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Implement evaluators for sparse * sparse products

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This commit is contained in:
Gael Guennebaud 2014-07-01 11:50:20 +02:00
parent 0ad7a644df
commit 441f97b2df
4 changed files with 88 additions and 4 deletions
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4
Eigen/SparseCore
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@ -49,12 +49,12 @@ struct Sparse {};
#include "src/SparseCore/SparseRedux.h"
#include "src/SparseCore/SparseView.h"
#include "src/SparseCore/SparseDiagonalProduct.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseProduct.h"
#ifndef EIGEN_TEST_EVALUATORS
#include "src/SparseCore/SparsePermutation.h"
#include "src/SparseCore/SparseFuzzy.h"
#include "src/SparseCore/ConservativeSparseSparseProduct.h"
#include "src/SparseCore/SparseSparseProductWithPruning.h"
#include "src/SparseCore/SparseProduct.h"
#include "src/SparseCore/SparseDenseProduct.h"
#include "src/SparseCore/SparseTriangularView.h"
#include "src/SparseCore/SparseSelfAdjointView.h"

13
Eigen/src/SparseCore/ConservativeSparseSparseProduct.h
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@ -37,6 +37,11 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
// Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros();
#ifdef EIGEN_TEST_EVALUATORS
typename evaluator<Lhs>::type lhsEval(lhs);
typename evaluator<Rhs>::type rhsEval(rhs);
#endif
res.setZero();
res.reserve(Index(estimated_nnz_prod));
// we compute each column of the result, one after the other
@ -45,11 +50,19 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
res.startVec(j);
Index nnz = 0;
#ifndef EIGEN_TEST_EVALUATORS
for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
#else
for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
#endif
{
Scalar y = rhsIt.value();
Index k = rhsIt.index();
#ifndef EIGEN_TEST_EVALUATORS
for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
#else
for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, k); lhsIt; ++lhsIt)
#endif
{
Index i = lhsIt.index();
Scalar x = lhsIt.value();

9
Eigen/src/SparseCore/SparseMatrixBase.h
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@ -190,8 +190,10 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
public:
#ifndef EIGEN_TEST_EVALUATORS
template<typename Lhs, typename Rhs>
inline Derived& operator=(const SparseSparseProduct<Lhs,Rhs>& product);
#endif
friend std::ostream & operator << (std::ostream & s, const SparseMatrixBase& m)
{
@ -264,12 +266,12 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
EIGEN_STRONG_INLINE const EIGEN_SPARSE_CWISE_PRODUCT_RETURN_TYPE
cwiseProduct(const MatrixBase<OtherDerived> &other) const;
#ifndef EIGEN_TEST_EVALUATORS
// sparse * sparse
template<typename OtherDerived>
const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
operator*(const SparseMatrixBase<OtherDerived> &other) const;
#ifndef EIGEN_TEST_EVALUATORS
// sparse * diagonal
template<typename OtherDerived>
const SparseDiagonalProduct<Derived,OtherDerived>
@ -292,6 +294,11 @@ template<typename Derived> class SparseMatrixBase : public EigenBase<Derived>
const Product<OtherDerived,Derived>
operator*(const DiagonalBase<OtherDerived> &lhs, const SparseMatrixBase& rhs)
{ return Product<OtherDerived,Derived>(lhs.derived(), rhs.derived()); }
// sparse * sparse
template<typename OtherDerived>
const Product<Derived,OtherDerived>
operator*(const SparseMatrixBase<OtherDerived> &other) const;
#endif // EIGEN_TEST_EVALUATORS
/** dense * sparse (return a dense object unless it is an outer product) */

64
Eigen/src/SparseCore/SparseProduct.h
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@ -12,6 +12,8 @@
namespace Eigen {
#ifndef EIGEN_TEST_EVALUATORS
template<typename Lhs, typename Rhs>
struct SparseSparseProductReturnType
{
@ -183,6 +185,68 @@ SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other
return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
}
#else // EIGEN_TEST_EVALUATORS
/** \returns an expression of the product of two sparse matrices.
* By default a conservative product preserving the symbolic non zeros is performed.
* The automatic pruning of the small values can be achieved by calling the pruned() function
* in which case a totally different product algorithm is employed:
* \code
* C = (A*B).pruned(); // supress numerical zeros (exact)
* C = (A*B).pruned(ref);
* C = (A*B).pruned(ref,epsilon);
* \endcode
* where \c ref is a meaningful non zero reference value.
* */
template<typename Derived>
template<typename OtherDerived>
inline const Product<Derived,OtherDerived>
SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
{
return Product<Derived,OtherDerived>(derived(), other.derived());
}
namespace internal {
template<typename Lhs, typename Rhs, int ProductType>
struct generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductType>
{
template<typename Dest>
static void evalTo(Dest& dst, const Lhs& lhs, const Rhs& rhs)
{
typedef typename nested_eval<Lhs,Dynamic>::type LhsNested;
typedef typename nested_eval<Rhs,Dynamic>::type RhsNested;
LhsNested lhsNested(lhs);
RhsNested rhsNested(rhs);
internal::conservative_sparse_sparse_product_selector<typename remove_all<LhsNested>::type,
typename remove_all<RhsNested>::type, Dest>::run(lhsNested,rhsNested,dst);
}
};
template<typename Lhs, typename Rhs, int ProductTag>
struct product_evaluator<Product<Lhs, Rhs, DefaultProduct>, ProductTag, SparseShape, SparseShape, typename Lhs::Scalar, typename Rhs::Scalar>
: public evaluator<typename Product<Lhs, Rhs, DefaultProduct>::PlainObject>::type
{
typedef Product<Lhs, Rhs, DefaultProduct> XprType;
typedef typename XprType::PlainObject PlainObject;
typedef typename evaluator<PlainObject>::type Base;
product_evaluator(const XprType& xpr)
: m_result(xpr.rows(), xpr.cols())
{
::new (static_cast<Base*>(this)) Base(m_result);
generic_product_impl<Lhs, Rhs, SparseShape, SparseShape, ProductTag>::evalTo(m_result, xpr.lhs(), xpr.rhs());
}
protected:
PlainObject m_result;
};
} // end namespace internal
#endif // EIGEN_TEST_EVALUATORS
} // end namespace Eigen
#endif // EIGEN_SPARSEPRODUCT_H