epanechnikov_kernel.hpp

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#ifndef MLPACK_CORE_KERNELS_EPANECHNIKOV_KERNEL_HPP
#define MLPACK_CORE_KERNELS_EPANECHNIKOV_KERNEL_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/core/kernels/kernel_traits.hpp>

namespace mlpack {
namespace kernel {

class EpanechnikovKernel
{
 public:
  EpanechnikovKernel(const double bandwidth = 1.0) :
      bandwidth(bandwidth),
      inverseBandwidthSquared(1.0 / (bandwidth * bandwidth))
  {  }

  template<typename VecTypeA, typename VecTypeB>
  double Evaluate(const VecTypeA& a, const VecTypeB& b) const;

  double Evaluate(const double distance) const;

  double Gradient(const double distance) const;

  double GradientForSquaredDistance(const double distanceSquared) const;
  template<typename VecTypeA, typename VecTypeB>
  double ConvolutionIntegral(const VecTypeA& a, const VecTypeB& b);

  double Normalizer(const size_t dimension);

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

 private:
  double bandwidth;
  double inverseBandwidthSquared;
};

template<>
class KernelTraits<EpanechnikovKernel>
{
 public:
  static const bool IsNormalized = true;
  static const bool UsesSquaredDistance = true;
};

} // namespace kernel
} // namespace mlpack

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

#endif