C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 2

Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
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 Ndaal
 NalgorithmsContains 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
 NadaboostContains classes for the AdaBoost classification algorithm
 Nassociation_rulesContains classes for the association rules algorithm
 Nbacon_outlier_detectionContains classes for computing the BACON outlier detection
 NboostingContains classes of boosting classification algorithms
 NbrownboostContains classes for the BrownBoost classification algorithm
 NcholeskyContains classes for computing Cholesky decomposition
 NclassifierContains classes for working with classifiers
 Ncorrelation_distanceContains classes for computing the correlation distance
 Ncosine_distanceContains classes for computing the cosine distance
 NcovarianceContains classes for computing the correlation or variance-covariance matrix
 Ndecision_forestContains classes of the decision forest algorithm
 Ndecision_treeContains classes for Decision tree algorithm
 NdistributionsContains classes for distributions
 Nem_gmmContains classes for the EM for GMM algorithm
 NenginesContains classes for engines
 NgbtContains classes of the gradient boosted trees algorithm
 Nimplicit_alsContains classes of the implicit ALS algorithm
 Ninterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 Nkdtree_knn_classificationContains classes for KD-tree based kNN algorithm
 Nkernel_functionContains classes for computing kernel functions
 NkmeansContains classes of the K-Means algorithm
 Nlinear_modelContains classes of the regression algorithm
 Nlinear_regressionContains classes of the linear regression algorithm
 NlogitboostContains classes for the LogitBoost classification algorithm
 Nlow_order_momentsContains classes for computing the results of the low order moments algorithm
 NmathContains classes for computing math functions
 Nmulti_class_classifierContains classes for computing the results of the multi-class classifier algorithm
 Nmultinomial_naive_bayesContains classes for multinomial Naive Bayes algorithm
 Nmultivariate_outlier_detectionContains classes for computing the multivariate outlier detection
 Nneural_networksContains classes for training and prediction using neural network
 NnormalizationContains classes to run the min-max normalization algorithms
 Noptimization_solverContains classes for optimization solver algorithms
 NpcaContains classes for computing the results of the principal component analysis (PCA) algorithm
 Npivoted_qrContains classes for computing the pivoted QR decomposition
 NqrContains classes for computing the results of the QR decomposition algorithm
 Nquality_metricContains classes to compute quality metrics
 Nquality_metric_setContains classes to compute a quality metric set
 NquantilesContains classes to run the quantile algorithms
 NregressionContains base classes for the regression algorithms
 Nridge_regressionContains classes of the ridge regression algorithm
 NsortingContains classes to run the sorting algorithms
 NstumpContains classes to work with the decision stump training algorithm
 NsvdContains classes to run the singular-value decomposition (SVD) algorithm
 NsvmContains classes to work with the support vector machine classifier
 Nunivariate_outlier_detectionContains classes for computing results of the univariate outlier detection algorithm
 Nweak_learnerContains classes for working with weak learners
 CAnalysisProvides methods for execution of operations over data, such as computation of Summary Statistics estimates. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the data analysis are derived classes of the Analysis class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms
 CAnalysisContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the analysis algorithms. It is associated with the Analysis class and supports the methods for computation and finalization of the analysis results in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each algorithm of data analysis
 CDistributedPrediction
 CDistributedPredictionContainerIface
 CPredictionProvides prediction methods depending on the model such as linear_regression::Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the Prediction class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms
 CPredictionContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the algorithms for model based prediction. Is associated with the Prediction class and supports the methods for computing the prediction results based on the trained model. The methods of the container are defined in derivative containers defined for each prediction algorithm
 CTrainingProvides methods to train models that depend on the data provided. For example, these methods enable training the linear regression model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of model training are derived classes of the Training class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CTrainingContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the model training algorithms. The class is associated with the Training class and supports the methods for computation and finalization of the training output in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each training algorithm
 Ndata_managementContains classes that implement data management functionality, including NumericTables, DataSources, and Compression
 Ninterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 CColumnFilterMethods of the class to filter out data source features from output numeric table
 CFeatureAuxDataStructure for auxiliary data used for feature extraction
 CMakeCategoricalMethods of the class to set a feature categorical
 CModifierIfaceAbstract interface class that defines the interface for a features modifier
 COneHotEncoderMethods of the class to set a feature binary categorical
 NservicesContains classes that implement service functionality, including error handling, memory allocation, and library version information
 Ninterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 CBaseBase class for Intel(R) Data Analytics Acceleration Library objects
 CIsSameType
 CIsSameType< U, U >

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