Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 1 | // This file is part of Eigen, a lightweight C++ template library |
| 2 | // for linear algebra. |
| 3 | // |
| 4 | // Copyright (C) 2008-2011 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> |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 20 | static void sparse_sparse_product_with_pruning_impl(const Lhs& lhs, const Rhs& rhs, ResultType& res, const typename ResultType::RealScalar& tolerance) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 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::Index Index; |
| 26 | |
| 27 | // make sure to call innerSize/outerSize since we fake the storage order. |
| 28 | Index rows = lhs.innerSize(); |
| 29 | Index cols = rhs.outerSize(); |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 30 | //Index size = lhs.outerSize(); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 31 | eigen_assert(lhs.outerSize() == rhs.innerSize()); |
| 32 | |
| 33 | // allocate a temporary buffer |
| 34 | AmbiVector<Scalar,Index> tempVector(rows); |
| 35 | |
| 36 | // estimate the number of non zero entries |
| 37 | // given a rhs column containing Y non zeros, we assume that the respective Y columns |
| 38 | // of the lhs differs in average of one non zeros, thus the number of non zeros for |
| 39 | // the product of a rhs column with the lhs is X+Y where X is the average number of non zero |
| 40 | // per column of the lhs. |
| 41 | // Therefore, we have nnz(lhs*rhs) = nnz(lhs) + nnz(rhs) |
| 42 | Index estimated_nnz_prod = lhs.nonZeros() + rhs.nonZeros(); |
| 43 | |
| 44 | // mimics a resizeByInnerOuter: |
| 45 | if(ResultType::IsRowMajor) |
| 46 | res.resize(cols, rows); |
| 47 | else |
| 48 | res.resize(rows, cols); |
| 49 | |
| 50 | res.reserve(estimated_nnz_prod); |
| 51 | double ratioColRes = double(estimated_nnz_prod)/double(lhs.rows()*rhs.cols()); |
| 52 | for (Index j=0; j<cols; ++j) |
| 53 | { |
| 54 | // FIXME: |
| 55 | //double ratioColRes = (double(rhs.innerVector(j).nonZeros()) + double(lhs.nonZeros())/double(lhs.cols()))/double(lhs.rows()); |
| 56 | // let's do a more accurate determination of the nnz ratio for the current column j of res |
| 57 | tempVector.init(ratioColRes); |
| 58 | tempVector.setZero(); |
| 59 | for (typename Rhs::InnerIterator rhsIt(rhs, j); rhsIt; ++rhsIt) |
| 60 | { |
| 61 | // FIXME should be written like this: tmp += rhsIt.value() * lhs.col(rhsIt.index()) |
| 62 | tempVector.restart(); |
| 63 | Scalar x = rhsIt.value(); |
| 64 | for (typename Lhs::InnerIterator lhsIt(lhs, rhsIt.index()); lhsIt; ++lhsIt) |
| 65 | { |
| 66 | tempVector.coeffRef(lhsIt.index()) += lhsIt.value() * x; |
| 67 | } |
| 68 | } |
| 69 | res.startVec(j); |
| 70 | for (typename AmbiVector<Scalar,Index>::Iterator it(tempVector,tolerance); it; ++it) |
| 71 | res.insertBackByOuterInner(j,it.index()) = it.value(); |
| 72 | } |
| 73 | res.finalize(); |
| 74 | } |
| 75 | |
| 76 | template<typename Lhs, typename Rhs, typename ResultType, |
| 77 | int LhsStorageOrder = traits<Lhs>::Flags&RowMajorBit, |
| 78 | int RhsStorageOrder = traits<Rhs>::Flags&RowMajorBit, |
| 79 | int ResStorageOrder = traits<ResultType>::Flags&RowMajorBit> |
| 80 | struct sparse_sparse_product_with_pruning_selector; |
| 81 | |
| 82 | template<typename Lhs, typename Rhs, typename ResultType> |
| 83 | struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,ColMajor> |
| 84 | { |
| 85 | typedef typename traits<typename remove_all<Lhs>::type>::Scalar Scalar; |
| 86 | typedef typename ResultType::RealScalar RealScalar; |
