Eigen/src/SparseCore/SparseVector.h - eigen - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.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_SPARSEVECTOR_H
 #define EIGEN_SPARSEVECTOR_H

 #include "./InternalHeaderCheck.h"

 namespace Eigen {

 /** \ingroup SparseCore_Module
   * \class SparseVector
   *
   * \brief a sparse vector class
   *
   * \tparam 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.
   *
   * This class can be extended with the help of the plugin mechanism described on the page
   * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
   */

 namespace internal {
 template<typename Scalar_, int Options_, typename StorageIndex_>
 struct traits<SparseVector<Scalar_, Options_, StorageIndex_> >
 {
   typedef Scalar_ Scalar;
   typedef StorageIndex_ StorageIndex;
   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 | LvalueBit | (IsColVector ? 0 : RowMajorBit) | CompressedAccessBit,
     SupportedAccessPatterns = InnerRandomAccessPattern
   };
 };

 // Sparse-Vector-Assignment kinds:
 enum {
   SVA_RuntimeSwitch,
   SVA_Inner,
   SVA_Outer
 };

 template< typename Dest, typename Src,
           int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
                              : Src::InnerSizeAtCompileTime==1 ? SVA_Outer
                              : SVA_Inner>
 struct sparse_vector_assign_selector;

 }

 template<typename Scalar_, int Options_, typename StorageIndex_>
 class SparseVector
   : public SparseCompressedBase<SparseVector<Scalar_, Options_, StorageIndex_> >
 {
     typedef SparseCompressedBase<SparseVector> Base;
     using Base::convert_index;
   public:
     EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
     EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
     EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)

     typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
     enum { IsColVector = internal::traits<SparseVector>::IsColVector };

     enum {
       Options = Options_
     };

     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 const Scalar* valuePtr() const { return m_data.valuePtr(); }
     EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }

     EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
     EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }

     inline const StorageIndex* outerIndexPtr() const { return 0; }
     inline StorageIndex* outerIndexPtr() { return 0; }
     inline const StorageIndex* innerNonZeroPtr() const { return 0; }
     inline StorageIndex* innerNonZeroPtr() { return 0; }

     /** \internal */
     inline Storage& data() { return m_data; }
     /** \internal */
     inline const Storage& data() const { return m_data; }

     inline Scalar coeff(Index row, Index col) const
     {
       eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
       return coeff(IsColVector ? row : col);
     }
     inline Scalar coeff(Index i) const
     {
       eigen_assert(i>=0 && i<m_size);
       return m_data.at(StorageIndex(i));
     }

     inline Scalar& coeffRef(Index row, Index col)
     {
       eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
       return coeffRef(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)
     {
       eigen_assert(i>=0 && i<m_size);

       return m_data.atWithInsertion(StorageIndex(i));
     }

   public:

     typedef typename Base::InnerIterator InnerIterator;
     typedef typename Base::ReverseInnerIterator ReverseInnerIterator;

     inline void setZero() { m_data.clear(); }

     /** \returns the number of non zero coefficients */
     inline Index nonZeros() const  { return m_data.size(); }

     inline void startVec(Index outer)
     {
       EIGEN_UNUSED_VARIABLE(outer);
       eigen_assert(outer==0);
     }

     inline Scalar& insertBackByOuterInner(Index outer, Index inner)
     {
       EIGEN_UNUSED_VARIABLE(outer);
       eigen_assert(outer==0);
       return insertBack(inner);
     }
     inline Scalar& insertBack(Index i)
     {
       m_data.append(0, i);
       return m_data.value(m_data.size()-1);
     }

     Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
     {
       EIGEN_UNUSED_VARIABLE(outer);
       eigen_assert(outer==0);
       return insertBackUnordered(inner);
     }
     inline Scalar& insertBackUnordered(Index i)
     {
       m_data.append(0, i);
       return m_data.value(m_data.size()-1);
     }

     inline Scalar& insert(Index row, Index col)
     {
       eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));

       Index inner = IsColVector ? row : col;
       Index outer = IsColVector ? col : row;
       EIGEN_ONLY_USED_FOR_DEBUG(outer);
       eigen_assert(outer==0);
       return insert(inner);
     }
     Scalar& insert(Index i)
     {
       eigen_assert(i>=0 && i<m_size);

