update sparse*sparse product: the default is now a conservative algorithm preserving symbolic non zeros. The previous with auto pruning of the small value is avaible doing: (A*B).pruned() or (A*B).pruned(ref) or (A*B).pruned(ref,eps)

1 files changed, 0 insertions, 401 deletions

diff --git a/Eigen/src/Sparse/SparseSparseProduct.h b/Eigen/src/Sparse/SparseSparseProduct.h
deleted file mode 100644
index b58c89561..000000000
--- a/Eigen/src/Sparse/SparseSparseProduct.h
+++ /dev/null
@@ -1,401 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Eigen is free software; you can redistribute it and/or
-// modify it under the terms of the GNU Lesser General Public
-// License as published by the Free Software Foundation; either
-// version 3 of the License, or (at your option) any later version.
-//
-// Alternatively, you can redistribute it and/or
-// modify it under the terms of the GNU General Public License as
-// published by the Free Software Foundation; either version 2 of
-// the License, or (at your option) any later version.
-//
-// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
-// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
-// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
-// GNU General Public License for more details.
-//
-// You should have received a copy of the GNU Lesser General Public
-// License and a copy of the GNU General Public License along with
-// Eigen. If not, see <http://www.gnu.org/licenses/>.
-
-#ifndef EIGEN_SPARSESPARSEPRODUCT_H
-#define EIGEN_SPARSESPARSEPRODUCT_H
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_product_impl2(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-{
-  typedef typename remove_all<Lhs>::type::Scalar Scalar;
-  typedef typename remove_all<Lhs>::type::Index Index;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  std::vector<bool> mask(rows,false);
-  Matrix<Scalar,Dynamic,1> values(rows);
-  Matrix<Index,Dynamic,1>    indices(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);
-
- int t200 = rows/(log2(200)*1.39);
- int t = (rows*100)/139;
-
-  res.resize(rows, cols);
-  res.reserve(Index(ratioRes*rows*cols));
-  // we compute each column of the result, one after the other
-  for (Index j=0; j<cols; ++j)
-  {
-
-    res.startVec(j);
-    Index nnz = 0;
-    for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt)
-    {
-      Scalar y = rhsIt.value();
-      Index k = rhsIt.index();
-      for (typename Lhs::InnerIterator lhsIt(lhs, k); lhsIt; ++lhsIt)
-      {
-        Index i = lhsIt.index();
-        Scalar x = lhsIt.value();
-        if(!mask[i])
-        {
-          mask[i] = true;
-//           values[i] = x * y;
-//           indices[nnz] = i;
-          ++nnz;
-        }
-        else
-          values[i] += x * y;
-      }
-    }
-    // FIXME reserve nnz non zeros
-    // FIXME implement fast sort algorithms for very small nnz
-    // if the result is sparse enough => use a quick sort
-    // otherwise => loop through the entire vector
-    // In order to avoid to perform an expensive log2 when the
-    // result is clearly very sparse we use a linear bound up to 200.
-    if((nnz<200 && nnz<t200) || nnz * log2(nnz) < t)
-    {
-      if(nnz>1) std::sort(indices.data(),indices.data()+nnz);
-      for(int k=0; k<nnz; ++k)
-      {
-        int i = indices[k];
-        res.insertBackNoCheck(j,i) = values[i];
-        mask[i] = false;
-      }
-    }
-    else
-    {
-      // dense path
-      for(int i=0; i<rows; ++i)
-      {
-        if(mask[i])
-        {
-          mask[i] = false;
-          res.insertBackNoCheck(j,i) = values[i];
-        }
-      }
-    }
-
-  }
-  res.finalize();
-}
-
-// perform a pseudo in-place sparse * sparse product assuming all matrices are col major
-template<typename Lhs, typename Rhs, typename ResultType>
-static void sparse_product_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-{
-  // return sparse_product_impl2(lhs,rhs,res);
-
-  typedef typename remove_all<Lhs>::type::Scalar Scalar;
-  typedef typename remove_all<Lhs>::type::Index Index;
-
-  // make sure to call innerSize/outerSize since we fake the storage order.
-  Index rows = lhs.innerSize();
-  Index cols = rhs.outerSize();
-  //int size = lhs.outerSize();
-  eigen_assert(lhs.outerSize() == rhs.innerSize());
-
-  // allocate a temporary buffer
-  AmbiVector<Scalar,Index> 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);
-
-  // mimics a resizeByInnerOuter:
-  if(ResultType::IsRowMajor)
-    res.resize(cols, rows);
-  else
-    res.resize(rows, cols);
-
-  res.reserve(Index(ratioRes*rows*cols));
-  for (Index 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;
-      }
-    }
-    res.startVec(j);
-    for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector); it; ++it)
-      res.insertBackByOuterInner(j,it.index()) = it.value();
-  }
-  res.finalize();
-}
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
-  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
-  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
-struct sparse_product_selector;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-//     std::cerr << __LINE__ << "\n";
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    sparse_product_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-//     std::cerr << __LINE__ << "\n";
-    // we need a col-major matrix to hold the result
-    typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
-    SparseTemporaryType _res(res.rows(), res.cols());
-    sparse_product_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res);
-    res = _res;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-//     std::cerr << __LINE__ << "\n";
-    // let's transpose the product to get a column x column product
-    typename remove_all<ResultType>::type _res(res.rows(), res.cols());
-    sparse_product_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res);
-    res.swap(_res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-//     std::cerr << "here...\n";
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-    ColMajorMatrix colLhs(lhs);
-    ColMajorMatrix colRhs(rhs);
-//     std::cerr << "more...\n";
-    sparse_product_impl<ColMajorMatrix,ColMajorMatrix,ResultType>(colLhs, colRhs, res);
-//     std::cerr << "OK.\n";
-
-    // let's transpose the product to get a column x column product
-
-//     typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
-//     SparseTemporaryType _res(res.cols(), res.rows());
-//     sparse_product_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
-//     res = _res.transpose();
-  }
-};
-
-// NOTE the 2 others cases (col row *) must never occur since they are caught
-// by ProductReturnType which transforms it to (col col *) by evaluating rhs.
