Linear kernel#

The linear kernel is the simplest kernel function for pattern analysis.

Operation

Computational methods

Programming Interface

dense

dense

compute(…)

compute_input

compute_result

Mathematical formulation#

Refer to Developer Guide: Linear kernel.

Programming Interface#

All types and functions in this section are declared in the oneapi::dal::linear_kernel namespace and are available via inclusion of the oneapi/dal/algo/linear_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 an implementation of 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 scale#

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

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

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

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

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#

A \(n \times m\) 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>
linear_kernel::compute_result compute(const Descriptor &desc, const linear_kernel::compute_input &input)#
Parameters:

  • desc – Linear Kernel algorithm descriptor linear_kernel::descriptor.

  • input – Input data for the computing operation

Preconditions
input.data.is_empty == false