CAbsLayerDataId | Identifiers of input objects for the backward abs layer and results for the forward abs layer |
CAbsMethod | Available methods for the abs layer |
CNumericTable.AllocationFlag | |
CTensor.AllocationFlag | |
CAveragePooling1dLayerDataId | Identifiers of input objects for the backward one-dimensional average pooling layer and results for the forward one-dimensional average pooling layer |
CAveragePooling1dMethod | Available methods for the one-dimensional average pooling layer |
CAveragePooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional average pooling layer and results for the forward two-dimensional average pooling layer |
CAveragePooling2dMethod | Available methods for the two-dimensional average pooling layer |
CAveragePooling3dLayerDataId | Identifiers of input objects for the backward three-dimensional average pooling layer and results for the forward three-dimensional average pooling layer |
CAveragePooling3dMethod | Available methods for the three-dimensional average pooling layer |
CBackwardInputId | Available identifiers of input objects for the backward layer |
CBackwardInputLayerDataId | Available identifiers of input objects for the backward layer |
CBackwardResultId | Available identifiers of results for the backward layer |
CBackwardResultLayerDataId | Available identifiers of results for the backward layer |
CBatchNormalizationForwardInputLayerDataId | Available identifiers of input objects for the forward batch normalization layer |
CBatchNormalizationLayerDataId | Identifiers of input objects for the backward batch normalization layer and results for the forward batch normalization layer |
CBatchNormalizationMethod | Available methods for the batch normalization layer |
CBinaryConfusionMatrixInputId | Available identifiers of the input objects of the binary confusion matrix algorithm |
CBinaryConfusionMatrixMethod | Available methods for computing the binary confusion matrix |
CBinaryConfusionMatrixResultId | Available identifiers of results of the binary confusion matrix algorithm |
CBinaryMetricId | Available identifiers of binary metrics |
CCompressionLevel | Compression levels |
CCompressionMethod | Compression and decompression methods |
CComputationMode | Available modes of kernel function computation |
CComputeMode | |
CComputeStep | |
CConcatLayerDataId | Identifiers of input objects for the backward concat layer and results for the forward concat layer |
CConcatMethod | Available methods for the concat layer |
CConvolution2dIndices | Data structure representing the dimension for convolution kernels |
CConvolution2dKernelSize | Data structure representing the sizes of the two-dimensional kernel subtensor for the backward 2D convolution layer and results for the forward 2D convolution layer |
CConvolution2dLayerDataId | Identifiers of input objects for the backward 2D convolution layer and results for the forward 2D convolution layer |
CConvolution2dMethod | Available methods for the 2D convolution layer |
CConvolution2dPadding | Data structure representing the number of data to be implicitly added to the subtensor |
CConvolution2dStride | Data structure representing the intervals on which the kernel should be applied to the input |
CCovarianceStorageId | Available identifiers of covariance types in the EM for GMM algorithm |
CCpuType | CPU types |
CCpuTypeEnable | CPU types |
CDaalContext | Provides the context for managment of memory in the native C++ object |
CDataFeatureUtils | Class that provides different feature types |
CTensor.DataLayout | |
CDataSource.DataSourceStatus | |
CDataUseInModelId | The option to enable/disable an usage of the input dataset in k nearest neighbors model |
CDataSource.DictionaryCreationFlag | |
►CDisposable | Class that frees memory allocated for the native C++ object |
►CAnalysisDistributed | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates in distributed processing mode. Classes that implement specific algorithms of the data analysis in distributed processing mode are derived classes of the AnalysisDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms |
►CDistributedIface | Base interface for the correlation or variance-covariance matrix algorithm in the distributed processing mode |
CDistributedStep2Master | Computes the correlation or variance-covariance matrix in the second step of the distributed processing mode |
CDistributedStep10Local | Runs the DBSCAN algorithm in the tenth step of the distributed processing mode |
CDistributedStep11Local | Runs the DBSCAN algorithm in the eleventh step of the distributed processing mode |
CDistributedStep12Local | Runs the DBSCAN algorithm in the twelfth step of the distributed processing mode |
CDistributedStep13Local | Runs the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
CDistributedStep1Local | Runs the DBSCAN algorithm in the first step of the distributed processing mode |
CDistributedStep2Local | Runs the DBSCAN algorithm in the second step of the distributed processing mode |
CDistributedStep3Local | Runs the DBSCAN algorithm in the third step of the distributed processing mode |
CDistributedStep4Local | Runs the DBSCAN algorithm in the fourth step of the distributed processing mode |
CDistributedStep5Local | Runs the DBSCAN algorithm in the fifth step of the distributed processing mode |
CDistributedStep6Local | Runs the DBSCAN algorithm in the sixth step of the distributed processing mode |
CDistributedStep7Master | Runs the DBSCAN algorithm in the seventh step of the distributed processing mode |
CDistributedStep8Local | Runs the DBSCAN algorithm in the eigth step of the distributed processing mode |
CDistributedStep9Master | Runs the DBSCAN algorithm in the ninth step of the distributed processing mode |
CDistributedStep1Local | Computes K-Means in the distributed processing mode on local nodes |
CDistributedStep2Master | Computes K-Means in the distributed processing mode on the master node |
CInitDistributedStep1Local | First step of computing initial clusters for the K-Means algorithm on local nodes |
CInitDistributedStep2Local | Seconda step of computing initial centroids for the K-Means algorithm on local nodes |
CInitDistributedStep2Master | Computes initial clusters for the K-Means algorithm in the distributed processing mode on the master node |
CInitDistributedStep3Master | Third step of computing initial centroids for the K-Means algorithm on local nodes |
CInitDistributedStep4Local | Third step of computing initial centroids for the K-Means algorithm on local nodes |
CInitDistributedStep5Master | Third step of computing initial centroids for the K-Means algorithm on local nodes |
CDistributedStep2Master | Computes moments of low order in the distributed processing mode on the master node |
CDistributedStep1Local | Provides methods for neural network model-based training in the distributed processing mode |
CDistributedStep2Master | Computes neural network training in the distributed processing mode on the master node |
CDistributedStep2Master | Runs the PCA algorithm in the the second step of the distributed processing mode |
CDistributedStep2Master | Computes the results of the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedStep3Local | Computes the results of the QR decomposition algorithm on the third step in the distributed processing mode |
CDistributedStep2Master | Runs the second step of the SVD algorithm in the distributed processing mode |
CDistributedStep3Local | Runs the third step of the SVD algorithm in the distributed processing mode |
►CAnalysisOnline | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates in the online processing mode. Classes that implement specific algorithms of the data analysis in the online processing mode are derived classes of the AnalysisOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms |
►COnlineImpl | Base interface for the correlation or variance-covariance matrix algorithm in the online processing mode |
►COnline | Computes the correlation or variance-covariance matrix in the online processing mode |
CDistributedStep1Local | Computes the results of the correlation or variance-covariance matrix algorithm in the first step of the distributed processing mode |
►COnline | Computes moments of low order in the online processing mode |
CDistributedStep1Local | Computes moments of low order in the distributed processing mode on local nodes |
►COnline | Runs the PCA algorithm in the online processing mode |
CDistributedStep1Local | Runs the PCA algorithm in the first step of the distributed processing mode |
►COnline | Computes the results of the QR decomposition algorithm in the online processing mode |
CDistributedStep1Local | Computes the results of the QR decomposition algorithm on the first step in the distributed processing mode |
►COnline | Runs the SVD algorithm in the online processing mode |
CDistributedStep1Local | Runs the first step of the SVD algorithm in the distributed processing mode |
►CPrediction | Provides prediction methods depending on the model such as linearregression.Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the Prediction class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
►CPredictionBatch | Base class for classifier model-based prediction in the batch processing mode |
►CPredictionBatch | Base class for training models of boosting algorithms in the batch processing mode |
CPredictionBatch | Predicts AdaBoost classification results |
CPredictionBatch | Predicts BrownBoost classification results |
CPredictionBatch | Predicts LogitBoost classification results |
CPredictionBatch | Predicts decision forest classification classification results |
CPredictionBatch | Predicts decision tree classification classification results |
CPredictionBatch | Predicts gradient boosted trees classification classification results |
CPredictionBatch | Runs k nearest neighbors model based prediction algorithm |
CPredictionBatch | Provides methods for logistic regression model-based prediction |
CPredictionBatch | Runs multi-class classifier model based prediction algorithm |
CPredictionBatch | Runs multinomial naive Bayes model based prediction |
CPredictionBatch | Algorithm class for predictions based on the SVM model |
►CPredictionBatch | Base class for making predictions based on the weak learner model |
CPredictionBatch | Predicts results of the decision stump classification |
CPredictionBatch | Predicts decision forest regression regression results |
CPredictionBatch | Predicts decision tree regression regression results |
CPredictionBatch | Predicts gradient boosted trees regression regression results |
CRatingsBatch | Predicts the results of implicit ALS model-based ratings prediction in the batch processing mode |
CPredictionBatch | Provides methods for lasso regression model-based prediction |
CPredictionBatch | Provides methods for linear regression model-based prediction |
CPredictionBatch | Provides methods for neural network model-based prediction in the batch processing mode |
CPredictionBatch | Provides methods for ridge regression model-based prediction |
►CPredictionDistributed | Provides prediction methods depending on the model such as linearregression.Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the PredictionDistributed class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
CRatingsDistributed | Predicts the results of implicit ALS model-based ratings prediction in the distributed processing mode |
►CTrainingBatch | Provides methods to train models that depend on the data provided in batch mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in batch mode are derived classes of the TrainingBatch class. The class additionally provides methods for validation of input and output parameters of the algorithms |
►CTrainingBatch | Algorithm class for training the classifier model |
►CTrainingBatch | Base class for training models of boosting algorithms in the batch processing mode |
CTrainingBatch | Trains a model of the AdaBoost algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the BrownBoost algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the LogitBoost algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the decision forest classification algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the gradient boosted trees classification algorithm in the batch processing mode |
CTrainingBatch | Provides methods for k nearest neighbors model-based training in the batch processing mode |
CTrainingBatch | Provides methods for logistic regression model-based training in the batch processing mode |
CTrainingBatch | Class for multi-class classifier model training |
CTrainingBatch | Algorithm class for training naive Bayes model in the batch processing mode |
CTrainingBatch | Algorithm class to train the SVM model |
►CTrainingBatch | Base class for training the weak learner model in the batch processing mode |
CTrainingBatch | Trains the decision stump model |
CTrainingBatch | Trains a model of the decision forest regression algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the decision tree classification algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the decision tree regression algorithm in the batch processing mode |
CTrainingBatch | Trains a model of the gradient boosted trees regression algorithm in the batch processing mode |
CInitBatch | Computes the initial model for the implicit ALS algorithm in the batch processing mode |
CTrainingBatch | Algorithm class for training the implicit ALS model in the batch processing mode |
CTrainingBatch | Provides methods for lasso regression model-based training in the batch processing mode |
CTrainingBatch | Provides methods for linear regression model-based training in the batch processing mode |
CTrainingBatch | Provides methods for neural network model-based training in the batch processing mode |
CTrainingBatch | Provides methods for ridge regression model-based training in the batch processing mode |
►CTrainingDistributed | Provides methods to train models that depend on the data provided in the distributed processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the distributed processing mode are derived classes of the TrainingDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CDistributedStep1Local | Runs the implicit ALS training algorithm in the first step of the distributed processing mode |
CDistributedStep2Master | Runs the implicit ALS training algorithm in the second step of the distributed processing mode |
CDistributedStep3Local | Runs the implicit ALS training algorithm in the third step of the distributed processing mode |
CDistributedStep4Local | Runs the implicit ALS training algorithm in the fourth step of the distributed processing mode |
CInitDistributed | Computes initial values for the implicit ALS algorithm in the distributed processing mode |
CInitDistributedStep1Local | Initializes the implicit ALS model in the first step of the distributed processing mode |
CInitDistributedStep2Local | Initializes the implicit ALS model in the second step of the distributed processing mode |
CTrainingDistributedStep2Master | Runs linear regression model-based training in the second step of the distributed processing mode |
CTrainingDistributedStep2Master | Algorithm class for training naive Bayes model on the second step in the distributed processing mode |
CTrainingDistributedStep2Master | Runs ridge regression model-based training in the second step of the distributed processing mode |
►CTrainingOnline | Provides methods to train models that depend on the data provided in the online processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the online processing mode are derived classes of the TrainingOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms |
►CTrainingOnline | Algorithm class for the classifier model training algorithm |
►CTrainingOnline | Algorithm class for training naive Bayes model in the online processing mode |
CTrainingDistributedStep1Local | Algorithm class for training naive Bayes model on the first step in the distributed processing mode |
►CTrainingOnline | Provides methods for linear regression model-based training in the online processing mode |
CTrainingDistributedStep1Local | Runs linear regression model-based training in the first step of the distributed processing mode |
►CTrainingOnline | Provides methods for ridge regression model-based training in the online processing mode |
CTrainingDistributedStep1Local | Runs ridge regression model-based training in the first step of the distributed processing mode |
►CSerializableBase | Class that provides methods for serialization and deserialization |
CModelBuilder | Class for building model of the decision forest classification algorithm |
CModelBuilder | Class for building model of the gradient boosted trees classification algorithm |
CModelBuilder | Class for building model of the gradient boosted trees regression algorithm |
CModelBuilder | Class for building model of the linear regression algorithm |
CModelBuilder | Class for building model of the logistic regression algorithm |
►CModel | Model is the base class for the classes that represent the models, such as linear regression or Support Vector Machine classifier |
►CModel | Base class for models of the classification algorithms |
►CModel | Base class for models of boosting algorithms. Contains a collection of weak learner algorithm models constructed during training of the boosting algorithm |
CModel | Model of the classifier trained by the AdaBoost algorithm in the batch processing mode |
CModel | Model of the classifier trained by the BrownBoost algorithm in the batch processing mode |
CModel | Model of the classifier trained by the LogitBoost algorithm in the batch processing mode |
CModel | Model of the classifier trained by decision forest classification algorithm in batch processing mode |
CModel | Model of the classifier trained by decision tree classification algorithm in batch processing mode |
CModel | Model of the classifier trained by gradient boosted trees classification algorithm in batch processing mode |
CModel | Base class for models trained by the k nearest neighbors training algorithm |
CModel | Base class for models trained by the logistic regression training algorithm |
CModel | Model of the classifier trained by the multi_class_classifier.training.TrainingBatch algorithm |
CModel | Model of the multinomial naive Bayes classifier trained in the batch processing mode |
CPartialModel | Multinomial naive Bayes PartialModel |
CModel | Model of the classifier trained by the svm.training.TrainingBatch algorithm |
CModel | Base class for the weak learner model |
CModel | Model trained by decision forest regression algorithm in batch processing mode |
CModel | Model trained by decision tree regression algorithm in batch processing mode |
CModel | Model trained by gradient boosted trees regression algorithm in batch processing mode |
