Python* API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1
▼Ndaal | Main module |
►Nalgorithms | Algorithms |
►Nadaboost | |
►Nprediction | |
CBatch | Predict AdaBoost classification results |
CInput | Input objects in the prediction stage of the adaboost algorithm |
Cinterface2_Input | Input objects in the prediction stage of the adaboost algorithm |
►Nquality_metric_set | |
CBatch | Class that represents a set of quality metrics to check the model trained with the AdaBoost algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
Cinterface2_Batch | Class that represents a set of quality metrics to check the model trained with the AdaBoost algorithm |
Cinterface2_InputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
Cinterface2_ResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
CParameter | Parameters for the AdaBoost compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
►Ntraining | |
CBatch | Trains model of the AdaBoost algorithms in batch mode |
Cinterface2_Result | Provides methods to access final results obtained with the compute() method of the AdaBoost training algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method |
Cinterface2_Model | Model of the classifier trained by the adaboost.training.Batch algorithm |
Cinterface2_Parameter | AdaBoost algorithm parameters |
CModel | Model of the classifier trained by the adaboost.training.Batch algorithm |
CParameter | AdaBoost algorithm parameters |
►Nassociation_rules | |
CBatch | Computes the result of the association rules algorithm in the batch processing mode |
CInput | Input for the association rules algorithm |
CParameter | Parameters for the association rules compute() method |
CResult | Results obtained with the compute() method of the association rules algorithm in the batch processing mode |
►Nbacon_outlier_detection | |
CBatch | Abstract class that specifies interface of the algorithms for computing BACON outlier detection in the batch processing mode |
CInput | Input objects for the BACON outlier detection algorithm |
CParameter | Parameters of the outlier detection computation using the baconDense method |
CResult | Results obtained with the compute() method of the BACON outlier detection algorithm in the batch processing mode |
►Nboosting | |
CModel | |
CParameter | Base class for parameters of the boosting algorithm |
►Nbrownboost | |
►Nprediction | |
CBatch | Predicts BrownBoost classification results |
CInput | Input objects in the prediction stage of the brownboost algorithm |
Cinterface2_Input | Input objects in the prediction stage of the brownboost algorithm |
►Nquality_metric_set | |
CBatch | Class that represents a set of quality metrics to check the model trained with the BrownBoost algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
►Ntraining | |
CBatch | Trains model of the BrownBoost algorithms in the batch processing mode |
Cinterface2_Result | Provides methods to access final results obtained with the compute() method of the BrownBoost training algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method |
Cinterface2_Model | Model of the classifier trained by the brownboost.training.Batch algorithm |
Cinterface2_Parameter | BrownBoost algorithm parameters |
CModel | Model of the classifier trained by the brownboost.training.Batch algorithm |
CParameter | BrownBoost algorithm parameters |
►Ncholesky | |
CBatch | Computes Cholesky decomposition in the batch processing mode |
CInput | Input parameters for the Cholesky algorithm |
CResult | Results obtained with the compute() method of the Cholesky algorithm in the batch processing mode |
►Nclassifier | |
►Nprediction | |
CBatch | Base class for making predictions based on the model of the classification algorithms |
CInput | Input objects in the prediction stage of the classification algorithm |
CInputIface | Base class for working with input objects in the prediction stage of the classification algorithm |
Cinterface2_Batch | Base class for making predictions based on the model of the classification algorithms |
Cinterface2_Result | Provides methods to access prediction results obtained with the compute() method of the classifier prediction algorithm in the batch processing mode |
CResult | Provides methods to access prediction results obtained with the compute() method of the classifier prediction algorithm in the batch processing mode |
►Nquality_metric | |
►Nbinary_confusion_matrix | |
CBatch | Computes the confusion matrix for a binary classifier in the batch processing mode |
CInput | Base class for input objects of the binary confusion matrix algorithm |
CParameter | Parameters for the binary confusion matrix compute() method |
CResult | Results obtained with the compute() method of the binary confusion matrix algorithm in the batch processing mode |
►Nmulticlass_confusion_matrix | |
CBatch | Computes the confusion matrix for a multi-class classifier in the batch processing mode |
CInput | Base class for the input objects of the confusion matrix algorithm in the training stage of the classification algorithm |
CParameter | Parameters for the compute() method of the multi-class confusion matrix |
CResult | Results obtained with the compute() method of the multi-class confusion matrix algorithm in the batch processing mode |
►Ntraining | |
CBatch | Algorithm class for training the classifier model |
CInput | Base class for the input objects in the training stage of the classification algorithms |
CInputIface | Abstract class that specifies the interface of the classes of the classification algorithm input objects |
Cinterface2_Batch | Algorithm class for training the classifier model |
Cinterface2_Online | Algorithm class for training the classifier model in the online processing mode |
COnline | Algorithm class for training the classifier model in the online processing mode |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the classifier training algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method in the batch processing mode or finalizeCompute() method in the online or distributed processing mode of the classification algorithm |
Cinterface2_Parameter | Base class for the parameters of the classification algorithm |
CModel | Base class for the model of the classification algorithm |
CParameter | Base class for the parameters of the classification algorithm |
CTreeNodeVisitor | Interface of abstract visitor used in tree traversal methods |
►Ncorrelation_distance | |
CBatch | Computes the correlation distance in the batch processing mode |
CInput | Input objects for the correlation distance algorithm |
CResult | Results obtained with compute() method of the correlation distance algorithm in the batch processing mode |
►Ncosine_distance | |
CBatch | Computes the cosine distance in the batch processing mode |
CInput | Input objects for the cosine distance algorithm |
CResult | Results obtained with the compute() method of the cosine distance algorithm in the batch processing mode |
►Ncovariance | |
CBatch | Computes correlation or variance-covariance matrix in the batch processing mode |
CBatchImpl | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the batch processing mode |
CDistributed | Computes correlation or variance-covariance matrix in the first step of the distributed processing mode |
CDistributedIface | Interface for correlation or variance-covariance matrix computation algorithms in the distributed processing mode on local nodes |
CDistributedInput | Input parameters of the distributed Covariance algorithm |
CInput | Input objects of the correlation or variance-covariance matrix algorithm |
CInputIface | Abstract class that specifies interface for classes that declare input of the correlation or variance-covariance matrix algorithm |
COnline | Computes correlation or variance-covariance matrix in the online processing mode |
COnlineImpl | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the online processing mode |
COnlineParameter | Parameters of the correlation or variance-covariance matrix algorithm in the online processing mode |
CParameter | Parameters of the correlation or variance-covariance matrix algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the batch processing mode |
►Ndecision_forest | |
►Nclassification | |
►Nprediction | |
CBatch | Predicts decision_forest classification results |
CInput | Input objects in the prediction stage of the DECISION_FOREST_CLASSIFICATION algorithm |
Cinterface1_Parameter | Class for the parameters of the Decision Forest classification algorithm |
►Ntraining | |
CBatch | Trains model of the Decision forest algorithms in the batch processing mode |
Cinterface2_Parameter | Decision forest algorithm parameters |
CParameter | Decision forest algorithm parameters |
CResult | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
CModel | Model of the classifier trained by the decision_forest.training.Batch algorithm |
►Nregression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the decision forest model-based prediction |
CInput | Provides an interface for input objects for making decision forest model-based prediction |
CResult | Provides interface for the result of decision forest model-based prediction |
►Ntraining | |
CBatch | Provides methods for decision forest model-based training in the batch processing mode |
CInput | Input objects for decision forest model-based training |
CParameter | Parameters for the decision forest algorithm |
CResult | Provides methods to access the result obtained with the compute() method of decision forest model-based training |
CModel | Base class for models trained with the decision forest regression algorithm |
►Ntraining | |
CParameter | Parameters for the decision forest algorithm |
►Ndecision_tree | |
►Nclassification | |
►Nprediction | |
CBatch | Provides methods to run implementations of the Decision tree model-based prediction |
CInput | Provides an interface for input objects for making Decision tree model-based prediction |
►Ntraining | |
CBatch | Provides methods for Decision tree model-based training in the batch processing mode |
CInput | Base class for the input objects in the training stage of the classification algorithms |
CResult | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
Cinterface2_Parameter | Decision tree algorithm parameters |
CModel | Base class for models trained with the Decision tree algorithm |
CParameter | Decision tree algorithm parameters |
►Nregression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the Decision tree model-based prediction |
CInput | Provides an interface for input objects for making Decision tree model-based prediction |
CResult | Provides interface for the result of decision tree model-based prediction |
►Ntraining | |
CBatch | Provides methods for Decision tree model-based training in the batch processing mode |
CInput | Base class for the input objects in the training stage of the regression algorithms |
CResult | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
CModel | Base class for models trained with the Decision tree algorithm |
