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