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

#include <iostream>

#include "itkNumericTraits.h"
#include "otbMacro.h"
#include "otbGaussianModelComponent.h"

namespace otb
{
namespace Statistics
{

template <class TSample>
GaussianModelComponent<TSample>::GaussianModelComponent()
{
  m_CovarianceEstimator        = nullptr;
  m_GaussianMembershipFunction = nullptr;
}

template <class TSample>
void GaussianModelComponent<TSample>::PrintSelf(std::ostream& os, itk::Indent indent) const
{
  Superclass::PrintSelf(os, indent);

  os << indent << "Mean Estimator: " << m_CovarianceEstimator << std::endl;
  os << indent << "Covariance Estimator: " << m_CovarianceEstimator << std::endl;
  os << indent << "GaussianMembershipFunction: " << m_GaussianMembershipFunction << std::endl;
}

template <class TSample>
void GaussianModelComponent<TSample>::ShowParameters(std::ostream& os, itk::Indent indent) const
{
  unsigned int i, j;
  os << indent << "Gaussian model component : \n";
  os << indent << "Mean : ";
  for (i = 0; i < m_Mean.Size(); ++i)
    os << m_Mean[i] << "\t";
  os << "\n" << indent << "Covariance : ";
  for (i = 0; i < m_Mean.Size(); ++i)
  {
    for (j = 0; j < m_Mean.Size(); ++j)
      os << m_Covariance(i, j) << "\t";
    os << "\n" << indent << "              ";
  }
  os << "\n";
}

template <class TSample>
void GaussianModelComponent<TSample>::SetSample(const TSample* sample)
{
  Superclass::SetSample(sample);
  const MeasurementVectorSizeType measurementVectorLength = sample->GetMeasurementVectorSize();
  this->m_Parameters.SetSize(measurementVectorLength * (1 + measurementVectorLength));

  // Set the size of the mean vector
  m_Mean.SetSize(measurementVectorLength);


  // Set the parameters of the mean (internally) and the covariance estimator
  m_Covariance.SetSize(measurementVectorLength, measurementVectorLength);

  m_CovarianceEstimator = CovarianceEstimatorType::New();
  m_CovarianceEstimator->SetInput(sample);
  m_CovarianceEstimator->Update();

  m_GaussianMembershipFunction = NativeMembershipFunctionType::New();
  this->m_PdfFunction          = (MembershipFunctionType*)m_GaussianMembershipFunction;
  m_GaussianMembershipFunction->SetMeasurementVectorSize(measurementVectorLength);
  this->SetPdfMembershipFunction((MembershipFunctionType*)m_GaussianMembershipFunction.GetPointer());
}

template <class TSample>
void GaussianModelComponent<TSample>::SetParameters(const ParametersType& parameters)
{
  Superclass::SetParameters(parameters);

  unsigned int paramIndex = 0;
  unsigned int i, j;

  MeasurementVectorSizeType measurementVectorSize = this->GetSample()->GetMeasurementVectorSize();

  m_Mean.SetSize(measurementVectorSize);
  for (i = 0; i < measurementVectorSize; i++)
  {
    m_Mean[i] = parameters[paramIndex];
    paramIndex++;
  }

  m_Covariance.SetSize(measurementVectorSize, measurementVectorSize);
  for (i = 0; i < measurementVectorSize; i++)
    for (j = 0; j < measurementVectorSize; j++)
    {
      m_Covariance(i, j) = parameters[paramIndex];
      paramIndex++;
    }

  this->m_GaussianMembershipFunction->SetMean(m_Mean);
  this->m_GaussianMembershipFunction->SetCovariance(&m_Covariance);
}

template <class TSample>
void GaussianModelComponent<TSample>::GenerateData()
{
  if (this->IsSampleModified() == 0)
    return;

  MeasurementVectorSizeType measurementVectorSize = this->GetSample()->GetMeasurementVectorSize();

  unsigned int i, j;
  int          paramIndex = 0;

  // Get the mean using the convariance estimator (computed internally)
  typename CovarianceEstimatorType::MeasurementVectorType meanOutput = m_CovarianceEstimator->GetMean();

  for (i = 0; i < measurementVectorSize; i++)
  {
    m_Mean.SetElement(i, meanOutput.GetElement(i));
    this->m_Parameters[paramIndex] = meanOutput.GetElement(i);
    ++paramIndex;
  }

  // Get the covariance matrix and fill the parameters vector
  const typename CovarianceEstimatorType::MatrixType covariance = m_CovarianceEstimator->GetCovarianceMatrix();

  for (i = 0; i < measurementVectorSize; i++)
    for (j = 0; j < measurementVectorSize; j++)
    {
      this->m_Parameters[paramIndex] = covariance.GetVnlMatrix().get(i, j);
      m_Covariance(i, j) = covariance.GetVnlMatrix().get(i, j);
      paramIndex++;
    }

  this->m_GaussianMembershipFunction->SetMean(meanOutput);
  this->m_GaussianMembershipFunction->SetCovariance(m_Covariance);

  Superclass::GenerateData();
}

} // end of namespace Statistics
} // end of namesapce otb

#endif
