/usr/include/ITK-4.9/itkExhaustiveOptimizerv4.h is in libinsighttoolkit4-dev 4.9.0-4ubuntu1.
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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.
*
*=========================================================================*/
#ifndef itkExhaustiveOptimizerv4_h
#define itkExhaustiveOptimizerv4_h
#include "itkIntTypes.h"
#include "itkObjectToObjectOptimizerBase.h"
namespace itk
{
/** \class ExhaustiveOptimizerv4
* \brief Optimizer that fully samples a grid on the parametric space.
*
* This optimizer is equivalent to an exahaustive search in a discrete grid
* defined over the parametric space. The grid is centered on the initial
* position. The subdivisions of the grid along each one of the dimensions
* of the parametric space is defined by an array of number of steps.
*
* A typical use is to plot the metric space to get an idea of how noisy it
* is. An example is given below, where it is desired to plot the metric
* space with respect to translations along x, y and z in a 3D registration
* application:
* Here it is assumed that the transform is Euler3DTransform.
*
* \code
* OptimizerType::StepsType steps( m_Transform->GetNumberOfParameters() );
* steps[0] = 10;
* steps[1] = 10;
* steps[2] = 10;
* m_Optimizer->SetNumberOfSteps( steps );
* m_Optimizer->SetStepLength( 2 );
* \endcode
*
* The optimizer throws IterationEvents after every iteration. We use this to plot
* the metric space in an image as follows:
*
* \code
* if( itk::IterationEvent().CheckEvent(& event ) )
* {
* IndexType index;
* index[0] = m_Optimizer->GetCurrentIndex()[0];
* index[1] = m_Optimizer->GetCurrentIndex()[1];
* index[2] = m_Optimizer->GetCurrentIndex()[2];
* image->SetPixel( index, m_Optimizer->GetCurrentValue() );
* }
* \endcode
*
* The image size is expected to be 11 x 11 x 11.
*
* If you wish to use different step lengths along each parametric axis,
* you can use the SetScales() method. This accepts an array, each element
* represents the number of subdivisions per step length. For instance scales
* of [0.5 1 4] along with a step length of 2 will cause the optimizer
* to search the metric space on a grid with x,y,z spacing of [1 2 8].
*
* Physical dimensions of the grid are influenced by both the scales and
* the number of steps along each dimension, a side of the region is
* stepLength*(2*numberOfSteps[d]+1)*scaling[d].
*
* \ingroup ITKOptimizersv4
*/
template<typename TInternalComputationValueType>
class ExhaustiveOptimizerv4:
public ObjectToObjectOptimizerBaseTemplate<TInternalComputationValueType>
{
public:
/** Standard "Self" typedef. */
typedef ExhaustiveOptimizerv4 Self;
typedef ObjectToObjectOptimizerBaseTemplate<TInternalComputationValueType> Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** Run-time type information (and related methods). */
itkTypeMacro(ExhaustiveOptimizerv4, Superclass);
/** Steps type */
typedef Array< SizeValueType > StepsType;
/** Measure type */
typedef typename Superclass::MeasureType MeasureType;
/** Parameters type */
typedef typename Superclass::ParametersType ParametersType;
/** Scales type */
typedef typename Superclass::ScalesType ScalesType;
virtual void StartOptimization(bool doOnlyInitialization = false) ITK_OVERRIDE;
/** Start optimization */
void StartWalking();
/** Resume the optimization */
void ResumeWalking();
/** Stop optimization */
void StopWalking();
itkSetMacro(StepLength, double);
itkSetMacro(NumberOfSteps, StepsType);
itkGetConstReferenceMacro(StepLength, double);
itkGetConstReferenceMacro(NumberOfSteps, StepsType);
itkGetConstReferenceMacro(CurrentValue, MeasureType);
itkGetConstReferenceMacro(MaximumMetricValue, MeasureType);
itkGetConstReferenceMacro(MinimumMetricValue, MeasureType);
itkGetConstReferenceMacro(MinimumMetricValuePosition, ParametersType);
itkGetConstReferenceMacro(MaximumMetricValuePosition, ParametersType);
itkGetConstReferenceMacro(CurrentIndex, ParametersType);
/** Get the reason for termination */
virtual const std::string GetStopConditionDescription() const ITK_OVERRIDE;
/** Set the position to initialize the optimization. */
void SetInitialPosition(const ParametersType & param);
/** Get the position to initialize the optimization. */
ParametersType & GetInitialPosition(void)
{
return m_InitialPosition;
}
protected:
ExhaustiveOptimizerv4();
virtual ~ExhaustiveOptimizerv4() {}
virtual void PrintSelf(std::ostream & os, Indent indent) const ITK_OVERRIDE;
/** Advance to the next grid position. */
void AdvanceOneStep();
void IncrementIndex(ParametersType & param);
protected:
ParametersType m_InitialPosition;
MeasureType m_CurrentValue;
StepsType m_NumberOfSteps;
bool m_Stop;
double m_StepLength;
ParametersType m_CurrentIndex;
MeasureType m_MaximumMetricValue;
MeasureType m_MinimumMetricValue;
ParametersType m_MinimumMetricValuePosition;
ParametersType m_MaximumMetricValuePosition;
private:
ExhaustiveOptimizerv4(const Self &) ITK_DELETE_FUNCTION;
void operator=(const Self &) ITK_DELETE_FUNCTION;
std::ostringstream m_StopConditionDescription;
};
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkExhaustiveOptimizerv4.hxx"
#endif
#endif
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