| 87 | |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 88 | static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 89 | { |
| 90 | typename remove_all<ResultType>::type _res(res.rows(), res.cols()); |
| 91 | internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,ResultType>(lhs, rhs, _res, tolerance); |
| 92 | res.swap(_res); |
| 93 | } |
| 94 | }; |
| 95 | |
| 96 | template<typename Lhs, typename Rhs, typename ResultType> |
| 97 | struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,ColMajor,ColMajor,RowMajor> |
| 98 | { |
| 99 | typedef typename ResultType::RealScalar RealScalar; |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 100 | static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 101 | { |
| 102 | // we need a col-major matrix to hold the result |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 103 | typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename ResultType::Index> SparseTemporaryType; |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 104 | SparseTemporaryType _res(res.rows(), res.cols()); |
| 105 | internal::sparse_sparse_product_with_pruning_impl<Lhs,Rhs,SparseTemporaryType>(lhs, rhs, _res, tolerance); |
| 106 | res = _res; |
| 107 | } |
| 108 | }; |
| 109 | |
| 110 | template<typename Lhs, typename Rhs, typename ResultType> |
| 111 | struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,RowMajor> |
| 112 | { |
| 113 | typedef typename ResultType::RealScalar RealScalar; |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 114 | static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 115 | { |
| 116 | // let's transpose the product to get a column x column product |
| 117 | typename remove_all<ResultType>::type _res(res.rows(), res.cols()); |
| 118 | internal::sparse_sparse_product_with_pruning_impl<Rhs,Lhs,ResultType>(rhs, lhs, _res, tolerance); |
| 119 | res.swap(_res); |
| 120 | } |
| 121 | }; |
| 122 | |
| 123 | template<typename Lhs, typename Rhs, typename ResultType> |
| 124 | struct sparse_sparse_product_with_pruning_selector<Lhs,Rhs,ResultType,RowMajor,RowMajor,ColMajor> |
| 125 | { |
| 126 | typedef typename ResultType::RealScalar RealScalar; |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 127 | static void run(const Lhs& lhs, const Rhs& rhs, ResultType& res, const RealScalar& tolerance) |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 128 | { |
Carlos Hernandez | 7faaa9f | 2014-08-05 17:53:32 -0700 | [diff] [blame] | 129 | typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixLhs; |
| 130 | typedef SparseMatrix<typename ResultType::Scalar,ColMajor,typename Lhs::Index> ColMajorMatrixRhs; |
| 131 | ColMajorMatrixLhs colLhs(lhs); |
| 132 | ColMajorMatrixRhs colRhs(rhs); |
| 133 | internal::sparse_sparse_product_with_pruning_impl<ColMajorMatrixLhs,ColMajorMatrixRhs,ResultType>(colLhs, colRhs, res, tolerance); |
Narayan Kamath | c981c48 | 2012-11-02 10:59:05 +0000 | [diff] [blame] | 134 | |
| 135 | // let's transpose the product to get a column x column product |
| 136 | // typedef SparseMatrix<typename ResultType::Scalar> SparseTemporaryType; |
| 137 | // SparseTemporaryType _res(res.cols(), res.rows()); |
| 138 | // sparse_sparse_product_with_pruning_impl<Rhs,Lhs,SparseTemporaryType>(rhs, lhs, _res); |
| 139 | // res = _res.transpose(); |
| 140 | } |
| 141 | }; |
| 142 | |
| 143 | // NOTE the 2 others cases (col row *) must never occur since they are caught |
| 144 | // by ProductReturnType which transforms it to (col col *) by evaluating rhs. |
| 145 | |
| 146 | } // end namespace internal |
| 147 | |
| 148 | } // end namespace Eigen |
| 149 | |
| 150 | #endif // EIGEN_SPARSESPARSEPRODUCTWITHPRUNING_H |