Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5
| ▼Algorithms | |
| ▼Analysis | |
| ►Association Rules | Contains classes for the association rules algorithm |
| ►BACON Outlier Detection | Contains classes for computing the multivariate outlier detection |
| ►Cholesky Decomposition | Contains classes for computing Cholesky decomposition |
| ►Correlation Distance Matrix | Contains classes for computing the correlation distance |
| ►Correlation and Variance-Covariance Matrices | Contains classes for computing the correlation or variance-covariance matrix |
| ►Cosine Distance Matrix | Contains classes for computing the cosine distance |
| ►Distributions | Contains classes for distributions |
| ►Engines | Contains classes for engines |
| ►Expectation-Maximization | Contains classes for the EM for GMM algorithm |
| ►K-means Clustering | Contains classes for the K-Means algorithm |
| ►Kernel Functions | Contains classes for computing kernel functions |
| ►Math Functions | Contains classes for computing math functions |
| ►Moments of Low Order | Contains classes for computing the results of the low order moments algorithm |
| ►Multivariate Outlier Detection | Contains classes for computing the multivariate outlier detection |
| ►Normalization | Contains classes for computing normalization algorithms |
| ►Optimization Solvers | Contains classes for optimization solver algorithms |
| ►Principal Component Analysis | Contains classes for computing the results of the principal component analysis (PCA) algorithm |
| ►QR Decomposition | Contains classes for computing the results of the QR decomposition algorithm |
| ►Quality Metrics | Contains classes for checking the quality of the classification algorithms |
| ►Quantile | Contains classes to run the quantile algorithms |
| ►Singular Value Decomposition | Contains classes to run the singular-value decomposition (SVD) algorithm |
| ►Sorting | Contains classes to run the sorting algorithms |
| ►Univariate Outlier Detection | Contains classes for computing results of the univariate outlier detection algorithm |
| Base Classes | |
| ▼Training and Prediction | |
| ►Classification | |
| ►Decision forest | |
| ►Decision tree | |
| ►Gradient Boosted Trees | Contains base classes of the gradient boosted trees algorithm |
| ►Neural Networks | Contains classes for training and prediction using neural network |
| ►Recommendation Systems | |
| ►Regression | |
| ▼Computation | |
| Batch | |
| ▼Data Management | Contains classes that implement data management functionality, including NumericTables, DataSources, and Compression |
| Data Compression | Contains classes for data compression and decompression |
| Data Dictionaries | Contains classes that represent a dictionary of a data set and provide methods to work with the data dictionary |
| Data Model | Contains classes that provide functionality of Collection container for objects derived from SerializableBase |
| Data Serialization and Deserialization | Contains classes that implement serialization and deserialization |
| Data Sources | Specifies methods to access data |
| Numeric Tables | Contains classes for a data management component responsible for representation of data in the numeric format |
| Numeric Tensors | Contains classes for a data management component responsible for representation of data in the n-dimensions numeric format |
| Distributed | |
| ▼Services | Contains classes that implement service functionality, including error handling, memory allocation, and library version information |
| Extracting Version Information | Provides information about the version of Intel(R) Data Analytics Acceleration Library |
| Managing Memory | Contains classes that implement memory allocation and deallocation |
| Managing the Computational Environment | Provides methods to interact with the environment, including processor detection and control by the number of threads |
For more complete information about compiler optimizations, see our Optimization Notice.