/*=========================================================================
 *
 *  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 "itkSampleClassifierFilter.h"
#include "itkMaximumDecisionRule.h"

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

// Sample classifier test using Gaussian Mixture model and EM estimator
int
itkSampleClassifierFilterTest7(int argc, char * argv[])
{
  using PointSetType = itk::PointSet<double, 2>;
  using DataSampleType = itk::Statistics::PointSetToListSampleAdaptor<PointSetType>;
  using EstimatorType = itk::Statistics::ExpectationMaximizationMixtureModelEstimator<DataSampleType>;
  using ComponentType = itk::Statistics::GaussianMixtureModelComponent<DataSampleType>;

  if (argc < 3)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
    std::cout << "Input_data_sample Target_data_sample" << std::endl;
    return EXIT_FAILURE;
  }

  constexpr int          maximumIteration = 200;
  constexpr double       minStandardDeviation = 28.54746;
  constexpr unsigned int numberOfClasses = 2;
  using ParametersType = itk::Array<double>;
  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();
  PointSetType::PointsContainerPointer pointsContainer = PointSetType::PointsContainer::New();
  constexpr int                        dataSizeBig = 2000;
  pointsContainer->Reserve(dataSizeBig);
  pointSet->SetPoints(pointsContainer);

  PointSetType::PointsContainerIterator p_iter = pointsContainer->Begin();
  PointSetType::PointType               point;

  char * const  dataFileName = argv[1];
  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)
    {
      double temp;
      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;
  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;
    const unsigned int measurementVectorSize = sample->GetMeasurementVectorSize();
    for (unsigned int j = 0; j < measurementVectorSize; ++j)
    {
      double temp;
      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;
    }
  }

  // Set up a classifier
  using FilterType = itk::Statistics::SampleClassifierFilter<DataSampleType>;
  auto filter = FilterType::New();

  using ClassLabelVectorObjectType = FilterType::ClassLabelVectorObjectType;
  using ClassLabelVectorType = FilterType::ClassLabelVectorType;

  auto classLabelsObject = ClassLabelVectorObjectType::New();

  // Add class labels
  ClassLabelVectorType & classLabelVector = classLabelsObject->Get();

  using ClassLabelType = FilterType::ClassLabelType;

  ClassLabelType class1 = 0;
  classLabelVector.push_back(class1);

  ClassLabelType class2 = 1;
  classLabelVector.push_back(class2);

  // Set a decision rule type
  using DecisionRuleType = itk::Statistics::MaximumDecisionRule;

  auto decisionRule = DecisionRuleType::New();

  const FilterType::MembershipFunctionVectorObjectType * membershipFunctionsObject = estimator->GetOutput();

  /* Print out estimated parameters of the membership function */

  const FilterType::MembershipFunctionVectorType membershipFunctions = membershipFunctionsObject->Get();

  auto begin = membershipFunctions.begin();

  auto end = membershipFunctions.end();

  FilterType::MembershipFunctionVectorType::const_iterator functionIter;

  functionIter = begin;

  unsigned int counter = 1;
  std::cout << "Estimator membership function output " << std::endl;
  while (functionIter != end)
  {
    FilterType::MembershipFunctionPointer membershipFunction = *functionIter;
    const auto *                          gaussianMemberShpFunction =
      dynamic_cast<const EstimatorType::GaussianMembershipFunctionType *>(membershipFunction.GetPointer());
    std::cout << "\tMembership function:\t " << counter << std::endl;
    std::cout << "\t\tMean=" << gaussianMemberShpFunction->GetMean() << std::endl;
    std::cout << "\t\tCovariance matrix=" << gaussianMemberShpFunction->GetCovariance() << std::endl;
    functionIter++;
    counter++;
  }

  // Set membership functions weight array
  const FilterType::MembershipFunctionsWeightsArrayObjectType * weightArrayObjects =
    estimator->GetMembershipFunctionsWeightsArray();
  const FilterType::MembershipFunctionsWeightsArrayType weightsArray = weightArrayObjects->Get();

  std::cout << "Estimator membership function Weight/proporation output: " << std::endl;
  for (unsigned int i = 0; i < weightsArray.Size(); ++i)
  {
    std::cout << "Membership function: \t" << i << '\t' << weightsArray[i] << std::endl;
  }

  char *        targetFileName = argv[2];
  std::ifstream dataTargetStream(targetFileName);
  if (!dataTargetStream)
  {
    std::cout << "ERROR: fail to open the target data file." << std::endl;
    return EXIT_FAILURE;
  }

  auto                                 pointSet2 = PointSetType::New();
  PointSetType::PointsContainerPointer pointsContainer2 = PointSetType::PointsContainer::New();
  constexpr int                        dataSizeSmall = 200;
  pointsContainer2->Reserve(dataSizeSmall);
  pointSet2->SetPoints(pointsContainer2);

  p_iter = pointsContainer2->Begin();
  while (p_iter != pointsContainer2->End())
  {
    for (unsigned int i = 0; i < PointSetType::PointDimension; ++i)
    {
      double temp;
      dataTargetStream >> temp;
      point[i] = temp;
    }
    p_iter.Value() = point;
    ++p_iter;
  }

  dataTargetStream.close();

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

  sampleTarget->SetPointSet(pointSet2);

  filter->SetInput(sample);
  filter->SetNumberOfClasses(numberOfClasses);
  filter->SetClassLabels(classLabelsObject);
  filter->SetDecisionRule(decisionRule);
  filter->SetMembershipFunctions(membershipFunctionsObject);
  filter->SetMembershipFunctionsWeightsArray(weightArrayObjects);

  ITK_TRY_EXPECT_NO_EXCEPTION(filter->Update());


  // Check if the measurement vectors are correctly labelled.
  const FilterType::MembershipSampleType *        membershipSample = filter->GetOutput();
  FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();

  unsigned int sampleCounter = 0;

  unsigned int numberOfSamplesPerClass = 100;
  if (sampleCounter > numberOfSamplesPerClass)
  {
    if (iter.GetClassLabel() != class1)
    {
      std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetMeasurementVector() << '\t'
                << "Class label= " << iter.GetClassLabel() << "\tTrue label=" << class1 << std::endl;
      return EXIT_FAILURE;
    }
    ++iter;
    ++sampleCounter;
  }

  if (sampleCounter > numberOfSamplesPerClass)
  {
    if (iter.GetClassLabel() != class1)
    {
      std::cerr << "Classification error: " << sampleCounter << '\t' << iter.GetMeasurementVector() << '\t'
                << "Class label= " << iter.GetClassLabel() << "\tTrue label=" << class1 << std::endl;
      return EXIT_FAILURE;
    }
    ++iter;
    ++sampleCounter;
  }

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

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