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fix some warnings
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79e9e3f24c
commit
76dd0e5314
@ -102,7 +102,7 @@ class SparseLU
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void setOrderingMethod(int m)
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{
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ei_assert(m&~OrderingMask == 0 && m!=0 && "invalid ordering method");
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ei_assert( (m&~OrderingMask) == 0 && m!=0 && "invalid ordering method");
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m_flags = m_flags&~OrderingMask | m&OrderingMask;
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}
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@ -138,7 +138,7 @@ class SparseLU
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* using the default algorithm.
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*/
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template<typename MatrixType, int Backend>
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void SparseLU<MatrixType,Backend>::compute(const MatrixType& a)
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void SparseLU<MatrixType,Backend>::compute(const MatrixType& )
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{
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ei_assert(false && "not implemented yet");
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}
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@ -153,7 +153,7 @@ void SparseLU<MatrixType,Backend>::compute(const MatrixType& a)
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*/
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template<typename MatrixType, int Backend>
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template<typename BDerived, typename XDerived>
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bool SparseLU<MatrixType,Backend>::solve(const MatrixBase<BDerived> &b, MatrixBase<XDerived>* x, const int transposed) const
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bool SparseLU<MatrixType,Backend>::solve(const MatrixBase<BDerived> &, MatrixBase<XDerived>* , const int ) const
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{
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ei_assert(false && "not implemented yet");
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return false;
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@ -156,8 +156,8 @@ static void ei_sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType&
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float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
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float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
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int t200 = rows/(log2(200)*1.39);
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int t = (rows*100)/139;
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// int t200 = rows/(log2(200)*1.39);
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// int t = (rows*100)/139;
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res.resize(rows, cols);
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res.reserve(int(ratioRes*rows*cols));
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@ -377,6 +377,11 @@ struct ei_sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor
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{
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static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
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{
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// prevent warnings until the code is fixed
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(void) lhs;
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(void) rhs;
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(void) res;
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// typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
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// RowMajorMatrix rhsRow = rhs;
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// RowMajorMatrix resRow(res.rows(), res.cols());
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@ -214,7 +214,7 @@ class DenseTimeSparseSelfAdjointProduct
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DenseTimeSparseSelfAdjointProduct(const Lhs& lhs, const Rhs& rhs) : Base(lhs,rhs)
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{}
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template<typename Dest> void scaleAndAddTo(Dest& dest, Scalar alpha) const
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template<typename Dest> void scaleAndAddTo(Dest& /*dest*/, Scalar /*alpha*/) const
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{
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// TODO
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}
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@ -144,7 +144,7 @@ const int InnerRandomAccessPattern = 0x2 | CoherentAccessPattern;
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const int OuterRandomAccessPattern = 0x4 | CoherentAccessPattern;
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const int RandomAccessPattern = 0x8 | OuterRandomAccessPattern | InnerRandomAccessPattern;
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template<typename T> class ei_eval<T,Sparse>
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template<typename T> struct ei_eval<T,Sparse>
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{
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typedef typename ei_traits<T>::Scalar _Scalar;
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enum {
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@ -379,7 +379,7 @@ class SparseVector<Scalar,_Options>::InnerIterator
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{}
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template<unsigned int Added, unsigned int Removed>
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InnerIterator(const Flagged<SparseVector,Added,Removed>& vec, int outer)
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InnerIterator(const Flagged<SparseVector,Added,Removed>& vec, int )
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: m_data(vec._expression().m_data), m_id(0), m_end(m_data.size())
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{}
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