Eigen/src/Sparse/AmbiVector.h - mirror - Git at Google

 // This file is part of Eigen, a lightweight C++ template library
 // for linear algebra.
 //
 // Copyright (C) 2008 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_AMBIVECTOR_H
 #define EIGEN_AMBIVECTOR_H

 /** \internal
   * Hybrid sparse/dense vector class designed for intensive read-write operations.
   *
   * See BasicSparseLLT and SparseProduct for usage examples.
   */
 template<typename _Scalar, typename _Index>
 class AmbiVector
 {
   public:
     typedef _Scalar Scalar;
     typedef _Index Index;
     typedef typename NumTraits<Scalar>::Real RealScalar;

     AmbiVector(Index size)
       : m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
     {
       resize(size);
     }

     void init(double estimatedDensity);
     void init(int mode);

     Index nonZeros() const;

     /** Specifies a sub-vector to work on */
     void setBounds(Index start, Index end) { m_start = start; m_end = end; }

     void setZero();

     void restart();
     Scalar& coeffRef(Index i);
     Scalar& coeff(Index i);

     class Iterator;

     ~AmbiVector() { delete[] m_buffer; }

     void resize(Index size)
     {
       if (m_allocatedSize < size)
         reallocate(size);
       m_size = size;
     }

     Index size() const { return m_size; }

   protected:

     void reallocate(Index size)
     {
       // if the size of the matrix is not too large, let's allocate a bit more than needed such
       // that we can handle dense vector even in sparse mode.
       delete[] m_buffer;
       if (size<1000)
       {
         Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
         m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
         m_buffer = new Scalar[allocSize];
       }
       else
       {
         m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
         m_buffer = new Scalar[size];
       }
       m_size = size;
       m_start = 0;
       m_end = m_size;
     }

     void reallocateSparse()
     {
       Index copyElements = m_allocatedElements;
       m_allocatedElements = std::min(Index(m_allocatedElements*1.5),m_size);
       Index allocSize = m_allocatedElements * sizeof(ListEl);
       allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
       Scalar* newBuffer = new Scalar[allocSize];
       memcpy(newBuffer,  m_buffer,  copyElements * sizeof(ListEl));
       delete[] m_buffer;
       m_buffer = newBuffer;
     }

   protected:
     // element type of the linked list
     struct ListEl
     {
       Index next;
       Index index;
       Scalar value;
     };

     // used to store data in both mode
     Scalar* m_buffer;
     Scalar m_zero;
     Index m_size;
     Index m_start;
     Index m_end;
     Index m_allocatedSize;
     Index m_allocatedElements;
     Index m_mode;

     // linked list mode
     Index m_llStart;
     Index m_llCurrent;
     Index m_llSize;
 };

 /** \returns the number of non zeros in the current sub vector */
 template<typename _Scalar,typename _Index>
 _Index AmbiVector<_Scalar,_Index>::nonZeros() const
 {
   if (m_mode==IsSparse)
     return m_llSize;
   else
     return m_end - m_start;
 }

 template<typename _Scalar,typename _Index>
 void AmbiVector<_Scalar,_Index>::init(double estimatedDensity)
 {
   if (estimatedDensity>0.1)
     init(IsDense);
   else
     init(IsSparse);
 }

 template<typename _Scalar,typename _Index>
 void AmbiVector<_Scalar,_Index>::init(int mode)
 {
   m_mode = mode;
   if (m_mode==IsSparse)
   {
     m_llSize = 0;
     m_llStart = -1;
   }
 }

 /** Must be called whenever we might perform a write access
   * with an index smaller than the previous one.
   *
   * Don't worry, this function is extremely cheap.
   */
 template<typename _Scalar,typename _Index>
 void AmbiVector<_Scalar,_Index>::restart()
 {
   m_llCurrent = m_llStart;
 }

