// SPDX-FileCopyrightText: Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
// SPDX-License-Identifier: BSD-3-Clause

/**
 * @class   vtkHighestDensityRegionsStatistics
 * @brief   Compute a random vector of
 * density f from input observations points. f is computed using a smooth
 * kernel method.
 *
 *
 * Given a selection of pairs of columns of interest, this class provides the
 * following functionalities, depending on the chosen execution options:
 * * Learn: calculates density estimator f of a random vector using a smooth
 *   gaussian kernel. The output metadata on port OUTPUT_MODEL is a multiblock
 *   dataset containing at one vtkTable holding three columns which are for the
 *   first columns the input columns of interest and for the last columns the
 *   density estimators of each input pair of columns of interest.
 * * Derive: calculate normalized (as a percentage) quantiles coming from
 *   Learn output. The second block of the multiblock dataset contains a
 *   vtkTable holding some pairs of columns which are for the second one the
 *   quantiles ordered from the stronger to the lower and for the first one
 *   the correspondand quantile index.
 * * Assess: not implemented.
 * * Test: not implemented.
 */

#ifndef vtkHighestDensityRegionsStatistics_h
#define vtkHighestDensityRegionsStatistics_h

#include "vtkFiltersStatisticsModule.h" // For export macro
#include "vtkStatisticsAlgorithm.h"

VTK_ABI_NAMESPACE_BEGIN
class vtkStatisticalModel;
class vtkVariant;

class VTKFILTERSSTATISTICS_EXPORT vtkHighestDensityRegionsStatistics : public vtkStatisticsAlgorithm
{
public:
  vtkTypeMacro(vtkHighestDensityRegionsStatistics, vtkStatisticsAlgorithm);
  void PrintSelf(ostream& os, vtkIndent indent) override;
  static vtkHighestDensityRegionsStatistics* New();

  /// This filter requires bivariate requests.
  int GetMaximumNumberOfColumnsPerRequest() const override { return 2; }

  /**
   * Given a collection of models, calculate aggregate model
   */
  bool Aggregate(vtkDataObjectCollection*, vtkStatisticalModel*) override { return false; }

  /// Provide a string that can be used to recreate an instance of this algorithm.
  void AppendAlgorithmParameters(std::string& algorithmParameters) const override;

  /// Implement the inverse of AppendAlgorithmParameters(): given parameters, update this algorithm.
  std::size_t ConsumeNextAlgorithmParameter(
    vtkStringToken parameterName, const std::string& algorithmParameters) override;

  /**
   * Set the width of the gaussian kernel.
   */
  void SetSigma(double sigma);

  /**
   * Set the gaussian kernel matrix.
   */
  void SetSigmaMatrix(double s11, double s12, double s21, double s22);

  /**
   * Fill outDensity with density vector that is computed from
   * inObservations values. This method uses a Gaussian kernel.
   * For n observations and with X an observation point:
   * f(X) = (1 / n) * Sum(KH(X -Xi)) for (i = 1 to n).
   * Look ComputeSmoothGaussianKernel for KH kernel definition.
   */
  double ComputeHDR(vtkDataArray* inObservations, vtkDataArray* outDensity);

  /**
   * Fill outDensity with density vector defined by inPOI and computed from
   * the inObs values. This method uses a Gaussian kernel.
   * For n observations and with X an observation point:
   * f(X) = (1 / n) * Sum(KH(X -Xi)) for (i = 1 to n).
   * Look ComputeSmoothGaussianKernel for KH kernel definition.
   */
  double ComputeHDR(vtkDataArray* inObs, vtkDataArray* inPOI, vtkDataArray* outDensity);

protected:
  vtkHighestDensityRegionsStatistics();
  ~vtkHighestDensityRegionsStatistics() override;

  /**
   * Execute the calculations required by the Learn option.
   */
  void Learn(vtkTable*, vtkTable*, vtkStatisticalModel*) override;

  /**
   * Execute the calculations required by the Derive option.
   */
  void Derive(vtkStatisticalModel*) override;

  /**
   * Execute the calculations required by the Assess option.
   */
  void Assess(vtkTable*, vtkStatisticalModel*, vtkTable*) override {}

  /**
   * Execute the calculations required by the Test option.
   */
  void Test(vtkTable*, vtkStatisticalModel*, vtkTable*) override {}

  /**
   * Provide the appropriate assessment functor.
   */
  void SelectAssessFunctor(vtkTable*, vtkDataObject*, vtkStringArray*, AssessFunctor*&) override {}

  ///@{
  /**
   * Store the smooth matrix parameter H. Specify a smooth direction
   * for the Gaussian kernel.
   */
  double SmoothHC1[2];
  double SmoothHC2[2];
  double InvSigmaC1[2];
  double InvSigmaC2[2];
  double Determinant;
  ///@}

  /**
   * Store the number of requested columns pair computed by learn method.
   */
  vtkIdType NumberOfRequestedColumnsPair;

private:
  /**
   * Helper that returns a smooth gaussian kernel of a vector of dimension two,
   * using its coordinates. For X = [khx, khy] and H a positive matrix of dim 2,
   * KH(X) = sqrt(det(H)) * K((1 / sqrt(H)) * X).
   * Look ComputeStandardGaussianKernel for the K kernel definition.
   */
  double ComputeSmoothGaussianKernel(int dimension, double khx, double khy);

  vtkHighestDensityRegionsStatistics(const vtkHighestDensityRegionsStatistics&) = delete;
  void operator=(const vtkHighestDensityRegionsStatistics&) = delete;
};

VTK_ABI_NAMESPACE_END
#endif
