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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 itkGradientDescentOptimizer_h
#define itkGradientDescentOptimizer_h
#include "itkIntTypes.h"
#include "itkSingleValuedNonLinearOptimizer.h"
#include "ITKOptimizersExport.h"
#include <string>
namespace itk
{
/** \class GradientDescentOptimizerEnums
* \brief Contains all enum classes in the GradientDescentOptimizer class.
* \ingroup ITKOptimizers
*/
class GradientDescentOptimizerEnums
{
public:
/** \class StopConditionGradientDescentOptimizer
* \ingroup ITKOptimizers
* Codes of stopping conditions */
enum class StopConditionGradientDescentOptimizer : uint8_t
{
MaximumNumberOfIterations,
MetricError
};
};
// Define how to print enumeration
extern ITKOptimizers_EXPORT std::ostream &
operator<<(std::ostream & out, const GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer value);
/** \class GradientDescentOptimizer
* \brief Implement a gradient descent optimizer
*
* GradientDescentOptimizer implements a simple gradient descent optimizer.
* At each iteration the current position is updated according to
*
* \f[
* p_{n+1} = p_n
* + \mbox{learningRate}
\, \frac{\partial f(p_n) }{\partial p_n}
* \f]
*
* The learning rate is a fixed scalar defined via SetLearningRate().
* The optimizer steps through a user defined number of iterations;
* no convergence checking is done.
*
* Additionally, user can scale each component,
* \f$ \partial f / \partial p \f$,
* by setting a scaling vector using method SetScale().
*
* \sa RegularStepGradientDescentOptimizer
*
* \ingroup Numerics Optimizers
* \ingroup ITKOptimizers
*/
class ITKOptimizers_EXPORT GradientDescentOptimizer : public SingleValuedNonLinearOptimizer
{
public:
ITK_DISALLOW_COPY_AND_MOVE(GradientDescentOptimizer);
/** Standard class type aliases. */
using Self = GradientDescentOptimizer;
using Superclass = SingleValuedNonLinearOptimizer;
using Pointer = SmartPointer<Self>;
using ConstPointer = SmartPointer<const Self>;
/** Method for creation through the object factory. */
itkNewMacro(Self);
/** \see LightObject::GetNameOfClass() */
itkOverrideGetNameOfClassMacro(GradientDescentOptimizer);
using StopConditionGradientDescentOptimizerEnum =
GradientDescentOptimizerEnums::StopConditionGradientDescentOptimizer;
#if !defined(ITK_LEGACY_REMOVE)
// We need to expose the enum values at the class level
// for backwards compatibility
static constexpr StopConditionGradientDescentOptimizerEnum MaximumNumberOfIterations =
StopConditionGradientDescentOptimizerEnum::MaximumNumberOfIterations;
static constexpr StopConditionGradientDescentOptimizerEnum MetricError =
StopConditionGradientDescentOptimizerEnum::MetricError;
#endif
/** Methods to configure the cost function. */
itkGetConstReferenceMacro(Maximize, bool);
itkSetMacro(Maximize, bool);
itkBooleanMacro(Maximize);
bool
GetMinimize() const
{
return !m_Maximize;
}
void
SetMinimize(bool v)
{
this->SetMaximize(!v);
}
void
MinimizeOn()
{
this->MaximizeOff();
}
void
MinimizeOff()
{
this->MaximizeOn();
}
/** Advance one step following the gradient direction. */
virtual void
AdvanceOneStep();
/** Start optimization. */
void
StartOptimization() override;
/** Resume previously stopped optimization with current parameters
* \sa StopOptimization. */
void
ResumeOptimization();
/** Stop optimization.
* \sa ResumeOptimization */
void
StopOptimization();
/** Set the learning rate. */
itkSetMacro(LearningRate, double);
/** Get the learning rate. */
itkGetConstReferenceMacro(LearningRate, double);
/** Set the number of iterations. */
itkSetMacro(NumberOfIterations, SizeValueType);
/** Get the number of iterations. */
itkGetConstReferenceMacro(NumberOfIterations, SizeValueType);
/** Get the current iteration number. */
itkGetConstMacro(CurrentIteration, SizeValueType);
/** Get the current value. */
itkGetConstReferenceMacro(Value, double);
/** Get Stop condition. */
itkGetConstReferenceMacro(StopCondition, StopConditionGradientDescentOptimizerEnum);
const std::string
GetStopConditionDescription() const override;
/** Get Gradient condition. */
itkGetConstReferenceMacro(Gradient, DerivativeType);
protected:
GradientDescentOptimizer();
~GradientDescentOptimizer() override = default;
void
PrintSelf(std::ostream & os, Indent indent) const override;
// made protected so subclass can access
DerivativeType m_Gradient{};
bool m_Maximize{ false };
double m_LearningRate{ 1.0 };
private:
bool m_Stop{ false };
double m_Value{ 0.0 };
StopConditionGradientDescentOptimizerEnum m_StopCondition{
StopConditionGradientDescentOptimizerEnum::MaximumNumberOfIterations
};
SizeValueType m_NumberOfIterations{ 100 };
SizeValueType m_CurrentIteration{ 0 };
std::ostringstream m_StopConditionDescription{};
};
// Define how to print enumeration
extern ITKOptimizers_EXPORT std::ostream &
operator<<(std::ostream & out, const GradientDescentOptimizer::StopConditionGradientDescentOptimizerEnum value);
} // end namespace itk
#endif
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