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add lpNorm<p>() method to MatrixBase, implemented in Array module, with

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specializations for cases p=1,2,Eigen::Infinity.
This commit is contained in:
Benoit Jacob 2008-11-03 22:47:00 +00:00
parent a0ec0fca5a
commit e80099932a
5 changed files with 102 additions and 0 deletions
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1
Eigen/Array
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@ -28,6 +28,7 @@ namespace Eigen {
#include "src/Array/Select.h"
#include "src/Array/PartialRedux.h"
#include "src/Array/Random.h"
#include "src/Array/Norms.h"
} // namespace Eigen

80
Eigen/src/Array/Norms.h Normal file
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@ -0,0 +1,80 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra. Eigen itself is part of the KDE project.
//
// Copyright (C) 2008 Benoit Jacob <jacob@math.jussieu.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_ARRAY_NORMS_H
#define EIGEN_ARRAY_NORMS_H
template<typename Derived, int p>
struct ei_lpNorm_selector
{
typedef typename NumTraits<typename ei_traits<Derived>::Scalar>::Real RealScalar;
inline static RealScalar run(const MatrixBase<Derived>& m)
{
return ei_pow(m.cwise().abs().cwise().pow(p).sum(), RealScalar(1)/p);
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, 1>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwise().abs().sum();
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, 2>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.norm();
}
};
template<typename Derived>
struct ei_lpNorm_selector<Derived, Infinity>
{
inline static typename NumTraits<typename ei_traits<Derived>::Scalar>::Real run(const MatrixBase<Derived>& m)
{
return m.cwise().abs().maxCoeff();
}
};
/** \array_module
*
* \returns the \f$ \ell^p \f$ norm of *this, that is, returns the p-th root of the sum of the p-th powers of the absolute values
* of the coefficients of *this. If \a p is the special value \a Eigen::Infinity, this function returns the \f$ \ell^p\infty \f$
* norm, that is the maximum of the absolute values of the coefficients of *this.
*
* \sa norm()
*/
template<typename Derived>
template<int p>
inline typename NumTraits<typename ei_traits<Derived>::Scalar>::Real MatrixBase<Derived>::lpNorm() const
{
return ei_lpNorm_selector<Derived, p>::run(*this);
}
#endif // EIGEN_ARRAY_NORMS_H

2
Eigen/src/Core/MatrixBase.h
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@ -557,6 +557,8 @@ template<typename Derived> class MatrixBase
inline const Select<Derived, NestByValue<typename ElseDerived::ConstantReturnType>, ElseDerived >
select(typename ElseDerived::Scalar thenScalar, const MatrixBase<ElseDerived>& elseMatrix) const;
template<int p> RealScalar lpNorm() const;
/////////// LU module ///////////
const LU<EvalType> lu() const;

1
Eigen/src/Core/util/Constants.h
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@ -27,6 +27,7 @@
#define EIGEN_CONSTANTS_H
const int Dynamic = 10000;
const int Infinity = -1;
/** \defgroup flags flags
* \ingroup Core_Module

18
test/array.cpp
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@ -113,6 +113,16 @@ template<typename MatrixType> void comparisons(const MatrixType& m)
VERIFY_IS_APPROX( (m1.cwise().abs().cwise()<mid).select(0,m1), m3);
}
template<typename VectorType> void lpNorm(const VectorType& v)
{
VectorType u = VectorType::Random(v.size());
VERIFY_IS_APPROX(u.template lpNorm<Infinity>(), u.cwise().abs().maxCoeff());
VERIFY_IS_APPROX(u.template lpNorm<1>(), u.cwise().abs().sum());
VERIFY_IS_APPROX(u.template lpNorm<2>(), ei_sqrt(u.cwise().abs().cwise().square().sum()));
VERIFY_IS_APPROX(ei_pow(u.template lpNorm<5>(), typename VectorType::RealScalar(5)), u.cwise().abs().cwise().pow(5).sum());
}
void test_array()
{
for(int i = 0; i < g_repeat; i++) {
@ -130,4 +140,12 @@ void test_array()
CALL_SUBTEST( comparisons(MatrixXf(8, 12)) );
CALL_SUBTEST( comparisons(MatrixXi(8, 12)) );
}
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST( lpNorm(Matrix<float, 1, 1>()) );
CALL_SUBTEST( lpNorm(Vector2f()) );
CALL_SUBTEST( lpNorm(Vector3d()) );
CALL_SUBTEST( lpNorm(Vector4f()) );
CALL_SUBTEST( lpNorm(VectorXf(16)) );
CALL_SUBTEST( lpNorm(VectorXcd(10)) );
}
}