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 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
 //
 // This Source Code Form is subject to the terms of the Mozilla
 // Public License v. 2.0. If a copy of the MPL was not distributed
 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
 
 #ifndef EIGEN_RANDOMSETTER_H
 #define EIGEN_RANDOMSETTER_H
 
 #if defined(EIGEN_GOOGLEHASH_SUPPORT)
 // Ensure the ::google namespace exists, required for checking existence of 
 // ::google::dense_hash_map and ::google::sparse_hash_map.
 namespace google {}
 #endif
 
 namespace Eigen {
 
 /** Represents a std::map
   *
   * \see RandomSetter
   */
 template<typename Scalar> struct StdMapTraits
 {
   typedef int KeyType;
   typedef std::map<KeyType,Scalar> Type;
   enum {
     IsSorted = 1
   };
 
   static void setInvalidKey(Type&, const KeyType&) {}
 };
 
 #ifdef EIGEN_UNORDERED_MAP_SUPPORT
 /** Represents a std::unordered_map
   *
   * To use it you need to both define EIGEN_UNORDERED_MAP_SUPPORT and include the unordered_map header file
   * yourself making sure that unordered_map is defined in the std namespace.
   *
   * For instance, with current version of gcc you can either enable C++0x standard (-std=c++0x) or do:
   * \code
   * #include <tr1/unordered_map>
   * #define EIGEN_UNORDERED_MAP_SUPPORT
   * namespace std {
   *   using std::tr1::unordered_map;
   * }
   * \endcode
   *
   * \see RandomSetter
   */
 template<typename Scalar> struct StdUnorderedMapTraits
 {
   typedef int KeyType;
   typedef std::unordered_map<KeyType,Scalar> Type;
   enum {
     IsSorted = 0
   };
 
   static void setInvalidKey(Type&, const KeyType&) {}
 };
 #endif // EIGEN_UNORDERED_MAP_SUPPORT
 
 #if defined(EIGEN_GOOGLEHASH_SUPPORT)
 
 namespace google {
   
 // Namespace work-around, since sometimes dense_hash_map and sparse_hash_map
 // are in the global namespace, and other times they are under ::google.
 using namespace ::google;
 
 template<typename KeyType, typename Scalar>
 struct DenseHashMap {
   typedef dense_hash_map<KeyType, Scalar> type;
 };
 
 template<typename KeyType, typename Scalar>
 struct SparseHashMap {
   typedef sparse_hash_map<KeyType, Scalar> type;
 };
 
 } // namespace google
 
 /** Represents a google::dense_hash_map
   *
   * \see RandomSetter
   */
 template<typename Scalar> struct GoogleDenseHashMapTraits
 {
   typedef int KeyType;
   typedef typename google::DenseHashMap<KeyType,Scalar>::type Type;
   enum {
     IsSorted = 0
   };
 
   static void setInvalidKey(Type& map, const KeyType& k)
   { map.set_empty_key(k); }
 };
 
 /** Represents a google::sparse_hash_map
   *
   * \see RandomSetter
   */
 template<typename Scalar> struct GoogleSparseHashMapTraits
 {
   typedef int KeyType;
   typedef typename google::SparseHashMap<KeyType,Scalar>::type Type;
   enum {
     IsSorted = 0
   };
 
