C++ API Reference for Intel® Data Analytics Acceleration Library 2018 Update 1
▼Ndaal | |
▼Nalgorithms | Contains classes that implement algorithms for data analysis(data mining), and data modeling(training and prediction). These algorithms include matrix decompositions, clustering algorithms, classification and regression algorithms, as well as association rules discovery |
►Nadaboost | Contains classes for the AdaBoost classification algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the adaboost::training::Batch algorithm |
CParameter | AdaBoost algorithm parameters |
►Nprediction | Contains classes for making prediction based on the AdaBoost models |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predict AdaBoost classification results |
CBatchContainer | Provides methods to run implementations of the AdaBoost algorithm. It is associated with daal::algorithms::adaboost::prediction::interface1::Batch class and supports method to compute AdaBoost prediction |
CInput | Input objects in the prediction stage of the adaboost algorithm |
►Nquality_metric_set | Contains classes for checking the quality of the model trained with the AdaBoost algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a set of quality metrics to check the model trained with the AdaBoost algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
►Ntraining | Contains classes for AdaBoost models training |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains model of the AdaBoost algorithms in batch mode |
CBatchContainer | Provides methods to run implementations of AdaBoost model-based training. It is associated with daal::algorithms::adaboost::training::Batch class and supports method to train AdaBoost model |
CResult | Provides methods to access final results obtained with the compute() method of the AdaBoost training algorithm in the batch processing mode |
►Nassociation_rules | Contains classes for the association rules algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the association rules algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the association rules algorithm. This class is associated with daal::algorithms::association_rules::Batch class |
CInput | Input for the association rules algorithm |
CParameter | Parameters for the association rules compute() method |
CResult | Results obtained with the compute() method of the association rules algorithm in the batch processing mode |
►Nbacon_outlier_detection | Contains classes for computing the BACON outlier detection |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Abstract class that specifies interface of the algorithms for computing BACON outlier detection in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the BACON outlier detection algorithm. This class is associated with daal::algorithms::bacon_outlier_detection::Batch class and supports the methods of the BACON outlier detection in the batch processing mode |
CInput | Input objects for the BACON outlier detection algorithm |
CParameter | Parameters of the outlier detection computation using the baconDense method |
CResult | Results obtained with the compute() method of the BACON outlier detection algorithm in the batch processing mode |
►Nboosting | Contains classes of boosting classification algorithms |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for boosting algorithm models. Contains a collection of weak learner models constructed during training of the boosting algorithm |
CParameter | Base class for parameters of the boosting algorithm |
►Nprediction | Contains classes for prediction based on boosting models |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Base class for predicting results of boosting classifiers |
►Ntraining | Contains classes for training boosting models |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Base class for training models of boosting algorithms in the batch processing mode |
►Nbrownboost | Contains classes for the BrownBoost classification algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the brownboost::training::Batch algorithm |
CParameter | BrownBoost algorithm parameters |
►Nprediction | Contains classes for prediction based on BrownBoost models |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts BrownBoost classification results |
CBatchContainer | Provides methods to run implementations of the BrownBoost algorithm. This class is associated with daal::algorithms::brownboost::prediction::interface1::Batch class |
CInput | Input objects in the prediction stage of the brownboost algorithm |
►Nquality_metric_set | Contains classes for checking the quality of the model trained with the BrownBoost algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a set of quality metrics to check the model trained with the BrownBoost algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
►Ntraining | Contains classes for BrownBoost models training |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains model of the BrownBoost algorithms in the batch processing mode |
CBatchContainer | Provides methods to run implementations of BrownBoost model-based training. This class is associated with daal::algorithms::brownboost::training::Batch class |
CResult | Provides methods to access final results obtained with the compute() method of the BrownBoost training algorithm in the batch processing mode |
►Ncholesky | Contains classes for computing Cholesky decomposition |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes Cholesky decomposition in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the Cholesky decomposition algorithm. This class is associated with daal::algorithms::cholesky::Batch class |
CInput | Input parameters for the Cholesky algorithm |
CResult | Results obtained with the compute() method of the Cholesky algorithm in the batch processing mode |
►Nclassifier | Contains classes for working with classifiers |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for the model of the classification algorithm |
CParameter | Base class for the parameters of the classification algorithm |
CTreeNodeVisitor | Interface of abstract visitor used in tree traversal methods |
►Nprediction | Contains classes for making prediction based on the classifier model |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Base class for making predictions based on the model of the classification algorithms |
CInput | Input objects in the prediction stage of the classification algorithm |
CInputIface | Base class for working with input objects in the prediction stage of the classification algorithm |
CResult | Provides methods to access prediction results obtained with the compute() method of the classifier prediction algorithm in the batch processing mode |
►Nquality_metric | Contains classes for checking the quality of the classification algorithms |
►Nbinary_confusion_matrix | Contains classes for computing the binary confusion matrix |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the confusion matrix for a binary classifier in the batch processing mode |
CBatchContainer | Class containing methods to compute the confusion matrix for a binary classifier |
CInput | Base class for input objects of the binary confusion matrix algorithm |
CParameter | Parameters for the binary confusion matrix compute() method |
CResult | Results obtained with the compute() method of the binary confusion matrix algorithm in the batch processing mode |
►Nmulticlass_confusion_matrix | Contains classes for computing the multi-class confusion matrix |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the confusion matrix for a multi-class classifier in the batch processing mode |
CBatchContainer | Class containing methods to compute the confusion matrix for the multi-class classifier |
CInput | Base class for the input objects of the confusion matrix algorithm in the training stage of the classification algorithm |
CParameter | Parameters for the compute() method of the multi-class confusion matrix |
CResult | Results obtained with the compute() method of the multi-class confusion matrix algorithm in the batch processing mode |
►Ntraining | Contains classes for training the model of the classification algorithms |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class for training the classifier model |
CInput | Base class for the input objects in the training stage of the classification algorithms |
CInputIface | Abstract class that specifies the interface of the classes of the classification algorithm input objects |
COnline | Algorithm class for training the classifier model in the online processing mode |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the classifier training algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method in the batch processing mode or finalizeCompute() method in the online or distributed processing mode of the classification algorithm |
►Ncorrelation_distance | Contains classes for computing the correlation distance |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the correlation distance in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the correlation distance algorithm. This class is associated with daal::algorithms::correlation_distance::Batch class |
CInput | Input objects for the correlation distance algorithm |
CResult | Results obtained with compute() method of the correlation distance algorithm in the batch processing mode |
►Ncosine_distance | Contains classes for computing the cosine distance |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the cosine distance in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the cosine distance algorithm. This class is associated with daal::algorithms::cosine_distance::Batch class |
CInput | Input objects for the cosine distance algorithm |
CResult | Results obtained with the compute() method of the cosine distance algorithm in the batch processing mode |
►Ncovariance | Contains classes for computing the correlation or variance-covariance matrix |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes correlation or variance-covariance matrix in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using default computation method This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, fastCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using fast computation method that works with Compressed Sparse Rows (CSR) numeric tables This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, singlePassCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using single-pass computation method that works with Compressed Sparse Rows (CSR) numeric tables This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, singlePassDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using single-pass computation method This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, sumCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using precomputed sum computation method that works with Compressed Sparse Rows (CSR) numeric tables This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainer< algorithmFPType, sumDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using precomputed sum computation method This class is associated with daal::algorithms::covariance::Batch class |
CBatchContainerIface | Class that specifies interfaces of implementations of the correlation or variance-covariance matrix container. This class is associated with daal::algorithms::covariance::BatchContainerIface class |
CBatchImpl | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the batch processing mode |
CDistributed | Computes correlation or variance-covariance matrix in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Computes correlation or variance-covariance matrix in the first step of the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Computes correlation or variance-covariance matrix in the second step of the distributed processing mode |
CDistributedContainer | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm in the distributed processing mode. This class is associated with daal::algorithms::covariance::Distributed class |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm in the distributed processing mode on master node |
CDistributedContainerIface | Class that spcifies interfaces of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::DistributedIface class |
CDistributedContainerIface< step2Master > | Class that spcifies interfaces of the correlation or variance-covariance matrix algorithm on master node. This class is associated with daal::algorithms::covariance::DistributedIface class |
CDistributedIface | Interface for the correlation or variance-covariance matrix algorithm in the distributed processing mode |
CDistributedIface< step1Local > | Interface for correlation or variance-covariance matrix computation algorithms in the distributed processing mode on local nodes |
CDistributedIface< step2Master > | Interface for correlation or variance-covariance matrix computation algorithms in the distributed processing mode on master node |
CDistributedInput | Input parameters of the distributed Covariance algorithm |
CDistributedInput< step1Local > | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on local node |
CDistributedInput< step2Master > | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on master node |
CInput | Input objects of the correlation or variance-covariance matrix algorithm |
CInputIface | Abstract class that specifies interface for classes that declare input of the correlation or variance-covariance matrix algorithm |
COnline | Computes correlation or variance-covariance matrix in the online processing mode |
COnlineContainer | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using default computation method. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, fastCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using fast computation method that works with Compressed Sparse Rows (CSR) numeric tables. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, singlePassCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using single-pass computation method that works with Compressed Sparse Rows (CSR) numeric tables. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, singlePassDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using single-pass computation method. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, sumCSR, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using precomputed sum computation method that works with Compressed Sparse Rows (CSR) numeric tables. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainer< algorithmFPType, sumDense, cpu > | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm using sum computation method. This class is associated with daal::algorithms::covariance::Online class |
COnlineContainerIface | Class that spcifies interfaces of implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::OnlineImpl class |
COnlineImpl | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the online processing mode |
COnlineParameter | Parameters of the correlation or variance-covariance matrix algorithm in the online processing mode |
CParameter | Parameters of the correlation or variance-covariance matrix algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the batch processing mode |
►Ndecision_forest | Contains classes of the decision forest algorithm |
►Nclassification | Contains classes for the decision_forest classification algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the decision_forest::training::Batch algorithm |
►Nprediction | Contains classes for prediction based on decision forest models |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts decision_forest classification results |
CBatchContainer | Provides methods to run implementations of the decision_forest algorithm. This class is associated with daal::algorithms::decision_forest::prediction::interface1::Batch class and supports method to compute decision_forest prediction |
CInput | Input objects in the prediction stage of the DECISION_FOREST_CLASSIFICATION algorithm |
►Ntraining | Contains classes for Decision forest models training |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains model of the Decision forest algorithms in the batch processing mode |
CBatchContainer | Provides methods to run implementations of Decision forest model-based training. This class is associated with daal::algorithms::decision_forest::classification::training::Batch class |
CParameter | Decision forest algorithm parameters |
CResult | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
►Nregression | Contains classes for decision forest regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the decision forest regression algorithm |
►Nprediction | Contains a class for making decision forest model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the decision forest model-based prediction |
CBatchContainer | Class containing computation methods for decision forest model-based prediction |
CInput | Provides an interface for input objects for making decision forest model-based prediction |
CResult | Provides interface for the result of decision forest model-based prediction |
►Ntraining | Contains a class for decision forest model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for decision forest model-based training in the batch processing mode |
CBatchContainer | Class containing methods for decision forest regression model-based training using algorithmFPType precision arithmetic |
CInput | Input objects for decision forest model-based training |
CParameter | Parameters for the decision forest algorithm |
CResult | Provides methods to access the result obtained with the compute() method of decision forest model-based training |
►Ntraining | Contains a class for decision forest model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CParameter | Parameters for the decision forest algorithm |
►Ndecision_tree | Contains classes for Decision tree algorithm |
►Nclassification | Contains classes for Decision tree classification algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the Decision tree algorithm |
CParameter | Decision tree algorithm parameters |
►Nprediction | Contains a class for making Decision tree model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the Decision tree model-based prediction |
CBatchContainer | Class containing computation methods for Decision tree model-based prediction |
CInput | Provides an interface for input objects for making Decision tree model-based prediction |
►Ntraining | Contains a class for Decision tree model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for Decision tree model-based training in the batch processing mode |
CBatchContainer | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
CInput | Base class for the input objects in the training stage of the classification algorithms |
CResult | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
►Nregression | Contains classes for decision tree regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the Decision tree algorithm |
CParameter | Decision tree algorithm parameters |
►Nprediction | Contains a class for making Decision tree model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the Decision tree model-based prediction |
CBatchContainer | Class containing computation methods for Decision tree model-based prediction |
CInput | Provides an interface for input objects for making Decision tree model-based prediction |
CResult | Provides interface for the result of decision tree model-based prediction |
►Ntraining | Contains a class for Decision tree model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for Decision tree model-based training in the batch processing mode |
CBatchContainer | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
