Visible to Intel only — GUID: GUID-7E690C4E-545C-414A-A00A-395193DB48B5
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_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.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
heterogen_table.cpp
homogen_table.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_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_cor_dense_batch.cpp
pca_precomputed_cov_dense_batch.cpp
pca_svd_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_biased_batch.cpp
cov_dense_biased_online.cpp
cov_dense_online.cpp
csr_accessor.cpp
csr_table.cpp
dbscan_brute_force_batch.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
directed_graph.cpp
graph_service_functions.cpp
heterogen_table.cpp
homogen_table.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_cor_dense_batch.cpp
pca_cor_dense_online.cpp
pca_cov_dense_batch.cpp
pca_cov_dense_online.cpp
pca_precomputed_dense_batch.cpp
pca_svd_dense_batch.cpp
pca_svd_dense_online.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
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-7E690C4E-545C-414A-A00A-395193DB48B5
k-Nearest Neighbors Classification (k-NN)
k-NN classification, regression, and search algorithms are based on finding the k nearest observations to the training set. For classification, the problem is to infer the class of a new feature vector by computing the majority vote of its k nearest observations from the training set. For regression, the problem is to infer the target value of a new feature vector by computing the average target value of its k nearest observations from the training set. For search, the problem is to identify the k nearest observations from the training set to a new feature vector. The nearest observations are computed based on the chosen distance metric.
Operation |
Computational methods |
Programming Interface |
|||
Mathematical formulation
Refer to Developer Guide: k-Nearest Neighbors Classification.
Programming Interface
All types and functions in this section are declared in the oneapi::dal::knn namespace and be available via inclusion of the oneapi/dal/algo/knn.hpp header file.
Enum classes
enumclassvoting_mode
- voting_mode::uniform
-
Uniform weights for neighbors for prediction voting.
- voting_mode::distance
-
Weight neighbors by the inverse of their distance.
Result options
classresult_option_id
Public Methods
constexprresult_option_id()=default
constexprresult_option_id(constresult_option_id_base&base)
Descriptor
template<typenameFloat=float,typenameMethod=method::by_default,typenameTask=task::by_default,typenameDistance=oneapi::dal::minkowski_distance::descriptor<Float>>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::brute_force or method::kd_tree.
Task – Tag-type that specifies type of the problem to solve. Can be task::classification, task::regression, or task::search.
Distance – The descriptor of the distance used for computations. Can be minkowski_distance::descriptor or chebyshev_distance::descriptor.
Constructors
descriptor(std::int64_tclass_count, std::int64_tneighbor_count)
Creates a new instance of the class with the given class_count and neighbor_count property values.
template<typenameM=Method,typenameNone=detail::enable_if_brute_force_t<M>>descriptor(std::int64_tclass_count, std::int64_tneighbor_count, constdistance_t&distance)
Creates a new instance of the class with the given class_count, neighbor_count and distance property values. Used with method::brute_force only.
template<typenameT=Task,typenameNone=detail::enable_if_not_classification_t<T>>descriptor(std::int64_tneighbor_count)
Creates a new instance of the class with the given neighbor_count property value. Used with task::search and task::regression only.
template<typenameT=Task,typenameNone=detail::enable_if_not_classification_t<T>>descriptor(std::int64_tneighbor_count, constdistance_t&distance)
Creates a new instance of the class with the given neighbor_count and distance property values. Used with task::search and task::regression only.
Properties
std::int64_tclass_count
The number of classes c.
- Getter & Setter
-
std::int64_t get_class_count() const
auto & set_class_count(std::int64_t value)
- Invariants
-
class_count > 1
result_option_idresult_options
Choose which results should be computed and returned.
- Getter & Setter
-
result_option_id get_result_options() const
auto & set_result_options(const result_option_id &value)
constdistance_t&distance
Choose distance type for calculations. Used with method::brute_force only.
- Getter & Setter
-
template <typename M = Method, typename None = detail::enable_if_brute_force_t<M>> const distance_t & get_distance() const
template <typename M = Method, typename None = detail::enable_if_brute_force_t<M>> auto & set_distance(const distance_t &dist)
std::int64_tneighbor_count
The number of neighbors k.
- Getter & Setter
-
std::int64_t get_neighbor_count() const
auto & set_neighbor_count(std::int64_t value)
- Invariants
-
neighbor_count > 0
voting_modevoting_mode
The voting mode.
