/usr/include/ITK-4.5/itkMultiGradientOptimizerv4.h is in libinsighttoolkit4-dev 4.5.0-3.
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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 __itkMultiGradientOptimizerv4_h
#define __itkMultiGradientOptimizerv4_h
#include "itkObjectToObjectOptimizerBase.h"
#include "itkGradientDescentOptimizerv4.h"
namespace itk
{
/** \class MultiGradientOptimizerv4Template
* \brief Multiple gradient-based optimizers are combined in order to perform a multi-objective optimization.
*
* This optimizer will do a combined gradient descent optimization using whatever metric/optimizer gradient
* sub-optimizers are passed to it by the user. The learning rate or scaleestimator for each sub-optimizer
* controls the relative weight of each metric in the optimization. Denote the weights as \f$ w_1 \f$ and \f$ w_2 \f$ then
* the MultiGradientOptimizer will optimize \f$ \sum_i w_i Metric_i \f$ by using update rule:
*
* \f[
* params_{new} = params_{old} + \frac{1}{N_{Metrics}} * ( \sum_i w_i Grad(Metric_i) )
* \f]
*
* \note The scales, learning rates and weights options must be set individually for each sub-optimizer,
* and have no effect when set on this class.
*
* The test for this class illustrates the expected behavior.
*
* \ingroup ITKOptimizersv4
*/
template<typename TInternalComputationValueType>
class MultiGradientOptimizerv4Template
: public GradientDescentOptimizerv4Template<TInternalComputationValueType>
{
public:
/** Standard class typedefs. */
typedef MultiGradientOptimizerv4Template Self;
typedef GradientDescentOptimizerv4Template<TInternalComputationValueType> Superclass;
typedef SmartPointer< Self > Pointer;
typedef SmartPointer< const Self > ConstPointer;
/** Run-time type information (and related methods). */
itkTypeMacro(MultiGradientOptimizerv4Template, Superclass);
/** Method for creation through the object factory. */
itkNewMacro(Self);
typedef itk::GradientDescentOptimizerv4Template<TInternalComputationValueType> LocalOptimizerType;
typedef typename itk::GradientDescentOptimizerv4Template<TInternalComputationValueType>::Pointer LocalOptimizerPointer;
typedef typename Superclass::ParametersType ParametersType;
typedef ObjectToObjectOptimizerBaseTemplate<TInternalComputationValueType> OptimizerType;
typedef typename OptimizerType::Pointer OptimizerPointer;
typedef std::vector< LocalOptimizerPointer > OptimizersListType;
typedef typename OptimizersListType::size_type OptimizersListSizeType;
typedef typename Superclass::StopConditionType StopConditionType;
/** Stop condition return string type */
typedef std::string StopConditionReturnStringType;
/** Stop condition internal string type */
typedef std::ostringstream StopConditionDescriptionType;
/** It should be possible to derive the internal computation type from the class object. */
typedef TInternalComputationValueType InternalComputationValueType;
/** Metric type over which this class is templated */
typedef typename Superclass::MetricType MetricType;
typedef typename MetricType::Pointer MetricTypePointer;
/** Derivative type */
typedef typename MetricType::DerivativeType DerivativeType;
/** Measure type */
typedef typename Superclass::MeasureType MeasureType;
typedef std::vector< MeasureType > MetricValuesListType;
/** Get stop condition enum */
itkGetConstReferenceMacro(StopCondition, StopConditionType);
/** Set the number of iterations. */
itkSetMacro(NumberOfIterations, SizeValueType);
/** Get the number of iterations. */
itkGetConstReferenceMacro(NumberOfIterations, SizeValueType);
/** Get the current iteration number. */
itkGetConstMacro(CurrentIteration, SizeValueType);
/** Begin the optimization */
virtual void StartOptimization( bool doOnlyInitialization = false );
/** Stop optimization. The object is left in a state so the
* optimization can be resumed by calling ResumeOptimization. */
virtual void StopOptimization(void);
/** Resume the optimization. Can be called after StopOptimization to
* resume. The bulk of the optimization work loop is here. */
virtual void ResumeOptimization();
/** Get the reason for termination */
virtual const StopConditionReturnStringType GetStopConditionDescription() const;
/** Get the list of optimizers currently held. */
OptimizersListType & GetOptimizersList();
/** Set the list of optimizers to combine */
void SetOptimizersList(OptimizersListType & p);
/** Get the list of metric values that we produced after the multi-objective search. */
const MetricValuesListType & GetMetricValuesList() const;
protected:
/** Default constructor */
MultiGradientOptimizerv4Template();
virtual ~MultiGradientOptimizerv4Template();
virtual void PrintSelf(std::ostream & os, Indent indent) const;
/* Common variables for optimization control and reporting */
bool m_Stop;
StopConditionType m_StopCondition;
StopConditionDescriptionType m_StopConditionDescription;
SizeValueType m_NumberOfIterations;
SizeValueType m_CurrentIteration;
OptimizersListType m_OptimizersList;
MetricValuesListType m_MetricValuesList;
MeasureType m_MinimumMetricValue;
MeasureType m_MaximumMetricValue;
private:
MultiGradientOptimizerv4Template( const Self & ); //purposely not implemented
void operator=( const Self& ); //purposely not implemented
};
/** This helps to meet backward compatibility */
typedef MultiGradientOptimizerv4Template<double> MultiGradientOptimizerv4;
} // end namespace itk
#ifndef ITK_MANUAL_INSTANTIATION
#include "itkMultiGradientOptimizerv4.hxx"
#endif
#endif
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