kde_model.hpp

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#ifndef MLPACK_METHODS_KDE_MODEL_HPP
#define MLPACK_METHODS_KDE_MODEL_HPP

// Include trees.
#include <mlpack/core/tree/binary_space_tree.hpp>
#include <mlpack/core/tree/octree.hpp>
#include <mlpack/core/tree/cover_tree.hpp>
#include <mlpack/core/tree/rectangle_tree.hpp>

// Include core.
#include <mlpack/core.hpp>

// Remaining includes.
#include "kde.hpp"

namespace mlpack {
namespace kde {

class KernelNormalizer
{
 private:
  // SFINAE check if Normalizer function is present.
  HAS_MEM_FUNC(Normalizer, HasNormalizer);

 public:
  template<typename KernelType>
  static void ApplyNormalizer(
      KernelType& /* kernel */,
      const size_t /* dimension */,
      arma::vec& /* estimations */,
      const typename std::enable_if<
          !HasNormalizer<KernelType, double(KernelType::*)(size_t)>::value>::
          type* = 0)
  { return; }

  template<typename KernelType>
  static void ApplyNormalizer(
      KernelType& kernel,
      const size_t dimension,
      arma::vec& estimations,
      const typename std::enable_if<
          HasNormalizer<KernelType, double(KernelType::*)(size_t)>::value>::
          type* = 0)
  {
    estimations /= kernel.Normalizer(dimension);
  }
};

class KDEWrapperBase
{
 public:
  KDEWrapperBase() { }

  virtual KDEWrapperBase* Clone() const = 0;

  virtual ~KDEWrapperBase() { }

  virtual void Bandwidth(const double bw) = 0;

  virtual void RelativeError(const double relError) = 0;

  virtual void AbsoluteError(const double absError) = 0;

  virtual bool MonteCarlo() const = 0;
  virtual bool& MonteCarlo() = 0;

  virtual void MCProb(const double mcProb) = 0;

  virtual size_t MCInitialSampleSize() const = 0;
  virtual size_t& MCInitialSampleSize() = 0;

  virtual void MCEntryCoef(const double entryCoef) = 0;

  virtual void MCBreakCoef(const double breakCoef) = 0;

  virtual KDEMode Mode() const = 0;
  virtual KDEMode& Mode() = 0;

  virtual void Train(arma::mat&& referenceSet) = 0;

  virtual void Evaluate(arma::mat&& querySet,
                        arma::vec& estimates) = 0;

  virtual void Evaluate(arma::vec& estimates) = 0;
};

template<typename KernelType,
         template<typename TreeMetricType,
                  typename TreeStatType,
                  typename TreeMatType> class TreeType>
class KDEWrapper : public KDEWrapperBase
{
 public:
  KDEWrapper(const double relError,
             const double absError,
             const KernelType& kernel) :
      kde(relError, absError, kernel)
  {
    // Nothing left to do.
  }

  virtual KDEWrapper* Clone() const { return new KDEWrapper(*this); }

  virtual ~KDEWrapper() { }

  virtual void Bandwidth(const double bw) { kde.Kernel() = KernelType(bw); }

  virtual void RelativeError(const double eps) { kde.RelativeError(eps); }

  virtual void AbsoluteError(const double eps) { kde.AbsoluteError(eps); }

  virtual bool MonteCarlo() const { return kde.MonteCarlo(); }
  virtual bool& MonteCarlo() { return kde.MonteCarlo(); }

  virtual void MCProb(const double mcProb) { kde.MCProb(mcProb); }

  virtual size_t MCInitialSampleSize() const
  {
    return kde.MCInitialSampleSize();
  }
  virtual size_t& MCInitialSampleSize()
  {
    return kde.MCInitialSampleSize();
  }

  virtual void MCEntryCoef(const double e) { kde.MCEntryCoef(e); }

  virtual void MCBreakCoef(const double b) { kde.MCBreakCoef(b); }

  virtual KDEMode Mode() const { return kde.Mode(); }
  virtual KDEMode& Mode() { return kde.Mode(); }

  virtual void Train(arma::mat&& referenceSet);

  virtual void Evaluate(arma::mat&& querySet,
                        arma::vec& estimates);

  virtual void Evaluate(arma::vec& estimates);

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */)
  {
    ar(CEREAL_NVP(kde));
  }

 protected:
  typedef KDE<KernelType,
              metric::EuclideanDistance,
              arma::mat,
              TreeType> KDEType;

  KDEType kde;
};

class KDEModel
{
 public:
  enum TreeTypes
  {
    KD_TREE,
    BALL_TREE,
    COVER_TREE,
    OCTREE,
    R_TREE
  };

  enum KernelTypes
  {
    GAUSSIAN_KERNEL,
    EPANECHNIKOV_KERNEL,
    LAPLACIAN_KERNEL,
    SPHERICAL_KERNEL,
    TRIANGULAR_KERNEL
  };

 private:
  double bandwidth;

  double relError;

  double absError;

  KernelTypes kernelType;

  TreeTypes treeType;

  bool monteCarlo;

  double mcProb;

  size_t initialSampleSize;

  double mcEntryCoef;

  double mcBreakCoef;

  KDEWrapperBase* kdeModel;

 public:
  KDEModel(const double bandwidth = 1.0,
           const double relError = KDEDefaultParams::relError,
           const double absError = KDEDefaultParams::absError,
           const KernelTypes kernelType = KernelTypes::GAUSSIAN_KERNEL,
           const TreeTypes treeType = TreeTypes::KD_TREE,
           const bool monteCarlo = KDEDefaultParams::mode,
           const double mcProb = KDEDefaultParams::mcProb,
           const size_t initialSampleSize = KDEDefaultParams::initialSampleSize,
           const double mcEntryCoef = KDEDefaultParams::mcEntryCoef,
           const double mcBreakCoef = KDEDefaultParams::mcBreakCoef);

  KDEModel(const KDEModel& other);

  KDEModel(KDEModel&& other);

  KDEModel& operator=(const KDEModel& other);

  KDEModel& operator=(KDEModel&& other);

  ~KDEModel();

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t version);

  double Bandwidth() const { return bandwidth; }

  void Bandwidth(const double newBandwidth);

  double RelativeError() const { return relError; }

  void RelativeError(const double newRelError);

  double AbsoluteError() const { return absError; }

  void AbsoluteError(const double newAbsError);

  TreeTypes TreeType() const { return treeType; }

  TreeTypes& TreeType() { return treeType; }

  KernelTypes KernelType() const { return kernelType; }

  KernelTypes& KernelType() { return kernelType; }

  bool MonteCarlo() const { return monteCarlo; }

  void MonteCarlo(const bool newMonteCarlo);

  double MCProbability() const { return mcProb; }

  void MCProbability(const double newMCProb);

  size_t MCInitialSampleSize() const { return initialSampleSize; }

  void MCInitialSampleSize(const size_t newSampleSize);

  double MCEntryCoefficient() const { return mcEntryCoef; }

  void MCEntryCoefficient(const double newEntryCoef);

  double MCBreakCoefficient() const { return mcBreakCoef; }

  void MCBreakCoefficient(const double newBreakCoef);

  KDEMode Mode() const { return kdeModel->Mode(); }

  KDEMode& Mode() { return kdeModel->Mode(); }

  void InitializeModel();

  void BuildModel(arma::mat&& referenceSet);

  void Evaluate(arma::mat&& querySet, arma::vec& estimations);

  void Evaluate(arma::vec& estimations);


 private:
  void CleanMemory();
};

} // namespace kde
} // namespace mlpack

#include "kde_model_impl.hpp"

#endif