 /** Set all coefficients of current subvector to zero */
 template<typename _Scalar,typename _Index>
 void AmbiVector<_Scalar,_Index>::setZero()
 {
   if (m_mode==IsDense)
   {
     for (Index i=m_start; i<m_end; ++i)
       m_buffer[i] = Scalar(0);
   }
   else
   {
     ei_assert(m_mode==IsSparse);
     m_llSize = 0;
     m_llStart = -1;
   }
 }

 template<typename _Scalar,typename _Index>
 _Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i)
 {
   if (m_mode==IsDense)
     return m_buffer[i];
   else
   {
     ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
     // TODO factorize the following code to reduce code generation
     ei_assert(m_mode==IsSparse);
     if (m_llSize==0)
     {
       // this is the first element
       m_llStart = 0;
       m_llCurrent = 0;
       ++m_llSize;
       llElements[0].value = Scalar(0);
       llElements[0].index = i;
       llElements[0].next = -1;
       return llElements[0].value;
     }
     else if (i<llElements[m_llStart].index)
     {
       // this is going to be the new first element of the list
       ListEl& el = llElements[m_llSize];
       el.value = Scalar(0);
       el.index = i;
       el.next = m_llStart;
       m_llStart = m_llSize;
       ++m_llSize;
       m_llCurrent = m_llStart;
       return el.value;
     }
     else
     {
       Index nextel = llElements[m_llCurrent].next;
       ei_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
       while (nextel >= 0 && llElements[nextel].index<=i)
       {
         m_llCurrent = nextel;
         nextel = llElements[nextel].next;
       }

       if (llElements[m_llCurrent].index==i)
       {
         // the coefficient already exists and we found it !
         return llElements[m_llCurrent].value;
       }
       else
       {
         if (m_llSize>=m_allocatedElements)
         {
           reallocateSparse();
           llElements = reinterpret_cast<ListEl*>(m_buffer);
         }
         ei_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
         // let's insert a new coefficient
         ListEl& el = llElements[m_llSize];
         el.value = Scalar(0);
         el.index = i;
         el.next = llElements[m_llCurrent].next;
         llElements[m_llCurrent].next = m_llSize;
         ++m_llSize;
         return el.value;
       }
     }
   }
 }

 template<typename _Scalar,typename _Index>
 _Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i)
 {
   if (m_mode==IsDense)
     return m_buffer[i];
   else
   {
     ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
     ei_assert(m_mode==IsSparse);
     if ((m_llSize==0) || (i<llElements[m_llStart].index))
     {
       return m_zero;
     }
     else
     {
       Index elid = m_llStart;
       while (elid >= 0 && llElements[elid].index<i)
         elid = llElements[elid].next;

       if (llElements[elid].index==i)
         return llElements[m_llCurrent].value;
       else
         return m_zero;
     }
   }
 }

 /** Iterator over the nonzero coefficients */
 template<typename _Scalar,typename _Index>
 class AmbiVector<_Scalar,_Index>::Iterator
 {
   public:
     typedef _Scalar Scalar;
     typedef typename NumTraits<Scalar>::Real RealScalar;

     /** Default constructor
       * \param vec the vector on which we iterate
       * \param epsilon the minimal value used to prune zero coefficients.
       * In practice, all coefficients having a magnitude smaller than \a epsilon
       * are skipped.
       */
     Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*NumTraits<RealScalar>::dummy_precision())
       : m_vector(vec)
     {
       m_epsilon = epsilon;
       m_isDense = m_vector.m_mode==IsDense;
       if (m_isDense)
       {
         m_currentEl = 0;   // this is to avoid a compilation warning
         m_cachedValue = 0; // this is to avoid a compilation warning
         m_cachedIndex = m_vector.m_start-1;
         ++(*this);
       }
       else
       {
         ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
         m_currentEl = m_vector.m_llStart;
         while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon)
           m_currentEl = llElements[m_currentEl].next;
         if (m_currentEl<0)
         {
           m_cachedValue = 0; // this is to avoid a compilation warning
           m_cachedIndex = -1;
         }
         else
         {
           m_cachedIndex = llElements[m_currentEl].index;
           m_cachedValue = llElements[m_currentEl].value;
         }
       }
     }