CModel | Base class for the model trained by the implicit ALS algorithm in the batch processing mode |
CPartialModel | |
CModel | Base class for models trained by the lasso regression training algorithm |
►CModel | Base class for models trained by the linear regression training algorithm |
CModelNormEq | Model trained by the linear regression algorithm using the normal equations method |
CModelQR | Model trained by the linear regression algorithm using the QR decomposition-based method |
CPredictionModel | Class Model object for the prediction stage of neural network algorithm |
CTrainingModel | Class Model object for the training stage of neural network |
►CModel | Base class for models trained by the ridge regression training algorithm |
CModelNormEq | Model trained by the ridge regression algorithm using the normal equations method |
CModelBuilder | Class for building model of the multi-class classifier algorithm |
►CInitializationProcedureIface | Abstract interface class for setting initial parameters of multivariate outlier detection algorithm |
CInitializationProcedure | Class that specifies the default method for setting initial parameters of multivariate outlier detection algorithm |
COptionalArgument | Class that provides functionality of the Collection container for Serializable objects |
►CPartialResult | Base class to represent partial results of the computation. Algorithm-specific partial results are represented as derivative classes of the PartialResult class |
►CTrainingPartialResult | Provides methods to access partial results obtained with the compute() method of the classifier model training algorithm in the online or distributed processing mode |
CTrainingPartialResult | Provides methods to access results obtained with the compute() method of the naive Bayes training algorithm in the online or distributed processing mode |
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 |
CDistributedPartialResultStep1 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the first step of the distributed processing mode |
CDistributedPartialResultStep10 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the tenth step of the distributed processing mode |
CDistributedPartialResultStep11 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the eleventh step of the distributed processing mode |
CDistributedPartialResultStep12 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the twelfth step of the distributed processing mode |
CDistributedPartialResultStep13 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
CDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the second step of the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the third step of the distributed processing mode |
CDistributedPartialResultStep4 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the fourth step of the distributed processing mode |
CDistributedPartialResultStep5 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the fifth step of the distributed processing mode |
CDistributedPartialResultStep6 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the sixth step of the distributed processing mode |
CDistributedPartialResultStep7 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the seventh step of the distributed processing mode |
CDistributedPartialResultStep8 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the eigth step of the distributed processing mode |
CDistributedPartialResultStep9 | Provides methods to access partial results obtained with the compute() method of the DBSCAN algorithm in the ninth step of the distributed processing mode |
CRatingsPartialResult | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the rating prediction stage |
CDistributedPartialResultStep1 | Provides methods to access partial results obtained with the compute() method of the implicit ALS training 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 training 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 training algorithm in the third step of the distributed processing mode |
CDistributedPartialResultStep4 | Provides methods to access partial results obtained with the compute() method of the implicit ALS training algorithm in the fourth step of the distributed processing mode |
►CInitPartialResultBase | Provides interface to access partial results obtained with the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode |
CInitDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
CInitPartialResult | Provides methods to access partial results of computing the initial model for the implicit ALS training algorithm |
CInitDistributedStep2LocalPlusPlusPartialResult | Provides methods to access partial results of computing initial centroids for the K-Means algorithm in the distributed processing mode |
CInitDistributedStep3MasterPlusPlusPartialResult | Provides methods to access partial results of computing initial centroids for the K-Means algorithm in the distributed processing mode |
CInitDistributedStep4LocalPlusPlusPartialResult | Provides methods to access partial results of computing initial centroids for the K-Means algorithm in the distributed processing mode |
CInitDistributedStep5MasterPlusPlusPartialResult | Provides methods to access partial results of computing initial centroids for the K-Means algorithm in the distributed processing mode |
CInitPartialResult | Provides methods to access partial results of computing initial centroids for the K-Means algorithm in the distributed processing mode |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the K-Means algorithm in the distributed 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 |
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 |
CDistributedPartialResult | Provides methods to access partial results obtained with the compute() method of the neural network algorithm in the distributed processing mode on step 2 |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
CPartialCorrelationResult | Provides methods to access partial results obtained with compute() of the correlation method of PCA algorithm in the online or distributed processing mode |
CPartialSVDResult | Provides methods to access partial results obtained with the compute() of the SVD method of the PCA algorithm in the online or distributed processing mode |
CDistributedStep2MasterPartialResult | Provides methods to access partial results obtained with the compute() method of the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedStep3LocalPartialResult | Provides methods to access partial results obtained with the compute() method of the QR decomposition algorithm on the third step in the distributed processing mode |
►COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the QR decomposition algorithm in the online processing mode |
CDistributedStep1LocalPartialResult | Provides methods to access partial results obtained with the compute() method of the first step of the QR decomposition algorithm in the distributed 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 |
CDistributedStep2MasterPartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the second step in the distributed processing mode |
CDistributedStep3LocalPartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the third step in the distributed processing mode |
►COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the online processing or distributed processing modes |
CDistributedStep1LocalPartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the first step in the distributed processing mode |
►CResult | Base class to represent final results of the computation. Algorithm-specific final results are represented as derivative classes of the Result class |
CResult | Results obtained with the compute() method of the association rules algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the multivariate outlier detection algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the Cholesky decomposition algorithm in the batch processing mode |
►CPredictionResult | Provides methods to access final results obtained with the compute() method of the classifier model-based prediction algorithm in the batch processing mode |
CPredictionResult | Result object for logistic regression model-based prediction |
►CTrainingResult | Provides methods to access results obtained with the compute() method of the classifier training algorithm in the batch, online, or distributed processing mode |
►CTrainingResult | Provides methods to access final results obtained with the compute() method of a boosting training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the AdaBoost training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the BrownBoost training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the decision forest classification training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the decision tree classification training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the gradient boosted trees classification training algorithm in the batch processing mode |
CTrainingResult | Provides methods to access the results obtained with the compute() method of kdtree_knn_classification.training.TrainingBatch algorithm |
CTrainingResult | Provides methods to access the result of logistic regression model-based training |
CTrainingResult | Provides methods to access the results obtained with the compute() method of multi_class_classifier.training.TrainingBatch algorithm |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the naive Bayes training algorithm in the batch processing mode and with the finalizeCompute() method in the online and distributed processing mode |
CTrainingResult | Provides methods to access results obtained with the compute() method of the svm.training.TrainingBatch algorithm |
CResult | Results obtained with the compute() method of the correlation distance algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the cosine distance algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the batch processing mode |
CDistributedResultStep13 | Provides methods to access results obtained with the compute() method of the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
CDistributedResultStep9 | Provides methods to access results obtained with the compute() method of the DBSCAN algorithm in the ninth step of the distributed processing mode |
CResult | Results obtained with the compute() method of the DBSCAN algorithm in the batch processing mode |
CPredictionResult | Provides methods to access final results obtained with the compute() method of the decision forest regression model-based prediction algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the decision forest regression training algorithm in the batch processing mode |
CPredictionResult | Provides methods to access final results obtained with the compute() method of the decision_tree regression model-based prediction algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the decision tree regression training algorithm in the batch processing mode |
CResult | Provides methods to access results obtained with the compute() method of the distribution |
CInitResult | Provides methods to access final results obtained with the compute() method of InitBatch for the computation of initial values 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 |
CResult | Provides methods to access results obtained with the compute() method of the engine |
CPredictionResult | Provides methods to access final results obtained with the compute() method of the gradient boosted trees regression model-based prediction algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the gradient boosted trees regression training algorithm in the batch processing mode |
CRatingsResult | Provides methods to access the results obtained with the compute() method of the implicit ALS ratings prediction algorithm in the batch processing mode |
CInitResult | Provides methods to access the results of computing the initial model for the implicit ALS training algorithm |
CTrainingResult | Provides methods to access the results of the implicit ALS training algorithm |
CResult | Results obtained with the compute() method of the linear kernel function algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the RBF kernel function algorithm in the batch processing mode |
CInitResult | Results of computing initial clusters for the K-Means algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the K-Means algorithm in the batch processing mode |
CPredictionResult | Result object for lasso regression model-based prediction |
CTrainingResult | Provides methods to access the result of lasso regression model-based training |
CPredictionResult | Result object for linear regression model-based prediction |
CTrainingResult | Provides methods to access the result of linear regression model-based training |
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 the algorithm in the online or distributed processing mode |
CResult | Results obtained with the compute() method of the absolute value function in the batch processing mode |
CResult | Results obtained with the compute() method of the logistic function in the batch processing mode |
CResult | Results obtained with the compute() method of the rectified linear function in the batch processing mode |
CResult | Results obtained with the compute() method of the SmoothReLU algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the softmax function in the batch processing mode |
CResult | Results obtained with the compute() method of the hyperbolic tangent function in the batch processing mode |
CResult | Results obtained with the compute() method of the multivariate outlier detection algorithm in the batch processing mode |
CResult | Provides methods to access results obtained with the compute() method of the neural network weights and biases initializer |
►CBackwardResult | Provides methods to access results obtained with the compute() method of the backward layer |
CAbsBackwardResult | Provides methods to access results obtained with the compute() method of the backward abs layer |
CBatchNormalizationBackwardResult | Provides methods to access results obtained with the compute() method of the backward batch normalization layer |
CConcatBackwardResult | Provides methods to access results obtained with the compute() method of the backward concat layer |
CConvolution2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward 2D convolution layer |
CDropoutBackwardResult | Provides methods to access results obtained with the compute() method of the backward dropout layer |
CEltwiseSumBackwardResult | Provides methods to access results obtained with the compute() method of the backward element-wise sum layer |
CEluBackwardResult | Provides methods to access results obtained with the compute() method of the backward ELU layer |
CFullyConnectedBackwardResult | Provides methods to access results obtained with the compute() method of the backward fully-connected layer |
CLcnBackwardResult | Provides methods to access results obtained with the compute() method of the backward local contrast normalization layer |
CLocallyConnected2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward 2D locally connected layer |
CLogisticBackwardResult | Provides methods to access results obtained with the compute() method of the backward logistic layer |
►CLossBackwardResult | Provides methods to access results obtained with the compute() method of the backward loss layer |
CLogisticCrossBackwardResult | Provides methods to access results obtained with the compute() method of the backward logistic cross-entropy layer |
CSoftmaxCrossBackwardResult | Provides methods to access results obtained with the compute() method of the backward softmax cross-entropy layer |
CLrnBackwardResult | Provides methods to access results obtained with the compute() method of the backward local response normalization layer |
►CPooling1dBackwardResult | Provides methods to access results obtained with the compute() method of the backward one-dimensional pooling layer |
CAveragePooling1dBackwardResult | Provides methods to access results obtained with the compute() method of the backward one-dimensional average pooling layer |
CMaximumPooling1dBackwardResult | Provides methods to access results obtained with the compute() method of the backward one-dimensional maximum pooling layer |
►CPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional pooling layer |
CAveragePooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional average pooling layer |
CMaximumPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional maximum pooling layer |
CStochasticPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional stochastic pooling layer |
►CPooling3dBackwardResult | Provides methods to access results obtained with the compute() method of the backward pooling layer |
CAveragePooling3dBackwardResult | Provides methods to access results obtained with the compute() method of the backward three-dimensional average pooling layer |
CMaximumPooling3dBackwardResult | Provides methods to access results obtained with the compute() method of the backward three-dimensional maximum pooling layer |
CPreluBackwardResult | Provides methods to access results obtained with the compute() method of the backward prelu layer |
CReluBackwardResult | Provides methods to access results obtained with the compute() method of the backward relu layer |
CReshapeBackwardResult | Provides methods to access results obtained with the compute() method of the backward reshape layer |
CSmoothreluBackwardResult | Provides methods to access results obtained with the compute() method of the backward smoothrelu layer |
CSoftmaxBackwardResult | Provides methods to access results obtained with the compute() method of the backward softmax layer |
►CSpatialPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional spatial pooling layer |
CSpatialAveragePooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional spatial maximum pooling layer |
CSpatialStochasticPooling2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward two-dimensional spatial stochastic pooling layer |
CSplitBackwardResult | Provides methods to access results obtained with the compute() method of the backward split layer |
CTanhBackwardResult | Provides methods to access results obtained with the compute() method of the backward hyperbolic tangent (tanh) layer |
CTransposedConv2dBackwardResult | Provides methods to access results obtained with the compute() method of the backward 2D transposed convolution layer |
►CForwardResult | Provides methods to access results obtained with the compute() method of the forward layer |
CAbsForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward abs layer |
CBatchNormalizationForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward batch normalization layer |
CConcatForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward concat layer |
CConvolution2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward 2D convolution layer |
CDropoutForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward dropout layer |
CEltwiseSumForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward element-wise sum layer |
CEluForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward ELU layer |
CFullyConnectedForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward fully-connected layer |
CLcnForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward local contrast normalization layer |
CLocallyConnected2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward 2D locally connected layer |
CLogisticForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward logistic layer |
►CLossForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward loss layer |
CLogisticCrossForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward logistic cross-entropy layer |
CSoftmaxCrossForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward softmax cross-entropy layer |
CLrnForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward local response normalization layer |
►CPooling1dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward one-dimensional pooling layer |
CAveragePooling1dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward one-dimensional average pooling layer |
CMaximumPooling1dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward one-dimensional maximum pooling layer |
►CPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional pooling layer |
CAveragePooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional average pooling layer |
CMaximumPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional maximum pooling layer |
CStochasticPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional stochastic pooling layer |
►CPooling3dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward pooling layer |
CAveragePooling3dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward three-dimensional average pooling layer |
CMaximumPooling3dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward three-dimensional maximum pooling layer |
CPreluForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward prelu layer |
CReluForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward relu layer |
CReshapeForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward reshape layer |
CSmoothreluForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward smoothrelu layer |
CSoftmaxForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward softmax layer |
►CSpatialPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional spatial pooling layer |
CSpatialAveragePooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional spatial maximum pooling layer |
CSpatialStochasticPooling2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward two-dimensional spatial stochastic pooling layer |
CSplitForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward split layer |
CTanhForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward hyperbolic tangent (tanh) layer |
CTransposedConv2dForwardResult | Class that provides methods to access the result obtained with the compute() method of the forward 2D transposed convolution layer |
CPredictionResult | Provides methods to access result obtained with the compute() method of the neural networks prediction algorithm |
CTrainingResult | Provides methods to access result obtained with the compute() method of the neural network training algorithm |
CResult | Results obtained with the compute() method of the Min-max normalization algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the Z-score normalization algorithm in the batch processing mode |
►CResult | Provides methods to access the results obtained with the compute() method of the iterative algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the Adagrad algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the Coordinate Descent algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the SAGA algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the SGD algorithm in the batch processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the Objective funtion algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method of PCA algorithm in the batch processing mode, or finalizeCompute() method in the online or distributed processing mode |
CTransformResult | Results obtained with the compute() method of the PCA transformation algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the pivoted QR algorithm in the batch processing mode |
CResult | Provides methods to access final results obtained with the compute() method of QR decomposition algorithm in the batch processing mode or finalizeCompute() method in the online processing mode for the algorithm on the second or third steps in the distributed processing mode |
►CQualityMetricResult | Base class for the result of quality metrics |
CBinaryConfusionMatrixResult | Class for the results of the binary confusion matrix algorithm |
CMultiClassConfusionMatrixResult | Class for the results of the multi-class confusion matrix algorithm |
CGroupOfBetasResult | Class for the the result of linear regression quality metrics algorithm |
CSingleBetaResult | Class for the the result of linear regression quality metrics algorithm |
CExplainedVarianceResult | Class for the the result of PCA quality metrics algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the quantiles algorithm in the batch processing mode |
CPredictionResult | Result object for ridge regression model-based prediction |
CTrainingResult | Provides methods to access the result of ridge regression model-based training |
CResult | Provides methods to access final results obtained with the compute() method of the sorting in the batch processing mode |
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 or distributed processing modes |
CResult | Results obtained with the compute() method of the univariate outlier detection algorithm in the batch processing mode |
CTrainingResult | Provides methods to access final results obtained with the compute() method of the TrainingBatch weak learner algorithm |
CModelBuilder | Class for building model of the support vector machine algorithm |
►CInitializationProcedureIface | Abstract interface class for setting initial parameters of univariate outlier detection algorithm |
CInitializationProcedure | Class that specifies the default method for setting initial parameters of univariate outlier detection algorithm |
CDataCollection | Class that provides functionality of the Collection container for Serializable objects |
CDataDictionary | Class that represents the data set dictionary and provides methods to work with the data dictionary. Methods of the class use the com.intel.daal.data.DataFeature structure |
CDataFeature | Class used to describe a feature. The structure is used in the com.intel.daal.data.DataDictionary class |
►CKeyValueDataCollection | Class that provides functionality of the key-value container for Serializable objects with the key of integer type |
►CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm |
►CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
►CNumericTable | Class for the data management component responsible for the representation of the data in a numerical format |
CAOSNumericTable | Class that provides methods to access data that is stored as a contiguous array of heterogeneous feature vectors, and each feature vector is represented with a data structure. Therefore, the data is represented as an Array Of Structures(AOS) |
CCSRNumericTable | Numeric table that provides methods to access data that is stored in the Compressed Sparse Row(CSR) data layout |
►CHomogenNumericTable | A derivative class of the NumericTable class, that provides methods to access the data that is stored as a contiguous array of homogeneous feature vectors. Table rows contain feature vectors, and columns contain values of individual features |
CMatrix | A derivative class of the NumericTable class, that provides methods to access the data that is stored as a contiguous array of homogeneous feature vectors. Table rows contain feature vectors, and columns contain values of individual features |
CMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by columns |
CPackedSymmetricMatrix | Class that provides methods to access symmetric matrices |
CPackedTriangularMatrix | Class that provides methods to access triangular matrices |
CRowMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by rows |
CSOANumericTable | Class that provides methods to access data that is stored as a Structure Of Arrays(SOA), where each contiguous array represents values corresponding to a specific feature |
►CNumericTableImpl | Class for the data management component responsible for the representation of the data in a numerical format |
CAOSNumericTableImpl | Class that provides methods to access data that is stored as a contiguous array of heterogeneous feature vectors, and each feature vector is represented with a data structure. Therefore, the data is represented as an Array Of Structures(AOS) |
CCSRNumericTableImpl | Numeric table that provides methods to access data that is stored in the Compressed Sparse Row(CSR) data layout |
CMergedNumericTableImpl | Class that provides methods to access a collection of numeric tables as if they are joined by columns |
CRowMergedNumericTableImpl | Class that provides methods to access a collection of numeric tables as if they are joined by rows |
CSOANumericTableImpl | Class that provides methods to access data that is stored as a Structure Of Arrays(SOA), where each contiguous array represents values corresponding to a specific feature |
►CTensor | Class for the data management component responsible for the representation of the tensor data |
CHomogenTensor | A derivative class of the Tensor class, that provides methods to access the data that is stored as a contiguous homogeneous array |
CTensorImpl | Class for the data management component responsible for the representation of the data in a numerical format |
►CContextClient | Class for management by deletion of the memory allocated for the native C++ object |
►CAlgorithm | Algorithm is the base class for the classes interfacing the major stages of data processing: Analysis, Training and Prediction |
►CAnalysisBatch | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates in batch processing mode. Classes that implement specific algorithms of the data analysis in batch processing mode are derived classes of the AnalysisBatch class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CBatch | Computes the result of the association rules algorithm in the batch processing mode |
CBatch | Runs the multivariate outlier detection algorithm in the batch processing mode |
CBatch | Computes Cholesky decomposition in the batch processing mode |
CBatch | Computes correlation distance in the batch processing mode |
CBatch | Computes the cosine distance in the batch processing mode |
►CBatchImpl | Base interface for the correlation or variance-covariance matrix algorithm in the batch processing mode |
CBatch | Computes the correlation or variance-covariance matrix in the batch processing mode |
CBatch | Computes the results of the DBSCAN algorithm in the batch processing mode |
►CBatchBase | Class representing distributions |
CBatch | Provides methods for bernoulli distribution computations in the batch processing mode |
CBatch | Provides methods for normal distribution computations in the batch processing mode |
CBatch | Provides methods for uniform distribution computations in the batch processing mode |
CBatch | Runs the EM for GMM algorithm in the batch processing mode |
CInitBatch | Computes initial values for the EM for GMM algorithm in the batch processing mode |
►CBatchBase | Class representing engines |
►CFamilyBatchBase | Class representing engines |
CBatch | Provides methods for mt2203 engine computations in the batch processing mode |
CBatch | Provides methods for mcg59 engine computations in the batch processing mode |
CBatch | Provides methods for mt19937 engine computations in the batch processing mode |
►CBatch | Computes the kernel function in the batch processing mode |
CBatch | Computes the linear kernel function in the batch processing mode |
CBatch | Computes the radial basis function (RBF) kernel in the batch processing mode |
CBatch | Computes the results of the K-Means algorithm in the batch processing mode |
CInitBatch | Computes initial clusters for the K-Means algorithm in the batch processing mode |
►CBatchImpl | Base interface for the low order moments algorithm in the batch processing mode |
CBatch | Computes moments of low order in the batch processing mode |
CBatch | Computes absolute value function in the batch processing mode |
CBatch | Computes logistic function in the batch processing mode |
CBatch | Computes the rectified linear function in the batch processing mode |
CBatch | Computes SmoothReLU function in the batch processing mode |
CBatch | Computes the softmax function in the batch processing mode |
CBatch | Computes the hyperbolic tangent function in the batch processing mode |
CBatch | Runs the multivariate outlier detection algorithm in the batch processing mode |
CBatch | Runs multivariate outlier detection in the batch processing mode |
CBatch | Runs multivariate outlier detection in the batch processing mode |
►CInitializerIface | Class representing a neural network weights and biases initializer |
CGaussianBatch | Provides methods for gaussian initializer computations in the batch processing mode |
CTruncatedGaussianBatch | Provides methods for truncated gaussian initializer computations in the batch processing mode |
CUniformBatch | Provides methods for uniform initializer computations in the batch processing mode |
CXavierBatch | Provides methods for Xavier initializer computations in the batch processing mode |
►CBackwardLayer | Class representing a backward layer of neural network |
CAbsBackwardBatch | Class that computes the results of the backward abs layer in the batch processing mode |
CAveragePooling1dBackwardBatch | Class that computes the results of the one-dimensional average pooling layer in the batch processing mode |
CAveragePooling2dBackwardBatch | Class that computes the results of the two-dimensional average pooling layer in the batch processing mode |
CAveragePooling3dBackwardBatch | Class that computes the results of the three-dimensional average pooling layer in the batch processing mode |
CBatchNormalizationBackwardBatch | Class that computes the results of the backward batch normalization layer in the batch processing mode |
CConcatBackwardBatch | Class that computes the results of the backward concat layer in the batch processing mode |
CConvolution2dBackwardBatch | Class that computes the results of the 2D convolution layer in the batch processing mode |
CDropoutBackwardBatch | Class that computes the results of the backward dropout layer in the batch processing mode |
CEltwiseSumBackwardBatch | Class that computes the results of the backward element-wise sum layer in the batch processing mode |
CEluBackwardBatch | Class that computes the results of the backward Exponential Linear Unit (ELU) layer in the batch processing mode |
CFullyConnectedBackwardBatch | Class that computes the results of the backward fully-connected layer in the batch processing mode |
CLcnBackwardBatch | Class that computes the results of the local contrast normalization layer in the batch processing mode |
CLocallyConnected2dBackwardBatch | Class that computes the results of the 2D locally connected layer in the batch processing mode |
CLogisticBackwardBatch | Class that computes the results of the logistic layer in the batch processing mode |
►CLossBackwardBatch | Class that computes the results of the backward loss layer in the batch processing mode |
CLogisticCrossBackwardBatch | Class that computes the results of the backward logistic cross-entropy layer in the batch processing mode |
CSoftmaxCrossBackwardBatch | Class that computes the results of the backward softmax cross-entropy layer in the batch processing mode |
CLrnBackwardBatch | Class that computes the results of the local response normalization (lrn) layer in the batch processing mode |
CMaximumPooling1dBackwardBatch | Class that computes the results of the one-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling2dBackwardBatch | Class that computes the results of the two-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling3dBackwardBatch | Class that computes the results of the three-dimensional maximum pooling layer in the batch processing mode |
CPreluBackwardBatch | Class that computes the results of the prelu layer in the batch processing mode |
CReluBackwardBatch | Class that computes the results of the backward rectified linear unit (relu) layer in the batch processing mode |
CReshapeBackwardBatch | Class that computes the results of the backward reshape layer in the batch processing mode |
CSmoothreluBackwardBatch | Class that computes the results of the backward smooth rectified linear unit (smoothrelu) layer in the batch processing mode |
CSoftmaxBackwardBatch | Class that computes the results of the softmax layer in the batch processing mode |
CSpatialAveragePooling2dBackwardBatch | Class that computes the results of the two-dimensional spatial average pooling layer in the batch processing mode |
CSpatialMaximumPooling2dBackwardBatch | Class that computes the results of the two-dimensional spatial maximum pooling layer in the batch processing mode |
CSpatialStochasticPooling2dBackwardBatch | Class that computes the results of the two-dimensional spatial stochastic pooling layer in the batch processing mode |
CSplitBackwardBatch | Class that computes the results of the backward split layer in the batch processing mode |
CStochasticPooling2dBackwardBatch | Class that computes the results of the two-dimensional stochastic pooling layer in the batch processing mode |
CTanhBackwardBatch | Class that computes the results of the backward hyperbolic tangent (tanh) layer in the batch processing mode |
CTransposedConv2dBackwardBatch | Class that computes the results of the 2D transposed convolution layer in the batch processing mode |
►CForwardLayer | Class representing a forward layer of neural network |
CAbsForwardBatch | Class that computes the results of the forward abs layer in the batch processing mode |
CAveragePooling1dForwardBatch | Class that computes the results of the forward one-dimensional average pooling layer in the batch processing mode |
CAveragePooling2dForwardBatch | Class that computes the results of the forward two-dimensional average pooling layer in the batch processing mode |
