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
|
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkKalmanLinearEstimatorTest.cxx,v $
Language: C++
Date: $Date: 2003-09-10 14:30:03 $
Version: $Revision: 1.8 $
Copyright (c) Insight Software Consortium. All rights reserved.
See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.
This software is distributed WITHOUT ANY WARRANTY; without even
the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the above copyright notices for more information.
=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif
#include "itkKalmanLinearEstimator.h"
#include <iostream>
/**
* This program test one instantiation of the itk::KalmanLinearEstimator class
*
* The test is done by providing a Linear Equation in 6D for which the
* coefficients are known. A population of samples is generated and
* passed to the KalmanLinearEstimator.
*
*/
int itkKalmanLinearEstimatorTest(int, char* [] )
{
typedef itk::KalmanLinearEstimator<double,6> KalmanFilterType;
typedef KalmanFilterType::VectorType VectorType;
typedef KalmanFilterType::MatrixType MatrixType;
typedef KalmanFilterType::ValueType ValueType;
KalmanFilterType filter;
filter.ClearEstimation();
filter.SetVariance(1.0);
ValueType measure;
VectorType predictor;
VectorType planeEquation;
planeEquation(0) = 9.0;
planeEquation(1) = 6.0;
planeEquation(2) = 7.0;
planeEquation(3) = 9.0;
planeEquation(4) = 4.0;
planeEquation(5) = 6.0;
const unsigned int N = 10;
predictor(5) = 1.0;
for(unsigned int ax=0; ax < N; ax++)
{
predictor(0) = ax;
for(unsigned int bx=0; bx < N; bx++)
{
predictor(1) = bx;
for(unsigned int cx=0; cx < N; cx++)
{
predictor(2) = cx;
for(unsigned int dx=0; dx < N; dx++)
{
predictor(3) = dx;
for(unsigned int ex=0; ex < N; ex++)
{
predictor(4) = ex;
measure = dot_product( predictor, planeEquation );
filter.UpdateWithNewMeasure(measure,predictor);
}
}
}
}
}
VectorType estimation = filter.GetEstimator();
std::cout << std::endl << "The Right answer should be : " << std::endl;
std::cout << planeEquation;
std::cout << std::endl << "The Estimation is : " << std::endl;
std::cout << estimation;
VectorType error = estimation - planeEquation;
ValueType errorMagnitude = dot_product( error, error );
std::cout << std::endl << "Errors : " << std::endl;
std::cout << error;
std::cout << std::endl << "Error Magnitude : " << std::endl;
std::cout << errorMagnitude;
std::cout << std::endl << "Variance : " << std::endl;
std::cout << filter.GetVariance();
std::cout << std::endl << std::endl;
bool pass = true;
const float tolerance = 1e-4;
if( errorMagnitude > tolerance )
{
pass = false;
}
if( !pass )
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
|