       Index startId = 0;
       Index p = Index(m_data.size()) - 1;
       // TODO smart realloc
       m_data.resize(p+2,1);

       while ( (p >= startId) && (m_data.index(p) > i) )
       {
         m_data.index(p+1) = m_data.index(p);
         m_data.value(p+1) = m_data.value(p);
         --p;
       }
       m_data.index(p+1) = convert_index(i);
       m_data.value(p+1) = 0;
       return m_data.value(p+1);
     }

     /**
       */
     inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }


     inline void finalize() {}

     /** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
     void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
     {
       m_data.prune(reference,epsilon);
     }

     /** Resizes the sparse vector to \a rows x \a cols
       *
       * This method is provided for compatibility with matrices.
       * For a column vector, \a cols must be equal to 1.
       * For a row vector, \a rows must be equal to 1.
       *
       * \sa resize(Index)
       */
     void resize(Index rows, Index cols)
     {
       eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
       resize(IsColVector ? rows : cols);
     }

     /** Resizes the sparse vector to \a newSize
       * This method deletes all entries, thus leaving an empty sparse vector
       *
       * \sa  conservativeResize(), setZero() */
     void resize(Index newSize)
     {
       m_size = newSize;
       m_data.clear();
     }

     /** Resizes the sparse vector to \a newSize, while leaving old values untouched.
       *
       * If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
       * Call .data().squeeze() to free extra memory.
       *
       * \sa reserve(), setZero()
       */
     void conservativeResize(Index newSize)
     {
       if (newSize < m_size)
       {
         Index i = 0;
         while (i<m_data.size() && m_data.index(i)<newSize) ++i;
         m_data.resize(i);
       }
       m_size = newSize;
     }

     void resizeNonZeros(Index size) { m_data.resize(size); }

     inline SparseVector() : m_size(0) { resize(0); }

     explicit 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 SparseMatrixBase<OtherDerived>& other)
       : m_size(0)
     {
       #ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
         EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
       #endif
       *this = other.derived();
     }

     inline SparseVector(const SparseVector& other)
       : Base(other), m_size(0)
     {
       *this = other.derived();
     }

     /** Swaps the values of \c *this and \a other.
       * Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
       * \sa SparseMatrixBase::swap()
       */
     inline void swap(SparseVector& other)
     {
       std::swap(m_size, other.m_size);
       m_data.swap(other.m_data);
     }

     template<int OtherOptions>
     inline void swap(SparseMatrix<Scalar,OtherOptions,StorageIndex>& other)
     {
       eigen_assert(other.outerSize()==1);
       std::swap(m_size, other.m_innerSize);
       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)
     {
       SparseVector tmp(other.size());
       internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
       this->swap(tmp);
       return *this;
     }

     #ifndef EIGEN_PARSED_BY_DOXYGEN
     template<typename Lhs, typename Rhs>
     inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
     {
       return Base::operator=(product);
     }
     #endif

     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;
     }

     /** Destructor */
     inline ~SparseVector() {}

     /** Overloaded for performance */
     Scalar sum() const;

   public:

     /** \internal \deprecated use setZero() and reserve() */
     EIGEN_DEPRECATED void startFill(Index reserve)
     {
       setZero();
       m_data.reserve(reserve);
     }

     /** \internal \deprecated use insertBack(Index,Index) */
     EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
     {
       eigen_assert(r==0 || c==0);
       return fill(IsColVector ? r : c);
     }

     /** \internal \deprecated use insertBack(Index) */
     EIGEN_DEPRECATED Scalar& fill(Index i)
     {
       m_data.append(0, i);
       return m_data.value(m_data.size()-1);
     }

     /** \internal \deprecated use insert(Index,Index) */
     EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
     {
       eigen_assert(r==0 || c==0);
       return fillrand(IsColVector ? r : c);
     }

     /** \internal \deprecated use insert(Index) */
     EIGEN_DEPRECATED Scalar& fillrand(Index i)
     {
       return insert(i);
     }

