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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2009 Gael Guennebaud <g.gael@free.fr>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 3 of the License, or (at your option) any later version.
//
// Alternatively, you can redistribute it and/or
// modify it under the terms of the GNU General Public License as
// published by the Free Software Foundation; either version 2 of
// the License, or (at your option) any later version.
//
// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License and a copy of the GNU General Public License along with
// Eigen. If not, see <http://www.gnu.org/licenses/>.
#ifndef EIGEN_SPARSEVECTOR_H
#define EIGEN_SPARSEVECTOR_H
/** \class SparseVector
*
* \brief a sparse vector class
*
* \param _Scalar the scalar type, i.e. the type of the coefficients
*
* See http://www.netlib.org/linalg/html_templates/node91.html for details on the storage scheme.
*
*/
template<typename _Scalar, int _Options>
struct ei_traits<SparseVector<_Scalar, _Options> >
{
typedef _Scalar Scalar;
typedef Sparse StorageKind;
typedef MatrixXpr XprKind;
enum {
IsColVector = _Options & RowMajorBit ? 0 : 1,
RowsAtCompileTime = IsColVector ? Dynamic : 1,
ColsAtCompileTime = IsColVector ? 1 : Dynamic,
MaxRowsAtCompileTime = RowsAtCompileTime,
MaxColsAtCompileTime = ColsAtCompileTime,
Flags = _Options | NestByRefBit,
CoeffReadCost = NumTraits<Scalar>::ReadCost,
SupportedAccessPatterns = InnerRandomAccessPattern
};
};
template<typename _Scalar, int _Options>
class SparseVector
: public SparseMatrixBase<SparseVector<_Scalar, _Options> >
{
public:
EIGEN_SPARSE_GENERIC_PUBLIC_INTERFACE(SparseVector)
EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)
// EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, =)
protected:
public:
typedef SparseMatrixBase<SparseVector> SparseBase;
enum { IsColVector = ei_traits<SparseVector>::IsColVector };
CompressedStorage<Scalar> m_data;
Index m_size;
CompressedStorage<Scalar>& _data() { return m_data; }
CompressedStorage<Scalar>& _data() const { return m_data; }
public:
EIGEN_STRONG_INLINE Index rows() const { return IsColVector ? m_size : 1; }
EIGEN_STRONG_INLINE Index cols() const { return IsColVector ? 1 : m_size; }
EIGEN_STRONG_INLINE Index innerSize() const { return m_size; }
EIGEN_STRONG_INLINE Index outerSize() const { return 1; }
EIGEN_STRONG_INLINE Index innerNonZeros(Index j) const { ei_assert(j==0); return m_size; }
EIGEN_STRONG_INLINE const Scalar* _valuePtr() const { return &m_data.value(0); }
EIGEN_STRONG_INLINE Scalar* _valuePtr() { return &m_data.value(0); }
EIGEN_STRONG_INLINE const Index* _innerIndexPtr() const { return &m_data.index(0); }
EIGEN_STRONG_INLINE Index* _innerIndexPtr() { return &m_data.index(0); }
inline Scalar coeff(Index row, Index col) const
{
ei_assert((IsColVector ? col : row)==0);
return coeff(IsColVector ? row : col);
}
inline Scalar coeff(Index i) const { return m_data.at(i); }
inline Scalar& coeffRef(Index row, Index col)
{
ei_assert((IsColVector ? col : row)==0);
return coeff(IsColVector ? row : col);
}
/** \returns a reference to the coefficient value at given index \a i
* This operation involes a log(rho*size) binary search. If the coefficient does not
* exist yet, then a sorted insertion into a sequential buffer is performed.
*
* This insertion might be very costly if the number of nonzeros above \a i is large.
*/
inline Scalar& coeffRef(Index i)
{
return m_data.atWithInsertion(i);
}
public:
class InnerIterator;
inline void setZero() { m_data.clear(); }
/** \returns the number of non zero coefficients */
inline Index nonZeros() const { return static_cast<Index>(m_data.size()); }
inline void startVec(Index outer)
{
ei_assert(outer==0);
}
inline Scalar& insertBack(Index outer, Index inner)
{
ei_assert(outer==0);
return insertBack(inner);
}
inline Scalar& insertBack(Index i)
{
m_data.append(0, i);
return m_data.value(m_data.size()-1);
}
inline Scalar& insert(Index outer, Index inner)
{
ei_assert(outer==0);
return insert(inner);
}
Scalar& insert(Index i)
{
Index startId = 0;
Index id = m_data.size() - 1;
// TODO smart realloc
m_data.resize(id+2,1);
while ( (id >= startId) && (m_data.index(id) > i) )
{
m_data.index(id+1) = m_data.index(id);
m_data.value(id+1) = m_data.value(id);
--id;
}
m_data.index(id+1) = i;
m_data.value(id+1) = 0;
return m_data.value(id+1);
}
/**
*/
inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }
/** \deprecated use setZero() and reserve() */
EIGEN_DEPRECATED void startFill(Index reserve)
{
setZero();
m_data.reserve(reserve);
}
/** \deprecated use insertBack(Index,Index) */
EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
{
ei_assert(r==0 || c==0);
return fill(IsColVector ? r : c);
}
/** \deprecated use insertBack(Index) */
EIGEN_DEPRECATED Scalar& fill(Index i)
{
m_data.append(0, i);
return m_data.value(m_data.size()-1);
}
/** \deprecated use insert(Index,Index) */
EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
{
ei_assert(r==0 || c==0);
return fillrand(IsColVector ? r : c);
}
