oneDAL documentation
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oneAPI Examples
DPC++
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
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
C++
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
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
Developer Guide
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Glossary
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Computational Modes
Data Management
Array
Accessors
Column accessor
Row accessor
Data Sources
CSV data source
Graphs
Undirected adjacency vector graph
Directed adjacency vector graph
Tables
Homogeneous table
Algorithms
Clustering
DBSCAN
K-Means
K-Means initialization
Covariance
Covariance
Decomposition
Principal Components Analysis (PCA)
Ensembles
Decision Forest Classification and Regression (DF)
Graph
Subgraph Isomorphism
Connected Components
Kernel Functions
Linear kernel
Polynomial kernel
Radial Basis Function (RBF) kernel
Sigmoid kernel
Nearest Neighbors (kNN)
k-Nearest Neighbors Classification and Search (k-NN)
Pairwise Distances
Minkowski distance
Chebyshev distance
Cosine distance
Statistics
Basic Statistics
Support Vector Machines
Support Vector Machine Classifier and Regression (SVM)
Single Program Multiple Data
Appendix
Decision Tree
k-d Tree
DAAL Interfaces
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Library Usage
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Training and Prediction
Classification Usage Model
Regression Usage Model
Recommendation Systems Usage Model
Data Management
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Types of Numeric Tables
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Data Serialization and Deserialization
Data Compression
Data Model
Analysis
K-Means Clustering
Batch Processing
Distributed Processing
Batch Processing
Distributed Processing
Density-Based Spatial Clustering of Applications with Noise
Batch Processing
Distributed Processing
Correlation and Variance-Covariance Matrices
Batch Processing
Online Processing
Distributed Processing
Principal Component Analysis
Batch Processing
Online Processing
Distributed Processing
Principal Components Analysis Transform
Singular Value Decomposition
Batch and Online Processing
Distributed Processing
Association Rules
Kernel Functions
Expectation-Maximization
Cholesky Decomposition
QR Decomposition
QR Decomposition without Pivoting
Pivoted QR Decomposition
Outlier Detection
Multivariate Outlier Detection
Multivariate BACON Outlier Detection
Univariate Outlier Detection
Distance Matrix
Correlation Distance Matrix
Cosine Distance Matrix
Distributions
Uniform Distribution
Normal Distribution
Bernoulli Distribution
Engines
mt19937
mcg59
mt2203
Moments of Low Order
Batch Processing
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Gradient Boosted Trees
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Classification Gradient Boosted Trees
Stump
Classification Stump
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Linear and Ridge Regressions
Linear Regression
Ridge Regression
Linear and Ridge Regressions Computation
LASSO and Elastic Net Regressions
LASSO
Elastic Net
LASSO and Elastic Net Computation
k-Nearest Neighbors (kNN) Classifier
Implicit Alternating Least Squares
Batch Processing
Distributed Processing
Batch Processing
Distributed Processing: Training
Distributed Processing: Prediction of Ratings
Logistic Regression
Naïve Bayes Classifier
Batch Processing
Online Processing
Distributed Processing
Support Vector Machine Classifier
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AdaBoost Classifier
AdaBoost Multiclass Classifier
BrownBoost Classifier
LogitBoost Classifier
Services
Extracting Version Information
Handling Errors
Managing Memory
Managing the Computational Environment
Providing a Callback for the Host Application
Bibliography
Developer Reference
C++ API
Data Management
Array
Accessors
Column accessor
Row accessor
Data Sources
CSV data source
Graphs
Undirected adjacency vector graph
Directed adjacency vector graph
Graph Service
Undirected adjacency vector graph service
Directed adjacency vector graph service
Tables
Homogeneous table
Algorithms
Clustering
DBSCAN
K-Means
K-Means initialization
Covariance
Covariance
Decomposition
Principal Components Analysis (PCA)
Ensembles
Decision Forest Classification and Regression (DF)
Graph
Subgraph Isomorphism
Connected Components
Kernel Functions
Linear kernel
Polynomial kernel
Radial Basis Function (RBF) kernel
Sigmoid kernel
Nearest Neighbors (kNN)
k-Nearest Neighbors Classification (k-NN)
Pairwise Distances
Minkowski distance
Chebyshev distance
Cosine distance
Statistics
Basic Statistics
Support Vector Machines
Support Vector Machine Classifier (SVM)
Distributed Model: Single Process Multiple Data
Distributed SPMD model
Communicators
Contributing Guide
Coding Guidelines
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Library Usage
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Algorithms
Algorithm Input
Algorithm Output
Algorithm Parameters
Computation Modes
Batch processing
Online processing
Distributed processing
Training and Prediction
Classification Usage Model
Regression Usage Model
Recommendation Systems Usage Model
CPU and GPU Support
Algorithms
