Visible to Intel only — GUID: GUID-4FBDD817-9C67-4604-B952-4DA2FCC3E4DE
basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_online.cpp
csr_accessor.cpp
dbscan_brute_force_batch.cpp
df_cls_hist_batch.cpp
df_cls_hist_batch_random.cpp
df_cls_traverse_model.cpp
df_reg_hist_batch.cpp
df_reg_hist_batch_random.cpp
df_reg_traverse_model.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_reg_brute_force_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
linear_regression_dense_online.cpp
logistic_regression_dense_batch.cpp
pca_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
basic_statistics_dense_batch.cpp
basic_statistics_dense_online.cpp
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cor_dense_online.cpp
cov_dense_batch.cpp
cov_dense_online.cpp
csr_accessor.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
jaccard_batch.cpp
jaccard_batch_app.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
knn_cls_kd_tree_dense_batch.cpp
knn_search_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
linear_regression_dense_batch.cpp
linear_regression_dense_online.cpp
logloss_dense_batch.cpp
louvain_batch.cpp
pca_dense_batch.cpp
pca_dense_online.cpp
pca_precomputed_dense_batch.cpp
polynomial_kernel_dense_batch.cpp
rbf_kernel_dense_batch.cpp
shortest_paths_batch.cpp
sigmoid_kernel_dense_batch.cpp
subgraph_isomorphism_batch.cpp
svm_multi_class_thunder_csr_batch.cpp
svm_multi_class_thunder_dense_batch.cpp
svm_nu_cls_thunder_csr_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_csr_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp
svm_reg_thunder_csr_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_two_class_smo_csr_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_csr_batch.cpp
svm_two_class_thunder_dense_batch.cpp
triangle_counting_batch.cpp
K-Means Clustering
Glossary
Correlation and Variance-Covariance Matrices
Principal Component Analysis
Principal Components Analysis Transform
Singular Value Decomposition
Association Rules
Kernel Functions
Expectation-Maximization
Cholesky Decomposition
QR Decomposition
Outlier Detection
Distance Matrix
Distributions
Engines
Moments of Low Order
Quantile
Quality Metrics
Sorting
Normalization
Optimization Solvers
Decision Forest
Decision Trees
Gradient Boosted Trees
Stump
Linear and Ridge Regressions
LASSO and Elastic Net Regressions
k-Nearest Neighbors (kNN) Classifier
Implicit Alternating Least Squares
Logistic Regression
Naïve Bayes Classifier
Support Vector Machine Classifier
Multi-class Classifier
Boosting
Visible to Intel only — GUID: GUID-4FBDD817-9C67-4604-B952-4DA2FCC3E4DE
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
Refer to Developer Guide: K-Means Initialization.
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<typenameFloat=float,typenameMethod=method::by_default,typenameTask=task::by_default>classdescriptor
- 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_tcluster_count=2)
Creates a new instance of the class with the given cluster_count.
Properties
auto&seed
- Getter & Setter
-
template <typename M = Method, typename None = detail::v1::enable_if_not_default_dense<M>> auto & get_seed() const
template <typename M = Method, typename None = detail::v1::enable_if_not_default_dense<M>> auto & set_seed(std::int64_t value)
auto&local_trials_count
Number of attempts to find the best sample in terms of potential value If the value is equal to -1, the number of trials is 2 + int(log(cluster_count)). Default value: -1.
- Getter & Setter
-
template <typename M = Method, typename None = detail::v1::enable_if_plus_plus_dense<M>> auto & get_local_trials_count() const
template <typename M = Method, typename None = detail::v1::enable_if_plus_plus_dense<M>> auto & set_local_trials_count(std::int64_t value=-1)
- Invariants
-
local_trials > 0 or :expr`local_trials = -1`
std::int64_tcluster_count
The number of clusters k. Default value: 2.
- Getter & Setter
-
std::int64_t get_cluster_count() const
auto & set_cluster_count(std::int64_t value)
- Invariants
-
cluster_count > 0
Method tags
structdense
Tag-type that denotes dense computational method.
structparallel_plus_dense
Tag-type that denotes parallel_plus_dense computational method.
structplus_plus_dense
Tag-type that denotes plus_plus_dense computational method.
structrandom_dense
Tag-type that denotes random_dense computational method.
usingby_default=dense
Task tags
structinit
Tag-type that parameterizes entities used for obtaining the initial K-Means centroids.
usingby_default=init
Alias tag-type for the initialization task.
Computing compute(...)
Input
template<typenameTask=task::by_default>classcompute_input
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::init.
Constructors
compute_input(consttable&data)
Creates a new instance of the class with the given data.
Properties
consttable&data
An 
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<typenameTask=task::by_default>classcompute_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
consttable¢roids
A 
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<typenameDescriptor>kmeans_init::compute_resultcompute(constDescriptor&desc, constkmeans_init::compute_input&input)
- Parameters
-
desc – K-Means algorithm descriptor kmeans_init::descriptor
input – Input data for the computing operation
Usage Example
Computing
table run_compute(const table& data) {
const auto kmeans_desc = kmeans_init::descriptor<float,
kmeans_init::method::dense>{}
.set_cluster_count(10)
const auto result = compute(kmeans_desc, data);
print_table("centroids", result.get_centroids());
return result.get_centroids();
}
Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing: