itkSPSAOptimizer.h

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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 __itkSPSAOptimizer_h
#define __itkSPSAOptimizer_h

#include "itkSingleValuedNonLinearOptimizer.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"

namespace itk
{
class ITK_EXPORT SPSAOptimizer:
  public SingleValuedNonLinearOptimizer
{
public:

  typedef SPSAOptimizer                  Self;
  typedef SingleValuedNonLinearOptimizer Superclass;
  typedef SmartPointer< Self >           Pointer;
  typedef SmartPointer< const Self >     ConstPointer;

  itkNewMacro(Self);

  itkTypeMacro(SPSAOptimizer, SingleValuedNonLinearOptimizer);

  typedef enum {
    Unknown,
    MaximumNumberOfIterations,
    BelowTolerance,
    MetricError
    } StopConditionType;

  virtual void AdvanceOneStep(void);

  void StartOptimization(void);

  void ResumeOptimization(void);

  void StopOptimization(void);

  virtual MeasureType GetValue(void) const;

  virtual MeasureType GetValue(const ParametersType & parameters) const;

  virtual void GuessParameters(
    SizeValueType numberOfGradientEstimates,
    double initialStepSize);

  itkGetConstMacro(CurrentIteration, SizeValueType);

  itkGetConstMacro(StopCondition, StopConditionType);

  itkGetConstMacro(LearningRate, double);

  itkGetConstMacro(GradientMagnitude, double);

  itkGetConstReferenceMacro(Gradient, DerivativeType);

  itkSetMacro(Sa, double);
  itkGetConstMacro(Sa, double);
  // For backward compatibility
  void Seta(double a) { SetSa(a); }
  double Geta() { return GetSa(); }

  itkSetMacro(Sc, double);
  itkGetConstMacro(Sc, double);
  // For backward compatibility
  void Setc(double c) { SetSc(c); }
  double Getc() { return GetSc(); }

  itkSetMacro(A, double);
  itkGetConstMacro(A, double);

  itkSetMacro(Alpha, double);
  itkGetConstMacro(Alpha, double);

  itkSetMacro(Gamma, double);
  itkGetConstMacro(Gamma, double);

  itkGetConstMacro(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(); }

  itkSetMacro(NumberOfPerturbations, SizeValueType);
  itkGetConstMacro(NumberOfPerturbations, SizeValueType);

  itkGetConstMacro(StateOfConvergence, double);

  itkSetMacro(StateOfConvergenceDecayRate, double);
  itkGetConstMacro(StateOfConvergenceDecayRate, double);

  itkSetMacro(MinimumNumberOfIterations, SizeValueType);
  itkGetConstMacro(MinimumNumberOfIterations, SizeValueType);

  itkSetMacro(MaximumNumberOfIterations, SizeValueType);
  itkGetConstMacro(MaximumNumberOfIterations, SizeValueType);

  itkSetMacro(Tolerance, double);
  itkGetConstMacro(Tolerance, double);

  const std::string GetStopConditionDescription() const;

protected:

  SPSAOptimizer();
  virtual ~SPSAOptimizer() {}

  void PrintSelf(std::ostream & os, Indent indent) const;

  DerivativeType m_Gradient;

  double m_LearningRate;

  DerivativeType m_Delta;

  bool m_Stop;

  StopConditionType m_StopCondition;

  double m_StateOfConvergence;

  SizeValueType m_CurrentIteration;

  Statistics::MersenneTwisterRandomVariateGenerator::Pointer m_Generator;

  virtual double Compute_a(SizeValueType k) const;

  virtual double Compute_c(SizeValueType k) const;

  virtual void GenerateDelta(const unsigned int spaceDimension);

  virtual void ComputeGradient(
    const ParametersType & parameters,
    DerivativeType & gradient);

private:

  SPSAOptimizer(const Self &);    // purposely not implemented
  void operator=(const Self &);   // purposely not implemented

  SizeValueType m_MinimumNumberOfIterations;
  SizeValueType m_MaximumNumberOfIterations;
  double        m_StateOfConvergenceDecayRate;
  double        m_Tolerance;
  bool          m_Maximize;
  double        m_GradientMagnitude;
  SizeValueType m_NumberOfPerturbations;

  double m_Sa;
  double m_Sc;
  double m_A;
  double m_Alpha;
  double m_Gamma;
}; // end class SPSAOptimizer
} // end namespace itk

#endif // end #ifndef __itkSPSAOptimizer_h