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

83
test/product_selfadjoint.cpp
View File

@@ -9,8 +9,8 @@
#include "main.h"
template<typename MatrixType> void product_selfadjoint(const MatrixType& m)
{
template <typename MatrixType>
void product_selfadjoint(const MatrixType& m) {
typedef typename MatrixType::Scalar Scalar;
typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> VectorType;
typedef Matrix<Scalar, 1, MatrixType::RowsAtCompileTime> RowVectorType;
@@ -20,67 +20,66 @@ template<typename MatrixType> void product_selfadjoint(const MatrixType& m)
Index rows = m.rows();
Index cols = m.cols();
MatrixType m1 = MatrixType::Random(rows, cols),
m2 = MatrixType::Random(rows, cols),
m3;
VectorType v1 = VectorType::Random(rows),
v2 = VectorType::Random(rows),
v3(rows);
RowVectorType r1 = RowVectorType::Random(rows),
r2 = RowVectorType::Random(rows);
RhsMatrixType m4 = RhsMatrixType::Random(rows,10);
MatrixType m1 = MatrixType::Random(rows, cols), m2 = MatrixType::Random(rows, cols), m3;
VectorType v1 = VectorType::Random(rows), v2 = VectorType::Random(rows), v3(rows);
RowVectorType r1 = RowVectorType::Random(rows), r2 = RowVectorType::Random(rows);
RhsMatrixType m4 = RhsMatrixType::Random(rows, 10);
Scalar s1 = internal::random<Scalar>(),
s2 = internal::random<Scalar>(),
s3 = internal::random<Scalar>();
Scalar s1 = internal::random<Scalar>(), s2 = internal::random<Scalar>(), s3 = internal::random<Scalar>();
m1 = (m1.adjoint() + m1).eval();
// rank2 update
m2 = m1.template triangularView<Lower>();
m2.template selfadjointView<Lower>().rankUpdate(v1,v2);
VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint()+ v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
m2.template selfadjointView<Lower>().rankUpdate(v1, v2);
VERIFY_IS_APPROX(m2, (m1 + v1 * v2.adjoint() + v2 * v1.adjoint()).template triangularView<Lower>().toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-v1,s2*v2,s3);
VERIFY_IS_APPROX(m2, (m1 + (s3*(-v1)*(s2*v2).adjoint()+numext::conj(s3)*(s2*v2)*(-v1).adjoint())).template triangularView<Upper>().toDenseMatrix());
m2.template selfadjointView<Upper>().rankUpdate(-v1, s2 * v2, s3);
VERIFY_IS_APPROX(m2, (m1 + (s3 * (-v1) * (s2 * v2).adjoint() + numext::conj(s3) * (s2 * v2) * (-v1).adjoint()))
.template triangularView<Upper>()
.toDenseMatrix());
m2 = m1.template triangularView<Upper>();
m2.template selfadjointView<Upper>().rankUpdate(-s2*r1.adjoint(),r2.adjoint()*s3,s1);
VERIFY_IS_APPROX(m2, (m1 + s1*(-s2*r1.adjoint())*(r2.adjoint()*s3).adjoint() + numext::conj(s1)*(r2.adjoint()*s3) * (-s2*r1.adjoint()).adjoint()).template triangularView<Upper>().toDenseMatrix());
m2.template selfadjointView<Upper>().rankUpdate(-s2 * r1.adjoint(), r2.adjoint() * s3, s1);
VERIFY_IS_APPROX(m2, (m1 + s1 * (-s2 * r1.adjoint()) * (r2.adjoint() * s3).adjoint() +
numext::conj(s1) * (r2.adjoint() * s3) * (-s2 * r1.adjoint()).adjoint())
.template triangularView<Upper>()
.toDenseMatrix());
if (rows>1)
{
if (rows > 1) {
m2 = m1.template triangularView<Lower>();
m2.block(1,1,rows-1,cols-1).template selfadjointView<Lower>().rankUpdate(v1.tail(rows-1),v2.head(cols-1));
m2.block(1, 1, rows - 1, cols - 1)
.template selfadjointView<Lower>()
.rankUpdate(v1.tail(rows - 1), v2.head(cols - 1));
m3 = m1;
m3.block(1,1,rows-1,cols-1) += v1.tail(rows-1) * v2.head(cols-1).adjoint()+ v2.head(cols-1) * v1.tail(rows-1).adjoint();
m3.block(1, 1, rows - 1, cols - 1) +=
v1.tail(rows - 1) * v2.head(cols - 1).adjoint() + v2.head(cols - 1) * v1.tail(rows - 1).adjoint();
VERIFY_IS_APPROX(m2, m3.template triangularView<Lower>().toDenseMatrix());
}
}
EIGEN_DECLARE_TEST(product_selfadjoint)
{
EIGEN_DECLARE_TEST(product_selfadjoint) {
int s = 0;
for(int i = 0; i < g_repeat ; i++) {
CALL_SUBTEST_1( product_selfadjoint(Matrix<float, 1, 1>()) );
CALL_SUBTEST_2( product_selfadjoint(Matrix<float, 2, 2>()) );
CALL_SUBTEST_3( product_selfadjoint(Matrix3d()) );
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_4( product_selfadjoint(MatrixXcf(s, s)) );
for (int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(product_selfadjoint(Matrix<float, 1, 1>()));
CALL_SUBTEST_2(product_selfadjoint(Matrix<float, 2, 2>()));
CALL_SUBTEST_3(product_selfadjoint(Matrix3d()));
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2);
CALL_SUBTEST_4(product_selfadjoint(MatrixXcf(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE/2);
CALL_SUBTEST_5( product_selfadjoint(MatrixXcd(s,s)) );
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE / 2);
CALL_SUBTEST_5(product_selfadjoint(MatrixXcd(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_6( product_selfadjoint(MatrixXd(s,s)) );
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_6(product_selfadjoint(MatrixXd(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
s = internal::random<int>(1,EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_7( product_selfadjoint(Matrix<float,Dynamic,Dynamic,RowMajor>(s,s)) );
s = internal::random<int>(1, EIGEN_TEST_MAX_SIZE);
CALL_SUBTEST_7(product_selfadjoint(Matrix<float, Dynamic, Dynamic, RowMajor>(s, s)));
TEST_SET_BUT_UNUSED_VARIABLE(s)
}
}