File: itkOptimizersHierarchyTest.cxx

package info (click to toggle)
insighttoolkit5 5.4.3-5
  • links: PTS, VCS
  • area: main
  • in suites: forky, sid, trixie
  • size: 704,384 kB
  • sloc: cpp: 783,592; ansic: 628,724; xml: 44,704; fortran: 34,250; python: 22,874; sh: 4,078; pascal: 2,636; lisp: 2,158; makefile: 464; yacc: 328; asm: 205; perl: 203; lex: 146; tcl: 132; javascript: 98; csh: 81
file content (158 lines) | stat: -rw-r--r-- 5,775 bytes parent folder | download
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
/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/

#include "itkAmoebaOptimizer.h"
#include "itkConjugateGradientOptimizer.h"
#include "itkCumulativeGaussianOptimizer.h"
#include "itkLBFGSOptimizer.h"
#include "itkVersorTransformOptimizer.h"
#include "itkQuaternionRigidTransformGradientDescentOptimizer.h"
#include "itkOnePlusOneEvolutionaryOptimizer.h"
#include "itkTestingMacros.h"


#include <iostream>


/**
 *
 *  This file performs only simple C++ tests of
 *  the base classes in the Optimizers hierarchy.
 *
 *  Nothing numerical is computed in these tests,
 *  only code conformance.
 */

int
itkOptimizersHierarchyTest(int, char *[])
{

  using OptimizerType = itk::Optimizer;
  auto genericOptimizer = OptimizerType::New();

  unsigned int spaceDimension = 10;

  OptimizerType::ParametersType initialPosition(spaceDimension);
  OptimizerType::ParametersType currentPosition(spaceDimension);
  OptimizerType::ScalesType     parameterScale(spaceDimension);

  parameterScale.Fill(1.5);
  initialPosition.Fill(2.0);

  genericOptimizer->SetInitialPosition(initialPosition);
  genericOptimizer->SetScales(parameterScale);

  const OptimizerType::ScalesType & parameterScaleGot = genericOptimizer->GetScales();

  const double tolerance = 1e-10;

  for (unsigned int i = 0; i < spaceDimension; ++i)
  {
    if (itk::Math::abs(parameterScaleGot[i] - parameterScale[i]) > tolerance)
    {
      std::cout << "Test failed." << std::endl;
      std::cout << "Scale parameters are damaged after being set." << std::endl;
      std::cout << "Scale was set to: " << parameterScale << std::endl;
      std::cout << "Scale was got as: " << parameterScaleGot << std::endl;
      return EXIT_FAILURE;
    }
  }

  OptimizerType::ParametersType initialPositionGot = genericOptimizer->GetInitialPosition();

  for (unsigned int i = 0; i < spaceDimension; ++i)
  {
    if (itk::Math::abs(initialPositionGot[i] - initialPosition[i]) > tolerance)
    {
      std::cout << "Test failed." << std::endl;
      std::cout << "InitialPosition parameters are damaged after being set." << std::endl;
      std::cout << "InitialPosition was set to: " << initialPosition << std::endl;
      std::cout << "InitialPosition was got as: " << initialPositionGot << std::endl;
      return EXIT_FAILURE;
    }
  }

  using NonLinearOptimizerType = itk::NonLinearOptimizer;
  auto nonLinearOptimizer = NonLinearOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(nonLinearOptimizer, NonLinearOptimizer, Optimizer);


  using SingleValuedNonLinearOptimizerType = itk::SingleValuedNonLinearOptimizer;
  auto singleValuedOptimizer = SingleValuedNonLinearOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(singleValuedOptimizer, SingleValuedNonLinearOptimizer, NonLinearOptimizer);


  using AmoebaOptimizerType = itk::AmoebaOptimizer;
  auto amoeba = AmoebaOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(amoeba, AmoebaOptimizer, SingleValuedNonLinearVnlOptimizer);


  using ConjugateGradientOptimizerType = itk::ConjugateGradientOptimizer;
  auto conjugate = ConjugateGradientOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(conjugate, ConjugateGradientOptimizer, SingleValuedNonLinearVnlOptimizer);


  using LBFGSOptimizerType = itk::LBFGSOptimizer;
  auto lbfgs = LBFGSOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(lbfgs, LBFGSOptimizer, SingleValuedNonLinearVnlOptimizer);


  // Note that a "Versor" is a Unit Quaternion
  using VersorOptimizerType = itk::VersorTransformOptimizer;
  auto versorOpt = VersorOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(versorOpt, VersorTransformOptimizer, RegularStepGradientDescentBaseOptimizer);


  using QuaternionOptimizerType = itk::QuaternionRigidTransformGradientDescentOptimizer;
  auto quaternionOpt = QuaternionOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(
    quaternionOpt, QuaternionRigidTransformGradientDescentOptimizer, GradientDescentOptimizer);

  using OnePlusOneEvolutionaryOptimizerType = itk::OnePlusOneEvolutionaryOptimizer;
  auto onePlusOne = OnePlusOneEvolutionaryOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(onePlusOne, OnePlusOneEvolutionaryOptimizer, SingleValuedNonLinearOptimizer);

  using CumulativeGaussianOptimizerType = itk::CumulativeGaussianOptimizer;
  auto cumGaussOpt = CumulativeGaussianOptimizerType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(cumGaussOpt, CumulativeGaussianOptimizer, MultipleValuedNonLinearOptimizer);


  using CumulativeGaussianCostFunctionType = itk::CumulativeGaussianCostFunction;
  auto cumGaussCostFunc = CumulativeGaussianCostFunctionType::New();

  ITK_EXERCISE_BASIC_OBJECT_METHODS(cumGaussCostFunc, CumulativeGaussianCostFunction, MultipleValuedCostFunction);


  // Not used; empty method body; called for coverage purposes
  CumulativeGaussianCostFunctionType::ParametersType parameters{};
  CumulativeGaussianCostFunctionType::DerivativeType derivative;
  cumGaussCostFunc->GetDerivative(parameters, derivative);


  std::cout << "Test finished." << std::endl;
  return EXIT_SUCCESS;
}