Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

  • 2021.5
  • 12/06/2021
  • Public Content
Contents

Linear kernel

The linear kernel is the simplest kernel function for pattern analysis.
Operation
Computational methods
Programming Interface

Mathematical formulation

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
shift
The coefficient LaTex Math image. of the linear kernel.
Default value
: 0.0.
Getter & Setter


double get_shift() const
auto & set_shift(double value)

double
scale
The coefficient LaTex Math image. of the linear 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 &
x
An LaTex Math image. 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 LaTex Math image. 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 LaTex Math image. 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

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.