File: itkSampleClassifierFilterTest2.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 (191 lines) | stat: -rw-r--r-- 6,644 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
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
/*=========================================================================
 *
 *  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 "itkListSample.h"
#include "itkSampleClassifierFilter.h"
#include "itkMaximumDecisionRule.h"
#include "itkGaussianMembershipFunction.h"
#include "itkNormalVariateGenerator.h"


// Test if the SampleClassifier filter labels observations correctly
int
itkSampleClassifierFilterTest2(int, char *[])
{

  constexpr unsigned int numberOfComponents = 1;
  using MeasurementType = float;

  constexpr unsigned int numberOfClasses = 2;

  using MeasurementVectorType = itk::Array<MeasurementType>;
  using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;
  using FilterType = itk::Statistics::SampleClassifierFilter<SampleType>;

  auto filter = FilterType::New();

  auto sample = SampleType::New();
  sample->SetMeasurementVectorSize(numberOfComponents);

  filter->SetNumberOfClasses(numberOfClasses);

  if (filter->GetNumberOfClasses() != numberOfClasses)
  {
    std::cerr << "GetNumberOfClasses() didn't matched SetNumberOfClasses()" << std::endl;
    return EXIT_FAILURE;
  }

  using ClassLabelVectorObjectType = FilterType::ClassLabelVectorObjectType;
  using ClassLabelVectorType = FilterType::ClassLabelVectorType;
  using MembershipFunctionVectorObjectType = FilterType::MembershipFunctionVectorObjectType;
  using MembershipFunctionVectorType = FilterType::MembershipFunctionVectorType;

  using MembershipFunctionType = itk::Statistics::GaussianMembershipFunction<MeasurementVectorType>;
  using MeanVectorType = MembershipFunctionType::MeanVectorType;
  using CovarianceMatrixType = MembershipFunctionType::CovarianceMatrixType;

  using MembershipFunctionPointer = MembershipFunctionType::Pointer;

  auto classLabelsObject = ClassLabelVectorObjectType::New();
  filter->SetClassLabels(classLabelsObject);

  auto membershipFunctionsObject = MembershipFunctionVectorObjectType::New();
  filter->SetMembershipFunctions(membershipFunctionsObject);
  // Add three membership functions and rerun the filter
  MembershipFunctionVectorType & membershipFunctionsVector = membershipFunctionsObject->Get();

  MembershipFunctionPointer membershipFunction1 = MembershipFunctionType::New();
  membershipFunction1->SetMeasurementVectorSize(numberOfComponents);
  MeanVectorType mean1;
  itk::NumericTraits<MeanVectorType>::SetLength(mean1, numberOfComponents);
  mean1[0] = 10.5;

  membershipFunction1->SetMean(mean1);
  CovarianceMatrixType covariance1;
  covariance1.SetSize(numberOfComponents, numberOfComponents);
  covariance1.SetIdentity();
  covariance1[0][0] = 0.5;
  membershipFunction1->SetCovariance(covariance1);
  membershipFunctionsVector.push_back(membershipFunction1);

  MembershipFunctionPointer membershipFunction2 = MembershipFunctionType::New();
  membershipFunction1->SetMeasurementVectorSize(numberOfComponents);

  MeanVectorType mean2;
  itk::NumericTraits<MeanVectorType>::SetLength(mean2, numberOfComponents);
  mean2[0] = 200.5;
  membershipFunction2->SetMean(mean2);

  CovarianceMatrixType covariance2;
  covariance2.SetSize(numberOfComponents, numberOfComponents);
  covariance2.SetIdentity();
  covariance2[0][0] = 0.5;
  membershipFunction2->SetCovariance(covariance2);
  membershipFunctionsVector.push_back(membershipFunction2);

  // Add class labels
  ClassLabelVectorType & classLabelVector = classLabelsObject->Get();

  using ClassLabelType = FilterType::ClassLabelType;

  ClassLabelType class1 = 0;
  classLabelVector.push_back(class1);

  ClassLabelType class2 = 1;
  classLabelVector.push_back(class2);

  // Set a decision rule type
  using DecisionRuleType = itk::Statistics::MaximumDecisionRule;

  auto decisionRule = DecisionRuleType::New();
  filter->SetDecisionRule(decisionRule);

  // Generate samples from a Gaussian distribution with mean and
  // covariance parameter as the first membership function.
  // All the samples should be labeled by the classifier as
  // the first class

  using NormalGeneratorType = itk::Statistics::NormalVariateGenerator;
  auto normalGenerator = NormalGeneratorType::New();

  normalGenerator->Initialize(101);

  MeasurementVectorType mv;
  itk::NumericTraits<MeasurementVectorType>::SetLength(mv, numberOfComponents);
  double       mean = mean1[0];
  double       standardDeviation = std::sqrt(covariance1[0][0]);
  unsigned int numberOfSampleEachClass = 10;
  for (unsigned int i = 0; i < numberOfSampleEachClass; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    sample->PushBack(mv);
  }

  // Add samples for the second gaussian
  mean = mean2[0];
  standardDeviation = std::sqrt(covariance1[0][0]);
  for (unsigned int i = 0; i < numberOfSampleEachClass; ++i)
  {
    mv[0] = (normalGenerator->GetVariate() * standardDeviation) + mean;
    sample->PushBack(mv);
  }

  filter->SetInput(sample);

  try
  {
    filter->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }

  // Check if the measurement vectors are correctly labelled.
  const FilterType::MembershipSampleType *        membershipSample = filter->GetOutput();
  FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();

  unsigned int sampleCounter = 0;
  while (iter != membershipSample->End())
  {
    if (sampleCounter < numberOfSampleEachClass)
    {
      if (iter.GetClassLabel() != class1)
      {
        std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetClassLabel() << "\tclass1=" << class1
                  << std::endl;
        return EXIT_FAILURE;
      }
    }
    else
    {
      if (iter.GetClassLabel() != class2)
      {
        std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetClassLabel() << "\tclass2=" << class2
                  << std::endl;
        return EXIT_FAILURE;
      }
    }
    ++iter;
    ++sampleCounter;
  }

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