CParameter | Decision tree algorithm parameters |
►Ndistributions | |
►Nbernoulli | |
CBatch | Provides methods for bernoulli distribution computations in the batch processing mode |
CParameter | Bernoulli distribution parameters |
►Nnormal | |
CBatch | Provides methods for normal distribution computations in the batch processing mode |
CParameter | Normal distribution parameters |
►Nuniform | |
CBatch | Provides methods for uniform distribution computations in the batch processing mode |
CParameter | Uniform distribution parameters |
CBatchBase | Class representing distributions |
CInput | Input objects for distributions |
CParameterBase | |
CResult | Provides methods to access the result obtained with the compute() method of the distribution |
►Nem_gmm | |
►Ninit | |
CBatch | Computes initial values for the EM for GMM algorithm in the batch processing mode |
CInput | Input objects for the computation of initial values for the EM for GMM algorithm |
CParameter | Parameter for the computation of initial values for the EM for GMM algorithm |
CResult | Results obtained with the compute() method of the initialization of the EM for GMM algorithm in the batch processing mode |
CBatch | Computes EM for GMM in the batch processing mode |
CInput | Input objects for the EM for GMM algorithm |
CParameter | Parameter for the EM for GMM algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the EM for GMM algorithm in the batch processing mode |
►Nengines | |
►Nmcg59 | |
CBatch | Provides methods for mcg59 engine computations in the batch processing mode |
►Nmt19937 | |
CBatch | Provides methods for mt19937 engine computations in the batch processing mode |
►Nmt2203 | |
CBatch | Provides methods for mt2203 engine computations in the batch processing mode |
CBatchBase | Class representing an engine |
CFamilyBatchBase | Class representing an engine that has collection of independent streams obtained from RNGs from same family |
CInput | Input objects for engines |
CResult | Provides methods to access the result obtained with the compute() method of the engine |
►Ngbt | |
►Nclassification | |
►Nprediction | |
CBatch | Predicts gradient boosted trees classification results |
CInput | Input objects in the prediction stage of the GBT_CLASSIFICATION algorithm |
Cinterface2_Parameter | Parameters of the prediction algorithm |
CParameter | Parameters of the prediction algorithm |
►Ntraining | |
CBatch | Trains model of the Gradient Boosted Trees algorithms in the batch processing mode |
Cinterface2_Parameter | Gradient Boosted Trees algorithm parameters |
CParameter | Gradient Boosted Trees algorithm parameters |
CResult | Provides methods to access the result obtained with the compute() method of model-based training |
CModel | Model of the classifier trained by the gbt.training.Batch algorithm |
►Nregression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the model-based prediction |
CInput | Provides an interface for input objects for making model-based prediction |
CParameter | Parameters of the prediction algorithm |
CResult | Provides interface for the result of model-based prediction |
►Ntraining | |
CBatch | Provides methods for model-based training in the batch processing mode |
CInput | Input objects for model-based training |
CParameter | Parameters for the gradient boosted trees algorithm |
CResult | Provides methods to access the result obtained with the compute() method of model-based training |
CModel | Base class for models trained with the gradient boosted trees regression algorithm |
►Ntraining | |
CParameter | Parameters for the gradient boosted trees algorithm |
►Nimplicit_als | |
►Nprediction | |
►Nratings | |
CBatch | Predicts the results of the implicit ALS algorithm |
CDistributed | Performs implicit ALS model-based prediction in the first step of the distributed processing mode |
CDistributedInput | Input objects for the first step of the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
CInput | Input objects for the rating prediction stage of the implicit ALS algorithm |
CInputIface | Input interface for the rating prediction stage of the implicit ALS algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm in the rating prediction stage |
CResult | Provides methods to access the prediction results obtained with the compute() method of the implicit ALS algorithm in the batch processing mode |
►Ntraining | |
►Ninit | |
CBatch | Algorithm class for initializing the implicit ALS model |
CDistributed | Initializes the implicit ALS model in the first step of the distributed processing mode |
CDistributedInput | Input objects for the implicit ALS initialization algorithm in the first step of the distributed processing mode |
CDistributedParameter | Parameters of the compute() method of the implicit ALS initialization algorithm in the distributed computing mode |
CDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
CInput | Input objects for the implicit ALS initialization algorithm |
CParameter | Parameters of the compute() method of the implicit ALS initialization algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
CPartialResultBase | Provides interface to access partial results obtained with the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the implicit ALS initialization algorithm |
CBatch | Algorithm class for training the implicit ALS model |
CDistributed | Trains the implicit ALS model in the first step of the distributed processing mode |
CDistributedInput | Input objects for the implicit ALS training algorithm in the first step of the distributed processing mode |
CDistributedPartialResultStep1 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the first step of the distributed processing mode |
CDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the second step of the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the the third step of the distributed processing mode |
CDistributedPartialResultStep4 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the the fourth step of the distributed processing mode |
CInput | Input objects for the implicit ALS training algorithm |
CResult | Provides methods to access the results obtained with the compute() method of the implicit ALS training algorithm in the batch processing mode |
CModel | Model trained by the implicit ALS algorithm in the batch processing mode |
CParameter | Parameters for the compute() method of the implicit ALS algorithm |
CPartialModel | Partial model trained by the implicit ALS training algorithm in the distributed processing mode |
►Nkdtree_knn_classification | |
►Nprediction | |
CBatch | Provides methods to run implementations of the KD-tree based kNN model-based prediction |
CInput | Provides an interface for input objects for making KD-tree based kNN model-based prediction |
►Ntraining | |
CBatch | Provides methods for KD-tree based kNN model-based training in the batch processing mode |
CResult | Provides methods to access the result obtained with the compute() method of KD-tree based kNN model-based training |
Cinterface2_Parameter | KD-tree based kNN algorithm parameters |
CModel | Base class for models trained with the KD-tree based kNN algorithm |
CParameter | KD-tree based kNN algorithm parameters |
►Nkernel_function | |
►Nlinear | |
CBatch | Computes a linear kernel function in the batch processing mode |
CInput | Input objects for the kernel function linear algorithm |
CParameter | Parameters for the linear kernel function k(X,Y) + b |
►Nrbf | |
CBatch | Computes the RBF kernel function in the batch processing mode |
CInput | Input objects for the RBF kernel algorithm |
CParameter | Parameters for the radial basis function (RBF) kernel |
CInput | Input objects for the kernel function algorithm |
CKernelIface | Abstract class that specifies the interface of the algorithms for computing kernel functions in the batch processing mode |
CParameterBase | Optional input objects for the kernel function algorithm |
CResult | Results obtained with the compute() method of the kernel function algorithm in the batch processing mode |
►Nkmeans | |
►Ninit | |
CBatch | Computes initial clusters for K-Means algorithm in the batch processing mode |
CBatchBase | Base class representing K-Means algorithm initialization in the batch processing mode |
CDistributed | Computes initial clusters for K-Means algorithm in the first step of the distributed processing mode |
CDistributedBase | Base class representing K-Means algorithm initialization in the distributed processing mode |
CDistributedStep2LocalPlusPlusBase | Base class representing K-Means algorithm initialization in the distributed processing mode |
CDistributedStep2LocalPlusPlusInput | Interface for K-Means initialization distributed Input classes used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CDistributedStep2LocalPlusPlusParameter | Parameters for computing initial centroids for K-Means algorithm |
CDistributedStep2LocalPlusPlusPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
CDistributedStep2MasterInput | Input objects for computing initials clusters for K-Means algorithm in the second step of the distributed processing mode |
CDistributedStep3MasterPlusPlusInput | Interface for K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
CDistributedStep3MasterPlusPlusPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
CDistributedStep4LocalPlusPlusInput | Interface for K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 4th step on a local node |
CDistributedStep4LocalPlusPlusPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
CDistributedStep5MasterPlusPlusInput | Interface for K-Means distributed Input classes |
CDistributedStep5MasterPlusPlusPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
CInput | Input objects for computing initial centroids for K-Means algorithm |
CInputIface | Interface for K-Means initialization batch and distributed Input classes |
Cinterface1_Parameter | Base classes parameters for computing initial centroids for K-Means algorithm |
CParameter | Parameters for computing initial centroids for K-Means algorithm of the batch mode |
CPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the batch processing mode |
CResult | Results obtained with the compute() method that computes initial centroids for K-Means algorithm in the batch processing mode |
CBatch | Computes the results of K-Means algorithm in the batch processing mode |
CDistributed | Computes the results of K-Means algorithm in the first step of the distributed processing mode |
CDistributedStep2MasterInput | Input objects for K-Means algorithm in the distributed processing mode |
CInput | Input objects for K-Means algorithm |
CInputIface | Interface for input objects for K-Means algorithm in the batch and distributed processing modes |
CParameter | Parameters for K-Means algorithm |
CPartialResult | Partial results obtained with the compute() method of K-Means algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of K-Means algorithm in the batch processing mode |
►Nlinear_model | |
►Nprediction | |