     Index index() const { return m_cachedIndex; }
     Scalar value() const { return m_cachedValue; }

     operator bool() const { return m_cachedIndex>=0; }

     Iterator& operator++()
     {
       if (m_isDense)
       {
         do {
           ++m_cachedIndex;
         } while (m_cachedIndex<m_vector.m_end && ei_abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
         if (m_cachedIndex<m_vector.m_end)
           m_cachedValue = m_vector.m_buffer[m_cachedIndex];
         else
           m_cachedIndex=-1;
       }
       else
       {
         ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
         do {
           m_currentEl = llElements[m_currentEl].next;
         } while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon);
         if (m_currentEl<0)
         {
           m_cachedIndex = -1;
         }
         else
         {
           m_cachedIndex = llElements[m_currentEl].index;
           m_cachedValue = llElements[m_currentEl].value;
         }
       }
       return *this;
     }

   protected:
     const AmbiVector& m_vector; // the target vector
     Index m_currentEl;            // the current element in sparse/linked-list mode
     RealScalar m_epsilon;       // epsilon used to prune zero coefficients
     Index m_cachedIndex;          // current coordinate
     Scalar m_cachedValue;       // current value
     bool m_isDense;             // mode of the vector
 };


 #endif // EIGEN_AMBIVECTOR_H
	// This file is part of Eigen, a lightweight C++ template library
	// for linear algebra.
	//
	// Copyright (C) 2008 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_AMBIVECTOR_H
	#define EIGEN_AMBIVECTOR_H

	/** \internal
	* Hybrid sparse/dense vector class designed for intensive read-write operations.
	*
	* See BasicSparseLLT and SparseProduct for usage examples.
	*/
	template<typename _Scalar, typename _Index>
	class AmbiVector
	{
	public:
	typedef _Scalar Scalar;
	typedef _Index Index;
	typedef typename NumTraits<Scalar>::Real RealScalar;

	AmbiVector(Index size)
	: m_buffer(0), m_zero(0), m_size(0), m_allocatedSize(0), m_allocatedElements(0), m_mode(-1)
	{
	resize(size);
	}

	void init(double estimatedDensity);
	void init(int mode);

	Index nonZeros() const;

	/** Specifies a sub-vector to work on */
	void setBounds(Index start, Index end) { m_start = start; m_end = end; }

	void setZero();

	void restart();
	Scalar& coeffRef(Index i);
	Scalar& coeff(Index i);

	class Iterator;

	~AmbiVector() { delete[] m_buffer; }

	void resize(Index size)
	{
	if (m_allocatedSize < size)
	reallocate(size);
	m_size = size;
	}

	Index size() const { return m_size; }

	protected:

	void reallocate(Index size)
	{
	// if the size of the matrix is not too large, let's allocate a bit more than needed such
	// that we can handle dense vector even in sparse mode.
	delete[] m_buffer;
	if (size<1000)
	{
	Index allocSize = (size * sizeof(ListEl))/sizeof(Scalar);
	m_allocatedElements = (allocSize*sizeof(Scalar))/sizeof(ListEl);
	m_buffer = new Scalar[allocSize];
	}
	else
	{
	m_allocatedElements = (size*sizeof(Scalar))/sizeof(ListEl);
	m_buffer = new Scalar[size];
	}
	m_size = size;
	m_start = 0;
	m_end = m_size;
	}

	void reallocateSparse()
	{
	Index copyElements = m_allocatedElements;
	m_allocatedElements = std::min(Index(m_allocatedElements*1.5),m_size);
	Index allocSize = m_allocatedElements * sizeof(ListEl);
	allocSize = allocSize/sizeof(Scalar) + (allocSize%sizeof(Scalar)>0?1:0);
	Scalar* newBuffer = new Scalar[allocSize];
	memcpy(newBuffer, m_buffer, copyElements * sizeof(ListEl));
	delete[] m_buffer;
	m_buffer = newBuffer;
	}

	protected:
	// element type of the linked list
	struct ListEl
	{
	Index next;
	Index index;
	Scalar value;
	};

