Index refactoring: StorageIndex must be used for storage only (and locally when it make sense). In all other cases use the global Index type.

1 files changed, 38 insertions, 36 deletions

diff --git a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
index c413e9e1a..6d63d45e4 100644
--- a/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
+++ b/Eigen/src/IterativeLinearSolvers/IncompleteLUT.h
@@ -25,14 +25,14 @@ namespace internal {
   * \param ind The array of index for the elements in @p row
   * \param ncut  The number of largest elements to keep
   **/ 
-template <typename VectorV, typename VectorI, typename Index>
+template <typename VectorV, typename VectorI>
 Index QuickSplit(VectorV &row, VectorI &ind, Index ncut)
 {
   typedef typename VectorV::RealScalar RealScalar;
   using std::swap;
   using std::abs;
   Index mid;
-  Index n = convert_index<Index>(row.size()); /* length of the vector */
+  Index n = row.size(); /* length of the vector */
   Index first, last ;
   
   ncut--; /* to fit the zero-based indices */
@@ -124,9 +124,9 @@ class IncompleteLUT : public SparseSolverBase<IncompleteLUT<_Scalar> >
       compute(mat); 
     }
     
-    StorageIndex rows() const { return m_lu.rows(); }
+    Index rows() const { return m_lu.rows(); }
     
-    StorageIndex cols() const { return m_lu.cols(); }
+    Index cols() const { return m_lu.cols(); }
 
     /** \brief Reports whether previous computation was successful.
       *
@@ -239,9 +239,10 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
   using std::sqrt;
   using std::swap;
   using std::abs;
+  using internal::convert_index;
 
   eigen_assert((amat.rows() == amat.cols()) && "The factorization should be done on a square matrix");
-  StorageIndex n = amat.cols();  // Size of the matrix
+  Index n = amat.cols();  // Size of the matrix
   m_lu.resize(n,n);
   // Declare Working vectors and variables
   Vector u(n) ;     // real values of the row -- maximum size is n --
@@ -259,36 +260,36 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
   u.fill(0);
 
   // number of largest elements to keep in each row:
-  StorageIndex fill_in =   static_cast<StorageIndex> (amat.nonZeros()*m_fillfactor)/n+1;
+  Index fill_in = (amat.nonZeros()*m_fillfactor)/n + 1;
   if (fill_in > n) fill_in = n;
 
   // number of largest nonzero elements to keep in the L and the U part of the current row:
-  StorageIndex nnzL = fill_in/2;
-  StorageIndex nnzU = nnzL;
+  Index nnzL = fill_in/2;
+  Index nnzU = nnzL;
   m_lu.reserve(n * (nnzL + nnzU + 1));
 
   // global loop over the rows of the sparse matrix
-  for (StorageIndex ii = 0; ii < n; ii++)
+  for (Index ii = 0; ii < n; ii++)
   {
     // 1 - copy the lower and the upper part of the row i of mat in the working vector u
 
-    StorageIndex sizeu = 1; // number of nonzero elements in the upper part of the current row
-    StorageIndex sizel = 0; // number of nonzero elements in the lower part of the current row
-    ju(ii)    = ii;
+    Index sizeu = 1; // number of nonzero elements in the upper part of the current row
+    Index sizel = 0; // number of nonzero elements in the lower part of the current row
+    ju(ii)    = convert_index<StorageIndex>(ii);
     u(ii)     = 0;
-    jr(ii)    = ii;
+    jr(ii)    = convert_index<StorageIndex>(ii);
     RealScalar rownorm = 0;
 
