📄 itkamoebaoptimizertest.cxx
字号:
/*=========================================================================
Program: Insight Segmentation & Registration Toolkit
Module: $RCSfile: itkAmoebaOptimizerTest.cxx,v $
Language: C++
Date: $Date: 2003/09/10 14:30:10 $
Version: $Revision: 1.14 $
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.
=========================================================================*/
#include <itkAmoebaOptimizer.h>
#include <vnl/vnl_vector_fixed.h>
#include <vnl/vnl_vector.h>
#include <vnl/vnl_matrix.h>
#include <vnl/vnl_math.h>
#include <iostream>
/**
* The objectif function is the quadratic form:
*
* 1/2 x^T A x - b^T x
*
* Where A is represented as an itkMatrix and
* b is represented as a itkVector
*
* The system in this example is:
*
* | 3 2 ||x| | 2| |0|
* | 2 6 ||y| + |-8| = |0|
*
*
* the solution is the vector | 2 -2 |
*
*/
class amoebaCostFunction : public itk::SingleValuedCostFunction
{
public:
typedef amoebaCostFunction Self;
typedef itk::SingleValuedCostFunction Superclass;
typedef itk::SmartPointer<Self> Pointer;
typedef itk::SmartPointer<const Self> ConstPointer;
itkNewMacro( Self );
itkTypeMacro( amoebaCostFunction, SingleValuedCostFunction );
enum { SpaceDimension=2 };
typedef Superclass::ParametersType ParametersType;
typedef Superclass::DerivativeType DerivativeType;
typedef Superclass::MeasureType MeasureType;
typedef vnl_vector<double> VectorType;
typedef vnl_matrix<double> MatrixType;
amoebaCostFunction():m_A(SpaceDimension,SpaceDimension),m_b(SpaceDimension)
{
m_A[0][0] = 3;
m_A[0][1] = 2;
m_A[1][0] = 2;
m_A[1][1] = 6;
m_b[0] = 2;
m_b[1] = -8;
}
double GetValue( const ParametersType & parameters ) const
{
VectorType v( parameters.Size() );
for(unsigned int i=0; i<SpaceDimension; i++)
{
v[i] = parameters[i];
}
std::cout << "GetValue( " << v << " ) = ";
VectorType Av = m_A * v;
double val = ( inner_product<double>( Av , v ) )/2.0;
val -= inner_product< double >( m_b , v );
std::cout << val << std::endl;
return val;
}
void GetDerivative( const ParametersType & parameters,
DerivativeType & derivative ) const
{
VectorType v( parameters.Size() );
for(unsigned int i=0; i<SpaceDimension; i++)
{
v[i] = parameters[i];
}
std::cout << "GetDerivative( " << v << " ) = ";
VectorType gradient = m_A * v - m_b;
std::cout << gradient << std::endl;
derivative = DerivativeType(SpaceDimension);
for(unsigned int i=0; i<SpaceDimension; i++)
{
derivative[i] = gradient[i];
}
}
unsigned int GetNumberOfParameters(void) const
{
return SpaceDimension;
}
private:
MatrixType m_A;
VectorType m_b;
};
int itkAmoebaOptimizerTest(int, char* [] )
{
std::cout << "Amoeba Optimizer Test \n \n";
typedef itk::AmoebaOptimizer OptimizerType;
typedef OptimizerType::InternalOptimizerType vnlOptimizerType;
// Declaration of a itkOptimizer
OptimizerType::Pointer itkOptimizer = OptimizerType::New();
// set optimizer parameters
itkOptimizer->SetMaximumNumberOfIterations( 10 );
double xTolerance = 0.01;
itkOptimizer->SetParametersConvergenceTolerance( xTolerance );
double fTolerance = 0.001;
itkOptimizer->SetFunctionConvergenceTolerance( fTolerance );
// Declaration of the CostFunction adaptor
amoebaCostFunction::Pointer costFunction = amoebaCostFunction::New();
itkOptimizer->SetCostFunction( costFunction.GetPointer() );
vnlOptimizerType * vnlOptimizer = itkOptimizer->GetOptimizer();
OptimizerType::ParametersType initialValue(2); // constructor requires vector size
initialValue[0] = 100; // We start not far from | 2 -2 |
initialValue[1] = -100;
OptimizerType::ParametersType currentValue(2);
currentValue = initialValue;
itkOptimizer->SetInitialPosition( currentValue );
try
{
vnlOptimizer->verbose = true;
std::cout << "Run for " << itkOptimizer->GetMaximumNumberOfIterations();
std::cout << " iterations." << std::endl;
itkOptimizer->StartOptimization();
std::cout << "Continue for " << itkOptimizer->GetMaximumNumberOfIterations();
std::cout << " iterations." << std::endl;
itkOptimizer->SetMaximumNumberOfIterations( 100 );
itkOptimizer->SetInitialPosition( itkOptimizer->GetCurrentPosition() );
itkOptimizer->StartOptimization();
}
catch( itk::ExceptionObject & e )
{
std::cout << "Exception thrown ! " << std::endl;
std::cout << "An error ocurred during Optimization" << std::endl;
std::cout << "Location = " << e.GetLocation() << std::endl;
std::cout << "Description = " << e.GetDescription() << std::endl;
return EXIT_FAILURE;
}
std::cout << "Number of evals = " << vnlOptimizer->get_num_evaluations() << std::endl;
std::cout << "Optimizer: " << itkOptimizer;
//
// check results to see if it is within range
//
OptimizerType::ParametersType finalPosition;
finalPosition = itkOptimizer->GetCurrentPosition();
double trueParameters[2] = { 2, -2 };
bool pass = true;
std::cout << "Right answer = " << trueParameters[0] << " , " << trueParameters[1] << std::endl;
std::cout << "Final position = " << finalPosition << std::endl;
for( unsigned int j = 0; j < 2; j++ )
{
if( vnl_math_abs( finalPosition[j] - trueParameters[j] ) > xTolerance )
pass = false;
}
if( !pass )
{
std::cout << "Test failed." << std::endl;
return EXIT_FAILURE;
}
std::cout << "Test passed." << std::endl;
return EXIT_SUCCESS;
}
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -