Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3

Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12345678]
 Ncom
 Nintel
 NdaalIntel(R) Data Analytics Acceleration Library (Intel(R) DAAL) package
 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 results of the multivariate outlier detection algorithm with BACON method
 NboostingContains base classes for working with boosting classifiers
 NbrownboostContains classes of the BrownBoost classification algorithm
 NcholeskyContains classes for computing the Cholesky decomposition
 NclassifierContains base classes for working with classification algorithms
 Ncordistance
 Ncosdistance
 NcovarianceContains classes for computing the correlation or variance-covariance matrix in the batch processing mode
 Ndecision_forest
 Ndecision_tree
 NdistributionsContains classes for the distributions
 Nem_gmmContains classes for running the EM for GMM algorithm
 NenginesContains classes for the engines
 Ngbt
 Nimplicit_alsContains classes for computing the results of the implicit ALS algorithm
 Nkdtree_knn_classification
 Nkernel_functionContains classes for computing kernel functions
 NkmeansContains classes for computing K-Means
 Nlinear_regressionContains classes for computing the result of the linear regression algorithm
 NlogitboostContains classes of the LogitBoost classification algorithm
 Nlow_order_momentsContains classes for computing moments of low order
 Nmath
 Nmulti_class_classifierContains classes for computing the results of the multi-class classifier
 Nmultinomial_naive_bayesContains classes for computing the Naive Bayes
 Nmultivariate_outlier_detectionContains classes for computing the results of the multivariate outlier detection algorithm with the default method
 Nneural_networksContains classes for for training and prediction using neural network
 Nnormalization
 Noptimization_solverContains classes for computing the optimization solvers
 NpcaContains classes for running the principal component analysis (PCA) algorithm in the batch processing mode
 Npivoted_qrContains classes for computing the pivoted QR decomposition
 NqrContains classes for computing the QR decomposition
 Nquality_metricContains classes to compute quality metrics
 Nquality_metric_setContains classes to compute a quality metric set
 NquantilesContains classes to run the quantile algorithms
 NregressionInterface of callback object for decision tree regression model traversal
 Nridge_regressionContains classes for computing the result of the ridge regression algorithm
 NsortingContains classes to run the sorting
 Nstump
 NsvdContains classes to run the singular-value decomposition (SVD) algorithm
 NsvmContains classes of the support vector machine (SVM) classification algorithm
 Nunivariate_outlier_detectionContains classes for computing results of the univariate outlier detection algorithm
 Nweak_learnerContains classes for working with weak learner
 CAlgorithmAlgorithm is the base class for the classes interfacing the major stages of data processing: Analysis, Training and Prediction
 CAnalysisBatchProvides methods for execution of operations over data, such as computation of Summary Statistics estimates in batch processing mode. Classes that implement specific algorithms of the data analysis in batch processing mode are derived classes of the AnalysisBatch class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CAnalysisDistributedProvides methods for execution of operations over data, such as computation of Summary Statistics estimates in distributed processing mode. Classes that implement specific algorithms of the data analysis in distributed processing mode are derived classes of the AnalysisDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CAnalysisOnlineProvides methods for execution of operations over data, such as computation of Summary Statistics estimates in the online processing mode. Classes that implement specific algorithms of the data analysis in the online processing mode are derived classes of the AnalysisOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CComputeMode
 CComputeStep
 CInputBase class to represent computation input arguments. Algorithm-specific input arguments are represented as derivative classes of the Input class
 CInputBatchBase class to represent input arguments of the computation in the batch processing mode. Algorithm-specific input arguments are represented as derivative classes of the InputBatch class
 CModelModel is the base class for the classes that represent the models, such as linear regression or Support Vector Machine classifier
 COptionalArgumentClass that provides functionality of the Collection container for Serializable objects
 CParameterBase class to represent computation parameters. Algorithm-specific parameters are represented as derivative classes of the Parameter class
 CPartialResultBase class to represent partial results of the computation. Algorithm-specific partial results are represented as derivative classes of the PartialResult class
 CPrecisionAvailable precisions for algorithms
 CPredictionProvides prediction methods depending on the model such as linearregression.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
 CPredictionDistributedProvides prediction methods depending on the model such as linearregression.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 PredictionDistributed class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms
 CResultBase class to represent final results of the computation. Algorithm-specific final results are represented as derivative classes of the Result class
 CTrainingBatchProvides methods to train models that depend on the data provided in batch mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in batch mode are derived classes of the TrainingBatch class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CTrainingDistributedProvides methods to train models that depend on the data provided in the distributed processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the distributed processing mode are derived classes of the TrainingDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms
 CTrainingOnlineProvides methods to train models that depend on the data provided in the online processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the online processing mode are derived classes of the TrainingOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms
 Ndata_management
 NcompressionContains classes for data compression and decompression
 NdataContains classes that implement the data management component responsible for representaion of the data in numerical format
 Ndata_sourceContains classes that implement the data source component responsible for representation of the data in a raw format
 NservicesContains classes that implement service functionality including memory management, information about environment, and library version information
 CContextClientClass for management by deletion of the memory allocated for the native C++ object
 CCpuTypeCPU types
 CCpuTypeEnableCPU types
 CDaalContextProvides the context for managment of memory in the native C++ object
 CDisposableClass that frees memory allocated for the native C++ object
 CEnvironmentProvides information about computational environment
 CLibraryVersionInfoProvides information about the version of Intel(R) Data Analytics Acceleration Library
 CSerializationTag

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