Radial Basis Function (RBF) kernel#
The Radial Basis Function (RBF) kernel is a popular kernel function used in kernelized learning algorithms.
Operation |
Computational methods |
Programming Interface |
||
Mathematical formulation#
Refer to Developer Guide: Radial Basis Function (RBF) kernel.
Programming Interface#
All types and functions in this section are declared in the
oneapi::dal::rbf_kernel
namespace and are available via inclusion of the
oneapi/dal/algo/rbf_kernel.hpp
header file.
Descriptor#
-
template<typename Float = float, typename Method = method::by_default, typename Task = task::by_default>
class descriptor# - Template Parameters:
Constructors
-
descriptor() = default#
Creates a new instance of the class with the default property values.
Properties
-
double sigma#
The coefficient \(\sigma\) of the RBF kernel. Default value: 1.0.
- Getter & Setter
double get_sigma() const
auto & set_sigma(double value)
Training compute(...)#
Input#
-
template<typename Task = task::by_default>
class compute_input# - Template Parameters:
Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.
Constructors
-
compute_input(const table &x, const table &y)#
Creates a new instance of the class with the given
x
andy
.
Properties
Result#
-
template<typename Task = task::by_default>
class compute_result# - Template Parameters:
Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.
Constructors
-
compute_result()#
Creates a new instance of the class with the default property values.
Properties
Operation#
-
template<typename Descriptor>
rbf_kernel::compute_result compute(const Descriptor &desc, const rbf_kernel::compute_input &input)# - Parameters:
desc – RBF Kernel algorithm descriptor rbf_kernel::descriptor.
input – Input data for the computing operation
- Preconditions
- input.data.is_empty == false