▼Ncom | |
▼Nintel | |
▼Ndaal | Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) package |
▼Nalgorithms | 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 |
►Nadaboost | Contains classes for the AdaBoost classification algorithm |
Nprediction | Contains classes for predictions based on AdaBoost models |
Nquality_metric_set | Contains classes to check the quality of the model trained with the AdaBoost algorithm |
Ntraining | Contains classes for training AdaBoost models |
Nassociation_rules | Contains classes for the association rules algorithm |
Nbacon_outlier_detection | Contains classes for computing results of the multivariate outlier detection algorithm with BACON method |
►Nboosting | Contains base classes for working with boosting classifiers |
Nprediction | Contains classes for predictions based on boosting classifiers models |
Ntraining | Contains classes for training models of boosting classifiers |
►Nbrownboost | Contains classes of the BrownBoost classification algorithm |
Nprediction | Contains classes for predictions based on BrownBoost models |
Nquality_metric_set | Contains classes to check the quality of the model trained with the BrownBoost algorithm |
Ntraining | Contains classes for training BrownBoost models |
Ncholesky | Contains classes for computing the Cholesky decomposition |
►Nclassifier | Contains base classes for working with classification algorithms |
Nprediction | Contains classes for making prediction based on the classification model |
►Nquality_metric | |
Nbinary_confusion_matrix | Contains classes for computing the binary confusion matrix |
Nmulti_class_confusion_matrix | Contains classes for computing the multi-class confusion matrix |
Ntraining | Contains classes for training the classification model |
Ncovariance | Contains classes for computing the correlation or variance-covariance matrix in the batch processing mode |
►Ndecision_forest | |
►Nclassification | |
Nprediction | Contains classes for predictions based on decision forest classification models |
Ntraining | Contains classes of the decision forest classification algorithm training |
►Nregression | Contains classes of the decision forest regression algorithm |
Nprediction | Contains classes for making prediction based on the decision_forest regression model |
Ntraining | Contains classes for training the regression model |
►Ndecision_tree | |
►Nclassification | Contains classes of the decision tree classification algorithm |
Nprediction | Contains classes for predictions based on decision tree classification models |
Ntraining | Contains classes for training decision tree classification algorithm |
►Nregression | Contains classes of the decision tree regression algorithm |
Nprediction | Contains classes for making prediction based on the decision_tree regression model |
Ntraining | Contains classes for training decision tree regression models |
►Ndistributions | Contains classes for the distributions |
Nbernoulli | Contains classes for the bernoulli distribution |
Nnormal | Contains classes for the normal distribution |
Nuniform | Contains classes for the uniform distribution |
►Nem_gmm | Contains classes for running the EM for GMM algorithm |
Ninit | Contains classes for initializing the EM for GMM algorithm |
►Nengines | Contains classes for the engines |
Nmcg59 | Contains classes for the mcg59 engine |
Nmt19937 | Contains classes for the mt19937 engine |
►Ngbt | |
►Nclassification | |
Nprediction | Contains classes for predictions based on gradient boosted trees classification models |
Ntraining | Contains classes of the gradient boosted trees classification algorithm training |
►Nregression | Contains classes of the gradient boosted trees regression algorithm |
Nprediction | Contains classes for making prediction based on the gbt regression model |
Ntraining | Contains classes for training the regression model |
Ntraining | Contains classes of the gradient boosted trees algorithm training |
►Nimplicit_als | Contains classes for computing the results of the implicit ALS algorithm |
►Nprediction | |
Nratings | Contains classes for computing ratings based on the implicit ALS model |
►Ntraining | Contains classes of the implicit ALS training algorithm |
Ninit | Contains classes for the implicit ALS initialization algorithm |
►Nkdtree_knn_classification | |
Nprediction | Contains classes for making prediction based on the K nearest neighbors models |
Nkernel_function | Contains classes for computing kernel functions |
►Nkmeans | Contains classes for computing K-Means |
Ninit | Contains classes for computing initial clusters for the K-Means algorithm in the batch processing mode |
