/*
 * Copyright (C) 2005-2019 Centre National d'Etudes Spatiales (CNES)
 * Copyright (C) 2007-2012 Institut Mines Telecom / Telecom Bretagne
 *
 * 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 otbSOMMap_h
#define otbSOMMap_h

#include "itkVariableLengthVector.h"
#include "itkEuclideanDistanceMetric.h"
#include "otbVectorImage.h"

namespace otb
{
/**
 * \class SOMMap
 * \brief This class represent a Self Organizing Map.
 *
 * The Self organizing map (or Kohonen map) is a type of neural networks. It is based on an analogy with
 * the visual cortex, where similar inputs activate neighbor neurons.
 *
 * This class extends the Image object, where each pixel represents a neuron in the map.
 * It is templated with a distance parameter used to compute the neuron response to an input.
 * Thanks to the extension of the Image object, reading and writing is supported through standard image
 * readers and writers.
 *
 * The training is done via the SOM class, and the activation map can be produced with the SOMActivationBuilder
 * class.
 *
 * \sa SOM
 * \sa SOMActivationBuilder
 *
 * \ingroup OTBSOM
 */
template <class TNeuron = itk::VariableLengthVector<double>, class TDistance = itk::Statistics::EuclideanDistanceMetric<TNeuron>,
          unsigned int VMapDimension = 2>
class ITK_EXPORT       SOMMap : public otb::VectorImage<typename TNeuron::ComponentType, VMapDimension>
{
public:
  /** Standard typedefs */
  typedef SOMMap Self;
  typedef otb::VectorImage<typename TNeuron::ComponentType, VMapDimension> Superclass;
  typedef itk::SmartPointer<Self>       Pointer;
  typedef itk::SmartPointer<const Self> ConstPointer;

  /** Creation through object factory macro */
  itkNewMacro(Self);
  /**
   * There is no runtime information macro since
   * this class has to be considered to as a simple VectorImage
   * // itkTypeMacro(SOMMap, VectorImage);
   * */

  /** Template parameters related typedefs */
  typedef TNeuron                        NeuronType;
  typedef TDistance                      DistanceType;
  typedef typename DistanceType::Pointer DistancePointerType;

  /** Superclass related typedefs */
  typedef typename Superclass::IndexType     IndexType;
  typedef typename Superclass::SizeType      SizeType;
  typedef typename Superclass::DirectionType DirectionType;
  typedef typename Superclass::RegionType    RegionType;
  typedef typename Superclass::SpacingType   SpacingType;
  typedef typename Superclass::PointType     PointType;
  /**
   * Get The index of the winning neuron for a sample.
   * \param sample the sample.
   * \return The index of the winning neuron.
   */
  IndexType GetWinner(const NeuronType& sample);

protected:
  /** Constructor */
  SOMMap();
  /** Destructor */
  ~SOMMap() override;
  /** PrintSelf method */
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;

private:
  SOMMap(const Self&) = delete;
  void operator=(const Self&) = delete;
};
} // end namespace otb

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

#endif