-
-
Notifications
You must be signed in to change notification settings - Fork 659
/
itkRegularStepGradientDescentOptimizerTest.cxx
270 lines (207 loc) · 7.75 KB
/
itkRegularStepGradientDescentOptimizerTest.cxx
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
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
/*=========================================================================
*
* 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 <set>
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkMath.h"
#include "itkTestingMacros.h"
/**
* The objectif function is the quadratic form:
*
* 1/2 x^T A x - b^T x
*
* Where A is a matrix and b is a vector
* The system in this example is:
*
* | 3 2 ||x| | 2| |0|
* | 2 6 ||y| + |-8| = |0|
*
*
* the solution is the vector | 2 -2 |
*
* \class RSGCostFunction
*/
class RSGCostFunction : public itk::SingleValuedCostFunction
{
public:
using Self = RSGCostFunction;
using Superclass = itk::SingleValuedCostFunction;
using Pointer = itk::SmartPointer<Self>;
using ConstPointer = itk::SmartPointer<const Self>;
itkNewMacro(Self);
enum
{
SpaceDimension = 2
};
using ParametersType = Superclass::ParametersType;
using DerivativeType = Superclass::DerivativeType;
using MeasureType = Superclass::MeasureType;
RSGCostFunction() = default;
MeasureType
GetValue(const ParametersType & parameters) const override
{
double x = parameters[0];
double y = parameters[1];
std::cout << "GetValue( ";
std::cout << x << ' ';
std::cout << y << ") = ";
MeasureType measure = 0.5 * (3 * x * x + 4 * x * y + 6 * y * y) - 2 * x + 8 * y;
std::cout << measure << std::endl;
return measure;
}
void
GetDerivative(const ParametersType & parameters, DerivativeType & derivative) const override
{
double x = parameters[0];
double y = parameters[1];
std::cout << "GetDerivative( ";
std::cout << x << ' ';
std::cout << y << ") = ";
derivative = DerivativeType(SpaceDimension);
derivative[0] = 3 * x + 2 * y - 2;
derivative[1] = 2 * x + 6 * y + 8;
}
unsigned int
GetNumberOfParameters() const override
{
return SpaceDimension;
}
private:
};
int
itkRegularStepGradientDescentOptimizerTest(int, char *[])
{
using OptimizerType = itk::RegularStepGradientDescentOptimizer;
using ScalesType = OptimizerType::ScalesType;
// Declaration of an itkOptimizer
auto itkOptimizer = OptimizerType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(
itkOptimizer, RegularStepGradientDescentOptimizer, RegularStepGradientDescentBaseOptimizer);
// Declaration of the CostFunction
auto costFunction = RSGCostFunction::New();
itkOptimizer->SetCostFunction(costFunction);
using ParametersType = RSGCostFunction::ParametersType;
const unsigned int spaceDimension = costFunction->GetNumberOfParameters();
// We start not so far from | 2 -2 |
ParametersType initialPosition(spaceDimension);
initialPosition[0] = 100;
initialPosition[1] = -100;
ScalesType parametersScale(spaceDimension);
parametersScale[0] = 1.0;
parametersScale[1] = 1.0;
auto minimize = true;
ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Minimize, minimize);
ITK_TEST_SET_GET_BOOLEAN(itkOptimizer, Maximize, !minimize);
itkOptimizer->SetScales(parametersScale);
ITK_TEST_SET_GET_VALUE(parametersScale, itkOptimizer->GetScales());
auto gradientMagnitudeTolerance = 1e-6;
itkOptimizer->SetGradientMagnitudeTolerance(gradientMagnitudeTolerance);
ITK_TEST_SET_GET_VALUE(gradientMagnitudeTolerance, itkOptimizer->GetGradientMagnitudeTolerance());
auto maximumStepLength = 30.0;
itkOptimizer->SetMaximumStepLength(maximumStepLength);
ITK_TEST_SET_GET_VALUE(maximumStepLength, itkOptimizer->GetMaximumStepLength());
auto minimumStepLength = 1e-6;
itkOptimizer->SetMinimumStepLength(minimumStepLength);
ITK_TEST_SET_GET_VALUE(minimumStepLength, itkOptimizer->GetMinimumStepLength());
itk::SizeValueType numberOfIterations = static_cast<itk::SizeValueType>(900);
itkOptimizer->SetNumberOfIterations(numberOfIterations);
ITK_TEST_SET_GET_VALUE(numberOfIterations, itkOptimizer->GetNumberOfIterations());
itkOptimizer->SetInitialPosition(initialPosition);
ITK_TEST_SET_GET_VALUE(initialPosition, itkOptimizer->GetInitialPosition());
ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer->StartOptimization());
ParametersType finalPosition = itkOptimizer->GetCurrentPosition();
std::cout << "Solution = (";
std::cout << finalPosition[0] << ',';
std::cout << finalPosition[1] << ')' << std::endl;
// Check results to see if it is within range
bool pass = true;
double trueParameters[2] = { 2, -2 };
for (unsigned int j = 0; j < 2; ++j)
{
if (itk::Math::abs(finalPosition[j] - trueParameters[j]) > 0.01)
{
pass = false;
}
}
if (!pass)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
// Run now with a different relaxation factor
{
itkOptimizer->SetInitialPosition(initialPosition);
auto relaxationFactor = 0.8;
itkOptimizer->SetRelaxationFactor(relaxationFactor);
ITK_TEST_SET_GET_VALUE(relaxationFactor, itkOptimizer->GetRelaxationFactor());
ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer->StartOptimization());
finalPosition = itkOptimizer->GetCurrentPosition();
std::cout << "Solution = (";
std::cout << finalPosition[0] << ',';
std::cout << finalPosition[1] << ')' << std::endl;
// Check results to see if it is within range
pass = true;
for (unsigned int j = 0; j < 2; ++j)
{
if (itk::Math::abs(finalPosition[j] - trueParameters[j]) > 0.01)
{
pass = false;
}
}
if (!pass)
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
}
// Verify that the optimizer doesn't run if the
// number of iterations is set to zero.
{
itkOptimizer->SetNumberOfIterations(0);
itkOptimizer->SetInitialPosition(initialPosition);
ITK_TRY_EXPECT_NO_EXCEPTION(itkOptimizer->StartOptimization());
if (itkOptimizer->GetCurrentIteration() > 0)
{
std::cerr << "The optimizer is running iterations despite of ";
std::cerr << "having a maximum number of iterations set to zero" << std::endl;
return EXIT_FAILURE;
}
}
//
// Test the Exception if the GradientMagnitudeTolerance is set to a negative value
//
itkOptimizer->SetGradientMagnitudeTolerance(-1.0);
ITK_TRY_EXPECT_EXCEPTION(itkOptimizer->StartOptimization());
// Test streaming enumeration for
// RegularStepGradientDescentBaseOptimizerEnums::StopCondition elements
const std::set<itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition> allStopCondition{
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::GradientMagnitudeTolerance,
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::StepTooSmall,
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::ImageNotAvailable,
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::CostFunctionError,
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::MaximumNumberOfIterations,
itk::RegularStepGradientDescentBaseOptimizerEnums::StopCondition::Unknown
};
for (const auto & ee : allStopCondition)
{
std::cout << "STREAMED ENUM VALUE "
"RegularStepGradientDescentBaseOptimizerEnums::StopCondition: "
<< ee << std::endl;
}
std::cout << "Test finished." << std::endl;
return EXIT_SUCCESS;
}