/*
 * Copyright (C) 2005-2017 Centre National d'Etudes Spatiales (CNES)
 *
 * This file is part of Orfeo Toolbox
 *
 *     https://www.orfeo-toolbox.org/
 *
 * 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
 *
 * 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 otbMRFSamplerRandomMAP_h
#define otbMRFSamplerRandomMAP_h

#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "otbMRFSampler.h"

namespace otb
{
/**
   * \class MRFSamplerRandomMAP
   * \brief This is the base class for sampler methods used in the MRF framework.
   *
   * This is one sampler to be used int he MRF framework. This sampler select the
   * value randomly according to the apriori probability.
   *
   * The probability is defined from the energy as:
   *
   *  \f[ P(X=x)= \frac{1}{Z} \exp^{-U(x)} \f]
   *
   * where \f$ Z = \sum_x \exp^{-U(x)}\f$
   *
   *
   * This class is meant to be used in the MRF framework with the otb::MarkovRandomFieldFilter
   *
   * \ingroup Markov
 *
 * \ingroup OTBMarkov
 */

template<class TInput1, class TInput2>
class ITK_EXPORT MRFSamplerRandomMAP : public MRFSampler<TInput1, TInput2>
{
public:

  typedef MRFSamplerRandomMAP               Self;
  typedef otb::MRFSampler<TInput1, TInput2> Superclass;
  typedef itk::SmartPointer<Self>           Pointer;
  typedef itk::SmartPointer<const Self>     ConstPointer;

  typedef typename Superclass::InputImageNeighborhoodIterator    InputImageNeighborhoodIterator;
  typedef typename Superclass::LabelledImageNeighborhoodIterator LabelledImageNeighborhoodIterator;
  typedef typename Superclass::LabelledImagePixelType            LabelledImagePixelType;
  typedef typename Superclass::InputImagePixelType               InputImagePixelType;
  typedef typename Superclass::EnergyFidelityType                EnergyFidelityType;
  typedef typename Superclass::EnergyRegularizationType          EnergyRegularizationType;
  typedef typename Superclass::EnergyFidelityPointer             EnergyFidelityPointer;
  typedef typename Superclass::EnergyRegularizationPointer       EnergyRegularizationPointer;
  typedef itk::Statistics::MersenneTwisterRandomVariateGenerator RandomGeneratorType;

  itkNewMacro(Self);

  itkTypeMacro(MRFSamplerRandomMAP, MRFSampler);

  void SetNumberOfClasses(const unsigned int nClasses) override
  {
    if ((nClasses != this->m_NumberOfClasses) || (m_EnergiesInvalid == true))
      {
      this->m_NumberOfClasses = nClasses;
      if (m_Energy != ITK_NULLPTR) free(m_Energy);
      if (m_RepartitionFunction != ITK_NULLPTR) free(m_RepartitionFunction);
      m_Energy = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
      m_RepartitionFunction = (double *) calloc(this->m_NumberOfClasses, sizeof(double));
      this->Modified();
      }
  }

  inline int Compute(const InputImageNeighborhoodIterator& itData, const LabelledImageNeighborhoodIterator& itRegul) override
  {
    if (this->m_NumberOfClasses == 0)
      {
      itkExceptionMacro(<< "NumberOfClasse has to be greater than 0.");
      }

    this->m_EnergyBefore = this->m_EnergyFidelity->GetValue(itData, itRegul.GetCenterPixel());
    this->m_EnergyBefore += this->m_Lambda
                            * this->m_EnergyRegularization->GetValue(itRegul, itRegul.GetCenterPixel());

    //Try all possible value (how to be generic ?)
    this->m_EnergyAfter = this->m_EnergyBefore; //default values to current one
    this->m_Value = itRegul.GetCenterPixel();

    //Compute probability for each possibility
    double       totalProba = 0.0;
    unsigned int valueCurrent = 0;
    for (valueCurrent = 0; valueCurrent < this->m_NumberOfClasses; ++valueCurrent)
      {
      this->m_EnergyCurrent = this->m_EnergyFidelity->GetValue(itData, static_cast<LabelledImagePixelType>(valueCurrent));
      this->m_EnergyCurrent += this->m_Lambda
                               * this->m_EnergyRegularization->GetValue(itRegul,
                                                                        static_cast<LabelledImagePixelType>(
                                                                          valueCurrent));

      m_Energy[valueCurrent] = this->m_EnergyCurrent;
      m_RepartitionFunction[valueCurrent] = vcl_exp(-this->m_EnergyCurrent) + totalProba;
      totalProba = m_RepartitionFunction[valueCurrent];

      }

    //Pick a value according to probability

    //double select = (m_Generator->GetIntegerVariate()/(double(RAND_MAX)+1) * totalProba);
    double select =
      (m_Generator->GetIntegerVariate() /
       (double(itk::NumericTraits<RandomGeneratorType::IntegerType>::max()) + 1) * totalProba);
    valueCurrent = 0;
    while ((valueCurrent < this->GetNumberOfClasses())
           && (m_RepartitionFunction[valueCurrent] <= select))
      {
      valueCurrent++;
      }

    if (valueCurrent == this->GetNumberOfClasses())
      {
      valueCurrent = this->GetNumberOfClasses() - 1;
      }

    if (this->m_Value != static_cast<LabelledImagePixelType>(valueCurrent))
      {
      this->m_Value = static_cast<LabelledImagePixelType>(valueCurrent);
      this->m_EnergyAfter = m_Energy[static_cast<unsigned int>(valueCurrent)];
      }

    this->m_DeltaEnergy =  this->m_EnergyAfter - this->m_EnergyBefore;

    return 0;
  }

  /** Methods to cancel random effects.*/
  void InitializeSeed(int seed)
  {
    m_Generator->SetSeed(seed);
  }
  void InitializeSeed()
  {
    m_Generator->SetSeed();
  }

protected:
  // The constructor and destructor.
  MRFSamplerRandomMAP() :
    m_RepartitionFunction(ITK_NULLPTR),
    m_Energy(ITK_NULLPTR),
    m_EnergiesInvalid(true)
    {
    m_Generator = RandomGeneratorType::GetInstance();
    m_Generator->SetSeed();
    }
  ~MRFSamplerRandomMAP() override
    {
    if (m_Energy != ITK_NULLPTR) free(m_Energy);
    if (m_RepartitionFunction != ITK_NULLPTR) free(m_RepartitionFunction);
    }

private:
  double *                     m_RepartitionFunction;
  double *                     m_Energy;
  bool                         m_EnergiesInvalid;
  RandomGeneratorType::Pointer m_Generator;
};
}

#endif
