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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Guillaume Saupin <guillaume.saupin@cea.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_SKYLINEMATRIX_H
#define EIGEN_SKYLINEMATRIX_H
#include "SkylineStorage.h"
#include "SkylineMatrixBase.h"
#include "./InternalHeaderCheck.h"
namespace Eigen {
/** \ingroup Skyline_Module
*
* \class SkylineMatrix
*
* \brief The main skyline matrix class
*
* This class implements a skyline matrix using the very uncommon storage
* scheme.
*
* \param Scalar_ the scalar type, i.e. the type of the coefficients
* \param Options_ Union of bit flags controlling the storage scheme. Currently the only possibility
* is RowMajor. The default is 0 which means column-major.
*
*
*/
namespace internal {
template<typename Scalar_, int Options_>
struct traits<SkylineMatrix<Scalar_, Options_> > {
typedef Scalar_ Scalar;
typedef Sparse StorageKind;
enum {
RowsAtCompileTime = Dynamic,
ColsAtCompileTime = Dynamic,
MaxRowsAtCompileTime = Dynamic,
MaxColsAtCompileTime = Dynamic,
Flags = SkylineBit | Options_,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
};
};
}
template<typename Scalar_, int Options_>
class SkylineMatrix
: public SkylineMatrixBase<SkylineMatrix<Scalar_, Options_> > {
public:
EIGEN_SKYLINE_GENERIC_PUBLIC_INTERFACE(SkylineMatrix)
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, +=)
EIGEN_SKYLINE_INHERIT_ASSIGNMENT_OPERATOR(SkylineMatrix, -=)
using Base::IsRowMajor;
protected:
typedef SkylineMatrix<Scalar, (Flags&~RowMajorBit) | (IsRowMajor ? RowMajorBit : 0) > TransposedSkylineMatrix;
Index m_outerSize;
Index m_innerSize;
public:
Index* m_colStartIndex;
Index* m_rowStartIndex;
SkylineStorage<Scalar> m_data;
public:
inline Index rows() const {
return IsRowMajor ? m_outerSize : m_innerSize;
}
inline Index cols() const {
return IsRowMajor ? m_innerSize : m_outerSize;
}
inline Index innerSize() const {
return m_innerSize;
}
inline Index outerSize() const {
return m_outerSize;
}
inline Index upperNonZeros() const {
return m_data.upperSize();
}
inline Index lowerNonZeros() const {
return m_data.lowerSize();
}
inline Index upperNonZeros(Index j) const {
return m_colStartIndex[j + 1] - m_colStartIndex[j];
}
inline Index lowerNonZeros(Index j) const {
return m_rowStartIndex[j + 1] - m_rowStartIndex[j];
}
inline const Scalar* _diagPtr() const {
return &m_data.diag(0);
}
inline Scalar* _diagPtr() {
return &m_data.diag(0);
}
inline const Scalar* _upperPtr() const {
return &m_data.upper(0);
}
inline Scalar* _upperPtr() {
return &m_data.upper(0);
}
inline const Scalar* _lowerPtr() const {
return &m_data.lower(0);
}
inline Scalar* _lowerPtr() {
return &m_data.lower(0);
}
inline const Index* _upperProfilePtr() const {
return &m_data.upperProfile(0);
}
inline Index* _upperProfilePtr() {
return &m_data.upperProfile(0);
}
inline const Index* _lowerProfilePtr() const {
return &m_data.lowerProfile(0);
}
inline Index* _lowerProfilePtr() {
return &m_data.lowerProfile(0);
}
inline Scalar coeff(Index row, Index col) const {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
if (outer == inner)
return this->m_data.diag(outer);
if (IsRowMajor) {
if (inner > outer) //upper matrix
{
const Index minOuterIndex = inner - m_data.upperProfile(inner);
if (outer >= minOuterIndex)
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
else
return Scalar(0);
}
if (inner < outer) //lower matrix
{
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
if (inner >= minInnerIndex)
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
else
return Scalar(0);
}
return m_data.upper(m_colStartIndex[inner] + outer - inner);
} else {
if (outer > inner) //upper matrix
{
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
if (outer <= maxOuterIndex)
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
else
return Scalar(0);
}
if (outer < inner) //lower matrix
{
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
if (inner <= maxInnerIndex)
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
else
return Scalar(0);
}
}
}
inline Scalar& coeffRef(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
if (outer == inner)
return this->m_data.diag(outer);
if (IsRowMajor) {
