laplacian_kernel.hpp

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#ifndef MLPACK_CORE_KERNELS_LAPLACIAN_KERNEL_HPP
#define MLPACK_CORE_KERNELS_LAPLACIAN_KERNEL_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace kernel {

class LaplacianKernel
{
 public:
  LaplacianKernel() : bandwidth(1.0)
  { }

  LaplacianKernel(double bandwidth) :
      bandwidth(bandwidth)
  { }

  template<typename VecTypeA, typename VecTypeB>
  double Evaluate(const VecTypeA& a, const VecTypeB& b) const
  {
    // The precalculation of gamma saves us a little computation time.
    return exp(-metric::EuclideanDistance::Evaluate(a, b) / bandwidth);
  }

  double Evaluate(const double t) const
  {
    // The precalculation of gamma saves us a little computation time.
    return exp(-t / bandwidth);
  }

  double Gradient(const double t) const  {
    return exp(-t / bandwidth) / -bandwidth;
  }

  double Bandwidth() const { return bandwidth; }
  double& Bandwidth() { return bandwidth; }

  template<typename Archive>
  void Serialize(Archive& ar, const unsigned int /* version */)
  {
    ar & data::CreateNVP(bandwidth, "bandwidth");
  }

 private:
  double bandwidth;
};

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

} // namespace kernel
} // namespace mlpack

#endif