mlpack::kernel::KernelTraits< KernelType > Class Template Reference

This is a template class that can provide information about various kernels. More...

Static Public Attributes
static const bool	IsNormalized = false
	If true, then the kernel is normalized: K(x, x) = K(y, y) = 1 for all x. More...
static const bool	UsesSquaredDistance = false
	If true, then the kernel include a squared distance, ||x - y||^2 . More...

Detailed Description

template<typename KernelType>
class mlpack::kernel::KernelTraits< KernelType >

This is a template class that can provide information about various kernels.

By default, this class will provide the weakest possible assumptions on kernels, and each kernel should override values as necessary. If a kernel doesn't need to override a value, then there's no need to write a KernelTraits specialization for that class.

Definition at line 27 of file kernel_traits.hpp.

Member Data Documentation

template<typename KernelType >

const bool mlpack::kernel::KernelTraits< KernelType >::IsNormalized = false

static

If true, then the kernel is normalized: K(x, x) = K(y, y) = 1 for all x.

Definition at line 33 of file kernel_traits.hpp.

template<typename KernelType >

const bool mlpack::kernel::KernelTraits< KernelType >::UsesSquaredDistance = false

static

If true, then the kernel include a squared distance, ||x - y||^2 .

Definition at line 38 of file kernel_traits.hpp.

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

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