Correlation distance

The Correlation Distance is a distance measure that quantifies the dissimilarity between vectors based on their linear correlation patterns.

Operation	Computational methods		Programming Interface
dense	dense	compute(…)	compute_input	compute_result

Mathematical formulation

Refer to Developer Guide: Correlation distance.

Programming Interface

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

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 distances.

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>
correlation_distance::compute_result compute(const Descriptor &desc, const correlation_distance::compute_input &input)

Parameters:

• desc – Correlation Distance algorithm descriptor correlation_distance::descriptor.

• input – Input data for the computing operation

Preconditions

input.data.is_empty == false