     typename FactorType::InnerIterator j_it(mat, ii); // Iterate through the current row ii
     for (; j_it; ++j_it)
     {
-      StorageIndex k = j_it.index();
+      Index k = j_it.index();
       if (k < ii)
       {
         // copy the lower part
-        ju(sizel) = k;
+        ju(sizel) = convert_index<StorageIndex>(k);
         u(sizel) = j_it.value();
-        jr(k) = sizel;
+        jr(k) = convert_index<StorageIndex>(sizel);
         ++sizel;
       }
       else if (k == ii)
@@ -298,10 +299,10 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
       else
       {
         // copy the upper part
-        StorageIndex jpos = ii + sizeu;
-        ju(jpos) = k;
+        Index jpos = ii + sizeu;
+        ju(jpos) = convert_index<StorageIndex>(k);
         u(jpos) = j_it.value();
-        jr(k) = jpos;
+        jr(k) = convert_index<StorageIndex>(jpos);
         ++sizeu;
       }
       rownorm += numext::abs2(j_it.value());
@@ -317,21 +318,22 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
     rownorm = sqrt(rownorm);
 
     // 3 - eliminate the previous nonzero rows
-    StorageIndex jj = 0;
-    StorageIndex len = 0;
+    Index jj = 0;
+    Index len = 0;
     while (jj < sizel)
     {
       // In order to eliminate in the correct order,
       // we must select first the smallest column index among  ju(jj:sizel)
-      StorageIndex k;
-      StorageIndex minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
+      Index k;
+      Index minrow = ju.segment(jj,sizel-jj).minCoeff(&k); // k is relative to the segment
       k += jj;
       if (minrow != ju(jj))
       {
         // swap the two locations
-        StorageIndex j = ju(jj);
+        Index j = ju(jj);
         swap(ju(jj), ju(k));
-        jr(minrow) = jj;   jr(j) = k;
+        jr(minrow) = convert_index<StorageIndex>(jj);
+        jr(j) = convert_index<StorageIndex>(k);
         swap(u(jj), u(k));
       }
       // Reset this location
@@ -355,11 +357,11 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
       for (; ki_it; ++ki_it)
       {
         Scalar prod = fact * ki_it.value();
-        StorageIndex j       = ki_it.index();
-        StorageIndex jpos    = jr(j);
+        Index j     = ki_it.index();
+        Index jpos  = jr(j);
         if (jpos == -1) // fill-in element
         {
-          StorageIndex newpos;
+          Index newpos;
           if (j >= ii) // dealing with the upper part
           {
             newpos = ii + sizeu;
@@ -372,23 +374,23 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
             sizel++;
             eigen_internal_assert(sizel<=ii);
           }
-          ju(newpos) = j;
+          ju(newpos) = convert_index<StorageIndex>(j);
           u(newpos) = -prod;
-          jr(j) = newpos;
+          jr(j) = convert_index<StorageIndex>(newpos);
         }
         else
           u(jpos) -= prod;
       }
       // store the pivot element
-      u(len) = fact;
-      ju(len) = minrow;
+      u(len)  = fact;
+      ju(len) = convert_index<StorageIndex>(minrow);
       ++len;
 
       jj++;
     } // end of the elimination on the row ii
 
     // reset the upper part of the pointer jr to zero
-    for(StorageIndex k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
+    for(Index k = 0; k <sizeu; k++) jr(ju(ii+k)) = -1;
 
     // 4 - partially sort and insert the elements in the m_lu matrix
 
@@ -401,7 +403,7 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
 
     // store the largest m_fill elements of the L part
     m_lu.startVec(ii);
-    for(StorageIndex k = 0; k < len; k++)
+    for(Index k = 0; k < len; k++)
       m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
 
     // store the diagonal element
@@ -413,7 +415,7 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
     // sort the U-part of the row
     // apply the dropping rule first
     len = 0;
-    for(StorageIndex k = 1; k < sizeu; k++)
+    for(Index k = 1; k < sizeu; k++)
     {
       if(abs(u(ii+k)) > m_droptol * rownorm )
       {
@@ -429,7 +431,7 @@ void IncompleteLUT<Scalar>::factorize(const _MatrixType& amat)
     internal::QuickSplit(uu, juu, len);
 
     // store the largest elements of the U part
-    for(StorageIndex k = ii + 1; k < ii + len; k++)
+    for(Index k = ii + 1; k < ii + len; k++)
       m_lu.insertBackByOuterInnerUnordered(ii,ju(k)) = u(k);
   }