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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* 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
*
* http://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";
typedef double MeasurementType;
const unsigned int MeasurementVectorSize = 3;
typedef itk::Statistics::Histogram< MeasurementType,
itk::Statistics::DenseFrequencyContainer2 > HistogramType;
typedef HistogramType SampleType;
HistogramType::Pointer 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();
typedef itk::Statistics::MahalanobisDistanceMetric<
HistogramType::MeasurementVectorType > MembershipFunctionType;
MembershipFunctionType::Pointer memberFunction = MembershipFunctionType::New();
typedef MembershipFunctionType::MeanVectorType MeanVectorType;
typedef MembershipFunctionType::CovarianceMatrixType 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();
typedef HistogramType::AbsoluteFrequencyType AbsoluteFrequencyType;
while( itr != end )
{
const double MahalanobisDistance =
memberFunction->Evaluate( itr.GetMeasurementVector() );
const double MahalanobisDistance2 = MahalanobisDistance * MahalanobisDistance;
AbsoluteFrequencyType frequency = (AbsoluteFrequencyType) std::floor( 1e5 * std::exp( -0.5 * MahalanobisDistance2 ) );
itr.SetFrequency( frequency );
++itr;
}
typedef itk::Statistics::MeanSampleFilter< SampleType > FilterType;
FilterType::Pointer filter = FilterType::New();
filter->SetInput( histogram );
try
{
filter->Update();
}
catch ( 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 ( std::fabs( 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;
}
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