Java* API Reference for Intel® Data Analytics Acceleration Library 2019

Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:
[detail level 1234567]
 CAbsLayerDataIdIdentifiers of input objects for the backward abs layer and results for the forward abs layer
 CAbsMethodAvailable methods for the abs layer
 CNumericTable.AllocationFlag
 CTensor.AllocationFlag
 CAveragePooling1dLayerDataIdIdentifiers of input objects for the backward one-dimensional average pooling layer and results for the forward one-dimensional average pooling layer
 CAveragePooling1dMethodAvailable methods for the one-dimensional average pooling layer
 CAveragePooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional average pooling layer and results for the forward two-dimensional average pooling layer
 CAveragePooling2dMethodAvailable methods for the two-dimensional average pooling layer
 CAveragePooling3dLayerDataIdIdentifiers of input objects for the backward three-dimensional average pooling layer and results for the forward three-dimensional average pooling layer
 CAveragePooling3dMethodAvailable methods for the three-dimensional average pooling layer
 CBackwardInputIdAvailable identifiers of input objects for the backward layer
 CBackwardInputLayerDataIdAvailable identifiers of input objects for the backward layer
 CBackwardResultIdAvailable identifiers of results for the backward layer
 CBackwardResultLayerDataIdAvailable identifiers of results for the backward layer
 CBatchNormalizationForwardInputLayerDataIdAvailable identifiers of input objects for the forward batch normalization layer
 CBatchNormalizationLayerDataIdIdentifiers of input objects for the backward batch normalization layer and results for the forward batch normalization layer
 CBatchNormalizationMethodAvailable methods for the batch normalization layer
 CBinaryConfusionMatrixInputIdAvailable identifiers of the input objects of the binary confusion matrix algorithm
 CBinaryConfusionMatrixMethodAvailable methods for computing the binary confusion matrix
 CBinaryConfusionMatrixResultIdAvailable identifiers of results of the binary confusion matrix algorithm
 CBinaryMetricIdAvailable identifiers of binary metrics
 CCompressionLevelCompression levels
 CCompressionMethodCompression and decompression methods
 CComputationModeAvailable modes of kernel function computation
 CComputeMode
 CComputeStep
 CConcatLayerDataIdIdentifiers of input objects for the backward concat layer and results for the forward concat layer
 CConcatMethodAvailable methods for the concat layer
 CConvolution2dIndicesData structure representing the dimension for convolution kernels
 CConvolution2dKernelSizeData structure representing the sizes of the two-dimensional kernel subtensor for the backward 2D convolution layer and results for the forward 2D convolution layer
 CConvolution2dLayerDataIdIdentifiers of input objects for the backward 2D convolution layer and results for the forward 2D convolution layer
 CConvolution2dMethodAvailable methods for the 2D convolution layer
 CConvolution2dPaddingData structure representing the number of data to be implicitly added to the subtensor
 CConvolution2dStrideData structure representing the intervals on which the kernel should be applied to the input
 CCovarianceStorageIdAvailable identifiers of covariance types in the EM for GMM algorithm
 CCpuTypeCPU types
 CCpuTypeEnableCPU types
 CDaalContextProvides the context for managment of memory in the native C++ object
 CDataFeatureUtilsClass that provides different feature types
 CTensor.DataLayout
 CDataSource.DataSourceStatus
 CDataUseInModelIdThe option to enable/disable an usage of the input dataset in k nearest neighbors model
 CDataSource.DictionaryCreationFlag
 CDisposableClass that frees memory allocated for the native C++ object
 CDistanceTypeAvailable distance types for the K-Means algorithm
 CDistributedDataSetAbstract class that defines the interface for the data management component responsible for representation of the data in the distributed raw format
 CDistributedPartialResultCollectionIdAvailable types of partial results of the QR decomposition algorithm on the second step in the distributed processing mode
 CDistributedPartialResultCollectionIdAvailable types of partial results of the second step of the SVD algorithm in the distributed processing mode, stored in the DataCollection object
 CDistributedPartialResultIdAvailable types of the partial results of the QR decomposition algorithm on the second step in the distributed processing mode
 CDistributedPartialResultIdAvailable types of partial results of the second step of the SVD algorithm in the distributed processing mode, stored in the Result object
 CDistributedPartialResultIdAvailable identifiers of partial results of the neural network training algorithm
 CDistributedPartialResultStep1IdAvailable identifiers of partial results of the implicit ALS training algorithm obtained in the first step of the distributed processing mode
 CDistributedPartialResultStep2IdAvailable identifiers of partial results of the implicit ALS training algorithm obtained in the second step of the distributed processing mode
 CDistributedPartialResultStep3IdAvailable identifiers of partial results of the implicit ALS training algorithm obtained in the third step of the distributed processing mode
 CDistributedPartialResultStep3IdAvailable types of partial results obtained in the second step of the SVD algorithm in the distributed processing mode, stored in the Result object
