C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1
![]() ![]() | The environment structure |
![]() ![]() | Interface class used by the Factory class to register and create objects of a specific class |
![]() ![]() ![]() | Main class used by the Factory class to register and create objects of a class derived from SerializationIface and the default constructor without arguments |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() ![]() | Implements a container to dispatch algorithms to cpu-specific implementations |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() ![]() | Implements a container to dispatch batch processing algorithms to CPU-specific implementations |
![]() ![]() | Abstract class which defines interface for the library component related to data processing involving execution of the algorithms for analysis, modeling, and prediction |
![]() ![]() ![]() | Implements the abstract interface AlgorithmIface. AlgorithmIfaceImpl is, in turn, the base class for the classes interfacing the major compute modes: batch, online and distributed |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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) |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | Provides implementations of the compute and checkComputeParams methods of the Algorithm<batch> class |
![]() ![]() | Base class to represent computation input and output arguments |
![]() ![]() ![]() | Base class to represent computation input arguments. Algorithm-specific input arguments are represented as derivative classes of the Input class |
![]() ![]() ![]() | Base class to represent argument with serialization methods |
![]() ![]() ![]() ![]() | Base class to represent argument with serialization methods |
![]() ![]() ![]() ![]() | Base class to represent partial results of the computation. Algorithm-specific partial results are represented as derivative classes of the PartialResult class |
![]() ![]() ![]() ![]() | Base class to represent final results of the computation. Algorithm-specific final results are represented as derivative classes of the Result class |
![]() ![]() | Class that represents an atomic object |
![]() ![]() | Base class for Intel(R) Data Analytics Acceleration Library objects |
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![]() ![]() ![]() | Abstract class that specifies the interface of layer |
![]() ![]() ![]() | Class defining a neural network topology - a set of layers and connection between them - on the prediction stage |
![]() ![]() ![]() | Class defining a neural network topology - a set of layers and connection between them - on the training stage |
![]() ![]() ![]() | Base class that partially implements abstract feature id, intended for inheritance form user side |
![]() ![]() ![]() | Base class that partially implements abstract feature id collection, intended for inheritance form user side |
![]() ![]() ![]() | Base class that partially implements feature mapping interface, intended for inheritance form the user side |
![]() ![]() ![]() | Base class that represents the collection of feature indices, intended for inheritance from the user side |
![]() ![]() ![]() | Auxiliary class that simplifies definition of feature ids collections |
![]() ![]() ![]() | CompressionStream class compresses input raw data by blocks |
![]() ![]() ![]() | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
![]() ![]() ![]() ![]() | Abstract interface class that defines methods to access and modify a serialized object. This class implements the most general serialization methods |
![]() ![]() ![]() ![]() ![]() | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
![]() ![]() ![]() ![]() ![]() | Implements the abstract DataArchiveIface interface |
![]() ![]() ![]() ![]() ![]() | Abstract interface class that defines methods to access and modify a serialized object. This class declares the most generic access and modification methods |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class that stores a pointer to a byte array and its size. Not responsible for memory management |
![]() ![]() ![]() | DecompressionStream class decompresses compressed input data by blocks |
![]() ![]() ![]() | Provides methods to create an archive data object (serialized) and access this object |
![]() ![]() ![]() | Provides methods to restore an object from its serialized counterpart and access the restored object |
![]() ![]() ![]() | Abstract interface class that defines the interface for serialization and deserialization |
![]() ![]() ![]() ![]() | Class that provides functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself |
![]() ![]() ![]() ![]() | Data structure that describes the Data Source feature |
![]() ![]() ![]() ![]() | Class that represents a dictionary of a data set and provides methods to work with the data dictionary |
![]() ![]() ![]() ![]() | Serializable memory block, owner of the memory |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | Base class that provides methods to access data stored as a Apache Arrow table |
![]() ![]() ![]() ![]() ![]() ![]() | Base class that provides methods to access data stored as a immutable Apache Arrow table |
![]() ![]() ![]() ![]() ![]() ![]() ![]() | Class that provides methods to access data stored as a Apache Arrow table |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access data stored in the CSR layout |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() ![]() | Represents a two-dimensional table of numbers of the same type |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access a collection of numeric tables as if they are joined by columns |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access symmetric matrices stored as a one-dimensional array |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access a packed triangular matrix stored as a one-dimensional array |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access a collection of numeric tables as if they are joined by rows |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access data stored as a structure of arrays, where each (contiguous) array represents values corresponding to a specific feature |
![]() ![]() ![]() ![]() | Data structure describes the Numeric Table feature |
![]() ![]() ![]() ![]() | Class that provides functionality of a key-value container for objects derived from the SerializationIface interface with a key of the size_t type |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | Class that provides methods to access data stored as a contiguous array of homogeneous data in rows-major format |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | Class for a data management component responsible for representation of data layout in the HomogenTensor |
![]() ![]() ![]() | Class that holds auxiliary information about features being parsed |
![]() ![]() ![]() | Class that parses single row in CSV file and implements iterator-like interface to iterate over the parsed tokens separated by comma |
![]() ![]() ![]() | Class that holds auxiliary information about single SQL column |
![]() ![]() ![]() | Class that holds auxiliary information about multiple SQL columns |
![]() ![]() ![]() | Class hold buffer for fetching data from SQL table, simplifies binding of SQL table columns |
![]() ![]() ![]() | Represents fragment of SQL fetch buffer |
![]() ![]() ![]() | Base class that represents modifier configuration, object of that class is passed to the modifier on initialization and finalization stages |
![]() ![]() ![]() | Base class that represents modifier context, object of that class is passed to the modifier as an argument of FeatureModifierIface::apply method |
![]() ![]() ![]() | Base class for feature modifier, intended for inheritance from the user side |
![]() ![]() ![]() | Primitive modifier that applicable to a single column |
![]() ![]() ![]() ![]() | Primitive feature modifier that parses tokens as categorical features |
![]() ![]() ![]() ![]() | Primitive feature modifier that parses tokens as continuous features |
![]() ![]() ![]() ![]() | Default implementation of primitive feature modifier |
![]() ![]() ![]() | Base class represents input feature for modifier, contains information about single input feature |
![]() ![]() ![]() ![]() | Class represents configuration of single input feature |
![]() ![]() ![]() ![]() | Class represents configuration of single input feature |
![]() ![]() ![]() | Class that binds feature identifiers to concrete feature indices, performs initialization of a modifier and manages Config and Context objects |
![]() ![]() ![]() | Class that creates and manages bindings for a modifier |
![]() ![]() ![]() | Class that holds modifiers and implements logic of modifiers applying flow |
![]() ![]() ![]() | Base class represents output feature for modifier, contains information about single output feature |
![]() ![]() ![]() ![]() | Class represents configuration of single output feature |
![]() ![]() ![]() ![]() | Class represents configuration of single feature |
![]() ![]() ![]() | Base class that represents modifier configuration, object of that class is passed to the modifier on initialization and finalization stages |
![]() ![]() ![]() | Base class that represents modifier context, object of that class is passed to the modifier as an argument of FeatureModifierIface::apply method |
![]() ![]() ![]() | Base class for feature modifier, intended for inheritance from the user side |
![]() ![]() ![]() | Class that refers to a contiguous sequence of objects, but doesn't control allocated memory buffer and objects lifetime, user is responsible for correct memory management and deallocation |
![]() ![]() ![]() | Class that provides methods to interact with the environment, including processor detection and control by the number of threads |
![]() ![]() ![]() | Abstract class which defines callback interface for the host application of this library to enable such features as computation cancelling, progress bar, status bar, verbose, etc |
![]() ![]() ![]() | Provides information about the version of Intel(R) Data Analytics Acceleration Library |
![]() ![]() ![]() | Class that implements functionality of the string, an object that represents a sequence of characters |
![]() ![]() ![]() | Class that implements functionality of the string but doesn't manage provided memory, user is responsible for correct memory management and deallocation |
![]() ![]() ![]() | Class that provides simple memory management routines for handling blocks of continues memory, also provides automatic memory deallocation. Note this class doesn't provide functionality for objects constructions and simply allocates and deallocates memory. In case of objects consider Collection or ObjectPtrCollection |
![]() ![]() ![]() | Wrapper for services::Collection that allocates and deallocates memory using internal new/delete operators |
![]() ![]() ![]() | Class that implements functionality of collection container and holds pointers to objects of specified type, also provides automatic objects disposal |
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![]() ![]() ![]() ![]() | Class that holds modifiers and implements logic of modifiers applying flow |
![]() ![]() ![]() ![]() | Class that holds modifiers and implements logic of modifiers applying flow |
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![]() ![]() | Provides methods to run implementations of the linear regression model-based prediction |
![]() ![]() | Provides methods to compute a quality metric set of an algorithm in the batch processing mode |
![]() ![]() | Provides methods to run implementations of the ridge regression model-based prediction |
![]() ![]() | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::Batch class |
![]() ![]() | Class that specifies the parameters of the PCA algorithm in the batch computing mode |
![]() ![]() | Base class that manages buffer memory for read/write operations required by numeric tables |
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![]() ![]() | Class that implements functionality of the Collection container |
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![]() ![]() ![]() | Class that represents a kernel error collection (collection that cannot throw exceptions) |
![]() ![]() | Abstract interface class for compression and decompression |
![]() ![]() ![]() | Base class for compression and decompression |
![]() ![]() ![]() ![]() | Base class for the Compressor |
![]() ![]() ![]() ![]() ![]() | Compressor class compresses an input data block and writes results into an output data block |
![]() ![]() ![]() ![]() | Base class for the Decompressor |
![]() ![]() ![]() ![]() ![]() | Decompressor class decompresses an input data block and writes results into an output data block |
![]() ![]() | Parameters for compression and decompression |
![]() ![]() | Base class for modifier configuration |
![]() ![]() ![]() | Internal implementation of feature modifier configuration |
![]() ![]() ![]() | Internal implementation of feature modifier configuration |
![]() ![]() | Abstract class that defines interface of modifier configuration |
![]() ![]() | Base class for modifier context |
![]() ![]() ![]() | Internal implementation of feature modifier context |
![]() ![]() ![]() | Internal implementation of feature modifier context |
![]() ![]() | Abstract class that defines interface of modifier context |
![]() ![]() | Base class that manages buffer memory for read/write operations required by CSR numeric tables |
![]() ![]() | Abstract class that defines the interface of CSR numeric tables |
![]() ![]() ![]() | Class that provides methods to access data stored in the CSR layout |
![]() ![]() | Options of CSV data source |
![]() ![]() | 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 |
![]() ![]() ![]() | Implements the abstract DataSourceIface interface |
![]() ![]() ![]() ![]() | Implements the abstract DataSourceIface interface |
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![]() ![]() ![]() ![]() ![]() | Connects to data sources with the KDB API |
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![]() ![]() ![]() ![]() ![]() | Specifies methods to access data stored in files |
![]() ![]() ![]() ![]() ![]() ![]() | Specifies methods to access data stored in files |
![]() ![]() ![]() ![]() ![]() ![]() | Specifies methods to access data stored in byte arrays in the C-string format |
![]() ![]() ![]() ![]() ![]() | Connects to data sources with the ODBC API |
![]() ![]() | Class that helps to define data source options |
![]() ![]() | Interface for a utility class used within SharedPtr to delete an object when the object owner is destroyed |
![]() ![]() ![]() | Implementation of DeleterIface without pointer destroying |
![]() ![]() ![]() | Implementation of DeleterIface to destroy a pointer by the delete operator |
![]() ![]() ![]() | Implementation of DeleterIface to destroy a pointer by the daal_free function |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() | Provides methods for neural network model-based training in the batch processing mode |
![]() ![]() | Computes moments of low order in the distributed processing mode |
![]() ![]() | Algorithm class for training naive Bayes model in the distributed processing mode |
![]() ![]() | Algorithm class for training naive Bayes model in the distributed processing mode |
![]() ![]() | Computes the results of the DBSCAN algorithm in the distributed processing mode |
![]() ![]() | Initializes the implicit ALS model in the distributed processing mode |
![]() ![]() | Computes the results of K-Means algorithm in the distributed processing mode |
![]() ![]() | Class containing methods for linear regression model-based training in the distributed processing mode |
![]() ![]() | Class containing methods to train neural network model in the distributed processing mode using algorithmFPType precision arithmetic |
![]() ![]() | 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 |
![]() ![]() | 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 |
![]() ![]() | Class containing methods to compute the results of the PCA algorithm in the distributed processing mode |
![]() ![]() | Class containing methods to compute naive Bayes training results in the distributed processing mode |
![]() ![]() | Provides methods to run implementations of the QR decomposition algorithm |
![]() ![]() | Class containing methods to compute naive Bayes training results in the distributed processing mode |
![]() ![]() | Class containing methods for ridge regression model-based training in the distributed processing mode |
![]() ![]() | Class containing methods to compute the result of DBSCAN algorithm in the distributed processing mode |
![]() ![]() | Provides methods to run implementations of the SVD algorithm |
![]() ![]() | Class that contains methods to run implicit ALS model-based prediction in the distributed processing mode |
![]() ![]() | Class containing methods to compute the result of implicit ALS model-based training in the distributed processing mode |
![]() ![]() | Class containing methods to compute the results of the implicit ALS initialization algorithm in the distributed processing mode |
![]() ![]() | Provides methods to run implementations of K-Means algorithm. This class is associated with the daal::algorithms::kmeans::Distributed class and supports the method of K-Means computation in the distributed processing mode |
![]() ![]() | Provides methods to run implementations of initialization of K-Means algorithm. This class is associated with the daal::algorithms::kmeans::init::Distributed class and supports the method of computing initial clusters for K-Means algorithm in the distributed processing mode |
