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

◆ IsNormalized

const bool 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.

◆ UsesSquaredDistance

const bool UsesSquaredDistance = false
static

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

Definition at line 38 of file kernel_traits.hpp.


The documentation for this class was generated from the following file:
  • /home/ryan/src/mlpack.org/_src/mlpack-3.3.2/src/mlpack/core/kernels/kernel_traits.hpp