[136] | 1 | // This file is part of Eigen, a lightweight C++ template library
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| 2 | // for linear algebra.
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| 3 | //
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| 4 | // Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.fr>
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| 5 | //
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| 6 | // This Source Code Form is subject to the terms of the Mozilla
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| 7 | // Public License v. 2.0. If a copy of the MPL was not distributed
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| 8 | // with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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| 9 |
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| 10 | #ifndef EIGEN_SKYLINEMATRIX_H
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| 11 | #define EIGEN_SKYLINEMATRIX_H
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| 12 |
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| 13 | #include "SkylineStorage.h"
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| 14 | #include "SkylineMatrixBase.h"
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| 15 |
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| 16 | namespace Eigen {
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| 17 |
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| 18 | /** \ingroup Skyline_Module
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| 19 | *
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| 20 | * \class SkylineMatrix
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| 21 | *
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| 22 | * \brief The main skyline matrix class
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| 23 | *
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| 24 | * This class implements a skyline matrix using the very uncommon storage
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| 25 | * scheme.
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| 26 | *
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| 27 | * \param _Scalar the scalar type, i.e. the type of the coefficients
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| 28 | * \param _Options Union of bit flags controlling the storage scheme. Currently the only possibility
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| 29 | * is RowMajor. The default is 0 which means column-major.
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| 30 | *
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| 31 | *
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| 32 | */
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| 33 | namespace internal {
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| 34 | template<typename _Scalar, int _Options>
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| 35 | struct traits<SkylineMatrix<_Scalar, _Options> > {
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| 36 | typedef _Scalar Scalar;
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| 37 | typedef Sparse StorageKind;
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| 38 |
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| 39 | enum {
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| 40 | RowsAtCompileTime = Dynamic,
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| 41 | ColsAtCompileTime = Dynamic,
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| 42 | MaxRowsAtCompileTime = Dynamic,
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| 43 | MaxColsAtCompileTime = Dynamic,
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| 44 | Flags = SkylineBit | _Options,
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| 45 | CoeffReadCost = NumTraits<Scalar>::ReadCost,
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| 46 | };
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| 47 | };
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| 48 | }
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| 49 |
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| 50 | template<typename _Scalar, int _Options>
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| 51 | class SkylineMatrix
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| 52 | : public SkylineMatrixBase<SkylineMatrix<_Scalar, _Options> > {
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| 53 | public:
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| 54 | EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
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| 55 | EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
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| 56 | EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
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| 57 |
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| 58 | using Base::IsRowMajor;
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| 59 |
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| 60 | protected:
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| 61 |
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| 62 | typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
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| 63 |
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| 64 | Index m_outerSize;
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| 65 | Index m_innerSize;
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| 66 |
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| 67 | public:
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| 68 | Index* m_colStartIndex;
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| 69 | Index* m_rowStartIndex;
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| 70 | SkylineStorage<Scalar> m_data;
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| 71 |
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| 72 | public:
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| 73 |
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| 74 | inline Index rows() const {
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| 75 | return IsRowMajor ? m_outerSize : m_innerSize;
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| 76 | }
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| 77 |
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| 78 | inline Index cols() const {
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| 79 | return IsRowMajor ? m_innerSize : m_outerSize;
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| 80 | }
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| 81 |
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| 82 | inline Index innerSize() const {
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| 83 | return m_innerSize;
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| 84 | }
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| 85 |
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| 86 | inline Index outerSize() const {
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| 87 | return m_outerSize;
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| 88 | }
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| 89 |
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| 90 | inline Index upperNonZeros() const {
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| 91 | return m_data.upperSize();
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| 92 | }
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| 93 |
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| 94 | inline Index lowerNonZeros() const {
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| 95 | return m_data.lowerSize();
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| 96 | }
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| 97 |
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| 98 | inline Index upperNonZeros(Index j) const {
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| 99 | return m_colStartIndex[j + 1] - m_colStartIndex[j];
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| 100 | }
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| 101 |
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| 102 | inline Index lowerNonZeros(Index j) const {
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| 103 | return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
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| 104 | }
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| 105 |
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| 106 | inline const Scalar* _diagPtr() const {
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| 107 | return &m_data.diag(0);
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| 108 | }
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| 109 |
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| 110 | inline Scalar* _diagPtr() {
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| 111 | return &m_data.diag(0);
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| 112 | }
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| 113 |
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| 114 | inline const Scalar* _upperPtr() const {
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| 115 | return &m_data.upper(0);
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| 116 | }
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| 117 |
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| 118 | inline Scalar* _upperPtr() {
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| 119 | return &m_data.upper(0);
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| 120 | }
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| 121 |
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| 122 | inline const Scalar* _lowerPtr() const {
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| 123 | return &m_data.lower(0);
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| 124 | }
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| 125 |
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| 126 | inline Scalar* _lowerPtr() {
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| 127 | return &m_data.lower(0);
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| 128 | }
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| 129 |
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| 130 | inline const Index* _upperProfilePtr() const {
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| 131 | return &m_data.upperProfile(0);
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| 132 | }
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| 133 |
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| 134 | inline Index* _upperProfilePtr() {
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| 135 | return &m_data.upperProfile(0);
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| 136 | }
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| 137 |
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| 138 | inline const Index* _lowerProfilePtr() const {