![]() ![]() | Class that spcifies interfaces of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::DistributedIface class |
![]() ![]() | Input object for linear regression model-based training in the distributed processing mode |
![]() ![]() | Input objects of the neural network training algorithm in the distributed processing mode |
![]() ![]() | Input objects for the PCA algorithm in the distributed processing mode |
![]() ![]() | Input parameters of the distributed Covariance algorithm |
![]() ![]() | Input object for ridge regression model-based training in the distributed processing mode |
![]() ![]() | Input objects for the implicit ALS initialization algorithm in the distributed processing mode |
![]() ![]() | Input objects for the DBSCAN algorithm in the distributed processing mode |
![]() ![]() | Input objects for the implicit ALS training algorithm in the distributed processing mode |
![]() ![]() | Input objects for the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
![]() ![]() | Class that stores collection of elements of specified type and pointers to the elements of that collection corresponding to the indices provided in ElementsPicker::pick method |
![]() ![]() | Class that represents an error |
![]() ![]() | Class that represents an error collection |
![]() ![]() | Base for error detail classes |
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![]() ![]() ![]() | Class that represents an exception |
![]() ![]() | Class that provides factory functionality for objects implementing the SerializationIface interface. Used within deserialization functionality |
![]() ![]() | Structure for auxiliary data used for feature extraction |
![]() ![]() | Abstract class that represents collection of feature ids |
![]() ![]() ![]() | Base class that partially implements abstract feature id collection, intended for inheritance form user side |
![]() ![]() | Abstract feature id interface |
![]() ![]() ![]() | Base class that partially implements abstract feature id, intended for inheritance form user side |
![]() ![]() | Abstract class that defines interface for mapping feature id to feature index |
![]() ![]() ![]() | Base class that partially implements feature mapping interface, intended for inheritance form the user side |
![]() ![]() | Static class that contains auxiliary methods for FeatureIndex |
![]() ![]() | Abstract class that defines interface for feature indices collection |
![]() ![]() ![]() | Base class that represents the collection of feature indices, intended for inheritance from the user side |
![]() ![]() | General feature modifier interface |
![]() ![]() | Data structure representing the indices of the dimension on which pooling is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which pooling is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which 2D transposed convolution is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which local contrast normalization is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which 2D locally connected is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which pooling is performed |
![]() ![]() | Data structure representing the indices of the three dimensions on which pooling is performed |
![]() ![]() | Data structure representing the indices of the two dimensions on which 2D convolution is performed |
![]() ![]() | Abstract interface class that provides function for the initialization procedure |
![]() ![]() ![]() | Class that specifies the default method for the initialization procedure |
![]() ![]() | Abstract class that provides a functor for the initial procedure |
![]() ![]() ![]() | Class that specifies the default method for initialization |
![]() ![]() | Class that implements functionality of the collection of quality metrics algorithms |
![]() ![]() | Abstract class that specifies the interface of input objects for linear regression model-based training |
![]() ![]() ![]() | Input object for linear regression model-based training in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for linear regression model-based training |
![]() ![]() | Abstract class that specifies the interface of input objects for elastic net model-based training |
![]() ![]() ![]() | Input objects for elastic net model-based training |
![]() ![]() | Abstract class that specifies the interface of input objects for ridge regression model-based training |
![]() ![]() ![]() | Input object for ridge regression model-based training in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for ridge regression model-based training |
![]() ![]() | Abstract class that specifies the interface of input objects for lasso regression model-based training |
![]() ![]() ![]() | Input objects for lasso regression model-based training |
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![]() ![]() | Contains KDB-specific commands |
![]() ![]() | Base class to represent algorithm implementation |
![]() ![]() | Data structure representing the size of the 1D subtensor from which the element is computed |
![]() ![]() | Data structure representing the size of the two-dimensional kernel subtensor |
![]() ![]() | Data structure representing the size of the 3D subtensor from which the element is computed |
![]() ![]() | Data structure representing the size of the two-dimensional kernel subtensor |
![]() ![]() | Data structure representing the size of the two-dimensional kernel subtensor |
![]() ![]() | Data structure representing the size of the 2D subtensor from which the element is computed |
![]() ![]() | Class that provides functionality of a key-value container for objects derived from the T with a key of the size_t type |
![]() ![]() ![]() | Class that provides functionality of a key-value container for objects derived from the SerializationIface interface with a key of the size_t type |
![]() ![]() | Class defining descriptor for layer on both forward and backward stages and its parameters |
![]() ![]() | Class defining descriptor for layer on forward stage |
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![]() ![]() | Builder for Model of the classifier trained by the multi_class_classifier::training::Batch algorithm |
![]() ![]() | Class for building model of the linear regression algorithm |
![]() ![]() | Class for building model of the logistic regression algorithm |
![]() ![]() | Model Builder class for gradient boosted trees classification model |
![]() ![]() | Model Builder class for class SVM Model |
![]() ![]() | Model Builder class for gradient boosted trees regression model |
![]() ![]() | Model Builder class for Decision Forest Classification Model algorithm |
![]() ![]() | Model Builder class for Decision Forest Classification Model algorithm |
![]() ![]() | Abstract interface class that defines the interface for a features modifier |
![]() ![]() ![]() | Methods of the class to filter out data source features from output numeric table |
![]() ![]() ![]() | Methods of the class to set a feature categorical |
![]() ![]() ![]() | Methods of the class to set a feature binary categorical |
![]() ![]() | Contains list of layer indices of layers following the current layer |
![]() ![]() | Struct containing base description of node in descision tree |
![]() ![]() ![]() | Struct containing description of split node in descision tree |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() | Options of ODBC data source |
![]() ![]() | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::Online class |
![]() ![]() | Abstract class that defines the interface of symmetric matrices stored as a one-dimensional array |
![]() ![]() ![]() | Class that provides methods to access symmetric matrices stored as a one-dimensional array |
![]() ![]() ![]() | Class that provides methods to access a packed triangular matrix stored as a one-dimensional array |
![]() ![]() | Data structure representing the number of data elements to implicitly add to each side of the 1D subtensor on which pooling is performed |
![]() ![]() | 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 |
![]() ![]() | 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 |
![]() ![]() | 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 |
![]() ![]() | Data structure representing the number of data elements to implicitly add to each size of the three-dimensional subtensor on which pooling is performed |
![]() ![]() | Data structure representing the number of data elements to implicitly add to each side of the 2D subtensor on which pooling is performed |
![]() ![]() | Parameters for the decision forest algorithm |
![]() ![]() | Class that specifies the parameters of the algorithm in the batch computing mode |
![]() ![]() | Parameters for the gradient boosted trees algorithm |
![]() ![]() | Base class to represent computation parameters. Algorithm-specific parameters are represented as derivative classes of the Parameter class |
![]() ![]() | 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 |
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![]() ![]() | Interprets the response of SQL data base and fill provided numeric table and dictionary |
![]() ![]() | Mode of fetching data from SQL table |
![]() ![]() | Class that holds the results of API calls. In case of API routine failure it contains the list of errors describing problems API encountered |
![]() ![]() | Data structure representing the intervals on which the subtensors for pooling are computed |
![]() ![]() | Data structure representing the intervals on which the subtensors for pooling are computed |
![]() ![]() | Data structure representing the intervals on which the subtensors for 2D locally connected are selected |
![]() ![]() | Data structure representing the intervals on which the subtensors for pooling are computed |
![]() ![]() | Data structure representing the intervals on which the subtensors for 2D transposed convolution are selected |
![]() ![]() | Data structure representing the intervals on which the subtensors for 2D convolution are selected |
![]() ![]() | 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 |
![]() ![]() ![]() | Methods of the class to preprocess data represented in the CSV format |
![]() ![]() | Class with descriptor of the subtensor retrieved from Tensor getSubTensor function |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() | Interface of abstract visitor used in tree traversal methods |
![]() ![]() | Interface of abstract visitor used in tree traversal methods |
![]() ![]() | Interface of abstract visitor used in tree traversal methods |
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![]() ![]() | Data structure representing the value sizes of the two dimensions on which 2D transposed convolution is performed |
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![]() ![]() ![]() ![]() | Provides methods to run implementations of the association rules algorithm. This class is associated with daal::algorithms::association_rules::Batch class |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the Cholesky decomposition algorithm. This class is associated with daal::algorithms::cholesky::Batch class |
![]() ![]() ![]() ![]() | Class containing methods to compute the confusion matrix for a binary classifier |
![]() ![]() ![]() ![]() | Class containing methods to compute the confusion matrix for the multi-class classifier |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the correlation distance algorithm. This class is associated with daal::algorithms::correlation_distance::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the cosine distance algorithm. This class is associated with daal::algorithms::cosine_distance::Batch class |
![]() ![]() ![]() ![]() | Class that specifies interfaces of implementations of the correlation or variance-covariance matrix container. This class is associated with daal::algorithms::covariance::BatchContainerIface class |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the DBSCAN algorithm. This class is associated with the daal::algorithms::dbscan::Batch class and supports the method of DBSCAN computation in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the mcg59 engine. This class is associated with the mcg59::Batch class and supports the method of mcg59 engine computation in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the mt2203 engine. This class is associated with the mt2203::Batch class and supports the method of mt2203 engine computation in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of initialization of K-Means algorithm. This class is associated with the daal::algorithms::kmeans::init::Batch class and supports the method of computing initial clusters for K-Means algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of 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 |
![]() ![]() ![]() ![]() | Class containing methods to compute regression quality metric |
![]() ![]() ![]() ![]() | Class containing methods to compute regression quality metric |
![]() ![]() ![]() ![]() | Class that specifies interfaces of implementations of the low order moments algorithm |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the low order moments algorithm. This class is associated with daal::algorithms::low_order_moments::Batch class |
![]() ![]() ![]() ![]() | Class containing methods for the absolute value function computing using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for the logistic function computing using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for the rectified linear function computing using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the SmoothReLU algorithm. This class is associated with daal::algorithms::math::smoothrelu::Batch class |
![]() ![]() ![]() ![]() | Class containing methods for the softmax function computing using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for the hyperbolic tangent function computing using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class that specifies interfaces of implementations of the neural network weights and biases initializer |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the of the backward ELU layer This class is associated with the daal::algorithms::neural_networks::layers::elu::backward::Batch class and supports the method of backward ELU layer computation in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods of base container for forward layers. This class is associated with the daal::algorithms::neural_networks::layers::forward::LayerContainerIfaceImpl class |
![]() ![]() ![]() ![]() ![]() | Implements a container to dispatch forward layers to cpu-specific implementations |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class containing methods for the backward softmax layer using algorithmFPType precision arithmetic |
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![]() ![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the backward stochastic 2D pooling layer |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the adaptive gradient descent algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the adaptive gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::adagrad::BatchContainer class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the coordinate descent algorithm. This class is associated with daal::algorithms::optimization_solver::coordinate_descent::BatchContainer class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the Cross-entropy loss objective function. This class is associated with the Batch class and supports the method of computing the Cross-entropy loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the Cross-entropy loss objective function. This class is associated with the Batch class and supports the method of computing the Cross-entropy loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the LBFGS algorithm. This class is associated with daal::algorithms::optimization_solver::lbfgs::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the LBFGS algorithm. This class is associated with daal::algorithms::optimization_solver::lbfgs::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the Logistic loss objective function. This class is associated with the Batch class and supports the method of computing the Logistic loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the Logistic loss objective function. This class is associated with the Batch class and supports the method of computing the Logistic loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the objective function with precomputed characteristics |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the stochastic average gradient descent algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the stochastic average gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::saga::BatchContainer class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the stochastic gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::sgd::BatchContainer class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the stochastic gradient descent algorithm. This class is associated with daal::algorithms::optimization_solver::sgd::BatchContainer class |
![]() ![]() ![]() ![]() | Contains version 3.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm |
![]() ![]() ![]() ![]() | Class containing methods to compute regression quality metric |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the PCA transformation algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the pivoted QR decomposition algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the QR decomposition algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the SVD algorithm |
