Intel® oneAPI Data Analytics Library Developer Guide and Reference
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Linear kernel
The linear kernel is the simplest kernel function for pattern analysis.
Operation |
Computational methods |
Programming Interface |
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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<typenameFloat=float,typenameMethod=method::by_default,typenameTask=task::by_default>classdescriptor
- Template Parameters
Constructors
descriptor()=default
Creates a new instance of the class with the default property values.
Properties
doubleshift
The coefficient \(b\) of the linear kernel. Default value: 0.0.
- Getter & Setter
-
double get_shift() const
auto & set_shift(double value)
doublescale
The coefficient \(k\) of the linear kernel. Default value: 1.0.
- Getter & Setter
-
double get_scale() const
auto & set_scale(double value)
Method tags
structdense
usingby_default=dense
Alias tag-type for the dense method.
Task tags
structcompute
Tag-type that parameterizes entities that are used to compute statistics, distance, and so on.
usingby_default=compute
Alias tag-type for the compute task.
Training compute(...)
Input
template<typenameTask=task::by_default>classcompute_input
- Template Parameters
-
Task – Tag-type that specifies the type of the problem to solve. Can be task::compute.
Constructors
compute_input(consttable&x, consttable&y)
Creates a new instance of the class with the given x and y.
Properties
consttable&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)
consttable&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<typenameTask=task::by_default>classcompute_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
consttable&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<typenameDescriptor>linear_kernel::compute_resultcompute(constDescriptor&desc, constlinear_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