CAveragePooling3dForwardBatch | Class that computes the results of the forward three-dimensional average pooling layer in the batch processing mode |
CBatchNormalizationForwardBatch | Class that computes the results of the forward batch normalization layer in the batch processing mode |
CConcatForwardBatch | Class that computes the results of the forward concat layer in the batch processing mode |
CConvolution2dForwardBatch | Class that computes the results of the forward 2D convolution layer in the batch processing mode |
CDropoutForwardBatch | Class that computes the results of the forward dropout layer in the batch processing mode |
CEltwiseSumForwardBatch | Class that computes the results of the forward element-wise sum layer in the batch processing mode |
CEluForwardBatch | Class that computes the results of the forward Exponential Linear Unit (ELU) layer in the batch processing mode |
CFullyConnectedForwardBatch | Class that computes the results of the forward fully-connected layer in the batch processing mode |
CLcnForwardBatch | Class that computes the results of the forward local contrast normalization layer in the batch processing mode |
CLocallyConnected2dForwardBatch | Class that computes the results of the forward 2D locally connected layer in the batch processing mode |
CLogisticForwardBatch | Class that computes the results of the forward logistic layer in the batch processing mode |
►CLossForwardBatch | Class that computes the results of the forward loss layer in the batch processing mode |
CLogisticCrossForwardBatch | Class that computes the results of the forward logistic cross-entropy layer in the batch processing mode |
CSoftmaxCrossForwardBatch | Class that computes the results of the forward softmax cross-entropy layer in the batch processing mode |
CLrnForwardBatch | Class that computes the results of the forward local response normalization layer in the batch processing mode |
CMaximumPooling1dForwardBatch | Class that computes the results of the forward one-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling2dForwardBatch | Class that computes the results of the forward two-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling3dForwardBatch | Class that computes the results of the forward three-dimensional maximum pooling layer in the batch processing mode |
CPreluForwardBatch | Class that computes the results of the forward prelu layer in the batch processing mode |
CReluForwardBatch | Class that computes the results of the forward rectified linear unit (relu) layer in the batch processing mode |
CReshapeForwardBatch | Class that computes the results of the forward reshape layer in the batch processing mode |
CSmoothreluForwardBatch | Class that computes the results of the forward smooth rectified linear unit (smoothrelu) layer in the batch processing mode |
CSoftmaxForwardBatch | Class that computes the results of the forward softmax layer in the batch processing mode |
CSpatialAveragePooling2dForwardBatch | Class that computes the results of the forward two-dimensional spatial average pooling layer in the batch processing mode |
CSpatialMaximumPooling2dForwardBatch | Class that computes the results of the forward two-dimensional spatial maximum pooling layer in the batch processing mode |
CSpatialStochasticPooling2dForwardBatch | Class that computes the results of the forward two-dimensional spatial stochastic pooling layer in the batch processing mode |
CSplitForwardBatch | Class that computes the results of the forward split layer in the batch processing mode |
CStochasticPooling2dForwardBatch | Class that computes the results of the forward two-dimensional stochastic pooling layer in the batch processing mode |
CTanhForwardBatch | Class that computes the results of the forward hyperbolic tangent (tanh) layer in the batch processing mode |
CTransposedConv2dForwardBatch | Class that computes the results of the forward 2D transposed convolution layer in the batch processing mode |
CBatch | Computes Min-max normalization in the batch processing mode |
CBatch | Computes Z-score normalization in the batch processing mode |
►CBatchIface | Base interface for the Optimization solver algorithm in the batch processing mode |
►CBatch | Base interface for the iterative solver algorithm in the batch processing mode |
CBatch | Base interface for the Adagrad algorithm in the batch processing mode |
CBatch | Base interface for the Coordinate Descent algorithm in the batch processing mode |
CBatch | Computes the results of LBFGS algorithm in the batch processing mode |
CBatch | Base interface for the SAGA algorithm in the batch processing mode |
CBatch | Base interface for the SGD algorithm in the batch processing mode |
►CBatch | Base interface for the Objective function algorithm in the batch processing mode |
►CBatch | Computes the Sum of functions algorithm in the batch processing mode |
CBatch | Computes the cross-entropy loss objective function in the batch processing mode |
CBatch | Computes the logistic loss objective function in the batch processing mode |
CBatch | Base interface for the MSE algorithm in the batch processing mode |
CBatch | Computes the objective function with precomputed characteristics in the batch processing mode |
CBatch | Runs the PCA algorithm in the batch processing mode |
CTransformBatch | Computes PCA transformation in the batch processing mode |
CBatch | Computes the results of the pivoted QR algorithm in the batch processing mode |
CBatch | Computes the results of the QR decomposition algorithm in the batch processing mode |
►CQualityMetricBatch | Provides methods to compute quality metrics of an algorithm in the batch processing mode. Quality metric is a numerical characteristic or a set of connected numerical characteristics that represents the qualitative aspect of a computed statistical estimate, model, or decision-making result |
CBinaryConfusionMatrixBatch | Computes the confusion matrix for a binary classifier in the batch processing mode |
CMultiClassConfusionMatrixBatch | Computes the confusion matrix for a multi-class classifier in the batch processing mode |
CGroupOfBetasBatch | Computes the linear regression regression group of betas quality metrics in batch processing mode |
CSingleBetaBatch | Computes the linear regression regression single beta quality metrics in batch processing mode |
CExplainedVarianceBatch | Computes the PCA explained variance quality metrics in batch processing mode |
CBatch | Computes values of quantiles in the batch processing mode |
CBatch | Sorts data in the batch processing mode |
CBatch | Runs the SVD algorithm in the batch processing mode |
CBatch | Runs the univariate outlier detection algorithm in the batch processing mode |
CAnalysisDistributed | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates in distributed processing mode. Classes that implement specific algorithms of the data analysis in distributed processing mode are derived classes of the AnalysisDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CAnalysisOnline | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates in the online processing mode. Classes that implement specific algorithms of the data analysis in the online processing mode are derived classes of the AnalysisOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CPrediction | Provides prediction methods depending on the model such as linearregression.Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the Prediction class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
CPredictionDistributed | Provides prediction methods depending on the model such as linearregression.Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the PredictionDistributed class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
CTrainingBatch | Provides methods to train models that depend on the data provided in batch mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in batch mode are derived classes of the TrainingBatch class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CTrainingDistributed | Provides methods to train models that depend on the data provided in the distributed processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the distributed processing mode are derived classes of the TrainingDistributed class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CTrainingOnline | Provides methods to train models that depend on the data provided in the online processing mode. For example, these methods enable training the linear regression model. Classes that implement specific algorithms of model training in the online processing mode are derived classes of the TrainingOnline class. The class additionally provides methods for validation of input and output parameters of the algorithms |
►CInput | Base class to represent computation input arguments. Algorithm-specific input arguments are represented as derivative classes of the Input class |
CInput | Input for the association rules algorithm |
CInput | Input objects for the multivariate outlier detection algorithm |
CInput | Input objects for the Cholesky decomposition algorithm |
►CPredictionInput | Input objects for the classification algorithm |
CPredictionInput | Input objects for the AdaBoost algorithm |
CPredictionInput | Input objects for the BrownBoost algorithm |
CPredictionInput | Input objects for the decision forest classification algorithm |
CPredictionInput | Input objects for the decision tree classification algorithm |
CPredictionInput | Input objects for the gradient boosted trees classification algorithm |
CPredictionInput | Input objects for the k nearest neighbors algorithm |
CPredictionInput | Input object for making logistic regression model-based prediction |
CPredictionInput | Input objects for the LogitBoost algorithm |
CPredictionInput | Input objects for the Multi-class classifier algorithm |
CPredictionInput | Input objects for the multinomial naive Bayes prediction algorithm |
CPredictionInput | Input objects for the Stump algorithm |
CPredictionInput | Input objects for the classification algorithm |
►CTrainingInput | Input objects for the classifier training algorithm |
CTrainingInput | Input objects for the decision tree classification algorithm |
CInput | Input objects for the correlation distance algorithm |
CInput | Input objects for the cosine distance algorithm |
CDistributedStep2MasterInput | Input objects for the correlation or variance-covariance matrix algorithm in the second step of the distributed processing mode |
CInput | Input objects for the correlation or variance-covariance matrix algorithm |
CDistributedStep10LocalInput | Input objects for the DBSCAN algorithm in the tenth step of the distributed processing mode |
CDistributedStep11LocalInput | Input objects for the DBSCAN algorithm in the eleventh step of the distributed processing mode |
CDistributedStep12LocalInput | Input objects for the DBSCAN algorithm in the twelfth step of the distributed processing mode |
CDistributedStep13LocalInput | Input objects for the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
CDistributedStep1LocalInput | Input objects for the DBSCAN algorithm in the first step of the distributed processing mode |
CDistributedStep2LocalInput | Input objects for the DBSCAN algorithm in the second step of the distributed processing mode |
CDistributedStep3LocalInput | Input objects for the DBSCAN algorithm in the third step of the distributed processing mode |
CDistributedStep4LocalInput | Input objects for the DBSCAN algorithm in the fourth step of the distributed processing mode |
CDistributedStep5LocalInput | Input objects for the DBSCAN algorithm in the fifth step of the distributed processing mode |
CDistributedStep6LocalInput | Input objects for the DBSCAN algorithm in the sixth step of the distributed processing mode |
CDistributedStep7MasterInput | Input objects for the DBSCAN algorithm in the seventh step of the distributed processing mode |
CDistributedStep8LocalInput | Input objects for the DBSCAN algorithm in the eigth step of the distributed processing mode |
CDistributedStep9MasterInput | Input objects for the DBSCAN algorithm in the ninth step of the distributed processing mode |
CInput | Input objects for the DBSCAN algorithm |
CPredictionInput | Input objects for the decision forest regression prediction algorithm |
CTrainingInput | Input objects for the decision_forest regression algorithm model training |
CPredictionInput | Input objects for the decision tree regression prediction algorithm |
CTrainingInput | Input objects for the decision_tree regression algorithm model training |
CInput | Input object for distributions |
CInitInput | Input objects for the default initialization of the EM for GMM algorithm |
CInput | Input objects for the EM for GMM algorithm |
CInput | Input object for engines |
CPredictionInput | Input objects for the gradient boosted trees regression prediction algorithm |
CTrainingInput | Input objects for the gbt regression algorithm model training |
CRatingsDistributedInput | Input objects for the first step of the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
CRatingsInput | Input objects for the rating prediction stage of the implicit ALS algorithm in the batch processing mode |
CDistributedStep1LocalInput | Input objects for the implicit ALS training algorithm in the first step of the distributed processing mode |
CDistributedStep2MasterInput | Input objects for the implicit ALS training algorithm in the second step of the distributed processing mode |
CDistributedStep3LocalInput | Input objects for the implicit ALS training algorithm in the third step of the distributed processing mode |
CDistributedStep4LocalInput | Input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode |
CInitDistributedStep2LocalInput | Input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode |
►CInitInput | Initializes input objects for the implicit ALS initialization algorithm |
CInitDistributedStep1LocalInput | Input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode |
CTrainingInput | Input objects for the implicit ALS training algorithm in the batch processing mode |
CInputBatch | Base class to represent input arguments of the computation in the batch processing mode. Algorithm-specific input arguments are represented as derivative classes of the InputBatch class |
►CInput | Input objects for the kernel function algorithm |
CInput | Input objects for the linear kernel function algorithm |
CInput | Input objects for the rbf kernel function algorithm |
CDistributedStep2MasterInput | Input objects for the K-Means algorithm in the second step of the distributed processing mode. Represents input objects for the algorithm on the master node |
CInitDistributedStep2MasterInput | Input objects for computing initial clusters for the K-Means algorithm. The class represents input objects for computing initial clusters for the algorithm on the master node |
CInitDistributedStep3MasterPlusPlusInput | Input objects for computing initial centroids for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
CInitDistributedStep5MasterPlusPlusInput | Input objects for computing initial centroids for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 5th step on a master node |
►CInitInput | InitInput objects for computing initial clusters for the K-Means algorithm |
CInitDistributedStep1LocalInput | Input objects for computing initial clusters for the K-Means algorithm. The class represents input objects for computing initial clusters for the algorithm on local nodes |
CInitDistributedStep2LocalPlusPlusInput | Input objects for computing initial centroids for the K-Means algorithm. The class represents input objects for computing initial centroids used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CInitDistributedStep4LocalPlusPlusInput | Input objects for computing initial centroids for the K-Means algorithm. The class represents input objects for computing initial centroids used with plusPlus and parallelPlus methods only on the 4th step on a local node |
►CInput | Input objects for the K-Means algorithm |
CDistributedStep1LocalInput | Input objects for the K-Means algorithm. Represents input objects for the algorithm on local nodes |
CInput | Input object for making lasso regression model-based prediction |
CInput | Input object for lasso regression model-based training in the batch and online processing modes and in the first step of the distributed processing mode |
CInput | Input object for making linear regression model-based prediction |
CDistributedStep2MasterInput | Input object for linear regression model-based training in the second step of the distributed processing mode |
CInput | Input object for linear regression model-based training in the batch and online processing modes and in the first step of the distributed processing mode |
CDistributedStep2MasterInput | Input objects for the low order moments algorithm on the master node |
CInput | Input for the low order moments algorithm |
CInput | Input objects for the absolute value function |
CInput | Input objects for the logistic function |
CInput | Input objects for the rectified linear function |
CInput | Input objects for the SmoothReLU algorithm algorithm |
CInput | Input objects for the softmax function function |
CInput | Input objects for the hyperbolic tangent function |
CTrainingDistributedInput | Input objects of the naive Bayes model training algorithm in the distributed computing mode |
CInput | Input objects for the multivariate outlier detection algorithm |
CInput | Input object for neural network weights and biases initializer |
►CBackwardInput | Input object for the backward layer |
CAbsBackwardInput | Input object for the backward abs layer |
CBatchNormalizationBackwardInput | Input object for the backward batch normalization layer |
CConcatBackwardInput | Input object for the backward concat layer |
CConvolution2dBackwardInput | Input object for the backward 2D convolution layer |
CDropoutBackwardInput | Input object for the backward dropout layer |
CEltwiseSumBackwardInput | Input object for the backward element-wise sum layer |
CEluBackwardInput | Input object for the backward ELU layer |
CFullyConnectedBackwardInput | Input object for the backward fully-connected layer |
CLcnBackwardInput | Input object for the backward local contrast normalization layer |
CLocallyConnected2dBackwardInput | Input object for the backward 2D locally connected layer |
CLogisticBackwardInput | Input object for the backward logistic layer |
►CLossBackwardInput | Input object for the backward loss layer |
CLogisticCrossBackwardInput | Input object for the backward logistic cross-entropy layer |
CSoftmaxCrossBackwardInput | Input object for the backward softmax cross-entropy layer |
CLrnBackwardInput | Input object for the backward local response normalization layer |
