C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

Modules
Here is a list of all modules:
[detail level 12345678]
oAlgorithms
|oAnalysisContains 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
||oAssociation RulesContains classes for the association rules algorithm.
||oBACON Outlier DetectionContains classes for computing the BACON outlier detection.
||oCholesky DecompositionContains classes for computing Cholesky decomposition.
||oCorrelation Distance MatrixContains classes for computing the correlation distance.
||oCorrelation and Variance-Covariance MatricesContains classes for computing the correlation or variance-covariance matrix.
||oCosine Distance MatrixContains classes for computing the cosine distance.
||oDistributionsContains classes for distributions.
||oEnginesContains classes for engines.
||oExpectation-MaximizationContains classes for the EM for GMM algorithm.
||oK-means ClusteringContains classes of K-Means algorithm.
||oKernel FunctionsContains classes for computing kernel functions.
||oMath FunctionsContains classes for computing math functions.
||oMoments of Low OrderContains classes for computing the results of the low order moments algorithm.
||oMultivariate Outlier DetectionContains classes for computing the multivariate outlier detection.
||oNormalizationContains classes to run the min-max normalization algorithms.
||oOptimization SolversContains classes for optimization solver algorithms.
||oPrincipal Component AnalysisContains classes for computing the results of the principal component analysis (PCA) algorithm.
||oQR DecompositionContains classes for computing the results of the QR decomposition algorithm.
||oQuality MetricsContains classes for checking the quality of the classification algorithms.
||oQuantileContains classes to run the quantile algorithms.
||oSingular Value DecompositionContains classes to run the singular-value decomposition (SVD) algorithm.
||oSortingContains classes to run the sorting algorithms.
||\Univariate Outlier DetectionContains classes for computing results of the univariate outlier detection algorithm.
|oBase 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
| oBase Decision ForestContains base classes of the decision forest algorithm
| oBase Decision TreeContains base classes for Decision tree algorithm
| oBase Gradient Boosted TreesContains base classes of the gradient boosted trees algorithm
| oClassificationContains classes for work with the classification algorithms
| oNeural NetworksContains classes for training and prediction using neural network.
| oRecommendation SystemsContains classes to work with recommendation systems
| oRegressionContains classes for work with the regression algorithms
| \Tree utilsContains classes for work with the tree-based algorithms
oComputationContains classes of the DBSCAN algorithm.
|oBatch
|\Distributed
oData ManagementContains classes that implement data management functionality, including NumericTables, DataSources, and Compression.
|oData CompressionContains classes for data compression and decompression
|oData DictionariesContains classes that represent a dictionary of a data set and provide methods to work with the data dictionary
|oData ModelContains classes that provide functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself
|oData Serialization and DeserializationContains classes that implement serialization and deserialization
|oData SourcesSpecifies methods to access data
||\ModifiersDefines special components which can be used to modify data during the loading through the data source components
|oNumeric 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
oPredictionContains classes to make prediction based on the decision stump model.
|\Batch
oPredictionContains classes to make prediction based on the decision stump model.
|\Batch
oServicesContains classes that implement service functionality, including error handling, memory allocation, and library version information.
|oExtracting Version InformationProvides information about the version of Intel(R) Data Analytics Acceleration Library
|oHandling ErrorsContains classes and methods to handle exceptions or errors that can occur during library operation
|oManaging 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
oStochastic average Gradient Descent AlgorithmContains classes for computing the Stochastic average gradient descent.
|\Batch
oTrainingContains classes to train the decision stump model.
|\Batch
\TrainingContains classes to train the decision stump model.
 \Batch

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