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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011-2014 Gael Guennebaud <gael.guennebaud@inria.fr>
// Copyright (C) 2010 Daniel Lowengrub <lowdanie@gmail.com>
//
// 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_SPARSEVIEW_H
#define EIGEN_SPARSEVIEW_H
namespace Eigen {
namespace internal {
template<typename MatrixType>
struct traits<SparseView<MatrixType> > : traits<MatrixType>
{
typedef typename MatrixType::StorageIndex StorageIndex;
typedef Sparse StorageKind;
enum {
Flags = int(traits<MatrixType>::Flags) & (RowMajorBit)
};
};
} // end namespace internal
/** \ingroup SparseCore_Module
* \class SparseView
*
* \brief Expression of a dense or sparse matrix with zero or too small values removed
*
* \tparam MatrixType the type of the object of which we are removing the small entries
*
* This class represents an expression of a given dense or sparse matrix with
* entries smaller than \c reference * \c epsilon are removed.
* It is the return type of MatrixBase::sparseView() and SparseMatrixBase::pruned()
* and most of the time this is the only way it is used.
*
* \sa MatrixBase::sparseView(), SparseMatrixBase::pruned()
*/
template<typename MatrixType>
class SparseView : public SparseMatrixBase<SparseView<MatrixType> >
{
typedef typename MatrixType::Nested MatrixTypeNested;
typedef typename internal::remove_all<MatrixTypeNested>::type _MatrixTypeNested;
typedef SparseMatrixBase<SparseView > Base;
public:
EIGEN_SPARSE_PUBLIC_INTERFACE(SparseView)
typedef typename internal::remove_all<MatrixType>::type NestedExpression;
explicit SparseView(const MatrixType& mat, const Scalar& reference = Scalar(0),
const RealScalar &epsilon = NumTraits<Scalar>::dummy_precision())
: m_matrix(mat), m_reference(reference), m_epsilon(epsilon) {}
inline Index rows() const { return m_matrix.rows(); }
inline Index cols() const { return m_matrix.cols(); }
inline Index innerSize() const { return m_matrix.innerSize(); }
inline Index outerSize() const { return m_matrix.outerSize(); }
/** \returns the nested expression */
const typename internal::remove_all<MatrixTypeNested>::type&
nestedExpression() const { return m_matrix; }
Scalar reference() const { return m_reference; }
RealScalar epsilon() const { return m_epsilon; }
protected:
MatrixTypeNested m_matrix;
Scalar m_reference;
RealScalar m_epsilon;
};
namespace internal {
// TODO find a way to unify the two following variants
// This is tricky because implementing an inner iterator on top of an IndexBased evaluator is
// not easy because the evaluators do not expose the sizes of the underlying expression.
template<typename ArgType>
struct unary_evaluator<SparseView<ArgType>, IteratorBased>
: public evaluator_base<SparseView<ArgType> >
{
typedef typename evaluator<ArgType>::InnerIterator EvalIterator;
public:
typedef SparseView<ArgType> XprType;
class InnerIterator : public EvalIterator
{
typedef typename XprType::Scalar Scalar;
public:
EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
: EvalIterator(sve.m_argImpl,outer), m_view(sve.m_view)
{
incrementToNonZero();
}
EIGEN_STRONG_INLINE InnerIterator& operator++()
{
EvalIterator::operator++();
incrementToNonZero();
return *this;
}
using EvalIterator::value;
protected:
const XprType &m_view;
private:
void incrementToNonZero()
{
while((bool(*this)) && internal::isMuchSmallerThan(value(), m_view.reference(), m_view.epsilon()))
{
EvalIterator::operator++();
}
}
};
enum {
CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
Flags = XprType::Flags
};
explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
protected:
evaluator<ArgType> m_argImpl;
const XprType &m_view;
};
template<typename ArgType>
struct unary_evaluator<SparseView<ArgType>, IndexBased>
: public evaluator_base<SparseView<ArgType> >
{
public:
typedef SparseView<ArgType> XprType;
protected:
enum { IsRowMajor = (XprType::Flags&RowMajorBit)==RowMajorBit };
typedef typename XprType::Scalar Scalar;
typedef typename XprType::StorageIndex StorageIndex;
public:
class InnerIterator
{
public:
EIGEN_STRONG_INLINE InnerIterator(const unary_evaluator& sve, Index outer)
: m_sve(sve), m_inner(0), m_outer(outer), m_end(sve.m_view.innerSize())
{
incrementToNonZero();
}
EIGEN_STRONG_INLINE InnerIterator& operator++()
{
m_inner++;
incrementToNonZero();
return *this;
}
EIGEN_STRONG_INLINE Scalar value() const
{
return (IsRowMajor) ? m_sve.m_argImpl.coeff(m_outer, m_inner)
: m_sve.m_argImpl.coeff(m_inner, m_outer);
}
EIGEN_STRONG_INLINE StorageIndex index() const { return m_inner; }
inline Index row() const { return IsRowMajor ? m_outer : index(); }
inline Index col() const { return IsRowMajor ? index() : m_outer; }
EIGEN_STRONG_INLINE operator bool() const { return m_inner < m_end && m_inner>=0; }
protected:
const unary_evaluator &m_sve;
Index m_inner;
const Index m_outer;
const Index m_end;
private:
void incrementToNonZero()
{
while((bool(*this)) && internal::isMuchSmallerThan(value(), m_sve.m_view.reference(), m_sve.m_view.epsilon()))
{
m_inner++;
}
}
};
enum {
CoeffReadCost = evaluator<ArgType>::CoeffReadCost,
Flags = XprType::Flags
};
explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_view(xpr) {}
protected:
evaluator<ArgType> m_argImpl;
const XprType &m_view;
};
} // end namespace internal
/** \ingroup SparseCore_Module
*
* \returns a sparse expression of the dense expression \c *this with values smaller than
* \a reference * \a epsilon removed.
*
* This method is typically used when prototyping to convert a quickly assembled dense Matrix \c D to a SparseMatrix \c S:
* \code
* MatrixXd D(n,m);
* SparseMatrix<double> S;
* S = D.sparseView(); // suppress numerical zeros (exact)
* S = D.sparseView(reference);
* S = D.sparseView(reference,epsilon);
* \endcode
* where \a reference is a meaningful non zero reference value,
* and \a epsilon is a tolerance factor defaulting to NumTraits<Scalar>::dummy_precision().
*
* \sa SparseMatrixBase::pruned(), class SparseView */
template<typename Derived>
const SparseView<Derived> MatrixBase<Derived>::sparseView(const Scalar& reference,
const typename NumTraits<Scalar>::Real& epsilon) const
{
return SparseView<Derived>(derived(), reference, epsilon);
}
/** \returns an expression of \c *this with values smaller than
* \a reference * \a epsilon removed.
*
* This method is typically used in conjunction with the product of two sparse matrices
* to automatically prune the smallest values as follows:
* \code
* C = (A*B).pruned(); // suppress numerical zeros (exact)
* C = (A*B).pruned(ref);
* C = (A*B).pruned(ref,epsilon);
* \endcode
* where \c ref is a meaningful non zero reference value.
* */
template<typename Derived>
const SparseView<Derived>
SparseMatrixBase<Derived>::pruned(const Scalar& reference,
const RealScalar& epsilon) const
{
return SparseView<Derived>(derived(), reference, epsilon);
}
} // end namespace Eigen
#endif
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