CInput | Base class for the input objects in the training stage of the regression algorithms |
CResult | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
►Ndistributions | Contains classes for distributions |
►Nbernoulli | Contains classes for bernoulli distribution |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for bernoulli distribution computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the bernoulli distribution. This class is associated with the bernoulli::Batch class and supports the method of bernoulli distribution computation in the batch processing mode |
CParameter | Bernoulli distribution parameters |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatchBase | Class representing distributions |
CInput | Input objects for distributions |
CParameterBase | |
CResult | Provides methods to access the result obtained with the compute() method of the distribution |
►Nnormal | Contains classes for normal distribution |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for normal distribution computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the normal distribution. This class is associated with the normal::Batch class and supports the method of normal distribution computation in the batch processing mode |
CParameter | Normal distribution parameters |
►Nuniform | Contains classes for uniform distribution |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for uniform distribution computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the uniform distribution. This class is associated with the uniform::Batch class and supports the method of uniform distribution computation in the batch processing mode |
CParameter | Uniform distribution parameters |
►Nem_gmm | Contains classes for the EM for GMM algorithm |
►Ninit | Contains classes for the EM for GMM initialization algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes initial values for the EM for GMM algorithm in the batch processing mode |
CBatchContainer | Provides methods to compute initial values for the EM for GMM algorithm. The class is associated with the daal::algorithms::em_gmm::init::Batch class |
CInput | Input objects for the computation of initial values for the EM for GMM algorithm |
CParameter | Parameter for the computation of initial values for the EM for GMM algorithm |
CResult | Results obtained with the compute() method of the initialization of the EM for GMM algorithm in the batch processing mode |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes EM for GMM in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the EM for GMM algorithm. This class is associated with the Batch class and supports the method of computing EM for GMM in the batch processing mode |
CInput | Input objects for the EM for GMM algorithm |
CParameter | Parameter for the EM for GMM algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the EM for GMM algorithm in the batch processing mode |
►Nengines | Contains classes for engines |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatchBase | Class representing an engine |
CInput | Input objects for engines |
CResult | Provides methods to access the result obtained with the compute() method of the engine |
►Nmt19937 | Contains classes for mt19937 engine |
►Ninterface1 | |
CBatch | Provides methods for mt19937 engine computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the mt19937 engine. This class is associated with the mt19937::Batch class and supports the method of mt19937 engine computation in the batch processing mode |
►Ngbt | Contains classes of the gradient boosted trees algorithm |
►Nclassification | Contains classes for the gbt classification algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the gbt::training::Batch algorithm |
►Nprediction | Contains classes for prediction based on models |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts gradient boosted trees classification results |
CBatchContainer | Provides methods to run implementations of the gradient boosted trees algorithm. This class is associated with daal::algorithms::gbt::prediction::interface1::Batch class and supports method to compute gbt prediction |
CInput | Input objects in the prediction stage of the GBT_CLASSIFICATION algorithm |
CParameter | Parameters of the prediction algorithm |
►Ntraining | Contains classes for Gradient Boosted Trees models training |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains model of the Gradient Boosted Trees algorithms in the batch processing mode |
CBatchContainer | Provides methods to run implementations of Gradient Boosted Trees model-based training. This class is associated with daal::algorithms::gbt::classification::training::Batch class |
CParameter | Gradient Boosted Trees algorithm parameters |
CResult | Provides methods to access the result obtained with the compute() method of model-based training |
►Nregression | Contains classes for gradient boosted trees regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the gradient boosted trees regression algorithm |
►Nprediction | Contains a class for making model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the model-based prediction |
CBatchContainer | Class containing computation methods for model-based prediction |
CInput | Provides an interface for input objects for making model-based prediction |
CParameter | Parameters of the prediction algorithm |
CResult | Provides interface for the result of model-based prediction |
►Ntraining | Contains a class for model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for model-based training in the batch processing mode |
CBatchContainer | Class containing methods for gradient boosted trees regression model-based training using algorithmFPType precision arithmetic |
CInput | Input objects for model-based training |
CParameter | Parameters for the gradient boosted trees algorithm |
CResult | Provides methods to access the result obtained with the compute() method of model-based training |
►Ntraining | Contains a class for model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CParameter | Parameters for the gradient boosted trees algorithm |
►Nimplicit_als | Contains classes of the implicit ALS algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model trained by the implicit ALS algorithm in the batch processing mode |
CParameter | Parameters for the compute() method of the implicit ALS algorithm |
CPartialModel | Partial model trained by the implicit ALS training algorithm in the distributed processing mode |
►Nprediction | Contains classes for making implicit ALS model-based prediction |
►Nratings | Contains classes for computing ratings based on the implicit ALS model |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts the results of the implicit ALS algorithm |
CBatchContainer | Provides methods to run implementations of the implicit ALS ratings prediction algorithm in the batch processing mode |
CDistributed | Runs implicit ALS model-based prediction in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Performs implicit ALS model-based prediction in the first step of the distributed processing mode |
CDistributedContainer | Class that contains methods to run implicit ALS model-based prediction in the distributed processing mode |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class that contains methods to run implicit ALS model-based prediction in the first step of the distributed processing mode |
CDistributedInput | Input objects for the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
CDistributedInput< step1Local > | Input objects for the first step of the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
CInput | Input objects for the rating prediction stage of the implicit ALS algorithm |
CInputIface | Input interface for the rating prediction stage of the implicit ALS algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm in the rating prediction stage |
CResult | Provides methods to access the prediction results obtained with the compute() method of the implicit ALS algorithm in the batch processing mode |
►Ntraining | Contains classes of the implicit ALS training algorithm |
►Ninit | Contains classes for the implicit ALS initialization algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class for initializing the implicit ALS model |
CBatchContainer | Provides methods to run implementations of the implicit ALS initialization algorithm |
CDistributed | Initializes the implicit ALS model in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Initializes the implicit ALS model in the first step of the distributed processing mode |
CDistributed< step2Local, algorithmFPType, method > | Initializes the implicit ALS model in the second step of the distributed processing mode |
CDistributedContainer | Class containing methods to compute the results of the implicit ALS initialization algorithm in the distributed processing mode |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the first step of the distributed processing mode |
CDistributedContainer< step2Local, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the third step of the distributed processing mode |
CDistributedInput | Input objects for the implicit ALS initialization algorithm in the distributed processing mode |
CDistributedInput< step1Local > | Input objects for the implicit ALS initialization algorithm in the first step of the distributed processing mode |
CDistributedInput< step2Local > | Input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode |
CDistributedParameter | Parameters of the compute() method of the implicit ALS initialization algorithm in the distributed computing mode |
CDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
CInput | Input objects for the implicit ALS initialization algorithm |
CParameter | Parameters of the compute() method of the implicit ALS initialization algorithm |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
CPartialResultBase | Provides interface to access partial results obtained with the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode |
CResult | Provides methods to access the results obtained with the compute() method of the implicit ALS initialization algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class for training the implicit ALS model |
CBatchContainer | Provides methods to run implementations of implicit ALS model-based training |
CDistributed | Trains the implicit ALS model in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Trains the implicit ALS model in the first step of the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Trains the implicit ALS model in the second step of the distributed processing mode |
CDistributed< step3Local, algorithmFPType, method > | Trains the implicit ALS model in the third step of the distributed processing mode |
CDistributed< step4Local, algorithmFPType, method > | Trains the implicit ALS model in the fourth step of the distributed processing mode |
CDistributedContainer | Class containing methods to compute the result of implicit ALS model-based training in the distributed processing mode |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the first step of the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the second step of the distributed processing mode |
CDistributedContainer< step3Local, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the third step of the distributed processing mode |
CDistributedContainer< step4Local, algorithmFPType, method, cpu > | Class containing methods to train the implicit ALS model in the fourth step of the distributed processing mode |
CDistributedInput | Input objects for the implicit ALS training algorithm in the distributed processing mode |
CDistributedInput< step1Local > | Input objects for the implicit ALS training algorithm in the first step of the distributed processing mode |
CDistributedInput< step2Master > | Input objects for the implicit ALS training algorithm in the second step of the distributed processing mode |
CDistributedInput< step3Local > | Input objects for the implicit ALS training algorithm in the third step of the distributed processing mode |
CDistributedInput< step4Local > | Input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode |
CDistributedPartialResultStep1 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the first step of the distributed processing mode |
CDistributedPartialResultStep2 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the second step of the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the the third step of the distributed processing mode |
CDistributedPartialResultStep4 | Provides methods to access partial results obtained with the compute() method of the implicit ALS algorithm in the the fourth step of the distributed processing mode |
CInput | Input objects for the implicit ALS training algorithm |
CResult | Provides methods to access the results obtained with the compute() method of the implicit ALS training algorithm in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CAlgorithm | Implements the abstract interface AlgorithmIface. Algorithm is, in turn, the base class for the classes interfacing the major stages of data processing: Analysis, Training and Prediction |
CAlgorithm< batch > | Implements the abstract interface AlgorithmIface. Algorithm<batch> is, in turn, the base class for the classes interfacing the major stages of data processing in batch mode: Analysis<batch>, Training<batch> and Prediction |
CAlgorithmContainer | Abstract interface class that provides virtual methods to access and run implementations of the algorithms. It is associated with the Algorithm class and supports the methods for computation and finalization of the algorithm results in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each algorithm |
CAlgorithmContainer< batch > | Abstract interface class that provides virtual methods to access and run implementations of the algorithms in batch mode. It is associated with the Algorithm<batch> class and supports the methods for computation of the algorithm results. The methods of the container are defined in derivative containers defined for each algorithm |
CAlgorithmContainerIface | Implements the abstract interface AlgorithmContainerIface. It is associated with the Algorithm class and supports the methods for computation and finalization of the algorithm results in the batch, distributed, and online modes |
CAlgorithmContainerIfaceImpl | Implements the abstract interface AlgorithmContainerIfaceImpl. It is associated with the Algorithm class and supports the methods for computation and finalization of the algorithm results in the batch, distributed, and online modes |
CAlgorithmContainerImpl | Abstract interface class that provides virtual methods to access and run implementations of the algorithms. It is associated with the Algorithm class and supports the methods for computation and finalization of the algorithm results in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each algorithm |
CAlgorithmContainerImpl< batch > | Abstract interface class that provides virtual methods to access and run implementations of the algorithms in batch mode. It is associated with the Algorithm<batch> class and supports the methods for computation of the algorithm results. The methods of the container are defined in derivative containers defined for each algorithm |
CAlgorithmDispatchContainer | Implements a container to dispatch algorithms to cpu-specific implementations |
CAlgorithmDispatchContainer< batch, sse2Container DAAL_KERNEL_AVX512_mic_ONLY(avx512_micContainer) > | Implements a container to dispatch batch processing algorithms to CPU-specific implementations |
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. AlgorithmIfaceImpl is, in turn, the base class for the classes interfacing the major compute modes: batch, online and distributed |
CAlgorithmImpl | Provides implementations of the compute and finalizeCompute methods of the Algorithm class. The methods of the class support different computation modes: batch, distributed and online(see ComputeMode) |
CAlgorithmImpl< batch > | Provides implementations of the compute and checkComputeParams methods of the Algorithm<batch> class |
CArgument | Base class to represent computation input and output arguments |
CInput | Base class to represent computation input arguments. Algorithm-specific input arguments are represented as derivative classes of the Input class |
CKernel | Base class to represent algorithm implementation |
CModel | The base class for the classes that represent the models, such as linear_regression::Model or svm::Model |
COptionalArgument | Base class to represent argument with serialization methods |
CParameter | Base class to represent computation parameters. Algorithm-specific parameters are represented as derivative classes of the Parameter class |
CPartialResult | Base class to represent partial results of the computation. Algorithm-specific partial results are represented as derivative classes of the PartialResult class |
CResult | Base class to represent final results of the computation. Algorithm-specific final results are represented as derivative classes of the Result class |
CSerializableArgument | Base class to represent argument with serialization methods |
CValidationMetricIface | |
►Nkdtree_knn_classification | Contains classes for KD-tree based kNN algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the KD-tree based kNN algorithm |
CParameter | KD-tree based kNN algorithm parameters |
►Nprediction | Contains a class for making KD-tree based kNN model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the KD-tree based kNN model-based prediction |
CBatchContainer | Class containing computation methods for KD-tree based kNN model-based prediction |
CInput | Provides an interface for input objects for making KD-tree based kNN model-based prediction |
►Ntraining | Contains a class for KD-tree based kNN model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for KD-tree based kNN model-based training in the batch processing mode |
CBatchContainer | Class containing methods for KD-tree based kNN model-based training using algorithmFPType precision arithmetic |
CResult | Provides methods to access the result obtained with the compute() method of KD-tree based kNN model-based training |
►Nkernel_function | Contains classes for computing kernel functions |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the kernel function algorithm |
CKernelIface | Abstract class that specifies the interface of the algorithms for computing kernel functions in the batch processing mode |
CParameterBase | Optional input objects for the kernel function algorithm |
CResult | Results obtained with the compute() method of the kernel function algorithm in the batch processing mode |
►Nlinear | Contains classes for computing linear kernel functions |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes a linear kernel function in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the linear kernel function algorithm. This class is associated with the Batch class and supports the method for computing linear kernel functions in the batch processing mode |
CInput | Input objects for the kernel function linear algorithm |
CParameter | Parameters for the linear kernel function k(X,Y) + b |
►Nrbf | Contains classes for computing the radial basis function (RBF) kernel |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the RBF kernel function in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the RBF kernel algorithm. This class is associated with the Batch class and supports the method for computing RBF kernel functions in the batch processing mode |
CInput | Input objects for the RBF kernel algorithm |
CParameter | Parameters for the radial basis function (RBF) kernel |
►Nkmeans | Contains classes of the K-Means algorithm |