   static void setInvalidKey(Type&, const KeyType&) {}
 };
 #endif
 
 /** \class RandomSetter
   *
   * \brief The RandomSetter is a wrapper object allowing to set/update a sparse matrix with random access
   *
   * \tparam SparseMatrixType the type of the sparse matrix we are updating
   * \tparam MapTraits a traits class representing the map implementation used for the temporary sparse storage.
   *                  Its default value depends on the system.
   * \tparam OuterPacketBits defines the number of rows (or columns) manage by a single map object
   *                        as a power of two exponent.
   *
   * This class temporarily represents a sparse matrix object using a generic map implementation allowing for
   * efficient random access. The conversion from the compressed representation to a hash_map object is performed
   * in the RandomSetter constructor, while the sparse matrix is updated back at destruction time. This strategy
   * suggest the use of nested blocks as in this example:
   *
   * \code
   * SparseMatrix<double> m(rows,cols);
   * {
   *   RandomSetter<SparseMatrix<double> > w(m);
   *   // don't use m but w instead with read/write random access to the coefficients:
   *   for(;;)
   *     w(rand(),rand()) = rand;
   * }
   * // when w is deleted, the data are copied back to m
   * // and m is ready to use.
   * \endcode
   *
   * Since hash_map objects are not fully sorted, representing a full matrix as a single hash_map would
   * involve a big and costly sort to update the compressed matrix back. To overcome this issue, a RandomSetter
   * use multiple hash_map, each representing 2^OuterPacketBits columns or rows according to the storage order.
   * To reach optimal performance, this value should be adjusted according to the average number of nonzeros
   * per rows/columns.
   *
   * The possible values for the template parameter MapTraits are:
   *  - \b StdMapTraits: corresponds to std::map. (does not perform very well)
   *  - \b GnuHashMapTraits: corresponds to __gnu_cxx::hash_map (available only with GCC)
   *  - \b GoogleDenseHashMapTraits: corresponds to google::dense_hash_map (best efficiency, reasonable memory consumption)
   *  - \b GoogleSparseHashMapTraits: corresponds to google::sparse_hash_map (best memory consumption, relatively good performance)
   *
   * The default map implementation depends on the availability, and the preferred order is:
   * GoogleSparseHashMapTraits, GnuHashMapTraits, and finally StdMapTraits.
   *
   * For performance and memory consumption reasons it is highly recommended to use one of
   * Google's hash_map implementations. To enable the support for them, you must define
   * EIGEN_GOOGLEHASH_SUPPORT. This will include both <google/dense_hash_map> and
   * <google/sparse_hash_map> for you.
   *
   * \see https://github.com/sparsehash/sparsehash
   */
 template<typename SparseMatrixType,
          template <typename T> class MapTraits =
 #if defined(EIGEN_GOOGLEHASH_SUPPORT)
           GoogleDenseHashMapTraits
 #elif defined(_HASH_MAP)
           GnuHashMapTraits
 #else
           StdMapTraits
 #endif
          ,int OuterPacketBits = 6>
 class RandomSetter
 {
     typedef typename SparseMatrixType::Scalar Scalar;
     typedef typename SparseMatrixType::StorageIndex StorageIndex;
 
     struct ScalarWrapper
     {
       ScalarWrapper() : value(0) {}
       Scalar value;
     };
     typedef typename MapTraits<ScalarWrapper>::KeyType KeyType;
     typedef typename MapTraits<ScalarWrapper>::Type HashMapType;
     static const int OuterPacketMask = (1 << OuterPacketBits) - 1;
     enum {
       SwapStorage = 1 - MapTraits<ScalarWrapper>::IsSorted,
       TargetRowMajor = (SparseMatrixType::Flags & RowMajorBit) ? 1 : 0,
       SetterRowMajor = SwapStorage ? 1-TargetRowMajor : TargetRowMajor
     };
 
   public:
 
     /** Constructs a random setter object from the sparse matrix \a target
       *
       * Note that the initial value of \a target are imported. If you want to re-set
       * a sparse matrix from scratch, then you must set it to zero first using the
       * setZero() function.
       */
     inline RandomSetter(SparseMatrixType& target)
       : mp_target(&target)
     {
       const Index outerSize = SwapStorage ? target.innerSize() : target.outerSize();
       const Index innerSize = SwapStorage ? target.outerSize() : target.innerSize();
       m_outerPackets = outerSize >> OuterPacketBits;
       if (outerSize&OuterPacketMask)
         m_outerPackets += 1;
       m_hashmaps = new HashMapType[m_outerPackets];
       // compute number of bits needed to store inner indices
       Index aux = innerSize - 1;
       m_keyBitsOffset = 0;
       while (aux)
       {
         ++m_keyBitsOffset;
         aux = aux >> 1;
       }
       KeyType ik = (1<<(OuterPacketBits+m_keyBitsOffset));
       for (Index k=0; k<m_outerPackets; ++k)
         MapTraits<ScalarWrapper>::setInvalidKey(m_hashmaps[k],ik);
 
       // insert current coeffs
       for (Index j=0; j<mp_target->outerSize(); ++j)
         for (typename SparseMatrixType::InnerIterator it(*mp_target,j); it; ++it)
           (*this)(TargetRowMajor?j:it.index(), TargetRowMajor?it.index():j) = it.value();
     }
 
