14 #ifndef __MLPACK_CORE_KERNELS_EPANECHNIKOV_KERNEL_HPP 15 #define __MLPACK_CORE_KERNELS_EPANECHNIKOV_KERNEL_HPP 52 template<
typename VecTypeA,
typename VecTypeB>
53 double Evaluate(
const VecTypeA& a,
const VecTypeB& b)
const;
59 double Evaluate(
const double distance)
const;
66 double Gradient(
const double distance)
const;
83 template<
typename VecTypeA,
typename VecTypeB>
96 template<
typename Archive>
97 void Serialize(Archive& ar,
const unsigned int version);
113 static const bool IsNormalized =
true;
115 static const bool UsesSquaredDistance =
true;
122 #include "epanechnikov_kernel_impl.hpp" This is a template class that can provide information about various kernels.
Linear algebra utility functions, generally performed on matrices or vectors.
EpanechnikovKernel(const double bandwidth=1.0)
Instantiate the Epanechnikov kernel with the given bandwidth (default 1.0).
double GradientForSquaredDistance(const double distanceSquared) const
Evaluate the Gradient of Epanechnikov kernel given that the squared distance between the two input po...
void Serialize(Archive &ar, const unsigned int version)
Serialize the kernel.
double Gradient(const double distance) const
Evaluate the Gradient of Epanechnikov kernel given that the distance between the two input points is ...
double ConvolutionIntegral(const VecTypeA &a, const VecTypeB &b)
Obtains the convolution integral [integral of K(||x-a||) K(||b-x||) dx] for the two vectors...
double inverseBandwidthSquared
Cached value of the inverse bandwidth squared (to speed up computation).
Include all of the base components required to write MLPACK methods, and the main MLPACK Doxygen docu...
double bandwidth
Bandwidth of the kernel.
The Epanechnikov kernel, defined as.
double Normalizer(const size_t dimension)
Compute the normalizer of this Epanechnikov kernel for the given dimension.
double Evaluate(const VecTypeA &a, const VecTypeB &b) const
Evaluate the Epanechnikov kernel on the given two inputs.