►Ninit | Contains classes for computing initial centroids for the K-Means algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes initial clusters for the K-Means algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of initialization of the K-Means algorithm. This class is associated with the daal::algorithms::kmeans::init::Batch class and supports the method of computing initial clusters for the K-Means algorithm in the batch processing mode |
CDistributed< step1Local, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the first step of the distributed processing mode |
CDistributed< step2Local, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the 2nd step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on a local node |
CDistributed< step2Master, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the 2nd step of the distributed processing mode |
CDistributed< step3Master, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the 3rd step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on the master node |
CDistributed< step4Local, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the 4th step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on a local node |
CDistributed< step5Master, algorithmFPType, method > | Computes initial clusters for the K-Means algorithm in the 5th step of the distributed processing mode. Used with parallelPlus method only |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the first step of the distributed processing mode |
CDistributedContainer< step2Local, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the 2nd step of the distributed processing mode performed on a local node |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the 2nd step of the distributed processing mode |
CDistributedContainer< step3Master, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the 3rd step of the distributed processing mode performed on the master mode |
CDistributedContainer< step4Local, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the 4th step of the distributed processing mode performed on a local node |
CDistributedContainer< step5Master, algorithmFPType, method, cpu > | Class containing methods for computing initial clusters for the K-Means algorithm in the 5th step of the distributed processing mode performed on the master node |
CDistributedStep2LocalPlusPlusInput | Interface for the K-Means initialization distributed Input classes used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
CDistributedStep2LocalPlusPlusParameter | Parameters for computing initial centroids for the K-Means algorithm |
CDistributedStep2LocalPlusPlusPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the distributed processing mode |
CDistributedStep2MasterInput | Input objects for computing initials clusters for the K-Means algorithm in the second step of the distributed processing mode |
CDistributedStep3MasterPlusPlusInput | Interface for the K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
CDistributedStep3MasterPlusPlusPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the distributed processing mode |
CDistributedStep4LocalPlusPlusInput | Interface for the K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 4th step on a local node |
CDistributedStep4LocalPlusPlusPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the distributed processing mode |
CDistributedStep5MasterPlusPlusInput | Interface for the K-Means distributed Input classes |
CDistributedStep5MasterPlusPlusPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the distributed processing mode |
CInput | Input objects for computing initial centroids for the K-Means algorithm |
CInputIface | Interface for the K-Means initialization batch and distributed Input classes |
CParameter | Parameters for computing initial centroids for the K-Means algorithm |
CPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the batch processing mode |
CResult | Results obtained with the compute() method that computes initial centroids for the K-Means algorithm in the batch processing mode |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the K-Means algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the K-Means algorithm. This class is associated with the daal::algorithms::kmeans::Batch class and supports the method of K-Means computation in the batch processing mode |
CDistributed | Computes the results of the K-Means algorithm in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Computes the results of the K-Means algorithm in the first step of the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Computes the results of the K-Means algorithm in the second step of the distributed processing mode |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class containing computation methods for the K-Means algorithm in the first step of the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing computation methods for the K-Means algorithm in the second step of the distributed processing mode |
CDistributedStep2MasterInput | Input objects for the K-Means algorithm in the distributed processing mode |
CInput | Input objects for the K-Means algorithm |
CInputIface | Interface for input objects for the the K-Means algorithm in the batch and distributed processing modes |
CParameter | Parameters for the K-Means algorithm |
CPartialResult | Partial results obtained with the compute() method of the K-Means algorithm in the batch processing mode |
CResult | Results obtained with the compute() method of the K-Means algorithm in the batch processing mode |
►Nlinear_model | Contains classes of the regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the regression algorithm |
CParameter | Parameters for the regression algorithm |
►Nprediction | Contains a class for making the regression model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the regression model-based prediction |
CBatchContainer | Class containing computation methods for the regression model-based prediction |
CInput | Provides an interface for input objects for making the regression model-based prediction |
CResult | Provides interface for the result of the regression model-based prediction |
►Ntraining | Contains a class for regression model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for linear model model-based training in the batch processing mode |
CInput | Input objects for the regression model-based training |
COnline | Provides methods for the linear model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of the linear model-based training in the online processing mode |
CResult | Provides methods to access the result obtained with the compute() method of the regression model-based training |
►Nlinear_regression | Contains classes of the linear regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the linear regression algorithm |
CModelNormEq | Model trained with the linear regression algorithm using the normal equations method |
CModelQR | Model trained with the linear regression algorithm using the QR decomposition-based method |
CParameter | Parameters for the linear regression algorithm |
►Nprediction | Contains a class for making linear regression model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the linear regression model-based prediction |
CBatch< algorithmFPType, defaultDense > | Provides methods to run implementations of the linear regression model-based prediction |
CInput | Provides an interface for input objects for making linear regression model-based prediction |
CResult | Provides interface for the result of linear regression model-based prediction |
►Nquality_metric | Contains classes for computing linear regression quality metrics |
►Ngroup_of_betas | Contains classes for computing linear regression quality metrics for group of betas |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CBatchContainer | Class containing methods to compute regression quality metric |
CInput | Input objects for a group of betas quality metrics |
CParameter | Parameters for the compute() method of a group of betas quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nsingle_beta | Contains classes for computing linear regression quality metrics for single beta |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CBatchContainer | Class containing methods to compute regression quality metric |
CInput | Input objects for single beta quality metrics |
CParameter | Parameters for the compute() method of single beta quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nquality_metric_set | Contains classes to check the quality of the model trained with the linear regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a quality metric set to check the model trained with linear regression algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
CParameter | Parameters for the quality metrics set compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
►Ntraining | Contains a class for linear regression model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for linear regression model-based training in the batch processing mode |
CBatchContainer | Class containing methods for normal equations linear regression model-based training using algorithmFPType precision arithmetic |
CDistributed | Provides methods for linear regression model-based training in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Performs linear regression model-based training in the the first step of the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Performs linear regression model-based training in the the second step of distributed processing mode |
CDistributedContainer | Class containing methods for linear regression model-based training in the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods for linear regression model-based training in the second step of the distributed processing mode |
CDistributedInput | Input object for linear regression model-based training in the distributed processing mode |
CDistributedInput< step2Master > | Input object for linear regression model-based training in the second step of the distributed processing mode |
CInput | Input objects for linear regression model-based training |
CInputIface | Abstract class that specifies the interface of input objects for linear regression model-based training |
COnline | Provides methods for linear regression model-based training in the online processing mode |
COnlineContainer | Class containing methods for linear regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of linear regression model-based training in the online or distributed processing mode |
CResult | Provides methods to access the result obtained with the compute() method of linear regression model-based training |
►Nlogitboost | Contains classes for the LogitBoost classification algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the logitboost::training::Batch algorithm |
CParameter | LogitBoost algorithm parameters |
►Nprediction | Contains classes for prediction based on LogitBoost models |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts LogitBoost classification results |
CBatchContainer | Provides methods to run implementations of the LogitBoost algorithm. This class is associated with daal::algorithms::logitboost::prediction::interface1::Batch class and supports method to compute LogitBoost prediction |
CInput | Input objects in the prediction stage of the logitboost algorithm |
►Nquality_metric_set | Contains classes for checking the quality of the model trained with the LogitBoost algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a set of quality metrics to check the model trained with the LogitBoost training algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
CParameter | Parameters for the LogitBoost compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
►Ntraining | Contains classes for LogitBoost models training |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains model of the LogitBoost algorithms in the batch processing mode |
CBatchContainer | Provides methods to run implementations of LogitBoost model-based training. This class is associated with daal::algorithms::logitboost::training::Batch class |
CResult | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
►Nlow_order_moments | Contains classes for computing the results of the low order moments algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes moments of low order in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the low order moments algorithm. This class is associated with daal::algorithms::low_order_moments::Batch class |
CBatchContainerIface | Class that specifies interfaces of implementations of the low order moments algorithm |
CBatchImpl | Abstract class that specifies interface of the algorithms for computing moments of low order in the batch processing mode |
CDistributed | Computes moments of low order in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Computes the result of the first step of the moments of low order algorithm in the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Computes the result of the second step of the moments of low order algorithm in the distributed processing mode |
CDistributedContainer | Provides methods to run implementations of the low order moments algorithm in the distributed processing mode. This class is associated with daal::algorithms::low_order_moments::Distributed class |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Provides methods to run implementations of the second step of the low order moments algorithm in the distributed processing mode. This class is associated with daal::algorithms::low_order_moments::Distributed class |
CDistributedInput | Input objects for the low order moments algorithm in the distributed processing mode on master node |
CInput | Input objects for the low order moments algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the low order moments algorithm |
COnline | Computes moments of low order in the online processing mode |
COnlineContainer | Provides methods to run implementations of the low order moments algorithm. This class is associated with daal::algorithms::low_order_moments::Online class |
CParameter | Low order moments algorithm parameters |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the low order moments algorithm in the online or distributed processing mode |
CResult | Provides methods to access final results obtained with the compute() method of the low order moments algorithm in the batch processing mode ; or finalizeCompute() method of algorithm in the online or distributed processing mode |
►Nmath | Contains classes for computing math functions |
►Nabs | Contains classes for computing the absolute value function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the absolute value function in the batch processing mode |
CBatchContainer | Class containing methods for the absolute value function computing using algorithmFPType precision arithmetic |
CInput | Input objects for the absolute value function |
CResult | Result obtained with the compute() method of the absolute value function in the batch processing mode |
►Nlogistic | Contains classes for computing the logistic function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the logistic function in the batch processing mode |
CBatchContainer | Class containing methods for the logistic function computing using algorithmFPType precision arithmetic |
CInput | Input objects for the logistic function |
CResult | Results obtained with the compute() method of the logistic function in the batch processing mode |
►Nrelu | Contains classes for computing the rectified linear function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the rectified linear function in the batch processing mode |
CBatchContainer | Class containing methods for the rectified linear function computing using algorithmFPType precision arithmetic |
CInput | Input objects for the rectified linear function |
CResult | Results obtained with the compute() method of the rectified linear function in the batch processing mode |
►Nsmoothrelu | Contains classes for computing smooth rectified linear unit |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes SmoothReLU in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the SmoothReLU algorithm. This class is associated with daal::algorithms::math::smoothrelu::Batch class |
CInput | Input parameters for the SmoothReLU algorithm |
CResult | Results obtained with the compute() method of the SmoothReLU algorithm in the batch processing mode |
►Nsoftmax | Contains classes for computing the softmax function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the softmax function in the batch processing mode |
CBatchContainer | Class containing methods for the softmax function computing using algorithmFPType precision arithmetic |
CInput | Input objects for the softmax function |
CResult | Results obtained with the compute() method of the softmax function in the batch processing mode |
►Ntanh | Contains classes for computing the hyperbolic tangent function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the hyperbolic tangent function in the batch processing mode |
CBatchContainer | Class containing methods for the hyperbolic tangent function computing using algorithmFPType precision arithmetic |
CInput | Input objects for the hyperbolic tangent function |
CResult | Result obtained with the compute() method of the hyperbolic tangent function in the batch processing mode |
►Nmulti_class_classifier | Contains classes for computing the results of the multi-class classifier algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the multi_class_classifier::training::Batch algorithm |
CParameter | Optional multi-class classifier algorithm parameters that are used with the MultiClassClassifierWu prediction method |
CParameterBase | Parameters of the multi-class classifier algorithm |
►Nprediction | Contains classes for prediction based on multi-class classifier models |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the multi-class classifier prediction algorithm |
CBatchContainer | Provides methods to run implementations of the multi-class classifier prediction algorithm |
CInput | Input objects in the prediction stage of the Multi-class classifier algorithm |
►Nquality_metric_set | Contains classes for checking the quality of the model trained with the multi-class classifier algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class containing a set of quality metrics to check the model trained with the multi-class classifier algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the multi-class classifier training algorithm |
CParameter | Parameters for the multi-class classifier compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the multi-class classifier training algorithm |
►Ntraining | Contains classes for training the multi-class classifier model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm for the multi-class classifier model training |
CBatchContainer | Class containing methods to compute the results of multi-class classifier model-based training |
CResult | Provides methods to access final results obtained with the compute() method for the multi-class classifier algorithm in the batch processing mode; or finalizeCompute() method of the algorithm in the online or distributed processing mode |
►Nmultinomial_naive_bayes | Contains classes for multinomial Naive Bayes algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Multinomial naive Bayes model |
CParameter | Naive Bayes algorithm parameters |
CPartialModel | PartialModel represents partial multinomial naive Bayes model |
►Nprediction | Contains classes for multinomial naive Bayes model based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts the results of the multinomial naive Bayes classification |