![]() ![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the correlation or variance-covariance matrix algorithm in the distributed processing mode on master node |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the 2nd step of the distributed processing mode performed on a local node |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the 2nd step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the 3rd step of the distributed processing mode performed on the master mode |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the 4th step of the distributed processing mode performed on a local node |
![]() ![]() ![]() ![]() | Class containing methods for computing initial clusters for K-Means algorithm in the 5th step of the distributed processing mode performed on the master node |
![]() ![]() ![]() ![]() | Class containing computation methods for K-Means algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for K-Means algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm on the master node |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm on the master node |
![]() ![]() ![]() ![]() | Provides methods to run implementations of QR decomposition algorithm on the first step in the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the QR decomposition algorithm on the third step in the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the second step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the third step of the SVD algorithm in the distributed processing mode |
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![]() ![]() ![]() ![]() | Class that spcifies interfaces of implementations of the correlation or variance-covariance matrix algorithm. This class is associated with daal::algorithms::covariance::OnlineImpl class |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the low order moments algorithm. This class is associated with daal::algorithms::low_order_moments::Online class |
![]() ![]() ![]() ![]() | Class containing methods to compute the result of the PCA algorithm |
![]() ![]() ![]() ![]() | Class containing methods to compute the result of the PCA algorithm |
![]() ![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm on the local node |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm |
![]() ![]() ![]() ![]() ![]() | Class containing methods to compute the results of the PCA algorithm on the local node |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the QR decomposition algorithm in the online processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the SVD algorithm in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the first step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | Class that contains methods to run implicit ALS model-based prediction in the first step of the distributed processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the AdaBoost algorithm |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the BrownBoost algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the BrownBoost algorithm. This class is associated with daal::algorithms::brownboost::prediction::interface1::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the decision_forest algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the decision_forest algorithm. This class is associated with daal::algorithms::decision_forest::prediction::interface2::Batch class and supports method to compute decision_forest prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the decision_forest algorithm. This class is associated with daal::algorithms::decision_forest::prediction::interface3::Batch class and supports method to compute decision_forest prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for decision forest model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for Decision tree model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for Decision tree model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for Decision tree model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for Decision tree model-based prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the gradient boosted trees algorithm |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class containing computation methods for model-based prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the implicit ALS ratings prediction algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for KD-tree based kNN model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for KD-tree based kNN model-based prediction |
![]() ![]() ![]() ![]() | Class containing computation methods for the regression model-based prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the logistic regression algorithm. This class is associated with daal::algorithms::logistic_regression::prediction::interface1::Batch class and supports method to compute logistic regression prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the logistic regression algorithm. This class is associated with daal::algorithms::logistic_regression::prediction::interface1::Batch class and supports method to compute logistic regression prediction |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the LogitBoost algorithm |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the multi-class classifier prediction algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the multi-class classifier prediction algorithm |
![]() ![]() ![]() ![]() | Runs the prediction based on the multinomial naive Bayes model |
![]() ![]() ![]() ![]() | Runs the prediction based on the multinomial naive Bayes model |
![]() ![]() ![]() ![]() | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the decision stump prediction algorithm. It is associated with the daal::algorithms::stump::classification::prediction::interface1::Batch class and supports methods to run based on the decision stump model |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the decision stump prediction algorithm. It is associated with the daal::algorithms::stump::regression::prediction::interface1::Batch class and supports methods to run based on the decision stump model |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | Provides methods to run implementations of AdaBoost model-based training. It is associated with daal::algorithms::adaboost::training::Batch class |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to run implementations of BrownBoost model-based training. This class is associated with daal::algorithms::brownboost::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of BrownBoost model-based training. This class is associated with daal::algorithms::brownboost::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of Decision forest model-based training. This class is associated with daal::algorithms::decision_forest::classification::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of Decision forest model-based training. This class is associated with daal::algorithms::decision_forest::classification::training::Batch class |
![]() ![]() ![]() ![]() | Class containing methods for decision forest regression model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for Decision tree model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for normal equations elastic net model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of Gradient Boosted Trees model-based training |
![]() ![]() ![]() ![]() | Provides methods to run implementations of Gradient Boosted Trees model-based training. This class is associated with daal::algorithms::gbt::classification::training::Batch class |
![]() ![]() ![]() ![]() | Class containing methods for gradient boosted trees regression model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the implicit ALS initialization algorithm |
![]() ![]() ![]() ![]() | Provides methods to run implementations of implicit ALS model-based training |
![]() ![]() ![]() ![]() | Class containing methods for KD-tree based kNN model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for KD-tree based kNN model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for normal equations lasso regression model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for normal equations linear regression model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of logistic regression model-based training |
![]() ![]() ![]() ![]() | Provides methods to run implementations of logistic regression model-based training. This class is associated with daal::algorithms::logistic_regression::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of logistic regression model-based training. This class is associated with daal::algorithms::logistic_regression::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of LogitBoost model-based training. This class is associated with daal::algorithms::logitboost::training::Batch class |
![]() ![]() ![]() ![]() | Provides methods to run implementations of LogitBoost model-based training. This class is associated with daal::algorithms::logitboost::training::Batch class |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of multi-class classifier model-based training |
![]() ![]() ![]() ![]() | Class containing methods to compute the results of multi-class classifier model-based training |
![]() ![]() ![]() ![]() | Class containing methods to compute naive Bayes training results |
![]() ![]() ![]() ![]() | Class containing methods to compute naive Bayes training results |
![]() ![]() ![]() ![]() | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for normal equations ridge regression model-based training using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the the decision stump training algorithm. It is associated with the daal::algorithms::stump::classification::training::Batch class and supports methods to train the decision stump model |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the the decision stump training algorithm. It is associated with the daal::algorithms::stump::regression::training::Batch class and supports methods to train the decision stump model |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Class containing methods to compute results of the SVM training |
![]() ![]() ![]() ![]() | Class containing methods to compute results of the SVM training |
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![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the tenth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the eleventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the twelfth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the third step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the fourth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the fifth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the sixth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the seventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the eighth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for the DBSCAN algorithm in the ninth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the third step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the third step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train the implicit ALS model in the fourth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods for linear regression model-based training in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train naive Bayes in the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train naive Bayes in the distributed processing mode |
![]() ![]() ![]() ![]() | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods to train neural network model using algorithmFPType precision arithmetic |
![]() ![]() ![]() ![]() | Class containing methods for ridge regression model-based training in the second step of the distributed processing mode |
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![]() ![]() ![]() ![]() | Class containing methods for linear regression model-based training in the online processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for naive Bayes training in the online processing mode |
![]() ![]() ![]() ![]() | Class containing computation methods for naive Bayes training in the online processing mode |
![]() ![]() ![]() ![]() | Class containing methods for ridge regression model-based training in the online processing mode |
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![]() ![]() ![]() ![]() | Computes the result of the association rules algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing BACON outlier detection in the batch processing mode |
![]() ![]() ![]() ![]() | Computes Cholesky decomposition in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the correlation distance in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the cosine distance in the batch processing mode |
![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Computes correlation or variance-covariance matrix in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Class representing distributions |
![]() ![]() ![]() ![]() | Computes initial values for the EM for GMM algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Computes EM for GMM in the batch processing mode |
![]() ![]() ![]() ![]() | Class representing an engine |
![]() ![]() ![]() ![]() | Abstract class that specifies the interface of the algorithms for computing kernel functions in the batch processing mode |
![]() ![]() ![]() ![]() | Base class representing K-Means algorithm initialization in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the results of K-Means algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing moments of low order in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Computes moments of low order in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the absolute value function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the logistic function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the rectified linear function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes SmoothReLU in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the softmax function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the hyperbolic tangent function in the batch processing mode |
![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing multivariate outlier detection in the batch processing mode |
![]() ![]() ![]() ![]() | Class representing a neural network weights and biases initializer |
![]() ![]() ![]() ![]() | Abstract class which defines interface for the layer |
![]() ![]() ![]() ![]() ![]() | Implements the abstract interface LayerIface. LayerIfaceImpl is, in turn, the base class for the classes interfacing the layers |
![]() ![]() ![]() ![]() | Abstract class which defines interface for the layer |
![]() ![]() ![]() ![]() ![]() | Implements the abstract interface LayerIface. LayerIfaceImpl is, in turn, the base class for the classes interfacing the layers |
![]() ![]() ![]() ![]() | Normalizes datasets in the batch processing mode |
![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Normalizes datasets in the batch processing mode |
![]() ![]() ![]() ![]() | Interface for computing the Optimization solver in the batch processing mode |
![]() ![]() ![]() ![]() | Interface for computing the Objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the results of the PCA algorithm |
![]() ![]() ![]() ![]() | Computes the results of the PCA transformation algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the results of the pivoted QR algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the results of the QR decomposition algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Computes values of quantiles in the batch processing mode |
![]() ![]() ![]() ![]() | Sorts the datasets by components of the random vector in the batch processing mode |
![]() ![]() ![]() ![]() | Computes results of the SVD algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Runs the univariate outlier detection algorithm in the batch processing mode |
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![]() ![]() ![]() ![]() | Interface for the correlation or variance-covariance matrix algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() ![]() | Computes correlation or variance-covariance matrix in the distributed processing mode |
![]() ![]() ![]() ![]() | Interface for correlation or variance-covariance matrix computation algorithms in the distributed processing mode on master node |
![]() ![]() ![]() ![]() ![]() | Computes correlation or variance-covariance matrix in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the tenth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the eleventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the eighth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the seventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the third step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the fourth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the fifth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the sixth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the seventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the eighth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the DBSCAN algorithm in the seventh step of the distributed processing mode |
![]() ![]() ![]() ![]() | Base class representing K-Means algorithm initialization in the distributed processing mode |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the 2nd step of the distributed processing mode |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the 3rd step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on the master node |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the 4th step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on a local node |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the 5th step of the distributed processing mode. Used with parallelPlus method only |