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| 139 | return &m_data.lowerProfile(0);
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| 140 | }
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| 141 |
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| 142 | inline Index* _lowerProfilePtr() {
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| 143 | return &m_data.lowerProfile(0);
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| 144 | }
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| 145 |
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| 146 | inline Scalar coeff(Index row, Index col) const {
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| 147 | const Index outer = IsRowMajor ? row : col;
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| 148 | const Index inner = IsRowMajor ? col : row;
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| 149 |
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| 150 | eigen_assert(outer < outerSize());
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| 151 | eigen_assert(inner < innerSize());
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| 152 |
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| 153 | if (outer == inner)
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| 154 | return this->m_data.diag(outer);
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| 155 |
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| 156 | if (IsRowMajor) {
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| 157 | if (inner > outer) //upper matrix
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| 158 | {
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| 159 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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| 160 | if (outer >= minOuterIndex)
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| 161 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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| 162 | else
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| 163 | return Scalar(0);
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| 164 | }
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| 165 | if (inner < outer) //lower matrix
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| 166 | {
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| 167 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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| 168 | if (inner >= minInnerIndex)
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| 169 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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| 170 | else
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| 171 | return Scalar(0);
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| 172 | }
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| 173 | return m_data.upper(m_colStartIndex[inner] + outer - inner);
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| 174 | } else {
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| 175 | if (outer > inner) //upper matrix
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| 176 | {
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| 177 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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| 178 | if (outer <= maxOuterIndex)
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| 179 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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| 180 | else
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| 181 | return Scalar(0);
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| 182 | }
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| 183 | if (outer < inner) //lower matrix
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| 184 | {
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| 185 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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| 186 |
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| 187 | if (inner <= maxInnerIndex)
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| 188 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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| 189 | else
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| 190 | return Scalar(0);
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| 191 | }
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| 192 | }
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| 193 | }
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| 194 |
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| 195 | inline Scalar& coeffRef(Index row, Index col) {
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| 196 | const Index outer = IsRowMajor ? row : col;
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| 197 | const Index inner = IsRowMajor ? col : row;
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| 198 |
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| 199 | eigen_assert(outer < outerSize());
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| 200 | eigen_assert(inner < innerSize());
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| 201 |
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| 202 | if (outer == inner)
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| 203 | return this->m_data.diag(outer);
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| 204 |
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| 205 | if (IsRowMajor) {
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| 206 | if (col > row) //upper matrix
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| 207 | {
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| 208 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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| 209 | eigen_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage");
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| 210 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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| 211 | }
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| 212 | if (col < row) //lower matrix
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| 213 | {
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| 214 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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| 215 | eigen_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage");
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| 216 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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| 217 | }
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| 218 | } else {
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| 219 | if (outer > inner) //upper matrix
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| 220 | {
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| 221 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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| 222 | eigen_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
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| 223 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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| 224 | }
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| 225 | if (outer < inner) //lower matrix
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| 226 | {
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| 227 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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| 228 | eigen_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
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| 229 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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| 230 | }
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| 231 | }
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| 232 | }
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| 233 |
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| 234 | inline Scalar coeffDiag(Index idx) const {
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| 235 | eigen_assert(idx < outerSize());
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| 236 | eigen_assert(idx < innerSize());
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| 237 | return this->m_data.diag(idx);
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| 238 | }
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| 239 |
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| 240 | inline Scalar coeffLower(Index row, Index col) const {
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| 241 | const Index outer = IsRowMajor ? row : col;
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| 242 | const Index inner = IsRowMajor ? col : row;
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| 243 |
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| 244 | eigen_assert(outer < outerSize());
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| 245 | eigen_assert(inner < innerSize());
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| 246 | eigen_assert(inner != outer);
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| 247 |
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| 248 | if (IsRowMajor) {
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| 249 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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| 250 | if (inner >= minInnerIndex)
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| 251 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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| 252 | else
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| 253 | return Scalar(0);
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| 254 |
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| 255 | } else {
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| 256 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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| 257 | if (inner <= maxInnerIndex)
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| 258 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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| 259 | else
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| 260 | return Scalar(0);
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| 261 | }
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| 262 | }
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| 263 |
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| 264 | inline Scalar coeffUpper(Index row, Index col) const {
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| 265 | const Index outer = IsRowMajor ? row : col;
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| 266 | const Index inner = IsRowMajor ? col : row;
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| 267 |
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| 268 | eigen_assert(outer < outerSize());
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| 269 | eigen_assert(inner < innerSize());
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| 270 | eigen_assert(inner != outer);