CBatch | Provides methods to run implementations of the regression model-based prediction |
CInput | Provides an interface for input objects for making the regression model-based prediction |
CResult | Provides interface for the result of the regression model-based prediction |
►Ntraining | |
CBatch | Provides methods for linear model model-based training in the batch processing mode |
CInput | Input objects for the regression model-based training |
COnline | Provides methods for the linear model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of the linear model-based training in the online processing mode |
CResult | Provides methods to access the result obtained with the compute() method of the regression model-based training |
CModel | Base class for models trained with the regression algorithm |
CParameter | Parameters for the regression algorithm |
►Nlinear_regression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the linear regression model-based prediction |
CInput | Provides an interface for input objects for making linear regression model-based prediction |
CResult | Provides interface for the result of linear regression model-based prediction |
►Nquality_metric | |
►Ngroup_of_betas | |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CInput | Input objects for a group of betas quality metrics |
CParameter | Parameters for the compute() method of a group of betas quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nsingle_beta | |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CInput | Input objects for single beta quality metrics |
CParameter | Parameters for the compute() method of single beta quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nquality_metric_set | |
CBatch | Class that represents a quality metric set to check the model trained with linear regression algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
CParameter | Parameters for the quality metrics set compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
►Ntraining | |
CBatch | Provides methods for linear regression model-based training in the batch processing mode |
CDistributed | Performs linear regression model-based training in the the first step of the distributed processing mode |
CDistributedInput | Input object for linear regression model-based training in the distributed processing mode |
CInput | Input objects for linear regression model-based training |
CInputIface | Abstract class that specifies the interface of input objects for linear regression model-based training |
COnline | Provides methods for linear regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of linear regression model-based training in the online or distributed processing mode |
CResult | Provides methods to access the result obtained with the compute() method of linear regression model-based training |
CModel | Base class for models trained with the linear regression algorithm |
CModelNormEq | Model trained with the linear regression algorithm using the normal equations method |
CModelQR | Model trained with the linear regression algorithm using the QR decomposition-based method |
CParameter | Parameters for the linear regression algorithm |
►Nlogistic_regression | |
►Nprediction | |
C_1 | |
C_1_ | Parameters of the prediction algorithm |
CBatch | Predicts logistic regression results |
CInput | Input objects in the prediction stage of the LOGISTIC_REGRESSION algorithm |
CResult | Provides interface for the result of model-based prediction |
►Ntraining | |
CBatch | Trains model of the logistic regression algorithms in the batch processing mode |
Cinterface2_Parameter | Logistic regression algorithm parameters |
Cinterface2_Result | Provides methods to access the result obtained with the compute() method of model-based training |
Cinterface3_Parameter | Logistic regression algorithm parameters |
CParameter | Logistic regression algorithm parameters |
CResult | Provides methods to access the result obtained with the compute() method of model-based training |
CModel | Model of the classifier trained by the logistic_regression.training.Batch algorithm |
►Nlogitboost | |
►Nprediction | |
CBatch | Predicts LogitBoost classification results |
CInput | Input objects in the prediction stage of the logitboost algorithm |
Cinterface2_Input | Input objects in the prediction stage of the logitboost algorithm |
►Nquality_metric_set | |
CBatch | Class that represents a set of quality metrics to check the model trained with the LogitBoost training algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
CParameter | Parameters for the LogitBoost compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
►Ntraining | |
CBatch | Trains model of the LogitBoost algorithms in the batch processing mode |
Cinterface2_Result | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
Cinterface2_Model | Model of the classifier trained by the logitboost.training.Batch algorithm |
Cinterface2_Parameter | LogitBoost algorithm parameters |
CModel | Model of the classifier trained by the logitboost.training.Batch algorithm |
CParameter | LogitBoost algorithm parameters |
►Nlow_order_moments | |
CBatch | Computes moments of low order in the batch processing mode |
CBatchImpl | Abstract class that specifies interface of the algorithms for computing moments of low order in the batch processing mode |
CDistributed | Computes the result of the first step of the moments of low order algorithm in the distributed processing mode |
CDistributedInput | Input objects for the low order moments algorithm in the distributed processing mode on master node |
CInput | Input objects for the low order moments algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the low order moments algorithm |
COnline | Computes moments of low order in the online processing mode |
CParameter | Low order moments algorithm parameters |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the low order moments algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the low order moments algorithm in the batch processing mode ; or finalizeCompute() method of algorithm in the online or distributed processing mode |
►Nmath | |
►Nabs | |
CBatch | Computes the absolute value function in the batch processing mode |
CInput | Input objects for the absolute value function |
CResult | Result obtained with the compute() method of the absolute value function in the batch processing mode |
►Nlogistic | |
CBatch | Computes the logistic function in the batch processing mode |
CInput | Input objects for the logistic function |
CResult | Results obtained with the compute() method of the logistic function in the batch processing mode |
►Nrelu | |
CBatch | Computes the rectified linear function in the batch processing mode |
CInput | Input objects for the rectified linear function |
CResult | Results obtained with the compute() method of the rectified linear function in the batch processing mode |
►Nsmoothrelu | |
CBatch | Computes SmoothReLU in the batch processing mode |
CInput | Input parameters for the SmoothReLU algorithm |
CResult | Results obtained with the compute() method of the SmoothReLU algorithm in the batch processing mode |
►Nsoftmax | |
CBatch | Computes the softmax function in the batch processing mode |
CInput | Input objects for the softmax function |
CResult | Results obtained with the compute() method of the softmax function in the batch processing mode |
►Ntanh | |
CBatch | Computes the hyperbolic tangent function in the batch processing mode |
CInput | Input objects for the hyperbolic tangent function |
CResult | Result obtained with the compute() method of the hyperbolic tangent function in the batch processing mode |
►Nmulti_class_classifier | |
►Nprediction | |
CBatch | Provides methods to run implementations of the multi-class classifier prediction algorithm |
CInput | Input objects in the prediction stage of the Multi-class classifier algorithm |
►Nquality_metric_set | |
CBatch | Class containing a set of quality metrics to check the model trained with the multi-class classifier algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the multi-class classifier training algorithm |
CParameter | Parameters for the multi-class classifier compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the multi-class classifier training algorithm |
►Ntraining | |
CBatch | Algorithm for the multi-class classifier model training |
CResult | Provides methods to access final results obtained with the compute() method for the multi-class classifier algorithm in the batch processing mode; or finalizeCompute() method of the algorithm in the online or distributed processing mode |
Cinterface2_Parameter | Optional multi-class classifier algorithm parameters that are used with the MultiClassClassifierWu prediction method |
Cinterface2_ParameterBase | Parameters of the multi-class classifier algorithm |
CModel | Model of the classifier trained by the multi_class_classifier.training.Batch algorithm |
CParameter | Optional multi-class classifier algorithm parameters that are used with the MultiClassClassifierWu prediction method |
CParameterBase | Parameters of the multi-class classifier algorithm |
►Nmultinomial_naive_bayes | |
►Nprediction | |
CBatch | Predicts the results of the multinomial naive Bayes classification |
CInput | Input objects in the prediction stage of the multinomial naive Bayes algorithm |
►Nquality_metric_set | |
CBatch | Class containing a quality metric set to check the model trained with the Naive Bayes algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
CParameter | Parameters for the Naive Bayes compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
►Ntraining | |
CBatch | Algorithm class for training the naive Bayes model |
CDistributed | Algorithm class for training Naive Bayes partial model in the distributed processing mode |
CDistributedInput | Input objects of the naive Bayes training algorithm in the distributed processing mode |
CInput | Input objects of the naive Bayes training algorithm in the batch and online processing mode |
COnline | Algorithm class for training naive Bayes model |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the naive Bayes training algorithm in the online or distributed processing |
CResult | Provides methods to access final results obtained with the compute() method of the naive Bayes training algorithm in the batch processing mode or with the finalizeCompute() method in the distributed or online processing mode |
Cinterface2_Parameter | Naive Bayes algorithm parameters |
CModel | Multinomial naive Bayes model |
CParameter | Naive Bayes algorithm parameters |
CPartialModel | PartialModel represents partial multinomial naive Bayes model |
►Nmultivariate_outlier_detection | |
CBatch | Abstract class that specifies interface of the algorithms for computing multivariate outlier detection in the batch processing mode |
CDefaultInit | Class that specifies the default method for the initialization procedure |
CInitIface | Abstract interface class that provides function for the initialization procedure |
