/*=========================================================================
 *
 *  Copyright NumFOCUS
 *
 *  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
 *
 *         https://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  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.
 *
 *=========================================================================*/


#include <fstream>

#include "itkPointSetToListSampleAdaptor.h"

#include "itkGaussianMixtureModelComponent.h"
#include "itkExpectationMaximizationMixtureModelEstimator.h"

int
itkExpectationMaximizationMixtureModelEstimatorTest(int argc, char * argv[])
{
  namespace stat = itk::Statistics;
  using PointSetType = itk::PointSet<double, 2>;
  using DataSampleType = stat::PointSetToListSampleAdaptor<PointSetType>;

  using EstimatorType = stat::ExpectationMaximizationMixtureModelEstimator<DataSampleType>;

  using ComponentType = stat::GaussianMixtureModelComponent<DataSampleType>;

  if (argc < 2)
  {
    std::cout << "ERROR: data file name argument missing." << std::endl;
    return EXIT_FAILURE;
  }

  char *        dataFileName = argv[1];
  constexpr int dataSize{ 2000 };
  constexpr int maximumIteration{ 200 };
  using ParametersType = itk::Array<double>;
  constexpr double            minStandardDeviation{ 28.54746 };
  constexpr unsigned int      numberOfClasses{ 2 };
  std::vector<ParametersType> trueParameters(numberOfClasses);
  ParametersType              params(6);
  params[0] = 99.261;
  params[1] = 100.078;
  params[2] = 814.95741;
  params[3] = 38.40308;
  params[4] = 38.40308;
  params[5] = 817.64446;
  trueParameters[0] = params;

  params[0] = 200.1;
  params[1] = 201.3;
  params[2] = 859.785295;
  params[3] = -3.617316;
  params[4] = -3.617316;
  params[5] = 848.991508;
  trueParameters[1] = params;

  // only the means are altered
  std::vector<ParametersType> initialParameters(numberOfClasses);
  params[0] = 80.0;
  params[1] = 80.0;
  params[2] = 814.95741;
  params[3] = 38.40308;
  params[4] = 38.40308;
  params[5] = 817.64446;
  initialParameters[0] = params;

  params[0] = 180.0;
  params[1] = 180.0;
  params[2] = 859.785295;
  params[3] = -3.617316;
  params[4] = -3.617316;
  params[5] = 848.991508;
  initialParameters[1] = params;

  itk::Array<double> trueProportions(numberOfClasses);
  trueProportions[0] = 0.5;
  trueProportions[1] = 0.5;

  itk::Array<double> initialProportions(numberOfClasses);
  initialProportions[0] = 0.5;
  initialProportions[1] = 0.5;

  /* Loading point data */
  auto                                       pointSet = PointSetType::New();
  const PointSetType::PointsContainerPointer pointsContainer = PointSetType::PointsContainer::New();
  pointsContainer->Reserve(dataSize);
  pointSet->SetPoints(pointsContainer);

  PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin();
  PointSetType::PointType               point;
  double                                temp = NAN;
  std::ifstream                         dataStream(dataFileName);
  if (!dataStream)
  {
    std::cout << "ERROR: fail to open the data file." << std::endl;
    return EXIT_FAILURE;
  }

  while (p_iter != pointsContainer->End())
  {
    for (unsigned int i = 0; i < PointSetType::PointDimension; ++i)
    {
      dataStream >> temp;
      point[i] = temp;
    }
    p_iter.Value() = point;
    ++p_iter;
  }

  dataStream.close();

  /* Importing the point set to the sample */
  auto sample = DataSampleType::New();

  sample->SetPointSet(pointSet);

  /* Preparing the gaussian mixture components */
  using ComponentPointer = ComponentType::Pointer;
  std::vector<ComponentPointer> components;
  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    components.push_back(ComponentType::New());
    (components[i])->SetSample(sample);
    (components[i])->SetParameters(initialParameters[i]);
  }

  /* Estimating */
  auto estimator = EstimatorType::New();
  estimator->SetSample(sample);
  estimator->SetMaximumIteration(maximumIteration);
  estimator->SetInitialProportions(initialProportions);

  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    estimator->AddComponent((ComponentType::Superclass *)(components[i]).GetPointer());
  }

  estimator->Update();

  std::cout << "DEBUG: current iteration = " << estimator->GetCurrentIteration() << std::endl;

  bool passed = true;

  const unsigned int measurementVectorSize = sample->GetMeasurementVectorSize();
  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    std::cout << "Cluster[" << i << ']' << std::endl;
    std::cout << "    Parameters:" << std::endl;
    std::cout << "         " << (components[i])->GetFullParameters() << std::endl;
    std::cout << "    Proportion: ";
    std::cout << "         " << (estimator->GetProportions())[i] << std::endl;
    double displacement = 0.0;
    for (unsigned int j = 0; j < measurementVectorSize; ++j)
    {
      temp = (components[i])->GetFullParameters()[j] - trueParameters[i][j];
      displacement += (temp * temp);
    }
    displacement = std::sqrt(displacement);
    std::cout << "    Mean displacement: " << std::endl;
    std::cout << "        " << displacement << std::endl << std::endl;
    if (displacement > (minStandardDeviation / 100.0) * 3)
    {
      passed = false;
    }
  }

  if (!passed)
  {
    std::cout << "Test failed." << std::endl;
    return EXIT_FAILURE;
  }

  estimator->Print(std::cout);

  // Test streaming enumeration for ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE elements
  const std::set<itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE>
    allTERMINATION_CODE{
      itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::CONVERGED,
      itk::Statistics::ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE::NOT_CONVERGED
    };
  for (const auto & ee : allTERMINATION_CODE)
  {
    std::cout << "STREAMED ENUM VALUE ExpectationMaximizationMixtureModelEstimatorEnums::TERMINATION_CODE: " << ee
              << std::endl;
  }

  std::cout << "Test passed." << std::endl;
  return EXIT_SUCCESS;
}
