C++ API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

Modules
Here is a list of all modules:
[detail level 12345678]
 Algorithms
 AnalysisContains classes for analysis algorithms that are intended to uncover the underlying structure of a data set and to characterize it by a set of quantitative measures, such as statistical moments, correlations coefficients, and so on
 Association RulesContains classes for the association rules algorithm.
 BACON Outlier DetectionContains classes for computing the BACON outlier detection.
 Cholesky DecompositionContains classes for computing Cholesky decomposition.
 Correlation Distance MatrixContains classes for computing the correlation distance.
 Correlation and Variance-Covariance MatricesContains classes for computing the correlation or variance-covariance matrix.
 Cosine Distance MatrixContains classes for computing the cosine distance.
 DistributionsContains classes for distributions.
 EnginesContains classes for engines.
 Expectation-MaximizationContains classes for the EM for GMM algorithm.
 K-means ClusteringContains classes of the K-Means algorithm.
 Kernel FunctionsContains classes for computing kernel functions.
 Math FunctionsContains classes for computing math functions.
 Moments of Low OrderContains classes for computing the results of the low order moments algorithm.
 Multivariate Outlier DetectionContains classes for computing the multivariate outlier detection.
 NormalizationContains classes to run the min-max normalization algorithms.
 Optimization SolversContains classes for optimization solver algorithms.
 Principal Component AnalysisContains classes for computing the results of the principal component analysis (PCA) algorithm.
 QR DecompositionContains classes for computing the results of the QR decomposition algorithm.
 Quality MetricsContains classes for checking the quality of the classification algorithms.
 QuantileContains classes to run the quantile algorithms.
 Singular Value DecompositionContains classes to run the singular-value decomposition (SVD) algorithm.
 SortingContains classes to run the sorting algorithms.
 Univariate Outlier DetectionContains classes for computing results of the univariate outlier detection algorithm.
 Base ClassesContains classes that implement algorithms for data analysis(data mining), and data modeling(training and prediction). These algorithms include matrix decompositions, clustering algorithms, classification and regression algorithms, as well as association rules discovery.
 Training and PredictionContains classes of machine learning algorithms. Unlike analysis algorithms, which are intended to characterize the structure of data sets, machine learning algorithms model the data. Modeling operates in two major stages: training and prediction or decision making
 Base Decision ForestContains base classes of the decision forest algorithm
 Base Decision TreeContains base classes for Decision tree algorithm
 Base Gradient Boosted TreesContains base classes of the gradient boosted trees algorithm
 ClassificationContains classes for work with the classification algorithms
 Neural NetworksContains classes for training and prediction using neural network.
 Recommendation SystemsContains classes to work with recommendation systems
 RegressionContains classes for work with the regression algorithms
 Tree utilsContains classes for work with the tree-based algorithms
 ComputationContains classes of the DBSCAN algorithm.
 Batch
 Distributed
 Data ManagementContains classes that implement data management functionality, including NumericTables, DataSources, and Compression.
 Data CompressionContains classes for data compression and decompression
 Data DictionariesContains classes that represent a dictionary of a data set and provide methods to work with the data dictionary
 Data ModelContains classes that provide functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself
 Data Serialization and DeserializationContains classes that implement serialization and deserialization
 Data SourcesSpecifies methods to access data
 ModifiersDefines special components which can be used to modify data during the loading through the data source components
 Numeric TablesContains classes for a data management component responsible for representation of data in the numeric format
 Numeric TensorsContains classes for a data management component responsible for representation of data in the n-dimensions numeric format
 ServicesContains classes that implement service functionality, including error handling, memory allocation, and library version information.
 Extracting Version InformationProvides information about the version of Intel(R) Data Analytics Acceleration Library
 Handling ErrorsContains classes and methods to handle exceptions or errors that can occur during library operation
 Managing MemoryContains classes that implement memory allocation and deallocation
 Managing the Computational EnvironmentProvides methods to interact with the environment, including processor detection and control by the number of threads
 Stochastic average Gradient Descent AlgorithmContains classes for computing the Stochastic average gradient descent.
 Batch

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