Visible to Intel only — GUID: GUID-10360984-3E04-4084-948B-FF3517ECA769
basic_statistics_dense_batch.cpp
column_accessor_homogen.cpp
cor_dense_batch.cpp
cov_dense_batch.cpp
dbscan_brute_force_batch.cpp
df_cls_hist_batch.cpp
df_cls_traverse_model.cpp
df_reg_hist_batch.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
pca_cor_dense_batch.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
column_accessor_homogen.cpp
connected_components_batch.cpp
cor_dense_batch.cpp
cov_dense_batch.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
louvain_batch.cpp
pca_dense_batch.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_dense_batch.cpp
svm_nu_cls_thunder_dense_batch.cpp
svm_nu_reg_thunder_dense_batch.cpp
svm_reg_thunder_dense_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
triangle_counting_batch.cpp
K-Means Clustering
Density-Based Spatial Clustering of Applications with Noise
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-10360984-3E04-4084-948B-FF3517ECA769
K-Means
The K-Means algorithm solves clustering problem by partitioning n feature vectors into k clusters minimizing some criterion. Each cluster is characterized by a representative point, called a centroid.
Operation |
Computational methods |
Programming Interface |
||
Mathematical formulation
Refer to Developer Guide: K-Means.
Programming Interface
All types and functions in this section are declared in the oneapi::dal::kmeans namespace and be available via inclusion of the oneapi/dal/algo/kmeans.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 algorithm. Can be method::lloyd_dense.
Task – Tag-type that specifies the type of the problem to solve. Can be task::clustering.
Constructors
descriptor(std::int64_tcluster_count=2)
Creates a new instance of the class with the given cluster_count.
Properties
std::int64_tmax_iteration_count
The maximum number of iterations T. Default value: 100.
- Getter & Setter
-
std::int64_t get_max_iteration_count() const
auto & set_max_iteration_count(std::int64_t value)
- Invariants
-
max_iteration_count >= 0
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
doubleaccuracy_threshold
The threshold 
for the stop condition. Default value: 0.0.
- Getter & Setter
-
double get_accuracy_threshold() const
auto & set_accuracy_threshold(double value)
- Invariants
-
accuracy_threshold >= 0.0
Method tags
structlloyd_dense
Tag-type that denotes Lloyd’s computational method.
usingby_default=lloyd_dense
Alias tag-type for Lloyd’s computational method.
Task tags
structclustering
Tag-type that parameterizes entities used for solving clustering problem.
usingby_default=clustering
Alias tag-type for the clustering task.
Model
template<typenameTask=task::by_default>classmodel
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
model()
Creates a new instance of the class with the default property values.
Public Methods
std::int64_tget_cluster_count()const
Number of clusters k in the trained model.
Properties
consttable¢roids
A 
table with the cluster 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)
Training train(...)
Input
template<typenameTask=task::by_default>classtrain_input
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
train_input(consttable&data)
train_input(consttable&data, consttable&initial_centroids)
Creates a new instance of the class with the given data and initial_centroids.
Properties
consttable&data
An 
table with the data to be clustered, where each row stores one feature vector.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &data)
consttable&initial_centroids
A table with the initial centroids, where each row stores one centroid.
- Getter & Setter
-
const table & get_initial_centroids() const
auto & set_initial_centroids(const table &data)
Result
template<typenameTask=task::by_default>classtrain_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
train_result()
Creates a new instance of the class with the default property values.
Properties
consttable&responses
An 
table with the responses 
assigned to the samples 
in the input data, 
. Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
auto & set_responses(const table &value)
std::int64_titeration_count
The number of iterations performed by the algorithm. Default value: 0.
- Getter & Setter
-
std::int64_t get_iteration_count() const
auto & set_iteration_count(std::int64_t value)
- Invariants
-
iteration_count >= 0
doubleobjective_function_value
The value of the objective function 
, where C is model.centroids.
- Getter & Setter
-
double get_objective_function_value() const
auto & set_objective_function_value(double value)
- Invariants
-
objective_function_value >= 0.0
consttable&labels
An table with the labels assigned to the samples in the input data, . Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
auto & set_labels(const table &value)
constmodel<Task>&model
The trained K-means model. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
Operation
template<typenameDescriptor>kmeans::train_resulttrain(constDescriptor&desc, constkmeans::train_input&input)
- Parameters
-
desc – K-Means algorithm descriptor kmeans::descriptor
input – Input data for the training operation
- Preconditions
-
input.data.has_data == true
input.initial_centroids.row_count == desc.cluster_count
input.initial_centroids.column_count == input.data.column_count
- Postconditions
-
result.labels.row_count == input.data.row_count
result.labels.column_count == 1
result.labels[i] >= 0
result.labels[i] < desc.cluster_count
result.iteration_count <= desc.max_iteration_count
result.model.centroids.row_count == desc.cluster_count
result.model.centroids.column_count == input.data.column_count
Inference infer(...)
Input
template<typenameTask=task::by_default>classinfer_input
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
infer_input(constmodel<Task>&trained_model, consttable&data)
Creates a new instance of the class with the given model and data.
Properties
consttable&data
The trained K-Means model. Default value: table{}.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &value)
constmodel<Task>&model
An table with the data to be assigned to the clusters, where each row stores one feature vector. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
Result
template<typenameTask=task::by_default>classinfer_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
infer_result()
Creates a new instance of the class with the default property values.
Properties
consttable&labels
An table with assignments labels to feature vectors in the input data. Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
auto & set_labels(const table &value)
doubleobjective_function_value
The value of the objective function , where C is defined by the corresponding infer_input::model::centroids. Default value: 0.0.
- Getter & Setter
-
double get_objective_function_value() const
auto & set_objective_function_value(double value)
- Invariants
-
objective_function_value >= 0.0
consttable&responses
An table with assignments responses to feature vectors in the input data. Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
auto & set_responses(const table &value)
Operation
template<typenameDescriptor>kmeans::infer_resultinfer(constDescriptor&desc, constkmeans::infer_input&input)
- Parameters
-
desc – K-Means algorithm descriptor kmeans::descriptor
input – Input data for the inference operation
- Preconditions
-
input.data.has_data == true
input.model.centroids.has_data == true
input.model.centroids.row_count == desc.cluster_count
input.model.centroids.column_count == input.data.column_count
- Postconditions
-
result.labels.row_count == input.data.row_count
result.labels.column_count == 1
result.labels[i] >= 0
result.labels[i] < desc.cluster_count
Usage example
Training
kmeans::model<> run_training(const table& data,
const table& initial_centroids) {
const auto kmeans_desc = kmeans::descriptor<float>{}
.set_cluster_count(10)
.set_max_iteration_count(50)
.set_accuracy_threshold(1e-4);
const auto result = train(kmeans_desc, data, initial_centroids);
print_table("labels", result.get_labels());
print_table("centroids", result.get_model().get_centroids());
print_value("objective", result.get_objective_function_value());
return result.get_model();
}
Inference
table run_inference(const kmeans::model<>& model,
const table& new_data) {
const auto kmeans_desc = kmeans::descriptor<float>{}
.set_cluster_count(model.get_cluster_count());
const auto result = infer(kmeans_desc, model, new_data);
print_table("labels", result.get_labels());
}
Examples
oneAPI DPC++
Batch Processing:
dpc_kmeans_lloyd_dense_batch.cpp
oneAPI C++
Batch Processing:
cpp_kmeans_lloyd_dense_batch.cpp
Python* with DPC++ support
Batch Processing:
Did you find the information on this page useful?
Feedback Message
Characters remaining: