Eigen  3.3.4
SparseSparseProductWithPruning.h
1 // This file is part of Eigen, a lightweight C++ template library
2 // for linear algebra.
3 //
4 // Copyright (C) 2008-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
5 //
6 // This Source Code Form is subject to the terms of the Mozilla
7 // Public License v. 2.0. If a copy of the MPL was not distributed
8 // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
9 
10 #ifndef EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
11 #define EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
12 
13 namespace Eigen {
14 
15 namespace internal {
16 
17 
18 // perform a pseudo in-place sparse * sparse product assuming all matrices are col major
19 template<typename Lhs, typename Rhs, typename ResultType>
20 static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance)
21 {
22  // return sparse_sparse_product_with_pruning_impl2(lhs,rhs,res);
23 
24  typedef typename remove_all<Lhs>::type::Scalar Scalar;
25  typedef typename remove_all<Lhs>::type::StorageIndex StorageIndex;
26 
27  // make sure to call innerSize/outerSize since we fake the storage order.
28  Index rows = lhs.innerSize();
29  Index cols = rhs.outerSize();
30  //Index size = lhs.outerSize();
31  eigen_assert(lhs.outerSize() == rhs.innerSize());
32 
33  // allocate a temporary buffer
34  AmbiVector<Scalar,StorageIndex> tempVector(rows);
35 
36  // mimics a resizeByInnerOuter:
37  if(ResultType::IsRowMajor)
38  res.resize(cols, rows);
39  else
40  res.resize(rows, cols);
41 
42  evaluator<Lhs> lhsEval(lhs);
43  evaluator<Rhs> rhsEval(rhs);
44 
45  // estimate the number of non zero entries
46  // given a rhs column containing Y non zeros, we assume that the respective Y columns
47  // of the lhs differs in average of one non zeros, thus the number of non zeros for
48  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
49  // per column of the lhs.
50  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
51  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
52 
53  res.reserve(estimated_nnz_prod);
54  double ratioColRes = double(estimated_nnz_prod)/(double(lhs.rows())*double(rhs.cols()));
55  for (Index j=0; j<cols; ++j)
56  {
57  // FIXME:
58  //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
59  // let's do a more accurate determination of the nnz ratio for the current column j of res
60  tempVector.init(ratioColRes);
61  tempVector.setZero();
62  for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
63  {
64  // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
65  tempVector.restart();
66  Scalar x = rhsIt.value();
67  for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
68  {
69  tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
70  }
71  }
72  res.startVec(j);
73  for (typename AmbiVector<Scalar,StorageIndex>::Iterator it(tempVector,tolerance); it; ++it)
74  res.insertBackByOuterInner(j,it.index()) = it.value();
75  }
76  res.finalize();
77 }
78 
79 template<typename Lhs, typename Rhs, typename ResultType,
80  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
81  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
82  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
83 struct sparse_sparse_product_with_pruning_selector;
84 
85 template<typename Lhs, typename Rhs, typename ResultType>
86 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor>
87 {
88  typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar;
89  typedef typename ResultType::RealScalar RealScalar;
90 
91  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
92  {
93  typename remove_all<ResultType>::type _res(res.rows(), res.cols());
94  internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance);
95  res.swap(_res);
96  }
97 };
98 
99 template<typename Lhs, typename Rhs, typename ResultType>
100 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor>
101 {
102  typedef typename ResultType::RealScalar RealScalar;
103  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
104  {
105  // we need a col-major matrix to hold the result
106  typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::StorageIndex> SparseTemporaryType;
107  SparseTemporaryType _res(res.rows(), res.cols());
108  internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance);
109  res = _res;
110  }
111 };
112 
113 template<typename Lhs, typename Rhs, typename ResultType>
114 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor>
115 {
116  typedef typename ResultType::RealScalar RealScalar;
117  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
118  {
119  // let's transpose the product to get a column x column product
120  typename remove_all<ResultType>::type _res(res.rows(), res.cols());
121  internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance);
122  res.swap(_res);
123  }
124 };
125 
126 template<typename Lhs, typename Rhs, typename ResultType>
127 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor>
128 {
129  typedef typename ResultType::RealScalar RealScalar;
130  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
131  {
132  typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
133  typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
134  ColMajorMatrixLhs colLhs(lhs);
135  ColMajorMatrixRhs colRhs(rhs);
136  internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance);
137 
138  // let's transpose the product to get a column x column product
139 // typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType;
140 // SparseTemporaryType _res(res.cols(), res.rows());
141 // sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res);
142 // res = _res.transpose();
143  }
144 };
145 
146 template<typename Lhs, typename Rhs, typename ResultType>
147 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,RowMajor>
148 {
149  typedef typename ResultType::RealScalar RealScalar;
150  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
151  {
152  typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixLhs;
153  RowMajorMatrixLhs rowLhs(lhs);
154  sparse_sparse_product_with_pruning_selector<RowMajorMatrixLhs,Rhs,ResultType,RowMajor,RowMajor>(rowLhs,rhs,res,tolerance);
155  }
156 };
157 
158 template<typename Lhs, typename Rhs, typename ResultType>
159 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,RowMajor>
160 {
161  typedef typename ResultType::RealScalar RealScalar;
162  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
163  {
164  typedef SparseMatrix<typename ResultType::Scalar,RowMajor,typename Lhs::StorageIndex> RowMajorMatrixRhs;
165  RowMajorMatrixRhs rowRhs(rhs);
166  sparse_sparse_product_with_pruning_selector<Lhs,RowMajorMatrixRhs,ResultType,RowMajor,RowMajor,RowMajor>(lhs,rowRhs,res,tolerance);
167  }
168 };
169 
170 template<typename Lhs, typename Rhs, typename ResultType>
171 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,RowMajor,ColMajor>
172 {
173  typedef typename ResultType::RealScalar RealScalar;
174  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
175  {
176  typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixRhs;
177  ColMajorMatrixRhs colRhs(rhs);
178  internal::sparse_sparse_product_with_pruning_impl<Lhs,ColMajorMatrixRhs,ResultType>(lhs, colRhs, res, tolerance);
179  }
180 };
181 
182 template<typename Lhs, typename Rhs, typename ResultType>
183 struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,ColMajor,ColMajor>
184 {
185  typedef typename ResultType::RealScalar RealScalar;
186  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
187  {
188  typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::StorageIndex> ColMajorMatrixLhs;
189  ColMajorMatrixLhs colLhs(lhs);
190  internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,Rhs,ResultType>(colLhs, rhs, res, tolerance);
191  }
192 };
193 
194 } // end namespace internal
195 
196 } // end namespace Eigen
197 
198 #endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H
Definition: Constants.h:320
Namespace containing all symbols from the Eigen library.
Definition: Core:287
const unsigned int RowMajorBit
Definition: Constants.h:61
EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:33
Definition: Eigen_Colamd.h:50
Definition: Constants.h:322