/*
 * 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 otbAutoencoderModel_h
#define otbAutoencoderModel_h

#include "otbMachineLearningModelTraits.h"
#include "otbMachineLearningModel.h"
#include <fstream>

#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic push
#pragma GCC diagnostic ignored "-Wshadow"
#pragma GCC diagnostic ignored "-Wunused-parameter"
#pragma GCC diagnostic ignored "-Woverloaded-virtual"
#pragma GCC diagnostic ignored "-Wsign-compare"
#pragma GCC diagnostic ignored "-Wunused-local-typedefs"
#if defined(__clang__)
#pragma clang diagnostic ignored "-Wheader-guard"
#pragma clang diagnostic ignored "-Wdivision-by-zero"
#pragma clang diagnostic ignored "-Wexpansion-to-defined"
#else
#pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
#endif
#endif
#include "otb_shark.h"
#include <shark/Algorithms/StoppingCriteria/AbstractStoppingCriterion.h>
#include <shark/Models/FFNet.h>
#include <shark/Models/Autoencoder.h>
#if defined(__GNUC__) || defined(__clang__)
#pragma GCC diagnostic pop
#endif

namespace otb
{
/**
 * \class AutoencoderModel
 *
 * Autoencoder model wrapper class
 *
 * \ingroup OTBDimensionalityReductionLearning
 */
template <class TInputValue, class NeuronType>
class ITK_EXPORT AutoencoderModel
  : public  MachineLearningModel<
    itk::VariableLengthVector< TInputValue>,
    itk::VariableLengthVector< TInputValue> >
{
public:
  typedef AutoencoderModel Self;
  typedef MachineLearningModel<
    itk::VariableLengthVector< TInputValue>,
    itk::VariableLengthVector< TInputValue> > Superclass;
  typedef itk::SmartPointer<Self> Pointer;
  typedef itk::SmartPointer<const Self> ConstPointer;

  typedef typename Superclass::InputValueType       InputValueType;
  typedef typename Superclass::InputSampleType      InputSampleType;
  typedef typename Superclass::InputListSampleType    InputListSampleType;
  typedef typename InputListSampleType::Pointer       ListSamplePointerType;
  typedef typename Superclass::TargetValueType      TargetValueType;
  typedef typename Superclass::TargetSampleType       TargetSampleType;
  typedef typename Superclass::TargetListSampleType     TargetListSampleType;

  /// Confidence map related typedefs
  typedef typename Superclass::ConfidenceValueType          ConfidenceValueType;
  typedef typename Superclass::ConfidenceSampleType         ConfidenceSampleType;
  typedef typename Superclass::ConfidenceListSampleType         ConfidenceListSampleType;

  /// Neural network related typedefs
  typedef shark::Autoencoder<NeuronType,shark::LinearNeuron> OutAutoencoderType;
  typedef shark::Autoencoder<NeuronType,NeuronType> AutoencoderType;
  typedef shark::FFNet<NeuronType,shark::LinearNeuron> NetworkType;

  itkNewMacro(Self);
  itkTypeMacro(AutoencoderModel, DimensionalityReductionModel);

  itkGetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);
  itkSetMacro(NumberOfHiddenNeurons,itk::Array<unsigned int>);

  itkGetMacro(NumberOfIterations,unsigned int);
  itkSetMacro(NumberOfIterations,unsigned int);

  itkGetMacro(NumberOfIterationsFineTuning,unsigned int);
  itkSetMacro(NumberOfIterationsFineTuning,unsigned int);

  itkGetMacro(Epsilon,double);
  itkSetMacro(Epsilon,double);

  itkGetMacro(InitFactor,double);
  itkSetMacro(InitFactor,double);

  itkGetMacro(Regularization,itk::Array<double>);
  itkSetMacro(Regularization,itk::Array<double>);

  itkGetMacro(Noise,itk::Array<double>);
  itkSetMacro(Noise,itk::Array<double>);

  itkGetMacro(Rho,itk::Array<double>);
  itkSetMacro(Rho,itk::Array<double>);

  itkGetMacro(Beta,itk::Array<double>);
  itkSetMacro(Beta,itk::Array<double>);

  itkGetMacro(WriteLearningCurve,bool);
  itkSetMacro(WriteLearningCurve,bool);

  itkSetMacro(WriteWeights, bool);
  itkGetMacro(WriteWeights, bool);

  itkGetMacro(LearningCurveFileName,std::string);
  itkSetMacro(LearningCurveFileName,std::string);

  bool CanReadFile(const std::string & filename) override;
  bool CanWriteFile(const std::string & filename) override;

  void Save(const std::string & filename, const std::string & name="")  override;
  void Load(const std::string & filename, const std::string & name="")  override;

  void Train() override;

  template <class T, class Autoencoder>
  void TrainOneLayer(
    shark::AbstractStoppingCriterion<T> & criterion,
    Autoencoder &,
    unsigned int,
    shark::Data<shark::RealVector> &,
    std::ostream&);

  template <class T, class Autoencoder>
  void TrainOneSparseLayer(
    shark::AbstractStoppingCriterion<T> & criterion,
    Autoencoder &,
    unsigned int,
    shark::Data<shark::RealVector> &,
    std::ostream&);

  template <class T>
  void TrainNetwork(
    shark::AbstractStoppingCriterion<T> & criterion,
    shark::Data<shark::RealVector> &,
    std::ostream&);

protected:
  AutoencoderModel();
  ~AutoencoderModel() override;

  virtual TargetSampleType DoPredict(
    const InputSampleType& input,
    ConfidenceValueType * quality = ITK_NULLPTR) const override;

  virtual void DoPredictBatch(
    const InputListSampleType *,
    const unsigned int & startIndex,
    const unsigned int & size,
    TargetListSampleType *,
    ConfidenceListSampleType * quality = ITK_NULLPTR) const override;

private:
  /** Internal Network */
  NetworkType m_Net;
  itk::Array<unsigned int> m_NumberOfHiddenNeurons;
  /** Training parameters */
  unsigned int m_NumberOfIterations; // stop the training after a fixed number of iterations
  unsigned int m_NumberOfIterationsFineTuning; // stop the fine tuning after a fixed number of iterations
  double m_Epsilon; // Stops the training when the training error seems to converge
  itk::Array<double> m_Regularization;  // L2 Regularization parameter
  itk::Array<double> m_Noise;  // probability for an input to be set to 0 (denosing autoencoder)
  itk::Array<double> m_Rho; // Sparsity parameter
  itk::Array<double> m_Beta; // Sparsity regularization parameter
  double m_InitFactor; // Weight initialization factor (the weights are intialized at m_initfactor/sqrt(inputDimension)  )

  bool m_WriteLearningCurve; // Flag for writing the learning curve into a txt file
  std::string m_LearningCurveFileName; // Name of the output learning curve printed after training
  bool m_WriteWeights;
};
} // end namespace otb

#ifndef OTB_MANUAL_INSTANTIATION
#include "otbAutoencoderModel.txx"
#endif

#endif

