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fix m = m*m with m sparse (gug found by Frederik Heinz)

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This commit is contained in:
Gael Guennebaud 2009-02-12 15:57:13 +00:00
parent 59a1ed0932
commit 20a8bb96eb
2 changed files with 98 additions and 87 deletions
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107
Eigen/src/Sparse/SparseProduct.h
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@ -171,6 +171,55 @@ class SparseProduct : ei_no_assignment_operator,
RhsNested m_rhs;
};
// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
template<typename Lhs, typename Rhs, typename ResultType>
static void ei_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{
typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
// make sure to call innerSize/outerSize since we fake the storage order.
int rows = lhs.innerSize();
int cols = rhs.outerSize();
//int size = lhs.outerSize();
ei_assert(lhs.outerSize() == rhs.innerSize());
// allocate a temporary buffer
AmbiVector<Scalar> tempVector(rows);
// estimate the number of non zero entries
float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
res.resize(rows, cols);
res.startFill(int(ratioRes*rows*cols));
for (int j=0; j<cols; ++j)
{
// let's do a more accurate determination of the nnz ratio for the current column j of res
//float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
// FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
float ratioColRes = ratioRes;
tempVector.init(ratioColRes);
tempVector.setZero();
for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
{
// FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
tempVector.restart();
Scalar x = rhsIt.value();
for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
{
tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
}
}
for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
if (ResultType::Flags&RowMajorBit)
res.fill(j,it.index()) = it.value();
else
res.fill(it.index(), j) = it.value();
}
res.endFill();
}
template<typename Lhs, typename Rhs, typename ResultType,
int LhsStorageOrder = ei_traits<Lhs>::Flags&RowMajorBit,
int RhsStorageOrder = ei_traits<Rhs>::Flags&RowMajorBit,
@ -184,58 +233,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{
// make sure to call innerSize/outerSize since we fake the storage order.
int rows = lhs.innerSize();
int cols = rhs.outerSize();
//int size = lhs.outerSize();
ei_assert(lhs.outerSize() == rhs.innerSize());
// allocate a temporary buffer
AmbiVector<Scalar> tempVector(rows);
// estimate the number of non zero entries
float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
res.resize(rows, cols);
res.startFill(int(ratioRes*rows*cols));
for (int j=0; j<cols; ++j)
{
// let's do a more accurate determination of the nnz ratio for the current column j of res
//float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
// FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
float ratioColRes = ratioRes;
tempVector.init(ratioColRes);
tempVector.setZero();
for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
{
// FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
tempVector.restart();
Scalar x = rhsIt.value();
for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
{
tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
}
}
for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
if (ResultType::Flags&RowMajorBit)
res.fill(j,it.index()) = it.value();
else
res.fill(it.index(), j) = it.value();
}
res.endFill();
typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
ei_sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
res.swap(_res);
}
};
template<typename Lhs, typename Rhs, typename ResultType>
struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
{
typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{
// we need a col-major matrix to hold the result
typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
SparseTemporaryType _res(res.rows(), res.cols());
ei_sparse_product_selector<Lhs,Rhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>::run(lhs, rhs, _res);
ei_sparse_product_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res);
res = _res;
}
};
@ -246,20 +258,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{
// let's transpose the product to get a column x column product
ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,ColMajor>::run(rhs, lhs, res);
typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
ei_sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
res.swap(_res);
}
};
template<typename Lhs, typename Rhs, typename ResultType>
struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
{
typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
{
// let's transpose the product to get a column x column product
typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
SparseTemporaryType _res(res.cols(), res.rows());
ei_sparse_product_selector<Rhs,Lhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>
::run(rhs, lhs, _res);
ei_sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
res = _res.transpose();
}
};