 CDistributedPartialResultStep4IdAvailable identifiers of partial results of the implicit ALS training algorithm obtained in the fourth step of the distributed processing mode
 CDistributedStep1LocalInputIdAvailable identifiers of input objects for the neural network model based training
 CDistributedStep2MasterInputIdPartial results required by the QR decomposition algorithm on the second step in the distributed processing mode
 CDistributedStep2MasterInputIdPartial results from previous steps of the SVD algorithm in the distributed processing mode, required by the second step
 CDistributedStep2MasterInputIdAvailable identifiers of input objects for the K-Means algorithm on the master node
 CDistributedStep2MasterInputIdAvailable identifiers of master-node input objects for the correlation or variance-covariance matrix algorithm
 CDistributedStep2MasterInputIdAvailable identifiers of master-node input objects for the low order momemnts algorithm
 CDistributedStep2MasterInputIdAvailable identifiers of input objects for the neural network training algorithm on the master node
 CDistributedStep3LocalInputIdPartial results required by the QR decomposition algorithm on the third step in the distributed processing mode
 CDistributedStep3LocalInputIdPartial results from previous steps of the SVD algorithm in the distributed processing mode, required by the third step
 CDropoutLayerDataIdIdentifiers of input objects for the backward dropout layer and results for the forward dropout layer
 CDropoutMethodAvailable methods for the dropout layer
 CEltwiseSumForwardInputIdIdentifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer
 CEltwiseSumLayerDataIdIdentifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer
 CEltwiseSumLayerDataNumericTableIdIdentifiers of input objects for the backward element-wise sum layer and results for the forward element-wise sum layer
 CEltwiseSumMethodAvailable methods for the element-wise sum layer
 CEluLayerDataIdIdentifiers of input objects for the backward ELU layer and results for the forward ELU layer
 CEluMethodAvailable methods for the ELU layer
 CEnvironmentProvides information about computational environment
 CEstimatesToComputeAvailable sets of estimates to compute of low order Moments
 CExplainedVarianceInputIdAvailable identifiers of input objects for a explained variance quality metrics
 CExplainedVarianceMethodAvailable methods for computing the quality metric
 CExplainedVarianceResultIdAvailable identifiers of the result of explained variance quality metrics
 CFactoryClass that provides factory functionality for objects derived from the SerializableBase class
 CDataFeatureUtils.FeatureType
 CForwardInputIdAvailable identifiers of input objects for the forward layer
 CForwardInputLayerDataIdAvailable identifiers of input objects for the forward layer
 CForwardResultIdAvailable identifiers of results for the forward layer
 CForwardResultLayerDataIdAvailable identifiers of results for the forward layer
 CFullyConnectedLayerDataIdIdentifiers of input objects for the backward fully-connected layer and results for the forward fully-connected layer
 CFullyConnectedMethodAvailable methods for the fully-connected layer
 CGaussianMethodAvailable methods for the gaussian initializer
 CGroupOfBetasInputIdAvailable identifiers of input objects for a single beta quality metrics
 CGroupOfBetasMethodAvailable methods for computing the quality metric
 CGroupOfBetasResultIdAvailable identifiers of the result of single beta quality metrics
 CCSRNumericTable.IndexingIndexing scheme used for accessing the data in CSR layout
 CDataFeatureUtils.IndexNumType
 CInitDistributedLocalPlusPlusInputDataIdAvailable identifiers of input objects for computing initial clusters for the K-Means algorithm used with plusPlus and parallelPlus methods only on a local node
 CInitDistributedPartialResultStep2IdAvailable identifiers of partial results of the implicit ALS initialization algorithm in the first step of the distributed processing mode
 CInitDistributedStep2LocalPlusPlusInputIdAvailable 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
 CInitDistributedStep2LocalPlusPlusPartialResultDataIdAvailable 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
 CInitDistributedStep2LocalPlusPlusPartialResultIdAvailable 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
 CInitDistributedStep2MasterInputIdAvailable identifiers of input objects for computing initial clusters for the K-Means algorithm on the master node
 CInitDistributedStep3MasterPlusPlusInputIdAvailable 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
 CInitDistributedStep3MasterPlusPlusPartialResultDataIdAvailable 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
 CInitDistributedStep3MasterPlusPlusPartialResultIdAvailable 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
 CInitDistributedStep4LocalPlusPlusInputIdAvailable 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
 CInitDistributedStep4LocalPlusPlusPartialResultIdAvailable 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
 CInitDistributedStep5MasterPlusPlusInputDataIdAvailable 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
 CInitDistributedStep5MasterPlusPlusInputIdAvailable 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
 CInitDistributedStep5MasterPlusPlusPartialResultIdAvailable 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
 CInitializationMethodAvailable initialization methods for the BACON multivariate outlier detection algorithm
 CInitializationMethodAvailable initialization methods for the BACON multivariate outlier detection algorithm
 CInitInputIdAvailable identifiers of input objects for initializing the implicit ALS training algorithm
 CInitInputIdAvailable identifiers of input objects for computing initial clusters for the K-Means algorithm
 CInitInputIdAvailable identifiers of input objects for the default initialization of the EM for GMM algorithm
 CInitMethodAvailable methods for computing initial values for the implicit ALS training algorithm
 CInitMethodMethods of computing initial clusters for the K-Means algorithm
 CInitMethodAvailable methods for computing initial values for the EM for GMM algorithm
 CInitPartialResultBaseIdAvailable identifiers of partial results of the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode
 CInitPartialResultCollectionIdAvailable identifiers of partial results of the implicit ALS initialization algorithm in the first step of the distributed processing mode
 CInitPartialResultIdAvailable identifiers of partial results of the default initialization of the implicit ALS training algorithm
 CInitPartialResultIdAvailable identifiers of partial results of computing initial clusters for the K-Means algorithm
 CInitResultCovariancesIdAvailable identifiers of results of the default initialization of the EM for GMM algorithm
 CInitResultIdAvailable identifiers of the results of the default initialization of the implicit ALS training algorithm
 CInitResultIdAvailable identifiers of the results of computing initial clusters for the K-Means algorithm
 CInitResultIdAvailable identifiers of results of the default initialization of the EM for GMM algorithm
 CInitStep2LocalInputIdAvailable identifiers of input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode
 CInputCovariancesIdAvailable identifiers of input covariance objects for the EM for GMM algorithm
 CInputIdAvailable identifiers of input objects for the cross-entropy loss objective function algorithm
 CInputIdAvailable identifiers of input objects for the iterative algorithm
 CInputIdAvailable identifiers of input objects for the logistic loss objective function algorithm
 CInputIdAvailable identifiers of input objects for the MSE algorithm
 CInputIdAvailable identifiers of input objects for the objective function algorithm
 CInputIdAvailable identifiers of input objects for the Sum of functions algorithm
 CInputIdAvailable identifiers of input objects for the PCA algorithm
 CInputIdAvailable identifiers of input objects for the multivariate outlier detection algorithm
 CInputIdAvailable identifiers of input objects for the pivoted QR algorithm
 CInputIdAvailable identifiers of input objects for the decision_forest regression algorithm
 CInputIdAvailable identifiers of input objects for neural network weights and biases initializer
 CInputIdAvailable identifiers of input objects for the QR decomposition algorithm
 CInputIdAvailable identifiers of input objects for the quantiles algorithm
 CInputIdAvailable identifiers of input objects for the sorting
 CInputIdAvailable identifiers of input arguments for the association rules algorithm
 CInputIdAvailable identifiers of input objects for the SVD algorithm
 CInputIdAvailable identifiers of input objects for the univariate outlier detection algorithm
 CInputIdAvailable identifiers of input objects for the classifier algorithm
 CInputIdAvailable identifiers of input objects for the kernel function algorithm
 CInputIdAvailable identifiers of distributions
 CInputIdAvailable identifiers of input objects for the correlation distance algorithm
 CInputIdAvailable identifiers of input objects for the K-Means algorithm
 CInputIdAvailable identifiers of input objects for the cosine distance algorithm
 CInputIdAvailable identifiers of input objects for the EM for GMM algorithm
 CInputIdAvailable identifiers of input objects for the Z-score normalization algorithm
 CInputIdAvailable identifiers of input objects for the Cholesky decomposition algorithm
 CInputIdAvailable identifiers of engines
 CInputIdAvailable identifiers of input objects for the correlation or variance-covariance matrix algorithm
 CInputIdAvailable identifiers of input objects for the multivariate outlier detection algorithm
 CInputIdAvailable identifiers of input objects for the low order moments
 CInputIdAvailable identifiers of input objects for the gbt regression algorithm
 CInputIdAvailable identifiers of input objects for the absolute value function
 CInputIdAvailable identifiers of input objects for the logistic function
 CInputIdAvailable identifiers of input objects for the rectified linear function
 CInputIdAvailable identifiers of input objects for the SmoothReLU algorithm
 CInputIdAvailable identifiers of input objects for the Min-max normalization algorithm
 CInputIdAvailable identifiers of input objects for the softmax function
 CInputIdAvailable identifiers of input objects for the hyperbolic tangent function
 CInputValuesIdAvailable identifiers of input objects for the EM for GMM algorithm
 CDataFeatureUtils.InternalNumType
 CInternalOptionalDataIdAvailable identifiers of InternalOptionalDataId objects for the algorithm
 CItemsetsOrderIdAvailable sort order options for resulting itemsets
 CLcnIndicesData structure representing the indices of the two dimensions on which local contrast normalization is performed
 CLcnLayerDataIdIdentifiers of input objects for the backward local contrast normalization layer and results for the forward local contrast normalization layer
 CLcnMethodAvailable methods for the local contrast normalization layer