- Getter & Setter
-
voting_mode get_voting_mode() const
auto & set_voting_mode(voting_mode value)
Method tags
structkd_tree
Tag-type that denotes k-d tree computational method.
structbrute_force
Tag-type that denotes brute-force computational method.
usingby_default=brute_force
Alias tag-type for brute-force computational method.
Task tags
structclassification
Tag-type that parameterizes entities used for solving classification problem.
structregression
Tag-type that parameterizes entities used for solving the regression problem.
structsearch
Tag-type that parameterizes entities used for solving the search problem.
usingby_default=classification
Alias tag-type for classification task.
Model
template<typenameTask=task::by_default>classmodel
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::classification, task::search and task::regression.
Constructors
model()
Creates a new instance of the class with the default property values.
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::classification or task::search.
Constructors
train_input(consttable&data, consttable&responses)
Creates a new instance of the class with the given data and responses property values.
train_input(consttable&data)
Properties
consttable&data
The training set X. Default value: table{}.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &data)
consttable&responses
Vector of responses y for the training set X. Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_responses(const table &responses)
consttable&labels
Vector of labels y for the training set X. Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_labels(const table &value)
Result
template<typenameTask=task::by_default>classtrain_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::classification or task::search.
Constructors
train_result()
Creates a new instance of the class with the default property values.
Properties
constmodel<Task>&model
The trained k-NN model. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &value)
Operation
template<typenameDescriptor>knn::train_resulttrain(constDescriptor&desc, constknn::train_input&input)
- Parameters
-
desc – k-NN algorithm descriptor knn::descriptor
input – Input data for the training operation
- Preconditions
-
input.data.has_data == true
input.labels.has_data == true
input.data.row_count == input.labels.row_count
input.labels.column_count == 1
input.labels[i] >= 0
input.labels[i] < desc.class_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::classification or task::search.
Constructors
infer_input(consttable&data, constmodel<Task>&model)
Creates a new instance of the class with the given model and data property values.
Properties
consttable&data
The dataset for inference 
. Default value: table{}.
- Getter & Setter
-
const table & get_data() const
auto & set_data(const table &data)
constmodel<Task>&model
The trained k-NN model. Default value: model<Task>{}.
- Getter & Setter
-
const model< Task > & get_model() const
auto & set_model(const model< Task > &m)
Result
template<typenameTask=task::by_default>classinfer_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::classification or task::search.
Constructors
infer_result()
Creates a new instance of the class with the default property values.
Properties
constresult_option_id&result_options
Result options that indicates availability of the properties.
- Getter & Setter
-
const result_option_id & get_result_options() const
auto & set_result_options(const result_option_id &value)
consttable&responses
The predicted responses. Default value: table{}.
- Getter & Setter
-
const table & get_responses() const
template <typename T = Task, typename None = detail::enable_if_not_search_t<T>> auto & set_responses(const table &value)
consttable&indices
Indices of nearest neighbors. Default value: table{}.
- Getter & Setter
-
const table & get_indices() const
auto & set_indices(const table &value)
consttable&distances
Distances to nearest neighbors. Default value: table{}.
- Getter & Setter
-
const table & get_distances() const
auto & set_distances(const table &value)
consttable&labels
The predicted labels. Default value: table{}.
- Getter & Setter
-
const table & get_labels() const
template <typename T = Task, typename None = detail::enable_if_classification_t<T>> auto & set_labels(const table &value)
Operation
template<typenameDescriptor>knn::infer_resultinfer(constDescriptor&desc, constknn::infer_input&input)
- Parameters
-
desc – k-NN algorithm descriptor knn::descriptor
input – Input data for the inference operation
Usage Example
Training
knn::model<> run_training(const table& data,
const table& labels) {
const std::int64_t class_count = 10;
const std::int64_t neighbor_count = 5;
const auto knn_desc = knn::descriptor<float>{class_count, neighbor_count};
const auto result = train(knn_desc, data, labels);
return result.get_model();
}Inference
table run_inference(const knn::model<>& model,
const table& new_data) {
const std::int64_t class_count = 10;
const std::int64_t neighbor_count = 5;
const auto knn_desc = knn::descriptor<float>{class_count, neighbor_count};
const auto result = infer(knn_desc, model, new_data);
print_table("labels", result.get_labels());
}Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing:
Python* with DPC++ support
Distributed Processing: