itkkalmanlinearestimatortest.cxx

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/*=========================================================================

  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;


}