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| 271 |
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| 272 | if (IsRowMajor) {
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| 273 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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| 274 | if (outer >= minOuterIndex)
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| 275 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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| 276 | else
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| 277 | return Scalar(0);
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| 278 | } else {
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| 279 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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| 280 | if (outer <= maxOuterIndex)
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| 281 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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| 282 | else
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| 283 | return Scalar(0);
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| 284 | }
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| 285 | }
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| 286 |
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| 287 | inline Scalar& coeffRefDiag(Index idx) {
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| 288 | eigen_assert(idx < outerSize());
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| 289 | eigen_assert(idx < innerSize());
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| 290 | return this->m_data.diag(idx);
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| 291 | }
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| 292 |
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| 293 | inline Scalar& coeffRefLower(Index row, Index col) {
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| 294 | const Index outer = IsRowMajor ? row : col;
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| 295 | const Index inner = IsRowMajor ? col : row;
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| 296 |
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| 297 | eigen_assert(outer < outerSize());
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| 298 | eigen_assert(inner < innerSize());
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| 299 | eigen_assert(inner != outer);
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| 300 |
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| 301 | if (IsRowMajor) {
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| 302 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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| 303 | eigen_assert(inner >= minInnerIndex && "you try to acces a coeff that do not exist in the storage");
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| 304 | return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
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| 305 | } else {
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| 306 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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| 307 | eigen_assert(inner <= maxInnerIndex && "you try to acces a coeff that do not exist in the storage");
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| 308 | return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
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| 309 | }
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| 310 | }
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| 311 |
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| 312 | inline bool coeffExistLower(Index row, Index col) {
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| 313 | const Index outer = IsRowMajor ? row : col;
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| 314 | const Index inner = IsRowMajor ? col : row;
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| 315 |
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| 316 | eigen_assert(outer < outerSize());
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| 317 | eigen_assert(inner < innerSize());
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| 318 | eigen_assert(inner != outer);
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| 319 |
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| 320 | if (IsRowMajor) {
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| 321 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
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| 322 | return inner >= minInnerIndex;
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| 323 | } else {
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| 324 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
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| 325 | return inner <= maxInnerIndex;
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| 326 | }
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| 327 | }
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| 328 |
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| 329 | inline Scalar& coeffRefUpper(Index row, Index col) {
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| 330 | const Index outer = IsRowMajor ? row : col;
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| 331 | const Index inner = IsRowMajor ? col : row;
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| 332 |
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| 333 | eigen_assert(outer < outerSize());
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| 334 | eigen_assert(inner < innerSize());
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| 335 | eigen_assert(inner != outer);
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| 336 |
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| 337 | if (IsRowMajor) {
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| 338 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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| 339 | eigen_assert(outer >= minOuterIndex && "you try to acces a coeff that do not exist in the storage");
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| 340 | return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
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| 341 | } else {
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| 342 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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| 343 | eigen_assert(outer <= maxOuterIndex && "you try to acces a coeff that do not exist in the storage");
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| 344 | return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
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| 345 | }
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| 346 | }
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| 347 |
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| 348 | inline bool coeffExistUpper(Index row, Index col) {
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| 349 | const Index outer = IsRowMajor ? row : col;
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| 350 | const Index inner = IsRowMajor ? col : row;
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| 351 |
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| 352 | eigen_assert(outer < outerSize());
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| 353 | eigen_assert(inner < innerSize());
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| 354 | eigen_assert(inner != outer);
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| 355 |
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| 356 | if (IsRowMajor) {
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| 357 | const Index minOuterIndex = inner - m_data.upperProfile(inner);
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| 358 | return outer >= minOuterIndex;
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| 359 | } else {
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| 360 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
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| 361 | return outer <= maxOuterIndex;
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| 362 | }
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| 363 | }
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| 364 |
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| 365 |
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| 366 | protected:
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| 367 |
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| 368 | public:
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| 369 | class InnerUpperIterator;
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| 370 | class InnerLowerIterator;
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| 371 |
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| 372 | class OuterUpperIterator;
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| 373 | class OuterLowerIterator;
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| 374 |
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| 375 | /** Removes all non zeros */
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| 376 | inline void setZero() {
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| 377 | m_data.clear();
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| 378 | memset(m_colStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
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| 379 | memset(m_rowStartIndex, 0, (m_outerSize + 1) * sizeof (Index));
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| 380 | }
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| 381 |
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| 382 | /** \returns the number of non zero coefficients */
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| 383 | inline Index nonZeros() const {
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| 384 | return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
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| 385 | }
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| 386 |
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| 387 | /** Preallocates \a reserveSize non zeros */
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| 388 | inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
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| 389 | m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
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| 390 | }
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| 391 |
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| 392 | /** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
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| 393 |
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| 394 | *
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| 395 | * \warning This function can be extremely slow if the non zero coefficients
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| 396 | * are not inserted in a coherent order.
