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

int
itkKdTreeTestSamplePoints(int, char *[])
{
  using MeasurementVectorType = itk::Array<double>;
  using SampleType = itk::Statistics::ListSample<MeasurementVectorType>;

  constexpr SampleType::MeasurementVectorSizeType measurementVectorSize{ 2 };

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

  constexpr unsigned int numberOfDataPoints{ 5 };
  MeasurementVectorType  mv(measurementVectorSize);
  mv[0] = 0.0342;
  mv[1] = 0.5175;
  sample->PushBack(mv);

  MeasurementVectorType mv2(measurementVectorSize);
  mv2[0] = 0.9650;
  mv2[1] = -0.9379;
  sample->PushBack(mv2);

  MeasurementVectorType mv3(measurementVectorSize);
  mv3[0] = -0.0471;
  mv3[1] = 0.8177;
  sample->PushBack(mv3);

  MeasurementVectorType mv4(measurementVectorSize);
  mv4[0] = 0.4737;
  mv4[1] = -1.0447;
  sample->PushBack(mv4);

  MeasurementVectorType mv5(measurementVectorSize);
  mv5[0] = -0.6307;
  mv5[1] = -2.7600;
  sample->PushBack(mv5);

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

  constexpr unsigned int bucketSize{ 1 };

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

  using TreeType = TreeGeneratorType::KdTreeType;

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

  MeasurementVectorType queryPoint(measurementVectorSize);

  constexpr unsigned int                 numberOfNeighbors{ 1 };
  TreeType::InstanceIdentifierVectorType neighbors;

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

  //
  //  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, neighbors);

    for (unsigned int i = 0; i < numberOfNeighbors; ++i)
    {
      const double distance = distanceMetric->Evaluate(tree->GetMeasurementVector(neighbors[i]));

      if (distance > itk::Math::eps)
      {
        std::cerr << "kd-tree knn search result:" << std::endl
                  << "query point = [" << queryPoint << ']' << std::endl
                  << "k = " << numberOfNeighbors << std::endl;
        std::cerr << "measurement vector : distance" << std::endl;
        std::cerr << '[' << tree->GetMeasurementVector(neighbors[i]) << "] : " << distance << std::endl;
      }
    }
  }

  double min_dist = itk::NumericTraits<double>::max();

  /*
  queryPoint[0] = 1.16651;
  queryPoint[1] = 0.16395;
  */

  /*
  queryPoint[0] = 1.0;
  queryPoint[1] = 0.12;
  */

  queryPoint[0] = 1.0;
  queryPoint[1] = 0.1;


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

  //
  // The first neighbor should be the closest point.
  //
  result = tree->GetMeasurementVector(neighbors[0]);

  //
  // Compute the distance to the "presumed" nearest neighbor
  //
  const double result_dist = std::sqrt((result[0] - queryPoint[0]) * (result[0] - queryPoint[0]) +
                                       (result[1] - queryPoint[1]) * (result[1] - queryPoint[1]));

  //
  // Compute the distance to all other points, to verify
  // whether the first neighbor was the closest one or not.
  //
  for (unsigned int i = 0; i < numberOfDataPoints; ++i)
  {
    test_point = tree->GetMeasurementVector(i);

    std::cout << "Compute distance with: " << test_point;

    const double dist = std::sqrt((test_point[0] - queryPoint[0]) * (test_point[0] - queryPoint[0]) +
                                  (test_point[1] - queryPoint[1]) * (test_point[1] - queryPoint[1]));

    std::cout << '\t' << dist << std::endl;

    if (dist < min_dist)
    {
      min_dist = dist;
      min_point = test_point;
    }
  }

  if (min_dist < result_dist)
  {
    std::cerr << "Problem found " << std::endl;
    std::cerr << "Query point " << queryPoint << std::endl;
    std::cerr << "Reported closest point " << result << " distance " << result_dist << std::endl;
    std::cerr << "Actual   closest point " << min_point << " distance " << min_dist << std::endl;
    std::cerr << std::endl;
    std::cerr << "Test FAILED." << std::endl;
  }

  //
  // Plot out the tree structure to the console in the format used by Graphviz dot
  //
  std::ofstream plotFile;
  plotFile.open("plot.dot");
  tree->PlotTree(plotFile);
  plotFile.close();

  return EXIT_SUCCESS;
}