68
test/sparse_product.cpp
View File

@ -37,7 +37,6 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
Scalar eps = 1e-6;
// test matrix-matrix product
/*
{
DenseMatrix refMat2 = DenseMatrix::Zero(rows, rows);
DenseMatrix refMat3 = DenseMatrix::Zero(rows, rows);
@ -65,8 +64,9 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
VERIFY_IS_APPROX(dm4=refMat2*m3.transpose(), refMat4=refMat2*refMat3.transpose());
VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3, refMat4=refMat2.transpose()*refMat3);
VERIFY_IS_APPROX(dm4=refMat2.transpose()*m3.transpose(), refMat4=refMat2.transpose()*refMat3.transpose());
VERIFY_IS_APPROX(m3=m3*m3, refMat3=refMat3*refMat3);
}
*/
// test matrix - diagonal product
{
@ -77,46 +77,44 @@ template<typename SparseMatrixType> void sparse_product(const SparseMatrixType&
SparseMatrixType m3(rows, rows);
initSparse<Scalar>(density, refM2, m2);
initSparse<Scalar>(density, refM3, m3);
// std::cerr << "foo\n" << (m2*d1).toDense() << "\n\n" << refM2*d1 << "\n\n";
VERIFY_IS_APPROX(m3=m2*d1, refM3=refM2*d1);
VERIFY_IS_APPROX(m3=m2.transpose()*d1, refM3=refM2.transpose()*d1);
VERIFY_IS_APPROX(m3=d1*m2, refM3=d1*refM2);
// std::cerr << "foo\n" << (d1*m2.transpose()).toDense() << "\n\n" << d1 * refM2.transpose() << "\n\n";
VERIFY_IS_APPROX(m3=d1*m2.transpose(), refM3=d1 * refM2.transpose());
}
// test self adjoint products
// {
// DenseMatrix b = DenseMatrix::Random(rows, rows);
// DenseMatrix x = DenseMatrix::Random(rows, rows);
// DenseMatrix refX = DenseMatrix::Random(rows, rows);
// DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
// DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
// DenseMatrix refS = DenseMatrix::Zero(rows, rows);
// SparseMatrixType mUp(rows, rows);
// SparseMatrixType mLo(rows, rows);
// SparseMatrixType mS(rows, rows);
// do {
// initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
// } while (refUp.isZero());
// refLo = refUp.transpose().conjugate();
// mLo = mUp.transpose().conjugate();
// refS = refUp + refLo;
// refS.diagonal() *= 0.5;
// mS = mUp + mLo;
// for (int k=0; k<mS.outerSize(); ++k)
// for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
// if (it.index() == k)
// it.valueRef() *= 0.5;
//
// VERIFY_IS_APPROX(refS.adjoint(), refS);
// VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
// VERIFY_IS_APPROX(mS, refS);
// VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
// VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
// VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
// VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
// }
{
DenseMatrix b = DenseMatrix::Random(rows, rows);
DenseMatrix x = DenseMatrix::Random(rows, rows);
DenseMatrix refX = DenseMatrix::Random(rows, rows);
DenseMatrix refUp = DenseMatrix::Zero(rows, rows);
DenseMatrix refLo = DenseMatrix::Zero(rows, rows);
DenseMatrix refS = DenseMatrix::Zero(rows, rows);
SparseMatrixType mUp(rows, rows);
SparseMatrixType mLo(rows, rows);
SparseMatrixType mS(rows, rows);
do {
initSparse<Scalar>(density, refUp, mUp, ForceRealDiag|/*ForceNonZeroDiag|*/MakeUpperTriangular);
} while (refUp.isZero());
refLo = refUp.transpose().conjugate();
mLo = mUp.transpose().conjugate();
refS = refUp + refLo;
refS.diagonal() *= 0.5;
mS = mUp + mLo;
for (int k=0; k<mS.outerSize(); ++k)
for (typename SparseMatrixType::InnerIterator it(mS,k); it; ++it)
if (it.index() == k)
it.valueRef() *= 0.5;
VERIFY_IS_APPROX(refS.adjoint(), refS);
VERIFY_IS_APPROX(mS.transpose().conjugate(), mS);
VERIFY_IS_APPROX(mS, refS);
VERIFY_IS_APPROX(x=mS*b, refX=refS*b);
VERIFY_IS_APPROX(x=mUp.template marked<UpperTriangular|SelfAdjoint>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mLo.template marked<LowerTriangular|SelfAdjoint>()*b, refX=refS*b);
VERIFY_IS_APPROX(x=mS.template marked<SelfAdjoint>()*b, refX=refS*b);
}
}