SparseSparseProductWithPruning.h
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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<Rhs>::type::Scalar RhsScalar;
25  typedef typename remove_all<ResultType>::type::Scalar ResScalar;
26  typedef typename remove_all<Lhs>::type::StorageIndex StorageIndex;
27 
28  // make sure to call innerSize/outerSize since we fake the storage order.
29  Index rows = lhs.innerSize();
30  Index cols = rhs.outerSize();
31  //Index size = lhs.outerSize();
32  eigen_assert(lhs.outerSize() == rhs.innerSize());
33 
34  // allocate a temporary buffer
35  AmbiVector<ResScalar,StorageIndex> tempVector(rows);
36 
37  // mimics a resizeByInnerOuter:
38  if(ResultType::IsRowMajor)
39  res.resize(cols, rows);
40  else
41  res.resize(rows, cols);
42 
43  evaluator<Lhs> lhsEval(lhs);
44  evaluator<Rhs> rhsEval(rhs);
45 
46  // estimate the number of non zero entries
47  // given a rhs column containing Y non zeros, we assume that the respective Y columns
48  // of the lhs differs in average of one non zeros, thus the number of non zeros for
49  // the product of a rhs column with the lhs is X+Y where X is the average number of non zero
50  // per column of the lhs.
51  // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs)
52  Index estimated_nnz_prod = lhsEval.nonZerosEstimate() + rhsEval.nonZerosEstimate();
53 
54  res.reserve(estimated_nnz_prod);
55  double ratioColRes = double(estimated_nnz_prod)/(double(lhs.rows())*double(rhs.cols()));
56  for (Index j=0; j<cols; ++j)
57  {
58  // FIXME:
59  //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows());
60  // let's do a more accurate determination of the nnz ratio for the current column j of res
61  tempVector.init(ratioColRes);
62  tempVector.setZero();
63  for (typename evaluator<Rhs>::InnerIterator rhsIt(rhsEval, j); rhsIt; ++rhsIt)
64  {
65  // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index())
66  tempVector.restart();
67  RhsScalar x = rhsIt.value();
68  for (typename evaluator<Lhs>::InnerIterator lhsIt(lhsEval, rhsIt.index()); lhsIt; ++lhsIt)
69  {
70  tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x;
71  }
72  }
73  res.startVec(j);
74  for (typename AmbiVector<ResScalar,StorageIndex>::Iterator it(tempVector,tolerance); it; ++it)
75  res.insertBackByOuterInner(j,it.index()) = it.value();
76  }
77  res.finalize();
78 }
79 
80 template<typename Lhs, typename Rhs, typename ResultType,
81  int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit,
82  int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit,
83  int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit>
85 
86 template<typename Lhs, typename Rhs, typename ResultType>
88 {
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>
101 {
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
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>
115 {
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>
128 {
130  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
131  {
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>
148 {
150  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
151  {
153  RowMajorMatrixLhs rowLhs(lhs);
155  }
156 };
157 
158 template<typename Lhs, typename Rhs, typename ResultType>
160 {
162  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
163  {
165  RowMajorMatrixRhs rowRhs(rhs);
167  }
168 };
169 
170 template<typename Lhs, typename Rhs, typename ResultType>
172 {
174  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
175  {
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>
184 {
186  static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance)
187  {
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
A versatible sparse matrix representation.
Definition: SparseMatrix.h:96
static void sparse_sparse_product_with_pruning_impl(const Lhs &lhs, const Rhs &rhs, ResultType &res, const typename ResultType::RealScalar &tolerance)
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
Namespace containing all symbols from the Eigen library.
Definition: jet.h:637
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
const unsigned int RowMajorBit
Definition: Constants.h:66
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
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EIGEN_DEFAULT_DENSE_INDEX_TYPE Index
The Index type as used for the API.
Definition: Meta.h:74
#define eigen_assert(x)
Definition: Macros.h:1037
static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
NumTraits< Scalar >::Real RealScalar
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static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)
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static void run(const Lhs &lhs, const Rhs &rhs, ResultType &res, const RealScalar &tolerance)


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autogenerated on Tue Jul 4 2023 02:36:11