     /** \internal \deprecated use finalize() */
     EIGEN_DEPRECATED void endFill() {}

     // These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
     /** \internal \deprecated use data() */
     EIGEN_DEPRECATED Storage& _data() { return m_data; }
     /** \internal \deprecated use data() */
     EIGEN_DEPRECATED const Storage& _data() const { return m_data; }

 #   ifdef EIGEN_SPARSEVECTOR_PLUGIN
 #     include EIGEN_SPARSEVECTOR_PLUGIN
 #   endif

 protected:
     EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
     EIGEN_STATIC_ASSERT((Options_&(ColMajor|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS)

     Storage m_data;
     Index m_size;
 };

 namespace internal {

 template<typename Scalar_, int Options_, typename Index_>
 struct evaluator<SparseVector<Scalar_,Options_,Index_> >
   : evaluator_base<SparseVector<Scalar_,Options_,Index_> >
 {
   typedef SparseVector<Scalar_,Options_,Index_> SparseVectorType;
   typedef evaluator_base<SparseVectorType> Base;
   typedef typename SparseVectorType::InnerIterator InnerIterator;
   typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;

   enum {
     CoeffReadCost = NumTraits<Scalar_>::ReadCost,
     Flags = SparseVectorType::Flags
   };

   evaluator() : Base() {}

   explicit evaluator(const SparseVectorType &mat) : m_matrix(&mat)
   {
     EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
   }

   inline Index nonZerosEstimate() const {
     return m_matrix->nonZeros();
   }

   operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
   operator const SparseVectorType&() const { return *m_matrix; }

   const SparseVectorType *m_matrix;
 };

 template< typename Dest, typename Src>
 struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
   static void run(Dest& dst, const Src& src) {
     eigen_internal_assert(src.innerSize()==src.size());
     typedef internal::evaluator<Src> SrcEvaluatorType;
     SrcEvaluatorType srcEval(src);
     for(typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it)
       dst.insert(it.index()) = it.value();
   }
 };

 template< typename Dest, typename Src>
 struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
   static void run(Dest& dst, const Src& src) {
     eigen_internal_assert(src.outerSize()==src.size());
     typedef internal::evaluator<Src> SrcEvaluatorType;
     SrcEvaluatorType srcEval(src);
     for(Index i=0; i<src.size(); ++i)
     {
       typename SrcEvaluatorType::InnerIterator it(srcEval, i);
       if(it)
         dst.insert(i) = it.value();
     }
   }
 };

 template< typename Dest, typename Src>
 struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
   static void run(Dest& dst, const Src& src) {
     if(src.outerSize()==1)  sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
     else                    sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
   }
 };

 }

 } // end namespace Eigen

 #endif // EIGEN_SPARSEVECTOR_H
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2008-2015 Gael Guennebaud <gael.guennebaud@inria.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_SPARSEVECTOR_H
	#define EIGEN_SPARSEVECTOR_H

	#include "./InternalHeaderCheck.h"

	namespace Eigen {

	/** \ingroup SparseCore_Module
	* \class SparseVector
	*
	* \brief a sparse vector class
	*
	* \tparam 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.
	*
	* This class can be extended with the help of the plugin mechanism described on the page
	* \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_SPARSEVECTOR_PLUGIN.
	*/

	namespace internal {
	template<typename Scalar_, int Options_, typename StorageIndex_>
	struct traits<SparseVector<Scalar_, Options_, StorageIndex_> >
	{
	typedef Scalar_ Scalar;
	typedef StorageIndex_ StorageIndex;
	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 \| LvalueBit \| (IsColVector ? 0 : RowMajorBit) \| CompressedAccessBit,
	SupportedAccessPatterns = InnerRandomAccessPattern
	};
	};

	// Sparse-Vector-Assignment kinds:
	enum {
	SVA_RuntimeSwitch,
	SVA_Inner,
	SVA_Outer
	};

	template< typename Dest, typename Src,
	int AssignmentKind = !bool(Src::IsVectorAtCompileTime) ? SVA_RuntimeSwitch
	: Src::InnerSizeAtCompileTime==1 ? SVA_Outer
	: SVA_Inner>
	struct sparse_vector_assign_selector;