CBatchContainer | Runs the prediction based on the multinomial naive Bayes model |
CInput | Input objects in the prediction stage of the multinomial naive Bayes algorithm |
►Nquality_metric_set | Contains classes for checking the quality of the model trained with the Naive Bayes algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class containing a quality metric set to check the model trained with the Naive Bayes algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects for the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
CParameter | Parameters for the Naive Bayes compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
►Ntraining | Contains classes for training the naive Bayes model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class for training the naive Bayes model |
CBatchContainer | Class containing methods to compute naive Bayes training results |
CDistributed | Algorithm class for training naive Bayes model in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Algorithm class for training Naive Bayes partial model in the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Algorithm class for training naive Bayes final model on the second step in the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods to train naive Bayes in the distributed processing mode |
CDistributedInput | Input objects of the naive Bayes training algorithm in the distributed processing mode |
COnline | Algorithm class for training naive Bayes model |
COnlineContainer | Class containing computation methods for naive Bayes training in the online 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 |
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 |
►Nmultivariate_outlier_detection | Contains classes for computing the multivariate outlier detection |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Abstract class that specifies interface of the algorithms for computing multivariate outlier detection in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the multivariate outlier detection algorithm. This class is associated with daal::algorithms::multivariate_outlier_detection::Batch class and supports the methods of the multivariate outlier detection in the batch processing mode |
CDefaultInit | Class that specifies the default method for the initialization procedure |
CInitIface | Abstract interface class that provides function for the initialization procedure |
CInput | Input objects for the multivariate outlier detection algorithm |
CParameter | |
CParameter< baconDense > | Parameters of the outlier detection computation using the baconDense method |
CParameter< defaultDense > | Parameters of the outlier detection computation using the defaultDense method |
CResult | Results obtained with the compute() method of the multivariate outlier detection algorithm in the batch processing mode |
►Nneural_networks | Contains classes for training and prediction using neural network |
►Ninitializers | Contains classes for neural network weights and biases initializers |
►Ngaussian | Contains classes for neural network weights and biases gaussian initializer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for gaussian initializer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the gaussian initializer. This class is associated with the gaussian::Batch class and supports the method of gaussian initializer computation in the batch processing mode |
CParameter | Gaussian initializer parameters |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInitializerContainerIface | Class that specifies interfaces of implementations of the neural network weights and biases initializer |
CInitializerIface | Class representing a neural network weights and biases initializer |
CInput | Input objects for initializer algorithm |
CParameter | |
CResult | Provides methods to access the result obtained with the compute() method of the neural network weights and biases initializer |
►Ntruncated_gaussian | Contains classes for neural network weights and biases truncated gaussian initializer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for truncated gaussian initializer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the truncated gaussian initializer. This class is associated with the truncated_gaussian::Batch class and supports the method of truncated gaussian initializer computation in the batch processing mode |
CParameter | Truncated gaussian initializer parameters |
►Nuniform | Contains classes for neural network weights and biases uniform initializer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for uniform initializer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the uniform initializer. This class is associated with the uniform::Batch class and supports the method of uniform initializer computation in the batch processing mode |
CParameter | Uniform initializer parameters |
►Nxavier | Contains classes for neural network weights and biases Xavier initializer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for Xavier initializer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the Xavier initializer. This class is associated with the xavier::Batch class and supports the method of Xavier initializer computation in the batch processing mode |
CParameter | Xavier initializer parameters |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CLearnableParametersIface | Learnable parameters for the prediction stage of neural network algorithm |
CModelImpl | Class Model object for the prediction stage of neural network algorithm |
►Nlayers | Contains classes for neural network layers |
►Nabs | Contains classes of the abs layer |
►Nbackward | Contains classes of the backward abs layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward abs layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward abs layer This class is associated with the daal::algorithms::neural_networks::layers::abs::backward::Batch class and supports the method of backward abs layer computation in the batch processing mode |
CInput | Input objects for the backward abs layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward abs layer |
►Nforward | Contains classes of the forward abs layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward abs layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward abs layer This class is associated with the daal::algorithms::neural_networks::layers::abs::forward::Batch class and supports the method of forward abs layer computation in the batch processing mode |
CInput | Input objects for the forward abs layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward abs layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the abs layer in the batch processing mode |
CParameter | Parameters for the abs layer |
►Naverage_pooling1d | Contains classes for average one-dimensional (1D) pooling layer |
►Nbackward | Contains classes for backward average 1D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward average 1D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward average 1D pooling layer. This class is associated with the backward::Batch class and supports the method of backward average 1D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward average 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 1D pooling layer |
►Nforward | Contains classes for forward average 1D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward average 1D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward average 1D pooling layer. This class is associated with the forward::Batch class and supports the method of forward average 1D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward average 1D pooling layer. See pooling1d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 1D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the average 1D pooling layer in the batch processing mode |
CParameter | Parameters for the average 1D pooling layer |
►Naverage_pooling2d | Contains classes for average two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward average 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward average 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward average 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward average 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 2D pooling layer |
►Nforward | Contains classes for forward average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward average 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward average 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward average 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward average 2D pooling layer. See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the average 2D pooling layer in the batch processing mode |
CParameter | Parameters for the average 2D pooling layer |
►Naverage_pooling3d | Contains classes for average three-dimensional (3D) pooling layer |
►Nbackward | Contains classes for backward average 3D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward average 3D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward average 3D pooling layer. This class is associated with the backward::Batch class and supports the method of backward average 3D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward average 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward average 3D pooling layer |
►Nforward | Contains classes for forward average 3D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward average 3D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward average 3D pooling layer. This class is associated with the forward::Batch class and supports the method of forward average 3D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward average 3D pooling layer. See pooling3d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward average 3D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the average 3D pooling layer in the batch processing mode |
CParameter | Parameters for the average 3D pooling layer |
►Nbackward | Contains classes for the backward stage of the neural network layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input parameters for the layer algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the neural network layer algorithm |
CLayerIface | Abstract class which defines interface for the layer |
CLayerIfaceImpl | Implements the abstract interface LayerIface. LayerIfaceImpl is, in turn, the base class for the classes interfacing the layers |
CResult | Provides methods to access the result obtained with the compute() method of the layer algorithm |
►Nbatch_normalization | Contains classes for batch normalization layer |
►Nbackward | Contains classes for the backward batch normalization layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward batch normalization layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward batch normalization layer. This class is associated with the backward::Batch class and supports the method of backward batch normalization layer computation in the batch processing mode |
CInput | Input objects for the backward batch normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward batch normalization layer |
►Nforward | Contains classes for forward batch normalization layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward batch normalization layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward batch normalization layer. This class is associated with the forward::Batch class and supports the method of forward batch normalization layer computation in the batch processing mode |
CInput | Input objects for the forward batch normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward batch normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the batch normalization layer in the batch processing mode |
CParameter | Parameters for the forward and backward batch normalization layers |
►Nconcat | Contains classes for the concat layer |
►Nbackward | Contains classes for the backward concat layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward concat layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward concat layer This class is associated with the daal::algorithms::neural_networks::layers::concat::backward::Batch class and supports the method of backward concat layer computation in the batch processing mode |
CInput | Input parameters for the backward concat layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward concat layer |
►Nforward | Contains classes for the forward concat layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward concat layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward concat layer This class is associated with the daal::algorithms::neural_networks::layers::concat::forward::Batch class and supports the method of forward concat layer computation in the batch processing mode |
CInput | Input objects for the forward concat layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward concat layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the concat layer in the batch processing mode |
CParameter | Concat layer parameters |
►Nconvolution2d | Contains classes for neural network 2D convolution layer |
►Nbackward | Contains classes for the backward 2D convolution layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward 2D convolution layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward 2D convolution layer This class is associated with the daal::algorithms::neural_networks::layers::convolution2d::backward::Batch class and supports the method of backward 2D convolution layer computation in the batch processing mode |
CInput | Input objects for the backward 2D convolution layer |
CResult | Results obtained with the compute() method of the backward 2D convolution layer |
►Nforward | Contains classes for the forward 2D convolution layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for forward 2D convolution layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward 2D convolution layer. This class is associated with the daal::algorithms::neural_networks::layers::convolution2d::forward::Batch class and supports the method of forward 2D convolution layer computation in the batch processing mode |
CInput | Input objects for the forward 2D convolution layer |
CResult | Results obtained with the compute() method of the forward 2D convolution layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward 2D convolution layer of neural network in the batch processing mode |
CParameter | 2D convolution layer parameters |
CIndices | Data structure representing the indices of the two dimensions on which 2D convolution is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D convolution is performed |
CStrides | Data structure representing the intervals on which the subtensors for 2D convolution are selected |
►Ndropout | Contains classes for dropout layer |
►Nbackward | Contains classes for the backward dropout layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward dropout layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward dropout layer This class is associated with the daal::algorithms::neural_networks::layers::dropout::backward::Batch class and supports the method of backward dropout layer computation in the batch processing mode |
CInput | Input objects for the backward dropout layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward dropout layer |
►Nforward | Contains classes for the forward dropout layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward dropout layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward dropout layer This class is associated with the daal::algorithms::neural_networks::layers::dropout::forward::Batch class and supports the method of forward dropout layer computation in the batch processing mode |
CInput | Input objects for the forward dropout layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward dropout layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the dropout layer in the batch processing mode |
CParameter | Parameters for the dropout layer |
►Neltwise_sum | Contains classes for neural network element-wise sum layer |
►Nbackward | Contains classes for backward element-wise sum layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward element-wise sum layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward element-wise sum layer This class is associated with the daal::algorithms::neural_networks::layers::eltwise_sum::backward::Batch class and supports the method of backward element-wise sum layer computation in the batch processing mode |
CInput | Input objects for the backward element-wise sum layer |
CResult | Results obtained with the compute() method of the backward element-wise sum layer |
►Nforward | Contains classes for the forward element-wise sum layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward element-wise sum layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward element-wise sum layer. This class is associated with the daal::algorithms::neural_networks::layers::eltwise_sum::forward::Batch class and supports the method of forward element-wise sum layer computation in the batch processing mode |
CInput | Input objects for the forward element-wise sum layer |
CResult | Results obtained with the compute() method of the forward element-wise sum layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward element-wise sum layer of neural network in the batch processing mode |
CParameter | Parameters for the element-wise sum layer |
►Nforward | Contains classes for the forward stage of the neural network layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CAlgorithmDispatchLayerContainer | Implements a container to dispatch forward layers to cpu-specific implementations |
CInput | Input objects for layer algorithm |
CInputIface | Abstract class that specifies interface of the input objects for the neural network layer algorithm |
CLayerContainerIfaceImpl | Provides methods of base container for forward layers. This class is associated with the daal::algorithms::neural_networks::layers::forward::LayerContainerIfaceImpl class |
CLayerDescriptor | Class defining descriptor for layer on forward stage |
CLayerIface | Abstract class which defines interface for the layer |
CLayerIfaceImpl | Implements the abstract interface LayerIface. LayerIfaceImpl is, in turn, the base class for the classes interfacing the layers |
CResult | Provides methods to access the result obtained with the compute() method of the layer algorithm |
►Nfullyconnected | Contains classes for neural network fully-connected layer |
►Nbackward | Contains classes for backward fully-connected layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward fully-connected layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward fully-connected layer This class is associated with the daal::algorithms::neural_networks::layers::fullyconnected::backward::Batch class and supports the method of backward fully-connected layer computation in the batch processing mode |
CInput | Input objects for the backward fully-connected layer |
CResult | Results obtained with the compute() method of the backward fully-connected layer |
►Nforward | Contains classes for the forward fully-connected layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for forward fully-connected layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward fully-connected layer. This class is associated with the daal::algorithms::neural_networks::layers::fullyconnected::forward::Batch class and supports the method of forward fully-connected layer computation in the batch processing mode |
CInput | Input objects for the forward fully-connected layer |
CResult | Results obtained with the compute() method of the forward fully-connected layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward fully-connected layer of neural network in the batch processing mode |
