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GURLS++
2.0.00
C++ Implementation of GURLS Matlab Toolbox
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GURLS++
2.0.00
C++ Implementation of GURLS Matlab Toolbox
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| gurls::BadConfidenceCreation | BadConfidenceCreation is thrown when factory tries to generate an unknown confidence method |
| gurls::BadKernelCreation | BadKernelCreation is thrown when factory tries to generate an unknown kernel |
| gurls::BadNormCreation | BadNormCreation is thrown when factory tries to generate an unknown norm |
| gurls::BadOptimizerCreation | BadOptimizerCreation is thrown when factory tries to generate an unknown optimizer |
| gurls::BadParamSelectionCreation | BadParamSelectionCreation is thrown when factory tries to generate an unknown parameter selection method |
| gurls::BadPerformanceCreation | BadPerformanceCreation is thrown when factory tries to generate an unknown performance evaluator |
| gurls::BadPredictionCreation | BadPredictionCreation is thrown when factory tries to generate an unknown prediction method |
| gurls::BadPredKernelCreation | BadPredKernelCreation is thrown when factory tries to generate an unknown prediction kernel |
| gurls::BadSplitCreation | BadSplitCreation is thrown when factory tries to generate an unknown split method |
| gurls::BaseArray< T > | BaseArray is the base class for all classes implementing vectors and matrices as arrays of cells |
| gurls::BlasUtils | BlasUtils is a convenience class to interface with Blas |
| gurls::ConfBoltzman< T > | ConfBoltzman is the sub-class of Confidence that computes the probability of belonging to the highest scoring class |
| gurls::ConfBoltzmanGap< T > | ConfBoltzmanGap is the sub-class of Confidence that computes a confidence estimation for the predicted class (i.e |
| gurls::ConfGap< T > | ConfGap is the sub-class of Confidence that computes a confidence estimation for the predicted class |
| gurls::Confidence< T > | Confidence is the class that computes a confidence score for the predicted labels |
| gurls::ConfMaxScore< T > | ConfMaxScore is the sub-class of Confidence that computes a confidence estimation for the predicted class (i.e |
| gurls::Functor | |
| gurls::gException | GException is the class designed to deal with exceptions in Gurls++ package |
| gurls::gMat2D< T > | GMat2D implements a matrix of generic size |
| gurls::GPRWrapper< T > | GPRWrapper is the sub-class of GurlsWrapper that implements .. |
| gurls::GURLS | GURLS is the class that implements a GURLS process |
| gurls::GurlsOption | GurlsOption is an abstraction of a generic `option', which is widely used within the GURLS++ package to store either numeric parameters necessary to configure specific algorigms or sequences of strings holding the names of the specific procedures that have to be performed |
| gurls::GurlsOptionsList | GurlsOptionsList is an option containing a list of options mapped by name |
| gurls::GurlsWrapper< T > | GurlsWrapper is the base class for all gurls++ wrappers |
| gurls::gVec< T > | GVec implements a vector of generic length |
| gurls::ICholWrapper< T > | |
| gurls::Kernel< T > | Kernel is the class that computes the kernel matrix |
| gurls::KernelChisquared< T > | KernelChisquared is the sub-class of Kernel that builds the kernel matrix for a chi-squared model |
| gurls::KernelLinear< T > | KernelLinear is the sub-class of Kernel that builds the kernel matrix for a linear model |
| gurls::KernelRBF< T > | KernelRBF is the sub-class of Kernel that builds the Gaussian kernel matrix |
| gurls::KernelRLSWrapper< T > | KernelRLSWrapper is the sub-class of GurlsWrapper that implements Regularized Least Squares with a possibly non-linear model by resorting to kernel methods |
| gurls::KernelWrapper< T > | KernelWrapper is the base class for all gurls++ wrappers |