/** \deprecated use insert(Index) */
EIGEN_DEPRECATED Scalar& fillrand(Index i)
{
return insert(i);
}
/** \deprecated use finalize() */
EIGEN_DEPRECATED void endFill() {}
inline void finalize() {}
void prune(Scalar reference, RealScalar epsilon = NumTraits<RealScalar>::dummy_precision())
{
m_data.prune(reference,epsilon);
}
void resize(Index rows, Index cols)
{
ei_assert(rows==1 || cols==1);
resize(IsColVector ? rows : cols);
}
void resize(Index newSize)
{
m_size = newSize;
m_data.clear();
}
void resizeNonZeros(Index size) { m_data.resize(size); }
inline SparseVector() : m_size(0) { resize(0); }
inline SparseVector(Index size) : m_size(0) { resize(size); }
inline SparseVector(Index rows, Index cols) : m_size(0) { resize(rows,cols); }
template<typename OtherDerived>
inline SparseVector(const MatrixBase<OtherDerived>& other)
: m_size(0)
{
*this = other.derived();
}
template<typename OtherDerived>
inline SparseVector(const SparseMatrixBase<OtherDerived>& other)
: m_size(0)
{
*this = other.derived();
}
inline SparseVector(const SparseVector& other)
: m_size(0)
{
*this = other.derived();
}
inline void swap(SparseVector& other)
{
std::swap(m_size, other.m_size);
m_data.swap(other.m_data);
}
inline SparseVector& operator=(const SparseVector& other)
{
if (other.isRValue())
{
swap(other.const_cast_derived());
}
else
{
resize(other.size());
m_data = other.m_data;
}
return *this;
}
template<typename OtherDerived>
inline SparseVector& operator=(const SparseMatrixBase<OtherDerived>& other)
{
if (int(RowsAtCompileTime)!=int(OtherDerived::RowsAtCompileTime))
return Base::operator=(other.transpose());
else
return Base::operator=(other);
}
// const bool needToTranspose = (Flags & RowMajorBit) != (OtherDerived::Flags & RowMajorBit);
// if (needToTranspose)
// {
// // two passes algorithm:
// // 1 - compute the number of coeffs per dest inner vector
// // 2 - do the actual copy/eval
// // Since each coeff of the rhs has to be evaluated twice, let's evauluate it if needed
// typedef typename ei_nested<OtherDerived,2>::type OtherCopy;
// OtherCopy otherCopy(other.derived());
// typedef typename ei_cleantype<OtherCopy>::type _OtherCopy;
//
// resize(other.rows(), other.cols());
// Eigen::Map<VectorXi>(m_outerIndex,outerSize()).setZero();
// // pass 1
// // FIXME the above copy could be merged with that pass
// for (int j=0; j<otherCopy.outerSize(); ++j)
// for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
// ++m_outerIndex[it.index()];
//
// // prefix sum
// int count = 0;
// VectorXi positions(outerSize());
// for (int j=0; j<outerSize(); ++j)
// {
// int tmp = m_outerIndex[j];
// m_outerIndex[j] = count;
// positions[j] = count;
// count += tmp;
// }
// m_outerIndex[outerSize()] = count;
// // alloc
// m_data.resize(count);
// // pass 2
// for (int j=0; j<otherCopy.outerSize(); ++j)
// for (typename _OtherCopy::InnerIterator it(otherCopy, j); it; ++it)
// {
// int pos = positions[it.index()]++;
// m_data.index(pos) = j;
// m_data.value(pos) = it.value();
// }
//
// return *this;
// }
// else
// {
// // there is no special optimization
// return SparseMatrixBase<SparseMatrix>::operator=(other.derived());
// }
// }
friend std::ostream & operator << (std::ostream & s, const SparseVector& m)
{
for (Index i=0; i<m.nonZeros(); ++i)
s << "(" << m.m_data.value(i) << "," << m.m_data.index(i) << ") ";
s << std::endl;
return s;
}
// this specialized version does not seems to be faster
// Scalar dot(const SparseVector& other) const
// {
// int i=0, j=0;
// Scalar res = 0;
// asm("#begindot");
// while (i<nonZeros() && j<other.nonZeros())
// {
// if (m_data.index(i)==other.m_data.index(j))
// {
// res += m_data.value(i) * ei_conj(other.m_data.value(j));
// ++i; ++j;
// }
// else if (m_data.index(i)<other.m_data.index(j))
// ++i;
// else
// ++j;
// }
// asm("#enddot");
// return res;
// }
/** Destructor */
inline ~SparseVector() {}
/** Overloaded for performance */
Scalar sum() const;
};
template<typename Scalar, int _Options>
class SparseVector<Scalar,_Options>::InnerIterator
{
public:
InnerIterator(const SparseVector& vec, Index outer=0)
: m_data(vec.m_data), m_id(0), m_end(static_cast<Index>(m_data.size()))
{
ei_assert(outer==0);
}
InnerIterator(const CompressedStorage<Scalar>& data)
: m_data(data), m_id(0), m_end(static_cast<Index>(m_data.size()))
{}
template<unsigned int Added, unsigned int Removed>
InnerIterator(const Flagged<SparseVector,Added,Removed>& vec, Index )
: m_data(vec._expression().m_data), m_id(0), m_end(m_data.size())
{}
inline InnerIterator& operator++() { m_id++; return *this; }
inline Scalar value() const { return m_data.value(m_id); }
inline Scalar& valueRef() { return const_cast<Scalar&>(m_data.value(m_id)); }
inline Index index() const { return m_data.index(m_id); }
inline Index row() const { return IsColVector ? index() : 0; }
inline Index col() const { return IsColVector ? 0 : index(); }
inline operator bool() const { return (m_id < m_end); }
protected:
const CompressedStorage<Scalar>& m_data;
Index m_id;
const Index m_end;
};
#endif // EIGEN_SPARSEVECTOR_H