## Developer Guide and Reference

• 2021.6
• 04/11/2022
• Public Content
Contents

# Linear kernel

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

## 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 of the linear kernel.
Default value
: 0.0.
Getter & Setter

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

double
scale
The coefficient 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 &
y
An 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 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
A 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.