 CLibraryVersionInfoProvides information about the version of Intel(R) Data Analytics Acceleration Library
 CLibUtils
 CLocallyConnected2dIndicesData structure representing the dimension for locally connected kernels
 CLocallyConnected2dKernelSizesData 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
 CLocallyConnected2dLayerDataIdIdentifiers of input objects for the backward 2D locally connected layer and results for the forward 2D locally connected layer
 CLocallyConnected2dMethodAvailable methods for the 2D locally connected layer
 CLocallyConnected2dPaddingsData structure representing the number of data to be implicitly added to the subtensor
 CLocallyConnected2dStridesData structure representing the intervals on which the kernel should be applied to the input
 CLogisticCrossLayerDataIdIdentifiers of input objects for the backward logistic cross-entropy layer and results for the forward logistic cross-entropy layer
 CLogisticCrossMethodAvailable methods for the logistic cross-entropy layer
 CLogisticLayerDataIdIdentifiers of input objects for the backward logistic layer and results for the forward logistic layer
 CLogisticMethodAvailable methods for logistic layer
 CLossForwardInputIdAvailable identifiers of input objects for the forward layer
 CLrnLayerDataIdIdentifiers of input objects for the backward local response normalization layer and results for the forward local response normalization layer
 CLrnMethodAvailable methods for the local response normalization layer
 CMasterInputIdAvailable identifiers of the PCA algorithm on the second step in the distributed processing mode
 CMasterInputIdAvailable identifiers of input objects for ridge regression model-based training on the master node
 CMasterInputIdAvailable identifiers of input objects for the implicit ALS training algorithm in the second step of the distributed processing mode
 CMasterInputIdAvailable identifiers of input objects for linear regression model-based training on the master node
 CMaximumPooling1dLayerDataIdIdentifiers of input objects for the backward one-dimensional maximum pooling layer and results for the forward one-dimensional maximum pooling layer
 CMaximumPooling1dLayerDataNumericTableIdIdentifiers of input numeric tables for the backward one-dimensional maximum pooling layer and results for the forward one-dimensional maximum pooling layer
 CMaximumPooling1dMethodAvailable methods for the one-dimensional maximum pooling layer
 CMaximumPooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional maximum pooling layer and results for the forward two-dimensional maximum pooling layer
 CMaximumPooling2dLayerDataNumericTableIdIdentifiers of input objects for the backward two-dimensional maximum pooling layer and results for the forward two-dimensional maximum pooling layer
 CMaximumPooling2dMethodAvailable methods for the two-dimensional maximum pooling layer
 CMaximumPooling3dLayerDataIdIdentifiers of input objects for the backward three-dimensional maximum pooling layer and results for the forward three-dimensional maximum pooling layer
 CMaximumPooling3dLayerDataNumericTableIdIdentifiers of input objects for the backward three-dimensional maximum pooling layer and results for the forward three-dimensional maximum pooling layer
 CMaximumPooling3dMethodAvailable methods for the three-dimensional maximum pooling layer
 CNumericTable.MemoryStatus
 CMethodAvailable methods for computing the Adagrad algorithm
 CMethodAvailable methods for computing the cross-entropy loss objective function algorithm
 CMethodAvailable methods for computing the LBFGS algorithm
 CMethodAvailable methods for computing the logistic loss objective function algorithm
 CMethodAvailable methods for computing the MSE algorithm
 CMethodAvailable methods for computing results of Objective function with precomputed characteristics
 CMethodAvailable methods for computing the SGD algorithm
 CMethodAvailable methods for running PCA algorithm
 CMethodAvailable methods for computing the results of the multivariate outlier detection
 CMethodAvailable methods to compute quantiles
 CMethodAvailable methods for the bernoulli distribution
 CMethodAvailable methods to sort data
 CMethodAvailable methods for computing results of the univariate outlier detection algorithm
 CMethodAvailable methods for RBF kernel function
 CMethodAvailable methods for computing large itemsets and association rules
 CMethodAvailable methods for computing the results of the pivoted QR algorithm
 CMethodAvailable methods for linear kernel function
 CMethodAvailable methods for the normal distribution
 CMethodAvailable methods for computing the correlation distance
 CMethodAvailable methods for the uniform distribution
 CMethodAvailable methods of the K-Means algorithm
 CMethodAvailable methods for Z-score normalization
 CMethodAvailable methods for computing the cosine distance
 CMethodAvailable methods for running the EM for GMM algorithm
 CMethodAvailable methods for the mcg59 engine
 CMethodAvailable methods for Cholesky decomposition
 CMethodAvailable methods for the mt19937 engine
 CMethodAvailable methods for computing the correlation or variance-covariance matrix
 CMethodAvailable methods for computing moments of low order Moments
 CMethodAvailable methods for absolute value function
 CMethodAvailable methods for computing the results of the multivariate outlier detection
 CMethodAvailable methods for logistic function
 CMethodAvailable methods for rectified linear function
 CMethodAvailable methods for SmoothReLU algorithm