CParameter | Fully-connected layer parameters |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CLayerDescriptor | Class defining descriptor for layer on both forward and backward stages and its parameters |
CLayerIface | Abstract class that specifies the interface of layer |
CNextLayers | Contains list of layer indices of layers following the current layer |
CParameter | |
►Nlcn | Contains classes for neural network local contrast normalization layer |
►Nbackward | Contains classes for the backward local contrast normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward local contrast normalization layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward local contrast normalization layer This class is associated with the daal::algorithms::neural_networks::layers::lcn::backward::Batch class and supports the method of backward local contrast normalization layer computation in the batch processing mode |
CInput | Input objects for the backward local contrast normalization layer |
CResult | Results obtained with the compute() method of the backward local contrast normalization layer |
►Nforward | Contains classes for the forward local contrast normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for forward local contrast normalization layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward local contrast normalization layer. This class is associated with the daal::algorithms::neural_networks::layers::lcn::forward::Batch class and supports the method of forward local contrast normalization layer computation in the batch processing mode |
CInput | Input objects for the forward local contrast normalization layer |
CResult | Results obtained with the compute() method of the forward local contrast normalization layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward local contrast normalization layer of neural network in the batch processing mode |
CParameter | Local contrast normalization layer parameters |
CIndices | Data structure representing the indices of the two dimensions on which local contrast normalization is performed |
►Nlocallyconnected2d | Contains classes for neural network 2D locally connected layer |
►Nbackward | Contains classes for the backward 2D locally connected layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward 2D locally connected layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward 2D locally connected layer This class is associated with the daal::algorithms::neural_networks::layers::locallyconnected2d::backward::Batch class and supports the method of backward 2D locally connected layer computation in the batch processing mode |
CInput | Input objects for the backward 2D locally connected layer |
CResult | Results obtained with the compute() method of the backward 2D locally connected layer |
►Nforward | Contains classes for the forward 2D locally connected layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for forward 2D locally connected layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward 2D locally connected layer. This class is associated with the daal::algorithms::neural_networks::layers::locallyconnected2d::forward::Batch class and supports the method of forward 2D locally connected layer computation in the batch processing mode |
CInput | Input objects for the forward 2D locally connected layer |
CResult | Results obtained with the compute() method of the forward 2D locally connected layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward 2D locally connected layer of neural network in the batch processing mode |
CParameter | 2D locally connected layer parameters |
CIndices | Data structure representing the indices of the two dimensions on which 2D locally connected is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D locally connected is performed |
CStrides | Data structure representing the intervals on which the subtensors for 2D locally connected are selected |
►Nlogistic | Contains classes for the logistic layer |
►Nbackward | Contains classes for the backward logistic layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward logistic layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward logistic layer This class is associated with the daal::algorithms::neural_networks::layers::logistic::backward::Batch class and supports the method of backward logistic layer computation in the batch processing mode |
CInput | Input objects for the backward logistic layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward logistic layer |
►Nforward | Contains classes for the forward logistic layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward logistic layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward logistic layer This class is associated with the daal::algorithms::neural_networks::layers::logistic::forward::Batch class and supports the method of forward logistic layer computation in the batch processing mode |
CInput | Input objects for the forward logistic layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward logistic layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the logistic layer in the batch processing mode |
CParameter | Parameters for the logistic layer |
►Nloss | Contains classes for loss layer |
►Nbackward | Contains classes for the backward loss layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward loss layer in the batch processing mode |
CInput | Input objects for the backward loss layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward loss layer |
►Nforward | Contains classes for the forward loss layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward loss layer in the batch processing mode |
CInput | Input objects for the forward loss layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward loss layer |
►Ninterface1 | |
CBatch | Provides methods for the loss layer in the batch processing mode |
►Nlogistic_cross | Contains classes for logistic cross-entropy layer |
►Nbackward | Contains classes for the backward logistic cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward logistic cross-entropy layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward logistic cross-entropy layer This class is associated with the daal::algorithms::neural_networks::layers::loss::logistic_cross::backward::Batch class and supports the method of backward logistic cross-entropy layer computation in the batch processing mode |
CInput | Input objects for the backward logistic cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward logistic cross-entropy layer |
►Nforward | Contains classes for the forward logistic cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward logistic cross layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward logistic cross-entropy layer This class is associated with the daal::algorithms::neural_networks::layers::loss::logistic_cross::forward::Batch class and supports the method of forward logistic cross-entropy layer computation in the batch processing mode |
CInput | Input objects for the forward logistic cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward logistic cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the logistic cross-entropy layer in the batch processing mode |
CParameter | Parameters for the logistic cross-entropy layer |
►Nsoftmax_cross | Contains classes for softmax cross-entropy layer |
►Nbackward | Contains classes for the backward softmax cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward softmax cross-entropy layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward softmax cross-entropy layer This class is associated with the daal::algorithms::neural_networks::layers::loss::softmax_cross::backward::Batch class and supports the method of backward softmax cross-entropy layer computation in the batch processing mode |
CInput | Input objects for the backward softmax cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward softmax cross-entropy layer |
►Nforward | Contains classes for the forward softmax cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward softmax cross layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward softmax cross-entropy layer This class is associated with the daal::algorithms::neural_networks::layers::loss::softmax_cross::forward::Batch class and supports the method of forward softmax cross-entropy layer computation in the batch processing mode |
CInput | Input objects for the forward softmax cross-entropy layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward softmax cross-entropy layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the softmax cross-entropy layer in the batch processing mode |
CParameter | Parameters for the softmax cross-entropy layer |
►Nlrn | Contains classes for local response normalization layer |
►Nbackward | Contains classes for the backward local response normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward local response normalization layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward local response normalization layer This class is associated with the daal::algorithms::neural_networks::layers::lrn::backward::Batch class and supports the method of backward local response normalization layer computation in the batch processing mode |
CInput | Input parameters for the backward local response normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward local response normalization layer |
►Nforward | Contains classes for the forward local response normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward local response normalization layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward local response normalization layer This class is associated with the daal::algorithms::neural_networks::layers::lrn::forward::Batch class and supports the method of forward local response normalization layer computation in the batch processing mode |
CInput | Input parameters for the forward local response normalization layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward local response normalization layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the local response normalization layer in the batch processing mode |
CParameter | Parameters for the local response normalization layer |
►Nmaximum_pooling1d | Contains classes for maximum one-dimensional (1D) pooling layer |
►Nbackward | Contains classes for backward maximum 1D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward maximum 1D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward maximum 1D pooling layer. This class is associated with the backward::Batch class and supports the method of backward maximum 1D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward maximum 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 1D pooling layer |
►Nforward | Contains classes for forward maximum 1D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward maximum 1D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward maximum 1D pooling layer. This class is associated with the forward::Batch class and supports the method of forward maximum 1D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward maximum 1D pooling layer See pooling1d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 1D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the maximum 1D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 1D pooling layer |
►Nmaximum_pooling2d | Contains classes for maximum two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward maximum 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward maximum 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward maximum 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward maximum 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 2D pooling layer |
►Nforward | Contains classes for forward maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward maximum 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward maximum 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward maximum 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward maximum 2D pooling layer See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the maximum 2D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 2D pooling layer |
►Nmaximum_pooling3d | Contains classes for maximum three-dimensional (3D) pooling layer |
►Nbackward | Contains classes for backward maximum 3D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward maximum 3D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward maximum 3D pooling layer. This class is associated with the backward::Batch class and supports the method of backward maximum 3D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward maximum 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward maximum 3D pooling layer |
►Nforward | Contains classes for forward maximum 3D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward maximum 3D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward maximum 3D pooling layer. This class is associated with the forward::Batch class and supports the method of forward maximum 3D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward maximum 3D pooling layer See pooling3d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward maximum 3D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the maximum 3D pooling layer in the batch processing mode |
CParameter | Parameters for the maximum 3D pooling layer |
►Npooling1d | Contains classes for the one-dimensional (1D) pooling layer |
►Nbackward | Contains classes for backward one-dimensional (1D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the backward 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 1D pooling layer |
►Nforward | Contains classes for the forward one-dimensional (1D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the forward 1D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 1D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CIndex | Data structure representing the indices of the dimension on which pooling is performed |
CKernelSize | Data structure representing the size of the 1D subtensor from which the element is computed |
CPadding | Data structure representing the number of data elements to implicitly add to each side of the 1D subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward pooling layers |
CStride | Data structure representing the intervals on which the subtensors for pooling are computed |
►Npooling2d | Contains classes for the two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward two-dimensional (2D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the backward 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 2D pooling layer |
►Nforward | Contains classes for the forward two-dimensional (2D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the forward 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CIndices | Data structure representing the indices of the two dimensions on which pooling is performed |
CKernelSizes | Data structure representing the size of the 2D subtensor from which the element is computed |
CPaddings | Data structure representing the number of data elements to implicitly add to each side of the 2D subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward two-dimensional pooling layers |
CStrides | Data structure representing the intervals on which the subtensors for pooling are computed |
►Npooling3d | Contains classes for the three-dimensional (3D) pooling layer |
►Nbackward | Contains classes for backward three-dimensional (3D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the backward 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 3D pooling layer |
►Nforward | Contains classes for the forward three-dimensional (3D) pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the forward 3D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 3D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CIndices | Data structure representing the indices of the three dimensions on which pooling is performed |
CKernelSizes | Data structure representing the size of the 3D subtensor from which the element is computed |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the three-dimensional subtensor on which pooling is performed |
CParameter | Parameters for the forward and backward pooling layers |
CStrides | Data structure representing the intervals on which the subtensors for pooling are computed |
►Nprelu | Contains classes for the prelu layer |
►Nbackward | Contains classes for the backward prelu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward prelu layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward prelu layer This class is associated with the daal::algorithms::neural_networks::layers::prelu::backward::Batch class and supports the method of backward prelu layer computation in the batch processing mode |
CInput | Input parameters for the backward prelu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward prelu layer |
►Nforward | Contains classes for the forward prelu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward prelu layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward prelu layer This class is associated with the daal::algorithms::neural_networks::layers::prelu::forward::Batch class and supports the method of forward prelu layer computation in the batch processing mode |
CInput | Input objects for the forward prelu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward prelu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the prelu layer in the batch processing mode |
CParameter | Parameters for the prelu layer |
►Nrelu | Contains classes for the relu layer |
►Nbackward | Contains classes for the backward relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward relu layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward relu layer This class is associated with the daal::algorithms::neural_networks::layers::relu::backward::Batch class and supports the method of backward relu layer computation in the batch processing mode |
CInput | Input objects for the backward relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward relu layer |
►Nforward | Contains classes for the forward relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward relu layer in the batch processing mode |
CBatchContainer | Class containing methods for the forward relu layer using algorithmFPType precision arithmetic |
CInput | Input objects for the forward relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the relu layer in the batch processing mode |
CParameter | Parameters for the relu layer |
►Nreshape | Contains classes of the reshape layer |
►Nbackward | Contains classes of the backward reshape layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward reshape layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward reshape layer This class is associated with the daal::algorithms::neural_networks::layers::reshape::backward::Batch class and supports the method of backward reshape layer computation in the batch processing mode |
CInput | Input objects for the backward reshape layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward reshape layer |
►Nforward | Contains classes of the forward reshape layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward reshape layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward reshape layer This class is associated with the daal::algorithms::neural_networks::layers::reshape::forward::Batch class and supports the method of forward reshape layer computation in the batch processing mode |
CInput | Input objects for the forward reshape layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward reshape layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the reshape layer in the batch processing mode |
CParameter | Parameters for the reshape layer |
