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* LU unit test: finally test fixed sizes

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* ReturnByValue: after all don't eval to temporary for generic MatrixBase impl
This commit is contained in:
Benoit Jacob 2009-10-19 10:56:37 -04:00
parent 47eeb40380
commit 9a700c2974
3 changed files with 56 additions and 45 deletions
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18
Eigen/src/Core/ReturnByValue.h
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@ -65,22 +65,8 @@ template<typename Derived>
template<typename OtherDerived>
Derived& MatrixBase<Derived>::operator=(const ReturnByValue<OtherDerived>& other)
{
// Here we evaluate to a temporary matrix tmp, which we then copy. The main purpose
// of this is to limit the number of instantiations of the template method evalTo<Destination>():
// we only instantiate for PlainMatrixType.
// Notice that this behaviour is specific to this operator in MatrixBase. The corresponding operator in class Matrix
// does not evaluate into a temporary first.
// TODO find a way to avoid evaluating into a temporary in the cases that matter. At least Block<> matters
// for the implementation of blocked algorithms.
// Should we:
// - try a trick like for the products, where the destination is abstracted as an array with stride?
// - or just add an operator in class Block, so we get a separate instantiation there (bad) but at least not more
// than that, and at least that's easy to make work?
// - or, since here we're talking about a compromise between code size and performance, let the user choose?
// Not obvious: many users will never find out about this feature, and it's hard to find a good API.
PlainMatrixType tmp;
other.evalTo(tmp);
return derived() = tmp;
other.evalTo(derived());
return derived();
}
#endif // EIGEN_RETURNBYVALUE_H

33
Eigen/src/LU/LU.h
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@ -504,25 +504,24 @@ struct ei_lu_kernel_impl : public ReturnByValue<ei_lu_kernel_impl<MatrixType> >
typedef typename MatrixType::Scalar Scalar;
typedef typename MatrixType::RealScalar RealScalar;
const LUType& m_lu;
int m_rank, m_dimker;
int m_rank, m_cols;
ei_lu_kernel_impl(const LUType& lu)
: m_lu(lu),
m_rank(lu.rank()),
m_dimker(m_lu.matrixLU().cols() - m_rank) {}
m_cols(m_rank==lu.matrixLU().cols() ? 1 : lu.matrixLU().cols() - m_rank){}
inline int rows() const { return m_lu.matrixLU().cols(); }
inline int cols() const { return m_dimker; }
inline int cols() const { return m_cols; }
template<typename Dest> void evalTo(Dest& dst) const
{
const int cols = m_lu.matrixLU().cols();
if(m_dimker == 0)
const int cols = m_lu.matrixLU().cols(), dimker = cols - m_rank;
if(dimker == 0)
{
// The Kernel is just {0}, so it doesn't have a basis properly speaking, but let's
// avoid crashing/asserting as that depends on floating point calculations. Let's
// just return a single column vector filled with zeros.
dst.resize(cols,1);
dst.setZero();
return;
}
@ -543,8 +542,6 @@ struct ei_lu_kernel_impl : public ReturnByValue<ei_lu_kernel_impl<MatrixType> >
* independent vectors in Ker U.
*/
dst.resize(cols, m_dimker);
Matrix<int, Dynamic, 1, 0, LUType::MaxSmallDimAtCompileTime, 1> pivots(m_rank);
RealScalar premultiplied_threshold = m_lu.maxPivot() * m_lu.threshold();
int p = 0;
@ -569,15 +566,15 @@ struct ei_lu_kernel_impl : public ReturnByValue<ei_lu_kernel_impl<MatrixType> >
m.corner(TopLeft, m_rank, m_rank)
.template triangularView<UpperTriangular>().solveInPlace(
m.corner(TopRight, m_rank, m_dimker)
m.corner(TopRight, m_rank, dimker)
);
for(int i = m_rank-1; i >= 0; --i)
m.col(i).swap(m.col(pivots.coeff(i)));
for(int i = 0; i < m_rank; ++i) dst.row(m_lu.permutationQ().coeff(i)) = -m.row(i).end(m_dimker);
for(int i = 0; i < m_rank; ++i) dst.row(m_lu.permutationQ().coeff(i)) = -m.row(i).end(dimker);
for(int i = m_rank; i < cols; ++i) dst.row(m_lu.permutationQ().coeff(i)).setZero();
for(int k = 0; k < m_dimker; ++k) dst.coeffRef(m_lu.permutationQ().coeff(m_rank+k), k) = Scalar(1);
for(int k = 0; k < dimker; ++k) dst.coeffRef(m_lu.permutationQ().coeff(m_rank+k), k) = Scalar(1);
}
};
@ -603,14 +600,16 @@ struct ei_lu_image_impl : public ReturnByValue<ei_lu_image_impl<MatrixType> >
typedef LU<MatrixType> LUType;
typedef typename MatrixType::RealScalar RealScalar;
const LUType& m_lu;
int m_rank;
int m_rank, m_cols;
const MatrixType& m_originalMatrix;
ei_lu_image_impl(const LUType& lu, const MatrixType& originalMatrix)
: m_lu(lu), m_rank(lu.rank()), m_originalMatrix(originalMatrix) {}
: m_lu(lu), m_rank(lu.rank()),
m_cols(m_rank == 0 ? 1 : m_rank),
m_originalMatrix(originalMatrix) {}
inline int rows() const { return m_lu.matrixLU().cols(); }
inline int cols() const { return m_rank; }
inline int rows() const { return m_lu.matrixLU().rows(); }
inline int cols() const { return m_cols; }
template<typename Dest> void evalTo(Dest& dst) const
{
@ -619,7 +618,6 @@ struct ei_lu_image_impl : public ReturnByValue<ei_lu_image_impl<MatrixType> >
// The Image is just {0}, so it doesn't have a basis properly speaking, but let's
// avoid crashing/asserting as that depends on floating point calculations. Let's
// just return a single column vector filled with zeros.
dst.resize(m_originalMatrix.rows(), 1);
dst.setZero();
return;
}
@ -632,7 +630,6 @@ struct ei_lu_image_impl : public ReturnByValue<ei_lu_image_impl<MatrixType> >
pivots.coeffRef(p++) = i;
ei_assert(p == m_rank && "You hit a bug in Eigen! Please report (backtrace and matrix)!");
dst.resize(m_originalMatrix.rows(), m_rank);
for(int i = 0; i < m_rank; ++i)
dst.col(i) = m_originalMatrix.col(m_lu.permutationQ().coeff(pivots.coeff(i)));
}
@ -682,8 +679,6 @@ struct ei_lu_solve_impl : public ReturnByValue<ei_lu_solve_impl<MatrixType, Rhs>
ei_assert(m_rhs.rows() == rows);
const int smalldim = std::min(rows, cols);
dst.resize(m_lu.matrixLU().cols(), m_rhs.cols());
if(nonzero_pivots == 0)
{
dst.setZero();