CInput | Input objects for the multivariate outlier detection algorithm |
CParameter | Parameters of the outlier detection computation using the defaultDense method |
CResult | Results obtained with the compute() method of the multivariate outlier detection algorithm in the batch processing mode |
►Nneural_networks | |
►Ninitializers | |
►Ngaussian | |
CBatch | Provides methods for gaussian initializer computations in the batch processing mode |
CParameter | Gaussian initializer parameters |
►Ntruncated_gaussian | |
CBatch | Provides methods for truncated gaussian initializer computations in the batch processing mode |
CParameter | Truncated gaussian initializer parameters |
►Nuniform | |
CBatch | Provides methods for uniform initializer computations in the batch processing mode |
CParameter | Uniform initializer parameters |
►Nxavier | |
CBatch | Provides methods for Xavier initializer computations in the batch processing mode |
CParameter | Xavier initializer parameters |
CInitializerContainerIface | Class that specifies interfaces of implementations of the neural network weights and biases initializer |
CInitializerIface | Class representing a neural network weights and biases initializer |
CInput | Input objects for initializer algorithm |
CParameter | |
CResult | Provides methods to access the result obtained with the compute() method of the neural network weights and biases initializer |
►Nlayers | |
►Nabs | |
►Nbackward | |
CBatch | Computes the results of the backward abs layer in the batch processing mode |
CInput | Input objects for the backward abs layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward abs layer |
►Nforward | |
CBatch | Computes the result of the forward abs layer in the batch processing mode |
CInput | Input objects for the forward abs layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward abs layer |
CBatch | Provides methods for the abs layer in the batch processing mode |
CParameter | Parameters for the abs layer |
►Naverage_pooling1d | |
►Nbackward | |
CBatch | Provides methods for the backward average 1D pooling layer in the batch processing mode |
CInput | Input objects for the backward average 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 1D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward average 1D pooling layer in the batch processing mode |
CInput | Input objects for the forward average 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 1D pooling layer |
CBatch | Provides methods for the average 1D pooling layer in the batch processing mode |
CParameter | Parameters for the average 1D pooling layer |
►Naverage_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward average 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward average 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward average 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward average 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 2D pooling layer |
CBatch | Provides methods for the average 2D pooling layer in the batch processing mode |
CParameter | Parameters for the average 2D pooling layer |
►Naverage_pooling3d | |
►Nbackward | |
CBatch | Provides methods for the backward average 3D pooling layer in the batch processing mode |
CInput | Input objects for the backward average 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 3D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward average 3D pooling layer in the batch processing mode |
CInput | Input objects for the forward average 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 3D pooling layer |
CBatch | Provides methods for the average 3D pooling layer in the batch processing mode |
CParameter | Parameters for the average 3D pooling layer |
►Nbackward | |
CInput | Input parameters for the layer algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the neural network layer algorithm |
CLayerIface | Abstract class which defines interface for the layer |
CLayerIfaceImpl | |
CResult | Provides methods to access the result obtained with the compute() method of the layer algorithm |
►Nbatch_normalization | |
►Nbackward | |
CBatch | Provides methods for the backward batch normalization layer in the batch processing mode |
CInput | Input objects for the backward batch normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward batch normalization layer |
►Nforward | |
CBatch | Provides methods for the forward batch normalization layer in the batch processing mode |
CInput | Input objects for the forward batch normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward batch normalization layer |
CBatch | Provides methods for the batch normalization layer in the batch processing mode |
CParameter | Parameters for the forward and backward batch normalization layers |
►Nconcat | |
►Nbackward | |
CBatch | Computes the results of the backward concat layer in the batch processing mode |
CInput | Input parameters for the backward concat layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward concat layer |
►Nforward | |
CBatch | Computes the results of the forward concat layer in the batch processing mode |
CInput | Input objects for the forward concat layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward concat layer |
CBatch | Provides methods for the concat layer in the batch processing mode |
CParameter | Concat layer parameters |
►Nconvolution2d | |
►Nbackward | |
CBatch | Provides methods for backward 2D convolution layer computations in the batch processing mode |
CInput | Input objects for the backward 2D convolution layer |
CResult | Results obtained with the compute() method of the backward 2D convolution layer |
►Nforward | |
CBatch | Provides methods for forward 2D convolution layer computations in the batch processing mode |
CInput | Input objects for the forward 2D convolution layer |
CResult | Results obtained with the compute() method of the forward 2D convolution layer in the batch processing mode |
CBatch | Computes the result of the forward and backward 2D convolution layer of neural network in the batch processing mode |
CIndices | Data structure representing the indices of the two dimensions on which 2D convolution is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D convolution is performed |
CParameter | 2D convolution layer parameters |
CStrides | Data structure representing the intervals on which the subtensors for 2D convolution are selected |
►Ndropout | |
►Nbackward | |
CBatch | Provides methods for the backward dropout layer in the batch processing mode |
CInput | Input objects for the backward dropout layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward dropout layer |
►Nforward | |
CBatch | Provides methods for the forward dropout layer in the batch processing mode |
CInput | Input objects for the forward dropout layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward dropout layer |
CBatch | Provides methods for the dropout layer in the batch processing mode |
CParameter | Parameters for the dropout layer |
►Neltwise_sum | |
►Nbackward | |
CBatch | Provides methods for backward element-wise sum layer computations in the batch processing mode |
CInput | Input objects for the backward element-wise sum layer |
CResult | Results obtained with the compute() method of the backward element-wise sum layer |
►Nforward | |
CBatch | Computes the results of the forward element-wise sum layer in the batch processing mode |
CInput | Input objects for the forward element-wise sum layer |
CResult | Results obtained with the compute() method of the forward element-wise sum layer in the batch processing mode |
CBatch | Computes the result of the forward and backward element-wise sum layer of neural network in the batch processing mode |
CParameter | Parameters for the element-wise sum layer |
►Nelu | |
►Nbackward | |
CBatch | Computes the results of the backward ELU layer in the batch processing mode |
CInput | Input objects for the backward ELU layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward ELU layer |
►Nforward | |
CBatch | Computes the results of the forward ELU layer in the batch processing mode |
CInput | Input objects for the forward ELU layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward ELU layer |
CBatch | Provides methods for the ELU layer in the batch processing mode |
CParameter | Parameters for the ELU layer |
►Nforward | |
CInput | Input objects for layer algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the neural network layer |
CLayerContainerIfaceImpl | Provides methods of base container for forward layers |
CLayerDescriptor | Class defining descriptor for layer on forward stage |
CLayerIface | Abstract class which defines interface for the layer |
CLayerIfaceImpl | |
CResult | Provides methods to access the result obtained with the compute() method of the layer algorithm |
►Nfullyconnected | |
►Nbackward | |
CBatch | Provides methods for backward fully-connected layer computations in the batch processing mode |
CInput | Input objects for the backward fully-connected layer |
CResult | Results obtained with the compute() method of the backward fully-connected layer |
►Nforward | |
CBatch | Provides methods for forward fully-connected layer computations in the batch processing mode |
CInput | Input objects for the forward fully-connected layer |
CResult | Results obtained with the compute() method of the forward fully-connected layer in the batch processing mode |
CBatch | Computes the result of the forward and backward fully-connected layer of neural network in the batch processing mode |
CParameter | Fully-connected layer parameters |
►Nlcn | |
►Nbackward | |
CBatch | Provides methods for backward local contrast normalization layer computations in the batch processing mode |
CInput | Input objects for the backward local contrast normalization layer |
CResult | Results obtained with the compute() method of the backward local contrast normalization layer |
►Nforward | |
CBatch | Provides methods for forward local contrast normalization layer computations in the batch processing mode |
CInput | Input objects for the forward local contrast normalization layer |
CResult | Results obtained with the compute() method of the forward local contrast normalization layer in the batch processing mode |
CBatch | Computes the result of the forward and backward local contrast normalization layer of neural network in the batch processing mode |
CIndices | Data structure representing the indices of the two dimensions on which local contrast normalization is performed |
CParameter | Local contrast normalization layer parameters |
►Nlocallyconnected2d | |
►Nbackward | |
CBatch | Provides methods for backward 2D locally connected layer computations in the batch processing mode |
CInput | Input objects for the backward 2D locally connected layer |
CResult | Results obtained with the compute() method of the backward 2D locally connected layer |
►Nforward | |
CBatch | Provides methods for forward 2D locally connected layer computations in the batch processing mode |
CInput | Input objects for the forward 2D locally connected layer |
