Java* API Reference for Intel® Data Analytics Acceleration Library 2019 Update 5

Packages | Classes
Package com.intel.daal.algorithms

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  dbscan
 Contains classes for computing DBSCAN.
 
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  lasso_regression
 Contains classes for computing the result of the lasso regression algorithm.
 
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.