![]() ![]() ![]() ![]() | Base class representing K-Means algorithm initialization in the distributed processing mode |
![]() ![]() ![]() ![]() ![]() | Computes initial clusters for K-Means algorithm in the 2nd step of the distributed processing mode. Used with plusPlus and parallelPlus methods only on a local node |
![]() ![]() ![]() ![]() | Computes the results of K-Means algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of K-Means algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the result of the second step of the moments of low order algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the result of the PCA algorithm |
![]() ![]() ![]() ![]() | Computes the result of the PCA Correlation algorithm on local nodes |
![]() ![]() ![]() ![]() | Computes the result of the PCA SVD algorithm on local nodes |
![]() ![]() ![]() ![]() | Computes the results of the QR decomposition algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the QR decomposition algorithm on the second step in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the QR decomposition algorithm on the third step in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes results of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Runs the second step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Runs the third step of the SVD algorithm in the distributed processing mode |
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![]() ![]() ![]() ![]() | Abstract class that specifies interface of the algorithms for computing correlation or variance-covariance matrix in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Interface for correlation or variance-covariance matrix computation algorithms in the distributed processing mode on local nodes |
![]() ![]() ![]() ![]() ![]() | Computes correlation or variance-covariance matrix in the online processing mode |
![]() ![]() ![]() ![]() ![]() ![]() | Computes correlation or variance-covariance matrix in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Computes moments of low order in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Computes the result of the first step of the moments of low order algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes the results of the PCA algorithm |
![]() ![]() ![]() ![]() ![]() | Computes the results of the PCA algorithm on the local nodes |
![]() ![]() ![]() ![]() | Computes the results of the PCA Correlation algorithm |
![]() ![]() ![]() ![]() | Computes the results of the PCA SVD algorithm |
![]() ![]() ![]() ![]() | Computes the results of the QR decomposition algorithm in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Computes the result of the first step of the QR decomposition algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Computes results of the SVD algorithm in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Runs the first step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | Runs implicit ALS model-based prediction in the distributed processing mode |
![]() ![]() ![]() ![]() | Performs implicit ALS model-based prediction in the first step of the distributed processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Base class for making predictions based on the model of the classification algorithms |
![]() ![]() ![]() ![]() ![]() | Base class for predicting results of boosting classifiers |
![]() ![]() ![]() ![]() ![]() | Predicts decision_forest classification results |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the Decision tree model-based prediction |
![]() ![]() ![]() ![]() ![]() | Predicts gradient boosted trees classification results |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the KD-tree based kNN model-based prediction |
![]() ![]() ![]() ![]() ![]() | Predicts logistic regression results |
![]() ![]() ![]() ![]() ![]() | Provides methods to run implementations of the multi-class classifier prediction algorithm |
![]() ![]() ![]() ![]() ![]() | Predicts the results of the multinomial naive Bayes classification |
![]() ![]() ![]() ![]() ![]() | Algorithm class for making predictions based on the SVM model |
![]() ![]() ![]() ![]() ![]() | Base class for making predictions based on the weak learner model |
![]() ![]() ![]() ![]() | Base class for making predictions based on the model of the classification algorithms |
![]() ![]() ![]() ![]() | Predicts the results of the implicit ALS algorithm |
![]() ![]() ![]() ![]() | Provides methods for neural network model-based prediction in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to run implementations of the regression model-based prediction |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() ![]() | Algorithm class for training the classifier model |
![]() ![]() ![]() ![]() ![]() | Base class for training models of boosting algorithms in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Trains model of the Decision forest algorithms in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Provides methods for Decision tree model-based training in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Trains model of the Gradient Boosted Trees algorithms in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Provides methods for KD-tree based kNN model-based training in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Trains model of the logistic regression algorithms in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Trains model of the logistic regression algorithms in the batch processing mode |
![]() ![]() ![]() ![]() ![]() | Algorithm for the multi-class classifier model training |
![]() ![]() ![]() ![]() ![]() | Algorithm class for training the naive Bayes model |
![]() ![]() ![]() ![]() ![]() | Algorithm class to train the SVM model |
![]() ![]() ![]() ![]() ![]() | Base class for training the weak learner model in the batch processing mode |
![]() ![]() ![]() ![]() | Algorithm class for training the classifier model |
![]() ![]() ![]() ![]() | Algorithm class for initializing the implicit ALS model |
![]() ![]() ![]() ![]() | Algorithm class for training the implicit ALS model |
![]() ![]() ![]() ![]() | Provides methods for neural network model-based training in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods for the regression model-based training in the batch processing mode |
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![]() ![]() ![]() ![]() | Initializes the implicit ALS model in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Initializes the implicit ALS model in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Trains the implicit ALS model in the distributed processing mode |
![]() ![]() ![]() ![]() | Trains the implicit ALS model in the first step of the distributed processing mode |
![]() ![]() ![]() ![]() | Trains the implicit ALS model in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Trains the implicit ALS model in the third step of the distributed processing mode |
![]() ![]() ![]() ![]() | Trains the implicit ALS model in the fourth step of the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods for linear regression model-based training in the distributed processing mode |
![]() ![]() ![]() ![]() | Performs linear regression model-based training in the the second step of distributed processing mode |
![]() ![]() ![]() ![]() | Algorithm class for training naive Bayes final model on the second step in the distributed processing mode |
![]() ![]() ![]() ![]() | Algorithm class for training naive Bayes final model on the second step in the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods for neural network model-based training in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods for neural network model-based training in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods for ridge regression model-based training in the distributed processing mode |
![]() ![]() ![]() ![]() | Performs ridge regression model-based training in the the second step of distributed processing mode |
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![]() ![]() ![]() ![]() | Algorithm class for training the classifier model in the online processing mode |
![]() ![]() ![]() ![]() ![]() | Algorithm class for training naive Bayes model |
![]() ![]() ![]() ![]() ![]() ![]() | Algorithm class for training Naive Bayes partial model in the distributed processing mode |
![]() ![]() ![]() ![]() | Algorithm class for training the classifier model in the online processing mode |
![]() ![]() ![]() ![]() | Provides methods for the regression model-based training in the online processing mode |
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![]() ![]() ![]() | Basic statistics for each column of original Numeric Table |
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![]() ![]() ![]() | Implementation of reference counter |
![]() ![]() ![]() ![]() | Provides implementations of the operator() method of the RefCounter class |
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![]() ![]() ![]() | Predict AdaBoost classification results |
![]() ![]() ![]() | Class that represents a set of quality metrics to check the model trained with the AdaBoost algorithm |
![]() ![]() ![]() | Class that represents a set of quality metrics to check the model trained with the AdaBoost algorithm |
![]() ![]() ![]() | Trains model of the AdaBoost algorithms in batch mode |
![]() ![]() ![]() | Predicts BrownBoost classification results |
![]() ![]() ![]() | Class that represents a set of quality metrics to check the model trained with the BrownBoost algorithm |
![]() ![]() ![]() | Trains model of the BrownBoost algorithms in the batch processing mode |
![]() ![]() ![]() | Computes the confusion matrix for a binary classifier in the batch processing mode |
![]() ![]() ![]() | Computes the confusion matrix for a multi-class classifier in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the decision forest model-based prediction |
![]() ![]() ![]() | Provides methods for decision forest model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the Decision tree model-based prediction |
![]() ![]() ![]() | Provides methods to run implementations of the Decision tree model-based prediction |
![]() ![]() ![]() | Provides methods for Decision tree model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods for Decision tree model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the elastic net model-based prediction |
![]() ![]() ![]() | Provides methods for elastic net model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the model-based prediction |
![]() ![]() ![]() | Provides methods for model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the lasso regression model-based prediction |
![]() ![]() ![]() | Provides methods for lasso regression model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the regression model-based prediction |
![]() ![]() ![]() | Provides methods for linear model model-based training in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the linear regression model-based prediction |
![]() ![]() ![]() | Computes the linear regression quality metric in the batch processing mode |
![]() ![]() ![]() | Computes the linear regression quality metric in the batch processing mode |
![]() ![]() ![]() | Class that represents a quality metric set to check the model trained with linear regression algorithm |
![]() ![]() ![]() | Provides methods for linear regression model-based training in the batch processing mode |
![]() ![]() ![]() | Predicts LogitBoost classification results |
![]() ![]() ![]() | Class that represents a set of quality metrics to check the model trained with the LogitBoost training algorithm |
![]() ![]() ![]() | Trains model of the LogitBoost algorithms in the batch processing mode |
![]() ![]() ![]() | Class containing a set of quality metrics to check the model trained with the multi-class classifier algorithm |
![]() ![]() ![]() | Class containing a quality metric set to check the model trained with the Naive Bayes algorithm |
![]() ![]() ![]() | Provides methods for the backward logistic cross-entropy layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward logistic cross layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the logistic cross-entropy layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward softmax cross-entropy layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward softmax cross layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the softmax cross-entropy layer in the batch processing mode |
![]() ![]() ![]() | Interface for computing the Sum of functions in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the Cross-entropy loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the Logistic loss objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the Mean squared error objective function in the batch processing mode |
![]() ![]() ![]() ![]() | Computes the objective function with precomputed characteristics in the batch processing mode |
![]() ![]() ![]() | Interface for computing the Sum of functions in the batch processing mode |
![]() ![]() ![]() | Computes the linear regression quality metric in the batch processing mode |
![]() ![]() ![]() | Class that represents a quality metric set of the pca algorithm |
![]() ![]() ![]() | Provides methods to run implementations of the ridge regression model-based prediction |
![]() ![]() ![]() | Provides methods for ridge regression model-based training in the batch processing mode |
![]() ![]() ![]() | Predicts results of the decision stump classification |
![]() ![]() ![]() | Predicts results of the decision stump regression |
![]() ![]() ![]() | Trains the decision stump model |
![]() ![]() ![]() | Trains the decision stump model |
![]() ![]() ![]() | Class that represents a quality metric set to check the model trained with the SVM algorithm |
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![]() ![]() ![]() | Predict AdaBoost classification results |
![]() ![]() ![]() | Trains model of the AdaBoost algorithms in batch mode |
![]() ![]() ![]() | Predicts BrownBoost classification results |
![]() ![]() ![]() | Trains model of the BrownBoost algorithms in the batch processing mode |
![]() ![]() ![]() | Predicts decision_forest classification results |
![]() ![]() ![]() | Predicts decision_forest classification results |
![]() ![]() ![]() | Trains model of the Decision forest algorithms in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the Decision tree model-based prediction |
![]() ![]() ![]() | Provides methods for Decision tree model-based training in the batch processing mode |
![]() ![]() ![]() | Predicts gradient boosted trees classification results |
![]() ![]() ![]() | Trains model of the Gradient Boosted Trees algorithms in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the KD-tree based kNN model-based prediction |
![]() ![]() ![]() | Provides methods for KD-tree based kNN model-based training in the batch processing mode |
![]() ![]() ![]() | Predicts logistic regression results |
![]() ![]() ![]() | Trains model of the logistic regression algorithms in the batch processing mode |
![]() ![]() ![]() | Predicts LogitBoost classification results |
![]() ![]() ![]() | Trains model of the LogitBoost algorithms in the batch processing mode |
![]() ![]() ![]() | Provides methods to run implementations of the multi-class classifier prediction algorithm |
![]() ![]() ![]() | Algorithm for the multi-class classifier model training |
![]() ![]() ![]() | Predicts the results of the multinomial naive Bayes classification |
![]() ![]() ![]() | Algorithm class for training the naive Bayes model |
![]() ![]() ![]() | Computes Adaptive gradient descent in the batch processing mode |
![]() ![]() ![]() | Computes Coordinate descent in the batch processing mode |
![]() ![]() ![]() | Computes the Cross-entropy loss objective function in the batch processing mode |
![]() ![]() ![]() | Computes LBFGS in the batch processing mode |
![]() ![]() ![]() | Computes the Logistic loss objective function in the batch processing mode |
![]() ![]() ![]() | Computes the Mean squared error objective function in the batch processing mode |
![]() ![]() ![]() | Computes the objective function with precomputed characteristics in the batch processing mode |
![]() ![]() ![]() | Computes Stochastic average gradient descent in the batch processing mode |
![]() ![]() ![]() | Computes Stochastic gradient descent in the batch processing mode |
![]() ![]() ![]() | Predicts results of the decision stump classification |
![]() ![]() ![]() | Trains the decision stump model |
![]() ![]() ![]() | Algorithm class for making predictions based on the SVM model |
![]() ![]() ![]() | Algorithm class to train the SVM model |
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![]() ![]() ![]() | Provides methods for bernoulli distribution computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for normal distribution computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for uniform distribution computations in the batch processing mode |
![]() ![]() ![]() | Class representing an engine that has collection of independent streams obtained from RNGs from same family |
![]() ![]() ![]() | Provides methods for mcg59 engine computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for mt19937 engine computations in the batch processing mode |
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![]() ![]() ![]() | Interface for computing the iterative solver in the batch processing mode |
