mlpack::kernel::SphericalKernel Class Reference

The spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise. More...

Public Member Functions
	SphericalKernel (const double bandwidth=1.0)
	Construct the SphericalKernel with the given bandwidth. More...
template<typename VecTypeA , typename VecTypeB >
double	ConvolutionIntegral (const VecTypeA &a, const VecTypeB &b) const
	Obtains the convolution integral [integral K(||x-a||)K(||b-x||)dx] for the two vectors. More...
template<typename VecTypeA , typename VecTypeB >
double	Evaluate (const VecTypeA &a, const VecTypeB &b) const
	Evaluate the spherical kernel with the given two vectors. More...
double	Evaluate (const double t) const
	Evaluate the kernel when only a distance is given, not two points. More...
double	Gradient (double t)
double	Normalizer (size_t dimension) const
template<typename Archive >
void	Serialize (Archive &ar, const unsigned int)
	Serialize the object. More...

Private Attributes
double	bandwidth
double	bandwidthSquared

Detailed Description

The spherical kernel, which is 1 when the distance between the two argument points is less than or equal to the bandwidth, or 0 otherwise.

Definition at line 23 of file spherical_kernel.hpp.

Constructor & Destructor Documentation

mlpack::kernel::SphericalKernel::SphericalKernel ( const double  bandwidth = 1.0 )

inline

Construct the SphericalKernel with the given bandwidth.

Definition at line 29 of file spherical_kernel.hpp.

Member Function Documentation

template<typename VecTypeA , typename VecTypeB >

double mlpack::kernel::SphericalKernel::ConvolutionIntegral ( const VecTypeA &  a, const VecTypeB &  b  ) const

inline

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

Template Parameters

VecTypeA	Type of first vector (arma::vec, arma::sp_vec should be expected).
VecTypeB	Type of second vector.

Parameters

a	First vector.
b	Second vector.

Returns

the convolution integral value.

Definition at line 62 of file spherical_kernel.hpp.

References bandwidth, mlpack::metric::LMetric< TPower, TTakeRoot >::Evaluate(), mlpack::Log::Fatal, and Normalizer().

template<typename VecTypeA , typename VecTypeB >

double mlpack::kernel::SphericalKernel::Evaluate ( const VecTypeA &  a, const VecTypeB &  b  ) const

inline

Evaluate the spherical kernel with the given two vectors.

Template Parameters

VecTypeA	Type of first vector.
VecTypeB	Type of second vector.

Parameters

a	First vector.
b	Second vector.

Returns

The kernel evaluation between the two vectors.

Definition at line 44 of file spherical_kernel.hpp.

References bandwidthSquared, and mlpack::metric::LMetric< TPower, TTakeRoot >::Evaluate().

double mlpack::kernel::SphericalKernel::Evaluate ( const double  t ) const

inline

Evaluate the kernel when only a distance is given, not two points.

Parameters

t	Argument to kernel.

Definition at line 99 of file spherical_kernel.hpp.

References bandwidth.

double mlpack::kernel::SphericalKernel::Gradient ( double  t )

inline

Definition at line 103 of file spherical_kernel.hpp.

References bandwidth.

double mlpack::kernel::SphericalKernel::Normalizer ( size_t  dimension ) const

inline

Definition at line 88 of file spherical_kernel.hpp.

References bandwidth, and M_PI.

Referenced by ConvolutionIntegral().

template<typename Archive >

void mlpack::kernel::SphericalKernel::Serialize ( Archive &  ar, const unsigned  int  )

inline

Serialize the object.

Definition at line 109 of file spherical_kernel.hpp.

References bandwidth, bandwidthSquared, and mlpack::data::CreateNVP().

Member Data Documentation

double mlpack::kernel::SphericalKernel::bandwidth

private

Definition at line 116 of file spherical_kernel.hpp.

Referenced by ConvolutionIntegral(), Evaluate(), Gradient(), Normalizer(), and Serialize().

double mlpack::kernel::SphericalKernel::bandwidthSquared

private

Definition at line 117 of file spherical_kernel.hpp.

Referenced by Evaluate(), and Serialize().

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

• src/mlpack/core/kernels/spherical_kernel.hpp