►Nlinear_regression | Contains classes for computing the result of the linear regression algorithm |
Nprediction | Contains classes for linear regression model-based prediction |
Nquality_metric | Contains classes for computing the single beta metric |
Nquality_metric_set | Contains classes to check the quality of the model trained with linear regression algorithm |
Ntraining | Contains classes for linear regression model-based training |
►Nlogitboost | Contains classes of the LogitBoost classification algorithm |
Nprediction | Contains classes for predictions based on LogitBoost models |
Nquality_metric_set | Contains classes to check the quality of the model trained with the LogitBoost algorithm |
Ntraining | Contains classes for training LogitBoost models |
Nlow_order_moments | Contains classes for computing moments of low order |
►Nmath | |
Nabs | Contains classes for computing the absolute value function |
Nlogistic | Contains classes for computing the logistic function |
Nrelu | Contains classes for computing the rectified linear function |
Nsmoothrelu | Contains classes for computing the SmoothReLU algorithm |
Nsoftmax | Contains classes for computing the softmax function |
Ntanh | Contains classes for computing the hyperbolic tangent function |
►Nmulti_class_classifier | Contains classes for computing the results of the multi-class classifier |
Nprediction | Contains classes for making prediction based on the Multi-class classifier models |
Nquality_metric_set | Contains classes to check the quality of the model trained with the multi-class SVM algorithm |
Ntraining | Contains classes for multi-class classifier model training |
►Nmultinomial_naive_bayes | Contains classes for computing the Naive Bayes |
Nprediction | Contains classes for multinomial naive Bayes model based prediction |
Nquality_metric_set | Contains classes to check the quality of the model trained with the multinomial naive Bayes algorithm |
Ntraining | Contains classes for multinomial naive Bayes models training |
►Nmultivariate_outlier_detection | Contains classes for computing the results of the multivariate outlier detection algorithm with the default method |
Nbacondense | Contains classes for computing results of the multivariate outlier detection algorithm with BACON method |
Ndefaultdense | Contains classes for computing the results of the multivariate outlier detection algorithm with the default method |
►Nneural_networks | Contains classes for for training and prediction using neural network |
►Ninitializers | Contains classes for the neural network weights and biases initializers |
Ngaussian | Contains classes for the gaussian initializer |
Ntruncated_gaussian | Contains classes for the truncated gaussian initializer |
Nuniform | Contains classes for the uniform initializer |
Nxavier | Contains classes for the Xavier initializer |
►Nlayers | Contains classes for the neural network layers |
Nabs | Contains classes of the abs layer |
Naverage_pooling1d | Contains classes of the one-dimensional (1D) average pooling layer |
Naverage_pooling2d | Contains classes of the two-dimensional (2D) average pooling layer |
Naverage_pooling3d | Contains classes of the three-dimensional (3D) average pooling layer |
Nbatch_normalization | Contains classes of the batch normalization layer |
Nconcat | Contains classes of the concat layer |
Nconvolution2d | Contains classes of the two-dimensional (2D) convolution layer |
Ndropout | Contains classes of the dropout layer |
Neltwise_sum | Contains classes of the element-wise sum layer |
Nelu | Contains classes of the Exponential Linear Unit (ELU) layer |
Nfullyconnected | Contains classes of the fully-connected layer |
Nlcn | Contains classes of the local contrast normalization layer |
Nlocallyconnected2d | Contains classes of the two-dimensional (2D) locally connected layer |
Nlogistic | Contains classes of the logistic layer |
Nlogistic_cross | Contains classes of thelogistic cross-entropy layer |
Nloss | Contains classes of the loss layer |
Nlrn | Contains classes of the local response normalization layer |
Nmaximum_pooling1d | Contains classes of the one-dimensional (1D) maximum pooling layer |
Nmaximum_pooling2d | Contains classes of the two-dimensional (2D) maximum pooling layer |
Nmaximum_pooling3d | Contains classes of the three-dimensional (3D) maximum pooling layer |
Npooling1d | Contains classes of the one-dimensional (1D) pooling layers |
Npooling2d | Contains classes of the two-dimensional (2D) pooling layers |
Npooling3d | Contains classes of the three-dimensional (3D) pooling layers |