![]() ![]() ![]() ![]() | Computes Adaptive gradient descent in the batch processing mode |
![]() ![]() ![]() ![]() | Computes LBFGS in the batch processing mode |
![]() ![]() ![]() ![]() | Computes Stochastic average gradient descent in the batch processing mode |
![]() ![]() ![]() ![]() | Computes Stochastic gradient descent in the batch processing mode |
![]() ![]() ![]() | Interface for computing the iterative solver in the batch processing mode |
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![]() ![]() ![]() | Class that provides functionality of Collection container for objects derived from SerializationIface interface and implements SerializationIface itself |
![]() ![]() ![]() | Wrapper for services::Collection that allocates and deallocates memory using internal new/delete operators |
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![]() ![]() ![]() | Parameter for bzip2 compression and decompression |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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) |
![]() ![]() ![]() | Parameter for zlib compression and decompression |
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![]() ![]() ![]() | Implementation of the Compressor class for the bzip2 compression method |
![]() ![]() ![]() | Implementation of the Compressor class for the LZO compression method |
![]() ![]() ![]() | Implementation of the Compressor class for the run-length encoding method |
![]() ![]() ![]() | Implementation of the Compressor class for the zlib compression method |
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![]() ![]() ![]() | Internal implementation of feature modifier configuration |
![]() ![]() ![]() | Internal implementation of feature modifier configuration |
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![]() ![]() ![]() | Abstract class that defines interface of modifier configuration |
![]() ![]() ![]() ![]() | Base class that represents modifier configuration, object of that class is passed to the modifier on initialization and finalization stages |
![]() ![]() ![]() | Abstract class that defines interface of modifier configuration |
![]() ![]() ![]() ![]() | Base class that represents modifier configuration, object of that class is passed to the modifier on initialization and finalization stages |
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![]() ![]() ![]() | Internal implementation of feature modifier context |
![]() ![]() ![]() | Internal implementation of feature modifier context |
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![]() ![]() ![]() | Abstract class that defines interface of modifier context |
![]() ![]() ![]() ![]() | Base class that represents modifier context, object of that class is passed to the modifier as an argument of FeatureModifierIface::apply method |
![]() ![]() ![]() | Abstract class that defines interface of modifier context |
![]() ![]() ![]() ![]() | Base class that represents modifier context, object of that class is passed to the modifier as an argument of FeatureModifierIface::apply method |
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![]() ![]() ![]() | Specialization of Decompressor class for Bzip2 compression method |
![]() ![]() ![]() | Specialization of Decompressor class for LZO compression method |
![]() ![]() ![]() | Implementation of the Decompressor class for the run-length decoding method |
![]() ![]() ![]() | Implementation of the Decompressor class for the zlib compression method |
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![]() ![]() ![]() | Provides methods for mt2203 engine computations in the batch processing mode |
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![]() ![]() ![]() | Implementation of FeatureId that uses number as a reference to particular feature |
![]() ![]() ![]() | Implementation of FeatureId that uses string as a reference to particular feature |
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![]() ![]() ![]() | Implementation of FeatureIdCollection to store a list of feature identifiers |
![]() ![]() ![]() | Implementation of FeatureIdCollection to store a range of feature identifiers |
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![]() ![]() ![]() | Default implementation of feature mapping |
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![]() ![]() ![]() | Implementation of FeatureIndices to store a list of feature indices |
![]() ![]() ![]() | Implementation of FeatureIndices to store a range of feature indices |
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![]() ![]() ![]() | Feature modifier that determines suitable feature type and parses tokens according to determined type |
![]() ![]() ![]() | Feature modifier that parses tokens as categorical features |
![]() ![]() ![]() | Feature modifier that parses tokens as continuous features |
![]() ![]() ![]() | Feature modifier that parses tokens as continuous features |
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![]() ![]() ![]() | Specialization of modifiers::FeatureModifierIface for CSV feature modifier |
![]() ![]() ![]() ![]() | Base class for feature modifier, intended for inheritance from the user side |
![]() ![]() ![]() | Specialization of modifiers::FeatureModifierIface for SQL feature modifier |
![]() ![]() ![]() ![]() | Base class for feature modifier, intended for inheritance from the user side |
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![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() | Provides methods for gaussian initializer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for truncated gaussian initializer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for uniform initializer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for Xavier initializer computations in the batch processing mode |
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![]() ![]() ![]() | Input objects in the prediction stage of the adaboost algorithm |
![]() ![]() ![]() | Input for the association rules algorithm |
![]() ![]() ![]() | Input objects for the BACON outlier detection algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the brownboost algorithm |
![]() ![]() ![]() | Input parameters for the Cholesky algorithm |
![]() ![]() ![]() | Base class for working with input objects in the prediction stage of the classification algorithm |
![]() ![]() ![]() ![]() | Input objects in the prediction stage of the classification algorithm |
![]() ![]() ![]() ![]() ![]() | Input objects in the prediction stage of the adaboost algorithm |
![]() ![]() ![]() ![]() ![]() | Input objects in the prediction stage of the brownboost algorithm |
![]() ![]() ![]() ![]() ![]() | Input objects in the prediction stage of the logitboost algorithm |
![]() ![]() ![]() | Base class for input objects of the binary confusion matrix algorithm |
![]() ![]() ![]() | Base class for the input objects of the confusion matrix algorithm in the training stage of the classification algorithm |
![]() ![]() ![]() | Abstract class that specifies the interface of the classes of the classification algorithm input objects |
![]() ![]() ![]() ![]() | Base class for the input objects in the training stage of the classification algorithms |
![]() ![]() ![]() | Input objects for the correlation distance algorithm |
![]() ![]() ![]() | Input objects for the cosine distance algorithm |
![]() ![]() ![]() | Abstract class that specifies interface for classes that declare input of the correlation or variance-covariance matrix algorithm |
![]() ![]() ![]() ![]() | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on master node |
![]() ![]() ![]() ![]() | Input objects of the correlation or variance-covariance matrix algorithm |
![]() ![]() ![]() ![]() ![]() | Input parameters of the distributed Covariance algorithm. Represents inputs of the algorithm on local node |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the tenth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the eleventh step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the twelfth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the thirteenth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the third step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the fourth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the fifth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the sixth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the seventh step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the eighth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm in the ninth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the DBSCAN algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the DECISION_FOREST_CLASSIFICATION algorithm |
![]() ![]() ![]() | Provides an interface for input objects for making decision forest model-based prediction |
![]() ![]() ![]() | Input objects for decision forest model-based training |
![]() ![]() ![]() | Provides an interface for input objects for making Decision tree model-based prediction |
![]() ![]() ![]() | Base class for the input objects in the training stage of the classification algorithms |
![]() ![]() ![]() | Provides an interface for input objects for making Decision tree model-based prediction |
![]() ![]() ![]() | Base class for the input objects in the training stage of the regression algorithms |
![]() ![]() ![]() | Input objects for distributions |
![]() ![]() ![]() | Provides an interface for input objects for making elastic net model-based prediction |
![]() ![]() ![]() | Input objects for elastic net model-based training |
![]() ![]() ![]() | Input objects for the computation of initial values for the EM for GMM algorithm |
![]() ![]() ![]() | Input objects for the EM for GMM algorithm |
![]() ![]() ![]() | Input objects for engines |
![]() ![]() ![]() | Input objects in the prediction stage of the GBT_CLASSIFICATION algorithm |
![]() ![]() ![]() | Provides an interface for input objects for making model-based prediction |
![]() ![]() ![]() | Input objects for model-based training |
![]() ![]() ![]() | Input interface for the rating prediction stage of the implicit ALS algorithm |
![]() ![]() ![]() ![]() | Input objects for the first step of the rating prediction stage of the implicit ALS algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Input objects for the rating prediction stage of the implicit ALS algorithm |
![]() ![]() ![]() | Input objects for the implicit ALS initialization algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS initialization algorithm |
![]() ![]() ![]() ![]() | Input objects for the implicit ALS initialization algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS training algorithm in the first step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS training algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS training algorithm in the third step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS training algorithm in the fourth step of the distributed processing mode |
![]() ![]() ![]() | Input objects for the implicit ALS training algorithm |
![]() ![]() ![]() | Provides an interface for input objects for making KD-tree based kNN model-based prediction |
![]() ![]() ![]() | Input objects for the kernel function algorithm |
![]() ![]() ![]() | Input objects for the kernel function linear algorithm |
![]() ![]() ![]() | Input objects for the RBF kernel algorithm |
![]() ![]() ![]() | Interface for K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 3rd step on a master node |
![]() ![]() ![]() | Interface for K-Means distributed Input classes |
![]() ![]() ![]() | Interface for K-Means initialization batch and distributed Input classes |
![]() ![]() ![]() ![]() | Input objects for computing initials clusters for K-Means algorithm in the second step of the distributed processing mode |
![]() ![]() ![]() ![]() | Input objects for computing initial centroids for K-Means algorithm |
![]() ![]() ![]() ![]() ![]() | Interface for K-Means initialization distributed Input classes used with plusPlus and parallelPlus methods only on the 2nd step on a local node |
![]() ![]() ![]() ![]() ![]() | Interface for K-Means distributed Input classes used with plusPlus and parallelPlus methods only on the 4th step on a local node |
![]() ![]() ![]() | Interface for input objects for K-Means algorithm in the batch and distributed processing modes |
![]() ![]() ![]() ![]() | Input objects for K-Means algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Input objects for K-Means algorithm |
![]() ![]() ![]() | Provides an interface for input objects for making lasso regression model-based prediction |
![]() ![]() ![]() | Input objects for lasso regression model-based training |
![]() ![]() ![]() | Provides an interface for input objects for making the regression model-based prediction |
![]() ![]() ![]() | Input objects for the regression model-based training |
![]() ![]() ![]() | Provides an interface for input objects for making linear regression model-based prediction |
![]() ![]() ![]() | Input objects for a group of betas quality metrics |
![]() ![]() ![]() | Input objects for single beta quality metrics |
![]() ![]() ![]() | Input object for linear regression model-based training in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for linear regression model-based training |
![]() ![]() ![]() | Input objects in the prediction stage of the LOGISTIC_REGRESSION algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the logitboost algorithm |
![]() ![]() ![]() | Abstract class that specifies interface of the input objects for the low order moments algorithm |
![]() ![]() ![]() ![]() | Input objects for the low order moments algorithm in the distributed processing mode on master node |
![]() ![]() ![]() ![]() | Input objects for the low order moments algorithm |
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![]() ![]() ![]() | Input objects for the absolute value function |
![]() ![]() ![]() | Input objects for the logistic function |
![]() ![]() ![]() | Input objects for the rectified linear function |
![]() ![]() ![]() | Input parameters for the SmoothReLU algorithm |
![]() ![]() ![]() | Input objects for the softmax function |
![]() ![]() ![]() | Input objects for the hyperbolic tangent function |
![]() ![]() ![]() | Input objects in the prediction stage of the Multi-class classifier algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the multinomial naive Bayes algorithm |
![]() ![]() ![]() | Input objects of the naive Bayes training algorithm in the batch and online processing mode |
![]() ![]() ![]() | Input objects for the multivariate outlier detection algorithm |
![]() ![]() ![]() | Input objects for initializer algorithm |
![]() ![]() ![]() | Input objects for the backward abs layer |
![]() ![]() ![]() | Input objects for the forward abs layer |
![]() ![]() ![]() | Input objects for the backward average 1D pooling layer |
![]() ![]() ![]() | Input objects for the forward average 1D pooling layer. See pooling1d::forward::Input |
![]() ![]() ![]() | Input objects for the backward average 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward average 2D pooling layer. See pooling2d::forward::Input |
![]() ![]() ![]() | Input objects for the backward average 3D pooling layer |
![]() ![]() ![]() | Input objects for the forward average 3D pooling layer. See pooling3d::forward::Input |
![]() ![]() ![]() | Abstract class that specifies interface of the input objects for the neural network layer algorithm |
![]() ![]() ![]() ![]() | Input parameters for the layer algorithm |
![]() ![]() ![]() | Input objects for the backward batch normalization layer |
![]() ![]() ![]() | Input objects for the forward batch normalization layer |
![]() ![]() ![]() | Input parameters for the backward concat layer |
![]() ![]() ![]() | Input objects for the forward concat layer |
![]() ![]() ![]() | Input objects for the backward 2D convolution layer |
![]() ![]() ![]() | Input objects for the forward 2D convolution layer |
![]() ![]() ![]() | Input objects for the backward dropout layer |
![]() ![]() ![]() | Input objects for the forward dropout layer |
![]() ![]() ![]() | Input objects for the backward element-wise sum layer |
![]() ![]() ![]() | Input objects for the forward element-wise sum layer |
![]() ![]() ![]() | Input objects for the backward ELU layer |
![]() ![]() ![]() | Input objects for the forward ELU layer |
![]() ![]() ![]() | Abstract class that specifies interface of the input objects for the neural network layer |
![]() ![]() ![]() ![]() | Input objects for layer algorithm |
![]() ![]() ![]() | Input objects for the backward fully-connected layer |
![]() ![]() ![]() | Input objects for the forward fully-connected layer |
![]() ![]() ![]() | Input objects for the backward local contrast normalization layer |
![]() ![]() ![]() | Input objects for the forward local contrast normalization layer |
![]() ![]() ![]() | Input objects for the backward 2D locally connected layer |
![]() ![]() ![]() | Input objects for the forward 2D locally connected layer |
![]() ![]() ![]() | Input objects for the backward logistic layer |
![]() ![]() ![]() | Input objects for the forward logistic layer |
![]() ![]() ![]() | Input objects for the backward loss layer |
![]() ![]() ![]() | Input objects for the forward loss layer |
![]() ![]() ![]() | Input objects for the backward logistic cross-entropy layer |
![]() ![]() ![]() | Input objects for the forward logistic cross-entropy layer |
![]() ![]() ![]() | Input objects for the backward softmax cross-entropy layer |
![]() ![]() ![]() | Input objects for the forward softmax cross-entropy layer |