     /** Destructor updating back the sparse matrix target */
     ~RandomSetter()
     {
       KeyType keyBitsMask = (1<<m_keyBitsOffset)-1;
       if (!SwapStorage) // also means the map is sorted
       {
         mp_target->setZero();
         mp_target->makeCompressed();
         mp_target->reserve(nonZeros());
         Index prevOuter = -1;
         for (Index k=0; k<m_outerPackets; ++k)
         {
           const Index outerOffset = (1<<OuterPacketBits) * k;
           typename HashMapType::iterator end = m_hashmaps[k].end();
           for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
           {
             const Index outer = (it->first >> m_keyBitsOffset) + outerOffset;
             const Index inner = it->first & keyBitsMask;
             if (prevOuter!=outer)
             {
               for (Index j=prevOuter+1;j<=outer;++j)
                 mp_target->startVec(j);
               prevOuter = outer;
             }
             mp_target->insertBackByOuterInner(outer, inner) = it->second.value;
           }
         }
         mp_target->finalize();
       }
       else
       {
         VectorXi positions(mp_target->outerSize());
         positions.setZero();
         // pass 1
         for (Index k=0; k<m_outerPackets; ++k)
         {
           typename HashMapType::iterator end = m_hashmaps[k].end();
           for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
           {
             const Index outer = it->first & keyBitsMask;
             ++positions[outer];
           }
         }
         // prefix sum
         StorageIndex count = 0;
         for (Index j=0; j<mp_target->outerSize(); ++j)
         {
           StorageIndex tmp = positions[j];
           mp_target->outerIndexPtr()[j] = count;
           positions[j] = count;
           count += tmp;
         }
         mp_target->makeCompressed();
         mp_target->outerIndexPtr()[mp_target->outerSize()] = count;
         mp_target->resizeNonZeros(count);
         // pass 2
         for (Index k=0; k<m_outerPackets; ++k)
         {
           const Index outerOffset = (1<<OuterPacketBits) * k;
           typename HashMapType::iterator end = m_hashmaps[k].end();
           for (typename HashMapType::iterator it = m_hashmaps[k].begin(); it!=end; ++it)
           {
             const Index inner = (it->first >> m_keyBitsOffset) + outerOffset;
             const Index outer = it->first & keyBitsMask;
             // sorted insertion
             // Note that we have to deal with at most 2^OuterPacketBits unsorted coefficients,
             // moreover those 2^OuterPacketBits coeffs are likely to be sparse, an so only a
             // small fraction of them have to be sorted, whence the following simple procedure:
             Index posStart = mp_target->outerIndexPtr()[outer];
             Index i = (positions[outer]++) - 1;
             while ( (i >= posStart) && (mp_target->innerIndexPtr()[i] > inner) )
             {
               mp_target->valuePtr()[i+1] = mp_target->valuePtr()[i];
               mp_target->innerIndexPtr()[i+1] = mp_target->innerIndexPtr()[i];
               --i;
             }
             mp_target->innerIndexPtr()[i+1] = internal::convert_index<StorageIndex>(inner);
             mp_target->valuePtr()[i+1] = it->second.value;
           }
         }
       }
       delete[] m_hashmaps;
     }
 
     /** \returns a reference to the coefficient at given coordinates \a row, \a col */
     Scalar& operator() (Index row, Index col)
     {
       const Index outer = SetterRowMajor ? row : col;
       const Index inner = SetterRowMajor ? col : row;
       const Index outerMajor = outer >> OuterPacketBits; // index of the packet/map
       const Index outerMinor = outer & OuterPacketMask;  // index of the inner vector in the packet
       const KeyType key = internal::convert_index<KeyType>((outerMinor<<m_keyBitsOffset) | inner);
       return m_hashmaps[outerMajor][key].value;
     }
 
     /** \returns the number of non zero coefficients
       *
       * \note According to the underlying map/hash_map implementation,
       * this function might be quite expensive.
       */
     Index nonZeros() const
     {
       Index nz = 0;
       for (Index k=0; k<m_outerPackets; ++k)
         nz += static_cast<Index>(m_hashmaps[k].size());
       return nz;
     }
 
 
   protected:
 
     HashMapType* m_hashmaps;
     SparseMatrixType* mp_target;
     Index m_outerPackets;
     unsigned char m_keyBitsOffset;
 };
 
 } // end namespace Eigen
 
 #endif // EIGEN_RANDOMSETTER_H

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