mlpack::kernel::EpanechnikovKernel Class Reference

The Epanechnikov kernel, defined as. More...

List of all members.

Public Member Functions

 EpanechnikovKernel (const double bandwidth=1.0)
 Instantiate the Epanechnikov kernel with the given bandwidth (default 1.0).
template<typename VecType >
double ConvolutionIntegral (const VecType &a, const VecType &b)
 Obtains the convolution integral [integral of K(||x-a||) K(||b-x||) dx] for the two vectors.
double Evaluate (const double distance) const
 Evaluate the Epanechnikov kernel given that the distance between the two input points is known.
template<typename Vec1Type , typename Vec2Type >
double Evaluate (const Vec1Type &a, const Vec2Type &b) const
 Evaluate the Epanechnikov kernel on the given two inputs.
double Normalizer (const size_t dimension)
 Compute the normalizer of this Epanechnikov kernel for the given dimension.

Private Attributes

double bandwidth
 Bandwidth of the kernel.
double inverseBandwidthSquared
 Cached value of the inverse bandwidth squared (to speed up computation).

Detailed Description

The Epanechnikov kernel, defined as.

\[ K(x, y) = \max \{0, 1 - || x - y ||^2_2 / b^2 \} \]

where $ b $ is the bandwidth the of the kernel (defaults to 1.0).

Definition at line 39 of file epanechnikov_kernel.hpp.


Constructor & Destructor Documentation

mlpack::kernel::EpanechnikovKernel::EpanechnikovKernel ( const double  bandwidth = 1.0  )  [inline]

Instantiate the Epanechnikov kernel with the given bandwidth (default 1.0).

Parameters:
bandwidth Bandwidth of the kernel.

Definition at line 47 of file epanechnikov_kernel.hpp.


Member Function Documentation

template<typename VecType >
double mlpack::kernel::EpanechnikovKernel::ConvolutionIntegral ( const VecType &  a,
const VecType &  b 
) [inline]

Obtains the convolution integral [integral of K(||x-a||) K(||b-x||) dx] for the two vectors.

Template Parameters:
VecType Type of vector (arma::vec, arma::spvec should be expected).
Parameters:
a First vector.
b Second vector.
Returns:
the convolution integral value.
double mlpack::kernel::EpanechnikovKernel::Evaluate ( const double  distance  )  const

Evaluate the Epanechnikov kernel given that the distance between the two input points is known.

template<typename Vec1Type , typename Vec2Type >
double mlpack::kernel::EpanechnikovKernel::Evaluate ( const Vec1Type &  a,
const Vec2Type &  b 
) const [inline]

Evaluate the Epanechnikov kernel on the given two inputs.

Parameters:
a One input vector.
b The other input vector.
double mlpack::kernel::EpanechnikovKernel::Normalizer ( const size_t  dimension  ) 

Compute the normalizer of this Epanechnikov kernel for the given dimension.

Parameters:
dimension Dimension to calculate the normalizer for.

Member Data Documentation

Bandwidth of the kernel.

Definition at line 88 of file epanechnikov_kernel.hpp.

Cached value of the inverse bandwidth squared (to speed up computation).

Definition at line 90 of file epanechnikov_kernel.hpp.


The documentation for this class was generated from the following file:

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