Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

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

Minkowski distance

The Minkowski distances are the set of distance metrics with different degree LaTex Math image. and are widely used for distance computation in different algorithms. The most commonly used distance metric, Euclidean distance, is also a Minkowski distance with LaTex Math image..
Operation
Computational methods

Mathematical formulation

Programming Interface

All types and functions in this section are declared in the
oneapi::dal::minkowski_distance
 namespace.
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 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.
descriptor
(double
degree
)
Creates a new instance of the class with the external property values.
Properties
double
degree
The coefficient LaTex Math image. of the Minkowski distance.
Default value
: 2.0.
Getter & Setter


double get_degree() const
auto & set_degree(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 distances.
using
by_default
 = compute
Alias tag-type for the compute task.

Product and Performance Information

1

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