/*
 * Copyright (C) 2005-2022 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 otbNeuralNetworkMachineLearningModel_h
#define otbNeuralNetworkMachineLearningModel_h

#include "otbRequiresOpenCVCheck.h"
#include "otbOpenCVUtils.h"

#include "itkLightObject.h"
#include "itkFixedArray.h"
#include "otbMachineLearningModel.h"

namespace otb
{
template <class TInputValue, class TTargetValue>
class ITK_EXPORT NeuralNetworkMachineLearningModel : public MachineLearningModel<TInputValue, TTargetValue>
{
public:
  /** Standard class typedefs. */
  typedef NeuralNetworkMachineLearningModel Self;
  typedef MachineLearningModel<TInputValue, TTargetValue> 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 Superclass::TargetValueType      TargetValueType;
  typedef typename Superclass::TargetSampleType     TargetSampleType;
  typedef typename Superclass::TargetListSampleType TargetListSampleType;
  typedef typename Superclass::ConfidenceValueType  ConfidenceValueType;
  typedef typename Superclass::ProbaSampleType      ProbaSampleType;
  typedef std::map<TargetValueType, unsigned int> MapOfLabelsType;

  /** Run-time type information (and related methods). */
  itkNewMacro(Self);
  itkTypeMacro(NeuralNetworkMachineLearningModel, MachineLearningModel);

  /** Setters/Getters to the train method
   *  2 methods are available:
   *   - CvANN_MLP_TrainParams::BACKPROP The back-propagation algorithm.
   *   - CvANN_MLP_TrainParams::RPROP The RPROP algorithm.
   *  Default is CvANN_MLP_TrainParams::RPROP.
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(TrainMethod, int);
  itkSetMacro(TrainMethod, int);

  /**
   * Set the number of neurons in each layer (including input and output layers).
   * The number of neuron in the first layer (input layer) must be equal
   * to the number of samples in the \c InputListSample
   */
  void SetLayerSizes(const std::vector<unsigned int> layers);


  /** Setters/Getters to the neuron activation function
   *  3 methods are available:
   *   - CvANN_MLP::IDENTITY
   *   - CvANN_MLP::SIGMOID_SYM
   *   - CvANN_MLP::GAUSSIAN
   *  Default is CvANN_MLP::SIGMOID_SYM
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(ActivateFunction, int);
  itkSetMacro(ActivateFunction, int);

  /** Setters/Getters to the alpha parameter of the activation function
   *  Default is 0.
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(Alpha, double);
  itkSetMacro(Alpha, double);

  /** Setters/Getters to the beta parameter of the activation function
   *  Default is 0.
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(Beta, double);
  itkSetMacro(Beta, double);

  /** Strength of the weight gradient term in the BACKPROP method.
   *  The recommended value is about 0.1
   *  Default is 0.1
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(BackPropDWScale, double);
  itkSetMacro(BackPropDWScale, double);

  /** Strength of the momentum term (the difference between weights on the 2 previous iterations).
   *  This parameter provides some inertia to smooth the random fluctuations of the weights.
   *  It can vary from 0 (the feature is disabled) to 1 and beyond.
   *  The value 0.1 or so is good enough
   *  Default is 0.1
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(BackPropMomentScale, double);
  itkSetMacro(BackPropMomentScale, double);

  /** Initial value \f$ \Delta_0 \f$ of update-values \f$ \Delta_{ij} \f$ in RPROP method.
   *  Default is 0.1
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(RegPropDW0, double);
  itkSetMacro(RegPropDW0, double);

  /** Update-values lower limit \f$ \Delta_{min} \f$ in RPROP method.
   * It must be positive. Default is FLT_EPSILON
   *  \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(RegPropDWMin, double);
  itkSetMacro(RegPropDWMin, double);

  /** Termination criteria.
   * It can be CV_TERMCRIT_ITER or CV_TERMCRIT_EPS or CV_TERMCRIT_ITER+CV_TERMCRIT_EPS
   * default is CV_TERMCRIT_ITER+CV_TERMCRIT_EPS.
   * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(TermCriteriaType, int);
  itkSetMacro(TermCriteriaType, int);

  /** Maximum number of iteration used in the Termination criteria.
   * default is 1000
   * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(MaxIter, int);
  itkSetMacro(MaxIter, int);

  /** Epsilon value used in the Termination criteria.
   * default is 0.01
   * \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
   */
  itkGetMacro(Epsilon, double);
  itkSetMacro(Epsilon, double);

  /** Train the machine learning model */
  void Train() override;

  /** Save the model to file */
  void Save(const std::string& filename, const std::string& name = "") override;

  /** Load the model from file */
  void Load(const std::string& filename, const std::string& name = "") override;

  /**\name Classification model file compatibility tests */
  //@{
  /** Is the input model file readable and compatible with the corresponding classifier ? */
  bool CanReadFile(const std::string&) override;

  /** Is the input model file writable and compatible with the corresponding classifier ? */
  bool CanWriteFile(const std::string&) override;
  //@}

protected:
  /** Constructor */
  NeuralNetworkMachineLearningModel();

  /** Destructor */
  ~NeuralNetworkMachineLearningModel() override = default;

  /** Predict values using the model */
  TargetSampleType DoPredict(const InputSampleType& input, ConfidenceValueType* quality = nullptr, ProbaSampleType* proba = nullptr) const override;

  void LabelsToMat(const TargetListSampleType* listSample, cv::Mat& output);

  /** PrintSelf method */
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;

private:
  NeuralNetworkMachineLearningModel(const Self&) = delete;
  void operator=(const Self&) = delete;

  void CreateNetwork();
  void SetupNetworkAndTrain(cv::Mat& labels);
  cv::Ptr<cv::ml::ANN_MLP> m_ANNModel;
  int                       m_TrainMethod;
  int                       m_ActivateFunction;
  std::vector<unsigned int> m_LayerSizes;

  double m_Alpha;
  double m_Beta;
  double m_BackPropDWScale;
  double m_BackPropMomentScale;
  double m_RegPropDW0;
  double m_RegPropDWMin;
  int    m_TermCriteriaType;
  int    m_MaxIter;
  double m_Epsilon;

  cv::Mat            m_MatrixOfLabels;
  MapOfLabelsType    m_MapOfLabels;

};
} // end namespace otb

#ifndef OTB_MANUAL_INSTANTIATION
#include "otbNeuralNetworkMachineLearningModel.hxx"
#endif

#endif
