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

Fix bug in partial reduction of expressions requiring evaluation

Browse Source
This commit is contained in:
Eugene Zhulenev
2018-10-10 13:23:52 -07:00
parent f3130ee1ba
commit 8e6dc2c81d
2 changed files with 8 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
test/redux.cpp
View File

@@ -28,6 +28,9 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
// failures if we underflow into denormals. Thus, we scale so that entries are close to 1.
MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
m2.setRandom();
VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
@@ -46,6 +49,10 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
VERIFY_IS_APPROX(m1_for_prod.prod(), p);
VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
// test that partial reduction works if nested expressions is forced to evaluate early
VERIFY_IS_APPROX((m1.matrix() * m1.matrix().transpose()) .cwiseProduct(m2.matrix()).rowwise().sum().sum(),
(m1.matrix() * m1.matrix().transpose()).eval().cwiseProduct(m2.matrix()).rowwise().sum().sum());
// test slice vectorization assuming assign is ok
Index r0 = internal::random<Index>(0,rows-1);
@@ -72,8 +79,6 @@ template<typename MatrixType> void matrixRedux(const MatrixType& m)
// test nesting complex expression
VERIFY_EVALUATION_COUNT( (m1.matrix()*m1.matrix().transpose()).sum(), (MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1) );
Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> m2(rows,rows);
m2.setRandom();
VERIFY_EVALUATION_COUNT( ((m1.matrix()*m1.matrix().transpose())+m2).sum(),(MatrixType::IsVectorAtCompileTime && MatrixType::SizeAtCompileTime!=1 ? 0 : 1));
}