CResult | Results obtained with the compute() method of the forward 2D locally connected layer in the batch processing mode |
CBatch | Computes the result of the forward and backward 2D locally connected layer of neural network in the batch processing mode |
CIndices | Data structure representing the indices of the two dimensions on which 2D locally connected is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D locally connected is performed |
CParameter | 2D locally connected layer parameters |
CStrides | Data structure representing the intervals on which the subtensors for 2D locally connected are selected |
►Nlogistic | |
►Nbackward | |
CBatch | Computes the results of the backward logistic layer in the batch processing mode |
CInput | Input objects for the backward logistic layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward logistic layer |
►Nforward | |
CBatch | Computes the results of the forward logistic layer in the batch processing mode |
CInput | Input objects for the forward logistic layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward logistic layer |
CBatch | Provides methods for the logistic layer in the batch processing mode |
CParameter | Parameters for the logistic layer |
►Nloss | |
►Nbackward | |
CBatch | Provides methods for the backward loss layer in the batch processing mode |
CInput | Input objects for the backward loss layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward loss layer |
►Nforward | |
CBatch | Provides methods for the forward loss layer in the batch processing mode |
CInput | Input objects for the forward loss layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward loss layer |
►Nlogistic_cross | |
►Nbackward | |
CBatch | Provides methods for the backward logistic cross-entropy layer in the batch processing mode |
CInput | Input objects for the backward logistic cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward logistic cross-entropy layer |
►Nforward | |
CBatch | Provides methods for the forward logistic cross layer in the batch processing mode |
CInput | Input objects for the forward logistic cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward logistic cross-entropy layer |
CBatch | Provides methods for the logistic cross-entropy layer in the batch processing mode |
CParameter | Parameters for the logistic cross-entropy layer |
►Nsoftmax_cross | |
►Nbackward | |
CBatch | Provides methods for the backward softmax cross-entropy layer in the batch processing mode |
CInput | Input objects for the backward softmax cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward softmax cross-entropy layer |
►Nforward | |
CBatch | Provides methods for the forward softmax cross layer in the batch processing mode |
CInput | Input objects for the forward softmax cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward softmax cross-entropy layer |
CBatch | Provides methods for the softmax cross-entropy layer in the batch processing mode |
CParameter | Parameters for the softmax cross-entropy layer |
CBatch | Provides methods for the loss layer in the batch processing mode |
►Nlrn | |
►Nbackward | |
CBatch | Provides methods for the backward local response normalization layer in the batch processing mode |
CInput | Input parameters for the backward local response normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward local response normalization layer |
►Nforward | |
CBatch | Provides methods for the forward local response normalization layer in the batch processing mode |
CInput | Input parameters for the forward local response normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward local response normalization layer |
CBatch | Provides methods for the local response normalization layer in the batch processing mode |
CParameter | Parameters for the local response normalization layer |
►Nmaximum_pooling1d | |
►Nbackward | |
CBatch | Provides methods for the backward maximum 1D pooling layer in the batch processing mode |
CInput | Input objects for the backward maximum 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 1D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward maximum 1D pooling layer in the batch processing mode |
CInput | Input objects for the forward maximum 1D pooling layer See pooling1d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 1D pooling layer |
CBatch | Provides methods for the maximum 1D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 1D pooling layer |
►Nmaximum_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward maximum 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward maximum 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward maximum 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward maximum 2D pooling layer See pooling2d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 2D pooling layer |
CBatch | Provides methods for the maximum 2D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 2D pooling layer |
►Nmaximum_pooling3d | |
►Nbackward | |
CBatch | Provides methods for the backward maximum 3D pooling layer in the batch processing mode |
CInput | Input objects for the backward maximum 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 3D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward maximum 3D pooling layer in the batch processing mode |
CInput | Input objects for the forward maximum 3D pooling layer See pooling3d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 3D pooling layer |
CBatch | Provides methods for the maximum 3D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 3D pooling layer |
►Npooling1d | |
►Nbackward | |
CInput | Input objects for the backward 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 1D pooling layer |
►Nforward | |
CInput | Input objects for the forward 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 1D pooling layer |
CIndex | Data structure representing the indices of the dimension on which pooling is performed |
CKernelSize | Data structure representing the size of the 1D subtensor from which the element is computed |
CPadding | Data structure representing the number of data elements to implicitly add to each side of the 1D subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward pooling layers |
CStride | Data structure representing the intervals on which the subtensors for pooling are computed |
►Npooling2d | |
►Nbackward | |
CInput | Input objects for the backward 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 2D pooling layer |
►Nforward | |
CInput | Input objects for the forward 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 2D pooling layer |
CIndices | Data structure representing the indices of the two dimensions on which pooling is performed |
CKernelSizes | Data structure representing the size of the 2D subtensor from which the element is computed |
CPaddings | Data structure representing the number of data elements to implicitly add to each side of the 2D subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward two-dimensional pooling layers |
CStrides | Data structure representing the intervals on which the subtensors for pooling are computed |
►Npooling3d | |
►Nbackward | |
CInput | Input objects for the backward 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 3D pooling layer |
►Nforward | |
CInput | Input objects for the forward 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 3D pooling layer |
CIndices | Data structure representing the indices of the three dimensions on which pooling is performed |
CKernelSizes | Data structure representing the size of the 3D subtensor from which the element is computed |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the three-dimensional subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward pooling layers |
CStrides | Data structure representing the intervals on which the subtensors for pooling are computed |
►Nprelu | |
►Nbackward | |
CBatch | Provides methods for the backward prelu layer in the batch processing mode |
CInput | Input parameters for the backward prelu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward prelu layer |
►Nforward | |
CBatch | Computes the results of the forward prelu layer in the batch processing mode |
CInput | Input objects for the forward prelu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward prelu layer |
CBatch | Provides methods for the prelu layer in the batch processing mode |
CParameter | Parameters for the prelu layer |
►Nrelu | |
►Nbackward | |
CBatch | Computes the results of the backward relu layer in the batch processing mode |
CInput | Input objects for the backward relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward relu layer |
►Nforward | |
CBatch | Computes the results of the forward relu layer in the batch processing mode |
CInput | Input objects for the forward relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward relu layer |
CBatch | Provides methods for the relu layer in the batch processing mode |
CParameter | Parameters for the relu layer |
►Nreshape | |
►Nbackward | |
CBatch | Computes the results of the backward reshape layer in the batch processing mode |
CInput | Input objects for the backward reshape layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward reshape layer |
►Nforward | |
CBatch | Computes the result of the forward reshape layer in the batch processing mode |
CInput | Input objects for the forward reshape layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward reshape layer |
CBatch | Provides methods for the reshape layer in the batch processing mode |
CParameter | Parameters for the reshape layer |
►Nsmoothrelu | |
►Nbackward | |
CBatch | Provides methods for the backward smooth relu layer in the batch processing mode |
CInput | Input objects for the backward smooth relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward smooth relu layer |
►Nforward | |
CBatch | Provides methods for the forward smooth relu layer in the batch processing mode |
CInput | Input objects for the forward smooth relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward smooth relu layer |
CBatch | Provides methods for the smooth relu layer in the batch processing mode |
CParameter | Parameters for the smoothrelu layer |
►Nsoftmax | |
►Nbackward | |
CBatch | Computes the results of the backward softmax layer in the batch processing mode |
CInput | Input objects for the backward softmax layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward softmax layer |
►Nforward | |
CBatch | Computes the results of the forward softmax layer in the batch processing mode |
CInput | Input objects for the forward softmax layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward softmax layer |
CBatch | Provides methods for the softmax layer in the batch processing mode |
CParameter | Parameters for the softmax layer |
►Nspatial_average_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward spatial pyramid average 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward spatial pyramid average 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid average 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward spatial pyramid average 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward spatial pyramid average 2D pooling layer See pooling2d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid average 2D pooling layer |