 CMethodAvailable methods for softmax function
 CMethodAvailable methods for Min-max normalization
 CMethodAvailable methods for hyperbolic tangent function
 CModelInputIdAvailable identifiers of input objects of the decision_forest regression predication algorithm
 CModelInputIdAvailable identifiers of input model objects for the implicit ALS training algorithm
 CModelInputIdAvailable identifiers of input models for making decision tree model-based prediction
 CModelInputIdAvailable identifiers of input objects of the gbt regression predication algorithm
 CModelInputIdAvailable identifiers of input objects of the classification algorithms
 CMultiClassConfusionMatrixInputIdAvailable identifiers of the input objects of the confusion matrix
 CMultiClassConfusionMatrixMethodAvailable methods for computing the confusion matrix
 CMultiClassConfusionMatrixResultIdAvailable identifiers of the results of the confusion matrix algorithm
 CMultiClassMetricIdAvailable identifiers of multi-class metrics
 CNodeDescriptorStruct containing base description of node in descision tree
 CNumericTable.NormalizationType
 CDataSource.NumericTableAllocationFlag
 CNumericTableInputIdAvailable identifiers of input objects of decision_forest regression prediction algorithms
 CNumericTableInputIdAvailable identifiers of input numeric table objects for the implicit ALS training algorithm in the distributed processing mode
 CNumericTableInputIdAvailable identifiers of input numeric tables for making decision tree model-based prediction
 CNumericTableInputIdAvailable identifiers of input objects of gbt regression prediction algorithms
 CNumericTableInputIdAvailable identifiers of input objects of classification algorithms
 COptionalDataIdAvailable identifiers of optional data objects for the iterative algorithm
 COptionalDataIdAvailable identifiers of input objects for the iterative algorithm
 COptionalDataIdAvailable identifiers of OptionalData objects for the algorithm
 COptionalInputIdAvailable identifiers of optional input objects for the iterative algorithm
 COptionalResultIdAvailable result identifiers for the iterative solver algorithm
 COutputMatrixTypeAvailable types of the computed correlation or variance-covariance matrix
 CPartialCorrelationResultIDAvailable identifiers of partial results of the correlation method of the PCA algorithm
 CPartialModelInputIdAvailable identifiers of input partial model objects for the implicit ALS training algorithm in the distributed processing mode
 CPartialResultIdAvailable 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
 CPartialResultIdAvailable identifiers of partial results of the SVD algorithm obtained in the online processing mode and in the first step in the distributed processing mode
 CPartialResultIdAvailable identifiers of partial results of the classification algorithm
 CPartialResultIdAvailable identifiers of a partial result of ridge regression model-based training
 CPartialResultIdAvailable identifiers of partial results of the K-Means algorithm
 CPartialResultIdAvailable identifiers of a partial result of linear regression model-based training
 CPartialResultIdAvailable identifiers of partial results of the low order moments algorithm
 CPartialResultIdAvailable identifiers of partial results of the correlation or variance-covariance matrix algorithm
 CPartialResultIdAvailable identifiers of partial results of the neural network training algorithm
 CPartialSVDCollectionResultIDAvailable identifiers of partial results of the SVD method of the PCA algorithm
 CPartialSVDTableResultIDAvailable identifiers of partial results of the SVD method of the PCA algorithm
 CDataFeatureUtils.PMMLNumType
 CPooling1dIndexData structure representing the indices of the dimension on which one-dimensional pooling is performed
 CPooling1dKernelSizeData structure representing the size of the 1D subtensor from which the element is computed
 CPooling1dPaddingData structure representing the number of data elements to implicitly add to each side of the 1D subtensor on which one-dimensional pooling is performed
 CPooling1dStrideData structure representing the intervals on which the subtensors for one-dimensional pooling are computed
 CPooling2dIndicesData structure representing the indices of the dimension on which two-dimensional pooling is performed
 CPooling2dKernelSizesData structure representing the size of the 2D subtensor from which the element is computed
 CPooling2dPaddingsData structure representing the number of data elements to implicitly add to each side of the 2D subtensor on which two-dimensional pooling is performed
 CPooling2dStridesData structure representing the intervals on which the subtensors for two-dimensional pooling are computed
 CPooling3dIndicesData structure representing the dimension for convolution kernels
 CPooling3dKernelSizesData structure representing the size of the three-dimensional subtensor
 CPooling3dPaddingsData structure representing the number of data elements to implicitly add to each size of the three-dimensional subtensor on which pooling is performed
 CPooling3dStridesData structure representing the intervals on which the subtensors for pooling are selected
 CPredictionInputIdAvailable identifiers of input objects for ridge regression model-based prediction
 CPredictionInputIdAvailable identifiers of input objects for linear regression model-based prediction
 CPredictionInputIdAvailable identifiers of input objects for logistic regression model-based prediction
 CPredictionMethodAvailable methods for the multi-class classifier prediction
 CPredictionMethodAvailable methods for computing the results of the naive Bayes model based prediction
 CPredictionMethodAvailable methods of ridge regression model-based prediction
 CPredictionMethodAvailable methods for predictions based on the decision tree regression model
 CPredictionMethodComputation methods for the neural networks model based prediction
 CPredictionMethodAvailable methods to compute the results of the SVM prediction algorithm
 CPredictionMethodAvailable methods for making Decision tree model-based prediction
 CPredictionMethodAvailable methods for the k nearest neighbors prediction
 CPredictionMethodAvailable methods to make prediction based on the decision stump model
 CPredictionMethodAvailable methods for predictions based on the BrownBoost model
 CPredictionMethodAvailable methods for predictions based on the decision forest regression model
 CPredictionMethodAvailable methods of linear regression model-based prediction
 CPredictionMethodAvailable methods for predictions based on the AdaBoost model
 CPredictionMethodAvailable methods of logistic regression model-based prediction
 CPredictionMethodAvailable methods for predictions based on the gradient boosted trees classification model
 CPredictionMethodAvailable methods for predictions based on the LogitBoost model
 CPredictionMethodAvailable methods for predictions based on the gradient boosted trees regression model
 CPredictionMethodAvailable methods for predictions based on the decision forest classification model
 CPredictionModelInputIdAvailable identifiers of input Model objects in the prediction stage of the Neural Networks algorithm
 CPredictionResultCollectionIdAvailable identifiers of results obtained in the prediction stage of the Neural Networks algorithm
 CPredictionResultIdAvailable identifiers of results of the classifier model-based prediction algorithm
 CPredictionResultIdAvailable identifiers of results of the classifier model-based prediction algorithm
 CPredictionResultIdAvailable identifiers of results obtained in the prediction stage of the Neural Networks algorithm
 CPredictionResultIdAvailable identifiers of the result of ridge regression model-based prediction
 CPredictionResultIdAvailable identifiers of the result for making decision tree model-based prediction
 CPredictionResultIdAvailable identifiers of the result of linear regression model-based prediction
 CPredictionResultIdAvailable identifiers of results of the classifier model-based prediction algorithm
 CPredictionResultNumericTableIdAvailable identifiers of the result of logistic regression model-based prediction
 CPredictionResultsToComputeIdAvailable identifiers of the result of logistic regression model-based prediction
 CPredictionTensorInputIdAvailable identifiers of input Tensor objects in the prediction stage of the Neural Networks algorithm
 CPreluLayerDataIdIdentifiers of input objects for the backward prelu layer and results for the forward prelu layer
 CPreluMethodAvailable methods for the prelu layer
 CPruningIdPruning method for Decision tree algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the multi-class SVM algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the SVM algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the linear regression algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the BrownBoost algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the PCA algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the multinomial Naive Bayes algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the LogitBoost algorithm
 CQualityMetricIdAvailable identifiers of the quality metrics available for the model trained with the AdaBoost algorithm
 CRatingsModelInputIdAvailable identifiers of input model objects for the rating prediction stage of the implicit ALS algorithm
 CRatingsPartialModelInputIdAvailable identifiers of input PartialModel objects for the rating prediction stage of the implicit ALS algorithm
 CRatingsPartialResultIdAvailable identifiers of input partial model objects for the rating prediction stage of the implicit ALS algorithm
 CRatingsResultIdAvailable identifiers of the results of the rating prediction stage of the implicit ALS algorithm
 CReluLayerDataIdIdentifiers of input objects for the backward relu layer and results for the forward relu layer
 CReluMethodAvailable methods for the relu layer
 CReshapeLayerDataIdIdentifiers of input objects for the backward reshape layer and results for the forward reshape layer
 CReshapeMethodAvailable methods for the reshape layer
 CResultCovariancesIdAvailable identifiers of results of the EM for GMM algorithm
 CResultFormatAvailable options to return result matrices
 CResultIdAvailable types of results of the SVD algorithm
 CResultIdAvailable result identifiers for the iterative solver algorithm
 CResultIdAvailable result identifiers for the objective funtion algorithm
 CResultIdAvailable identifiers of results of the rectified linear function
 CResultIdAvailable identifiers of the results of the multivariate outlier detection algorithm
 CResultIdAvailable types of results of the PCA algorithm
 CResultIdAvailable types of the results of the QR decomposition algorithm
 CResultIdAvailable identifiers of the results of the multivariate outlier detection algorithm
 CResultIdAvailable identifiers of results of the sorting
 CResultIdAvailable identifiers of results of the quantiles algorithm
 CResultIdAvailable result identifiers for the kernel function algorithm
