Java* API Reference for Intel® Data Analytics Acceleration Library 2018 Update 3
Contains 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.
Packages | |
package | adaboost |
Contains classes for the AdaBoost classification algorithm. | |
package | association_rules |
Contains classes for the association rules algorithm. | |
package | bacon_outlier_detection |
Contains classes for computing results of the multivariate outlier detection algorithm with BACON method. | |
package | boosting |
Contains base classes for working with boosting classifiers. | |
package | brownboost |
Contains classes of the BrownBoost classification algorithm. | |
package | cholesky |
Contains classes for computing the Cholesky decomposition. | |
package | classifier |
Contains base classes for working with classification algorithms. | |
package | covariance |
Contains classes for computing the correlation or variance-covariance matrix in the batch processing mode. | |
package | distributions |
Contains classes for the distributions. | |
package | em_gmm |
Contains classes for running the EM for GMM algorithm. | |
package | engines |
Contains classes for the engines. | |
package | implicit_als |
Contains classes for computing the results of the implicit ALS algorithm. | |
package | kernel_function |
Contains classes for computing kernel functions. | |
package | kmeans |
Contains classes for computing K-Means. | |
package | linear_regression |
Contains classes for computing the result of the linear regression algorithm. | |
package | logitboost |
Contains classes of the LogitBoost classification algorithm. | |
package | low_order_moments |
Contains classes for computing moments of low order. | |
package | multi_class_classifier |
Contains classes for computing the results of the multi-class classifier. | |
package | multinomial_naive_bayes |
Contains classes for computing the Naive Bayes. | |
package | multivariate_outlier_detection |
Contains classes for computing the results of the multivariate outlier detection algorithm with the default method. | |
package | neural_networks |
Contains classes for for training and prediction using neural network. | |
package | optimization_solver |
Contains classes for computing the optimization solvers. | |
package | pca |
Contains classes for running the principal component analysis (PCA) algorithm in the batch processing mode. | |
package | pivoted_qr |
Contains classes for computing the pivoted QR decomposition. | |
package | qr |
Contains classes for computing the QR decomposition. | |
package | quality_metric |
Contains classes to compute quality metrics. | |
package | quality_metric_set |
Contains classes to compute a quality metric set. | |
package | quantiles |
Contains classes to run the quantile algorithms. | |
package | regression |
Interface of callback object for decision tree regression model traversal. | |
package | ridge_regression |
Contains classes for computing the result of the ridge regression algorithm. | |
package | sorting |
Contains classes to run the sorting. | |
package | svd |
Contains classes to run the singular-value decomposition (SVD) algorithm. | |
package | svm |
Contains classes of the support vector machine (SVM) classification algorithm. | |
package | univariate_outlier_detection |
Contains classes for computing results of the univariate outlier detection algorithm. | |
package | weak_learner |
Contains classes for working with weak learner. | |
Classes | |
class | Algorithm |
Algorithm is the base class for the classes interfacing the major stages of data processing: Analysis, Training and Prediction. More... | |
class | AnalysisBatch |
Provides 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. More... | |
class | AnalysisDistributed |
Provides 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. More... | |
class | AnalysisOnline |
Provides 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. More... | |
class | ComputeMode |
class | ComputeStep |
class | Input |
Base class to represent computation input arguments. Algorithm-specific input arguments are represented as derivative classes of the Input class. More... | |
class | InputBatch |
Base 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. More... | |
class | Model |
Model is the base class for the classes that represent the models, such as linear regression or Support Vector Machine classifier. More... | |
class | OptionalArgument |
Class that provides functionality of the Collection container for Serializable objects. More... | |
class | Parameter |
Base class to represent computation parameters. Algorithm-specific parameters are represented as derivative classes of the Parameter class. More... | |
class | PartialResult |
Base class to represent partial results of the computation. Algorithm-specific partial results are represented as derivative classes of the PartialResult class. More... | |
class | Precision |
Available precisions for algorithms. More... | |
class | Prediction |
Provides 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. More... | |
class | PredictionDistributed |
Provides 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. More... | |
class | Result |
Base class to represent final results of the computation. Algorithm-specific final results are represented as derivative classes of the Result class. More... | |
class | TrainingBatch |
Provides 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. More... | |
class | TrainingDistributed |
Provides 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. More... | |
class | TrainingOnline |
Provides 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. More... | |
For more complete information about compiler optimizations, see our Optimization Notice.