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https://gitlab.com/libeigen/eigen.git
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@@ -66,9 +66,9 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
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}
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// unordered insertion
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for(int k=0; k<nnz; ++k)
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for(Index k=0; k<nnz; ++k)
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{
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int i = indices[k];
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Index i = indices[k];
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res.insertBackByOuterInnerUnordered(j,i) = values[i];
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mask[i] = false;
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}
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@@ -76,8 +76,8 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
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#if 0
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// alternative ordered insertion code:
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int t200 = rows/(log2(200)*1.39);
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int t = (rows*100)/139;
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Index t200 = rows/(log2(200)*1.39);
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Index t = (rows*100)/139;
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// FIXME reserve nnz non zeros
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// FIXME implement fast sort algorithms for very small nnz
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@@ -90,9 +90,9 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
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if(true)
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{
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if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
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for(int k=0; k<nnz; ++k)
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for(Index k=0; k<nnz; ++k)
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{
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int i = indices[k];
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Index i = indices[k];
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res.insertBackByOuterInner(j,i) = values[i];
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mask[i] = false;
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}
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@@ -100,7 +100,7 @@ static void conservative_sparse_sparse_product_impl(const Lhs& lhs, const Rhs& r
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else
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{
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// dense path
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for(int i=0; i<rows; ++i)
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for(Index i=0; i<rows; ++i)
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{
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if(mask[i])
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{
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@@ -125,7 +125,7 @@ class SparseDenseOuterProduct<Lhs,Rhs,Transpose>::InnerIterator : public _LhsNes
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inline Scalar value() const { return Base::value() * m_factor; }
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protected:
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int m_outer;
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Index m_outer;
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Scalar m_factor;
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};
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@@ -155,7 +155,7 @@ struct sparse_time_dense_product_impl<SparseLhsType,DenseRhsType,DenseResType, R
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{
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for(Index c=0; c<rhs.cols(); ++c)
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{
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int n = lhs.outerSize();
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Index n = lhs.outerSize();
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for(Index j=0; j<n; ++j)
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{
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typename Res::Scalar tmp(0);
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@@ -402,7 +402,7 @@ class SparseMatrix
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* \sa insertBack, insertBackByOuterInner */
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inline void startVec(Index outer)
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{
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eigen_assert(m_outerIndex[outer]==int(m_data.size()) && "You must call startVec for each inner vector sequentially");
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eigen_assert(m_outerIndex[outer]==Index(m_data.size()) && "You must call startVec for each inner vector sequentially");
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eigen_assert(m_outerIndex[outer+1]==0 && "You must call startVec for each inner vector sequentially");
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m_outerIndex[outer+1] = m_outerIndex[outer];
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}
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@@ -480,7 +480,7 @@ class SparseMatrix
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if(m_innerNonZeros != 0)
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return;
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m_innerNonZeros = static_cast<Index*>(std::malloc(m_outerSize * sizeof(Index)));
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for (int i = 0; i < m_outerSize; i++)
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for (Index i = 0; i < m_outerSize; i++)
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{
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m_innerNonZeros[i] = m_outerIndex[i+1] - m_outerIndex[i];
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}
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@@ -752,8 +752,8 @@ class SparseMatrix
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else
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for (Index i=0; i<m.outerSize(); ++i)
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{
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int p = m.m_outerIndex[i];
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int pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
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Index p = m.m_outerIndex[i];
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Index pe = m.m_outerIndex[i]+m.m_innerNonZeros[i];
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Index k=p;
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for (; k<pe; ++k)
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s << "(" << m.m_data.value(k) << "," << m.m_data.index(k) << ") ";
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@@ -1022,7 +1022,7 @@ void SparseMatrix<Scalar,_Options,_Index>::sumupDuplicates()
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wi.fill(-1);
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Index count = 0;
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// for each inner-vector, wi[inner_index] will hold the position of first element into the index/value buffers
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for(int j=0; j<outerSize(); ++j)
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for(Index j=0; j<outerSize(); ++j)
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{
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Index start = count;
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Index oldEnd = m_outerIndex[j]+m_innerNonZeros[j];
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@@ -27,7 +27,7 @@ static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& r
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// make sure to call innerSize/outerSize since we fake the storage order.
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Index rows = lhs.innerSize();
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Index cols = rhs.outerSize();
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//int size = lhs.outerSize();
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//Index size = lhs.outerSize();
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eigen_assert(lhs.outerSize() == rhs.innerSize());
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// allocate a temporary buffer
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