GPUMLib  0.2.2
GPU Machine Learning Library
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12]
 NGPUMLib
 CArgumentThis class acts like a placeholder for an argument, composed of a parameter and a attribute/value
 CBackPropagationRepresents a feed-forward network that can be trained using the CUDA implementation of the Back-Propagation algorithm
 CBaseArrayBase class for HostArray and DeviceArray classes (Array base class)
 CBaseMatrixBase class for HostMatrix and DeviceMatrix classes (Matrix base class)
 CCudaArrayCreate an array of any type, that automatically manages the memory used to hold its elements (data will be stored both on the host and on the device)
 CCudaMatrixCreate a matrix of any type, that automatically manages the memory used to hold its elements (data will be stored both on the host and on the device)
 CCudaStreamRepresents a CUDA stream
 CDBNRepresents a Deep Belief Network (Device - GPU)
 CDBNhostRepresents a Deep Belief Network (Host - CPU)
 CDeviceAccessibleVariableRepresents a variable residing in memory that is page-locked and accessible to the device
 CDeviceArrayCreate an array of any type, on the device, that automatically manages the memory used to hold its elements
 CDeviceMatrixCreate a matrix of any type, on the device, that automatically manages the memory used to hold its elements
 CHostArrayCreate an array of any type, on the host, that automatically manages the memory used to hold its elements
 CHostMatrixCreate a matrix of any type, on the host, that automatically manages the memory used to hold its elements
 CKMeansRepresents a clustering algorithm using the K-Means technique, implemented in CUDA
 CMultipleBackPropagationRepresents a multiple feed-forward network that can be trained using the CUDA implementation of the Multiple Back-Propagation algorithm
 CNMFBase class for all Non-Negative Matrix Factorization classes
 CNMF_AdditiveDivergenceRepresents a Non-Negative Matrix Factorization (NMF) algorithm that uses additive update rules and the (Kullback-Leibler) divergence metric
 CNMF_AdditiveEuclidianRepresents a Non-Negative Matrix Factorization (NMF) algorithm that uses additive update rules and the Euclidean distance metric
 CNMF_MultiplicativeDivergenceRepresents a Non-Negative Matrix Factorization (NMF) algorithm that uses multiplicative update rules and the (Kullback-Leibler) divergence metric
 CNMF_MultiplicativeEuclidianDistanceRepresents a Non-Negative Matrix Factorization (NMF) algorithm that uses multiplicative update rules and the Euclidean distance metric
 CRadialBasisFunctionRepresents a radial basis function network that can be trained using the CUDA implementation of the Radial Basis Function algorithm
 CRandomClass for generating random values on the device. Uses the CURAND library
 CRBMRepresents a Restricted Boltzman Machine (GPU)
 CRBMhostRepresents a Restricted Boltzman Machine (Host - CPU)
 CReductionProvides reduction functions (Sum, Average, Max, Min, ...)
 CResourceAllocatingNetworkRepresents a resource allocating network with long term memory that can be trained using a CUDA implementation of the algorithm
 CSettingsUtility class to parse main()'s arguments, store them in a convenient list and access when needed
 CSVMRepresents an SVM which can be used to train and classify datasets on the GPU device