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| 397 | *
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| 398 | * After an insertion session, you should call the finalize() function.
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| 399 | */
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| 400 | EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
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| 401 | const Index outer = IsRowMajor ? row : col;
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| 402 | const Index inner = IsRowMajor ? col : row;
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| 403 |
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| 404 | eigen_assert(outer < outerSize());
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| 405 | eigen_assert(inner < innerSize());
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| 406 |
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| 407 | if (outer == inner)
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| 408 | return m_data.diag(col);
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| 409 |
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| 410 | if (IsRowMajor) {
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| 411 | if (outer < inner) //upper matrix
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| 412 | {
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| 413 | Index minOuterIndex = 0;
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| 414 | minOuterIndex = inner - m_data.upperProfile(inner);
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| 415 |
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| 416 | if (outer < minOuterIndex) //The value does not yet exist
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| 417 | {
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| 418 | const Index previousProfile = m_data.upperProfile(inner);
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| 419 |
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| 420 | m_data.upperProfile(inner) = inner - outer;
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| 421 |
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| 422 |
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| 423 | const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
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| 424 | //shift data stored after this new one
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| 425 | const Index stop = m_colStartIndex[cols()];
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| 426 | const Index start = m_colStartIndex[inner];
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| 427 |
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| 428 |
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| 429 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
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| 430 | m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
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| 431 | }
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| 432 |
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| 433 | for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
|
---|
| 434 | m_colStartIndex[innerIdx] += bandIncrement;
|
---|
| 435 | }
|
---|
| 436 |
|
---|
| 437 | //zeros new data
|
---|
| 438 | memset(this->_upperPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
| 439 |
|
---|
| 440 | return m_data.upper(m_colStartIndex[inner]);
|
---|
| 441 | } else {
|
---|
| 442 | return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
|
---|
| 443 | }
|
---|
| 444 | }
|
---|
| 445 |
|
---|
| 446 | if (outer > inner) //lower matrix
|
---|
| 447 | {
|
---|
| 448 | const Index minInnerIndex = outer - m_data.lowerProfile(outer);
|
---|
| 449 | if (inner < minInnerIndex) //The value does not yet exist
|
---|
| 450 | {
|
---|
| 451 | const Index previousProfile = m_data.lowerProfile(outer);
|
---|
| 452 | m_data.lowerProfile(outer) = outer - inner;
|
---|
| 453 |
|
---|
| 454 | const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
---|