if (col > row) //upper matrix
{
const Index minOuterIndex = inner - m_data.upperProfile(inner);
eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
}
if (col < row) //lower matrix
{
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
}
} else {
if (outer > inner) //upper matrix
{
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
}
if (outer < inner) //lower matrix
{
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
}
}
}
inline Scalar coeffDiag(Index idx) const {
eigen_assert(idx < outerSize());
eigen_assert(idx < innerSize());
return this->m_data.diag(idx);
}
inline Scalar coeffLower(Index row, Index col) const {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
if (inner >= minInnerIndex)
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
else
return Scalar(0);
} else {
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
if (inner <= maxInnerIndex)
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
else
return Scalar(0);
}
}
inline Scalar coeffUpper(Index row, Index col) const {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minOuterIndex = inner - m_data.upperProfile(inner);
if (outer >= minOuterIndex)
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
else
return Scalar(0);
} else {
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
if (outer <= maxOuterIndex)
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
else
return Scalar(0);
}
}
inline Scalar& coeffRefDiag(Index idx) {
eigen_assert(idx < outerSize());
eigen_assert(idx < innerSize());
return this->m_data.diag(idx);
}
inline Scalar& coeffRefLower(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
eigen_assert(inner >= minInnerIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
} else {
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
eigen_assert(inner <= maxInnerIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.lower(m_rowStartIndex[outer] + (inner - outer));
}
}
inline bool coeffExistLower(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
return inner >= minInnerIndex;
} else {
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
return inner <= maxInnerIndex;
}
}
inline Scalar& coeffRefUpper(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minOuterIndex = inner - m_data.upperProfile(inner);
eigen_assert(outer >= minOuterIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
} else {
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
eigen_assert(outer <= maxOuterIndex && "You tried to access a coeff that does not exist in the storage");
return this->m_data.upper(m_colStartIndex[inner] + (outer - inner));
}
}
inline bool coeffExistUpper(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
eigen_assert(inner != outer);
if (IsRowMajor) {
const Index minOuterIndex = inner - m_data.upperProfile(inner);
return outer >= minOuterIndex;
} else {
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
return outer <= maxOuterIndex;
}
}
protected:
public:
class InnerUpperIterator;
class InnerLowerIterator;
class OuterUpperIterator;
class OuterLowerIterator;
/** Removes all non zeros */
inline void setZero() {
m_data.clear();
std::fill_n(m_colStartIndex, m_outerSize + 1, Index(0));
std::fill_n(m_rowStartIndex, m_outerSize + 1, Index(0));
}
/** \returns the number of non zero coefficients */
inline Index nonZeros() const {
return m_data.diagSize() + m_data.upperSize() + m_data.lowerSize();
}
/** Preallocates \a reserveSize non zeros */
inline void reserve(Index reserveSize, Index reserveUpperSize, Index reserveLowerSize) {
m_data.reserve(reserveSize, reserveUpperSize, reserveLowerSize);
}
/** \returns a reference to a novel non zero coefficient with coordinates \a row x \a col.
*
* \warning This function can be extremely slow if the non zero coefficients
* are not inserted in a coherent order.
*
* After an insertion session, you should call the finalize() function.
*/
EIGEN_DONT_INLINE Scalar & insert(Index row, Index col) {
const Index outer = IsRowMajor ? row : col;
const Index inner = IsRowMajor ? col : row;
eigen_assert(outer < outerSize());
eigen_assert(inner < innerSize());
if (outer == inner)
return m_data.diag(col);
if (IsRowMajor) {
if (outer < inner) //upper matrix
{
Index minOuterIndex = 0;
