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| ITK optimizers are generic classes, which can be used independently of registration. This example demonstrates use of the itk::LevenbergMarquardtOptimizer class in optimizing a curve corrupted by Gaussian noise. | | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions. |
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| Contributed by: Davis Vigneault
| | [https://itk.org/ITKExamples[ITK Sphinx Examples]] |
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| ==LMOptimization.cxx==
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| <source lang="cpp">
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| // Include the Levenberg-Marquardt optimizer and a custom cost function
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| #include "itkLevenbergMarquardtOptimizer.h"
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| #include "itkExampleCostFunction.h"
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| // Typedef the optimizer and cost function, for convenience
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| typedef itk::LevenbergMarquardtOptimizer OptimizerType;
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| typedef itk::ExampleCostFunction CostType;
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| int main(int, char *[])
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| {
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| // Instantiate the cost function and optimizer
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| CostType::Pointer cost = CostType::New();
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| OptimizerType::Pointer optimizer = OptimizerType::New();
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| optimizer->SetNumberOfIterations( 100 );
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| optimizer->UseCostFunctionGradientOff();
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| optimizer->SetCostFunction( cost );
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| // This is the initial guess for the parameter values, which we set to one
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| CostType::ParametersType p(cost->GetNumberOfParameters());
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| p.Fill( 1 );
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| optimizer->SetInitialPosition(p);
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| optimizer->StartOptimization();
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| // Print out some information about the optimization
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| // The parameters come out to be near to, but not exactly [5.5, 0.5]
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| std::cout << "Position: " << optimizer->GetCurrentPosition() << std::endl;
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| std::cout << "Value: " << optimizer->GetValue() << std::endl;
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|
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| return EXIT_SUCCESS;
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| }
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| </source>
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| ==itkExampleCostFunction.h==
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| <source lang="cpp">
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| #ifndef itkExampleCostFunction_h
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| #define itkExampleCostFunction_h
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| #include "itkMultipleValuedCostFunction.h"
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| #include "itkMersenneTwisterRandomVariateGenerator.h"
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| #include <cmath>
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| #include <vector>
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| namespace itk
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| {
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| class ExampleCostFunction :
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| public MultipleValuedCostFunction
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| {
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| public:
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| /** Standard class typedefs. */
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| typedef ExampleCostFunction Self;
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| typedef MultipleValuedCostFunction Superclass;
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| typedef SmartPointer<Self> Pointer;
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| typedef SmartPointer<const Self> ConstPointer;
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| /** Method for creation through the object factory. */
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| itkNewMacro(Self);
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| /** Run-time type information (and related methods). */
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| itkTypeMacro(ExampleCostFunction, MultipleValuedCostFunction);
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| // The equation we're fitting is y=C*e^(K*x)
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| // The free parameters which we're trying to fit are C and K
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| // Therefore, there are two parameters
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| unsigned int GetNumberOfParameters() const { return 2; }
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|
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| // We will take a curve with concrete values for C and K,
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| // which has been corrupted by Gaussian noise, and sample
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| // it at 100 points on the interval [0,1]. Each of these
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| // points will produce a residual with the expected value.
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| // Therefore, there are 100 values (aka residuals).
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| unsigned int GetNumberOfValues() const { return 100; }
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| // Calculate the residual array, given a set of parameters.
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| // We take parameters[0] to be C and parameters[1] to be K.
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| // Therefore, this is a matter of calculating the value of y
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| // at each of the sampled points, given the provided guesses
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| // for C and K, and returning the difference from the data.
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| MeasureType GetValue(const ParametersType ¶meters) const
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| {
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| MeasureType residual(this->GetNumberOfValues());
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| double predictedC = parameters[0];
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| double predictedK = parameters[1];
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| for (unsigned int i = 0; i < 100; ++i)
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| {
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| double position = double(i)/100;
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| double prediction = predictedC*exp(position*predictedK);
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| residual[i] = prediction - y[i];
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| }
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| return residual;
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| }
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| // The "derivative" is the Jacobian, which takes the derivative
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| // of each residual with respect to each parameter. Since this
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| // class does not provide a derivative, any optimizer using this
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| // cost function must be told explicitly not to ask for derivative,
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| // otherwise an exception will the thrown.
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| void GetDerivative(const ParametersType ¶meters, DerivativeType & derivative ) const
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| {
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| throw ExceptionObject(__FILE__,__LINE__,"No derivative available.");
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| }
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| protected:
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| ExampleCostFunction()
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| {
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| // Create some data
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| // Take the curve y = C*e^(K*x), add noise, and sample at 100 points on [0,1]
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| // C and K are our parameters
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| // In the actual data, these parameters are 5.5 and 0.5
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| typedef itk::Statistics::MersenneTwisterRandomVariateGenerator
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| GeneratorType;
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| GeneratorType::Pointer randomEngine = GeneratorType::New();
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| double actualC = 5.5;
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| double actualK = 0.5;
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| for (unsigned int i = 0; i < 100; ++i)
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| {
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| double position = double(i)/100;
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| this->x.push_back(position);
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| this->y.push_back(actualC*exp(position*actualK) +
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| randomEngine->GetUniformVariate(0.0, 0.5));
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| }
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| };
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| ~ExampleCostFunction(){};
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| private:
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| ExampleCostFunction(const Self &); //purposely not implemented
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| void operator = (const Self &); //purposely not implemented
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| // The x and y positions of the data, created in the constructor
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| std::vector<double> x;
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| std::vector<double> y;
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| };
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| } // end namespace itk
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| #endif
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| </source>
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| {{ITKCMakeLists|{{SUBPAGENAME}}}}
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