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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 itkSingleValuedVnlCostFunctionAdaptor_h
#define itkSingleValuedVnlCostFunctionAdaptor_h
#include "itkSingleValuedCostFunction.h"
#include "vnl/vnl_cost_function.h"
#include "ITKOptimizersExport.h"
namespace itk
{
/** \class SingleValuedVnlCostFunctionAdaptor
* \brief This class is an Adaptor that allows to pass
* itk::SingleValuedCostFunctions to vnl_optimizers expecting
* a vnl_cost_function.
*
* This class returns a single valued.
*
* \ingroup Numerics Optimizers
* \ingroup ITKOptimizers
*/
class ITKOptimizers_EXPORT SingleValuedVnlCostFunctionAdaptor : public vnl_cost_function
{
public:
/** InternalParametersType type alias. */
using InternalParametersType = vnl_vector<double>;
/** InternalMeasureType type alias. */
using InternalMeasureType = double;
/** InternalGradientType type alias. */
using InternalDerivativeType = vnl_vector<double>;
/** Parameters of the SingleValuedCostFunction */
using ParametersType = SingleValuedCostFunction::ParametersType;
/** Derivatives of the SingleValuedCostFunction */
using DerivativeType = SingleValuedCostFunction::DerivativeType;
/** Type of the SingleValuedCostFunction value */
using MeasureType = SingleValuedCostFunction::MeasureType;
/** Scales type alias */
using ScalesType = Array<double>;
/** Constructor with size */
SingleValuedVnlCostFunctionAdaptor(unsigned int spaceDimension);
/** Set the CostFunction deriving from SingleValuedCostFunction */
void
SetCostFunction(SingleValuedCostFunction * costFunction)
{
m_CostFunction = costFunction;
}
/** Get the CostFunction deriving from SingleValuedCostFunction */
const SingleValuedCostFunction *
GetCostFunction() const
{
return m_CostFunction;
}
/** Delegate computation of the value to the CostFunction. */
InternalMeasureType
f(const InternalParametersType & inparameters) override;
/** Delegate computation of the gradient to the costFunction. */
void
gradf(const InternalParametersType & inparameters, InternalDerivativeType & gradient) override;
/** Delegate computation of value and gradient to the costFunction. */
void
compute(const InternalParametersType & x, InternalMeasureType * fun, InternalDerivativeType * g) override;
/** Convert external derivative measures into internal type */
void
ConvertExternalToInternalGradient(const DerivativeType & input, InternalDerivativeType & output) const;
/** Set current parameters scaling. */
void
SetScales(const ScalesType & scales);
/** Set/Get Negate cost function. The purpose of this boolean flag is to make
* possible to take certain VNL optimizers that are only minimizers, and use
* them for maximizing functions. When the boolean flag is set to true, the
* values returned by GetValue in the internal ITK cost function will be
* multiplied by -1 before returning it in the f() function. Similar
* operations will be done for the gradf() and compute() methods. When the
* boolean flag is set to false, then the values returned by the ITK cost
* function will be passed unchanged to the VNL optimizers. */
void
SetNegateCostFunction(bool flag);
bool
GetNegateCostFunction() const;
void
NegateCostFunctionOn()
{
m_NegateCostFunction = true;
}
void
NegateCostFunctionOff()
{
m_NegateCostFunction = false;
}
/** This AddObserver method allows to simulate that this class derives from
* an itkObject for the purpose of reporting iteration events. The goal of
* this method is to allow ITK-vnl optimizer adaptors to get iteration events
* despite the fact that VNL does not provide callbacks. */
unsigned long
AddObserver(const EventObject & event, Command *) const;
/** Return the value of the last evaluation to the value of the cost function.
* Note that this method DOES NOT triggers a computation of the function or
* the derivatives, it only returns previous values. Therefore the values here
* are only valid after you invoke the f() or gradf() methods. */
const MeasureType &
GetCachedValue() const;
const DerivativeType &
GetCachedDerivative() const;
const ParametersType &
GetCachedCurrentParameters() const;
protected:
/** This method is intended to be called by the derived classes in order to
* notify of an iteration event to any Command/Observers */
void
ReportIteration(const EventObject & event) const;
private:
/** Get current parameters scaling. */
itkGetConstReferenceMacro(InverseScales, ScalesType);
SingleValuedCostFunction::Pointer m_CostFunction{};
bool m_ScalesInitialized{};
ScalesType m_InverseScales{};
bool m_NegateCostFunction{};
Object::Pointer m_Reporter{};
mutable MeasureType m_CachedValue{};
mutable DerivativeType m_CachedDerivative{};
mutable ParametersType m_CachedCurrentParameters{};
}; // end of Class CostFunction
} // end namespace itk
#endif
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