Contents

Sigmoid kernel

The Sigmoid kernel is a popular kernel function used in kernelized learning algorithms.

Operation

Computational methods

Programming Interface

dense

dense

compute(…)

compute_input

compute_result

Mathematical formulation

Refer to Developer Guide: Sigmoid kernel.

Programming Interface

All types and functions in this section are declared in the oneapi::dal::sigmoid_kernel namespace and are available via inclusion of the oneapi/dal/algo/sigmoid_kernel.hpp header file.

Descriptor

template<typename Float = float, typename Method = method::by_default, typename Task = task::by_default>
class descriptor
Template Parameters

  • Float – The floating-point type that the algorithm uses for intermediate computations. Can be float or double.

  • Method – Tag-type that specifies the implementation of the algorithm. Can be method::dense.

  • Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.

Constructors

descriptor() = default

Creates a new instance of the class with the default property values.

Properties

double shift

The coefficient \(b\) of the sigmoid kernel. Default value: 0.0.

Getter & Setter
double get_shift() const
auto & set_shift(double value)
double scale

The coefficient \(k\) of the sigmoid kernel. Default value: 1.0.

Getter & Setter
double get_scale() const
auto & set_scale(double value)

Method tags

struct dense
using by_default = dense

Alias tag-type for the dense method.

Task tags

struct compute

Tag-type that parameterizes entities that are used to compute statistics, distance, and so on.

using by_default = compute

Alias tag-type for the compute task.

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 and y.

Properties

const table &y

An \(m \times p\) table with the data y, where each row stores one feature vector. Default value: table{}.

Getter & Setter
const table & get_y() const
auto & set_y(const table &data)
const table &x

An \(n \times p\) table with the data x, where each row stores one feature vector. Default value: table{}.

Getter & Setter
const table & get_x() const
auto & set_x(const table &data)

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

const table &values

An \(n \times m\) table with the result kernel functions. Default value: table{}.

Getter & Setter
const table & get_values() const
auto & set_values(const table &value)

Operation

template<typename Descriptor>
sigmoid_kernel::compute_result compute(const Descriptor &desc, const sigmoid_kernel::compute_input &input)
Parameters

  • desc – Sigmoid Kernel algorithm descriptor sigmoid_kernel::descriptor

  • input – Input data for the computing operation

Preconditions
input.x.is_empty == false
input.y.is_empty == false
input.x.column_count == input.y.column_count
Postconditions
result.values.has_data == true
result.values.row_count == input.x.row_count
result.values.column_count == input.y.row_count

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Radial Basis Function (RBF) kernel

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Nearest Neighbors (kNN)