| 455 | //shift data stored after this new one
|
---|
| 456 | const Index stop = m_rowStartIndex[rows()];
|
---|
| 457 | const Index start = m_rowStartIndex[outer];
|
---|
| 458 |
|
---|
| 459 |
|
---|
| 460 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
| 461 | m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
---|
| 462 | }
|
---|
| 463 |
|
---|
| 464 | for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
|
---|
| 465 | m_rowStartIndex[innerIdx] += bandIncrement;
|
---|
| 466 | }
|
---|
| 467 |
|
---|
| 468 | //zeros new data
|
---|
| 469 | memset(this->_lowerPtr() + start, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
| 470 | return m_data.lower(m_rowStartIndex[outer]);
|
---|
| 471 | } else {
|
---|
| 472 | return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
|
---|
| 473 | }
|
---|
| 474 | }
|
---|
| 475 | } else {
|
---|
| 476 | if (outer > inner) //upper matrix
|
---|
| 477 | {
|
---|
| 478 | const Index maxOuterIndex = inner + m_data.upperProfile(inner);
|
---|
| 479 | if (outer > maxOuterIndex) //The value does not yet exist
|
---|
| 480 | {
|
---|
| 481 | const Index previousProfile = m_data.upperProfile(inner);
|
---|
| 482 | m_data.upperProfile(inner) = outer - inner;
|
---|
| 483 |
|
---|
| 484 | const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
|
---|
| 485 | //shift data stored after this new one
|
---|
| 486 | const Index stop = m_rowStartIndex[rows()];
|
---|
| 487 | const Index start = m_rowStartIndex[inner + 1];
|
---|
| 488 |
|
---|
| 489 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
| 490 | m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
|
---|
| 491 | }
|
---|
| 492 |
|
---|
| 493 | for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
---|
| 494 | m_rowStartIndex[innerIdx] += bandIncrement;
|
---|
| 495 | }
|
---|
| 496 | memset(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
| 497 | return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
|
---|
| 498 | } else {
|
---|
| 499 | return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
|
---|
| 500 | }
|
---|
| 501 | }
|
---|
| 502 |
|
---|
| 503 | if (outer < inner) //lower matrix
|
---|
| 504 | {
|
---|
| 505 | const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
|
---|
| 506 | if (inner > maxInnerIndex) //The value does not yet exist
|
---|
| 507 | {
|
---|
| 508 | const Index previousProfile = m_data.lowerProfile(outer);
|
---|
| 509 | m_data.lowerProfile(outer) = inner - outer;
|
---|
| 510 |
|
---|
| 511 | const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
|
---|
| 512 | //shift data stored after this new one
|
---|
| 513 | const Index stop = m_colStartIndex[cols()];
|
---|
| 514 | const Index start = m_colStartIndex[outer + 1];
|
---|
| 515 |
|
---|
| 516 | for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
|
---|
| 517 | m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
|
---|
| 518 | }
|
---|
| 519 |
|
---|
| 520 | for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
|
---|
| 521 | m_colStartIndex[innerIdx] += bandIncrement;
|
---|
| 522 | }
|
---|
| 523 | memset(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, 0, (bandIncrement - 1) * sizeof (Scalar));
|
---|
| 524 | return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
|
---|
| 525 | } else {
|
---|
| 526 | return m_data.lower(m_colStartIndex[outer] + (inner - outer));
|
---|
| 527 | }
|
---|
| 528 | }
|
---|
| 529 | }
|
---|
| 530 | }
|
---|
| 531 |
|
---|
| 532 | /** Must be called after inserting a set of non zero entries.
|
---|
| 533 | */
|
---|
| 534 | inline void finalize() {
|
---|
| 535 | if (IsRowMajor) {
|
---|
| 536 | if (rows() > cols())
|
---|
| 537 | m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
---|
| 538 | else
|
---|
| 539 | m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
|
---|
| 540 |
|
---|
| 541 | // eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
|
---|
| 542 | //
|
---|
| 543 | // Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
|
---|
| 544 | // Index dataIdx = 0;
|
---|
| 545 | // for (Index row = 0; row < rows(); row++) {
|
---|
| 546 | //
|
---|