	// used to store data in both mode
	Scalar* m_buffer;
	Scalar m_zero;
	Index m_size;
	Index m_start;
	Index m_end;
	Index m_allocatedSize;
	Index m_allocatedElements;
	Index m_mode;

	// linked list mode
	Index m_llStart;
	Index m_llCurrent;
	Index m_llSize;
	};

	/** \returns the number of non zeros in the current sub vector */
	template<typename _Scalar,typename _Index>
	_Index AmbiVector<_Scalar,_Index>::nonZeros() const
	{
	if (m_mode==IsSparse)
	return m_llSize;
	else
	return m_end - m_start;
	}

	template<typename _Scalar,typename _Index>
	void AmbiVector<_Scalar,_Index>::init(double estimatedDensity)
	{
	if (estimatedDensity>0.1)
	init(IsDense);
	else
	init(IsSparse);
	}

	template<typename _Scalar,typename _Index>
	void AmbiVector<_Scalar,_Index>::init(int mode)
	{
	m_mode = mode;
	if (m_mode==IsSparse)
	{
	m_llSize = 0;
	m_llStart = -1;
	}
	}

	/** Must be called whenever we might perform a write access
	* with an index smaller than the previous one.
	*
	* Don't worry, this function is extremely cheap.
	*/
	template<typename _Scalar,typename _Index>
	void AmbiVector<_Scalar,_Index>::restart()
	{
	m_llCurrent = m_llStart;
	}

	/** Set all coefficients of current subvector to zero */
	template<typename _Scalar,typename _Index>
	void AmbiVector<_Scalar,_Index>::setZero()
	{
	if (m_mode==IsDense)
	{
	for (Index i=m_start; i<m_end; ++i)
	m_buffer[i] = Scalar(0);
	}
	else
	{
	ei_assert(m_mode==IsSparse);
	m_llSize = 0;
	m_llStart = -1;
	}
	}

	template<typename _Scalar,typename _Index>
	_Scalar& AmbiVector<_Scalar,_Index>::coeffRef(_Index i)
	{
	if (m_mode==IsDense)
	return m_buffer[i];
	else
	{
	ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
	// TODO factorize the following code to reduce code generation
	ei_assert(m_mode==IsSparse);
	if (m_llSize==0)
	{
	// this is the first element
	m_llStart = 0;
	m_llCurrent = 0;
	++m_llSize;
	llElements[0].value = Scalar(0);
	llElements[0].index = i;
	llElements[0].next = -1;
	return llElements[0].value;
	}
	else if (i<llElements[m_llStart].index)
	{
	// this is going to be the new first element of the list
	ListEl& el = llElements[m_llSize];
	el.value = Scalar(0);
	el.index = i;
	el.next = m_llStart;
	m_llStart = m_llSize;
	++m_llSize;
	m_llCurrent = m_llStart;
	return el.value;
	}
	else
	{
	Index nextel = llElements[m_llCurrent].next;
	ei_assert(i>=llElements[m_llCurrent].index && "you must call restart() before inserting an element with lower or equal index");
	while (nextel >= 0 && llElements[nextel].index<=i)
	{
	m_llCurrent = nextel;
	nextel = llElements[nextel].next;
	}

	if (llElements[m_llCurrent].index==i)
	{
	// the coefficient already exists and we found it !
	return llElements[m_llCurrent].value;
	}
	else
	{
	if (m_llSize>=m_allocatedElements)
	{
	reallocateSparse();
	llElements = reinterpret_cast<ListEl*>(m_buffer);
	}
	ei_internal_assert(m_llSize<m_allocatedElements && "internal error: overflow in sparse mode");
	// let's insert a new coefficient
	ListEl& el = llElements[m_llSize];
	el.value = Scalar(0);
	el.index = i;
	el.next = llElements[m_llCurrent].next;
	llElements[m_llCurrent].next = m_llSize;
	++m_llSize;
	return el.value;
	}
	}
	}
	}

