oneAPI Data Analytics Library 2021.1 documentation
Data Analytics Pipeline
oneAPI Interfaces
Get Started with oneDAL
Build applications with oneDAL
Glossary
Computational Modes
Data Management
Array
Accessors
Column accessor
Row accessor
Data Sources
CSV data source
Tables
Homogeneous table
Algorithms
Clustering
K-Means
K-Means initialization
Decomposition
Principal Components Analysis (PCA)
Ensembles
Decision Forest Classification and Regression (DF)
Kernel Functions
Linear kernel
Radial Basis Function (RBF) kernel
Nearest Neighbors (kNN)
k-Nearest Neighbors Classification (k-NN)
Support Vector Machines
Support Vector Machine Classifier (SVM)
oneAPI Examples
DPC++
column_accessor_homogen.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_brute_force_dense_batch.cpp
linear_kernel_dense_batch.cpp
pca_cor_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
C++
column_accessor_homogen.cpp
df_cls_dense_batch.cpp
df_reg_dense_batch.cpp
graph_service_functions.cpp
jaccard_batch.cpp
jaccard_batch_app.cpp
kmeans_init_dense.cpp
kmeans_lloyd_dense_batch.cpp
knn_cls_kd_tree_dense_batch.cpp
linear_kernel_dense_batch.cpp
load_graph.cpp
pca_dense_batch.cpp
rbf_kernel_dense_batch.cpp
svm_two_class_smo_dense_batch.cpp
svm_two_class_thunder_dense_batch.cpp
Appendix
k-d Tree
DAAL Interfaces
CPU and GPU Support
Library Usage
Algorithms
Computation Modes
Training and Prediction
Classification Usage Model
Regression Usage Model
Recommendation Systems Usage Model
Data Management
Numeric Tables
Generic Interfaces
Essential Interfaces for Algorithms
Types of Numeric Tables
Data Sources
Data Dictionaries
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
Batch and Online Processing
Distributed Processing
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
Online Processing
Distributed Processing
Quantile
Quality Metrics
Working with the Default Metric Set
Quality Metrics for Binary Classification Algorithms
Quality Metrics for Multi-class Classification Algorithms
Quality Metrics for Linear Regression
Quality Metrics for Principal Components Analysis
Working with User-defined Quality Metrics
Sorting
Normalization
Z-score
Min-max
Optimization Solvers
Objective Function
Computation
Sum of Functions
Mean Squared Error Algorithm
Objective Function with Precomputed Characteristics Algorithm
Logistic Loss
Cross-entropy Loss
Iterative Solver
Computation
Limited-Memory Broyden-Fletcher-Goldfarb-Shanno Algorithm
Stochastic Gradient Descent Algorithm
Adaptive Subgradient Method
Coordinate Descent Algorithm
Stochastic Average Gradient Accelerated Method
Training and Prediction
Decision Forest
Decision Forest
Regression Decision Forest
Classification Decision Forest
Decision Trees
Decision Tree
Regression Decision Tree
Classification Decision Tree
Gradient Boosted Trees
Gradient Boosted Trees
Regression Gradient Boosted Trees
Classification Gradient Boosted Trees
Stump
Classification Stump
Regression Stump
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
Multi-class Classifier
Boosting
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
Notices and Disclaimers
.rst
.pdf
Usage Model: Training and Prediction
¶
Typical workflows:
Classification Usage Model
Regression Usage Model
Recommendation Systems Usage Model
Computation Modes
Classification Usage Model