►CPooling1dBackwardInput | Input object for the backward one-dimensional pooling layer |
CAveragePooling1dBackwardInput | Input object for the backward one-dimensional average pooling layer |
CMaximumPooling1dBackwardInput | Input object for the backward one-dimensional maximum pooling layer |
►CPooling2dBackwardInput | Input object for the backward two-dimensional pooling layer |
CAveragePooling2dBackwardInput | Input object for the backward two-dimensional average pooling layer |
CMaximumPooling2dBackwardInput | Input object for the backward two-dimensional maximum pooling layer |
CStochasticPooling2dBackwardInput | Input object for the backward two-dimensional stochastic pooling layer |
►CPooling3dBackwardInput | Input object for the backward pooling layer |
CAveragePooling3dBackwardInput | Input object for the backward three-dimensional average pooling layer |
CMaximumPooling3dBackwardInput | Input object for the backward three-dimensional maximum pooling layer |
CPreluBackwardInput | Input object for the backward prelu layer |
CReluBackwardInput | Input object for the backward relu layer |
CReshapeBackwardInput | Input object for the backward reshape layer |
CSmoothreluBackwardInput | Input object for the backward smoothrelu layer |
CSoftmaxBackwardInput | Input object for the backward softmax layer |
►CSpatialPooling2dBackwardInput | Input object for the backward two-dimensional spatial pooling layer |
CSpatialAveragePooling2dBackwardInput | Input object for the backward two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dBackwardInput | Input object for the backward two-dimensional spatial maximum pooling layer |
CSpatialStochasticPooling2dBackwardInput | Input object for the backward two-dimensional spatial stochastic pooling layer |
CSplitBackwardInput | Input object for the backward split layer |
CTanhBackwardInput | Input object for the backward hyperbolic tangent (tanh) layer |
CTransposedConv2dBackwardInput | Input object for the backward 2D transposed convolution layer |
►CForwardInput | Input object for the forward layer |
CAbsForwardInput | Input object for the forward abs layer |
CBatchNormalizationForwardInput | Input object for the forward batch normalization layer |
CConcatForwardInput | Input object for the forward concat layer |
CConvolution2dForwardInput | Input object for the forward 2D convolution layer |
CDropoutForwardInput | Input object for the forward dropout layer |
CEltwiseSumForwardInput | Input object for the forward element-wise sum layer |
CEluForwardInput | Input object for the forward ELU layer |
CFullyConnectedForwardInput | Input object for the forward fully-connected layer |
CLcnForwardInput | Input object for the forward local contrast normalization layer |
CLocallyConnected2dForwardInput | Input object for the forward 2D locally connected layer |
CLogisticForwardInput | Input object for the forward logistic layer |
►CLossForwardInput | Input object for the forward loss layer |
CLogisticCrossForwardInput | Input object for the forward logistic cross-entropy layer |
CSoftmaxCrossForwardInput | Input object for the forward softmax cross-entropy layer |
CLrnForwardInput | Input object for the forward local response normalization layer |
►CPooling1dForwardInput | Input object for the forward one-dimensional pooling layer |
CAveragePooling1dForwardInput | Input object for the forward one-dimensional average pooling layer |
CMaximumPooling1dForwardInput | Input object for the forward one-dimensional maximum pooling layer |
►CPooling2dForwardInput | Input object for the forward two-dimensional pooling layer |
CAveragePooling2dForwardInput | Input object for the forward two-dimensional average pooling layer |
CMaximumPooling2dForwardInput | Input object for the forward two-dimensional maximum pooling layer |
CStochasticPooling2dForwardInput | Input object for the forward two-dimensional stochastic pooling layer |
►CPooling3dForwardInput | Input object for the forward pooling layer |
CAveragePooling3dForwardInput | Input object for the forward three-dimensional average pooling layer |
CMaximumPooling3dForwardInput | Input object for the forward three-dimensional maximum pooling layer |
CPreluForwardInput | Input object for the forward prelu layer |
CReluForwardInput | Input object for the forward relu layer |
CReshapeForwardInput | Input object for the forward reshape layer |
CSmoothreluForwardInput | Input object for the forward smoothrelu layer |
CSoftmaxForwardInput | Input object for the forward softmax layer |
►CSpatialPooling2dForwardInput | Input object for the forward two-dimensional spatial pooling layer |
CSpatialAveragePooling2dForwardInput | Input object for the forward two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dForwardInput | Input object for the forward two-dimensional spatial maximum pooling layer |
CSpatialStochasticPooling2dForwardInput | Input object for the forward two-dimensional spatial stochastic pooling layer |
CSplitForwardInput | Input object for the forward split layer |
CTanhForwardInput | Input object for the forward hyperbolic tangent (tanh) layer |
CTransposedConv2dForwardInput | Input object for the forward 2D transposed convolution layer |
CPredictionInput | Input objects of the neural networks prediction algorithm |
CDistributedStep2MasterInput | Input objects for the neural networks training algorithm in the second step of the distributed processing mode. Represents input objects for the algorithm on the master node |
►CTrainingInput | Input object for the training layer |
CDistributedStep1LocalInput | Input objects for the K-Means algorithm. Represents input objects for the algorithm on local nodes |
CInput | Input objects for the Min-max normalization algorithm |
CInput | Input objects for the Z-score normalization algorithm |
►CInput | Input objects for the iterative algorithm |
CInput | Input objects for the Adagrad algorithm |
CInput | Input objects for the Coordinate Descent algorithm |
CInput | Input objects for the LBFGS algorithm |
CInput | Input objects for the SAGA algorithm |
►CInput | Input objects for the Objective function algorithm |
►CInput | Input objects for the Sum of functions algorithm |
CInput | Input objects for the cross-entropy loss objective function algorithm |
CInput | Input objects for the logistic loss objective function algorithm |
CInput | Input objects for the MSE algorithm |
CDistributedStep2MasterInput | Input objects for the second step of the PCA algorithm in the distributed processing mode |
CInput | Input objects for the PCA algorithm |
CTransformInput | Input objects for the PCA transformation algorithm |
CInput | Input objects for the pivoted QR algorithm in the batch processing mode |
CDistributedStep2MasterInput | Input objects for the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedStep3LocalInput | 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 mode and for the QR decomposition algorithm on the first step in the distributed processing mode |
►CQualityMetricInput | Base class for input objects of quality metrics |
CBinaryConfusionMatrixInput | Class for the input objects of the binary confusion matrix algorithm |
CMultiClassConfusionMatrixInput | Class for the input objects of the multi-class confusion matrix algorithm |
CGroupOfBetasInput | Class for the input objects of the algorithm |
CSingleBetaInput | Class for the input objects of the algorithm |
CExplainedVarianceInput | Class for the input objects of the algorithm |
CInput | Input objects for the quantiles algorithm |
CInput | Input object for making ridge regression model-based prediction |
CDistributedStep2MasterInput | Input object for ridge regression model-based training in the second step of the distributed processing mode |
CInput | Input object for ridge regression model-based training in the batch and online processing modes and in the first step of the distributed processing mode |
CInput | Input objects for the sorting |
CDistributedStep2MasterInput | DistributedStep2MasterInput objects for the SVD algorithm in the batch processing and online processing modes, and the first step in the distributed processing mode |
CDistributedStep3LocalInput | 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 |
CInput | Input objects for the univariate outlier detection algorithm |
CBackwardLayers | Represents a collection of backward stages of neural network layers |
CForwardLayers | Represents a collection of forward stages of neural network layers |
CForwardLayerDescriptor | Class defining descriptor for layer on forward stage |
CLayerDescriptor | Class defining descriptor for layer on both forward and backward stages and its parameters |
►CLayerIface | Abstract class that specifies the interface of layer |
CAbsBatch | Provides methods for the abs layer in the batch processing mode |
CAveragePooling1dBatch | Provides methods for the one-dimensional average pooling layer in the batch processing mode |
CAveragePooling2dBatch | Provides methods for the two-dimensional average pooling layer in the batch processing mode |
CAveragePooling3dBatch | Provides methods for the three-dimensional average pooling layer in the batch processing mode |
CBatchNormalizationBatch | Provides methods for the batch normalization layer in the batch processing mode |
CConcatBatch | Provides methods for the concat layer in the batch processing mode |
CConvolution2dBatch | Provides methods for the 2D convolution layer in the batch processing mode |
CDropoutBatch | Provides methods for the dropout layer in the batch processing mode |
CEltwiseSumBatch | Provides methods for the element-wise sum layer in the batch processing mode |
CEluBatch | Provides methods for the Exponential Linear Unit (ELU) layer in the batch processing mode |
CFullyConnectedBatch | Provides methods for the fully-connected layer in the batch processing mode |
CLcnBatch | Provides methods for the local contrast normalization layer in the batch processing mode |
CLocallyConnected2dBatch | Provides methods for the 2D locally connected layer in the batch processing mode |
CLogisticBatch | Provides methods for the logistic layer in the batch processing mode |
►CLossBatch | Provides methods for the loss layer in the batch processing mode |
CLogisticCrossBatch | Provides methods for the logistic cross-entropy layer in the batch processing mode |
CSoftmaxCrossBatch | Provides methods for thesoftmax cross-entropy layer in the batch processing mode |
CLrnBatch | Provides methods for the local response normalization layer in the batch processing mode |
CMaximumPooling1dBatch | Provides methods for the one-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling2dBatch | Provides methods for the two-dimensional maximum pooling layer in the batch processing mode |
CMaximumPooling3dBatch | Provides methods for the three-dimensional maximum pooling layer in the batch processing mode |
CPreluBatch | Provides methods for the prelu layer in the batch processing mode |
CReluBatch | Provides methods for the rectified linear unit (relu) layer in the batch processing mode |
CReshapeBatch | Provides methods for the reshape layer in the batch processing mode |
CSmoothreluBatch | Provides methods for the smooth rectified linear unit (smoothrelu) layer in the batch processing mode |
CSoftmaxBatch | Provides methods for the softmax layer in the batch processing mode |
CSpatialAveragePooling2dBatch | Provides methods for the two-dimensional spatial average pooling layer in the batch processing mode |
CSpatialMaximumPooling2dBatch | Provides methods for the two-dimensional spatial maximum pooling layer in the batch processing mode |
CSpatialStochasticPooling2dBatch | Provides methods for the two-dimensional spatial stochastic pooling layer in the batch processing mode |
CSplitBatch | Provides methods for the split layer in the batch processing mode |
CStochasticPooling2dBatch | Provides methods for the two-dimensional stochastic pooling layer in the batch processing mode |
CTanhBatch | Provides methods for the hyperbolic tangent (tanh) layer in the batch processing mode |
CTransposedConv2dBatch | Provides methods for the 2D transposed convolution layer in the batch processing mode |
CNextLayers | Contains list of layer indices of layers following the current layer |
CNextLayersCollection | Represents a collection of neural network NextLayers objects |
CPredictionTopology | Represents a collection of neural network forward layer descriptors |
CTrainingTopology | Represents a collection of neural network layer descriptors |
►CParameter | Base class to represent computation parameters. Algorithm-specific parameters are represented as derivative classes of the Parameter class |
CParameter | Parameters for the association rules compute method |
CParameter | Parameters of the multivariate outlier detection compute() method used with the defaultDense method |
►CParameter | Base class for the parameters of the classification algorithms |
►CParameter | Base class for the parameters of the boosting algorithm |
CParameter | AdaBoost algorithm parameters |
CParameter | Base class for the parameters of the BrownBoost training algorithm |
CParameter | Base class for parameters of the LogitBoost training algorithm |
CParameter | Base class for parameters of the decision forest classification training algorithm |
CParameter | Base class for parameters of the decision tree classification algorithm |
CPredictionParameter | PredictionParameter of the gradient boosted trees classification prediction algorithm |
CParameter | Base class for parameters of the gradient boosted trees classification training algorithm |
CPredictionParameter | Logistic regression algorithm parameters |
CTrainingParameter | Logistic regression training algorithm parameters |
CParameter | Parameters of the multi-class classifier algorithm |
CParameter | Parameters for multinomial naive Bayes algorithm |
CParameter | Optional SVM algorithm parameters |
CParameter | Base class for the input objects of the weak learner training and prediction algorithm |
CBinaryConfusionMatrixParameter | Base class for the parameters of the classification algorithms |
CMultiClassConfusionMatrixParameter | Base class for the parameters of the multi-class confusion matrix algorithm |
►CParameter | Parameters of the correlation or variance-covariance matrix algorithm |
COnlineParameter | Parameters of the correlation or variance-covariance matrix algorithm in the online processing mode |
CParameter | Parameters of the DBSCAN computation method |
CParameter | Parameter of the decision forest regression prediction algorithm |
CParameter | Base class for parameters of the decision forest regression training algorithm |
CParameter | Parameter of the decision tree regression algorithm |
►CParameterBase | Class that specifies parameters of the distribution |
CParameter | Class that specifies parameters of the bernoulli distribution |
CParameter | Class that specifies parameters of the normal distribution |
CParameter | Class that specifies parameters of the uniform distribution |
CInitParameter | Parameters for the default initialization of the EM for GMM algorithm |
CParameter | Parameters of the EM for GMM algorithm |
CParameter | Parameter of the gradient boosted trees regression prediction algorithm |
CParameter | Base class for parameters of the gradient boosted trees regression training algorithm |
CParameter | Parameters for the compute() method of the implicit ALS algorithm |
►CInitParameter | Parameters of the implicit ALS initialization algorithm |
CInitDistributedParameter | Parameters of the implicit ALS initialization algorithm in the distributed compute mode |
CParameter | K nearest neighbors algorithm parameters |
►CParameter | Optional parameters for computing kernel functions |
CParameter | Parameters for computing the linear kernel function k(X,Y) + b |
CParameter | Parameters for computing the radial base function (RBF) kernel |
►CInitParameter | Parameters for computing initial clusters for the K-Means method |
CInitDistributedStep2LocalPlusPlusParameter | Parameters for computing initial clusters on the step 2 on local nodes. Kmeans++ and || only |
CParameter | Parameters of the K-Means computation method |
CTrainParameter | Lasso regression algorithm parameters |
CParameter | Linear regression algorithm parameters |
CGroupOfBetasParameter | Base class for the parameters of the algorithm |
CSingleBetaParameter | Base class for the parameters of the algorithm |
CQualityMetricSetParameter | Class for the parameter of the linear regression quality metrics set algorithm |
CQualityMetricSetParameter | Class for the parameter of the LogitBoost algorithm |
CParameter | Parameters of the low order moments algorithm |
CQualityMetricSetParameter | Class for the parameter of the multi-class SVM algorithm |
CQualityMetricSetParameter | Class for the parameter of the multinomial Naive Bayes algorithm |
CParameter | Parameters of the multivariate outlier detection compute() method used with the baconDense method |
CParameter | Parameters for the multivariate outlier detection compute() used with the defaultDense method |
►CParameter | Class that specifies parameters of the neural network weights and biases initializer |
CGaussianParameter | Class that specifies parameters of the neural network weights and biases gaussian initializer |
CTruncatedGaussianParameter | Class that specifies parameters of the neural network weights and biases truncated gaussian initializer |
CUniformParameter | Class that specifies parameters of the neural network weights and biases uniform initializer |
CXavierParameter | Class that specifies parameters of the neural network weights and biases Xavier initializer |
►CParameter | Class that specifies parameters of the neural network layer |
CBatchNormalizationParameter | Class that specifies parameters of the batch normalization layer |
CConcatParameter | Class that specifies parameters of the concat layer |
CConvolution2dParameter | Class that specifies parameters of the 2D convolution layer |
CDropoutParameter | Class that specifies parameters of the dropout layer |
CEltwiseSumParameter | Class that specifies parameters of the element-wise sum layer |
CFullyConnectedParameter | Class that specifies parameters of the fully-connected layer |
CLcnParameter | Class that specifies parameters of the local contrast normalization layer |
CLocallyConnected2dParameter | Class that specifies parameters of the 2D locally connected layer |
►CLossParameter | Class that specifies parameters of the neural network layer |
CLogisticCrossParameter | Class that specifies parameters of the logistic cross-entropy layer |
CSoftmaxCrossParameter | Class that specifies parameters of the softmax cross-entropy layer |
CLrnParameter | Class that specifies parameters of the local response normalization layer |