minOuterIndex = inner - m_data.upperProfile(inner);
if (outer < minOuterIndex) //The value does not yet exist
{
const Index previousProfile = m_data.upperProfile(inner);
m_data.upperProfile(inner) = inner - outer;
const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
//shift data stored after this new one
const Index stop = m_colStartIndex[cols()];
const Index start = m_colStartIndex[inner];
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
}
for (Index innerIdx = cols(); innerIdx > inner; innerIdx--) {
m_colStartIndex[innerIdx] += bandIncrement;
}
//zeros new data
std::fill_n(this->_upperPtr() + start, bandIncrement - 1, Scalar(0));
return m_data.upper(m_colStartIndex[inner]);
} else {
return m_data.upper(m_colStartIndex[inner] + outer - (inner - m_data.upperProfile(inner)));
}
}
if (outer > inner) //lower matrix
{
const Index minInnerIndex = outer - m_data.lowerProfile(outer);
if (inner < minInnerIndex) //The value does not yet exist
{
const Index previousProfile = m_data.lowerProfile(outer);
m_data.lowerProfile(outer) = outer - inner;
const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
//shift data stored after this new one
const Index stop = m_rowStartIndex[rows()];
const Index start = m_rowStartIndex[outer];
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
}
for (Index innerIdx = rows(); innerIdx > outer; innerIdx--) {
m_rowStartIndex[innerIdx] += bandIncrement;
}
//zeros new data
std::fill_n(this->_lowerPtr() + start, bandIncrement - 1, Scalar(0));
return m_data.lower(m_rowStartIndex[outer]);
} else {
return m_data.lower(m_rowStartIndex[outer] + inner - (outer - m_data.lowerProfile(outer)));
}
}
} else {
if (outer > inner) //upper matrix
{
const Index maxOuterIndex = inner + m_data.upperProfile(inner);
if (outer > maxOuterIndex) //The value does not yet exist
{
const Index previousProfile = m_data.upperProfile(inner);
m_data.upperProfile(inner) = outer - inner;
const Index bandIncrement = m_data.upperProfile(inner) - previousProfile;
//shift data stored after this new one
const Index stop = m_rowStartIndex[rows()];
const Index start = m_rowStartIndex[inner + 1];
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
m_data.upper(innerIdx + bandIncrement) = m_data.upper(innerIdx);
}
for (Index innerIdx = inner + 1; innerIdx < outerSize() + 1; innerIdx++) {
m_rowStartIndex[innerIdx] += bandIncrement;
}
std::fill_n(this->_upperPtr() + m_rowStartIndex[inner] + previousProfile + 1, bandIncrement - 1, Scalar(0));
return m_data.upper(m_rowStartIndex[inner] + m_data.upperProfile(inner));
} else {
return m_data.upper(m_rowStartIndex[inner] + (outer - inner));
}
}
if (outer < inner) //lower matrix
{
const Index maxInnerIndex = outer + m_data.lowerProfile(outer);
if (inner > maxInnerIndex) //The value does not yet exist
{
const Index previousProfile = m_data.lowerProfile(outer);
m_data.lowerProfile(outer) = inner - outer;
const Index bandIncrement = m_data.lowerProfile(outer) - previousProfile;
//shift data stored after this new one
const Index stop = m_colStartIndex[cols()];
const Index start = m_colStartIndex[outer + 1];
for (Index innerIdx = stop; innerIdx >= start; innerIdx--) {
m_data.lower(innerIdx + bandIncrement) = m_data.lower(innerIdx);
}
for (Index innerIdx = outer + 1; innerIdx < outerSize() + 1; innerIdx++) {
m_colStartIndex[innerIdx] += bandIncrement;
}
std::fill_n(this->_lowerPtr() + m_colStartIndex[outer] + previousProfile + 1, bandIncrement - 1, Scalar(0));
return m_data.lower(m_colStartIndex[outer] + m_data.lowerProfile(outer));
} else {
return m_data.lower(m_colStartIndex[outer] + (inner - outer));
}
}
}
}
/** Must be called after inserting a set of non zero entries.
*/
inline void finalize() {
if (IsRowMajor) {
if (rows() > cols())
m_data.resize(cols(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
else
m_data.resize(rows(), cols(), rows(), m_colStartIndex[cols()] + 1, m_rowStartIndex[rows()] + 1);
// eigen_assert(rows() == cols() && "memory reorganisatrion only works with suare matrix");
//
// Scalar* newArray = new Scalar[m_colStartIndex[cols()] + 1 + m_rowStartIndex[rows()] + 1];
// Index dataIdx = 0;
// for (Index row = 0; row < rows(); row++) {
//