►Nsmoothrelu | Contains classes for smooth relu layer |
►Nbackward | Contains classes for the backward smooth relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward smooth relu layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward smooth relu layer This class is associated with the daal::algorithms::neural_networks::layers::smoothrelu::backward::Batch class and supports the method of backward smooth relu layer computation in the batch processing mode |
CInput | Input objects for the backward smooth relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward smooth relu layer |
►Nforward | Contains classes for the forward smooth relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward smooth relu layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward smooth relu layer This class is associated with the daal::algorithms::neural_networks::layers::smoothrelu::forward::Batch class and supports the method of forward smooth relu layer computation in the batch processing mode |
CInput | Input objects for the forward smooth relu layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward smooth relu layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the smooth relu layer in the batch processing mode |
CParameter | Parameters for the smoothrelu layer |
►Nsoftmax | Contains classes of the softmax layer |
►Nbackward | Contains classes of the backward softmax layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward softmax layer in the batch processing mode |
CBatchContainer | Class containing methods for the backward softmax layer using algorithmFPType precision arithmetic |
CInput | Input objects for the backward softmax layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward softmax layer |
►Nforward | Contains classes of the forward softmax layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward softmax layer in the batch processing mode |
CBatchContainer | Class containing methods for the forward softmax layer using algorithmFPType precision arithmetic |
CInput | Input objects for the forward softmax layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward softmax layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the softmax layer in the batch processing mode |
CParameter | Parameters for the softmax layer |
►Nspatial_average_pooling2d | Contains classes for spatial pyramid average two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward spatial pyramid average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward spatial pyramid average 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the backward spatial pyramid average 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward spatial pyramid average 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward spatial pyramid average 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid average 2D pooling layer |
►Nforward | Contains classes for forward spatial pyramid average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward spatial pyramid average 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the forward spatial pyramid average 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward spatial pyramid average 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward spatial pyramid average 2D pooling layer See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid average 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the spatial pyramid average 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid average 2D pooling layer |
►Nspatial_maximum_pooling2d | Contains classes for spatial pyramid maximum two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward spatial pyramid maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward spatial pyramid maximum 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the backward spatial pyramid maximum 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward spatial pyramid maximum 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward spatial pyramid maximum 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid maximum 2D pooling layer |
►Nforward | Contains classes for forward spatial pyramid maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward spatial pyramid maximum 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the forward spatial pyramid maximum 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward spatial pyramid maximum 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward spatial pyramid maximum 2D pooling layer See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid maximum 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the spatial pyramid maximum 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid maximum 2D pooling layer |
►Nspatial_pooling2d | Contains classes for the two-dimensional (2D) spatial layer |
►Nbackward | Contains classes for backward two-dimensional (2D) spatial layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the backward 2D spatial layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward 2D spatial layer |
►Nforward | Contains classes for the forward two-dimensional (2D) spatial layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the forward 2D spatial layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward 2D spatial layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CIndices | Data structure representing the indices of the two dimensions on which pooling is performed |
CParameter | Parameters for the forward and backward two-dimensional spatial layers |
►Nspatial_stochastic_pooling2d | Contains classes for spatial pyramid stochastic two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward spatial pyramid stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the backward spatial pyramid stochastic 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward spatial pyramid stochastic 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward spatial pyramid stochastic 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid stochastic 2D pooling layer |
►Nforward | Contains classes for forward spatial pyramid stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CBatchContainer | |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the forward spatial pyramid stochastic 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward spatial pyramid stochastic 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward spatial pyramid stochastic 2D pooling layer See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the spatial pyramid stochastic 2D pooling layer in the batch processing mode |
CParameter | Parameters for the spatial pyramid stochastic 2D pooling layer |
►Nsplit | Contains classes for the split layer |
►Nbackward | Contains classes for the backward split layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward split layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward split layer This class is associated with the daal::algorithms::neural_networks::layers::split::backward::Batch class and supports the method of backward split layer computation in the batch processing mode |
CInput | Input parameters for the backward split layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward split layer |
►Nforward | Contains classes for the forward split layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward split layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward split layer This class is associated with the daal::algorithms::neural_networks::layers::split::forward::Batch class and supports the method of forward split layer computation in the batch processing mode |
CInput | Input objects for the forward split layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward split layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the split layer in the batch processing mode |
CParameter | Split layer parameters |
►Nstochastic_pooling2d | Contains classes for stochastic two-dimensional (2D) pooling layer |
►Nbackward | Contains classes for backward stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the backward stochastic 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the backward stochastic 2D pooling layer |
CBatchContainer< algorithmFPType, defaultDense, cpu > | Provides methods to run implementations of the backward stochastic 2D pooling layer. This class is associated with the backward::Batch class and supports the method of backward stochastic 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the backward stochastic 2D pooling layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward stochastic 2D pooling layer |
►Nforward | Contains classes for forward stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the forward stochastic 2D pooling layer in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward stochastic 2D pooling layer. This class is associated with the forward::Batch class and supports the method of forward stochastic 2D pooling layer computation in the batch processing mode |
CInput | Input objects for the forward stochastic 2D pooling layer See pooling2d::forward::Input |
CResult | Provides methods to access the result obtained with the compute() method of the forward stochastic 2D pooling layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the stochastic 2D pooling layer in the batch processing mode |
CParameter | Parameters for the stochastic 2D pooling layer |
►Ntanh | Contains classes for the hyperbolic tangent layer |
►Nbackward | Contains classes for the backward hyperbolic tangent layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the backward hyperbolic tangent in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward tanh layer This class is associated with the daal::algorithms::neural_networks::layers::tanh::backward::Batch class and supports the method of backward tanh layer computation in the batch processing mode |
CInput | Input objects for the backward hyperbolic tangent layer |
CResult | Provides methods to access the result obtained with the compute() method of the backward hyperbolic tangent layer |
►Nforward | Contains classes for the forward hyperbolic tangent layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the forward hyperbolic tangent in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the forward tanh layer This class is associated with the daal::algorithms::neural_networks::layers::tanh::forward::Batch class and supports the method of forward tanh layer computation in the batch processing mode |
CInput | Input objects for the forward hyperbolic tangent layer |
CResult | Provides methods to access the result obtained with the compute() method of the forward hyperbolic tangent layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the hyperbolic tangent layer in the batch processing mode |
CParameter | Parameters for the tanh layer |
►Ntransposed_conv2d | Contains classes for neural network 2D transposed convolution layer |
►Nbackward | Contains classes for the backward 2D transposed convolution layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for backward 2D transposed convolution layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the of the backward 2D transposed convolution layer This class is associated with the daal::algorithms::neural_networks::layers::transposed_conv2d::backward::Batch class and supports the method of backward 2D transposed convolution layer computation in the batch processing mode |
CInput | Input objects for the backward 2D transposed convolution layer |
CResult | Results obtained with the compute() method of the backward 2D transposed convolution layer |
►Nforward | Contains classes for the forward 2D transposed convolution layer |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for forward 2D transposed convolution layer computations in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the forward 2D transposed convolution layer. This class is associated with the daal::algorithms::neural_networks::layers::transposed_conv2d::forward::Batch class and supports the method of forward 2D transposed convolution layer computation in the batch processing mode |
CInput | Input objects for the forward 2D transposed convolution layer |
CResult | Results obtained with the compute() method of the forward 2D transposed convolution layer in the batch processing mode |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the result of the forward and backward 2D transposed convolution layer of neural network in the batch processing mode |
CParameter | 2D transposed convolution layer parameters |
CIndices | Data structure representing the indices of the two dimensions on which 2D transposed convolution is performed |
CKernelSizes | Data structure representing the size of the two-dimensional kernel subtensor |
CPaddings | Data structure representing the number of data elements to implicitly add to each size of the two-dimensional subtensor on which 2D transposed convolution is performed |
CStrides | Data structure representing the intervals on which the subtensors for 2D transposed convolution are selected |
CValueSizes | Data structure representing the value sizes of the two dimensions on which 2D transposed convolution is performed |
►Nprediction | Contains classes for making prediction based on the trained model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for neural network model-based prediction in the batch processing mode |
CBatchContainer | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
CInput | Input objects of the neural networks prediction algorithm |
CModel | Class Model object for the prediction stage of neural network algorithm |
CParameter | Class representing the parameters of neural network prediction |
CResult | Provides methods to access result obtained with the compute() method of the neural networks prediction algorithm |
CTopology | Class defining a neural network topology - a set of layers and connection between them - on the prediction stage |
►Ntraining | Contains classes for training the model of the neural network |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for neural network model-based training in the batch processing mode |
CBatchContainer | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
CDistributed | Provides methods for neural network model-based training in the batch processing mode |
CDistributed< step1Local, algorithmFPType, method > | Provides methods for neural network model-based training in the batch processing mode |
CDistributed< step2Master, algorithmFPType, method > | Provides methods for neural network model-based training in the batch processing mode |
CDistributedContainer | Class containing methods to train neural network model in the distributed processing mode using algorithmFPType precision arithmetic |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
CDistributedInput | Input objects of the neural network training algorithm in the distributed processing mode |
CDistributedInput< step1Local > | Input objects of the neural network training algorithm in the distributed processing mode |
CDistributedInput< step2Master > | Input objects of the neural network training algorithm |
CDistributedPartialResult | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
CInput | Input objects of the neural network training algorithm |
CModel | Class representing the model of neural network |
CParameter | Class representing the parameters of neural network |
CPartialResult | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
CResult | Provides methods to access result obtained with the compute() method of the neural network training algorithm |
CTopology | Class defining a neural network topology - a set of layers and connection between them - on the training stage |
►Nnormalization | Contains classes to run the min-max normalization algorithms |
►Nminmax | Contains classes for computing the min-max normalization |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Normalizes datasets in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the min-max normalization algorithm. It is associated with the daal::algorithms::normalization::minmax::Batch class and supports methods of min-max normalization computation in the batch processing mode |
CInput | Input objects for the min-max normalization algorithm |
CParameter | Class that specifies the parameters of the algorithm in the batch computing mode |
CParameterBase | Base class that specifies the parameters of the algorithm in the batch computing mode |
CResult | Provides methods to access final results obtained with the compute() method of the min-max normalization algorithm in the batch processing mode |
►Nzscore | Contains classes for computing the z-score normalization |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CInput | Input objects for the z-score normalization algorithm |
►Ninterface2 | Contains version 2.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBaseParameter | Class that specifies the base parameters of the algorithm in the batch computing mode |
CBatch | Normalizes datasets in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the z-score normalization algorithm. It is associated with the daal::algorithms::normalization::zscore::Batch class and supports methods of z-score normalization computation 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 |
CParameter | Class that specifies the parameters of the algorithm in the batch computing mode |
CParameter< algorithmFPType, defaultDense > | Class that specifies the parameters of the default algorithm in the batch computing mode |
CParameter< algorithmFPType, sumDense > | Class that specifies the parameters of the default algorithm in the batch computing mode |
CResult | Provides methods to access final results obtained with the compute() method of the z-score normalization algorithm in the batch processing mode |
►Noptimization_solver | Contains classes for optimization solver algorithms |
►Nadagrad | Contains classes for computing the Adaptive gradient descent |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes Adaptive gradient descent in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the adaptive gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::adagrad::BatchContainer class |
CInput | Input class for the Adaptive gradient descent algorithm |
CParameter | Parameter base class for the Adaptive gradient descent algorithm |
CResult | Results obtained with the compute() method of the adagrad algorithm in the batch processing mode |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatchIface | Interface for computing the Optimization solver in the batch processing mode |
►Niterative_solver | Contains classes for computing the iterative solver |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Interface for computing the iterative solver in the batch processing mode |
CInput | Input parameters for the iterative solver algorithm |
CParameter | Parameter base class for the iterative solver algorithm |
CResult | Results obtained with the compute() method of the iterative solver algorithm in the batch processing mode |
►Nlbfgs | Contains classes for computing the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes LBFGS in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the LBFGS algorithm. This class is associated with daal::algorithms::optimization_solver::lbfgs::Batch class |
CInput | Input class for LBFGS algorithm |
CParameter | Parameter class for LBFGS algorithm |
CResult | Results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
►Nmse | Contains classes for computing the Mean squared error objective function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the Mean squared error objective function in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the mean squared error objective function. This class is associated with the Batch class and supports the method of computing the Mean squared error objective function in the batch processing mode |
