/*
 * Copyright (C) 1999-2011 Insight Software Consortium
 * Copyright (C) 2005-2019 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 otbEigenvalueLikelihoodMaximisation_h
#define otbEigenvalueLikelihoodMaximisation_h

#include "itkObjectFactory.h"
#include "itkLightObject.h"
#include "vnl/vnl_vector.h"
#include "vnl/vnl_matrix.h"
#include "vnl/algo/vnl_symmetric_eigensystem.h"

namespace otb
{

/** \class EigenvalueLikelihoodMaximisation
 * \brief Estimates the number of endmembers in a hyperspectral image
 *
 * This filter applies the ELM (Eigenvalue Likelihood Maximisation) algorithm to a
 * hyperspectral image and outputs the number of endmember.
 * It takes as input the covariance and correlation matrices of the input data,
 * the number of observed pixel for thoses matrices estimations,
 * and outputs the number of endmembers, and the log-likelihood.
 *
 * References :
 * "Unsupervised Endmember Extraction of Martian Hyperspectral Images",
 * B.Luo, J. Chanussot, S. Dout\'e and X. Ceamanos,
 * IEEE Whispers 2009, Grenoble France, 2009
 *
 * "Unsupervised classification of hyperspectral images by using linear unmixing algorithm",
 * Luo, B. and Chanussot, J.,
 * IEEE Int. Conf. On Image Processing(ICIP) 2009, Cairo, Egypte, 2009
 *
 * \ingroup Hyperspectral
 *
 * \ingroup OTBEndmembersExtraction
 */
template <class TPrecision>
class ITK_EXPORT EigenvalueLikelihoodMaximisation : public itk::LightObject
{
public:
  /** Standard Self typedef */
  typedef EigenvalueLikelihoodMaximisation Self;
  typedef itk::LightObject                 Superclass;
  typedef itk::SmartPointer<Self>          Pointer;
  typedef itk::SmartPointer<const Self>    ConstPointer;

  /** Method for creation through the object factory. */
  itkNewMacro(Self);

  /** Runtime information support. */
  itkTypeMacro(EigenvalueLikelihoodMaximisation, itk::LightObject);

  /** Types to use for computations. */
  typedef TPrecision PrecisionType;

  typedef vnl_vector<PrecisionType> VectorType;
  typedef vnl_matrix<PrecisionType> MatrixType;

  void SetCovariance(const MatrixType& m)
  {
    m_Covariance = m;
  }

  void SetCorrelation(const MatrixType& m)
  {
    m_Correlation = m;
  }

  void SetNumberOfPixels(unsigned int n)
  {
    m_NumberOfPixels = n;
  }

  void Compute();

  unsigned int GetNumberOfEndmembers()
  {
    return m_NumberOfEndmembers;
  }

  const VectorType& GetLikelihood()
  {
    return m_Likelihood;
  }

protected:
  EigenvalueLikelihoodMaximisation();
  ~EigenvalueLikelihoodMaximisation() override
  {
  }
  void PrintSelf(std::ostream& os, itk::Indent indent) const override;

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

  MatrixType m_Covariance;
  MatrixType m_Correlation;

  unsigned int m_NumberOfPixels;
  unsigned int m_NumberOfEndmembers;
  VectorType   m_Likelihood;
};
}

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

#endif