50
test/lu.cpp
View File

@ -30,15 +30,43 @@ template<typename MatrixType> void lu_non_invertible()
/* this test covers the following files:
LU.h
*/
int rows = ei_random<int>(20,200), cols = ei_random<int>(20,200), cols2 = ei_random<int>(20,200);
int rows, cols, cols2;
if(MatrixType::RowsAtCompileTime==Dynamic)
{
rows = ei_random<int>(20,200);
}
else
{
rows = MatrixType::RowsAtCompileTime;
}
if(MatrixType::ColsAtCompileTime==Dynamic)
{
cols = ei_random<int>(20,200);
cols2 = ei_random<int>(20,200);
}
else
{
cols2 = cols = MatrixType::ColsAtCompileTime;
}
typedef typename ei_lu_kernel_impl<MatrixType>::ReturnMatrixType KernelMatrixType;
typedef typename ei_lu_image_impl <MatrixType>::ReturnMatrixType ImageMatrixType;
typedef Matrix<typename MatrixType::Scalar, Dynamic, Dynamic> DynamicMatrixType;
typedef Matrix<typename MatrixType::Scalar, MatrixType::ColsAtCompileTime, MatrixType::ColsAtCompileTime>
CMatrixType;
int rank = ei_random<int>(1, std::min(rows, cols)-1);
MatrixType m1(rows, cols), m2(cols, cols2), m3(rows, cols2), k(1,1);
// The image of the zero matrix should consist of a single (zero) column vector
VERIFY((MatrixType::Zero(rows,cols).lu().image(MatrixType::Zero(rows,cols)).cols() == 1));
MatrixType m1(rows, cols), m3(rows, cols2);
CMatrixType m2(cols, cols2);
createRandomMatrixOfRank(rank, rows, cols, m1);
LU<MatrixType> lu(m1);
typename ei_lu_kernel_impl<MatrixType>::ReturnMatrixType m1kernel = lu.kernel();
typename ei_lu_image_impl <MatrixType>::ReturnMatrixType m1image = lu.image(m1);
KernelMatrixType m1kernel = lu.kernel();
ImageMatrixType m1image = lu.image(m1);
// std::cerr << rank << " " << lu.rank() << std::endl;
VERIFY(rank == lu.rank());
@ -48,19 +76,18 @@ template<typename MatrixType> void lu_non_invertible()
VERIFY(!lu.isSurjective());
VERIFY((m1 * m1kernel).isMuchSmallerThan(m1));
VERIFY(m1image.lu().rank() == rank);
MatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
DynamicMatrixType sidebyside(m1.rows(), m1.cols() + m1image.cols());
sidebyside << m1, m1image;
VERIFY(sidebyside.lu().rank() == rank);
m2 = MatrixType::Random(cols,cols2);
m2 = CMatrixType::Random(cols,cols2);
m3 = m1*m2;
m2 = MatrixType::Random(cols,cols2);
m2 = CMatrixType::Random(cols,cols2);
// test that the code, which does resize(), may be applied to an xpr
m2.block(0,0,m2.rows(),m2.cols()) = lu.solve(m3);
VERIFY_IS_APPROX(m3, m1*m2);
typedef Matrix<typename MatrixType::Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;
SquareMatrixType m4(rows, rows), m5(rows, rows);
createRandomMatrixOfRank(rows/2, rows, rows, m4);
CMatrixType m4(cols, cols), m5(cols, cols);
createRandomMatrixOfRank(cols/2, cols, cols, m4);
VERIFY(!m4.computeInverseWithCheck(&m5));
}
@ -84,6 +111,7 @@ template<typename MatrixType> void lu_invertible()
LU<MatrixType> lu(m1);
VERIFY(0 == lu.dimensionOfKernel());
VERIFY(lu.kernel().cols() == 1); // the kernel() should consist of a single (zero) column vector
VERIFY(size == lu.rank());
VERIFY(lu.isInjective());
VERIFY(lu.isSurjective());
@ -125,6 +153,8 @@ template<typename MatrixType> void lu_verify_assert()
void test_lu()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( lu_non_invertible<Matrix3f>() );
CALL_SUBTEST( (lu_non_invertible<Matrix<double, 4, 6> >()) );
CALL_SUBTEST( lu_non_invertible<MatrixXf>() );
CALL_SUBTEST( lu_non_invertible<MatrixXd>() );
CALL_SUBTEST( lu_non_invertible<MatrixXcf>() );