CBatch | Provides methods for the spatial pyramid average 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid average 2D pooling layer |
►Nspatial_maximum_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward spatial pyramid maximum 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward spatial pyramid maximum 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid maximum 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward spatial pyramid maximum 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward spatial pyramid maximum 2D pooling layer See pooling2d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid maximum 2D pooling layer |
CBatch | Provides methods for the spatial pyramid maximum 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid maximum 2D pooling layer |
►Nspatial_pooling2d | |
►Nbackward | |
CInput | Input objects for the backward 2D spatial layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 2D spatial layer |
►Nforward | |
CInput | Input objects for the forward 2D spatial layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 2D spatial layer |
CIndices | Data structure representing the indices of the two dimensions on which pooling is performed |
CParameter | Parameters for the forward and backward two-dimensional spatial layers |
►Nspatial_stochastic_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward spatial pyramid stochastic 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid stochastic 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward spatial pyramid stochastic 2D pooling layer See pooling2d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid stochastic 2D pooling layer |
CBatch | Provides methods for the spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid stochastic 2D pooling layer |
►Nsplit | |
►Nbackward | |
CBatch | Computes the results of the backward split layer in the batch processing mode |
CInput | Input parameters for the backward split layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward split layer |
►Nforward | |
CBatch | Computes the results of the forward split layer in the batch processing mode |
CInput | Input objects for the forward split layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward split layer |
CBatch | Provides methods for the split layer in the batch processing mode |
CParameter | Split layer parameters |
►Nstochastic_pooling2d | |
►Nbackward | |
CBatch | Provides methods for the backward stochastic 2D pooling layer in the batch processing mode |
CInput | Input objects for the backward stochastic 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward stochastic 2D pooling layer |
►Nforward | |
CBatch | Provides methods for the forward stochastic 2D pooling layer in the batch processing mode |
CInput | Input objects for the forward stochastic 2D pooling layer See pooling2d.forward.Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward stochastic 2D pooling layer |
CBatch | Provides methods for the stochastic 2D pooling layer in the batch processing mode |
CParameter | Parameters for the stochastic 2D pooling layer |
►Ntanh | |
►Nbackward | |
CBatch | Computes the results of the backward hyperbolic tangent in the batch processing mode |
CInput | Input objects for the backward hyperbolic tangent layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward hyperbolic tangent layer |
►Nforward | |
CBatch | Computes the results of the forward hyperbolic tangent in the batch processing mode |
CInput | Input objects for the forward hyperbolic tangent layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward hyperbolic tangent layer |
CBatch | Provides methods for the hyperbolic tangent layer in the batch processing mode |
CParameter | Parameters for the tanh layer |
►Ntransposed_conv2d | |
►Nbackward | |
CBatch | Provides methods for backward 2D transposed convolution layer computations in the batch processing mode |
CInput | Input objects for the backward 2D transposed convolution layer |
CResult | Results obtained with the compute() method of the backward 2D transposed convolution layer |
►Nforward | |
CBatch | Provides methods for forward 2D transposed convolution layer computations in the batch processing mode |
CInput | Input objects for the forward 2D transposed convolution layer |
CResult | Results obtained with the compute() method of the forward 2D transposed convolution layer in the batch processing mode |
CBatch | Computes the result of the forward and backward 2D transposed convolution layer of neural network in the batch processing mode |
CIndices | Data structure representing the indices of the two dimensions on which 2D transposed convolution is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D transposed convolution is performed |
CParameter | 2D transposed convolution layer parameters |
CStrides | Data structure representing the intervals on which the subtensors for 2D transposed convolution are selected |
CValueSizes | Data structure representing the value sizes of the two dimensions on which 2D transposed convolution is performed |
CLayerDescriptor | Class defining descriptor for layer on both forward and backward stages and its parameters |
CLayerIface | Abstract class that specifies the interface of layer |
CNextLayers | Contains list of layer indices of layers following the current layer |
CParameter | |
►Nprediction | |
CBatch | Provides methods for neural network model-based prediction in the batch processing mode |
CInput | Input objects of the neural networks prediction algorithm |
CModel | Class Model object for the prediction stage of neural network algorithm |
CParameter | Class representing the parameters of neural network prediction |
CResult | Provides methods to access result obtained with the compute() method of the neural networks prediction algorithm |
CTopology | Class defining a neural network topology - a set of layers and connection between them - on the prediction stage |
►Ntraining | |
CBatch | Provides methods for neural network model-based training in the batch processing mode |
CDistributed | Provides methods for neural network model-based training in the batch processing mode |
CDistributedInput | Input objects of the neural network training algorithm in the distributed processing mode |
CDistributedPartialResult | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
CInput | Input objects of the neural network training algorithm |
CModel | Class representing the model of neural network |
CParameter | Class representing the parameters of neural network |
CPartialResult | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
CResult | Provides methods to access result obtained with the compute() method of the neural network training algorithm |
CTopology | Class defining a neural network topology - a set of layers and connection between them - on the training stage |
CBackwardLayers | Class that implements functionality of the Collection container |
CForwardLayers | Class that implements functionality of the Collection container |
CLearnableParametersIface | Learnable parameters for the prediction stage of neural network algorithm |
CModelImpl | Class Model object for the prediction stage of neural network algorithm |
►Nnormalization | |
►Nminmax | |
CBatch | Normalizes datasets in the batch processing mode |
CInput | Input objects for the min-max normalization algorithm |
CParameter | Class that specifies the parameters of the algorithm in the batch computing mode |
CParameterBase | Base class that specifies the parameters of the algorithm in the batch computing mode |
CResult | Provides methods to access final results obtained with the compute() method of the min-max normalization algorithm in the batch processing mode |
►Nzscore | |
CBaseParameter | Class that specifies the base parameters of the algorithm in the batch computing mode |
CBatch | Normalizes datasets in the batch processing mode |
CBatchImpl | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the batch processing mode |
CInput | Input objects for the z-score normalization algorithm |
CParameter | |
CResult | Provides methods to access final results obtained with the compute() method of the z-score normalization algorithm in the batch processing mode |
►Noptimization_solver | |
►Ncross_entropy_loss | |
CBatch | Computes the Cross-entropy loss objective function in the batch processing mode |
CInput | Input objects for the Cross-entropy loss objective function |
Cinterface1_Input | Input objects for the Cross-entropy loss objective function |
Cinterface1_Parameter | Parameter for Cross-entropy loss objective function |
CParameter | Parameter for Cross-entropy loss objective function |
►Niterative_solver | |
CBatch | Interface for computing the iterative solver in the batch processing mode |
CInput | Input parameters for the iterative solver algorithm |
Cinterface1_Batch | Interface for computing the iterative solver in the batch processing mode |
Cinterface1_Input | Input parameters for the iterative solver algorithm |
Cinterface1_Parameter | Parameter base class for the iterative solver algorithm |
Cinterface1_Result | Results obtained with the compute() method of the iterative solver algorithm in the batch processing mode |
CParameter | Parameter base class for the iterative solver algorithm |
CResult | Results obtained with the compute() method of the iterative solver algorithm in the batch processing mode |
►Nlbfgs | |
CBatch | Computes LBFGS in the batch processing mode |
CInput | Input class for LBFGS algorithm |
Cinterface1_Input | Input class for LBFGS algorithm |
Cinterface1_Parameter | Parameter class for LBFGS algorithm |
Cinterface1_Result | Results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
CParameter | Parameter class for LBFGS algorithm |
CResult | Results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
►Nlogistic_loss | |
CBatch | Computes the Logistic loss objective function in the batch processing mode |
CInput | Input objects for the Logistic loss objective function |
Cinterface1_Input | Input objects for the Logistic loss objective function |
Cinterface1_Parameter | Parameter for Logistic loss objective function |
CParameter | Parameter for Logistic loss objective function |
►Nmse | |
CBatch | Computes the Mean squared error objective function in the batch processing mode |
CInput | Input objects for the Mean squared error objective function |
Cinterface1_Input | Input objects for the Mean squared error objective function |
Cinterface1_Parameter | Parameter for Mean squared error objective function |
CParameter | Parameter for Mean squared error objective function |
►Nobjective_function | |
CBatch | Interface for computing the Objective function in the batch processing mode |
CInput | Input objects for the Objective function |
CParameter | Parameter for the Objective function |