| gurls::LtCompare< T > | Auxiliary class performing floating point comparison used for std routines |
| gurls::Max | Computes the largest element in a vector v of lenght n |
| gurls::Mean | Computes the mean value of a vector v of lenght n |
| gurls::Median | Computes the median value of a vector v of lenght n |
| gurls::Min | Computes the smallest element in a vector v of lenght n |
| gurls::Norm< T > | Norm is a class that spherifies the data |
| gurls::NormL2< T > | NormL2 is the sub-class of Norm that spheriphies the data according to the l2 norm |
| gurls::NormTestZScore< T > | NormTestZScore is the sub-class of Norm that spheriphies the data according to the statistics cmoputed on the training set |
| gurls::NormZScore< T > | NormZScore is the sub-class of Norm that centers and rescales the input data matrix X |
| gurls::NystromWrapper< T > | NystromWrapper is the sub-class of GurlsWrapper that allows to train a possibly non linear model for large data sets, for which the complete nxn kernel matrix may not fit into RAM |
| gurls::OptArray | Optarray is an option containing an indexed array of options |
| gurls::OptFunction | OptFunction is an option representing a pointer to a generic function T (*function)(T* , int) operating over an array of floating point numbers |
| gurls::Optimizer< T > | Optimizer is a class that implements a Regularized Least Square algorithm |
| gurls::OptMatrix< Matrix > | OptMatrix is an option containing a matrix |
| gurls::OptMatrixBase | OptMatrixBase is the base class for all options containing matrices |
| gurls::OptNumber | OptNumber is an option containing a double precision floating point number |
| gurls::OptNumberList | OptNumberList is an option containing a list of double precision floating point numbers |
| gurls::OptProcess | OptProcess is an option containing a sequence of actions that form a Gurls process |
| gurls::OptString | OptString is an option containing a generic string |
| gurls::OptStringList | OptStringList is an option containing a list of strings |
| gurls::OptTaskSequence | OptTaskSequence is an option containing a sequence of task that forms a pipeline |
| gurls::ParamSelCalibrateSGD< T > | ParamselCalibrateSGD is the sub-class of ParamSelection that implements parameter selection for pegasos |
| gurls::ParamSelection< T > | ParamSelection is the class that implements parameter selection |
| gurls::ParamSelFixLambda< T > | ParamSelFixLambda is the sub-class of ParamSelection that sets the regularization parameter to a constant |
| gurls::ParamSelFixSigLam< T > | ParamSelFixSigLam is the sub-class of ParamSelection that sets the regularization parameters to constants |
| gurls::ParamSelHoDual< T > | ParamSelHoDual is the subclass of ParamSelection that implements hold-out cross validation with the dual formulation of RLS |
| gurls::ParamSelHoDualr< T > | ParamSelHoDualr is the randomized version of ParamSelHoDual |
| gurls::ParamSelHoGPRegr< T > | ParamSelHoGPRegr is the sub-class of ParamSelection that implements |
| gurls::ParamSelHoPrimal< T > | ParamSelHoPrimal is the subclass of ParamSelection that implements hold-out cross validation with the primal formulation of RLS |
| gurls::ParamSelHoPrimalr< T > | ParamSelHoPrimalr is the randomized version of ParamSelHoPrimal |
| gurls::ParamSelLoocvDual< T > | ParamSelLoocvDual is the sub-class of ParamSelection that implements LOO cross-validation with the dual formulation |
| gurls::ParamSelLoocvPrimal< T > | ParamSelLoocvPrimal is the sub-class of ParamSelection that implements LOO cross-validation with the primal formulation |
| gurls::ParamSelLooGPRegr< T > | ParamSelLooGPRegr is the sub-class of ParamSelection that implements |
| gurls::ParamSelSiglam< T > | ParamSelSiglam is the sub-class of ParamSelection that implements LOO cross-validation with the dual formulation for a rbf kernel |
| gurls::ParamSelSiglamHo< T > | ParamSelSiglam is the sub-class of ParamSelection that implements hold-out cross validation with the dual formulation for a rbf kernel |