►CPooling1dParameter | Class that specifies parameters of the one-dimensional pooling layer |
CAveragePooling1dParameter | Class that specifies parameters of the one-dimensional average pooling layer |
CMaximumPooling1dParameter | Class that specifies parameters of the one-dimensional maximum pooling layer |
►CPooling2dParameter | Class that specifies parameters of the two-dimensional pooling layer |
CAveragePooling2dParameter | Class that specifies parameters of the two-dimensional average pooling layer |
CMaximumPooling2dParameter | Class that specifies parameters of the two-dimensional maximum pooling layer |
CStochasticPooling2dParameter | Class that specifies parameters of the two-dimensional stochastic pooling layer |
►CPooling3dParameter | Class that specifies parameters of the pooling layer |
CAveragePooling3dParameter | Class that specifies parameters of the three-dimensional average pooling layer |
CMaximumPooling3dParameter | Class that specifies parameters of the three-dimensional maximum pooling layer |
CPreluParameter | Class that specifies parameters of the prelu layer |
CReshapeParameter | Class that specifies parameters of the reshape layer |
CSoftmaxParameter | Class that specifies parameters of the softmax layer |
►CSpatialPooling2dParameter | Class that specifies parameters of the two-dimensional spatial pooling layer |
CSpatialAveragePooling2dParameter | Class that specifies parameters of the two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dParameter | Class that specifies parameters of the two-dimensional spatial maximum pooling layer |
CSpatialStochasticPooling2dParameter | Class that specifies parameters of the two-dimensional spatial stochastic pooling layer |
CSplitParameter | Class that specifies parameters of the split layer |
CTransposedConv2dParameter | Class that specifies parameters of the 2D transposed convolution layer |
CPredictionParameter | Class representing the parameters of neural network |
CTrainingParameter | Class representing the parameters of neural network |
CParameter | Parameters of the Min-max normalization algorithm |
CParameter | Parameters of the z-score normalization algorithm |
►CParameter | Parameter of the iterative solver algorithm |
CParameter | Parameter of the Adagrad algorithm |
CParameter | Parameter of the Coordinate Descent algorithm |
CParameter | Parameters of the LBFGS algorithm |
CParameter | Parameter of the SAGA algorithm |
►CBaseParameter | Base parameter of the Sum of functions algorithm |
CParameterDefaultDense | ParameterDefaultDense of the SGD algorithm |
CParameterMiniBatch | ParameterMiniBatch of the SGD algorithm |
CParameterMomentum | ParameterMomentum of the SGD algorithm |
►CParameter | Parameters of the Objective function algorithm |
►CParameter | Parameters of the Sum of functions algorithm |
CParameter | Parameters of the cross-entropy loss objective function algorithm |
CParameter | Parameters of the logistic loss objective function algorithm |
CParameter | Parameters of the MSE algorithm |
►CBaseParameter | Common parameters of the PCA algorithm |
CBatchParameter | Parameters of the PCA algorithm in the batch processing mode |
CDistributedStep2MasterParameter | Parameters of the PCA algorithm in the second step of the distributed processing mode |
COnlineParameter | Parameters of the PCA algorithm in the online processing mode |
CExplainedVarianceParameter | Base class for the parameters of the algorithm |
CQualityMetricSetParameter | Parameters for the quality metrics set computation for PCA algorithm |
CTransformParameter | Parameters of the PCA transformation algorithm |
CParameter | Pivoted QR algorithm parameters |
CParameter | Parameters of the quantiles algorithm |
►CParameter | Ridge regression algorithm parameters |
CTrainParameter | Ridge regression algorithm parameters |
CParameter | Parameters of the compute() method of the SVD algorithm |
CParameter | Parameters of the univariate outlier detection algorithm |
►CCompressionParameter | Parameters for the compression and decompression |
CBzip2CompressionParameter | Parameter for the BZIP2 compression and decompression, CompressionLevel.defaultLevel is equal to BZIP2 compression level 9 |
CLzoCompressionParameter | Parameter for the LZO compression and decompression LZO compressed block header consists of four sections: 1) optional, 2) uncompressed data size(4 bytes), 3) compressed data size(4 bytes), 4) optional |
CRleCompressionParameter | Parameter for the RLE encoding and decoding. RLE encoded block may contain header that consists of two sections: decoded data size(4 bytes), and encoded data size(4 bytes) |
CZlibCompressionParameter | Parameter for ZLIB compression and decompression |
►CQualityMetricSetBatch | Provides methods to compute a quality metric set of an algorithm in the batch processing mode |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the AdaBoost algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the BrownBoost algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with linear regression algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the LogitBoost algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the multi-class SVM algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the multinomial Naive Bayes algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the PCA algorithm |
CQualityMetricSetBatch | Class that represents a quality metric set to check the model trained with the SVM algorithm |
►CCompression | The base class that provides methods for the compression and decompression operation |
►CCompressor | The base class that provides methods for the compression |
CBzip2Compressor | Implementation of the Compressor class for the BZIP2 compression method |
CLzoCompressor | Specialization of the Compressor class for the LZO compression method |
CRleCompressor | Specialization of the Compressor class for the RLE compression method |
CZlibCompressor | Specialization of the Compressor class for ZLIB compression method |
►CDecompressor | The base class that provides methods for the decompression |
CBzip2Decompressor | Implementation of the Decompressor class for the BZIP2 decompression method |
CLzoDecompressor | Specialization of the Decompressor class for the LZO decompression method |
CRleDecompressor | Specialization of the Decompressor class for the RLE decompression method |
CZlibDecompressor | Specialization of the Decompressor class for ZLIB decompression method |
CCompressionStream | The class that provides methods for compressing input raw data by the blocks. * |
CDecompressionStream | The class that provides methods for decompressing the input compressed data arriving by the blocks |
CSerializableBase | Class that provides methods for serialization and deserialization |
►CDataSource | Abstract class that defines the interface for the data management component responsible for the representation of the data in a raw format. This class declares the most generic methods for data access |
CFileDataSource | Specifies the methods for accessing the data stored in files |
CStringDataSource | Specifies the methods for accessing the data stored as a text in java.io.Strings format |
CFeatureManager | |
►CModifierIface | |
CColumnFilter | |
CMakeCategorical | |
COneHotEncoder | |
CDistanceType | Available distance types for the K-Means algorithm |
CDistanceType | Available distance types for the DBSCAN algorithm |
CDistributedDataSet | Abstract class that defines the interface for the data management component responsible for representation of the data in the distributed raw format |
CDistributedPartialResultCollectionId | Available types of partial results of the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedPartialResultCollectionId | Available types of partial results of the second step of the SVD algorithm in the distributed processing mode, stored in the DataCollection object |
CDistributedPartialResultId | Available identifiers of partial results of the neural network training algorithm |
CDistributedPartialResultId | Available types of the partial results of the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedPartialResultId | Available types of partial results of the second step of the SVD algorithm in the distributed processing mode, stored in the Result object |
CDistributedPartialResultStep10CollectionId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the tenth step of the distributed processing mode |
CDistributedPartialResultStep10NumericTableId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the tenth step of the distributed processing mode |
CDistributedPartialResultStep11CollectionId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the eleventh step of the distributed processing mode |
CDistributedPartialResultStep11NumericTableId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the eleventh step of the distributed processing mode |
CDistributedPartialResultStep12Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the twelfth step of the distributed processing mode |
CDistributedPartialResultStep13Id | Available identifiers of results of the DBSCAN training algorithm obtained in the thirteenth step of the distributed processing mode |
CDistributedPartialResultStep1Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the first step of the distributed processing mode |
CDistributedPartialResultStep1Id | Available identifiers of partial results of the implicit ALS training algorithm obtained in the first step of the distributed processing mode |
CDistributedPartialResultStep2Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the second step of the distributed processing mode |
CDistributedPartialResultStep2Id | Available identifiers of partial results of the implicit ALS training algorithm obtained in the second step of the distributed processing mode |
CDistributedPartialResultStep3Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the third step of the distributed processing mode |
CDistributedPartialResultStep3Id | Available types of partial results obtained in the second step of the SVD algorithm in the distributed processing mode, stored in the Result object |
CDistributedPartialResultStep3Id | Available identifiers of partial results of the implicit ALS training algorithm obtained in the third step of the distributed processing mode |
CDistributedPartialResultStep4Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the fourth step of the distributed processing mode |
CDistributedPartialResultStep4Id | Available identifiers of partial results of the implicit ALS training algorithm obtained in the fourth step of the distributed processing mode |
CDistributedPartialResultStep5Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the fifth step of the distributed processing mode |
CDistributedPartialResultStep6CollectionId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the sixth step of the distributed processing mode |
CDistributedPartialResultStep6NumericTableId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the sixth step of the distributed processing mode |
CDistributedPartialResultStep7Id | Available identifiers of results of the DBSCAN training algorithm obtained in the seventh step of the distributed processing mode |
CDistributedPartialResultStep8CollectionId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the eigth step of the distributed processing mode |
CDistributedPartialResultStep8NumericTableId | Available identifiers of partial results of the DBSCAN training algorithm obtained in the eigth step of the distributed processing mode |
CDistributedPartialResultStep9Id | Available identifiers of partial results of the DBSCAN training algorithm obtained in the ninth step of the distributed processing mode |
CDistributedResultStep13Id | Available identifiers of results of the DBSCAN training algorithm obtained in the thirteenth step of the distributed processing mode |
CDistributedResultStep9Id | Available identifiers of results of the DBSCAN training algorithm obtained in the ninth step of the distributed processing mode |
CDistributedStep1LocalInputId | Available identifiers of input objects for the neural network model based training |
CDistributedStep2MasterInputId | Available identifiers of input objects for the neural network training algorithm on the master node |
CDistributedStep2MasterInputId | Available identifiers of master-node input objects for the low order momemnts algorithm |
CDistributedStep2MasterInputId | Partial results required by the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributedStep2MasterInputId | Partial results from previous steps of the SVD algorithm in the distributed processing mode, required by the second step |
CDistributedStep2MasterInputId | Available identifiers of input objects for the K-Means algorithm on the master node |
CDistributedStep2MasterInputId | Available identifiers of master-node input objects for the correlation or variance-covariance matrix algorithm |
CDistributedStep3LocalInputId | Partial results required by the QR decomposition algorithm on the third step in the distributed processing mode |
CDistributedStep3LocalInputId | Partial results from previous steps of the SVD algorithm in the distributed processing mode, required by the third step |
CDropoutLayerDataId | Identifiers of input objects for the backward dropout layer and results for the forward dropout layer |
CDropoutMethod | Available methods for the dropout layer |
CEltwiseSumForwardInputId | Identifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer |
CEltwiseSumLayerDataId | Identifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer |
CEltwiseSumLayerDataNumericTableId | Identifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer |
CEltwiseSumMethod | Available methods for the element-wise sum layer |
CEluLayerDataId | Identifiers of input objects for the backward ELU layer and results for the forward ELU layer |
CEluMethod | Available methods for the ELU layer |
CEnvironment | Provides information about computational environment |
CEstimatesToCompute | Available sets of estimates to compute of low order Moments |
CExplainedVarianceInputId | Available identifiers of input objects for a explained variance quality metrics |
CExplainedVarianceMethod | Available methods for computing the quality metric |
CExplainedVarianceResultId | Available identifiers of the result of explained variance quality metrics |
CFactory | Class that provides factory functionality for objects derived from the SerializableBase class |
CDataFeatureUtils.FeatureType | |
CForwardInputId | Available identifiers of input objects for the forward layer |
CForwardInputLayerDataId | Available identifiers of input objects for the forward layer |
CForwardResultId | Available identifiers of results for the forward layer |
CForwardResultLayerDataId | Available identifiers of results for the forward layer |
CFullyConnectedLayerDataId | Identifiers of input objects for the backward fully-connected layer and results for the forward fully-connected layer |
CFullyConnectedMethod | Available methods for the fully-connected layer |
CGaussianMethod | Available methods for the gaussian initializer |
CGroupOfBetasInputId | Available identifiers of input objects for a single beta quality metrics |
CGroupOfBetasMethod | Available methods for computing the quality metric |
CGroupOfBetasResultId | Available identifiers of the result of single beta quality metrics |
CCSRNumericTable.Indexing | Indexing scheme used for accessing the data in CSR layout |
CDataFeatureUtils.IndexNumType | |
CInitDistributedLocalPlusPlusInputDataId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on a local node |
CInitDistributedPartialResultStep2Id | Available identifiers of partial results of the implicit ALS initialization algorithm in the first step of the distributed processing mode |
CInitDistributedStep2LocalPlusPlusInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CInitDistributedStep2LocalPlusPlusPartialResultDataId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CInitDistributedStep2LocalPlusPlusPartialResultId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CInitDistributedStep2MasterInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm on the master node |
CInitDistributedStep3MasterPlusPlusInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
CInitDistributedStep3MasterPlusPlusPartialResultDataId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with parallelPlus method only on the 3rd step on a master node |
CInitDistributedStep3MasterPlusPlusPartialResultId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
CInitDistributedStep4LocalPlusPlusInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 4th step on a local node |
CInitDistributedStep4LocalPlusPlusPartialResultId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with plusPlus and parallelPlus methods only on the 4th step on a local node |
CInitDistributedStep5MasterPlusPlusInputDataId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with parallelPlus methods only on the 5th step on a master node |
CInitDistributedStep5MasterPlusPlusInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on the 5th step on a master node |
CInitDistributedStep5MasterPlusPlusPartialResultId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm in the distributed processing mode used with parallelPlus method only on the 5th step on a master node |
CInitializationMethod | Available initialization methods for the BACON multivariate outlier detection algorithm |
CInitializationMethod | Available initialization methods for the BACON multivariate outlier detection algorithm |
CInitInputId | Available identifiers of input objects for the default initialization of the EM for GMM algorithm |
CInitInputId | Available identifiers of input objects for initializing the implicit ALS training algorithm |
CInitInputId | Available identifiers of input objects for computing initial clusters for the K-Means algorithm |
CInitMethod | Available methods for computing initial values for the EM for GMM algorithm |
CInitMethod | Available methods for computing initial values for the implicit ALS training algorithm |
CInitMethod | Methods of computing initial clusters for the K-Means algorithm |
CInitPartialResultBaseId | Available identifiers of partial results of the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode |
CInitPartialResultCollectionId | Available identifiers of partial results of the implicit ALS initialization algorithm in the first step of the distributed processing mode |
CInitPartialResultId | Available identifiers of partial results of the default initialization of the implicit ALS training algorithm |
CInitPartialResultId | Available identifiers of partial results of computing initial clusters for the K-Means algorithm |
CInitResultCovariancesId | Available identifiers of results of the default initialization of the EM for GMM algorithm |