CResult | Provides methods to access final results obtained with the compute() method of the Objective function in the batch processing mode |
►Nprecomputed | |
CBatch | Computes the objective function with precomputed characteristics in the batch processing mode |
►Nsgd | |
CBaseParameter | BaseParameter base class for the Stochastic gradient descent algorithm |
CBatch | Computes Stochastic gradient descent in the batch processing mode |
CInput | |
Cinterface1_BaseParameter | BaseParameter base class for the Stochastic gradient descent algorithm |
Cinterface1_Input | |
Cinterface1_Result | |
CParameter | Parameter for the Stochastic gradient descent algorithm |
CResult | |
►Nsum_of_functions | |
CBatch | Interface for computing the Sum of functions in the batch processing mode |
CInput | Input objects for the Sum of functions |
Cinterface1_Batch | Interface for computing the Sum of functions in the batch processing mode |
Cinterface1_Input | Input objects for the Sum of functions |
Cinterface1_Parameter | Parameter for the Sum of functions |
CParameter | Parameter for the Sum of functions |
CBatchIface | Interface for computing the Optimization solver in the batch processing mode |
►Npca | |
►Nquality_metric | |
►Nexplained_variance | |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CInput | Input objects for explained variance quality metrics |
CParameter | Parameters for the compute() method of explained variance quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nquality_metric_set | |
CBatch | Class that represents a quality metric set of the pca algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the pca algorithm |
CParameter | Parameters for the quality metrics set compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the pca algorithm |
►Ntransform | |
CBatch | Computes the results of the PCA transformation algorithm in the batch processing mode |
CInput | Input objects for the PCA transformation algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
CParameter | Parameters for the PCA transformation compute method |
CResult | Provides methods to access final results obtained with the compute() method of the PCA transformation algorithm in the batch processing mode or finalizeCompute() method of algorithm in the online processing mode or on the second and third steps of the algorithm in the distributed processing mode |
CBaseBatchParameter | Class that specifies the common parameters of the PCA Batch algorithms |
CBatch | Computes the results of the PCA algorithm |
CBatchParameter | Class that specifies the parameters of the PCA SVD algorithm in the batch computing mode |
CDistributed | Computes the results of the PCA algorithm on the local nodes |
CDistributedInput | Input objects of the PCA SVD algorithm in the distributed processing mode |
CDistributedParameter | Class that specifies the parameters of the PCA algorithm in the distributed computing mode |
CInput | Input objects for the PCA algorithm |
CInputIface | Abstract class that specifies interface for classes that declare input of the PCA algorithm |
COnline | Computes the results of the PCA SVD algorithm |
COnlineParameter | Class that specifies the parameters of the PCA SVD algorithm in the online computing mode |
CPartialResult | Provides methods to access partial results obtained with the compute() method of PCA SVD algorithm in the online or distributed processing mode |
CPartialResultBase | Provides interface to access partial results obtained with the compute() method of the PCA algorithm in the online or distributed processing mode |
CResult | Provides methods to access results obtained with the PCA algorithm |
►Npivoted_qr | |
CBatch | Computes the results of the pivoted QR algorithm in the batch processing mode |
CInput | Input objects for the pivoted QR algorithm in the batch processing mode |
CParameter | Parameter for the pivoted QR computation method |
CResult | Provides methods to access final results obtained with the compute() method of the pivoted QR algorithm in the batch processing mode |
►Nqr | |
CBatch | Computes the results of the QR decomposition algorithm in the batch processing mode |
CDistributed | Computes the result of the first step of the QR decomposition algorithm in the distributed processing mode |
CDistributedPartialResult | Provides methods to access partial results obtained with the compute() method of the second step of the QR decomposition algorithm in the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the third step of the QR decomposition algorithm in the distributed processing mode |
CDistributedStep2Input | Input objects for the second step of the QR decomposition algorithm in the distributed processing mode |
CDistributedStep3Input | Input objects for the third step of the QR decomposition algorithm in the distributed processing mode |
CInput | Input objects for the QR decomposition algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
COnline | Computes the results of the QR decomposition algorithm in the online processing mode |
COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the QR decomposition algorithm in the online processing mode or on the first step of the algorithm in the distributed processing mode |
CParameter | Parameters for the QR decomposition compute method |
CResult | Provides methods to access final results obtained with the compute() method of the QR decomposition algorithm in the batch processing mode or finalizeCompute() method of algorithm in the online processing mode or on the second and third steps of the algorithm in the distributed processing mode |
►Nquality_metric | |
CBatch | Provides methods to compute quality metrics of an algorithm in the batch processing mode |
►Nquality_metric_set | |
CBatch | Provides methods to compute a quality metric set of an algorithm in the batch processing mode |
CInputAlgorithmsCollection | Class that implements functionality of the collection of quality metrics algorithms |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
►Nquantiles | |
CBatch | Computes values of quantiles in the batch processing mode |
CInput | Input objects for the quantiles algorithm |
CParameter | Parameters of the quantiles algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the quantiles algorithm in the batch processing mode |
►Nregression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the regression model-based prediction |
CInput | Provides an interface for input objects for making the regression model-based prediction |
CResult | Provides interface for the result of the regression model-based prediction |
►Ntraining | |
CBatch | Provides methods for the regression model-based training in the batch processing mode |
CInput | Input objects for the regression model-based training |
COnline | Provides methods for the regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of the regression model-based training in the online processing mode |
CResult | Provides methods to access the result obtained with the compute() method of the regression model-based training |
CModel | Base class for models trained with the regression algorithm |
CTreeNodeVisitor | Interface of abstract visitor used in tree traversal methods |
►Nridge_regression | |
►Nprediction | |
CBatch | Provides methods to run implementations of the ridge regression model-based prediction |
CInput | Provides an interface for input objects for making ridge regression model-based prediction |
CResult | Provides interface for the result of ridge regression model-based prediction |
►Ntraining | |
CBatch | Provides methods for ridge regression model-based training in the batch processing mode |
CDistributed | Performs ridge regression model-based training in the the first step of the distributed processing mode |
CDistributedInput | Input object for ridge regression model-based training in the distributed processing mode |
CInput | Input objects for ridge regression model-based training |
CInputIface | Abstract class that specifies the interface of input objects for ridge regression model-based training |
COnline | Provides methods for ridge regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of ridge regression model-based training in the online or distributed processing mode |
CResult | Provides methods to access the result obtained with the compute() method of ridge regression model-based training |
CModel | Base class for models trained with the ridge regression algorithm |
CModelNormEq | Model trained with the ridge regression algorithm using the normal equations method |
CParameter | Parameters for the ridge regression algorithm |
CTrainParameter | Parameters for the ridge regression algorithm |
►Nsorting | |
CBatch | Sorts the datasets by components of the random vector in the batch processing mode |
CInput | Input objects for the sorting algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the sorting algorithm in the batch processing mode |
►Nstump | |
►Nprediction | |
CBatch | Predicts results of the decision stump classification |
CInput | Input objects in the prediction stage of the stump algorithm |
►Ntraining | |
CBatch | Trains the decision stump model |
CResult | Provides methods to access final results obtained with the compute() method of the decision stump training algorithm in the batch processing mode |
CModel | Model of the classifier trained by the stump.training.Batch algorithm |
►Nsvd | |
CBatch | Computes results of the SVD algorithm in the batch processing mode |
CDistributed | Runs the first step of the SVD algorithm in the distributed processing mode |
CDistributedPartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the second step in the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the third step in the distributed processing mode |
CDistributedStep2Input | Input objects for the second step of the SVD algorithm in the distributed processing mode |
CDistributedStep3Input | Input objects for the third step of the SVD algorithm in the distributed processing mode |
CInput | Input objects for the SVD algorithm in the batch processing and online processing modes, and the first step in the distributed processing mode |
COnline | Computes results of the SVD algorithm in the online processing mode |
COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the online processing mode or the first step in the distributed processing mode |
CParameter | Parameters for the computation method of the SVD algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the SVD algorithm in the batch processing mode or with the finalizeCompute() method in the online processing mode or steps 2 and 3 in the distributed processing mode |
►Nsvm | |
►Nprediction | |
CBatch | Algorithm class for making predictions based on the SVM model |
CInput | Input objects in the prediction stage of the svm algorithm |
►Nquality_metric_set | |
CBatch | Class that represents a quality metric set to check the model trained with the SVM algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
►Ntraining | |
CBatch | Algorithm class to train the SVM model |
