Intel® oneAPI Data Analytics Library Developer Guide and Reference

Developer Guide and Reference

  • 2021.4
  • 09/27/2021
  • Public Content
Contents

K-Means initialization

The K-Means initialization algorithm receives
n
 feature vectors as input and chooses
k
 initial centroids. After initialization, K-Means algorithm uses the initialization result to partition input data into
k
 clusters.
Operation
Computational methods
Programming Interface

Mathematical formulation

Programming Interface

All types and functions in this section are declared in the
oneapi::dal::kmeans_init
 namespace and be available via inclusion of the
oneapi/dal/algo/kmeans_init.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 K-Means Initialization algorithm.
  • Task
     – Tag-type that specifies the type of the problem to solve. Can be
    task::init
    .
Constructors
descriptor
(std::int64_t
cluster_count
 = 2)
Creates a new instance of the class with the given
cluster_count
.
Properties
std::int64_t
cluster_count
The number of clusters k.
Default value
: 2.
Getter & Setter


std::int64_t get_cluster_count() const
auto & set_cluster_count(int64_t value)

Invariants


cluster_count > 0

Method tags
struct
dense
Tag-type that denotes dense computational method.
struct
parallel_plus_dense
struct
plus_plus_dense
struct
random_dense
using
by_default
 = dense
Task tags
struct
init
Tag-type that parameterizes entities used for obtaining the initial K-Means centroids.
using
by_default
 = init
Alias tag-type for the initialization task.
Computing
compute(...)
Input
template<typename
Task
 = task::by_default>
class
compute_input
Template Parameters
Task
 – Tag-type that specifies type of the problem to solve. Can be
task::init
.
Constructors
compute_input
(
const
 table &
data
)
Creates a new instance of the class with the given
data
.
Properties
const
 table &
data
An LaTex Math image. table with the data to be clustered, where each row stores one feature vector.
Default value
: table{}.
Getter & Setter


const table & get_data() const
auto & set_data(const table &data)

Result
template<typename
Task
 = task::by_default>
class
compute_result
Template Parameters
Task
 – Tag-type that specifies type of the problem to solve. Can be
oneapi::dal::kmeans::task::clustering
.
Constructors
compute_result
()
Creates a new instance of the class with the default property values.
Properties
const
 table &
centroids
A LaTex Math image. table with the initial centroids. Each row of the table stores one centroid.
Default value
: table{}.
Getter & Setter


const table & get_centroids() const
auto & set_centroids(const table &value)

Operation
template<typename
Descriptor
> kmeans_init::compute_result
compute
(
const
 Descriptor &
desc
,
const
 kmeans_init::compute_input &
input
)
Parameters
  • desc
     – K-Means algorithm descriptor
    kmeans_init::descriptor
  • input
     – Input data for the computing operation
Preconditions


input.data.has_data == true
input.data.row_count == desc.cluster_count

Postconditions


result.centroids.has_data == true
result.centroids.row_count == desc.cluster_count
result.centroids.column_count == input.data.column_count

Examples

oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:

Product and Performance Information

1

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