![]() ![]() ![]() | Input parameters for the backward local response normalization layer |
![]() ![]() ![]() | Input parameters for the forward local response normalization layer |
![]() ![]() ![]() | Input objects for the backward maximum 1D pooling layer |
![]() ![]() ![]() | Input objects for the forward maximum 1D pooling layer See pooling1d::forward::Input |
![]() ![]() ![]() | Input objects for the backward maximum 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward maximum 2D pooling layer See pooling2d::forward::Input |
![]() ![]() ![]() | Input objects for the backward maximum 3D pooling layer |
![]() ![]() ![]() | Input objects for the forward maximum 3D pooling layer See pooling3d::forward::Input |
![]() ![]() ![]() | Input objects for the backward 1D pooling layer |
![]() ![]() ![]() | Input objects for the forward 1D pooling layer |
![]() ![]() ![]() | Input objects for the backward 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward 2D pooling layer |
![]() ![]() ![]() | Input objects for the backward 3D pooling layer |
![]() ![]() ![]() | Input objects for the forward 3D pooling layer |
![]() ![]() ![]() | Input parameters for the backward prelu layer |
![]() ![]() ![]() | Input objects for the forward prelu layer |
![]() ![]() ![]() | Input objects for the backward relu layer |
![]() ![]() ![]() | Input objects for the forward relu layer |
![]() ![]() ![]() | Input objects for the backward reshape layer |
![]() ![]() ![]() | Input objects for the forward reshape layer |
![]() ![]() ![]() | Input objects for the backward smooth relu layer |
![]() ![]() ![]() | Input objects for the forward smooth relu layer |
![]() ![]() ![]() | Input objects for the backward softmax layer |
![]() ![]() ![]() | Input objects for the forward softmax layer |
![]() ![]() ![]() | Input objects for the backward spatial pyramid average 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward spatial pyramid average 2D pooling layer See pooling2d::forward::Input |
![]() ![]() ![]() | Input objects for the backward spatial pyramid maximum 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward spatial pyramid maximum 2D pooling layer See pooling2d::forward::Input |
![]() ![]() ![]() | Input objects for the backward 2D spatial layer |
![]() ![]() ![]() | Input objects for the forward 2D spatial layer |
![]() ![]() ![]() | Input objects for the backward spatial pyramid stochastic 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward spatial pyramid stochastic 2D pooling layer See pooling2d::forward::Input |
![]() ![]() ![]() | Input parameters for the backward split layer |
![]() ![]() ![]() | Input objects for the forward split layer |
![]() ![]() ![]() | Input objects for the backward stochastic 2D pooling layer |
![]() ![]() ![]() | Input objects for the forward stochastic 2D pooling layer See pooling2d::forward::Input |
![]() ![]() ![]() | Input objects for the backward hyperbolic tangent layer |
![]() ![]() ![]() | Input objects for the forward hyperbolic tangent layer |
![]() ![]() ![]() | Input objects for the backward 2D transposed convolution layer |
![]() ![]() ![]() | Input objects for the forward 2D transposed convolution layer |
![]() ![]() ![]() | Input objects of the neural networks prediction algorithm |
![]() ![]() ![]() | Input objects of the neural network training algorithm |
![]() ![]() ![]() | Input objects of the neural network training algorithm |
![]() ![]() ![]() ![]() | Input objects of the neural network training algorithm in the distributed processing mode |
![]() ![]() ![]() | Input objects for the min-max normalization algorithm |
![]() ![]() ![]() | Input objects for the z-score normalization algorithm |
![]() ![]() ![]() | Input parameters for the iterative solver algorithm |
![]() ![]() ![]() ![]() | Input class for the Adaptive gradient descent algorithm |
![]() ![]() ![]() ![]() | Input class for LBFGS algorithm |
![]() ![]() ![]() ![]() | Input class for the Stochastic average gradient descent algorithm |
![]() ![]() ![]() ![]() | Input for the Stochastic gradient descent algorithm |
![]() ![]() ![]() | Input parameters for the iterative solver algorithm |
![]() ![]() ![]() | Input objects for the Objective function |
![]() ![]() ![]() | Input objects for the Sum of functions |
![]() ![]() ![]() ![]() | Input objects for the Cross-entropy loss objective function |
![]() ![]() ![]() ![]() | Input objects for the Logistic loss objective function |
![]() ![]() ![]() ![]() | Input objects for the Mean squared error objective function |
![]() ![]() ![]() | Input objects for the Sum of functions |
![]() ![]() ![]() | Abstract class that specifies interface for classes that declare input of the PCA algorithm |
![]() ![]() ![]() ![]() | Input objects for the PCA Correlation algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Input objects of the PCA SVD algorithm in the distributed processing mode |
![]() ![]() ![]() ![]() | Input objects for the PCA algorithm |
![]() ![]() ![]() | Input objects for explained variance quality metrics |
![]() ![]() ![]() | Input objects for the PCA transformation algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
![]() ![]() ![]() | Input objects for the pivoted QR algorithm in the batch processing mode |
![]() ![]() ![]() | Input objects for the second step of the QR decomposition algorithm in the distributed processing mode |
![]() ![]() ![]() | Input objects for the third step of the QR decomposition algorithm in the distributed processing mode |
![]() ![]() ![]() | Input objects for the QR decomposition algorithm in the batch and online processing modes and for the first distributed step of the algorithm |
![]() ![]() ![]() | Input objects for the quantiles algorithm |
![]() ![]() ![]() | Provides an interface for input objects for making the regression model-based prediction |
![]() ![]() ![]() | Input objects for the regression model-based training |
![]() ![]() ![]() | Provides an interface for input objects for making ridge regression model-based prediction |
![]() ![]() ![]() | Input object for ridge regression model-based training in the second step of the distributed processing mode |
![]() ![]() ![]() | Input objects for ridge regression model-based training |
![]() ![]() ![]() | Input objects for the sorting algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the stump algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the stump algorithm |
![]() ![]() ![]() | Input objects in the prediction stage of the stump algorithm |
![]() ![]() ![]() | Input objects for the second step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() | Input objects for the third step of the SVD algorithm in the distributed processing mode |
![]() ![]() ![]() | Input objects for the SVD algorithm in the batch processing and online processing modes, and the first step in the distributed processing mode |
![]() ![]() ![]() | Input objects in the prediction stage of the svm algorithm |
![]() ![]() ![]() | Input objects for the univariate outlier detection algorithm |
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![]() ![]() ![]() | Input class for the Adaptive gradient descent algorithm |
![]() ![]() ![]() | Input class for the Coordinate descent algorithm |
![]() ![]() ![]() | Input objects for the Cross-entropy loss objective function |
![]() ![]() ![]() | Input class for LBFGS algorithm |
![]() ![]() ![]() | Input objects for the Logistic loss objective function |
![]() ![]() ![]() | Input objects for the Mean squared error objective function |
![]() ![]() ![]() | Input class for the Stochastic average gradient descent algorithm |
![]() ![]() ![]() | Input for the Stochastic gradient descent algorithm |
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![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects for the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the pca algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
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![]() ![]() ![]() | Input objects of the naive Bayes training algorithm in the distributed processing mode |
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![]() ![]() ![]() | Computes a linear kernel function in the batch processing mode |
![]() ![]() ![]() | Computes the RBF kernel function in the batch processing mode |
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![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
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![]() ![]() ![]() | Class that implements functionality of the collection of input objects of the quality metrics algorithm |
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![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Class containing methods for the forward ELU layer using algorithmFPType precision arithmetic |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Class containing methods for the forward relu layer using algorithmFPType precision arithmetic |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Class containing methods for the forward softmax layer using algorithmFPType precision arithmetic |
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![]() ![]() ![]() | 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 |
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![]() ![]() ![]() | 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 |
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![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() | Provides methods for the abs layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the average 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the average 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the batch normalization layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the concat layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward 2D convolution layer of neural network in the batch processing mode |
![]() ![]() ![]() | Provides methods for the dropout layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward element-wise sum layer of neural network in the batch processing mode |
![]() ![]() ![]() | Provides methods for the ELU layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward fully-connected layer of neural network in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward local contrast normalization layer of neural network in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward 2D locally connected layer of neural network in the batch processing mode |
![]() ![]() ![]() | Provides methods for the logistic layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the loss layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the local response normalization layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the maximum 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the maximum 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the prelu layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the relu layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the reshape layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the smooth relu layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the softmax layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the spatial pyramid average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the spatial pyramid maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the spatial pyramid stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the split layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the hyperbolic tangent layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward and backward 2D transposed convolution layer of neural network in the batch processing mode |
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![]() ![]() ![]() | Computes the results of the backward abs layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward abs layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward average 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward average 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward average 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward average 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward batch normalization layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward batch normalization layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward concat layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward concat layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward 2D convolution layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for forward 2D convolution layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward dropout layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward dropout layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward element-wise sum layer computations in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward element-wise sum layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward ELU layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward ELU layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward fully-connected layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for forward fully-connected layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward local contrast normalization layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for forward local contrast normalization layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward 2D locally connected layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for forward 2D locally connected layer computations in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward logistic layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward logistic layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward loss layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward loss layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward local response normalization layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward local response normalization layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward maximum 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward maximum 1D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward maximum 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward maximum 3D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward prelu layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward prelu layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward relu layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward relu layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward reshape layer in the batch processing mode |
![]() ![]() ![]() | Computes the result of the forward reshape layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward smooth relu layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward smooth relu layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward softmax layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward softmax layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward spatial pyramid average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward spatial pyramid average 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward spatial pyramid maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward spatial pyramid maximum 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward spatial pyramid stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward split layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward split layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the backward stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Provides methods for the forward stochastic 2D pooling layer in the batch processing mode |
![]() ![]() ![]() | Computes the results of the backward hyperbolic tangent in the batch processing mode |
![]() ![]() ![]() | Computes the results of the forward hyperbolic tangent in the batch processing mode |
![]() ![]() ![]() | Provides methods for backward 2D transposed convolution layer computations in the batch processing mode |
![]() ![]() ![]() | Provides methods for forward 2D transposed convolution layer computations in the batch processing mode |
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![]() ![]() ![]() | Model of the classifier trained by the adaboost::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the adaboost::training::Batch algorithm |
![]() ![]() ![]() | Base class for boosting algorithm models. Contains a collection of weak learner models constructed during training of the boosting algorithm |
![]() ![]() ![]() | Model of the classifier trained by the brownboost::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the brownboost::training::Batch algorithm |
![]() ![]() ![]() | Base class for the model of the classification algorithm |
![]() ![]() ![]() ![]() | Base class for the weak learner model |
![]() ![]() ![]() | Model of the classifier trained by the decision_forest::training::Batch algorithm |
![]() ![]() ![]() | Base class for models trained with the decision forest regression algorithm |
![]() ![]() ![]() | Base class for models trained with the Decision tree algorithm |
![]() ![]() ![]() | Base class for models trained with the Decision tree algorithm |
![]() ![]() ![]() | Base class for models trained with the elastic net algorithm |
![]() ![]() ![]() | Model of the classifier trained by the gbt::training::Batch algorithm |
![]() ![]() ![]() | Base class for models trained with the gradient boosted trees regression algorithm |
![]() ![]() ![]() | Model trained by the implicit ALS algorithm in the batch processing mode |
![]() ![]() ![]() | Partial model trained by the implicit ALS training algorithm in the distributed processing mode |
