From aa6c516ec17fb44dff85b1abf3a1b05d58d3bc01 Mon Sep 17 00:00:00 2001
From: Gael Guennebaud <g.gael@free.fr>
Date: Mon, 16 Feb 2015 13:19:05 +0100
Subject: Fix many long to int conversion warnings:  - fix usage of Index (API)
 versus StorageIndex (when multiple indexes are stored)  - use
 StorageIndex(val) when the input has already been check  - use
 internal::convert_index<StorageIndex>(val) when val is potentially unsafe
 (directly comes from user input)

---
 Eigen/src/OrderingMethods/Ordering.h | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

(limited to 'Eigen/src/OrderingMethods/Ordering.h')

diff --git a/Eigen/src/OrderingMethods/Ordering.h b/Eigen/src/OrderingMethods/Ordering.h
index f3c31f9cb..e88e637a4 100644
--- a/Eigen/src/OrderingMethods/Ordering.h
+++ b/Eigen/src/OrderingMethods/Ordering.h
@@ -111,12 +111,12 @@ class NaturalOrdering
   * Functor computing the \em column \em approximate \em minimum \em degree ordering 
   * The matrix should be in column-major and \b compressed format (see SparseMatrix::makeCompressed()).
   */
-template<typename Index>
+template<typename StorageIndex>
 class COLAMDOrdering
 {
   public:
-    typedef PermutationMatrix<Dynamic, Dynamic, Index> PermutationType; 
-    typedef Matrix<Index, Dynamic, 1> IndexVector;
+    typedef PermutationMatrix<Dynamic, Dynamic, StorageIndex> PermutationType; 
+    typedef Matrix<StorageIndex, Dynamic, 1> IndexVector;
     
     /** Compute the permutation vector \a perm form the sparse matrix \a mat
       * \warning The input sparse matrix \a mat must be in compressed mode (see SparseMatrix::makeCompressed()).
@@ -126,26 +126,26 @@ class COLAMDOrdering
     {
       eigen_assert(mat.isCompressed() && "COLAMDOrdering requires a sparse matrix in compressed mode. Call .makeCompressed() before passing it to COLAMDOrdering");
       
-      Index m = mat.rows();
-      Index n = mat.cols();
-      Index nnz = mat.nonZeros();
+      StorageIndex m = StorageIndex(mat.rows());
+      StorageIndex n = StorageIndex(mat.cols());
+      StorageIndex nnz = StorageIndex(mat.nonZeros());
       // Get the recommended value of Alen to be used by colamd
-      Index Alen = internal::colamd_recommended(nnz, m, n); 
+      StorageIndex Alen = internal::colamd_recommended(nnz, m, n); 
       // Set the default parameters
       double knobs [COLAMD_KNOBS]; 
-      Index stats [COLAMD_STATS];
+      StorageIndex stats [COLAMD_STATS];
       internal::colamd_set_defaults(knobs);
       
       IndexVector p(n+1), A(Alen); 
-      for(Index i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
-      for(Index i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
+      for(StorageIndex i=0; i <= n; i++)   p(i) = mat.outerIndexPtr()[i];
+      for(StorageIndex i=0; i < nnz; i++)  A(i) = mat.innerIndexPtr()[i];
       // Call Colamd routine to compute the ordering 
-      Index info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats); 
+      StorageIndex info = internal::colamd(m, n, Alen, A.data(), p.data(), knobs, stats); 
       EIGEN_UNUSED_VARIABLE(info);
       eigen_assert( info && "COLAMD failed " );
       
       perm.resize(n);
-      for (Index i = 0; i < n; i++) perm.indices()(p(i)) = i;
+      for (StorageIndex i = 0; i < n; i++) perm.indices()(p(i)) = i;
     }
 };
 
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