	template<typename _Scalar,typename _Index>
	_Scalar& AmbiVector<_Scalar,_Index>::coeff(_Index i)
	{
	if (m_mode==IsDense)
	return m_buffer[i];
	else
	{
	ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_buffer);
	ei_assert(m_mode==IsSparse);
	if ((m_llSize==0) \|\| (i<llElements[m_llStart].index))
	{
	return m_zero;
	}
	else
	{
	Index elid = m_llStart;
	while (elid >= 0 && llElements[elid].index<i)
	elid = llElements[elid].next;

	if (llElements[elid].index==i)
	return llElements[m_llCurrent].value;
	else
	return m_zero;
	}
	}
	}

	/** Iterator over the nonzero coefficients */
	template<typename _Scalar,typename _Index>
	class AmbiVector<_Scalar,_Index>::Iterator
	{
	public:
	typedef _Scalar Scalar;
	typedef typename NumTraits<Scalar>::Real RealScalar;

	/** Default constructor
	* \param vec the vector on which we iterate
	* \param epsilon the minimal value used to prune zero coefficients.
	* In practice, all coefficients having a magnitude smaller than \a epsilon
	* are skipped.
	*/
	Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*NumTraits<RealScalar>::dummy_precision())
	: m_vector(vec)
	{
	m_epsilon = epsilon;
	m_isDense = m_vector.m_mode==IsDense;
	if (m_isDense)
	{
	m_currentEl = 0; // this is to avoid a compilation warning
	m_cachedValue = 0; // this is to avoid a compilation warning
	m_cachedIndex = m_vector.m_start-1;
	++(*this);
	}
	else
	{
	ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
	m_currentEl = m_vector.m_llStart;
	while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon)
	m_currentEl = llElements[m_currentEl].next;
	if (m_currentEl<0)
	{
	m_cachedValue = 0; // this is to avoid a compilation warning
	m_cachedIndex = -1;
	}
	else
	{
	m_cachedIndex = llElements[m_currentEl].index;
	m_cachedValue = llElements[m_currentEl].value;
	}
	}
	}

	Index index() const { return m_cachedIndex; }
	Scalar value() const { return m_cachedValue; }

	operator bool() const { return m_cachedIndex>=0; }

	Iterator& operator++()
	{
	if (m_isDense)
	{
	do {
	++m_cachedIndex;
	} while (m_cachedIndex<m_vector.m_end && ei_abs(m_vector.m_buffer[m_cachedIndex])<m_epsilon);
	if (m_cachedIndex<m_vector.m_end)
	m_cachedValue = m_vector.m_buffer[m_cachedIndex];
	else
	m_cachedIndex=-1;
	}
	else
	{
	ListEl* EIGEN_RESTRICT llElements = reinterpret_cast<ListEl*>(m_vector.m_buffer);
	do {
	m_currentEl = llElements[m_currentEl].next;
	} while (m_currentEl>=0 && ei_abs(llElements[m_currentEl].value)<m_epsilon);
	if (m_currentEl<0)
	{
	m_cachedIndex = -1;
	}
	else
	{
	m_cachedIndex = llElements[m_currentEl].index;
	m_cachedValue = llElements[m_currentEl].value;
	}
	}
	return *this;
	}

	protected:
	const AmbiVector& m_vector; // the target vector
	Index m_currentEl; // the current element in sparse/linked-list mode
	RealScalar m_epsilon; // epsilon used to prune zero coefficients
	Index m_cachedIndex; // current coordinate
	Scalar m_cachedValue; // current value
	bool m_isDense; // mode of the vector
	};


	#endif // EIGEN_AMBIVECTOR_H