Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2
▼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 | |
▼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 |
▼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.