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
|
/*=========================================================================
*
* 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 "itkMeanSampleFilter.h"
#include "itkHistogram.h"
#include "itkMahalanobisDistanceMetric.h"
int
itkMeanSampleFilterTest3(int, char *[])
{
std::cout << "CovarianceSampleFilter test \n \n";
using MeasurementType = double;
constexpr unsigned int MeasurementVectorSize = 3;
using HistogramType = itk::Statistics::Histogram<MeasurementType, itk::Statistics::DenseFrequencyContainer2>;
using SampleType = HistogramType;
auto histogram = HistogramType::New();
HistogramType::SizeType size(MeasurementVectorSize);
HistogramType::MeasurementVectorType lowerBound(MeasurementVectorSize);
HistogramType::MeasurementVectorType upperBound(MeasurementVectorSize);
size.Fill(50);
lowerBound.Fill(-350);
upperBound.Fill(450);
histogram->SetMeasurementVectorSize(MeasurementVectorSize);
histogram->Initialize(size, lowerBound, upperBound);
histogram->SetToZero();
using MembershipFunctionType = itk::Statistics::MahalanobisDistanceMetric<HistogramType::MeasurementVectorType>;
auto memberFunction = MembershipFunctionType::New();
using MeanVectorType = MembershipFunctionType::MeanVectorType;
using CovarianceMatrixType = MembershipFunctionType::CovarianceMatrixType;
MeanVectorType mean(MeasurementVectorSize);
CovarianceMatrixType covariance(MeasurementVectorSize, MeasurementVectorSize);
mean[0] = 50;
mean[1] = 52;
mean[2] = 51;
covariance.set_identity();
covariance[0][0] = 10000.0;
covariance[1][1] = 8000.0;
covariance[2][2] = 6000.0;
for (unsigned int i = 0; i < MeasurementVectorSize; ++i)
{
for (unsigned int j = i; j < MeasurementVectorSize; ++j)
{
covariance[j][i] = covariance[i][j];
}
}
std::cout << "Initial Mean = " << std::endl << mean << std::endl;
std::cout << "Initial Covariance = " << std::endl << covariance << std::endl;
memberFunction->SetMean(mean);
memberFunction->SetCovariance(covariance);
HistogramType::Iterator itr = histogram->Begin();
HistogramType::Iterator end = histogram->End();
using AbsoluteFrequencyType = HistogramType::AbsoluteFrequencyType;
while (itr != end)
{
const double MahalanobisDistance = memberFunction->Evaluate(itr.GetMeasurementVector());
const double MahalanobisDistance2 = MahalanobisDistance * MahalanobisDistance;
auto frequency = (AbsoluteFrequencyType)std::floor(1e5 * std::exp(-0.5 * MahalanobisDistance2));
itr.SetFrequency(frequency);
++itr;
}
using FilterType = itk::Statistics::MeanSampleFilter<SampleType>;
auto filter = FilterType::New();
filter->SetInput(histogram);
try
{
filter->Update();
}
catch (const itk::ExceptionObject & excp)
{
std::cerr << "Exception caught: " << excp << std::endl;
}
FilterType::MeasurementVectorRealType meanOutput = filter->GetMean();
std::cout << "Mean: " << meanOutput << std::endl;
std::cout << "GetMeasurementVectorSize = " << filter->GetMeasurementVectorSize() << std::endl;
double epsilon = 1;
for (unsigned int i = 0; i < MeasurementVectorSize; ++i)
{
if (itk::Math::abs(meanOutput[i] - mean[i]) > epsilon)
{
std::cerr << "The computed mean value is incorrect" << std::endl;
std::cerr << "computed mean = " << meanOutput << std::endl;
std::cerr << "expected mean = " << mean << std::endl;
return EXIT_FAILURE;
}
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
|