CInput | Input objects for the Mean squared error objective function |
CParameter | Parameter for Mean squared error objective function |
►Nobjective_function | Contains classes for computing the Objective function |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Interface for computing the Objective function in the batch processing mode |
CInput | Input objects for the Objective function |
CParameter | Parameter for the Objective function |
CResult | Provides methods to access final results obtained with the compute() method of the Objective function in the batch processing mode |
►Nprecomputed | Contains classes for the Objective function with precomputed characteristics |
►Ninterface1 | |
CBatch | Computes the objective function with precomputed characteristics in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the objective function with precomputed characteristics. This class is associated with the Batch class and supports the method of computing the objective function with precomputed characteristics in the batch processing mode |
►Nsgd | Contains classes for computing the Stochastic gradient descent |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBaseParameter | BaseParameter base class for the Stochastic gradient descent algorithm |
CBatch | Computes Stochastic gradient descent in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the stochastic gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::sgd::BatchContainer class |
CInput | Input for the Stochastic gradient descent algorithm |
CParameter | |
CParameter< defaultDense > | Parameter for the Stochastic gradient descent algorithm |
CParameter< miniBatch > | Parameter for the Stochastic gradient descent algorithm |
CParameter< momentum > | Parameter for the Stochastic gradient descent algorithm |
CResult | Results obtained with the compute() method of the sgd algorithm in the batch processing mode |
►Nsum_of_functions | Contains classes for computing the Sum of functions |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Interface for computing the Sum of functions in the batch processing mode |
CInput | Input objects for the Sum of functions |
CParameter | Parameter for the Sum of functions |
►Npca | Contains classes for computing the results of the principal component analysis (PCA) algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBaseParameter | Class that specifies the common parameters of the PCA algorithm |
CDistributed | Computes the result of the PCA algorithm |
CDistributed< step1Local, algorithmFPType, method > | Computes the results of the PCA algorithm on the local nodes |
CDistributed< step2Master, algorithmFPType, correlationDense > | Computes the result of the PCA Correlation algorithm on local nodes |
CDistributed< step2Master, algorithmFPType, svdDense > | Computes the result of the PCA SVD algorithm on local nodes |
CDistributedContainer | Class containing methods to compute the results of the PCA algorithm in the distributed processing mode |
CDistributedContainer< step1Local, algorithmFPType, correlationDense, cpu > | Class containing methods to compute the results of the PCA algorithm on the local node |
CDistributedContainer< step1Local, algorithmFPType, svdDense, cpu > | Class containing methods to compute the results of the PCA algorithm on the local node |
CDistributedContainer< step2Master, algorithmFPType, correlationDense, cpu > | Class containing methods to compute the results of the PCA algorithm on the master node |
CDistributedContainer< step2Master, algorithmFPType, svdDense, cpu > | Class containing methods to compute the results of the PCA algorithm on the master node |
CDistributedInput | Input objects for the PCA algorithm in the distributed processing mode |
CDistributedInput< correlationDense > | Input objects for the PCA Correlation algorithm in the distributed processing mode |
CDistributedInput< svdDense > | Input objects of the PCA SVD algorithm in the distributed processing mode |
CDistributedParameter | Class that specifies the parameters of the PCA algorithm in the distributed computing mode |
CDistributedParameter< step2Master, algorithmFPType, correlationDense > | Class that specifies the parameters of the PCA Correlation algorithm in the distributed computing mode |
CInput | Input objects for the PCA algorithm |
CInputIface | Abstract class that specifies interface for classes that declare input of the PCA algorithm |
COnline | Computes the results of the PCA algorithm |
COnline< algorithmFPType, correlationDense > | Computes the results of the PCA Correlation algorithm |
COnline< algorithmFPType, svdDense > | Computes the results of the PCA SVD algorithm |
COnlineContainer | Class containing methods to compute the result of the PCA algorithm |
COnlineContainer< algorithmFPType, correlationDense, cpu > | Class containing methods to compute the result of the PCA algorithm |
COnlineContainer< algorithmFPType, svdDense, cpu > | Class containing methods to compute the results of the PCA algorithm |
COnlineParameter | Class that specifies the parameters of the PCA algorithm in the online computing mode |
COnlineParameter< algorithmFPType, correlationDense > | Class that specifies the parameters of the PCA Correlation algorithm in the online computing mode |
COnlineParameter< algorithmFPType, svdDense > | Class that specifies the parameters of the PCA SVD algorithm in the online computing mode |
CPartialResult | Provides methods to access partial results obtained with the compute() method of the PCA algorithm in the online or distributed processing mode |
CPartialResult< daal::algorithms::pca::correlationDense > | Provides methods to access partial results obtained with the compute() method of the PCA Correlation algorithm in the online or distributed processing mode |
CPartialResult< daal::algorithms::pca::svdDense > | Provides methods to access partial results obtained with the compute() method of PCA SVD algorithm in the online or distributed processing mode |
CPartialResultBase | Provides interface to access partial results obtained with the compute() method of the PCA algorithm in the online or distributed processing mode |
►Ninterface2 | |
CBaseBatchParameter | Class that specifies the common parameters of the PCA Batch algorithms |
CBatch | Computes the results of the PCA algorithm |
CBatchContainer | Class containing methods to compute the results of the PCA algorithm |
CBatchContainer< algorithmFPType, correlationDense, cpu > | Class containing methods to compute the results of the PCA algorithm |
CBatchContainer< algorithmFPType, svdDense, cpu > | Class containing methods to compute the results of the PCA algorithm |
CBatchParameter | Class that specifies the parameters of the PCA algorithm in the batch computing mode |
CBatchParameter< algorithmFPType, correlationDense > | Class that specifies the parameters of the PCA Correlation algorithm in the batch computing mode |
CBatchParameter< algorithmFPType, svdDense > | Class that specifies the parameters of the PCA SVD algorithm in the batch computing mode |
CResult | Provides methods to access results obtained with the PCA algorithm |
►Nquality_metric | |
►Nexplained_variance | Contains classes for computing pca quality metrics |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the linear regression quality metric in the batch processing mode |
CBatchContainer | Class containing methods to compute regression quality metric |
CInput | Input objects for explained variance quality metrics |
CParameter | Parameters for the compute() method of explained variance quality metrics |
CResult | Provides interface for the result of linear regression quality metrics |
►Nquality_metric_set | Contains classes to check the quality of the pca algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a quality metric set of the pca algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the pca algorithm |
CParameter | Parameters for the quality metrics set compute() method |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the pca algorithm |
►Ntransform | Contains classes for computing the results of the PCA transformation algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the PCA transformation algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the PCA transformation algorithm in the batch processing mode |
CInput | Input objects for the PCA transformation algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
CParameter | Parameters for the PCA transformation compute method |
CResult | Provides methods to access final results obtained with the compute() method of the PCA transformation algorithm in the batch processing mode or finalizeCompute() method of algorithm in the online processing mode or on the second and third steps of the algorithm in the distributed processing mode |
►Npivoted_qr | Contains classes for computing the pivoted QR decomposition |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the pivoted QR algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the pivoted QR decomposition algorithm |
CInput | Input objects for the pivoted QR algorithm in the batch processing mode |
CParameter | Parameter for the pivoted QR computation method |
CResult | Provides methods to access final results obtained with the compute() method of the pivoted QR algorithm in the batch processing mode |
►Nqr | Contains classes for computing the results of the QR decomposition algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes the results of the QR decomposition algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the QR decomposition algorithm in the batch processing mode |
CDistributed | Computes the results of the QR decomposition algorithm in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Computes the result of the first step of the QR decomposition algorithm in the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Computes the results of the QR decomposition algorithm on the second step in the distributed processing mode |
CDistributed< step3Local, algorithmFPType, method > | Computes the results of the QR decomposition algorithm on the third step in the distributed processing mode |
CDistributedContainer | Provides methods to run implementations of the QR decomposition algorithm |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Provides methods to run implementations of QR decomposition algorithm on the first step in the distributed processing mode |
CDistributedContainer< step3Local, algorithmFPType, method, cpu > | Provides methods to run implementations of the QR decomposition algorithm on the third step in the distributed processing mode |
CDistributedPartialResult | Provides methods to access partial results obtained with the compute() method of the second step of the QR decomposition algorithm in the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the third step of the QR decomposition algorithm in the distributed processing mode |
CDistributedStep2Input | Input objects for the second step of the QR decomposition algorithm in the distributed processing mode |
CDistributedStep3Input | Input objects for the third step of the QR decomposition algorithm in the distributed processing mode |
CInput | Input objects for the QR decomposition algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
COnline | Computes the results of the QR decomposition algorithm in the online processing mode |
COnlineContainer | Provides methods to run implementations of the QR decomposition algorithm in the online processing mode |
COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the QR decomposition algorithm in the online processing mode or on the first step of the algorithm in the distributed processing mode |
CParameter | Parameters for the QR decomposition compute method |
CResult | Provides methods to access final results obtained with the compute() method of the QR decomposition algorithm in the batch processing mode or finalizeCompute() method of algorithm in the online processing mode or on the second and third steps of the algorithm in the distributed processing mode |
►Nquality_metric | Contains classes to compute quality metrics |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to compute quality metrics of an algorithm in the batch processing mode. Quality metric is a numerical characteristic or a set of connected numerical characteristics that represents the qualitative aspect of a computed statistical estimate, model, or decision-making result |
►Nquality_metric_set | Contains classes to compute a quality metric set |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to compute a quality metric set of an algorithm in the batch processing mode |
CInputAlgorithmsCollection | Class that implements functionality of the collection of quality metrics algorithms |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
►Nquantiles | Contains classes to run the quantile algorithms |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes values of quantiles in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the quantiles algorithm. It is associated with the daal::algorithms::quantiles::Batch class and supports methods of quantiles computation in the batch processing mode |
CInput | Input objects for the quantiles algorithm |
CParameter | Parameters of the quantiles algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the quantiles algorithm in the batch processing mode |
►Nregression | Contains base classes for the regression algorithms |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the regression algorithm |
CTreeNodeVisitor | Interface of abstract visitor used in tree traversal methods |
►Nprediction | Contains a class for making the regression model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the regression model-based prediction |
CInput | Provides an interface for input objects for making the regression model-based prediction |
CResult | Provides interface for the result of the regression model-based prediction |
►Ntraining | Contains a class for regression model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for the regression model-based training in the batch processing mode |
CInput | Input objects for the regression model-based training |
COnline | Provides methods for the regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of the regression model-based training in the online processing mode |
CResult | Provides methods to access the result obtained with the compute() method of the regression model-based training |
►Nridge_regression | Contains classes of the ridge regression algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for models trained with the ridge regression algorithm |
CModelNormEq | Model trained with the ridge regression algorithm using the normal equations method |
CParameter | Parameters for the ridge regression algorithm |
CTrainParameter | Parameters for the ridge regression algorithm |
►Nprediction | Contains a class for making ridge regression model-based prediction |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods to run implementations of the ridge regression model-based prediction |
CBatch< algorithmFPType, defaultDense > | Provides methods to run implementations of the ridge regression model-based prediction |
CInput | Provides an interface for input objects for making ridge regression model-based prediction |
CResult | Provides interface for the result of ridge regression model-based prediction |
►Ntraining | Contains a class for ridge regression model-based training |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Provides methods for ridge regression model-based training in the batch processing mode |
CBatchContainer | Class containing methods for normal equations ridge regression model-based training using algorithmFPType precision arithmetic |
CDistributed | Provides methods for ridge regression model-based training in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Performs ridge regression model-based training in the the first step of the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Performs ridge regression model-based training in the the second step of distributed processing mode |
CDistributedContainer | Class containing methods for ridge regression model-based training in the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Class containing methods for ridge regression model-based training in the second step of the distributed processing mode |
CDistributedInput | Input object for ridge regression model-based training in the distributed processing mode |
CDistributedInput< step2Master > | Input object for ridge regression model-based training in the second step of the distributed processing mode |
CInput | Input objects for ridge regression model-based training |
CInputIface | Abstract class that specifies the interface of input objects for ridge regression model-based training |
COnline | Provides methods for ridge regression model-based training in the online processing mode |
COnlineContainer | Class containing methods for ridge regression model-based training in the online processing mode |
CPartialResult | Provides methods to access a partial result obtained with the compute() method of ridge regression model-based training in the online or distributed processing mode |
CResult | Provides methods to access the result obtained with the compute() method of ridge regression model-based training |
►Nsorting | Contains classes to run the sorting algorithms |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Sorts the datasets by components of the random vector in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the sorting algorithm. It is associated with the daal::algorithms::sorting::Batch class and supports methods of sorting computation in the batch processing mode |
CInput | Input objects for the sorting algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the sorting algorithm in the batch processing mode |
►Nstump | Contains classes to work with the decision stump training algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the stump::training::Batch algorithm |
►Nprediction | Contains classes to make prediction based on the decision stump model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Predicts results of the decision stump classification |
CBatchContainer | Provides methods to run implementations of the decision stump prediction algorithm. It is associated with the daal::algorithms::stump::prediction::interface1::Batch class and supports methods to run based on the decision stump model |
CInput | Input objects in the prediction stage of the stump algorithm |
►Ntraining | Contains classes to train the decision stump model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Trains the decision stump model |
CBatchContainer | Provides methods to run implementations of the the decision stump training algorithm. It is associated with the daal::algorithms::stump::training::Batch class and supports methods to train the decision stump model |
CResult | Provides methods to access final results obtained with the compute() method of the decision stump training algorithm in the batch processing mode |
►Nsvd | Contains classes to run the singular-value decomposition (SVD) algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Computes results of the SVD algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the SVD algorithm |
CDistributed | Computes results of the SVD algorithm in the distributed processing mode |
CDistributed< step1Local, algorithmFPType, method > | Runs the first step of the SVD algorithm in the distributed processing mode |
CDistributed< step2Master, algorithmFPType, method > | Runs the second step of the SVD algorithm in the distributed processing mode |
