// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008 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_AMBIVECTOR_H
#define EIGEN_AMBIVECTOR_H
namespace Eigen {
namespace internal {
/** \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
{
eigen_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
eigen_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;
eigen_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);
}
eigen_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);
eigen_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 = 0)
: 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 && internal::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 && internal::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 && internal::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
};
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_AMBIVECTOR_H