| 547 | // const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
|
---|
| 548 | // // std::cout << "nbLowerElts" << nbLowerElts << std::endl;
|
---|
| 549 | // memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
|
---|
| 550 | // m_rowStartIndex[row] = dataIdx;
|
---|
| 551 | // dataIdx += nbLowerElts;
|
---|
| 552 | //
|
---|
| 553 | // const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
|
---|
| 554 | // memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
|
---|
| 555 | // m_colStartIndex[row] = dataIdx;
|
---|
| 556 | // dataIdx += nbUpperElts;
|
---|
| 557 | //
|
---|
| 558 | //
|
---|
| 559 | // }
|
---|
| 560 | // //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
|
---|
| 561 | // m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
|
---|
| 562 | // m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
|
---|
| 563 | //
|
---|
| 564 | // delete[] m_data.m_lower;
|
---|
| 565 | // delete[] m_data.m_upper;
|
---|
| 566 | //
|
---|
| 567 | // m_data.m_lower = newArray;
|
---|
| 568 | // m_data.m_upper = newArray;
|
---|
| 569 | } else {
|
---|
| 570 | if (rows() > cols())
|
---|
| 571 | m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
|
---|
| 572 | else
|
---|
| 573 | m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
|
---|
| 574 | }
|
---|
| 575 | }
|
---|
| 576 |
|
---|
| 577 | inline void squeeze() {
|
---|
| 578 | finalize();
|
---|
| 579 | m_data.squeeze();
|
---|
| 580 | }
|
---|
| 581 |
|
---|
| 582 | void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
|
---|
| 583 | //TODO
|
---|
| 584 | }
|
---|
| 585 |
|
---|
| 586 | /** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
|
---|
| 587 | * \sa resizeNonZeros(Index), reserve(), setZero()
|
---|
| 588 | */
|
---|
| 589 | void resize(size_t rows, size_t cols) {
|
---|
| 590 | const Index diagSize = rows > cols ? cols : rows;
|
---|
| 591 | m_innerSize = IsRowMajor ? cols : rows;
|
---|
| 592 |
|
---|
| 593 | eigen_assert(rows == cols && "Skyline matrix must be square matrix");
|
---|
| 594 |
|
---|
| 595 | if (diagSize % 2) { // diagSize is odd
|
---|
| 596 | const Index k = (diagSize - 1) / 2;
|
---|
| 597 |
|
---|
| 598 | m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
|
---|
| 599 | 2 * k * k + k + 1,
|
---|
| 600 | 2 * k * k + k + 1);
|
---|
| 601 |
|
---|
| 602 | } else // diagSize is even
|
---|
| 603 | {
|
---|
| 604 | const Index k = diagSize / 2;
|
---|
| 605 | m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
|
---|
| 606 | 2 * k * k - k + 1,
|
---|
| 607 | 2 * k * k - k + 1);
|
---|
| 608 | }
|
---|
| 609 |
|
---|
| 610 | if (m_colStartIndex && m_rowStartIndex) {
|
---|
| 611 | delete[] m_colStartIndex;
|
---|
| 612 | delete[] m_rowStartIndex;
|
---|
| 613 | }
|
---|
| 614 | m_colStartIndex = new Index [cols + 1];
|
---|
| 615 | m_rowStartIndex = new Index [rows + 1];
|
---|
| 616 | m_outerSize = diagSize;
|
---|
| 617 |
|
---|
| 618 | m_data.reset();
|
---|
| 619 | m_data.clear();
|
---|
| 620 |
|
---|
| 621 | m_outerSize = diagSize;
|
---|
| 622 | memset(m_colStartIndex, 0, (cols + 1) * sizeof (Index));
|
---|
| 623 | memset(m_rowStartIndex, 0, (rows + 1) * sizeof (Index));
|
---|
| 624 | }
|
---|
| 625 |
|
---|
| 626 | void resizeNonZeros(Index size) {
|
---|
| 627 | m_data.resize(size);
|
---|
| 628 | }
|
---|
| 629 |
|
---|
| 630 | inline SkylineMatrix()
|
---|
| 631 | : m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
| 632 | resize(0, 0);
|
---|
| 633 | }
|
---|
| 634 |
|
---|
| 635 | inline SkylineMatrix(size_t rows, size_t cols)
|
---|
| 636 | : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
| 637 | resize(rows, cols);
|
---|
| 638 | }
|
---|
| 639 |
|
---|
| 640 | template<typename OtherDerived>
|
---|
| 641 | inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
|
---|
| 642 | : m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
| 643 | *this = other.derived();
|
---|
| 644 | }
|
---|
| 645 |
|
---|
| 646 | inline SkylineMatrix(const SkylineMatrix & other)
|
---|
| 647 | : Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
|
---|
| 648 | *this = other.derived();
|
---|
| 649 | }
|
---|
| 650 |
|
---|