CDistributed< step3Local, algorithmFPType, method > | Runs the third step of the SVD algorithm in the distributed processing mode |
CDistributedContainer | Provides methods to run implementations of the SVD algorithm |
CDistributedContainer< step1Local, algorithmFPType, method, cpu > | Provides methods to run implementations of the first step of the SVD algorithm in the distributed processing mode |
CDistributedContainer< step2Master, algorithmFPType, method, cpu > | Provides methods to run implementations of the second step of the SVD algorithm in the distributed processing mode |
CDistributedContainer< step3Local, algorithmFPType, method, cpu > | Provides methods to run implementations of the third step of the SVD algorithm in the distributed processing mode |
CDistributedPartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the second step in the distributed processing mode |
CDistributedPartialResultStep3 | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the third step in the distributed processing mode |
CDistributedStep2Input | Input objects for the second step of the SVD algorithm in the distributed processing mode |
CDistributedStep3Input | Input objects for the third step of the SVD algorithm in the distributed processing mode |
CInput | Input objects for the SVD algorithm in the batch processing and online processing modes, and the first step in the distributed processing mode |
COnline | Computes results of the SVD algorithm in the online processing mode |
COnlineContainer | Provides methods to run implementations of the SVD algorithm in the online processing mode |
COnlinePartialResult | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the online processing mode or the first step in the distributed processing mode |
CParameter | Parameters for the computation method of the SVD algorithm |
CResult | Provides methods to access final results obtained with the compute() method of the SVD algorithm in the batch processing mode or with the finalizeCompute() method in the online processing mode or steps 2 and 3 in the distributed processing mode |
►Nsvm | Contains classes to work with the support vector machine classifier |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Model of the classifier trained by the svm::training::Batch algorithm |
CParameter | Optional parameters |
►Nprediction | Contains classes to make predictions based on the SVM model |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class for making predictions based on the SVM model |
CBatchContainer | Provides methods to run implementations of the SVM algorithm. It is associated with the Prediction class and supports methods to run predictions based on the SVM model |
CInput | Input objects in the prediction stage of the svm algorithm |
►Nquality_metric_set | Contains classes to check the quality of the model trained with the SVM algorithm |
►Ninterface1 | Contains version 1.0 of the Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Class that represents a quality metric set to check the model trained with the SVM algorithm |
CInputDataCollection | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
CResultCollection | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
►Ntraining | Contains classes to train the SVM model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Algorithm class to train the SVM model |
CBatchContainer | Class containing methods to compute results of the SVM training |
CResult | Provides methods to access final results obtained with the compute() method of the SVM training algorithm in the batch processing mode |
►Nunivariate_outlier_detection | Contains classes for computing results of the univariate outlier detection algorithm |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Runs the univariate outlier detection algorithm in the batch processing mode |
CBatchContainer | Provides methods to run implementations of the univariate outlier detection algorithm. It is associated with the daal::algorithms::univariate_outlier_detection::Batch class and supports the methods of the univariate outlier detection in the batch processing mode |
CDefaultInit | Class that specifies the default method for initialization |
CInitIface | Abstract class that provides a functor for the initial procedure |
CInput | Input objects for the univariate outlier detection algorithm |
CParameter | Parameters of the univariate outlier detection algorithm |
CResult | Results obtained with the compute() method of the univariate outlier detection algorithm in the batch processing mode |
►Nweak_learner | Contains classes for working with weak learners |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CModel | Base class for the weak learner model |
CParameter | Base class for the input objects of the weak learner training and prediction algorithm |
►Nprediction | Contains classes to make predictions based on the weak learner model |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Base class for making predictions based on the weak learner model |
►Ntraining | Contains classes for training models of the weak learners algorithms |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CBatch | Base class for training the weak learner model in the batch processing mode |
CResult | Provides methods to access final results obtained with compute() method of Batch or finalizeCompute() method of Online and Distributed weak learners algorithms |
CAnalysis | Provides methods for execution of operations over data, such as computation of Summary Statistics estimates. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the data analysis are derived classes of the Analysis class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
CAnalysisContainerIface | Abstract interface class that provides virtual methods to access and run implementations of the analysis algorithms. It is associated with the Analysis class and supports the methods for computation and finalization of the analysis results in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each algorithm of data analysis |
CDistributedPrediction | |
CDistributedPredictionContainerIface | |
CPrediction | Provides prediction methods depending on the model such as linear_regression::Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the Prediction class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms |
CPredictionContainerIface | Abstract interface class that provides virtual methods to access and run implementations of the algorithms for model based prediction. Is associated with the Prediction class and supports the methods for computing the prediction results based on the trained model. The methods of the container are defined in derivative containers defined for each prediction algorithm |
CTraining | Provides methods to train models that depend on the data provided. For example, these methods enable training the linear regression model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of model training are derived classes of the Training class. The class additionally provides methods for validation of input and output parameters of the algorithms |
CTrainingContainerIface | Abstract interface class that provides virtual methods to access and run implementations of the model training algorithms. The class is associated with the Training class and supports the methods for computation and finalization of the training output in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each training algorithm |
▼Ndata_management | Contains classes that implement data management functionality, including NumericTables, DataSources, and Compression |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CAbstractCreator | Interface class used by the Factory class to register and create objects of a specific class |
CAOSNumericTable | Class that provides methods to access data stored as a contiguous array of heterogeneous feature vectors, while each feature vector is represented by a data structure. Therefore, the data is represented as an array of structures |
CBlockDescriptor | Base class that manages buffer memory for read/write operations required by numeric tables |
CBzip2CompressionParameter | Parameter for bzip2 compression and decompression |
CCategoricalFeatureDictionary | |
CCompressedDataArchive | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
CCompression | Base class for compression and decompression |
CCompressionIface | Abstract interface class for compression and decompression |
CCompressionParameter | Parameters for compression and decompression |
CCompressionStream | CompressionStream class compresses input raw data by blocks |
CCompressor | Compressor class compresses an input data block and writes results into an output data block |
CCompressor< bzip2 > | Implementation of the Compressor class for the bzip2 compression method |
CCompressor< lzo > | Implementation of the Compressor class for the LZO compression method |
CCompressor< rle > | Implementation of the Compressor class for the run-length encoding method |
CCompressor< zlib > | Implementation of the Compressor class for the zlib compression method |
CCompressorImpl | Base class for the Compressor |
CCreator | Main class used by the Factory class to register and create objects of a class derived from SerializationIface and the default constructor without arguments |
CCSRBlockDescriptor | Base class that manages buffer memory for read/write operations required by CSR numeric tables |
CCSRNumericTable | Class that provides methods to access data stored in the CSR layout |
CCSRNumericTableIface | Abstract class that defines the interface of CSR numeric tables |
CCsvDataSource | Specifies methods to access data stored in files |
CCSVFeatureManager | Methods of the class to preprocess data represented in the CSV format |
CDataArchive | Implements the abstract DataArchiveIface interface |
CDataArchiveIface | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
CDataArchiveImpl | Abstract interface class that defines methods to access and modify a serialized object. This class implements the most general serialization methods |
CDataBlock | Class that stores a pointer to a byte array and its size. Not responsible for memory management |
CDataBlockIface | Abstract interface class for a data management component responsible for a pointer to a byte array and its size. This class declares the most general methods for data access |
CDataCollection | Class that provides functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself |
CDataSource | Implements the abstract DataSourceIface interface |
CDataSourceFeature | Data structure that describes the Data Source feature |
CDataSourceIface | Abstract interface class that defines the interface for a data management component responsible for representation of data in the raw format. This class declares the most generic methods for data access |
CDataSourceTemplate | Implements the abstract DataSourceIface interface |
CDecompressedDataArchive | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
CDecompressionStream | DecompressionStream class decompresses compressed input data by blocks |
CDecompressor | Decompressor class decompresses an input data block and writes results into an output data block |
CDecompressor< bzip2 > | Specialization of Decompressor class for Bzip2 compression method |
CDecompressor< lzo > | Specialization of Decompressor class for LZO compression method |
CDecompressor< rle > | Implementation of the Decompressor class for the run-length decoding method |
CDecompressor< zlib > | Implementation of the Decompressor class for the zlib compression method |
CDecompressorImpl | Base class for the Decompressor |
CDenseNumericTableIface | Abstract interface class for a data management component responsible for accessing data in the numeric format. This class declares specific methods to access data in a dense homogeneous form |
CDenseTensorIface | Abstract interface class for a data management component responsible for accessing data in the numeric format. This class declares specific methods to access Tensor data in a dense homogeneous form |
CDictionary | Class that represents a dictionary of a data set and provides methods to work with the data dictionary |
CFactory | Class that provides factory functionality for objects implementing the SerializationIface interface. Used within deserialization functionality |
CFileDataSource | Specifies methods to access data stored in files |
CHomogenNumericTable | Class that provides methods to access data stored as a contiguous array of homogeneous feature vectors. Table rows contain feature vectors, and columns contain values of individual features |
CHomogenTensor | Class that provides methods to access data stored as a contiguous array of homogeneous data in rows-major format |
CInputDataArchive | Provides methods to create an archive data object (serialized) and access this object |
CKDBDataSource | Connects to data sources with the KDB API |
CKDBFeatureManager | Contains KDB-specific commands |
CKeyValueCollection | Class that provides functionality of a key-value container for objects derived from the T with a key of the size_t type |
CLzoCompressionParameter | Parameter for LZO compression and decompression. LZO compressed block header consists of four sections: 1) optional, 2) uncompressed data size (4 bytes), 3) compressed data size (4 bytes), 4) optional |
CMatrix | Represents a two-dimensional table of numbers of the same type |
CMemoryBlock | Serializable memory block, owner of the memory |
CMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by columns |
CMySQLFeatureManager | Contains MySQL-specific commands |
►CNumericTable | Class for a data management component responsible for representation of data in the numeric format. This class implements the most general methods for data access |
CBasicStatisticsDataCollection | Basic statistics for each column of original Numeric Table |
CNumericTableFeature | Data structure describes the Numeric Table feature |
CNumericTableIface | Abstract interface class for a data management component responsible for representation of data in the numeric format. This class declares the most general methods for data access |
CODBCDataSource | Connects to data sources with the ODBC API |
COutputDataArchive | Provides methods to restore an object from its serialized counterpart and access the restored object |
CPackedArrayNumericTableIface | Abstract class that defines the interface of symmetric matrices stored as a one-dimensional array |
CPackedSymmetricMatrix | Class that provides methods to access symmetric matrices stored as a one-dimensional array |
CPackedTriangularMatrix | Class that provides methods to access a packed triangular matrix stored as a one-dimensional array |
CRleCompressionParameter | Parameter for run-length encoding and decoding. A RLE encoded block may contain a header that consists of two sections: 1) decoded data size (4 bytes) and 2) encoded data size (4 bytes) |
CRowMergedNumericTable | Class that provides methods to access a collection of numeric tables as if they are joined by rows |
CSerializableKeyValueCollection | Class that provides functionality of a key-value container for objects derived from the SerializationIface interface with a key of the size_t type |
CSerializationIface | Abstract interface class that defines the interface for serialization and deserialization |
CSOANumericTable | Class that provides methods to access data stored as a structure of arrays, where each (contiguous) array represents values corresponding to a specific feature |
CStringDataSource | Specifies methods to access data stored in byte arrays in the C-string format |
CStringRowFeatureManagerIface | Abstract interface class that defines the interface to parse and convert the raw data represented as a string into a numeric format. The string must represent a row of data, a dictionary, or a vector of features |
CSubtensorDescriptor | Class with descriptor of the subtensor retrieved from Tensor getSubTensor function |
CTensor | Class for a data management component responsible for representation of data in the n-dimensions numeric format. This class implements the most general methods for data access |
CTensorIface | Abstract interface class for a data management component responsible for representation of data in the numeric format. This class declares the most general methods for data access |
CTensorLayout | Class for a data management component responsible for representation of data layout in the tensor. This class implements the most general methods for data layout |
CTensorLayoutIface | Abstract interface class for a data management component responsible for representation of data layout in the tensor. This class declares the most general methods for data access |
CTensorOffsetLayout | Class for a data management component responsible for representation of data layout in the HomogenTensor |
CZlibCompressionParameter | Parameter for zlib compression and decompression |
CColumnFilter | Methods of the class to filter out data source features from output numeric table |
CFeatureAuxData | Structure for auxiliary data used for feature extraction |
CMakeCategorical | Methods of the class to set a feature categorical |
CModifierIface | Abstract interface class that defines the interface for a features modifier |
COneHotEncoder | Methods of the class to set a feature binary categorical |
▼Nservices | Contains classes that implement service functionality, including error handling, memory allocation, and library version information |
►Ninterface1 | Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
CAtomic | Class that represents an atomic object |
CCollection | Class that implements functionality of the Collection container |
CDeleterIface | Interface for a utility class used within SharedPtr to delete an object when the object owner is destroyed |
CEmptyDeleter | Implementation of DeleterIface without pointer destroying |
►CEnvironment | Class that provides methods to interact with the environment, including processor detection and control by the number of threads |
C_envStruct | The environment structure |
CError | Class that represents an error |
CErrorCollection | Class that represents an error collection |
CErrorDetail | Base for error detail classes |
CException | Class that represents an exception |
CKernelErrorCollection | Class that represents a kernel error collection (collection that cannot throw exceptions) |
CLibraryVersionInfo | Provides information about the version of Intel(R) Data Analytics Acceleration Library |
CObjectDeleter | Implementation of DeleterIface to destroy a pointer by the delete operator |
CRefCounter | Implementation of reference counter |
CRefCounterImp | Provides implementations of the operator() method of the RefCounter class |
CServiceDeleter | Implementation of DeleterIface to destroy a pointer by the daal_free function |
CSharedPtr | Shared pointer that retains shared ownership of an object through a pointer. Several SharedPtr objects may own the same object. The object is destroyed and its memory deallocated when either of the following happens: 1) the last remaining SharedPtr owning the object is destroyed. 2) the last remaining SharedPtr owning the object is assigned another pointer via operator=. The object is destroyed using the delete operator |
CStatus | Class that holds the results of API calls. In case of API routine failure it contains the list of errors describing problems API encountered |
CString | Class that implements functionality of the string, an object that represents a sequence of characters |
CBase | Base class for Intel(R) Data Analytics Acceleration Library objects |
CIsSameType | |
CIsSameType< U, U > |
For more complete information about compiler optimizations, see our Optimization Notice.