denseLM.cpp File Reference

#include <iostream>
#include <fstream>
#include <iomanip>
#include "main.h"
#include <Eigen/LevenbergMarquardt>

Classes
struct	DenseLM< Scalar >

Functions
template<typename FunctorType , typename VectorType >
int	test_minimizeLM (FunctorType &functor, VectorType &uv)
template<typename FunctorType , typename VectorType >
int	test_lmder (FunctorType &functor, VectorType &uv)
template<typename FunctorType , typename VectorType >
int	test_minimizeSteps (FunctorType &functor, VectorType &uv)
template<typename T >
void	test_denseLM_T ()
	EIGEN_DECLARE_TEST (denseLM)

Function Documentation

EIGEN_DECLARE_TEST()

EIGEN_DECLARE_TEST

(

denseLM

)

                            {
  CALL_SUBTEST_2(test_denseLM_T<double>());
 
  // CALL_SUBTEST_2(test_sparseLM_T<std::complex<double>());
}

References CALL_SUBTEST_2.

test_denseLM_T()

template<typename T >

void test_denseLM_T

(

)

                      {
  typedef Matrix<T, Dynamic, 1> VectorType;
 
  int inputs = 10;
  int values = 1000;
  DenseLM<T> dense_gaussian(inputs, values);
  VectorType uv(inputs), uv_ref(inputs);
  VectorType x(values);
 
  // Generate the reference solution
  uv_ref << -2, 1, 4, 8, 6, 1.8, 1.2, 1.1, 1.9, 3;
 
  // Generate the reference data points
  x.setRandom();
  x = 10 * x;
  x.array() += 10;
  dense_gaussian.initPoints(uv_ref, x);
 
  // Generate the initial parameters
  VectorBlock<VectorType> u(uv, 0, inputs / 2);
  VectorBlock<VectorType> v(uv, inputs / 2, inputs / 2);
 
  // Solve the optimization problem
 
  // Solve in one go
  u.setOnes();
  v.setOnes();
  test_minimizeLM(dense_gaussian, uv);
 
  // Solve until the machine precision
  u.setOnes();
  v.setOnes();
  test_lmder(dense_gaussian, uv);
 
  // Solve step by step
  v.setOnes();
  u.setOnes();
  test_minimizeSteps(dense_gaussian, uv);
}

References DenseLM< Scalar >::initPoints(), test_lmder(), test_minimizeLM(), test_minimizeSteps(), v, and plotDoE::x.

test_lmder()

template<typename FunctorType , typename VectorType >

int test_lmder	(	FunctorType &	functor,
		VectorType &	uv
	)

                                                     {
  typedef typename VectorType::Scalar Scalar;
  LevenbergMarquardtSpace::Status info;
  LevenbergMarquardt<FunctorType> lm(functor);
  info = lm.lmder1(uv);
 
  VERIFY_IS_EQUAL(info, 1);
  // FIXME Check other parameters
  return info;
}

References info, Eigen::LevenbergMarquardt< FunctorType_ >::lmder1(), and VERIFY_IS_EQUAL.

Referenced by test_denseLM_T().

test_minimizeLM()

template<typename FunctorType , typename VectorType >

int test_minimizeLM	(	FunctorType &	functor,
		VectorType &	uv
	)

                                                          {
  LevenbergMarquardt<FunctorType> lm(functor);
  LevenbergMarquardtSpace::Status info;
 
  info = lm.minimize(uv);
 
  VERIFY_IS_EQUAL(info, 1);
  // FIXME Check other parameters
  return info;
}

References info, Eigen::LevenbergMarquardt< FunctorType_ >::minimize(), and VERIFY_IS_EQUAL.

Referenced by test_denseLM_T().

test_minimizeSteps()

template<typename FunctorType , typename VectorType >

int test_minimizeSteps	(	FunctorType &	functor,
		VectorType &	uv
	)

                                                             {
  LevenbergMarquardtSpace::Status info;
  LevenbergMarquardt<FunctorType> lm(functor);
  info = lm.minimizeInit(uv);
  if (info == LevenbergMarquardtSpace::ImproperInputParameters) return info;
  do {
    info = lm.minimizeOneStep(uv);
  } while (info == LevenbergMarquardtSpace::Running);
 
  VERIFY_IS_EQUAL(info, 1);
  // FIXME Check other parameters
  return info;
}

References Eigen::LevenbergMarquardtSpace::ImproperInputParameters, info, Eigen::LevenbergMarquardt< FunctorType_ >::minimizeInit(), Eigen::LevenbergMarquardt< FunctorType_ >::minimizeOneStep(), Eigen::LevenbergMarquardtSpace::Running, and VERIFY_IS_EQUAL.

Referenced by test_denseLM_T().