| 651 | inline void swap(SkylineMatrix & other) {
|
---|
| 652 | //EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
|
---|
| 653 | std::swap(m_colStartIndex, other.m_colStartIndex);
|
---|
| 654 | std::swap(m_rowStartIndex, other.m_rowStartIndex);
|
---|
| 655 | std::swap(m_innerSize, other.m_innerSize);
|
---|
| 656 | std::swap(m_outerSize, other.m_outerSize);
|
---|
| 657 | m_data.swap(other.m_data);
|
---|
| 658 | }
|
---|
| 659 |
|
---|
| 660 | inline SkylineMatrix & operator=(const SkylineMatrix & other) {
|
---|
| 661 | std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
|
---|
| 662 | if (other.isRValue()) {
|
---|
| 663 | swap(other.const_cast_derived());
|
---|
| 664 | } else {
|
---|
| 665 | resize(other.rows(), other.cols());
|
---|
| 666 | memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
|
---|
| 667 | memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
|
---|
| 668 | m_data = other.m_data;
|
---|
| 669 | }
|
---|
| 670 | return *this;
|
---|
| 671 | }
|
---|
| 672 |
|
---|
| 673 | template<typename OtherDerived>
|
---|
| 674 | inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
|
---|
| 675 | const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
|
---|
| 676 | if (needToTranspose) {
|
---|
| 677 | // TODO
|
---|
| 678 | // return *this;
|
---|
| 679 | } else {
|
---|
| 680 | // there is no special optimization
|
---|
| 681 | return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
|
---|
| 682 | }
|
---|
| 683 | }
|
---|
| 684 |
|
---|
| 685 | friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
|
---|
| 686 |
|
---|
| 687 | EIGEN_DBG_SKYLINE(
|
---|
| 688 | std::cout << "upper elements : " << std::endl;
|
---|
| 689 | for (Index i = 0; i < m.m_data.upperSize(); i++)
|
---|
| 690 | std::cout << m.m_data.upper(i) << "\t";
|
---|
| 691 | std::cout << std::endl;
|
---|
| 692 | std::cout << "upper profile : " << std::endl;
|
---|
| 693 | for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
|
---|
| 694 | std::cout << m.m_data.upperProfile(i) << "\t";
|
---|
| 695 | std::cout << std::endl;
|
---|
| 696 | std::cout << "lower startIdx : " << std::endl;
|
---|
| 697 | for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
|
---|
| 698 | std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
|
---|
| 699 | std::cout << std::endl;
|
---|
| 700 |
|
---|
| 701 |
|
---|
| 702 | std::cout << "lower elements : " << std::endl;
|
---|
| 703 | for (Index i = 0; i < m.m_data.lowerSize(); i++)
|
---|
| 704 | std::cout << m.m_data.lower(i) << "\t";
|
---|
| 705 | std::cout << std::endl;
|
---|
| 706 | std::cout << "lower profile : " << std::endl;
|
---|
| 707 | for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
|
---|
| 708 | std::cout << m.m_data.lowerProfile(i) << "\t";
|
---|
| 709 | std::cout << std::endl;
|
---|
| 710 | std::cout << "lower startIdx : " << std::endl;
|
---|
| 711 | for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
|
---|
| 712 | std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
|
---|
| 713 | std::cout << std::endl;
|
---|
| 714 | );
|
---|
| 715 | for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
|
---|
| 716 | for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
|
---|
| 717 | s << m.coeff(rowIdx, colIdx) << "\t";
|
---|
| 718 | }
|
---|
| 719 | s << std::endl;
|
---|
| 720 | }
|
---|
| 721 | return s;
|
---|
| 722 | }
|
---|
| 723 |
|
---|
| 724 | /** Destructor */
|
---|
| 725 | inline ~SkylineMatrix() {
|
---|
| 726 | delete[] m_colStartIndex;
|
---|
| 727 | delete[] m_rowStartIndex;
|
---|
| 728 | }
|
---|
| 729 |
|
---|
| 730 | /** Overloaded for performance */
|
---|
| 731 | Scalar sum() const;
|
---|
| 732 | };
|
---|
| 733 |
|
---|
| 734 | template<typename Scalar, int _Options>
|
---|
| 735 | class SkylineMatrix<Scalar, _Options>::InnerUpperIterator {
|
---|
| 736 | public:
|
---|
| 737 |
|
---|
| 738 | InnerUpperIterator(const SkylineMatrix& mat, Index outer)
|
---|
| 739 | : m_matrix(mat), m_outer(outer),
|
---|
| 740 | m_id(_Options == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
|
---|
| 741 | m_start(m_id),
|
---|