	}

	template<typename Scalar_, int Options_, typename StorageIndex_>
	class SparseVector
	: public SparseCompressedBase<SparseVector<Scalar_, Options_, StorageIndex_> >
	{
	typedef SparseCompressedBase<SparseVector> Base;
	using Base::convert_index;
	public:
	EIGEN_SPARSE_PUBLIC_INTERFACE(SparseVector)
	EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, +=)
	EIGEN_SPARSE_INHERIT_ASSIGNMENT_OPERATOR(SparseVector, -=)

	typedef internal::CompressedStorage<Scalar,StorageIndex> Storage;
	enum { IsColVector = internal::traits<SparseVector>::IsColVector };

	enum {
	Options = Options_
	};

	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 const Scalar* valuePtr() const { return m_data.valuePtr(); }
	EIGEN_STRONG_INLINE Scalar* valuePtr() { return m_data.valuePtr(); }

	EIGEN_STRONG_INLINE const StorageIndex* innerIndexPtr() const { return m_data.indexPtr(); }
	EIGEN_STRONG_INLINE StorageIndex* innerIndexPtr() { return m_data.indexPtr(); }

	inline const StorageIndex* outerIndexPtr() const { return 0; }
	inline StorageIndex* outerIndexPtr() { return 0; }
	inline const StorageIndex* innerNonZeroPtr() const { return 0; }
	inline StorageIndex* innerNonZeroPtr() { return 0; }

	/** \internal */
	inline Storage& data() { return m_data; }
	/** \internal */
	inline const Storage& data() const { return m_data; }

	inline Scalar coeff(Index row, Index col) const
	{
	eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
	return coeff(IsColVector ? row : col);
	}
	inline Scalar coeff(Index i) const
	{
	eigen_assert(i>=0 && i<m_size);
	return m_data.at(StorageIndex(i));
	}

	inline Scalar& coeffRef(Index row, Index col)
	{
	eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));
	return coeffRef(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)
	{
	eigen_assert(i>=0 && i<m_size);

	return m_data.atWithInsertion(StorageIndex(i));
	}

	public:

	typedef typename Base::InnerIterator InnerIterator;
	typedef typename Base::ReverseInnerIterator ReverseInnerIterator;

	inline void setZero() { m_data.clear(); }

	/** \returns the number of non zero coefficients */
	inline Index nonZeros() const { return m_data.size(); }

	inline void startVec(Index outer)
	{
	EIGEN_UNUSED_VARIABLE(outer);
	eigen_assert(outer==0);
	}

	inline Scalar& insertBackByOuterInner(Index outer, Index inner)
	{
	EIGEN_UNUSED_VARIABLE(outer);
	eigen_assert(outer==0);
	return insertBack(inner);
	}
	inline Scalar& insertBack(Index i)
	{
	m_data.append(0, i);
	return m_data.value(m_data.size()-1);
	}

	Scalar& insertBackByOuterInnerUnordered(Index outer, Index inner)
	{
	EIGEN_UNUSED_VARIABLE(outer);
	eigen_assert(outer==0);
	return insertBackUnordered(inner);
	}
	inline Scalar& insertBackUnordered(Index i)
	{
	m_data.append(0, i);
	return m_data.value(m_data.size()-1);
	}

	inline Scalar& insert(Index row, Index col)
	{
	eigen_assert(IsColVector ? (col==0 && row>=0 && row<m_size) : (row==0 && col>=0 && col<m_size));

	Index inner = IsColVector ? row : col;
	Index outer = IsColVector ? col : row;
	EIGEN_ONLY_USED_FOR_DEBUG(outer);
	eigen_assert(outer==0);
	return insert(inner);
	}
	Scalar& insert(Index i)
	{
	eigen_assert(i>=0 && i<m_size);

	Index startId = 0;
	Index p = Index(m_data.size()) - 1;
	// TODO smart realloc
	m_data.resize(p+2,1);

	while ( (p >= startId) && (m_data.index(p) > i) )
	{
	m_data.index(p+1) = m_data.index(p);
	m_data.value(p+1) = m_data.value(p);
	--p;
	}
	m_data.index(p+1) = convert_index(i);
	m_data.value(p+1) = 0;
	return m_data.value(p+1);
	}

