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Implement evaluators for sparse * sparse with auto pruning.

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This commit is contained in:
Gael Guennebaud
2014-07-01 13:18:56 +02:00
parent 441f97b2df
commit 746d2db6ed
5 changed files with 120 additions and 4 deletions
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63
Eigen/src/SparseCore/SparseSparseProductWithPruning.h
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@@ -46,6 +46,11 @@ static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& r
res.resize(cols, rows);
else
res.resize(rows, cols);
#ifdef EIGEN_TEST_EVALUATORS
typename evaluator<Lhs>::type lhsEval(lhs);
typename evaluator<Rhs>::type rhsEval(rhs);
#endif
res.reserve(estimated_nnz_prod);
double ratioColRes = double(estimated_nnz_prod)/double(lhs.rows()*rhs.cols());
@@ -56,12 +61,20 @@ static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& r
// let's do a more accurate determination of the nnz ratio for the current column j of res
tempVector.init(ratioColRes);
tempVector.setZero();
#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
{
// FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
tempVector.restart();
Scalar x = rhsIt.value();
#ifndef EIGEN_TEST_EVALUATORS
for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
#else
for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
#endif
{
tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
}
@@ -140,8 +153,58 @@ struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,R
}
};
#ifndef EIGEN_TEST_EVALUATORS
// NOTE the 2 others cases (col row *) must never occur since they are caught
// by ProductReturnType which transforms it to (col col *) by evaluating rhs.
#else
template<typename Lhs, typename Rhs, typename ResultType>
struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
{
typedef typename ResultType::RealScalar RealScalar;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
{
typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename Lhs::Index> RowMajorMatrixLhs;
RowMajorMatrixLhs rowLhs(lhs);
sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs,Rhs,ResultType,RowMajor,RowMajor>(rowLhs,rhs,res,tolerance);
}
};
template<typename Lhs, typename Rhs, typename ResultType>
struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
{
typedef typename ResultType::RealScalar RealScalar;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
{
typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename Lhs::Index> RowMajorMatrixRhs;
RowMajorMatrixRhs rowRhs(rhs);
sparse_sparse_product_with_pruning_selector<Lhs,RowMajorMatrixRhs,ResultType,RowMajor,RowMajor,RowMajor>(lhs,rowRhs,res,tolerance);
}
};
template<typename Lhs, typename Rhs, typename ResultType>
struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
{
typedef typename ResultType::RealScalar RealScalar;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
{
typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixRhs;
ColMajorMatrixRhs colRhs(rhs);
internal::sparse_sparse_product_with_pruning_impl<Lhs,ColMajorMatrixRhs,ResultType>(lhs, colRhs, res, tolerance);
}
};
template<typename Lhs, typename Rhs, typename ResultType>
struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
{
typedef typename ResultType::RealScalar RealScalar;
static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
{
typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixLhs;
ColMajorMatrixLhs colLhs(lhs);
internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,Rhs,ResultType>(colLhs, rhs, res, tolerance);
}
};
#endif
} // end namespace internal