// const Index nbLowerElts = m_rowStartIndex[row + 1] - m_rowStartIndex[row];
// // std::cout << "nbLowerElts" << nbLowerElts << std::endl;
// memcpy(newArray + dataIdx, m_data.m_lower + m_rowStartIndex[row], nbLowerElts * sizeof (Scalar));
// m_rowStartIndex[row] = dataIdx;
// dataIdx += nbLowerElts;
//
// const Index nbUpperElts = m_colStartIndex[row + 1] - m_colStartIndex[row];
// memcpy(newArray + dataIdx, m_data.m_upper + m_colStartIndex[row], nbUpperElts * sizeof (Scalar));
// m_colStartIndex[row] = dataIdx;
// dataIdx += nbUpperElts;
//
//
// }
// //todo : don't access m_data profile directly : add an accessor from SkylineMatrix
// m_rowStartIndex[rows()] = m_rowStartIndex[rows()-1] + m_data.lowerProfile(rows()-1);
// m_colStartIndex[cols()] = m_colStartIndex[cols()-1] + m_data.upperProfile(cols()-1);
//
// delete[] m_data.m_lower;
// delete[] m_data.m_upper;
//
// m_data.m_lower = newArray;
// m_data.m_upper = newArray;
} else {
if (rows() > cols())
m_data.resize(cols(), rows(), cols(), m_rowStartIndex[cols()] + 1, m_colStartIndex[cols()] + 1);
else
m_data.resize(rows(), rows(), cols(), m_rowStartIndex[rows()] + 1, m_colStartIndex[rows()] + 1);
}
}
inline void squeeze() {
finalize();
m_data.squeeze();
}
void prune(Scalar reference, RealScalar epsilon = dummy_precision<RealScalar > ()) {
//TODO
}
/** Resizes the matrix to a \a rows x \a cols matrix and initializes it to zero
* \sa resizeNonZeros(Index), reserve(), setZero()
*/
void resize(size_t rows, size_t cols) {
const Index diagSize = rows > cols ? cols : rows;
m_innerSize = IsRowMajor ? cols : rows;
eigen_assert(rows == cols && "Skyline matrix must be square matrix");
if (diagSize % 2) { // diagSize is odd
const Index k = (diagSize - 1) / 2;
m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
2 * k * k + k + 1,
2 * k * k + k + 1);
} else // diagSize is even
{
const Index k = diagSize / 2;
m_data.resize(diagSize, IsRowMajor ? cols : rows, IsRowMajor ? rows : cols,
2 * k * k - k + 1,
2 * k * k - k + 1);
}
if (m_colStartIndex && m_rowStartIndex) {
delete[] m_colStartIndex;
delete[] m_rowStartIndex;
}
m_colStartIndex = new Index [cols + 1];
m_rowStartIndex = new Index [rows + 1];
m_outerSize = diagSize;
m_data.reset();
m_data.clear();
m_outerSize = diagSize;
std::fill_n(m_colStartIndex, cols + 1, Index(0));
std::fill_n(m_rowStartIndex, rows + 1, Index(0));
}
void resizeNonZeros(Index size) {
m_data.resize(size);
}
inline SkylineMatrix()
: m_outerSize(-1), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
resize(0, 0);
}
inline SkylineMatrix(size_t rows, size_t cols)
: m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
resize(rows, cols);
}
template<typename OtherDerived>
inline SkylineMatrix(const SkylineMatrixBase<OtherDerived>& other)
: m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
*this = other.derived();
}
inline SkylineMatrix(const SkylineMatrix & other)
: Base(), m_outerSize(0), m_innerSize(0), m_colStartIndex(0), m_rowStartIndex(0) {
*this = other.derived();
}
inline void swap(SkylineMatrix & other) {
//EIGEN_DBG_SKYLINE(std::cout << "SkylineMatrix:: swap\n");
std::swap(m_colStartIndex, other.m_colStartIndex);
std::swap(m_rowStartIndex, other.m_rowStartIndex);
std::swap(m_innerSize, other.m_innerSize);
std::swap(m_outerSize, other.m_outerSize);
m_data.swap(other.m_data);
}
inline SkylineMatrix & operator=(const SkylineMatrix & other) {
std::cout << "SkylineMatrix& operator=(const SkylineMatrix& other)\n";
if (other.isRValue()) {
swap(other.const_cast_derived());
} else {
resize(other.rows(), other.cols());
memcpy(m_colStartIndex, other.m_colStartIndex, (m_outerSize + 1) * sizeof (Index));
memcpy(m_rowStartIndex, other.m_rowStartIndex, (m_outerSize + 1) * sizeof (Index));
m_data = other.m_data;
}
return *this;
}
template<typename OtherDerived>
inline SkylineMatrix & operator=(const SkylineMatrixBase<OtherDerived>& other) {
const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
if (needToTranspose) {
// TODO
// return *this;
} else {
// there is no special optimization
return SkylineMatrixBase<SkylineMatrix>::operator=(other.derived());
}
}
friend std::ostream & operator <<(std::ostream & s, const SkylineMatrix & m) {
EIGEN_DBG_SKYLINE(
