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

Namespaces | Classes
daal::algorithms Namespace Reference

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.

Namespaces

 adaboost
 Contains classes for the AdaBoost classification algorithm.
 
 association_rules
 Contains classes for the association rules algorithm.
 
 bacon_outlier_detection
 Contains classes for computing the BACON outlier detection.
 
 boosting
 Contains classes of boosting classification algorithms.
 
 brownboost
 Contains classes for the BrownBoost classification algorithm.
 
 cholesky
 Contains classes for computing Cholesky decomposition.
 
 classifier
 Contains classes for working with classifiers.
 
 correlation_distance
 Contains classes for computing the correlation distance.
 
 cosine_distance
 Contains classes for computing the cosine distance.
 
 covariance
 Contains classes for computing the correlation or variance-covariance matrix.
 
 dbscan
 Contains classes of the DBSCAN algorithm.
 
 decision_forest
 Contains classes of the decision forest algorithm.
 
 decision_tree
 Contains classes for Decision tree algorithm.
 
 distributions
 Contains classes for distributions.
 
 elastic_net
 Contains classes of the elastic net algorithm.
 
 em_gmm
 Contains classes for the EM for GMM algorithm.
 
 engines
 Contains classes for engines.
 
 gbt
 Contains classes of the gradient boosted trees algorithm.
 
 implicit_als
 Contains classes of the implicit ALS algorithm.
 
 interface1
 Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface.
 
 kdtree_knn_classification
 Contains classes for KD-tree based kNN algorithm.
 
 kernel_function
 Contains classes for computing kernel functions.
 
 kmeans
 Contains classes of K-Means algorithm.
 
 lasso_regression
 Contains classes of the lasso regression algorithm.
 
 linear_model
 Contains classes of the regression algorithm.
 
 linear_regression
 Contains classes of the linear regression algorithm.
 
 logistic_regression
 Contains classes for the logistic regression algorithm.
 
 logitboost
 Contains classes for the LogitBoost classification algorithm.
 
 low_order_moments
 Contains classes for computing the results of the low order moments algorithm.
 
 math
 Contains classes for computing math functions.
 
 multi_class_classifier
 Contains classes for computing the results of the multi-class classifier algorithm.
 
 multinomial_naive_bayes
 Contains classes for multinomial Naive Bayes algorithm.
 
 multivariate_outlier_detection
 Contains classes for computing the multivariate outlier detection.
 
 neural_networks
 Contains classes for training and prediction using neural network.
 
 normalization
 Contains classes to run the min-max normalization algorithms.
 
 optimization_solver
 Contains classes for optimization solver algorithms.
 
 pca
 Contains classes for computing the results of the principal component analysis (PCA) algorithm.
 
 pivoted_qr
 Contains classes for computing the pivoted QR decomposition.
 
 qr
 Contains classes for computing the results of the QR decomposition algorithm.
 
 quality_metric
 Contains classes to compute quality metrics.
 
 quality_metric_set
 Contains classes to compute a quality metric set.
 
 quantiles
 Contains classes to run the quantile algorithms.
 
 regression
 Contains base classes for the regression algorithms.
 
 ridge_regression
 Contains classes of the ridge regression algorithm.
 
 sorting
 Contains classes to run the sorting algorithms.
 
 stump
 Contains classes to work with the decision stump training algorithm.
 
 svd
 Contains classes to run the singular-value decomposition (SVD) algorithm.
 
 svm
 Contains classes to work with the support vector machine classifier.
 
 univariate_outlier_detection
 Contains classes for computing results of the univariate outlier detection algorithm.
 
 weak_learner
 Contains classes for working with weak learners.
 

Classes

class  AnalysisContainerIface
 Abstract 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. More...
 
class  Analysis
 Provides 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. More...
 
class  PredictionContainerIface
 Abstract 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. More...
 
class  DistributedPredictionContainerIface
 
class  Prediction
 Provides 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. More...
 
class  DistributedPrediction
 
class  TrainingContainerIface
 Abstract 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. More...
 
class  Training
 Provides 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. More...
 

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