CInitResultId | Available identifiers of results of the default initialization of the EM for GMM algorithm |
CInitResultId | Available identifiers of the results of the default initialization of the implicit ALS training algorithm |
CInitResultId | Available identifiers of the results of computing initial clusters for the K-Means algorithm |
CInitStep2LocalInputId | Available identifiers of input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode |
CInputCovariancesId | Available identifiers of input covariance objects for the EM for GMM algorithm |
CInputId | Available identifiers of input objects for the univariate outlier detection algorithm |
CInputId | Available identifiers of distributions |
CInputId | Available identifiers of input objects for the Min-max normalization algorithm |
CInputId | Available identifiers of input objects for the Z-score normalization algorithm |
CInputId | Available identifiers of input objects for the cross-entropy loss objective function algorithm |
CInputId | Available identifiers of input objects for the low order moments |
CInputId | Available identifiers of input objects for the iterative algorithm |
CInputId | Available identifiers of input objects for the EM for GMM algorithm |
CInputId | Available identifiers of input objects for the logistic loss objective function algorithm |
CInputId | Available identifiers of input objects for the MSE algorithm |
CInputId | Available identifiers of input objects for the absolute value function |
CInputId | Available identifiers of input objects for the objective function algorithm |
CInputId | Available identifiers of input objects for the logistic function |
CInputId | Available identifiers of input objects for the rectified linear function |
CInputId | Available identifiers of input objects for the Sum of functions algorithm |
CInputId | Available identifiers of engines |
CInputId | Available identifiers of input objects for the SmoothReLU algorithm |
CInputId | Available identifiers of input objects for the PCA algorithm |
CInputId | Available identifiers of input arguments for the association rules algorithm |
CInputId | Available identifiers of input objects for the softmax function |
CInputId | Available identifiers of input objects for the hyperbolic tangent function |
CInputId | Available identifiers of input objects for the pivoted QR algorithm |
CInputId | Available identifiers of input objects for the QR decomposition algorithm |
CInputId | Available identifiers of input objects for the quantiles algorithm |
CInputId | Available identifiers of input objects for the classifier algorithm |
CInputId | Available identifiers of input objects for the sorting |
CInputId | Available identifiers of input objects for the gbt regression algorithm |
CInputId | Available identifiers of input objects for the multivariate outlier detection algorithm |
CInputId | Available identifiers of input objects for the SVD algorithm |
CInputId | Available identifiers of input objects for neural network weights and biases initializer |
CInputId | Available identifiers of input objects for the DBSCAN algorithm |
CInputId | Available identifiers of input objects for the correlation distance algorithm |
CInputId | Available identifiers of input objects for the cosine distance algorithm |
CInputId | Available identifiers of input objects for the kernel function algorithm |
CInputId | Available identifiers of input objects for the Cholesky decomposition algorithm |
CInputId | Available identifiers of input objects for the decision_forest regression algorithm |
CInputId | Available identifiers of input objects for the correlation or variance-covariance matrix algorithm |
CInputId | Available identifiers of input objects for the multivariate outlier detection algorithm |
CInputId | Available identifiers of input objects for the K-Means algorithm |
CInputValuesId | Available identifiers of input objects for the EM for GMM algorithm |
CDataFeatureUtils.InternalNumType | |
CInternalOptionalDataId | Available identifiers of InternalOptionalDataId objects for the algorithm |
CItemsetsOrderId | Available sort order options for resulting itemsets |
CLcnIndices | Data structure representing the indices of the two dimensions on which local contrast normalization is performed |
CLcnLayerDataId | Identifiers of input objects for the backward local contrast normalization layer and results for the forward local contrast normalization layer |
CLcnMethod | Available methods for the local contrast normalization layer |
CLibraryVersionInfo | Provides information about the version of Intel(R) Data Analytics Acceleration Library |
CLibUtils | |
CLocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the distributed processing mode |
CLocallyConnected2dIndices | Data structure representing the dimension for locally connected kernels |
CLocallyConnected2dKernelSizes | Data structure representing the sizes of the two-dimensional kernel subtensor for the backward 2D locally connected layer and results for the forward 2D locally connected layer |
CLocallyConnected2dLayerDataId | Identifiers of input objects for the backward 2D locally connected layer and results for the forward 2D locally connected layer |
CLocallyConnected2dMethod | Available methods for the 2D locally connected layer |
CLocallyConnected2dPaddings | Data structure representing the number of data to be implicitly added to the subtensor |
CLocallyConnected2dStrides | Data structure representing the intervals on which the kernel should be applied to the input |
CLogisticCrossLayerDataId | Identifiers of input objects for the backward logistic cross-entropy layer and results for the forward logistic cross-entropy layer |
CLogisticCrossMethod | Available methods for the logistic cross-entropy layer |
CLogisticLayerDataId | Identifiers of input objects for the backward logistic layer and results for the forward logistic layer |
CLogisticMethod | Available methods for logistic layer |
CLossForwardInputId | Available identifiers of input objects for the forward layer |
CLrnLayerDataId | Identifiers of input objects for the backward local response normalization layer and results for the forward local response normalization layer |
CLrnMethod | Available methods for the local response normalization layer |
CMasterInputId | Available identifiers of input objects for linear regression model-based training on the master node |
CMasterInputId | Available identifiers of the PCA algorithm on the second step in the distributed processing mode |
CMasterInputId | Available identifiers of input objects for ridge regression model-based training on the master node |
CMasterInputId | Available identifiers of input objects for the implicit ALS training algorithm in the second step of the distributed processing mode |
CMaximumPooling1dLayerDataId | Identifiers of input objects for the backward one-dimensional maximum pooling layer and results for the forward one-dimensional maximum pooling layer |
CMaximumPooling1dLayerDataNumericTableId | Identifiers of input numeric tables for the backward one-dimensional maximum pooling layer and results for the forward one-dimensional maximum pooling layer |
CMaximumPooling1dMethod | Available methods for the one-dimensional maximum pooling layer |
CMaximumPooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional maximum pooling layer and results for the forward two-dimensional maximum pooling layer |
CMaximumPooling2dLayerDataNumericTableId | Identifiers of input objects for the backward two-dimensional maximum pooling layer and results for the forward two-dimensional maximum pooling layer |
CMaximumPooling2dMethod | Available methods for the two-dimensional maximum pooling layer |
CMaximumPooling3dLayerDataId | Identifiers of input objects for the backward three-dimensional maximum pooling layer and results for the forward three-dimensional maximum pooling layer |
CMaximumPooling3dLayerDataNumericTableId | Identifiers of input objects for the backward three-dimensional maximum pooling layer and results for the forward three-dimensional maximum pooling layer |
CMaximumPooling3dMethod | Available methods for the three-dimensional maximum pooling layer |
CNumericTable.MemoryStatus | |
CMethod | Available methods for the bernoulli distribution |
CMethod | Available methods for the normal distribution |
CMethod | Available methods for the uniform distribution |
CMethod | Available methods for Min-max normalization |
CMethod | Available methods for Z-score normalization |
CMethod | Available methods for computing the Adagrad algorithm |
CMethod | Available methods for computing the Coordinate Descent algorithm |
CMethod | Available methods for computing the cross-entropy loss objective function algorithm |
CMethod | Available methods for computing moments of low order Moments |
CMethod | Available methods for computing the LBFGS algorithm |
CMethod | Available methods for running the EM for GMM algorithm |
CMethod | Available methods for absolute value function |
CMethod | Available methods for computing results of Objective function with precomputed characteristics |
CMethod | Available methods for computing the SAGA algorithm |
CMethod | Available methods for logistic function |
CMethod | Available methods for computing the SGD algorithm |
CMethod | Available methods for rectified linear function |
CMethod | Available methods for SmoothReLU algorithm |
CMethod | Available methods for running PCA algorithm |
CMethod | Available methods for the mt19937 engine |
CMethod | Available methods for softmax function |
CMethod | Available methods for the mt2203 engine |
CMethod | Available methods for hyperbolic tangent function |
CMethod | Available methods for computing the results of the pivoted QR algorithm |
CMethod | Available methods for computing large itemsets and association rules |
CMethod | Available methods to compute quantiles |
CMethod | Available methods to sort data |
CMethod | Available methods for computing the results of the multivariate outlier detection |
CMethod | Available methods for computing results of the univariate outlier detection algorithm |
CMethod | Available methods of the DBSCAN algorithm |
CMethod | Available methods for computing the correlation distance |
CMethod | Available methods for computing the cosine distance |
CMethod | Available methods for the mcg59 engine |
CMethod | Available methods for linear kernel function |
CMethod | Available methods for RBF kernel function |
CMethod | Available methods for Cholesky decomposition |
CMethod | Available methods for computing the logistic loss objective function algorithm |
CMethod | Available methods for computing the MSE algorithm |
CMethod | Available methods for computing the correlation or variance-covariance matrix |
CMethod | Available methods of the K-Means algorithm |
CMethod | Available methods for computing the results of the multivariate outlier detection |
CModelInputId | Available identifiers of input objects of the gbt regression predication algorithm |
CModelInputId | Available identifiers of input model objects for the implicit ALS training algorithm |
CModelInputId | Available identifiers of input objects of the decision_forest regression predication algorithm |
CModelInputId | Available identifiers of input objects of the classification algorithms |
CModelInputId | Available identifiers of input models for making decision tree model-based prediction |
CMultiClassConfusionMatrixInputId | Available identifiers of the input objects of the confusion matrix |
CMultiClassConfusionMatrixMethod | Available methods for computing the confusion matrix |
CMultiClassConfusionMatrixResultId | Available identifiers of the results of the confusion matrix algorithm |
CMultiClassMetricId | Available identifiers of multi-class metrics |
►CNodeDescriptor | Struct containing base description of node in descision tree |
CLeafNodeDescriptor | Struct containing description of leaf node in classification descision tree |
CLeafNodeDescriptor | Struct containing description of leaf node in regression descision tree |
CSplitNodeDescriptor | Struct containing description of split node in descision tree |
CNumericTable.NormalizationType | |
CDataSource.NumericTableAllocationFlag | |
CNumericTableInputId | Available identifiers of input objects of gbt regression prediction algorithms |
CNumericTableInputId | Available identifiers of input numeric table objects for the implicit ALS training algorithm in the distributed processing mode |
CNumericTableInputId | Available identifiers of input objects of decision_forest regression prediction algorithms |
CNumericTableInputId | Available identifiers of input objects of classification algorithms |
CNumericTableInputId | Available identifiers of input numeric tables for making decision tree model-based prediction |
COptionalDataId | Available identifiers of optional data objects for the iterative algorithm |
COptionalDataId | Available identifiers of optional data objects for the iterative algorithm |
COptionalDataId | Available identifiers of input objects for the iterative algorithm |
COptionalDataId | Available identifiers of OptionalData objects for the algorithm |
COptionalInputId | Available identifiers of optional input objects for the iterative algorithm |
COptionalResultId | Available result identifiers for the iterative solver algorithm |
COutputMatrixType | Available types of the computed correlation or variance-covariance matrix |
CPartialCorrelationResultID | Available identifiers of partial results of the correlation method of the PCA algorithm |
CPartialModelInputId | Available identifiers of input partial model objects for the implicit ALS training algorithm in the distributed processing mode |
CPartialResultId | Available identifiers of a partial result of linear regression model-based training |
CPartialResultId | Available identifiers of partial results of the neural network training algorithm |
CPartialResultId | Available identifiers of partial results of the SVD algorithm obtained in the online processing mode and in the first step in the distributed processing mode |
CPartialResultId | Available identifiers of a partial result of ridge regression model-based training |
CPartialResultId | Available identifiers of partial results of the classification algorithm |
CPartialResultId | Available identifiers of partial results of the QR decomposition algorithm in the online processing mode and of the algorithm on the first step in the distributed processing mode |
CPartialResultId | Available identifiers of partial results of the low order moments algorithm |
CPartialResultId | Available identifiers of partial results of the K-Means algorithm |
CPartialResultId | Available identifiers of partial results of the correlation or variance-covariance matrix algorithm |
CPartialSVDCollectionResultID | Available identifiers of partial results of the SVD method of the PCA algorithm |
CPartialSVDTableResultID | Available identifiers of partial results of the SVD method of the PCA algorithm |
CDataFeatureUtils.PMMLNumType | |
CPooling1dIndex | Data structure representing the indices of the dimension on which one-dimensional pooling is performed |
CPooling1dKernelSize | Data structure representing the size of the 1D subtensor from which the element is computed |
CPooling1dPadding | Data structure representing the number of data elements to implicitly add to each side of the 1D subtensor on which one-dimensional pooling is performed |
CPooling1dStride | Data structure representing the intervals on which the subtensors for one-dimensional pooling are computed |
CPooling2dIndices | Data structure representing the indices of the dimension on which two-dimensional pooling is performed |
CPooling2dKernelSizes | Data structure representing the size of the 2D subtensor from which the element is computed |
CPooling2dPaddings | Data structure representing the number of data elements to implicitly add to each side of the 2D subtensor on which two-dimensional pooling is performed |
CPooling2dStrides | Data structure representing the intervals on which the subtensors for two-dimensional pooling are computed |
CPooling3dIndices | Data structure representing the dimension for convolution kernels |
CPooling3dKernelSizes | Data structure representing the size of the three-dimensional subtensor |
CPooling3dPaddings | Data structure representing the number of data elements to implicitly add to each size of the three-dimensional subtensor on which pooling is performed |
CPooling3dStrides | Data structure representing the intervals on which the subtensors for pooling are selected |
CPredictionInputId | Available identifiers of input objects for logistic regression model-based prediction |
CPredictionInputId | Available identifiers of input objects for ridge regression model-based prediction |
CPredictionInputId | Available identifiers of input objects for lasso regression model-based prediction |
CPredictionInputId | Available identifiers of input objects for linear regression model-based prediction |
CPredictionMethod | Available methods of logistic regression model-based prediction |
CPredictionMethod | Available methods for predictions based on the LogitBoost model |
CPredictionMethod | Available methods of ridge regression model-based prediction |
CPredictionMethod | Computation methods for the neural networks model based prediction |
CPredictionMethod | Available methods for the multi-class classifier prediction |
CPredictionMethod | Available methods for predictions based on the gradient boosted trees regression model |
CPredictionMethod | Available methods for computing the results of the naive Bayes model based prediction |
CPredictionMethod | Available methods for predictions based on the BrownBoost model |
CPredictionMethod | Available methods to compute the results of the SVM prediction algorithm |
CPredictionMethod | Available methods for predictions based on the gradient boosted trees classification model |
CPredictionMethod | Available methods for predictions based on the AdaBoost model |
CPredictionMethod | Available methods for the k nearest neighbors prediction |
CPredictionMethod | Available methods for predictions based on the decision forest classification model |
CPredictionMethod | Available methods to make prediction based on the decision stump model |
CPredictionMethod | Available methods for predictions based on the decision forest regression model |
CPredictionMethod | Available methods for making Decision tree model-based prediction |
CPredictionMethod | Available methods of lasso regression model-based prediction |
CPredictionMethod | Available methods of linear regression model-based prediction |
CPredictionMethod | Available methods for predictions based on the decision tree regression model |