| 742 | m_end(_Options == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
|
---|
| 743 | }
|
---|
| 744 |
|
---|
| 745 | inline InnerUpperIterator & operator++() {
|
---|
| 746 | m_id++;
|
---|
| 747 | return *this;
|
---|
| 748 | }
|
---|
| 749 |
|
---|
| 750 | inline InnerUpperIterator & operator+=(Index shift) {
|
---|
| 751 | m_id += shift;
|
---|
| 752 | return *this;
|
---|
| 753 | }
|
---|
| 754 |
|
---|
| 755 | inline Scalar value() const {
|
---|
| 756 | return m_matrix.m_data.upper(m_id);
|
---|
| 757 | }
|
---|
| 758 |
|
---|
| 759 | inline Scalar* valuePtr() {
|
---|
| 760 | return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
|
---|
| 761 | }
|
---|
| 762 |
|
---|
| 763 | inline Scalar& valueRef() {
|
---|
| 764 | return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
|
---|
| 765 | }
|
---|
| 766 |
|
---|
| 767 | inline Index index() const {
|
---|
| 768 | return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
|
---|
| 769 | m_outer + (m_id - m_start) + 1;
|
---|
| 770 | }
|
---|
| 771 |
|
---|
| 772 | inline Index row() const {
|
---|
| 773 | return IsRowMajor ? index() : m_outer;
|
---|
| 774 | }
|
---|
| 775 |
|
---|
| 776 | inline Index col() const {
|
---|
| 777 | return IsRowMajor ? m_outer : index();
|
---|
| 778 | }
|
---|
| 779 |
|
---|
| 780 | inline size_t size() const {
|
---|
| 781 | return m_matrix.m_data.upperProfile(m_outer);
|
---|
| 782 | }
|
---|
| 783 |
|
---|
| 784 | inline operator bool() const {
|
---|
| 785 | return (m_id < m_end) && (m_id >= m_start);
|
---|
| 786 | }
|
---|
| 787 |
|
---|
| 788 | protected:
|
---|
| 789 | const SkylineMatrix& m_matrix;
|
---|
| 790 | const Index m_outer;
|
---|
| 791 | Index m_id;
|
---|
| 792 | const Index m_start;
|
---|
| 793 | const Index m_end;
|
---|
| 794 | };
|
---|
| 795 |
|
---|
| 796 | template<typename Scalar, int _Options>
|
---|
| 797 | class SkylineMatrix<Scalar, _Options>::InnerLowerIterator {
|
---|
| 798 | public:
|
---|
| 799 |
|
---|
| 800 | InnerLowerIterator(const SkylineMatrix& mat, Index outer)
|
---|
| 801 | : m_matrix(mat),
|
---|
| 802 | m_outer(outer),
|
---|
| 803 | m_id(_Options == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
|
---|
| 804 | m_start(m_id),
|
---|
| 805 | m_end(_Options == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
|
---|
| 806 | }
|
---|
| 807 |
|
---|
| 808 | inline InnerLowerIterator & operator++() {
|
---|
| 809 | m_id++;
|
---|
| 810 | return *this;
|
---|
| 811 | }
|
---|
| 812 |
|
---|
| 813 | inline InnerLowerIterator & operator+=(Index shift) {
|
---|
| 814 | m_id += shift;
|
---|
| 815 | return *this;
|
---|
| 816 | }
|
---|
| 817 |
|
---|
| 818 | inline Scalar value() const {
|
---|
| 819 | return m_matrix.m_data.lower(m_id);
|
---|
| 820 | }
|
---|
| 821 |
|
---|
| 822 | inline Scalar* valuePtr() {
|
---|
| 823 | return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
|
---|
| 824 | }
|
---|
| 825 |
|
---|
| 826 | inline Scalar& valueRef() {
|
---|
| 827 | return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
|
---|
| 828 | }
|
---|
| 829 |
|
---|
| 830 | inline Index index() const {
|
---|
| 831 | return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
|
---|
| 832 | m_outer + (m_id - m_start) + 1;
|
---|
| 833 | ;
|
---|
| 834 | }
|
---|
| 835 |
|
---|
| 836 | inline Index row() const {
|
---|
| 837 | return IsRowMajor ? m_outer : index();
|
---|
| 838 | }
|
---|
| 839 |
|
---|
| 840 | inline Index col() const {
|
---|
| 841 | return IsRowMajor ? index() : m_outer;
|
---|
| 842 | }
|
---|
| 843 |
|
---|
| 844 | inline size_t size() const {
|
---|
| 845 | return m_matrix.m_data.lowerProfile(m_outer);
|
---|
| 846 | }
|
---|
| 847 |
|
---|
| 848 | inline operator bool() const {
|
---|
| 849 | return (m_id < m_end) && (m_id >= m_start);
|
---|
| 850 | }
|
---|
| 851 |
|
---|
| 852 | protected:
|
---|
| 853 | const SkylineMatrix& m_matrix;
|
---|
| 854 | const Index m_outer;
|
---|
| 855 | Index m_id;
|
---|
| 856 | const Index m_start;
|
---|
| 857 | const Index m_end;
|
---|
| 858 | };
|
---|
| 859 |
|
---|
| 860 | } // end namespace Eigen
|
---|
| 861 |
|
---|
| 862 | #endif // EIGEN_SkylineMatrix_H
|
---|