Visible to Intel only — GUID: GUID-A184F52E-1CEB-4580-800C-8591986630CF
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-A184F52E-1CEB-4580-800C-8591986630CF
DBSCAN
Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed in [Ester96]. It is a density-based clustering non-parametric algorithm: given a set of observations in some space, it groups together observations that are closely packed together (observations with many nearby neighbors), marking as outliers observations that lie alone in low-density regions (whose nearest neighbors are too far away).
Operation |
Computational methods |
Programming Interface |
||
Default method |
Mathematical formulation
Refer to Developer Guide: DBSCAN.
Programming Interface
All types and functions in this section are declared in the oneapi::dal::dbscan namespace and are available via inclusion of the oneapi/dal/algo/dbscan.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::brute_force.
Task – Tag-type that specifies the type of the problem to solve. Can be task::clustering.
Constructors
descriptor(doubleepsilon, std::int64_tmin_observations)
Creates a new instance of the class with the given epsilon, min_observations.
Properties
boolmem_save_mode
The flag for memory saving mode.
- Getter & Setter
-
bool get_mem_save_mode() const
auto & set_mem_save_mode(bool value)
doubleepsilon
The distance epsilon for neighbor search.
- Getter & Setter
-
double get_epsilon() const
auto & set_epsilon(double value)
- Invariants
-
epsilon >= 0.0
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)
std::int64_tmin_observations
The number of neighbors.
- Getter & Setter
-
std::int64_t get_min_observations() const
auto & set_min_observations(std::int64_t value)
Method tags
structbrute_force
usingby_default=brute_force
Task tags
structclustering
Tag-type that parameterizes entities used for solving clustering problem.
usingby_default=clustering
Alias tag-type for the clustering task.
Computation 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::clustering.
Constructors
compute_input(consttable&data={}, consttable&weights={})
Creates a new instance of the class with the given data and weights.
Properties
consttable&weights
A single column table with the weights, where each row stores one weight per observation.
- Getter & Setter
-
const table & get_weights() const
auto & set_weights(const table &weights)
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)
Result
template<typenameTask=task::by_default>classcompute_result
- Template Parameters
-
Task – Tag-type that specifies type of the problem to solve. Can be task::clustering.
Constructors
compute_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. Default value: default_result_options<Task>.
- Getter & Setter
-
const result_option_id & get_result_options() const
auto & set_result_options(const result_option_id &value)
consttable&core_flags
An 
table with the core flags 
assigned to the samples 
in the input data.
- Getter & Setter
-
const table & get_core_flags() const
auto & set_core_flags(const table &value)
consttable&core_observations
An 
table with the core observations in the input data. 
is a number of core observations.
- Getter & Setter
-
const table & get_core_observations() const
auto & set_core_observations(const table &value)
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)
consttable&core_observation_indices
An 
table with the indices of core observations in the input data. is a number of core observations.
- Getter & Setter
-
const table & get_core_observation_indices() const
auto & set_core_observation_indices(const table &value)
std::int64_tcluster_count
The number of clusters found by the algorithm.
- Getter & Setter
-
std::int64_t get_cluster_count() const
auto & set_cluster_count(std::int64_t value)
- Invariants
-
cluster_count >= 0
Operation
template<typenameDescriptor>dbscan::compute_resultcompute(constDescriptor&desc, constdbscan::compute_input&input)
- Parameters
-
desc – DBSCAN algorithm descriptor dbscan::descriptor
input – Input data for the compute operation
Usage Example
Compute
void run_compute(const table& data,
const table& weights) {
double epsilon = 1.0;
std::int64_t max_observations = 5;
const auto dbscan_desc = kmeans::descriptor<float>{epsilon, max_observations}
.set_result_options(dal::dbscan::result_options::responses);
const auto result = compute(dbscan_desc, data, weights);
print_table("responses", result.get_responses());
}
Examples
oneAPI DPC++
Batch Processing:
oneAPI C++
Batch Processing: