Scalar>::epsilon();
for (Index i = 0; i < (std::min)(rows, cols) - 1; ++i) {
RealScalar x = numext::abs(r(i, i));
RealScalar y = numext::abs(r(i + 1, i + 1));
@@ -237,29 +226,27 @@ template<typename MatrixType> void qr_kahan_matrix()
for (Index j = 0; j < (std::min)(rows, cols); ++j) {
std::cout << "i = " << j << ", |r_ii| = " << numext::abs(r(j, j)) << std::endl;
}
std::cout << "Failure at i=" << i << ", rank=" << qr.rank()
<< ", threshold=" << threshold << std::endl;
std::cout << "Failure at i=" << i << ", rank=" << qr.rank() << ", threshold=" << threshold << std::endl;
}
VERIFY_IS_APPROX_OR_LESS_THAN(y, x);
}
}
template<typename MatrixType> void qr_invertible()
{
using std::log;
template <typename MatrixType>
void qr_invertible() {
using std::abs;
using std::log;
typedef typename NumTraits<typename MatrixType::Scalar>::Real RealScalar;
typedef typename MatrixType::Scalar Scalar;
int size = internal::random<int>(10,50);
int size = internal::random<int>(10, 50);
MatrixType m1(size, size), m2(size, size), m3(size, size);
m1 = MatrixType::Random(size,size);
m1 = MatrixType::Random(size, size);
if (internal::is_same<RealScalar,float>::value)
{
if (internal::is_same<RealScalar, float>::value) {
// let's build a matrix more stable to inverse
MatrixType a = MatrixType::Random(size,size*2);
MatrixType a = MatrixType::Random(size, size * 2);
m1 += a * a.adjoint();
}
@@ -269,10 +256,10 @@ template<typename MatrixType> void qr_invertible()
// now construct a matrix with prescribed determinant
m1.setZero();
for(int i = 0; i < size; i++) m1(i,i) = internal::random<Scalar>();
for (int i = 0; i < size; i++) m1(i, i) = internal::random<Scalar>();
Scalar det = m1.diagonal().prod();
RealScalar absdet = abs(det);
m3 = qr.householderQ(); // get a unitary
m3 = qr.householderQ(); // get a unitary
m1 = m3 * m1 * m3.adjoint();
qr.compute(m1);
VERIFY_IS_APPROX(det, qr.determinant());
@@ -280,8 +267,8 @@ template<typename MatrixType> void qr_invertible()
VERIFY_IS_APPROX(log(absdet), qr.logAbsDeterminant());
}
template<typename MatrixType> void qr_verify_assert()
{
template <typename MatrixType>
void qr_verify_assert() {
MatrixType tmp;
ColPivHouseholderQR<MatrixType> qr;
@@ -300,8 +287,8 @@ template<typename MatrixType> void qr_verify_assert()
VERIFY_RAISES_ASSERT(qr.logAbsDeterminant())
}
template<typename MatrixType> void cod_verify_assert()
{
template <typename MatrixType>
void cod_verify_assert() {
MatrixType tmp;
CompleteOrthogonalDecomposition<MatrixType> cod;
@@ -320,31 +307,30 @@ template<typename MatrixType> void cod_verify_assert()
VERIFY_RAISES_ASSERT(cod.logAbsDeterminant())
}
EIGEN_DECLARE_TEST(qr_colpivoting)
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( qr<MatrixXf>() );
CALL_SUBTEST_2( qr<MatrixXd>() );
CALL_SUBTEST_3( qr<MatrixXcd>() );
CALL_SUBTEST_4(( qr_fixedsize<Matrix<float,3,5>, 4 >() ));
CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,6,2>, 3 >() ));
CALL_SUBTEST_5(( qr_fixedsize<Matrix<double,1,1>, 1 >() ));
EIGEN_DECLARE_TEST(qr_colpivoting) {
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(qr<MatrixXf>());
CALL_SUBTEST_2(qr<MatrixXd>());
CALL_SUBTEST_3(qr<MatrixXcd>());
CALL_SUBTEST_4((qr_fixedsize<Matrix<float, 3, 5>, 4>()));
CALL_SUBTEST_5((qr_fixedsize<Matrix<double, 6, 2>, 3>()));
CALL_SUBTEST_5((qr_fixedsize<Matrix<double, 1, 1>, 1>()));
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( cod<MatrixXf>() );
CALL_SUBTEST_2( cod<MatrixXd>() );
CALL_SUBTEST_3( cod<MatrixXcd>() );
CALL_SUBTEST_4(( cod_fixedsize<Matrix<float,3,5>, 4 >() ));
CALL_SUBTEST_5(( cod_fixedsize<Matrix<double,6,2>, 3 >() ));
CALL_SUBTEST_5(( cod_fixedsize<Matrix<double,1,1>, 1 >() ));
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(cod<MatrixXf>());
CALL_SUBTEST_2(cod<MatrixXd>());
CALL_SUBTEST_3(cod<MatrixXcd>());
CALL_SUBTEST_4((cod_fixedsize<Matrix<float, 3, 5>, 4>()));
CALL_SUBTEST_5((cod_fixedsize<Matrix<double, 6, 2>, 3>()));
CALL_SUBTEST_5((cod_fixedsize<Matrix<double, 1, 1>, 1>()));
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( qr_invertible<MatrixXf>() );
CALL_SUBTEST_2( qr_invertible<MatrixXd>() );
CALL_SUBTEST_6( qr_invertible<MatrixXcf>() );
CALL_SUBTEST_3( qr_invertible<MatrixXcd>() );
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(qr_invertible<MatrixXf>());
CALL_SUBTEST_2(qr_invertible<MatrixXd>());
CALL_SUBTEST_6(qr_invertible<MatrixXcf>());
CALL_SUBTEST_3(qr_invertible<MatrixXcd>());
}
CALL_SUBTEST_7(qr_verify_assert<Matrix3f>());
@@ -364,6 +350,6 @@ EIGEN_DECLARE_TEST(qr_colpivoting)
// Test problem size constructors
CALL_SUBTEST_9(ColPivHouseholderQR<MatrixXf>(10, 20));
CALL_SUBTEST_1( qr_kahan_matrix<MatrixXf>() );
CALL_SUBTEST_2( qr_kahan_matrix<MatrixXd>() );
CALL_SUBTEST_1(qr_kahan_matrix<MatrixXf>());
CALL_SUBTEST_2(qr_kahan_matrix<MatrixXd>());
}