kde_rules.hpp

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#ifndef MLPACK_METHODS_KDE_RULES_HPP
#define MLPACK_METHODS_KDE_RULES_HPP

#include <mlpack/core/tree/traversal_info.hpp>

namespace mlpack {
namespace kde {

template<typename MetricType, typename KernelType, typename TreeType>
class KDERules
{
 public:
  KDERules(const arma::mat& referenceSet,
           const arma::mat& querySet,
           arma::vec& densities,
           const double relError,
           const double absError,
           const double mcProb,
           const size_t initialSampleSize,
           const double mcAccessCoef,
           const double mcBreakCoef,
           MetricType& metric,
           KernelType& kernel,
           const bool monteCarlo,
           const bool sameSet);

  double BaseCase(const size_t queryIndex, const size_t referenceIndex);

  double Score(const size_t queryIndex, TreeType& referenceNode);

  double Rescore(const size_t queryIndex,
                 TreeType& referenceNode,
                 const double oldScore) const;

  double Score(TreeType& queryNode, TreeType& referenceNode);

  double Rescore(TreeType& queryNode,
                 TreeType& referenceNode,
                 const double oldScore) const;

  typedef typename tree::TraversalInfo<TreeType> TraversalInfoType;

  const TraversalInfoType& TraversalInfo() const { return traversalInfo; }

  TraversalInfoType& TraversalInfo() { return traversalInfo; }

  size_t BaseCases() const { return baseCases; }

  size_t Scores() const { return scores; }

  size_t MinimumBaseCases() const { return 0; }

 private:
  double EvaluateKernel(const size_t queryIndex,
                        const size_t referenceIndex) const;

  double EvaluateKernel(const arma::vec& query,
                        const arma::vec& reference) const;

  double CalculateAlpha(TreeType* node);

  const arma::mat& referenceSet;

  const arma::mat& querySet;

  arma::vec& densities;

  const double absError;

  const double relError;

  const double mcBeta;

  const size_t initialSampleSize;

  const double mcAccessCoef;

  const double mcBreakCoef;

  MetricType& metric;

  KernelType& kernel;

  const bool monteCarlo;

  arma::vec accumMCAlpha;

  arma::vec accumError;

  const bool sameSet;

  constexpr static bool kernelIsGaussian =
      std::is_same<KernelType, kernel::GaussianKernel>::value;

  const double absErrorTol;

  size_t lastQueryIndex;

  size_t lastReferenceIndex;

  TraversalInfoType traversalInfo;

  size_t baseCases;

  size_t scores;
};

template<typename TreeType>
class KDECleanRules
{
 public:
  KDECleanRules() { /* Nothing to do. */ }

  double BaseCase(const size_t /* queryIndex */, const size_t /* refIndex */);

  double Score(const size_t /* queryIndex */, TreeType& referenceNode);

  double Rescore(const size_t /* queryIndex */,
                 TreeType& /* referenceNode */,
                 const double oldScore) const { return oldScore; }

  double Score(TreeType& queryNode, TreeType& referenceNode);

  double Rescore(TreeType& /* queryNode */,
                 TreeType& /* referenceNode*/ ,
                 const double oldScore) const { return oldScore; }

  typedef typename tree::TraversalInfo<TreeType> TraversalInfoType;

  const TraversalInfoType& TraversalInfo() const { return traversalInfo; }

  TraversalInfoType& TraversalInfo() { return traversalInfo; }

 private:
  TraversalInfoType traversalInfo;
};

} // namespace kde
} // namespace mlpack

// Include implementation.
#include "kde_rules_impl.hpp"

#endif