	/**
	*/
	inline void reserve(Index reserveSize) { m_data.reserve(reserveSize); }


	inline void finalize() {}

	/** \copydoc SparseMatrix::prune(const Scalar&,const RealScalar&) */
	void prune(const Scalar& reference, const RealScalar& epsilon = NumTraits<RealScalar>::dummy_precision())
	{
	m_data.prune(reference,epsilon);
	}

	/** Resizes the sparse vector to \a rows x \a cols
	*
	* This method is provided for compatibility with matrices.
	* For a column vector, \a cols must be equal to 1.
	* For a row vector, \a rows must be equal to 1.
	*
	* \sa resize(Index)
	*/
	void resize(Index rows, Index cols)
	{
	eigen_assert((IsColVector ? cols : rows)==1 && "Outer dimension must equal 1");
	resize(IsColVector ? rows : cols);
	}

	/** Resizes the sparse vector to \a newSize
	* This method deletes all entries, thus leaving an empty sparse vector
	*
	* \sa conservativeResize(), setZero() */
	void resize(Index newSize)
	{
	m_size = newSize;
	m_data.clear();
	}

	/** Resizes the sparse vector to \a newSize, while leaving old values untouched.
	*
	* If the size of the vector is decreased, then the storage of the out-of bounds coefficients is kept and reserved.
	* Call .data().squeeze() to free extra memory.
	*
	* \sa reserve(), setZero()
	*/
	void conservativeResize(Index newSize)
	{
	if (newSize < m_size)
	{
	Index i = 0;
	while (i<m_data.size() && m_data.index(i)<newSize) ++i;
	m_data.resize(i);
	}
	m_size = newSize;
	}

	void resizeNonZeros(Index size) { m_data.resize(size); }

	inline SparseVector() : m_size(0) { resize(0); }

	explicit 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 SparseMatrixBase<OtherDerived>& other)
	: m_size(0)
	{
	#ifdef EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
	EIGEN_SPARSE_CREATE_TEMPORARY_PLUGIN
	#endif
	*this = other.derived();
	}

	inline SparseVector(const SparseVector& other)
	: Base(other), m_size(0)
	{
	*this = other.derived();
	}

	/** Swaps the values of \c *this and \a other.
	* Overloaded for performance: this version performs a \em shallow swap by swapping pointers and attributes only.
	* \sa SparseMatrixBase::swap()
	*/
	inline void swap(SparseVector& other)
	{
	std::swap(m_size, other.m_size);
	m_data.swap(other.m_data);
	}

	template<int OtherOptions>
	inline void swap(SparseMatrix<Scalar,OtherOptions,StorageIndex>& other)
	{
	eigen_assert(other.outerSize()==1);
	std::swap(m_size, other.m_innerSize);
	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)
	{
	SparseVector tmp(other.size());
	internal::sparse_vector_assign_selector<SparseVector,OtherDerived>::run(tmp,other.derived());
	this->swap(tmp);
	return *this;
	}

	#ifndef EIGEN_PARSED_BY_DOXYGEN
	template<typename Lhs, typename Rhs>
	inline SparseVector& operator=(const SparseSparseProduct<Lhs,Rhs>& product)
	{
	return Base::operator=(product);
	}
	#endif

	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;
	}

	/** Destructor */
	inline ~SparseVector() {}

	/** Overloaded for performance */
	Scalar sum() const;

	public:

	/** \internal \deprecated use setZero() and reserve() */
	EIGEN_DEPRECATED void startFill(Index reserve)
	{
	setZero();
	m_data.reserve(reserve);
	}

	/** \internal \deprecated use insertBack(Index,Index) */
	EIGEN_DEPRECATED Scalar& fill(Index r, Index c)
	{
	eigen_assert(r==0 \|\| c==0);
	return fill(IsColVector ? r : c);
	}

	/** \internal \deprecated use insertBack(Index) */
	EIGEN_DEPRECATED Scalar& fill(Index i)
	{
	m_data.append(0, i);
	return m_data.value(m_data.size()-1);
	}

