/*=========================================================================
 *
 *  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 "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkListSample.h"
#include "itkKdTreeGenerator.h"
#include "itkTestingMacros.h"
#include <fstream>
#include <algorithm>

int
itkKdTreeTest3(int argc, char * argv[])
{
  if (argc < 5)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
    std::cerr << " numberOfDataPoints numberOfTestPoints "
              << "numberOfNeighbors bucketSize [graphvizDotOutputFile]" << std::endl;
    return EXIT_FAILURE;
  }

  // Random number generator
  using NumberGeneratorType = itk::Statistics::MersenneTwisterRandomVariateGenerator;

  NumberGeneratorType::Pointer randomNumberGenerator = NumberGeneratorType::GetInstance();
  randomNumberGenerator->Initialize();

  using MeasurementVectorType = itk::Array<double>;
  using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;

  constexpr SampleType::MeasurementVectorSizeType measurementVectorSize = 2;

  auto sample = SampleType::New();
  sample->SetMeasurementVectorSize(measurementVectorSize);

  //
  // Generate a sample of random points
  //
  const unsigned int    numberOfDataPoints = std::stoi(argv[1]);
  MeasurementVectorType mv(measurementVectorSize);
  for (unsigned int i = 0; i < numberOfDataPoints; ++i)
  {
    mv[0] = randomNumberGenerator->GetNormalVariate(0.0, 1.0);
    mv[1] = randomNumberGenerator->GetNormalVariate(0.0, 1.0);
    sample->PushBack(mv);
  }

  using TreeGeneratorType = itk::Statistics::KdTreeGenerator<SampleType>;
  auto treeGenerator = TreeGeneratorType::New();

  const unsigned int bucketSize = std::stoi(argv[4]);

  treeGenerator->SetSample(sample);
  treeGenerator->SetBucketSize(bucketSize);
  treeGenerator->Update();

  using TreeType = TreeGeneratorType::KdTreeType;

  TreeType::Pointer tree = treeGenerator->GetOutput();

  MeasurementVectorType queryPoint(measurementVectorSize);

  unsigned int numberOfNeighbors = std::stoi(argv[3]);
  if (numberOfNeighbors > numberOfDataPoints)
  {
    numberOfNeighbors = numberOfDataPoints;
  }
  TreeType::InstanceIdentifierVectorType neighbors1;
  TreeType::InstanceIdentifierVectorType neighbors2;

  MeasurementVectorType result(measurementVectorSize);
  MeasurementVectorType test_point(measurementVectorSize);
  MeasurementVectorType min_point(measurementVectorSize);

  unsigned int numberOfFailedPoints1 = 0;

  const unsigned int numberOfTestPoints = std::stoi(argv[2]);

  //
  //  Check that for every point in the sample, its closest point is itself.
  //
  using DistanceMetricType = itk::Statistics::EuclideanDistanceMetric<MeasurementVectorType>;

  using OriginType = DistanceMetricType::OriginType;

  auto distanceMetric = DistanceMetricType::New();

  OriginType origin(measurementVectorSize);

  for (unsigned int k = 0; k < sample->Size(); ++k)
  {
    queryPoint = sample->GetMeasurementVector(k);

    for (unsigned int i = 0; i < sample->GetMeasurementVectorSize(); ++i)
    {
      origin[i] = queryPoint[i];
    }

    distanceMetric->SetOrigin(origin);

    tree->Search(queryPoint, numberOfNeighbors, neighbors1);
    double max_distance = 0.;

    for (const auto i : neighbors1)
    {
      const double distance = distanceMetric->Evaluate(tree->GetMeasurementVector(i));

      max_distance = std::max(distance, max_distance);
    }

    max_distance += itk::NumericTraits<double>::epsilon() * 10.0;
    tree->Search(queryPoint, max_distance, neighbors2);

    for (size_t i = 0; i < neighbors2.size(); ++i)
    {
      auto temp_it = std::find(neighbors1.begin(), neighbors1.end(), neighbors2[i]);
      if (temp_it == neighbors1.end())
      {
        std::cerr << "neighbors2[" << i << "] = " << neighbors2[i] << " is not in neighbors1" << std::endl;
        numberOfFailedPoints1++;
      }
    }

    for (size_t i = 0; i < neighbors1.size(); ++i)
    {
      auto temp_it = std::find(neighbors2.begin(), neighbors2.end(), neighbors1[i]);
      if (temp_it == neighbors2.end())
      {
        std::cerr << "neighbors1[" << i << "] = " << neighbors1[i] << " is not in neighbors2" << std::endl;
        numberOfFailedPoints1++;
      }
    }
  }

  //
  // Generate a second sample of random points
  // and use them to query the tree
  //
  unsigned int numberOfFailedPoints2 = 0;

  for (unsigned int j = 0; j < numberOfTestPoints; ++j)
  {
    double max_distance = 0.;

    queryPoint[0] = randomNumberGenerator->GetNormalVariate(0.0, 1.0);
    queryPoint[1] = randomNumberGenerator->GetNormalVariate(0.0, 1.0);

    for (unsigned int i = 0; i < sample->GetMeasurementVectorSize(); ++i)
    {
      origin[i] = queryPoint[i];
    }

    distanceMetric->SetOrigin(origin);

    tree->Search(queryPoint, numberOfNeighbors, neighbors1);

    for (const auto i : neighbors1)
    {
      const double distance = distanceMetric->Evaluate(tree->GetMeasurementVector(i));

      max_distance = std::max(distance, max_distance);
    }

    max_distance += itk::NumericTraits<double>::epsilon() * 10.0;
    tree->Search(queryPoint, max_distance, neighbors2);

    for (size_t i = 0; i < neighbors2.size(); ++i)
    {
      const double distance = distanceMetric->Evaluate(tree->GetMeasurementVector(neighbors2[i]));
      if (distance <= max_distance)
      {
        auto temp_it = std::find(neighbors1.begin(), neighbors1.end(), neighbors2[i]);
        if (temp_it == neighbors1.end())
        {
          std::cerr << "neighbors2[" << i << "] = " << neighbors2[i] << " is not in neighbors1" << std::endl;
          numberOfFailedPoints2++;
        }
      }
    }
  }

  if (argc > 5)
  {
    //
    // Plot out the tree structure to the console in the format used by Graphviz dot
    //
    std::ofstream plotFile;
    plotFile.open(argv[5]);
    tree->PlotTree(plotFile);
    plotFile.close();
  }


  if (numberOfFailedPoints1)
  {
    std::cerr << numberOfFailedPoints1 << " out of " << sample->Size();
    std::cerr << " points failed to find themselves as closest-point" << std::endl;
  }

  if (numberOfFailedPoints2)
  {
    std::cerr << numberOfFailedPoints2 << " out of " << numberOfTestPoints;
    std::cerr << " points failed to find the correct closest point." << std::endl;
  }


  if (numberOfFailedPoints1 || numberOfFailedPoints2)
  {
    return EXIT_FAILURE;
  }


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