CResult | Provides methods to access final results obtained with the compute() method of the SVM training algorithm in the batch processing mode |
Cinterface2_Parameter | Optional parameters |
CModel | Model of the classifier trained by the svm.training.Batch algorithm |
CParameter | Optional parameters |
►Nunivariate_outlier_detection | |
CBatch | Runs the univariate outlier detection algorithm in the batch processing mode |
CDefaultInit | Class that specifies the default method for initialization |
CInitIface | Abstract class that provides a functor for the initial procedure |
CInput | Input objects for the univariate outlier detection algorithm |
CParameter | Parameters of the univariate outlier detection algorithm |
CResult | Results obtained with the compute() method of the univariate outlier detection algorithm in the batch processing mode |
►Nweak_learner | |
►Nprediction | |
CBatch | Base class for making predictions based on the weak learner model |
►Ntraining | |
CBatch | Base class for training the weak learner model in the batch processing mode |
CResult | Provides methods to access final results obtained with compute() method of Batch or finalizeCompute() method of Online and Distributed weak learners algorithms |
CModel | Base class for the weak learner model |
CParameter | Base class for the input objects of the weak learner training and prediction algorithm |
CAlgorithm | Implements the abstract interface AlgorithmIface |
CAlgorithmContainer | Abstract interface class that provides virtual methods to access and run implementations of the algorithms in batch mode |
CAlgorithmContainerIfaceImpl | Implements the abstract interface AlgorithmContainerIfaceImpl |
CAlgorithmContainerImpl | Abstract interface class that provides virtual methods to access and run implementations of the algorithms in batch mode |
CAlgorithmIface | Abstract class which defines interface for the library component related to data processing involving execution of the algorithms for analysis, modeling, and prediction |
CAlgorithmIfaceImpl | Implements the abstract interface AlgorithmIface |
CAlgorithmImpl | Provides implementations of the compute and checkComputeParams methods of the Algorithm<batch> class |
CAnalysis | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates |
CArgument | Base class to represent computation input and output arguments |
CDistributedPrediction | |
CInput | Base class to represent computation input arguments |
CKernel | Base class to represent algorithm implementation |
CModel | The base class for the classes that represent the models, such as linear_regression.Model or svm.Model |
COptionalArgument | Base class to represent argument with serialization methods |
CParameter | Base class to represent computation parameters |
CPartialResult | Base class to represent partial results of the computation |
CPrediction | Provides prediction methods depending on the model such as linear_regression.Model |
CResult | Base class to represent final results of the computation |
CSerializableArgument | Base class to represent argument with serialization methods |
CTraining | Provides methods to train models that depend on the data provided |
CValidationMetricIface | |
►Ndata_management | |
►Ndb | |
CODBCDataSource | Connects to data sources with the ODBC API |
CODBCDataSourceOptions | Options of ODBC data source |
CSQLFeatureManager | Interprets the response of SQL data base and fill provided numeric table and dictionary |
►Nmodifiers | |
►Ncsv | |
CFeatureModifier | Base class for feature modifier, intended for inheritance from the user side |
CFeatureModifierIface | Specialization of modifiers.FeatureModifierIface for CSV feature modifier |
CAOSNumericTable | Class that provides methods to access data stored as a numpy structured array |
CBasicStatisticsDataCollection | Basic statistics for each column of original Numeric Table |
CBlockDescriptor | Base class that manages buffer memory for read/write operations required by numeric tables |
CCategoricalFeatureDictionary | |
CColumnFilter | Methods of the class to filter out data source features from output numeric table |
CCompressedDataArchive | Abstract interface class that defines methods to access and modify a serialized object |
CCompression | Base class for compression and decompression |
CCompressionIface | Abstract interface class for compression and decompression |
CCompressionParameter | Parameters for compression and decompression |
CCompressionStream | CompressionStream class compresses input raw data by blocks |
CCompressor | Implementation of the Compressor class for the bzip2 compression method |
CCompressorImpl | Base class for the Compressor |
CCSRBlockDescriptor | Base class that manages buffer memory for read/write operations required by CSR numeric tables |
CCSRNumericTable | Class that provides methods to access data stored in the CSR layout |
CCSRNumericTableIface | Abstract class that defines the interface of CSR numeric tables |
CCsvDataSource | Specifies methods to access data stored in files |
CCsvDataSourceOptions | Options of CSV data source |
CDataArchive | Implements the abstract DataArchiveIface interface |
CDataArchiveIface | Abstract interface class that defines methods to access and modify a serialized object |
CDataArchiveImpl | Abstract interface class that defines methods to access and modify a serialized object |
CDataBlock | Class that stores a pointer to a byte array and its size |
CDataCollection | Class that provides functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself |
CDataSource | Implements the abstract DataSourceIface interface |
CDataSourceDictionary | Class that represents a dictionary of a data set and provides methods to work with the data dictionary |
CDataSourceFeature | Data structure that describes the Data Source feature |
CDataSourceIface | Abstract interface class that defines the interface for a data management component responsible for representation of data in the raw format |
CDataSourceTemplate | Implements the abstract DataSourceIface interface |
CDecompressedDataArchive | Abstract interface class that defines methods to access and modify a serialized object |
CDecompressionStream | DecompressionStream class decompresses compressed input data by blocks |
CDecompressor | Specialization of Decompressor class for Bzip2 compression method |
CDecompressorImpl | Base class for the Decompressor |
CDenseNumericTableIface | Abstract interface class for a data management component responsible for accessing data in the numeric format |
CDenseTensorIface | Abstract interface class for a data management component responsible for accessing data in the numeric format |
CFeatureAuxData | Structure for auxiliary data used for feature extraction |
CFileDataSource | Specifies methods to access data stored in files |
CHomogenNumericTable | Class that provides methods to access data stored as a contiguous array of homogeneous feature vectors |
CHomogenTensor | Class that provides methods to access data stored as a contiguous array of homogeneous data in rows-major format |
CInputDataArchive | Provides methods to create an archive data object (serialized) and access this object |
CKeyValueCollection | Class that provides functionality of a key-value container for objects derived from the T with a key of the size_t type |
CKeyValueDataCollection | Class that provides functionality of a key-value container for objects derived from the SerializationIface interface with a key of the size_t type |
CLzoCompressionParameter | Parameter for LZO compression and decompression |
CMakeCategorical | Methods of the class to set a feature categorical |
CMatrix | Represents a two-dimensional table of numbers of the same type |
CMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by columns |
CModifierIface | Abstract interface class that defines the interface for a features modifier |
CNumericTable | Class for a data management component responsible for representation of data in the numeric format |
CNumericTableDictionary | Class that represents a dictionary of a data set and provides methods to work with the data dictionary |
CNumericTableIface | Abstract interface class for a data management component responsible for representation of data in the numeric format |
COneHotEncoder | Methods of the class to set a feature binary categorical |
COutputDataArchive | Provides methods to restore an object from its serialized counterpart and access the restored object |
CPackedArrayNumericTableIface | Abstract class that defines the interface of symmetric matrices stored as a one-dimensional array |
CPackedSymmetricMatrix | Class that provides methods to access symmetric matrices stored as a one-dimensional array |
CPackedTriangularMatrix | Class that provides methods to access a packed triangular matrix stored as a one-dimensional array |
CRleCompressionParameter | Parameter for run-length encoding and decoding |
CRowMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by rows |
CSerializableKeyValueCollection | Class that provides functionality of a key-value container for objects derived from the SerializationIface interface with a key of the size_t type |
CSerializationDesc | |
CSerializationIface | Abstract interface class that defines the interface for serialization and deserialization |
CSOANumericTable | Class that provides methods to access data stored as a structure of arrays, where each (contiguous) array represents values corresponding to a specific feature |
CStringDataSource | Specifies methods to access data stored in byte arrays in the C-string format |
CSubtensorDescriptor | Class with descriptor of the subtensor retrieved from Tensor getSubTensor function |
CTensor | Class for a data management component responsible for representation of data in the n-dimensions numeric format |
CTensorIface | Abstract interface class for a data management component responsible for representation of data in the numeric format |
CTensorLayout | Class for a data management component responsible for representation of data layout in the tensor |
CTensorLayoutIface | Abstract interface class for a data management component responsible for representation of data layout in the tensor |
CTensorOffsetLayout | Class for a data management component responsible for representation of data layout in the HomogenTensor |
CZlibCompressionParameter | Parameter for zlib compression and decompression |
►Nservices | |
CCollection | Class that implements functionality of the Collection container |
CEnvironment | Class that provides methods to interact with the environment, including processor detection and control by the number of threads |
CError | Class that represents an error |
CException | Class that represents an exception |
CKernelErrorCollection | Class that represents a kernel error collection (collection that cannot throw exceptions) |
CStatus | |
CBase | Base class for Intel(R) Data Analytics Acceleration Library objects |
For more complete information about compiler optimizations, see our Optimization Notice.