	/** \internal \deprecated use insert(Index,Index) */
	EIGEN_DEPRECATED Scalar& fillrand(Index r, Index c)
	{
	eigen_assert(r==0 \|\| c==0);
	return fillrand(IsColVector ? r : c);
	}

	/** \internal \deprecated use insert(Index) */
	EIGEN_DEPRECATED Scalar& fillrand(Index i)
	{
	return insert(i);
	}

	/** \internal \deprecated use finalize() */
	EIGEN_DEPRECATED void endFill() {}

	// These two functions were here in the 3.1 release, so let's keep them in case some code rely on them.
	/** \internal \deprecated use data() */
	EIGEN_DEPRECATED Storage& _data() { return m_data; }
	/** \internal \deprecated use data() */
	EIGEN_DEPRECATED const Storage& _data() const { return m_data; }

	# ifdef EIGEN_SPARSEVECTOR_PLUGIN
	# include EIGEN_SPARSEVECTOR_PLUGIN
	# endif

	protected:
	EIGEN_STATIC_ASSERT(NumTraits<StorageIndex>::IsSigned,THE_INDEX_TYPE_MUST_BE_A_SIGNED_TYPE)
	EIGEN_STATIC_ASSERT((Options_&(ColMajor\|RowMajor))==Options,INVALID_MATRIX_TEMPLATE_PARAMETERS)

	Storage m_data;
	Index m_size;
	};

	namespace internal {

	template<typename Scalar_, int Options_, typename Index_>
	struct evaluator<SparseVector<Scalar_,Options_,Index_> >
	: evaluator_base<SparseVector<Scalar_,Options_,Index_> >
	{
	typedef SparseVector<Scalar_,Options_,Index_> SparseVectorType;
	typedef evaluator_base<SparseVectorType> Base;
	typedef typename SparseVectorType::InnerIterator InnerIterator;
	typedef typename SparseVectorType::ReverseInnerIterator ReverseInnerIterator;

	enum {
	CoeffReadCost = NumTraits<Scalar_>::ReadCost,
	Flags = SparseVectorType::Flags
	};

	evaluator() : Base() {}

	explicit evaluator(const SparseVectorType &mat) : m_matrix(&mat)
	{
	EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
	}

	inline Index nonZerosEstimate() const {
	return m_matrix->nonZeros();
	}

	operator SparseVectorType&() { return m_matrix->const_cast_derived(); }
	operator const SparseVectorType&() const { return *m_matrix; }

	const SparseVectorType *m_matrix;
	};

	template< typename Dest, typename Src>
	struct sparse_vector_assign_selector<Dest,Src,SVA_Inner> {
	static void run(Dest& dst, const Src& src) {
	eigen_internal_assert(src.innerSize()==src.size());
	typedef internal::evaluator<Src> SrcEvaluatorType;
	SrcEvaluatorType srcEval(src);
	for(typename SrcEvaluatorType::InnerIterator it(srcEval, 0); it; ++it)
	dst.insert(it.index()) = it.value();
	}
	};

	template< typename Dest, typename Src>
	struct sparse_vector_assign_selector<Dest,Src,SVA_Outer> {
	static void run(Dest& dst, const Src& src) {
	eigen_internal_assert(src.outerSize()==src.size());
	typedef internal::evaluator<Src> SrcEvaluatorType;
	SrcEvaluatorType srcEval(src);
	for(Index i=0; i<src.size(); ++i)
	{
	typename SrcEvaluatorType::InnerIterator it(srcEval, i);
	if(it)
	dst.insert(i) = it.value();
	}
	}
	};

	template< typename Dest, typename Src>
	struct sparse_vector_assign_selector<Dest,Src,SVA_RuntimeSwitch> {
	static void run(Dest& dst, const Src& src) {
	if(src.outerSize()==1) sparse_vector_assign_selector<Dest,Src,SVA_Inner>::run(dst, src);
	else sparse_vector_assign_selector<Dest,Src,SVA_Outer>::run(dst, src);
	}
	};

	}

	} // end namespace Eigen

	#endif // EIGEN_SPARSEVECTOR_H