Nprelu | Contains classes of the prelu layer |
Nrelu | Contains classes of the rectified linear unit (relu) layer |
Nreshape | Contains classes of the reshape layer |
Nsmoothrelu | Contains classes of the smooth rectified linear unit (smoothrelu) layer |
Nsoftmax | Contains classes of the softmax layer |
Nsoftmax_cross | Contains classes of thesoftmax cross-entropy layer |
Nspatial_average_pooling2d | Contains classes of the two-dimensional (2D) spatial average pooling layer |
Nspatial_maximum_pooling2d | Contains classes of the two-dimensional (2D) spatial maximum pooling layer |
Nspatial_pooling2d | Contains classes of the two-dimensional (2D) pooling layers |
Nspatial_stochastic_pooling2d | Contains classes of the two-dimensional (2D) spatial stochastic pooling layer |
Nsplit | Contains classes of the split layer |
Nstochastic_pooling2d | Contains classes of the two-dimensional (2D) stochastic pooling layer |
Ntanh | Contains classes of the hyperbolic tangent (tanh) layer |
Ntransposed_conv2d | Contains classes of the two-dimensional (2D) transposed convolution layer |
Nprediction | Contains classes for making prediction based on the trained model |
Ntraining | Contains classes for training the model of the neural network |
►Nnormalization | |
Nminmax | Contains classes for computing Min-max normalization solvers |
Nzscore | Contains classes for computing Z-score normalization solvers |
►Noptimization_solver | Contains classes for computing the optimization solvers |
Nadagrad | Contains classes for computing Adagrad algorithm |
Niterative_solver | Contains classes for computing iterative solver algorithm |
Nlbfgs | Contains classes for computing limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm |
Nmse | Contains classes for computing the mse algorithm |
Nobjective_function | Contains classes for computing objective functions |
Nsgd | Contains classes for computing Stochastic gradient descent algorithm |
Nsum_of_functions | Contains classes for the objective functions that could be represented as a sum of functions |
►Npca | Contains classes for running the principal component analysis (PCA) algorithm in the batch processing mode |
Nquality_metric | Contains classes for computing the explained variance metric |
Nquality_metric_set | Contains classes to check the quality of the model trained with the PCA algorithm |
Ntransform | Contains classes for computing PCA transformation solvers |
Npivoted_qr | Contains classes for computing the pivoted QR decomposition |
Nqr | Contains classes for computing the QR decomposition |
Nquality_metric | Contains classes to compute quality metrics |
Nquality_metric_set | Contains classes to compute a quality metric set |
Nquantiles | Contains classes to run the quantile algorithms |
Nregression | Interface of callback object for decision tree regression model traversal |
►Nridge_regression | Contains classes for computing the result of the ridge regression algorithm |
Nprediction | Contains classes for ridge regression model-based prediction |
Ntraining | Contains classes for ridge regression model-based training |
Nsorting | Contains classes to run the sorting |
►Nstump | |
Nprediction | Contains classes to make prediction based on the decision stump model |
Ntraining | Contains classes to train the decision stump model |
Nsvd | Contains classes to run the singular-value decomposition (SVD) algorithm |
►Nsvm | Contains classes of the support vector machine (SVM) classification algorithm |
Nprediction | Contains classes to make predictions based on the SVM model |
Nquality_metric_set | Contains classes to check the quality of the model trained with the SVM algorithm |
Ntraining | Contains classes to train the SVM model |
Nunivariate_outlier_detection | Contains classes for computing results of the univariate outlier detection algorithm |
►Nweak_learner | Contains classes for working with weak learner |
Nprediction | Contains classes for making predictions based on the weak learner model |
Ntraining | Contains classes for training the weak learner model |
▼Ndata_management | |
Ncompression | Contains classes for data compression and decompression |
Ndata | Contains classes that implement the data management component responsible for representaion of the data in numerical format |
Ndata_source | Contains classes that implement the data source component responsible for representation of the data in a raw format |
Nservices | Contains classes that implement service functionality including memory management, information about environment, and library version information |