std::cout << "upper elements : " << std::endl;
for (Index i = 0; i < m.m_data.upperSize(); i++)
std::cout << m.m_data.upper(i) << "\t";
std::cout << std::endl;
std::cout << "upper profile : " << std::endl;
for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
std::cout << m.m_data.upperProfile(i) << "\t";
std::cout << std::endl;
std::cout << "lower startIdx : " << std::endl;
for (Index i = 0; i < m.m_data.upperProfileSize(); i++)
std::cout << (IsRowMajor ? m.m_colStartIndex[i] : m.m_rowStartIndex[i]) << "\t";
std::cout << std::endl;
std::cout << "lower elements : " << std::endl;
for (Index i = 0; i < m.m_data.lowerSize(); i++)
std::cout << m.m_data.lower(i) << "\t";
std::cout << std::endl;
std::cout << "lower profile : " << std::endl;
for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
std::cout << m.m_data.lowerProfile(i) << "\t";
std::cout << std::endl;
std::cout << "lower startIdx : " << std::endl;
for (Index i = 0; i < m.m_data.lowerProfileSize(); i++)
std::cout << (IsRowMajor ? m.m_rowStartIndex[i] : m.m_colStartIndex[i]) << "\t";
std::cout << std::endl;
);
for (Index rowIdx = 0; rowIdx < m.rows(); rowIdx++) {
for (Index colIdx = 0; colIdx < m.cols(); colIdx++) {
s << m.coeff(rowIdx, colIdx) << "\t";
}
s << std::endl;
}
return s;
}
/** Destructor */
inline ~SkylineMatrix() {
delete[] m_colStartIndex;
delete[] m_rowStartIndex;
}
/** Overloaded for performance */
Scalar sum() const;
};
template<typename Scalar, int Options_>
class SkylineMatrix<Scalar, Options_>::InnerUpperIterator {
public:
InnerUpperIterator(const SkylineMatrix& mat, Index outer)
: m_matrix(mat), m_outer(outer),
m_id(Options_ == RowMajor ? mat.m_colStartIndex[outer] : mat.m_rowStartIndex[outer] + 1),
m_start(m_id),
m_end(Options_ == RowMajor ? mat.m_colStartIndex[outer + 1] : mat.m_rowStartIndex[outer + 1] + 1) {
}
inline InnerUpperIterator & operator++() {
m_id++;
return *this;
}
inline InnerUpperIterator & operator+=(Index shift) {
m_id += shift;
return *this;
}
inline Scalar value() const {
return m_matrix.m_data.upper(m_id);
}
inline Scalar* valuePtr() {
return const_cast<Scalar*> (&(m_matrix.m_data.upper(m_id)));
}
inline Scalar& valueRef() {
return const_cast<Scalar&> (m_matrix.m_data.upper(m_id));
}
inline Index index() const {
return IsRowMajor ? m_outer - m_matrix.m_data.upperProfile(m_outer) + (m_id - m_start) :
m_outer + (m_id - m_start) + 1;
}
inline Index row() const {
return IsRowMajor ? index() : m_outer;
}
inline Index col() const {
return IsRowMajor ? m_outer : index();
}
inline size_t size() const {
return m_matrix.m_data.upperProfile(m_outer);
}
inline operator bool() const {
return (m_id < m_end) && (m_id >= m_start);
}
protected:
const SkylineMatrix& m_matrix;
const Index m_outer;
Index m_id;
const Index m_start;
const Index m_end;
};
template<typename Scalar, int Options_>
class SkylineMatrix<Scalar, Options_>::InnerLowerIterator {
public:
InnerLowerIterator(const SkylineMatrix& mat, Index outer)
: m_matrix(mat),
m_outer(outer),
m_id(Options_ == RowMajor ? mat.m_rowStartIndex[outer] : mat.m_colStartIndex[outer] + 1),
m_start(m_id),
m_end(Options_ == RowMajor ? mat.m_rowStartIndex[outer + 1] : mat.m_colStartIndex[outer + 1] + 1) {
}
inline InnerLowerIterator & operator++() {
m_id++;
return *this;
}
inline InnerLowerIterator & operator+=(Index shift) {
m_id += shift;
return *this;
}
inline Scalar value() const {
return m_matrix.m_data.lower(m_id);
}
inline Scalar* valuePtr() {
return const_cast<Scalar*> (&(m_matrix.m_data.lower(m_id)));
}
inline Scalar& valueRef() {
return const_cast<Scalar&> (m_matrix.m_data.lower(m_id));
}
inline Index index() const {
return IsRowMajor ? m_outer - m_matrix.m_data.lowerProfile(m_outer) + (m_id - m_start) :
m_outer + (m_id - m_start) + 1;
;
}
inline Index row() const {
return IsRowMajor ? m_outer : index();
}
inline Index col() const {
return IsRowMajor ? index() : m_outer;
}
inline size_t size() const {
return m_matrix.m_data.lowerProfile(m_outer);
}
inline operator bool() const {
return (m_id < m_end) && (m_id >= m_start);
}
protected:
const SkylineMatrix& m_matrix;
const Index m_outer;
Index m_id;
const Index m_start;
const Index m_end;
};
} // end namespace Eigen
#endif // EIGEN_SKYLINEMATRIX_H