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