![]() ![]() ![]() | Base class for models trained with the KD-tree based kNN algorithm |
![]() ![]() ![]() | Base class for models trained with the lasso regression algorithm |
![]() ![]() ![]() | Base class for models trained with the regression algorithm |
![]() ![]() ![]() | Base class for models trained with the linear regression algorithm |
![]() ![]() ![]() ![]() | Model trained with the linear regression algorithm using the normal equations method |
![]() ![]() ![]() ![]() | Model trained with the linear regression algorithm using the QR decomposition-based method |
![]() ![]() ![]() | Model of the classifier trained by the logistic_regression::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the logitboost::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the logitboost::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the multi_class_classifier::training::Batch algorithm |
![]() ![]() ![]() | Multinomial naive Bayes model |
![]() ![]() ![]() | PartialModel represents partial multinomial naive Bayes model |
![]() ![]() ![]() | Class Model object for the prediction stage of neural network algorithm |
![]() ![]() ![]() | Base class for models trained with the regression algorithm |
![]() ![]() ![]() | Base class for models trained with the ridge regression algorithm |
![]() ![]() ![]() ![]() | Model trained with the ridge regression algorithm using the normal equations method |
![]() ![]() ![]() | Model of the classifier trained by the stump::classification::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the stump::training::Batch algorithm |
![]() ![]() ![]() | Model of the regression trained by the stump::regression::training::Batch algorithm |
![]() ![]() ![]() | Model of the classifier trained by the svm::training::Batch algorithm |
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![]() ![]() ![]() | Class Model object for the prediction stage of neural network algorithm |
![]() ![]() ![]() | Class representing the model of neural network |
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![]() ![]() ![]() | Struct containing description of leaf node in classification descision tree |
![]() ![]() ![]() | Struct containing description of leaf node in classification descision tree |
![]() ![]() ![]() | Struct containing description of leaf node in regression descision tree |
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![]() ![]() ![]() | Algorithm class for training naive Bayes model |
![]() ![]() ![]() ![]() | Algorithm class for training Naive Bayes partial model in the distributed processing mode |
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![]() ![]() ![]() | Provides methods for the linear model-based training in the online processing mode |
![]() ![]() ![]() | Provides methods for linear regression model-based training in the online processing mode |
![]() ![]() ![]() ![]() | Performs linear regression model-based training in the the first step of the distributed processing mode |
![]() ![]() ![]() | Provides methods for ridge regression model-based training in the online processing mode |
![]() ![]() ![]() ![]() | Performs ridge regression model-based training in the the first step of the distributed processing mode |
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![]() ![]() ![]() | AdaBoost algorithm parameters |
![]() ![]() ![]() | BrownBoost algorithm parameters |
![]() ![]() ![]() | Class for the parameters of the Decision Forest classification algorithm |
![]() ![]() ![]() | Decision forest algorithm parameters |
![]() ![]() ![]() | Decision tree algorithm parameters |
![]() ![]() ![]() | Parameters of the prediction algorithm |
![]() ![]() ![]() | Gradient Boosted Trees algorithm parameters |
![]() ![]() ![]() | KD-tree based kNN algorithm parameters |
![]() ![]() ![]() | Logistic regression algorithm parameters |
![]() ![]() ![]() | LogitBoost algorithm parameters |
![]() ![]() ![]() | Parameters of the multi-class classifier algorithm |
![]() ![]() ![]() ![]() | Optional multi-class classifier algorithm parameters that are used with the MultiClassClassifierWu prediction method |
![]() ![]() ![]() | Naive Bayes algorithm parameters |
![]() ![]() ![]() | Parameter base class for the Adaptive gradient descent algorithm |
![]() ![]() ![]() | Parameter base class for the Coordinate descent algorithm |
![]() ![]() ![]() | Parameter for Cross-entropy loss objective function |
![]() ![]() ![]() | Parameter class for LBFGS algorithm |
![]() ![]() ![]() | Parameter for Logistic loss objective function |
![]() ![]() ![]() | Parameter for Mean squared error objective function |
![]() ![]() ![]() | Parameter base class for the Stochastic average gradient descent algorithm |
![]() ![]() ![]() | BaseParameter base class for the Stochastic gradient descent algorithm |
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![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() | Stump algorithm parameters |
![]() ![]() ![]() | Optional parameters |
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![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA Correlation algorithm in the distributed computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA Correlation algorithm in the online computing mode |
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![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA SVD algorithm in the online computing mode |
![]() ![]() ![]() | AdaBoost algorithm parameters |
![]() ![]() ![]() | Parameters for the AdaBoost compute() method |
![]() ![]() ![]() | Parameters for the association rules compute() method |
![]() ![]() ![]() | Parameters of the outlier detection computation using the baconDense method |
![]() ![]() ![]() | BrownBoost algorithm parameters |
![]() ![]() ![]() | Base class for the parameters of the classification algorithm |
![]() ![]() ![]() ![]() | Base class for parameters of the boosting algorithm |
![]() ![]() ![]() ![]() | Decision forest algorithm parameters |
![]() ![]() ![]() ![]() | Decision tree algorithm parameters |
![]() ![]() ![]() ![]() | Parameters of the prediction algorithm |
![]() ![]() ![]() ![]() | Gradient Boosted Trees algorithm parameters |
![]() ![]() ![]() ![]() | KD-tree based kNN algorithm parameters |
![]() ![]() ![]() ![]() | Parameters of the prediction algorithm |
![]() ![]() ![]() ![]() | Logistic regression algorithm parameters |
![]() ![]() ![]() ![]() | Logistic regression algorithm parameters |
![]() ![]() ![]() ![]() | Parameters of the multi-class classifier algorithm |
![]() ![]() ![]() ![]() ![]() | Optional multi-class classifier algorithm parameters that are used with the MultiClassClassifierWu prediction method |
![]() ![]() ![]() ![]() | Naive Bayes algorithm parameters |
![]() ![]() ![]() ![]() | Optional parameters |
![]() ![]() ![]() ![]() | Base class for the input objects of the weak learner training and prediction algorithm |
![]() ![]() ![]() | Base class for the parameters of the classification algorithm |
![]() ![]() ![]() | Parameters for the binary confusion matrix compute() method |
![]() ![]() ![]() | Parameters for the compute() method of the multi-class confusion matrix |
![]() ![]() ![]() | Parameters of the correlation or variance-covariance matrix algorithm |
![]() ![]() ![]() ![]() | Parameters of the correlation or variance-covariance matrix algorithm in the online processing mode |
![]() ![]() ![]() | Parameters for the DBSCAN algorithm |
![]() ![]() ![]() | Decision forest algorithm parameters |
![]() ![]() ![]() | Decision forest algorithm parameters |
![]() ![]() ![]() | Parameters for the decision forest algorithm |
![]() ![]() ![]() | Parameters for the decision forest algorithm |
![]() ![]() ![]() | Decision tree algorithm parameters |
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![]() ![]() ![]() | Parameters for the elastic net algorithm |
![]() ![]() ![]() | Parameter for the computation of initial values for the EM for GMM algorithm |
![]() ![]() ![]() | Parameter for the EM for GMM algorithm |
![]() ![]() ![]() | Gradient Boosted Trees algorithm parameters |
![]() ![]() ![]() | Gradient Boosted Trees algorithm parameters |
![]() ![]() ![]() | Parameters of the prediction algorithm |
![]() ![]() ![]() | Parameters for the gradient boosted trees algorithm |
![]() ![]() ![]() | Parameters for the gradient boosted trees algorithm |
![]() ![]() ![]() | Parameters for the compute() method of the implicit ALS algorithm |
![]() ![]() ![]() | Parameters of the compute() method of the implicit ALS initialization algorithm |
![]() ![]() ![]() ![]() | Parameters of the compute() method of the implicit ALS initialization algorithm in the distributed computing mode |
![]() ![]() ![]() | Optional input objects for the kernel function algorithm |
![]() ![]() ![]() | Base classes parameters for computing initial centroids for K-Means algorithm |
![]() ![]() ![]() ![]() | Parameters for computing initial centroids for K-Means algorithm |
![]() ![]() ![]() ![]() | Parameters for computing initial centroids for K-Means algorithm of the batch mode |
![]() ![]() ![]() | Parameters for K-Means algorithm |
![]() ![]() ![]() | Parameters for the lasso regression algorithm |
![]() ![]() ![]() | Parameters for the regression algorithm |
![]() ![]() ![]() | Parameters for the linear regression algorithm |
![]() ![]() ![]() | Parameters for the compute() method of a group of betas quality metrics |
![]() ![]() ![]() | Parameters for the compute() method of single beta quality metrics |
![]() ![]() ![]() | Parameters for the quality metrics set compute() method |
![]() ![]() ![]() | LogitBoost algorithm parameters |
![]() ![]() ![]() | Parameters for the LogitBoost compute() method |
![]() ![]() ![]() | Low order moments algorithm parameters |
![]() ![]() ![]() | Parameters for the multi-class classifier compute() method |
![]() ![]() ![]() | Parameters for the Naive Bayes compute() method |
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![]() ![]() ![]() | Parameters of the outlier detection computation using the baconDense method |
![]() ![]() ![]() | Parameters of the outlier detection computation using the defaultDense method |
![]() ![]() ![]() | Gaussian initializer parameters |
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![]() ![]() ![]() | Truncated gaussian initializer parameters |
![]() ![]() ![]() | Uniform initializer parameters |
![]() ![]() ![]() | Xavier initializer parameters |
![]() ![]() ![]() | Parameters for the abs layer |
![]() ![]() ![]() | Parameters for the average 1D pooling layer |
![]() ![]() ![]() | Parameters for the average 2D pooling layer |
![]() ![]() ![]() | Parameters for the average 3D pooling layer |
![]() ![]() ![]() | Parameters for the forward and backward batch normalization layers |
![]() ![]() ![]() | Concat layer parameters |
![]() ![]() ![]() | 2D convolution layer parameters |
![]() ![]() ![]() | Parameters for the dropout layer |
![]() ![]() ![]() | Parameters for the element-wise sum layer |
![]() ![]() ![]() | Parameters for the ELU layer |
![]() ![]() ![]() | Fully-connected layer parameters |
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![]() ![]() ![]() | Local contrast normalization layer parameters |
![]() ![]() ![]() | 2D locally connected layer parameters |
![]() ![]() ![]() | Parameters for the logistic layer |
![]() ![]() ![]() | Parameters for the logistic cross-entropy layer |
![]() ![]() ![]() | Parameters for the softmax cross-entropy layer |
![]() ![]() ![]() | Parameters for the local response normalization layer |
![]() ![]() ![]() | Parameters for the maximum 1D pooling layer |
![]() ![]() ![]() | Parameters for the maximum 2D pooling layer |
![]() ![]() ![]() | Parameters for the maximum 3D pooling layer |
![]() ![]() ![]() | Parameters for the forward and backward pooling layers |
![]() ![]() ![]() | Parameters for the forward and backward two-dimensional pooling layers |
![]() ![]() ![]() | Parameters for the forward and backward pooling layers |
![]() ![]() ![]() | Parameters for the prelu layer |
![]() ![]() ![]() | Parameters for the relu layer |
![]() ![]() ![]() | Parameters for the reshape layer |
![]() ![]() ![]() | Parameters for the smoothrelu layer |
![]() ![]() ![]() | Parameters for the softmax layer |
![]() ![]() ![]() | Parameters for the spatial pyramid average 2D pooling layer |
![]() ![]() ![]() | Parameters for the spatial pyramid maximum 2D pooling layer |
![]() ![]() ![]() | Parameters for the forward and backward two-dimensional spatial layers |
![]() ![]() ![]() | Parameters for the spatial pyramid stochastic 2D pooling layer |
![]() ![]() ![]() | Split layer parameters |
![]() ![]() ![]() | Parameters for the stochastic 2D pooling layer |
![]() ![]() ![]() | Parameters for the tanh layer |
![]() ![]() ![]() | 2D transposed convolution layer parameters |
![]() ![]() ![]() | Class representing the parameters of neural network prediction |
![]() ![]() ![]() | Class representing the parameters of neural network |
![]() ![]() ![]() | Base class that specifies the parameters of the algorithm in the batch computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the algorithm in the batch computing mode |
![]() ![]() ![]() | Class that specifies the base parameters of the algorithm in the batch computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the default algorithm in the batch computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the default algorithm in the batch computing mode |
![]() ![]() ![]() | Parameter base class for the iterative solver algorithm |
![]() ![]() ![]() ![]() | Parameter base class for the Adaptive gradient descent algorithm |
![]() ![]() ![]() ![]() | Parameter class for LBFGS algorithm |
![]() ![]() ![]() ![]() | Parameter base class for the Stochastic average gradient descent algorithm |
![]() ![]() ![]() ![]() | BaseParameter base class for the Stochastic gradient descent algorithm |
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![]() ![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() ![]() ![]() | Parameter for the Stochastic gradient descent algorithm |
![]() ![]() ![]() | Parameter base class for the iterative solver algorithm |
![]() ![]() ![]() | Parameter for the Objective function |
![]() ![]() ![]() | Parameter for the Sum of functions |
![]() ![]() ![]() ![]() | Parameter for Cross-entropy loss objective function |
![]() ![]() ![]() ![]() | Parameter for Logistic loss objective function |
![]() ![]() ![]() ![]() | Parameter for Mean squared error objective function |
![]() ![]() ![]() | Parameter for the Sum of functions |
![]() ![]() ![]() | Class that specifies the common parameters of the PCA algorithm |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA algorithm in the distributed computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA algorithm in the online computing mode |
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![]() ![]() ![]() | Class that specifies the common parameters of the PCA Batch algorithms |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA Correlation algorithm in the batch computing mode |
![]() ![]() ![]() ![]() | Class that specifies the parameters of the PCA SVD algorithm in the batch computing mode |
![]() ![]() ![]() | Parameters for the compute() method of explained variance quality metrics |
![]() ![]() ![]() | Parameters for the quality metrics set compute() method |
![]() ![]() ![]() | Parameters for the PCA transformation compute method |
![]() ![]() ![]() | Parameter for the pivoted QR computation method |
![]() ![]() ![]() | Parameters for the QR decomposition compute method |
![]() ![]() ![]() | Parameters of the quantiles algorithm |
![]() ![]() ![]() | Parameters for the ridge regression algorithm |
![]() ![]() ![]() ![]() | Parameters for the ridge regression algorithm |
![]() ![]() ![]() | Stump algorithm parameters |
![]() ![]() ![]() | Parameters for the computation method of the SVD algorithm |
![]() ![]() ![]() | Parameters of the univariate outlier detection algorithm |
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![]() ![]() ![]() | Bernoulli distribution parameters |
![]() ![]() ![]() | Normal distribution parameters |
![]() ![]() ![]() | Uniform distribution parameters |
![]() ![]() ![]() | Parameters for the linear kernel function k(X,Y) + b |
![]() ![]() ![]() | Parameters for the radial basis function (RBF) kernel |
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![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the classifier training algorithm in the online or distributed processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the first step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the tenth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the eleventh step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the twelfth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the thirteenth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the second step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the third step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the fourth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the fifth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the sixth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the seventh step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the eighth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the DBSCAN in the ninth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm in the rating prediction stage |
![]() ![]() ![]() | Provides interface to access partial results obtained with the implicit ALS initialization algorithm in the first and second steps of the distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
