aboutsummaryrefslogtreecommitdiffhomepage
path: root/test/denseLM.cpp
blob: 0aa736ea3fbe199eefb42c2d84e4fb48125776db (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2012 Desire Nuentsa <desire.nuentsa_wakam@inria.fr>
// Copyright (C) 2012 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/.

#include <iostream>
#include <fstream>
#include <iomanip>

#include "main.h"
#include <Eigen/LevenbergMarquardt>
using namespace std;
using namespace Eigen;

template<typename Scalar>
struct DenseLM : DenseFunctor<Scalar>
{
  typedef DenseFunctor<Scalar> Base;
  typedef typename Base::JacobianType JacobianType;
  typedef Matrix<Scalar,Dynamic,1> VectorType;
  
  DenseLM(int n, int m) : DenseFunctor<Scalar>(n,m) 
  { }
 
  VectorType model(const VectorType& uv, VectorType& x)
  {
    VectorType y; // Should change to use expression template
    int m = Base::values(); 
    int n = Base::inputs();
    eigen_assert(uv.size()%2 == 0);
    eigen_assert(uv.size() == n);
    eigen_assert(x.size() == m);
    y.setZero(m);
    int half = n/2;
    VectorBlock<const VectorType> u(uv, 0, half);
    VectorBlock<const VectorType> v(uv, half, half);
    for (int j = 0; j < m; j++)
    {
      for (int i = 0; i < half; i++)
        y(j) += u(i)*std::exp(-(x(j)-i)*(x(j)-i)/(v(i)*v(i)));
    }
    return y;
    
  }
  void initPoints(VectorType& uv_ref, VectorType& x)
  {
    m_x = x;
    m_y = this->model(uv_ref, x);
  }
  
  int operator()(const VectorType& uv, VectorType& fvec)
  {
    
    int m = Base::values(); 
    int n = Base::inputs();
    eigen_assert(uv.size()%2 == 0);
    eigen_assert(uv.size() == n);
    eigen_assert(fvec.size() == m);
    int half = n/2;
    VectorBlock<const VectorType> u(uv, 0, half);
    VectorBlock<const VectorType> v(uv, half, half);
    for (int j = 0; j < m; j++)
    {
      fvec(j) = m_y(j);
      for (int i = 0; i < half; i++)
      {
        fvec(j) -= u(i) *std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
      }
    }
    
    return 0;
  }
  int df(const VectorType& uv, JacobianType& fjac)
  {
    int m = Base::values(); 
    int n = Base::inputs();
    eigen_assert(n == uv.size());
    eigen_assert(fjac.rows() == m);
    eigen_assert(fjac.cols() == n);
    int half = n/2;
    VectorBlock<const VectorType> u(uv, 0, half);
    VectorBlock<const VectorType> v(uv, half, half);
    for (int j = 0; j < m; j++)
    {
      for (int i = 0; i < half; i++)
      {
        fjac.coeffRef(j,i) = -std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
        fjac.coeffRef(j,i+half) = -2.*u(i)*(m_x(j)-i)*(m_x(j)-i)/(std::pow(v(i),3)) * std::exp(-(m_x(j)-i)*(m_x(j)-i)/(v(i)*v(i)));
      }
    }
    return 0;
  }
  VectorType m_x, m_y; //Data Points
};

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;
}

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;
}

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;
}

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);
  
}

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