-
-} // end namespace internal
-
-// sparse = sparse * sparse
-template<typename Derived>
-template<typename Lhs, typename Rhs>
-inline Derived& SparseMatrixBase<Derived>::operator=(const SparseSparseProduct<Lhs,Rhs>& product)
-{
-//   std::cerr << "there..." << typeid(Lhs).name() << "  " << typeid(Lhs).name() << " " << (Derived::Flags&&RowMajorBit) << "\n";
-  internal::sparse_product_selector<
-    typename internal::remove_all<Lhs>::type,
-    typename internal::remove_all<Rhs>::type,
-    Derived>::run(product.lhs(),product.rhs(),derived());
-  return derived();
-}
-
-namespace internal {
-
-template<typename Lhs, typename Rhs, typename ResultType,
-  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
-  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
-  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
-struct sparse_product_selector2;
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    sparse_product_impl2<Lhs,Rhs,ResultType>(lhs, rhs, res);
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-      // prevent warnings until the code is fixed
-      EIGEN_UNUSED_VARIABLE(lhs);
-      EIGEN_UNUSED_VARIABLE(rhs);
-      EIGEN_UNUSED_VARIABLE(res);
-
-//     typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
-//     RowMajorMatrix rhsRow = rhs;
-//     RowMajorMatrix resRow(res.rows(), res.cols());
-//     sparse_product_impl2<RowMajorMatrix,Lhs,RowMajorMatrix>(rhsRow, lhs, resRow);
-//     res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
-    RowMajorMatrix lhsRow = lhs;
-    RowMajorMatrix resRow(res.rows(), res.cols());
-    sparse_product_impl2<Rhs,RowMajorMatrix,RowMajorMatrix>(rhs, lhsRow, resRow);
-    res = resRow;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,RowMajor> RowMajorMatrix;
-    RowMajorMatrix resRow(res.rows(), res.cols());
-    sparse_product_impl2<Rhs,Lhs,RowMajorMatrix>(rhs, lhs, resRow);
-    res = resRow;
-  }
-};
-
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
-{
-  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
-
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-    ColMajorMatrix resCol(res.rows(), res.cols());
-    sparse_product_impl2<Lhs,Rhs,ColMajorMatrix>(lhs, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-    ColMajorMatrix lhsCol = lhs;
-    ColMajorMatrix resCol(res.rows(), res.cols());
-    sparse_product_impl2<ColMajorMatrix,Rhs,ColMajorMatrix>(lhsCol, rhs, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-    ColMajorMatrix rhsCol = rhs;
-    ColMajorMatrix resCol(res.rows(), res.cols());
-    sparse_product_impl2<Lhs,ColMajorMatrix,ColMajorMatrix>(lhs, rhsCol, resCol);
-    res = resCol;
-  }
-};
-
-template<typename Lhs, typename Rhs, typename ResultType>
-struct sparse_product_selector2<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
-{
-  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res)
-  {
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-//     ColMajorMatrix lhsTr(lhs);
-//     ColMajorMatrix rhsTr(rhs);
-//     ColMajorMatrix aux(res.rows(), res.cols());
-//     sparse_product_impl2<Rhs,Lhs,ColMajorMatrix>(rhs, lhs, aux);
-// //     ColMajorMatrix aux2 = aux.transpose();
-//     res = aux;
-    typedef SparseMatrix<typename ResultType::Scalar,ColMajor> ColMajorMatrix;
-    ColMajorMatrix lhsCol(lhs);
-    ColMajorMatrix rhsCol(rhs);
-    ColMajorMatrix resCol(res.rows(), res.cols());
-    sparse_product_impl2<ColMajorMatrix,ColMajorMatrix,ColMajorMatrix>(lhsCol, rhsCol, resCol);
-    res = resCol;
-  }
-};
-
-} // end namespace internal
-
-template<typename Derived>
-template<typename Lhs, typename Rhs>
-inline void SparseMatrixBase<Derived>::_experimentalNewProduct(const Lhs& lhs, const Rhs& rhs)
-{
-  //derived().resize(lhs.rows(), rhs.cols());
-  internal::sparse_product_selector2<
-    typename internal::remove_all<Lhs>::type,
-    typename internal::remove_all<Rhs>::type,
-    Derived>::run(lhs,rhs,derived());
-}
-
-// sparse * sparse
-template<typename Derived>
-template<typename OtherDerived>
-inline const typename SparseSparseProductReturnType<Derived,OtherDerived>::Type
-SparseMatrixBase<Derived>::operator*(const SparseMatrixBase<OtherDerived> &other) const
-{
-  return typename SparseSparseProductReturnType<Derived,OtherDerived>::Type(derived(), other.derived());
-}
-
-#endif // EIGEN_SPARSESPARSEPRODUCT_H