![]() ![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the implicit ALS initialization algorithm |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the distributed processing mode |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the batch processing mode |
![]() ![]() ![]() | Partial results obtained with the compute() method of K-Means algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access a partial result obtained with the compute() method of the linear model-based training in the online processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the low order moments algorithm in the online or distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the naive Bayes training algorithm in the online or distributed processing |
![]() ![]() ![]() | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial result obtained with the compute() method of the neural network training algorithm in the distributed processing mode |
![]() ![]() ![]() | Provides interface to access partial results obtained with the compute() method of the PCA algorithm in the online or distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the PCA algorithm in the online or distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the PCA Correlation algorithm in the online or distributed processing mode |
![]() ![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of PCA SVD algorithm in the online or distributed processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access a partial result obtained with the compute() method of the regression model-based training in the online processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the second step in the distributed processing mode |
![]() ![]() ![]() | Provides methods to access partial results obtained with the compute() method of the SVD algorithm in the third step in the distributed processing mode |
![]() ![]() ![]() | 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 |
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![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the AdaBoost training algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the association rules algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the BACON outlier detection algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the BrownBoost training algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the Cholesky algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access prediction results obtained with the compute() method of the classifier prediction algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Provides interface for the result of model-based prediction |
![]() ![]() ![]() | Provides methods to access prediction results obtained with the compute() method of the classifier prediction algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the binary confusion matrix algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the multi-class confusion matrix algorithm in the batch processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method |
![]() ![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method |
![]() ![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Provides methods to access final results obtained with compute() method of Batch or finalizeCompute() method of Online and Distributed weak learners algorithms |
![]() ![]() ![]() | Results obtained with compute() method of the correlation distance algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the cosine distance algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the correlation or variance-covariance matrix algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access results obtained with the compute() method of the DBSCAN in the thirteenth step of the distributed processing mode |
![]() ![]() ![]() | Provides methods to access results obtained with the compute() method of the DBSCAN in the ninth step of the distributed processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the DBSCAN algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
![]() ![]() ![]() | Provides interface for the result of decision forest model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of decision forest model-based training |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
![]() ![]() ![]() | Provides interface for the result of decision tree model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of Decision tree model-based training |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the distribution |
![]() ![]() ![]() | Provides interface for the result of elastic net model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of elastic net model-based training |
![]() ![]() ![]() | Results obtained with the compute() method of the initialization of the EM for GMM algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the EM for GMM algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the engine |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of model-based training |
![]() ![]() ![]() | Provides interface for the result of model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of model-based training |
![]() ![]() ![]() | Provides methods to access the prediction results obtained with the compute() method of the implicit ALS algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the results obtained with the compute() method of the implicit ALS initialization algorithm |
![]() ![]() ![]() | Provides methods to access the results obtained with the compute() method of the implicit ALS training algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of KD-tree based kNN model-based training |
![]() ![]() ![]() | Results obtained with the compute() method of the kernel function algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method that computes initial centroids for K-Means algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of K-Means algorithm in the batch processing mode |
![]() ![]() ![]() | Provides interface for the result of lasso regression model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of lasso regression model-based training |
![]() ![]() ![]() | Provides interface for the result of the regression model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the regression model-based training |
![]() ![]() ![]() | Provides interface for the result of linear regression model-based prediction |
![]() ![]() ![]() | Provides interface for the result of linear regression quality metrics |
![]() ![]() ![]() | Provides interface for the result of linear regression quality metrics |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of linear regression model-based training |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of model-based training |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of model-based training |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the LogitBoost training algorithm in the batch processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Result obtained with the compute() method of the absolute value function in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the logistic function in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the rectified linear function in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the SmoothReLU algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the softmax function in the batch processing mode |
![]() ![]() ![]() | Result obtained with the compute() method of the hyperbolic tangent function in the batch processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Results obtained with the compute() method of the multivariate outlier detection algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the neural network weights and biases initializer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward abs layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward abs layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward average 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward average 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward average 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward average 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward average 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward average 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the layer algorithm |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward batch normalization layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward batch normalization layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward concat layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward concat layer |
![]() ![]() ![]() | Results obtained with the compute() method of the backward 2D convolution layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward 2D convolution layer in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward dropout layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward dropout layer |
![]() ![]() ![]() | Results obtained with the compute() method of the backward element-wise sum layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward element-wise sum layer in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward ELU layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward ELU layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the layer algorithm |
![]() ![]() ![]() | Results obtained with the compute() method of the backward fully-connected layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward fully-connected layer in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the backward local contrast normalization layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward local contrast normalization layer in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the backward 2D locally connected layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward 2D locally connected layer in the batch processing mode |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward logistic layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward logistic layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward loss layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward loss layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward logistic cross-entropy layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward logistic cross-entropy layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward softmax cross-entropy layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward softmax cross-entropy layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward local response normalization layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward local response normalization layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward maximum 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward maximum 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward maximum 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward maximum 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward maximum 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward maximum 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward 1D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward 3D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward prelu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward prelu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward relu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward relu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward reshape layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward reshape layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward smooth relu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward smooth relu layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward softmax layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward softmax layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid average 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid average 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid maximum 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid maximum 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward 2D spatial layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward 2D spatial layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward spatial pyramid stochastic 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward spatial pyramid stochastic 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward split layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward split layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward stochastic 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward stochastic 2D pooling layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the backward hyperbolic tangent layer |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the forward hyperbolic tangent layer |
![]() ![]() ![]() | Results obtained with the compute() method of the backward 2D transposed convolution layer |
![]() ![]() ![]() | Results obtained with the compute() method of the forward 2D transposed convolution layer in the batch processing mode |
![]() ![]() ![]() | Provides methods to access result obtained with the compute() method of the neural networks prediction algorithm |
![]() ![]() ![]() | Provides methods to access result obtained with the compute() method of the neural network training algorithm |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the min-max normalization algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the z-score normalization algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the iterative solver algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Results obtained with the compute() method of the adagrad algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Results obtained with the compute() method of the saga algorithm in the batch processing mode |
![]() ![]() ![]() ![]() | Results obtained with the compute() method of the sgd algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the iterative solver algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the Objective function in the batch processing mode |
![]() ![]() ![]() | Provides methods to access results obtained with the PCA algorithm |
![]() ![]() ![]() | Provides interface for the result of linear regression quality metrics |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the pivoted QR algorithm in the batch processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the quantiles algorithm in the batch processing mode |
![]() ![]() ![]() | Provides interface for the result of the regression model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of the regression model-based training |
![]() ![]() ![]() | Provides interface for the result of ridge regression model-based prediction |
![]() ![]() ![]() | Provides methods to access the result obtained with the compute() method of ridge regression model-based training |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the sorting algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the decision stump training algorithm in the batch processing mode |
![]() ![]() ![]() | Provides interface for the result of stump model-based prediction |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the decision stump training algorithm in the batch processing mode |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the decision stump training algorithm in the batch processing mode |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Provides methods to access final results obtained with the compute() method of the SVM training algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the univariate outlier detection algorithm in the batch processing mode |
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![]() ![]() ![]() | Results obtained with the compute() method of the adagrad algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the coordinate_descent algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the LBFGS algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the saga algorithm in the batch processing mode |
![]() ![]() ![]() | Results obtained with the compute() method of the sgd algorithm in the batch processing mode |
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![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the AdaBoost training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the BrownBoost training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the linear regression training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the LogitBoost training algorithm |
![]() ![]() ![]() | 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 |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the Naive Bayes training algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the pca algorithm |
![]() ![]() ![]() | Class that implements functionality of the collection of result objects of the quality metrics algorithm specialized for using with the SVM training algorithm |
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![]() ![]() ![]() | The base class for the classes that represent the models, such as linear_regression::Model or svm::Model |
![]() ![]() ![]() | Base class to represent argument with serialization methods |
![]() ![]() ![]() | Learnable parameters for the prediction stage of neural network algorithm |
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![]() ![]() ![]() | Shared pointer to the Collection object |
For more complete information about compiler optimizations, see our Optimization Notice.