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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2006-2008 Benoit Jacob <jacob@math.jussieu.fr>
// 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_FUZZY_H
#define EIGEN_FUZZY_H
template<typename Derived, typename OtherDerived=Derived, bool IsVector=Derived::IsVectorAtCompileTime>
struct ei_fuzzy_selector;
/** \returns \c true if \c *this is approximately equal to \a other, within the precision
* determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. Two vectors \f$ v \f$ and \f$ w \f$
* are considered to be approximately equal within precision \f$ p \f$ if
* \f[ \Vert v - w \Vert \leqslant p\,\min(\Vert v\Vert, \Vert w\Vert). \f]
* For matrices, the comparison is done on all columns.
*
* \note Because of the multiplicativeness of this comparison, one can't use this function
* to check whether \c *this is approximately equal to the zero matrix or vector.
* Indeed, \c isApprox(zero) returns false unless \c *this itself is exactly the zero matrix
* or vector. If you want to test whether \c *this is zero, use ei_isMuchSmallerThan(const
* RealScalar&, RealScalar) instead.
*
* \sa ei_isMuchSmallerThan(const RealScalar&, RealScalar) const
*/
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isApprox(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived,OtherDerived>::isApprox(derived(), other.derived(), prec);
}
/** \returns \c true if the norm of \c *this is much smaller than \a other,
* within the precision determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
* considered to be much smaller than \f$ x \f$ within precision \f$ p \f$ if
* \f[ \Vert v \Vert \leqslant p\,\vert x\vert. \f]
* For matrices, the comparison is done on all columns.
*
* \sa isApprox(), isMuchSmallerThan(const MatrixBase<OtherDerived>&, RealScalar) const
*/
template<typename Derived>
bool MatrixBase<Derived>::isMuchSmallerThan(
const typename NumTraits<Scalar>::Real& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived>::isMuchSmallerThan(derived(), other, prec);
}
/** \returns \c true if the norm of \c *this is much smaller than the norm of \a other,
* within the precision determined by \a prec.
*
* \note The fuzzy compares are done multiplicatively. A vector \f$ v \f$ is
* considered to be much smaller than a vector \f$ w \f$ within precision \f$ p \f$ if
* \f[ \Vert v \Vert \leqslant p\,\Vert w\Vert. \f]
* For matrices, the comparison is done on all columns.
*
* \sa isApprox(), isMuchSmallerThan(const RealScalar&, RealScalar) const
*/
template<typename Derived>
template<typename OtherDerived>
bool MatrixBase<Derived>::isMuchSmallerThan(
const MatrixBase<OtherDerived>& other,
typename NumTraits<Scalar>::Real prec
) const
{
return ei_fuzzy_selector<Derived,OtherDerived>::isMuchSmallerThan(derived(), other.derived(), prec);
}
template<typename Derived, typename OtherDerived>
struct ei_fuzzy_selector<Derived,OtherDerived,true>
{
typedef typename Derived::RealScalar RealScalar;
static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived);
ei_assert(self.size() == other.size());
return((self - other).norm2() <= std::min(self.norm2(), other.norm2()) * prec * prec);
}
static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
{
return(self.norm2() <= ei_abs2(other * prec));
}
static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_VECTOR_SIZE(Derived,OtherDerived);
ei_assert(self.size() == other.size());
return(self.norm2() <= other.norm2() * prec * prec);
}
};
template<typename Derived, typename OtherDerived>
struct ei_fuzzy_selector<Derived,OtherDerived,false>
{
typedef typename Derived::RealScalar RealScalar;
static bool isApprox(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived);
ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
typename Derived::Nested nested(self);
typename OtherDerived::Nested otherNested(other);
for(int i = 0; i < self.cols(); i++)
if((nested.col(i) - otherNested.col(i)).norm2()
> std::min(nested.col(i).norm2(), otherNested.col(i).norm2()) * prec * prec)
return false;
return true;
}
static bool isMuchSmallerThan(const Derived& self, const RealScalar& other, RealScalar prec)
{
typename Derived::Nested nested(self);
for(int i = 0; i < self.cols(); i++)
if(nested.col(i).norm2() > ei_abs2(other * prec))
return false;
return true;
}
static bool isMuchSmallerThan(const Derived& self, const OtherDerived& other, RealScalar prec)
{
EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived,OtherDerived);
ei_assert(self.rows() == other.rows() && self.cols() == other.cols());
typename Derived::Nested nested(self);
typename OtherDerived::Nested otherNested(other);
for(int i = 0; i < self.cols(); i++)
if(nested.col(i).norm2() > otherNested.col(i).norm2() * prec * prec)
return false;
return true;
}
};
#endif // EIGEN_FUZZY_H
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