CPredictionModelInputId | Available identifiers of input Model objects in the prediction stage of the Neural Networks algorithm |
CPredictionResultCollectionId | Available identifiers of results obtained in the prediction stage of the Neural Networks algorithm |
CPredictionResultId | Available identifiers of the result for making decision tree model-based prediction |
CPredictionResultId | Available identifiers of results of the classifier model-based prediction algorithm |
CPredictionResultId | Available identifiers of results obtained in the prediction stage of the Neural Networks algorithm |
CPredictionResultId | Available identifiers of results of the classifier model-based prediction algorithm |
CPredictionResultId | Available identifiers of the result of ridge regression model-based prediction |
CPredictionResultId | Available identifiers of results of the classifier model-based prediction algorithm |
CPredictionResultId | Available identifiers of the result of lasso regression model-based prediction |
CPredictionResultId | Available identifiers of the result of linear regression model-based prediction |
CPredictionResultNumericTableId | Available identifiers of the result of logistic regression model-based prediction |
CPredictionResultsToComputeId | Available identifiers of the result of logistic regression model-based prediction |
CPredictionTensorInputId | Available identifiers of input Tensor objects in the prediction stage of the Neural Networks algorithm |
CPreluLayerDataId | Identifiers of input objects for the backward prelu layer and results for the forward prelu layer |
CPreluMethod | Available methods for the prelu layer |
CPruningId | Pruning method for Decision tree algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the linear regression algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the LogitBoost algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the multinomial Naive Bayes algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the PCA algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the SVM algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the BrownBoost algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the multi-class SVM algorithm |
CQualityMetricId | Available identifiers of the quality metrics available for the model trained with the AdaBoost algorithm |
CRatingsModelInputId | Available identifiers of input model objects for the rating prediction stage of the implicit ALS algorithm |
CRatingsPartialModelInputId | Available identifiers of input PartialModel objects for the rating prediction stage of the implicit ALS algorithm |
CRatingsPartialResultId | Available identifiers of input partial model objects for the rating prediction stage of the implicit ALS algorithm |
CRatingsResultId | Available identifiers of the results of the rating prediction stage of the implicit ALS algorithm |
CReluLayerDataId | Identifiers of input objects for the backward relu layer and results for the forward relu layer |
CReluMethod | Available methods for the relu layer |
CReshapeLayerDataId | Identifiers of input objects for the backward reshape layer and results for the forward reshape layer |
CReshapeMethod | Available methods for the reshape layer |
CResultCovariancesId | Available identifiers of results of the EM for GMM algorithm |
CResultFormat | Available options to return result matrices |
CResultId | Available identifiers of the results of the multivariate outlier detection algorithm |
CResultId | Available identifiers of results for the distributions |
CResultId | Available identifiers of results of the Min-max normalization algorithm |
CResultId | Available identifiers of results of the logistic function |
CResultId | Available identifiers of results of the hyperbolic tangent function |
CResultId | Available types of results of the SVD algorithm |
CResultId | Available identifiers of results for the association rules algorithm |
CResultId | Available identifiers of results of the softmax function |
CResultId | Available identifiers of results of the Z-score normalization algorithm |
CResultId | Available result identifiers for the kernel function algorithm |
CResultId | Available identifiers of results of the SmoothReLU algorithm |
CResultId | Available identifiers of results of the quantiles algorithm |
CResultId | Available identifiers of results of the EM for GMM algorithm |
CResultId | Available identifiers of results of the absolute value function |
CResultId | Available result identifiers for the objective funtion algorithm |
CResultId | Available types of results of the low order moments algorithm |
CResultId | Available identifiers of the results of the DBSCAN algorithm |
CResultId | Available result identifiers for the correlation distance algorithm |
CResultId | Available types of the results of the pivoted QR algorithm |
CResultId | Available types of results of the PCA algorithm |
CResultId | Available types of the results of the QR decomposition algorithm |
CResultId | Available identifiers of results of the sorting |
CResultId | Available identifiers of results for the engines |
CResultId | Available identifiers of results of the rectified linear function |
CResultId | Available result identifiers for the cosine distance algorithm |
CResultId | Available identifiers of the results of the K-Means algorithm |
CResultId | Available identifiers of results for the neural network weights and biases initializers |
CResultId | Available identifiers of results of the Cholesky decomposition algorithm |
CResultId | Available identifiers of the results of the multivariate outlier detection algorithm |
CResultId | Available result identifiers for the iterative solver algorithm |
CResultId | Available result identifiers for the correlation or variance-covariance matrix algorithm |
CResultId | Available identifiers of results for the univariate outlier detection algorithm |
CResultNumericTableId | Available identifiers of the result of decision forest model-based training |
CResultNumericTableId | Available identifiers of the result of decision forest model-based training |
CResultsToComputeId | Available computation flag identifiers for the decision forest result |
CResultsToComputeId | Available identifiers of results of the Z-score normalization algorithm |
CResultsToComputeId | Available computation flag identifiers for the objective funtion result |
CResultsToComputeId | Available identifiers of results of the DBSCAN algorithm |
CResultsToComputeId | Available identifiers of results of the PCA algorithm |
CRulesOrderId | Available sort order options for resulting association rules |
CSerializationTag | |
CSingleBetaDataInputId | Available identifiers of input objects for a single beta quality metrics |
CSingleBetaMethod | Available methods for computing the quality metric |
CSingleBetaModelInputId | Available identifiers of input objects for a single beta quality metrics |
CSingleBetaResultDataCollectionId | Available identifiers of the result of single beta quality metrics |
CSingleBetaResultId | Available identifiers of the result of single beta quality metrics |
CSmoothreluLayerDataId | Identifiers of input objects for the backward smoothrelu layer and results for the forward smoothrelu layer |
CSmoothreluMethod | Available methods for smoothrelu layer |
CSoftmaxCrossLayerDataId | Identifiers of input objects for the backward softmax cross-entropy layer and results for the forward softmax cross-entropy layer |
CSoftmaxCrossMethod | Available methods for thesoftmax cross-entropy layer |
CSoftmaxLayerDataId | Identifiers of input objects for the backward softmax layer and results for the forward softmax layer |
CSoftmaxMethod | Available methods for the softmax layer |
CSpatialAveragePooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional spatial average pooling layer and results for the forward two-dimensional spatial average pooling layer |
CSpatialAveragePooling2dMethod | Available methods for the two-dimensional spatial average pooling layer |
CSpatialMaximumPooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional spatial maximum pooling layer and results for the forward two-dimensional spatial maximum pooling layer |
CSpatialMaximumPooling2dLayerDataNumericTableId | Identifiers of input objects for the backward two-dimensional spatial maximum pooling layer and results for the forward two-dimensional spatial maximum pooling layer |
CSpatialMaximumPooling2dMethod | Available methods for the two-dimensional spatial maximum pooling layer |
CSpatialPooling2dIndices | Data structure representing the indices of the dimension on which two-dimensional pooling is performed |
CSpatialStochasticPooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional spatial stochastic pooling layer and results for the forward two-dimensional spatial stochastic pooling layer |
CSpatialStochasticPooling2dLayerDataNumericTableId | Identifiers of input objects for the backward two-dimensional spatial stochastic pooling layer and results for the forward two-dimensional spatial stochastic pooling layer |
CSpatialStochasticPooling2dMethod | Available methods for the two-dimensional spatial stochastic pooling layer |
CSplitBackwardInputLayerDataId | Identifiers of input objects for the backward split layer and results for the forward split layer |
CSplitCriterionId | Split criterion for Decision tree classification algorithm |
CSplitForwardResultLayerDataId | Identifiers of input objects for the backward split layer and results for the forward split layer |
CSplitMethod | Available methods for the split layer |
CSplitMethod | Split finding method in gradient boosted trees algorithm |
CStep10LocalNumericTableInputId | Available identifiers of input numeric table objects for the DBSCAN algorithm in the tenth step of the distributed processing mode |
CStep11LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the eleventh step of the distributed processing mode |
CStep11LocalNumericTableInputId | Available identifiers of input numeric table objects for the DBSCAN algorithm in the eleventh step of the distributed processing mode |
CStep12LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the twelfth step of the distributed processing mode |
CStep12LocalNumericTableInputId | Available identifiers of input numeric table objects for the DBSCAN algorithm in the twelfth step of the distributed processing mode |
CStep13LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
CStep1LocalNumericTableInputId | Available identifiers of input data numeric table objects for the DBSCAN algorithm in the first step of the distributed processing mode |
CStep3LocalCollectionInputId | Available identifiers of input objects for the implicit ALS training algorithm in the third step of the distributed processing mode |
CStep3LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the third step of the distributed processing mode |
CStep3LocalNumericTableInputId | Available identifiers of input objects for the implicit ALS training algorithm in the third step of the distributed processing mode |
CStep4LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the fourth step of the distributed processing mode |
CStep4LocalNumericTableInputId | Available identifiers of input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode |
CStep4LocalPartialModelsInputId | Available identifiers of input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode |
CStep5LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the fifth step of the distributed processing mode |
CStep6LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the sixth step of the distributed processing mode |
CStep7MasterCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the seventh step of the distributed processing mode |
CStep8LocalCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the eighth step of the distributed processing mode |
CStep8LocalNumericTableInputId | Available identifiers of input numeric table objects for the DBSCAN algorithm in the eighth step of the distributed processing mode |
CStep9MasterCollectionInputId | Available identifiers of input data collection objects for the DBSCAN algorithm in the ninth step of the distributed processing mode |
CStochasticPooling2dLayerDataId | Identifiers of input objects for the backward two-dimensional stochastic pooling layer and results for the forward two-dimensional stochastic pooling layer |
CStochasticPooling2dLayerDataNumericTableId | Identifiers of input objects for the backward two-dimensional stochastic pooling layer and results for the forward two-dimensional stochastic pooling layer |
CStochasticPooling2dMethod | Available methods for the two-dimensional stochastic pooling layer |
CTanhLayerDataId | Identifiers of input objects for the backward hyperbolic tangent (tanh) layer and results for the forward tanh layer |
CTanhMethod | Available methods for hyperbolic tangent (tanh) layer |
CTrainingDistributedInputId | Available identifiers of input objects of the classifier model training algorithm |
CTrainingInputCollectionId | Available identifiers of input objects for the neural network model based training |
CTrainingInputId | Available identifiers of input objects for linear regression model-based training |
CTrainingInputId | Available identifiers of the results in the training stage of decision tree |
CTrainingInputId | Available identifiers of input objects for lasso regression model-based training |
CTrainingInputId | Available identifiers of the result of decision tree model-based training |
CTrainingInputId | Available identifiers of input objects for ridge regression model-based training |
CTrainingInputId | Available identifiers of input objects for the neural network model based training |
CTrainingMethod | Available methods for training decision tree regression models |
CTrainingMethod | Available methods for training BrownBoost models |
CTrainingMethod | Available methods to train the decision stump model |
CTrainingMethod | Available methods for computing the naive Bayes training results |
CTrainingMethod | Available methods for training LogitBoost models |
CTrainingMethod | Available methods for ridge regression model-based training |
CTrainingMethod | Available methods for linear regression model-based training |
CTrainingMethod | Available methods for lasso regression model-based training |
CTrainingMethod | Available methods for training gradient boosted trees classification models |
CTrainingMethod | Available methods for training decision tree classification models |
CTrainingMethod | Available methods for training decision forest classification models |
CTrainingMethod | Available methods for training the implicit ALS model |
CTrainingMethod | Available methods for k nearest neighbors model-based training |
CTrainingMethod | Available methods for training gradient boosted trees regression models |
CTrainingMethod | Available methods for training AdaBoost models |
CTrainingMethod | Available methods for logistic regression model-based training |
CTrainingMethod | Computation methods for the multi-class classifier algorithm |
CTrainingMethod | Available methods to train the SVM model |
CTrainingMethod | Available methods for training decision forest regression models |
CTrainingMethod | Computation methods for the neural networks model based training |
CTrainingResultId | Available identifiers of the result of linear regression model-based training |
CTrainingResultId | Available identifiers of results of decision_forest regression model training algorithm |
CTrainingResultId | Available identifiers of the results of the implicit ALS training algorithm |
CTrainingResultId | Available identifiers of the result of ridge regression model-based training |
CTrainingResultId | Available identifiers of results of gbt regression model training algorithm |
CTrainingResultId | Available identifiers of results of the classifier model training algorithm |
CTrainingResultId | Available identifiers of the result of decision tree model-based training |
CTrainingResultId | Available identifiers of result of the neural network model based training |
CTrainingResultId | Available identifiers of the result of lasso regression model-based training |
CTransformComponentId | Available identifiers of input objects for the PCA transformation algorithm |
CTransformDataInputId | Available identifiers of input objects for the PCA transformation algorithm |
CTransformInputId | Available identifiers of input objects for the PCA transformation algorithm |
CTransformMethod | Available methods for PCA transformation |
CTransformResultId | Available identifiers of results of the PCA transformation algorithm |
CTransposedConv2dIndices | Data structure representing the dimension for convolution kernels |
CTransposedConv2dKernelSize | Data structure representing the sizes of the two-dimensional kernel subtensor for the backward 2D transposed convolution layer and results for the forward 2D transposed convolution layer |
CTransposedConv2dLayerDataId | Identifiers of input objects for the backward 2D transposed convolution layer and results for the forward 2D transposed convolution layer |
CTransposedConv2dMethod | Available methods for the 2D transposed convolution layer |
CTransposedConv2dPadding | Data structure representing the number of data to be implicitly added to the subtensor |
CTransposedConv2dStride | Data structure representing the intervals on which the kernel should be applied to the input |
CTransposedConv2dValueSizes | Data structure representing the sizes of the two-dimensional value subtensor for the backward 2D transposed convolution layer and results for the forward 2D transposed convolution layer |
CTreeNodeVisitor | Interface of callback object for classification model traversal |
CTreeNodeVisitor | Interface of callback object for regression model traversal |
CTreeNodeVisitor | Interface of callback object for decision tree regression model traversal |
CTreeNodeVisitor | Interface of callback object for classification model traversal |
CTruncatedGaussianMethod | Available methods for the truncated gaussian initializer |
CUniformMethod | Available methods for the uniform initializer |
CVariableImportanceModeId | Variable importance computation mode |
CXavierMethod | Available methods for the Xavier initializer |
►CSerializable | |
CRatingsMethod | Available methods for computing the results of implicit ALS model-based ratings prediction |
CPrecision | Available precisions for algorithms |
CMethod | Available methods for computing the results of the QR decomposition algorithm |
CMethod | Available methods to run the SVD algorithm |
CDataDictionary.FeaturesEqual | |
CNumericTable.StorageLayout | |
CSerializableBase | Class that provides methods for serialization and deserialization |
CStringDataSource | Specifies the methods for accessing the data stored as a text in java.io.Strings format |