 CResultIdAvailable identifiers of results for the univariate outlier detection algorithm
 CResultIdAvailable identifiers of results of the Z-score normalization algorithm
 CResultIdAvailable identifiers of results of the hyperbolic tangent function
 CResultIdAvailable identifiers of results for the distributions
 CResultIdAvailable result identifiers for the correlation distance algorithm
 CResultIdAvailable identifiers of the results of the K-Means algorithm
 CResultIdAvailable types of the results of the pivoted QR algorithm
 CResultIdAvailable identifiers of results for the association rules algorithm
 CResultIdAvailable result identifiers for the cosine distance algorithm
 CResultIdAvailable identifiers of results of the EM for GMM algorithm
 CResultIdAvailable identifiers of results for the engines
 CResultIdAvailable identifiers of results of the Cholesky decomposition algorithm
 CResultIdAvailable types of results of the low order moments algorithm
 CResultIdAvailable identifiers of results of the absolute value function
 CResultIdAvailable identifiers of results of the softmax function
 CResultIdAvailable result identifiers for the correlation or variance-covariance matrix algorithm
 CResultIdAvailable identifiers of results of the logistic function
 CResultIdAvailable identifiers of results of the Min-max normalization algorithm
 CResultIdAvailable identifiers of results of the SmoothReLU algorithm
 CResultIdAvailable identifiers of results for the neural network weights and biases initializers
 CResultNumericTableIdAvailable identifiers of the result of decision forest model-based training
 CResultNumericTableIdAvailable identifiers of the result of decision forest model-based training
 CResultsToComputeIdAvailable identifiers of results of the PCA algorithm
 CResultsToComputeIdAvailable computation flag identifiers for the objective funtion result
 CResultsToComputeIdAvailable computation flag identifiers for the decision forest result
 CResultsToComputeIdAvailable identifiers of results of the Z-score normalization algorithm
 CRulesOrderIdAvailable sort order options for resulting association rules
 CSerializationTag
 CSingleBetaDataInputIdAvailable identifiers of input objects for a single beta quality metrics
 CSingleBetaMethodAvailable methods for computing the quality metric
 CSingleBetaModelInputIdAvailable identifiers of input objects for a single beta quality metrics
 CSingleBetaResultDataCollectionIdAvailable identifiers of the result of single beta quality metrics
 CSingleBetaResultIdAvailable identifiers of the result of single beta quality metrics
 CSmoothreluLayerDataIdIdentifiers of input objects for the backward smoothrelu layer and results for the forward smoothrelu layer
 CSmoothreluMethodAvailable methods for smoothrelu layer
 CSoftmaxCrossLayerDataIdIdentifiers of input objects for the backward softmax cross-entropy layer and results for the forward softmax cross-entropy layer
 CSoftmaxCrossMethodAvailable methods for thesoftmax cross-entropy layer
 CSoftmaxLayerDataIdIdentifiers of input objects for the backward softmax layer and results for the forward softmax layer
 CSoftmaxMethodAvailable methods for the softmax layer
 CSpatialAveragePooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional spatial average pooling layer and results for the forward two-dimensional spatial average pooling layer
 CSpatialAveragePooling2dMethodAvailable methods for the two-dimensional spatial average pooling layer
 CSpatialMaximumPooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional spatial maximum pooling layer and results for the forward two-dimensional spatial maximum pooling layer
 CSpatialMaximumPooling2dLayerDataNumericTableIdIdentifiers of input objects for the backward two-dimensional spatial maximum pooling layer and results for the forward two-dimensional spatial maximum pooling layer
 CSpatialMaximumPooling2dMethodAvailable methods for the two-dimensional spatial maximum pooling layer
 CSpatialPooling2dIndicesData structure representing the indices of the dimension on which two-dimensional pooling is performed
 CSpatialStochasticPooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional spatial stochastic pooling layer and results for the forward two-dimensional spatial stochastic pooling layer
 CSpatialStochasticPooling2dLayerDataNumericTableIdIdentifiers of input objects for the backward two-dimensional spatial stochastic pooling layer and results for the forward two-dimensional spatial stochastic pooling layer
 CSpatialStochasticPooling2dMethodAvailable methods for the two-dimensional spatial stochastic pooling layer
 CSplitBackwardInputLayerDataIdIdentifiers of input objects for the backward split layer and results for the forward split layer
 CSplitCriterionIdSplit criterion for Decision tree classification algorithm
 CSplitForwardResultLayerDataIdIdentifiers of input objects for the backward split layer and results for the forward split layer
 CSplitMethodAvailable methods for the split layer
 CSplitMethodSplit finding method in gradient boosted trees algorithm
 CStep3LocalCollectionInputIdAvailable identifiers of input objects for the implicit ALS training algorithm in the third step of the distributed processing mode
 CStep3LocalNumericTableInputIdAvailable identifiers of input objects for the implicit ALS training algorithm in the third step of the distributed processing mode
 CStep4LocalNumericTableInputIdAvailable identifiers of input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode
 CStep4LocalPartialModelsInputIdAvailable identifiers of input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode
 CStochasticPooling2dLayerDataIdIdentifiers of input objects for the backward two-dimensional stochastic pooling layer and results for the forward two-dimensional stochastic pooling layer
 CStochasticPooling2dLayerDataNumericTableIdIdentifiers of input objects for the backward two-dimensional stochastic pooling layer and results for the forward two-dimensional stochastic pooling layer
 CStochasticPooling2dMethodAvailable methods for the two-dimensional stochastic pooling layer
 CTanhLayerDataIdIdentifiers of input objects for the backward hyperbolic tangent (tanh) layer and results for the forward tanh layer
 CTanhMethodAvailable methods for hyperbolic tangent (tanh) layer
 CTrainingDistributedInputIdAvailable identifiers of input objects of the classifier model training algorithm
 CTrainingInputCollectionIdAvailable identifiers of input objects for the neural network model based training
 CTrainingInputIdAvailable identifiers of input objects for the neural network model based training
 CTrainingInputIdAvailable identifiers of the result of decision tree model-based training
 CTrainingInputIdAvailable identifiers of the results in the training stage of decision tree
 CTrainingInputIdAvailable identifiers of input objects for ridge regression model-based training
 CTrainingInputIdAvailable identifiers of input objects for linear regression model-based training
 CTrainingMethodAvailable methods for k nearest neighbors model-based training
 CTrainingMethodAvailable methods for training decision forest classification models
 CTrainingMethodAvailable methods for computing the naive Bayes training results
 CTrainingMethodAvailable methods for training LogitBoost models
 CTrainingMethodAvailable methods to train the decision stump model
 CTrainingMethodAvailable methods for ridge regression model-based training
 CTrainingMethodAvailable methods for training gradient boosted trees classification models
 CTrainingMethodAvailable methods for training decision forest regression models
 CTrainingMethodAvailable methods for training AdaBoost models
 CTrainingMethodAvailable methods for training decision tree classification models
 CTrainingMethodComputation methods for the neural networks model based training
 CTrainingMethodAvailable methods for logistic regression model-based training
 CTrainingMethodAvailable methods to train the SVM model
 CTrainingMethodComputation methods for the multi-class classifier algorithm
 CTrainingMethodAvailable methods for training decision tree regression models
 CTrainingMethodAvailable methods for linear regression model-based training
 CTrainingMethodAvailable methods for training BrownBoost models
 CTrainingMethodAvailable methods for training the implicit ALS model
 CTrainingMethodAvailable methods for training gradient boosted trees regression models
 CTrainingResultIdAvailable identifiers of results of the classifier model training algorithm
 CTrainingResultIdAvailable identifiers of result of the neural network model based training
 CTrainingResultIdAvailable identifiers of the result of linear regression model-based training
 CTrainingResultIdAvailable identifiers of the result of ridge regression model-based training
 CTrainingResultIdAvailable identifiers of results of gbt regression model training algorithm
 CTrainingResultIdAvailable identifiers of results of decision_forest regression model training algorithm
 CTrainingResultIdAvailable identifiers of the results of the implicit ALS training algorithm
 CTrainingResultIdAvailable identifiers of the result of decision tree model-based training
 CTransformComponentIdAvailable identifiers of input objects for the PCA transformation algorithm
 CTransformDataInputIdAvailable identifiers of input objects for the PCA transformation algorithm
 CTransformInputIdAvailable identifiers of input objects for the PCA transformation algorithm
 CTransformMethodAvailable methods for PCA transformation
 CTransformResultIdAvailable identifiers of results of the PCA transformation algorithm
 CTransposedConv2dIndicesData structure representing the dimension for convolution kernels
 CTransposedConv2dKernelSizeData 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
 CTransposedConv2dLayerDataIdIdentifiers of input objects for the backward 2D transposed convolution layer and results for the forward 2D transposed convolution layer
 CTransposedConv2dMethodAvailable methods for the 2D transposed convolution layer
 CTransposedConv2dPaddingData structure representing the number of data to be implicitly added to the subtensor
 CTransposedConv2dStrideData structure representing the intervals on which the kernel should be applied to the input
 CTransposedConv2dValueSizesData 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
 CTreeNodeVisitorInterface of callback object for decision tree regression model traversal
 CTreeNodeVisitorInterface of callback object for classification model traversal
 CTreeNodeVisitorInterface of callback object for classification model traversal
 CTreeNodeVisitorInterface of callback object for regression model traversal
 CTruncatedGaussianMethodAvailable methods for the truncated gaussian initializer
 CUniformMethodAvailable methods for the uniform initializer
 CVariableImportanceModeIdVariable importance computation mode
 CXavierMethodAvailable methods for the Xavier initializer
 CSerializable

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