ITK
4.1.0
Insight Segmentation and Registration Toolkit
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#include <itkMultipleValuedNonLinearVnlOptimizer.h>
This class is a base for the Optimization methods that optimize a multi-valued function.
It is an Adaptor class for optimizers provided by the vnl library
Definition at line 36 of file itkMultipleValuedNonLinearVnlOptimizer.h.
typedef ReceptorMemberCommand< Self > itk::MultipleValuedNonLinearVnlOptimizer::CommandType [protected] |
Command observer that will interact with the ITKVNL cost-function adaptor in order to generate iteration events. This will allow to overcome the limitation of VNL optimizers not offering callbacks for every iteration
Definition at line 111 of file itkMultipleValuedNonLinearVnlOptimizer.h.
typedef SmartPointer< const Self > itk::MultipleValuedNonLinearVnlOptimizer::ConstPointer |
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
Definition at line 44 of file itkMultipleValuedNonLinearVnlOptimizer.h.
typedef MultipleValuedVnlCostFunctionAdaptor itk::MultipleValuedNonLinearVnlOptimizer::CostFunctionAdaptorType [protected] |
Reimplemented in itk::LevenbergMarquardtOptimizer.
Definition at line 95 of file itkMultipleValuedNonLinearVnlOptimizer.h.
ParametersType typedef. It defines a position in the optimization search space.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Definition at line 48 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
Definition at line 43 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Standard class typedefs.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
Definition at line 41 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
Definition at line 42 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Return Cached Values. These method have the advantage of not triggering a recomputation of the metric value, but it has the disadvantage of returning a value that may not be the one corresponding to the current parameters. For GUI update purposes, this method is a good option, for mathematical validation you should rather call GetValue().
virtual itk::MultipleValuedNonLinearVnlOptimizer::~MultipleValuedNonLinearVnlOptimizer | ( | ) | [protected, virtual] |
Return Cached Values. These method have the advantage of not triggering a recomputation of the metric value, but it has the disadvantage of returning a value that may not be the one corresponding to the current parameters. For GUI update purposes, this method is a good option, for mathematical validation you should rather call GetValue().
itk::MultipleValuedNonLinearVnlOptimizer::MultipleValuedNonLinearVnlOptimizer | ( | const Self & | ) | [private] |
Command observer that will interact with the ITKVNL cost-function adaptor in order to generate iteration events. This will allow to overcome the limitation of VNL optimizers not offering callbacks for every iteration
virtual const ParametersType& itk::MultipleValuedNonLinearVnlOptimizer::GetCachedCurrentPosition | ( | ) | [virtual] |
Return Cached Values. These method have the advantage of not triggering a recomputation of the metric value, but it has the disadvantage of returning a value that may not be the one corresponding to the current parameters. For GUI update purposes, this method is a good option, for mathematical validation you should rather call GetValue().
virtual const DerivativeType& itk::MultipleValuedNonLinearVnlOptimizer::GetCachedDerivative | ( | ) | [virtual] |
Return Cached Values. These method have the advantage of not triggering a recomputation of the metric value, but it has the disadvantage of returning a value that may not be the one corresponding to the current parameters. For GUI update purposes, this method is a good option, for mathematical validation you should rather call GetValue().
virtual const MeasureType& itk::MultipleValuedNonLinearVnlOptimizer::GetCachedValue | ( | ) | [virtual] |
Return Cached Values. These method have the advantage of not triggering a recomputation of the metric value, but it has the disadvantage of returning a value that may not be the one corresponding to the current parameters. For GUI update purposes, this method is a good option, for mathematical validation you should rather call GetValue().
const CostFunctionAdaptorType* itk::MultipleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor | ( | void | ) | const [protected] |
CostFunctionAdaptorType* itk::MultipleValuedNonLinearVnlOptimizer::GetCostFunctionAdaptor | ( | void | ) | [protected] |
virtual const char* itk::MultipleValuedNonLinearVnlOptimizer::GetNameOfClass | ( | ) | const [virtual] |
Run-time type information (and related methods).
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
CostFunctionAdaptorType* itk::MultipleValuedNonLinearVnlOptimizer::GetNonConstCostFunctionAdaptor | ( | void | ) | const [protected] |
The purpose of this method is to get around the lack of const correctness in vnl cost_functions and optimizers
void itk::MultipleValuedNonLinearVnlOptimizer::IterationReport | ( | const EventObject & | event | ) | [private] |
Callback function for the Command Observer
void itk::MultipleValuedNonLinearVnlOptimizer::operator= | ( | const Self & | ) | [private] |
Command observer that will interact with the ITKVNL cost-function adaptor in order to generate iteration events. This will allow to overcome the limitation of VNL optimizers not offering callbacks for every iteration
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
Reimplemented in itk::LevenbergMarquardtOptimizer.
void itk::MultipleValuedNonLinearVnlOptimizer::PrintSelf | ( | std::ostream & | os, |
Indent | indent | ||
) | const [protected, virtual] |
Methods invoked by Print() to print information about the object including superclasses. Typically not called by the user (use Print() instead) but used in the hierarchical print process to combine the output of several classes.
Reimplemented from itk::MultipleValuedNonLinearOptimizer.
virtual void itk::MultipleValuedNonLinearVnlOptimizer::SetCostFunction | ( | MultipleValuedCostFunction * | costFunction | ) | [pure virtual] |
Set the cost Function. This method has to be overloaded by derived classes because the CostFunctionAdaptor requires to know the number of parameters at construction time. This number of parameters is obtained at run-time from the itkCostFunction. As a consequence each derived optimizer should construct its own CostFunctionAdaptor when overloading this method
Implemented in itk::LevenbergMarquardtOptimizer.
void itk::MultipleValuedNonLinearVnlOptimizer::SetCostFunctionAdaptor | ( | CostFunctionAdaptorType * | adaptor | ) | [protected] |
Define if the Cost function should provide a customized Gradient computation or the gradient can be computed internally using a default approach
void itk::MultipleValuedNonLinearVnlOptimizer::UseCostFunctionGradientOff | ( | ) | [inline] |
Definition at line 72 of file itkMultipleValuedNonLinearVnlOptimizer.h.
void itk::MultipleValuedNonLinearVnlOptimizer::UseCostFunctionGradientOn | ( | ) | [inline] |
Definition at line 67 of file itkMultipleValuedNonLinearVnlOptimizer.h.
ParametersType itk::MultipleValuedNonLinearVnlOptimizer::m_CachedCurrentPosition [mutable, private] |
Definition at line 125 of file itkMultipleValuedNonLinearVnlOptimizer.h.
DerivativeType itk::MultipleValuedNonLinearVnlOptimizer::m_CachedDerivative [mutable, private] |
Definition at line 127 of file itkMultipleValuedNonLinearVnlOptimizer.h.
MeasureType itk::MultipleValuedNonLinearVnlOptimizer::m_CachedValue [mutable, private] |
Definition at line 126 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Definition at line 123 of file itkMultipleValuedNonLinearVnlOptimizer.h.
Definition at line 120 of file itkMultipleValuedNonLinearVnlOptimizer.h.
bool itk::MultipleValuedNonLinearVnlOptimizer::m_UseGradient [private] |
Definition at line 121 of file itkMultipleValuedNonLinearVnlOptimizer.h.