fix m = m*m with m sparse (gug found by Frederik Heinz)

1 files changed, 61 insertions, 48 deletions

diff --git a/Eigen/src/Sparse/SparseProduct.h b/Eigen/src/Sparse/SparseProduct.h
index d3ef57455..c98a71e99 100644
--- a/Eigen/src/Sparse/SparseProduct.h
+++ b/Eigen/src/Sparse/SparseProduct.h
@@ -171,6 +171,55 @@ class SparseProduct : ei_no_assignment_operator,
     RhsNested m_rhs;
 };
 
+// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
+template<typename Lhs, typename Rhs, typename ResultType>
+static void ei_sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
+{
+  typedef typename ei_traits<typename ei_cleantype<Lhs>::type>::Scalar Scalar;
+
+  // make sure to call innerSize/outerSize since we fake the storage order.
+  int rows = lhs.innerSize();
+  int cols = rhs.outerSize();
+  //int size = lhs.outerSize();
+  ei_assert(lhs.outerSize() == rhs.innerSize());
+
+  // allocate a temporary buffer
+  AmbiVector<Scalar> tempVector(rows);
+
+  // estimate the number of non zero entries
+  float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
+  float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
+  float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
+
+  res.resize(rows, cols);
+  res.startFill(int(ratioRes*rows*cols));
+  for (int j=0; j<cols; ++j)
+  {
+    // let's do a more accurate determination of the nnz ratio for the current column j of res
+    //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
+    // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
+    float ratioColRes = ratioRes;
+    tempVector.init(ratioColRes);
+    tempVector.setZero();
+    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
+    {
+      // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
+      tempVector.restart();
+      Scalar x = rhsIt.value();
+      for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
+      {
+        tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
+      }
+    }
+    for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
+      if (ResultType::Flags&RowMajorBit)
+        res.fill(j,it.index()) = it.value();
+      else
+        res.fill(it.index(), j) = it.value();
+  }
+  res.endFill();
+}
+
 template<typename Lhs, typename Rhs, typename ResultType,
   int LhsStorageOrder = ei_traits<Lhs>::Flags&RowMajorBit,
   int RhsStorageOrder = ei_traits<Rhs>::Flags&RowMajorBit,
@@ -184,58 +233,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
 
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
-    // make sure to call innerSize/outerSize since we fake the storage order.
-    int rows = lhs.innerSize();
-    int cols = rhs.outerSize();
-    //int size = lhs.outerSize();
-    ei_assert(lhs.outerSize() == rhs.innerSize());
-
-    // allocate a temporary buffer
-    AmbiVector<Scalar> tempVector(rows);
-
-    // estimate the number of non zero entries
-    float ratioLhs = float(lhs.nonZeros())/(float(lhs.rows())*float(lhs.cols()));
-    float avgNnzPerRhsColumn = float(rhs.nonZeros())/float(cols);
-    float ratioRes = std::min(ratioLhs * avgNnzPerRhsColumn, 1.f);
-
-    res.resize(rows, cols);
-    res.startFill(int(ratioRes*rows*cols));
-    for (int j=0; j<cols; ++j)
-    {
-      // let's do a more accurate determination of the nnz ratio for the current column j of res
-      //float ratioColRes = std::min(ratioLhs * rhs.innerNonZeros(j), 1.f);
-      // FIXME find a nice way to get the number of nonzeros of a sub matrix (here an inner vector)
-      float ratioColRes = ratioRes;
-      tempVector.init(ratioColRes);
-      tempVector.setZero();
-      for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
-      {
-        // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
-        tempVector.restart();
-        Scalar x = rhsIt.value();
-        for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt)
-        {
-          tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
-        }
-      }
-      for (typename AmbiVector<Scalar>::Iterator it(tempVector); it; ++it)
-        if (ResultType::Flags&RowMajorBit)
-          res.fill(j,it.index()) = it.value();
-        else
-          res.fill(it.index(), j) = it.value();
-    }
-    res.endFill();
+    typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
+    ei_sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
+    res.swap(_res);
   }
 };
 
 template<typename Lhs, typename Rhs, typename ResultType>
 struct ei_sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
 {
-  typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
+    // we need a col-major matrix to hold the result
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
     SparseTemporaryType _res(res.rows(), res.cols());
-    ei_sparse_product_selector<Lhs,Rhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>::run(lhs, rhs, _res);
+    ei_sparse_product_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res);
     res = _res;
   }
 };
@@ -246,20 +258,21 @@ struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
     // let's transpose the product to get a column x column product
-    ei_sparse_product_selector<Rhs,Lhs,ResultType,ColMajor,ColMajor,ColMajor>::run(rhs, lhs, res);
+    typename ei_cleantype<ResultType>::type _res(res.rows(), res.cols());
+    ei_sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
+    res.swap(_res);
   }
 };
 
 template<typename Lhs, typename Rhs, typename ResultType>
 struct ei_sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
 {
-  typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
   static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
   {
     // let's transpose the product to get a column x column product
+    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
     SparseTemporaryType _res(res.cols(), res.rows());
-    ei_sparse_product_selector<Rhs,Lhs,SparseTemporaryType,ColMajor,ColMajor,ColMajor>
-      ::run(rhs, lhs, _res);
+    ei_sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
     res = _res.transpose();
   }
 };
@@ -322,7 +335,7 @@ inline Derived& SparseMatrixBase<Derived>::operator=(const SparseProduct<Lhs,Rhs
 template<typename Derived>
 template<typename Lhs, typename Rhs>
 Derived& MatrixBase<Derived>::lazyAssign(const SparseProduct<Lhs,Rhs,SparseTimeDenseProduct>& product)
-{ 
+{
   typedef typename ei_cleantype<Lhs>::type _Lhs;
   typedef typename ei_cleantype<Rhs>::type _Rhs;
   typedef typename _Lhs::InnerIterator LhsInnerIterator;