| gurls::ParamSelSiglamHoGPRegr< T > | ParamSelSiglamHoGPRegr is the sub-class of ParamSelection that implements |
| gurls::ParamSelSiglamLooGPRegr< T > | ParamSelSiglamLooGPRegr is the sub-class of ParamSelection that implements leave-one-ot parameter selection for Gaussian process regression |
| gurls::PerfMacroAvg< T > | PerfMacroAvg is the sub-class of Performance that evaluates prediction accuracy |
| gurls::Performance< T > | Performance is the class that evaluates prediction performance |
| gurls::PerfPrecRec< T > | PerfPrecRec is the sub-class of Performance that evaluates prediction precision |
| gurls::PerfRmse< T > | PerfRmse is the sub-class of Performance that evaluates prediction error |
| gurls::PredDual< T > | PredDual is the sub-class of Prediction that computes the predictions of a linear classifier in the dual formulation |
| gurls::PredGPRegr< T > | PredGPRegr is the sub-class of Prediction that computes the predictions of GP |
| gurls::Prediction< T > | Prediction is the class that computes predictions |
| gurls::PredKernel< T > | PredKernel is the class that computes the kernel matrix for prediction |
| gurls::PredKernelTrainTest< T > | PredKernelTrainTest is the sub-class of PredKernel that computes the kernel matrix between training and test sets |
| gurls::PredPrimal< T > | PredPrimal is the sub-class of Prediction that computes the predictions of a linear classifier in the primal formulation |
| gurls::PredRandFeats< T > | PredRandFeats is the sub-class of Prediction that computes the predictions of the linear classifier stored in opt.rls.W, and obtained the Random Features approach proposed in: Ali Rahimi, Ben Recht; Random Features for Large-Scale Kernel Machines; in Neural Information Processing Systems (NIPS) 2007 |
| gurls::RandomFeaturesWrapper< T > | RLSWrapper is the sub-class of GurlsWrapper that implements Regularized Least Squares with a linear model |
| gurls::RecursiveRLSWrapper< T > | RecursiveRLSWrapper is the sub-class of GurlsWrapper that implements recursive update of the RLS estimator with retraining capability |
| gurls::RLSAuto< T > | RLSAuto is the sub-class of Optimizer that implements automatic selection of primal/dual procedure |
| gurls::RLSDual< T > | RLSDual is the sub-class of Optimizer that implements RLS with the dual formulation |
| gurls::RLSDualr< T > | RLSDualr is the sub-class of Optimizer that implements RLS with the dual formulation, using a randomized version of SVD |
| gurls::RLSGPRegr< T > | RLSGPRegr is the sub-class of Optimizer that implements GP inference |
| gurls::RLSPegasos< T > | RLSPegasos is the sub-class of Optimizer that implements the Pegaosos algorithm |
| gurls::RLSPrimal< T > | RLSPrimal is the sub-class of Optimizer that implements RLS with the primal formulation |
| gurls::RLSPrimalr< T > | RLSPrimalr is the sub-class of Optimizer that implements RLS with the primal formulation, using a randomized version of SVD |
| gurls::RLSPrimalRecInit< T > | RLSPrimalRecInit is the sub-class of Optimizer that implements RLS with the primal formulation |
| gurls::RLSPrimalRecUpdate< T > | RLSPrimalRecUpdate is the sub-class of Optimizer that implements RLS with the primal formulation |
| gurls::RLSRandFeats< T > | RLSRandFeats is the sub-class of Optimizer that computes a classifier for the primal formulation of RLS using the Random Features approach proposed in: Ali Rahimi, Ben Recht; Random Features for Large-Scale Kernel Machines; in Neural Information Processing Systems (NIPS) 2007 |
| gurls::RLSWrapper< T > | RLSWrapper is the sub-class of GurlsWrapper that implements Regularized Least Squares with a linear model |
| gurls::Split< T > | Split is the class that splits data into pair(s) of training and test samples |
| gurls::SplitHo< T > | SplitHoMulti is the sub-class of Split that splits data into one or more pairs of training and test samples |
1.7.6.1 
