C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1
Contains classes for analysis algorithms that are intended to uncover the underlying structure of a data set and to characterize it by a set of quantitative measures, such as statistical moments, correlations coefficients, and so on. More...
References | |
Association Rules | |
Contains classes for the association rules algorithm. | |
BACON Outlier Detection | |
Contains classes for computing the BACON outlier detection. | |
Cholesky Decomposition | |
Contains classes for computing Cholesky decomposition. | |
Correlation Distance Matrix | |
Contains classes for computing the correlation distance. | |
Correlation and Variance-Covariance Matrices | |
Contains classes for computing the correlation or variance-covariance matrix. | |
Cosine Distance Matrix | |
Contains classes for computing the cosine distance. | |
Distributions | |
Contains classes for distributions. | |
Engines | |
Contains classes for engines. | |
Expectation-Maximization | |
Contains classes for the EM for GMM algorithm. | |
K-means Clustering | |
Contains classes of K-Means algorithm. | |
Kernel Functions | |
Contains classes for computing kernel functions. | |
Math Functions | |
Contains classes for computing math functions. | |
Moments of Low Order | |
Contains classes for computing the results of the low order moments algorithm. | |
Multivariate Outlier Detection | |
Contains classes for computing the multivariate outlier detection. | |
Normalization | |
Contains classes to run the min-max normalization algorithms. | |
Optimization Solvers | |
Contains classes for optimization solver algorithms. | |
Principal Component Analysis | |
Contains classes for computing the results of the principal component analysis (PCA) algorithm. | |
QR Decomposition | |
Contains classes for computing the results of the QR decomposition algorithm. | |
Quality Metrics | |
Contains classes for checking the quality of the classification algorithms. | |
Quantile | |
Contains classes to run the quantile algorithms. | |
Singular Value Decomposition | |
Contains classes to run the singular-value decomposition (SVD) algorithm. | |
Sorting | |
Contains classes to run the sorting algorithms. | |
Univariate Outlier Detection | |
Contains classes for computing results of the univariate outlier detection algorithm. | |
Classes | |
class | AnalysisContainerIface< mode > |
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< mode > |
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... | |
For more complete information about compiler optimizations, see our Optimization Notice.