Classes |
class | DynArray |
| Template Dynamic array class that creates an array that can be used like a list or an array. More...
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class | Parallel |
| Class Parallel provides helper functions for multithreading. More...
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struct | TParameter |
| parameter struct More...
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class | Parameter |
| Parameter class. More...
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class | SGParamInfo |
| Class that holds informations about a certain parameter of an CSGObject. Contains name, type, etc. This is used for mapping types that have changed in different versions of shogun. Instances of this class may be compared to each other. Ordering is based on name, equalness is based on all attributes. More...
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class | ParameterMapElement |
| Class to hold instances of a parameter map. Each element contains a key and a set of values, which each are of type SGParamInfo. May be compared to each other based on their keys. More...
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class | ParameterMap |
| Implements a map of ParameterMapElement instances Maps one key to a set of values. More...
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class | CSGObject |
| Class SGObject is the base class of all shogun objects. More...
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class | Version |
| Class Version provides version information. More...
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class | CAveragedPerceptron |
| Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron. More...
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class | CFeatureBlockLogisticRegression |
| class FeatureBlockLogisticRegression, a linear binary logistic loss classifier for problems with complex feature relations. Currently two feature relations are supported - feature group (done via CIndexBlockGroup) and feature tree (done via CIndexTree). Handling of feature relations is done via L1/Lq (for groups) and L1/L2 (for trees) regularization. More...
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class | CLDA |
| Class LDA implements regularized Linear Discriminant Analysis. More...
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class | CLPBoost |
| Class LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer. More...
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class | CLPM |
| Class LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer. More...
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class | CMKL |
| Multiple Kernel Learning. More...
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class | CMKLClassification |
| Multiple Kernel Learning for two-class-classification. More...
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class | CMKLMulticlass |
| MKLMulticlass is a class for L1-norm multiclass MKL. More...
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class | MKLMulticlassGLPK |
| MKLMulticlassGLPK is a helper class for MKLMulticlass. More...
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class | MKLMulticlassGradient |
| MKLMulticlassGradient is a helper class for MKLMulticlass. More...
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class | MKLMulticlassOptimizationBase |
| MKLMulticlassOptimizationBase is a helper class for MKLMulticlass. More...
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class | CMKLOneClass |
| Multiple Kernel Learning for one-class-classification. More...
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class | CNearestCentroid |
| Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier. More...
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class | CPerceptron |
| Class Perceptron implements the standard linear (online) perceptron. More...
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class | CPluginEstimate |
| class PluginEstimate More...
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class | CSubGradientLPM |
| Class SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer. More...
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class | CCPLEXSVM |
| CplexSVM a SVM solver implementation based on cplex (unfinished). More...
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class | CGNPPLib |
| class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP). More...
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class | CGNPPSVM |
| class GNPPSVM More...
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class | CGPBTSVM |
| class GPBTSVM More...
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class | CLibLinear |
| class to implement LibLinear More...
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class | CLibSVM |
| LibSVM. More...
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class | CLibSVMOneClass |
| class LibSVMOneClass More...
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class | CMPDSVM |
| class MPDSVM More...
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class | CNewtonSVM |
| NewtonSVM, In this Implementation linear SVM is trained in its primal form using Newton-like iterations. This Implementation is ported from the Olivier Chapelles fast newton based SVM solver, Which could be found here :http://mloss.org/software/view/30/ For further information on this implementation of SVM refer to this paper: http://www.kyb.mpg.de/publications/attachments/neco_%5B0%5D.pdf. More...
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class | COnlineLibLinear |
| Class implementing a purely online version of LibLinear, using the L2R_L1LOSS_SVC_DUAL solver only. More...
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class | COnlineSVMSGD |
| class OnlineSVMSGD More...
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class | CQPBSVMLib |
| class QPBSVMLib More...
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class | CSGDQN |
| class SGDQN More...
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class | CSubGradientSVM |
| class SubGradientSVM More...
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class | CSVM |
| A generic Support Vector Machine Interface. More...
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class | CSVMLight |
| class SVMlight More...
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class | CSVMLightOneClass |
| Trains a one class C SVM. More...
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class | CSVMLin |
| class SVMLin More...
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class | CSVMOcas |
| class SVMOcas More...
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class | CSVMSGD |
| class SVMSGD More...
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class | CWDSVMOcas |
| class WDSVMOcas More...
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class | CVwCacheReader |
| Base class from which all cache readers for VW should be derived. More...
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class | CVwCacheWriter |
| CVwCacheWriter is the base class for all VW cache creating classes. More...
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class | CVwNativeCacheReader |
| Class CVwNativeCacheReader reads from a cache exactly as that which has been produced by VW's default cache format. More...
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class | CVwNativeCacheWriter |
| Class CVwNativeCacheWriter writes a cache exactly as that which would be produced by VW's default cache format. More...
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class | CVwAdaptiveLearner |
| VwAdaptiveLearner uses an adaptive subgradient technique to update weights. More...
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class | CVwNonAdaptiveLearner |
| VwNonAdaptiveLearner uses a standard gradient descent weight update rule. More...
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class | CVowpalWabbit |
| Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit. More...
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class | VwFeature |
| One feature in VW. More...
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class | VwExample |
| Example class for VW. More...
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class | VwLabel |
| Class VwLabel holds a label object used by VW. More...
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class | CVwEnvironment |
| Class CVwEnvironment is the environment used by VW. More...
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class | CVwLearner |
| Base class for all VW learners. More...
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class | CVwParser |
| CVwParser is the object which provides the functions to parse examples from buffered input. More...
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class | CVwRegressor |
| Regressor used by VW. More...
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class | CGMM |
| Gaussian Mixture Model interface. More...
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class | CHierarchical |
| Agglomerative hierarchical single linkage clustering. More...
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class | CKMeans |
| KMeans clustering, partitions the data into k (a-priori specified) clusters. More...
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class | CConverter |
| class Converter used to convert data More...
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class | CDiffusionMaps |
| class DiffusionMaps (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess given data using Diffusion Maps dimensionality reduction technique as described in More...
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class | CEmbeddingConverter |
| class EmbeddingConverter (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of features, e.g. construct dense numeric embedding of string features More...
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class | CHessianLocallyLinearEmbedding |
| class HessianLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess data using Hessian Locally Linear Embedding algorithm as described in More...
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class | CIsomap |
| class Isomap (part of the Efficient Dimension Reduction Toolkit) used to embed data using Isomap algorithm as described in More...
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class | CKernelLocallyLinearEmbedding |
| class KernelLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using kernel formulation of Locally Linear Embedding algorithm as described in More...
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class | CKernelLocalTangentSpaceAlignment |
| class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using kernel extension of the Local Tangent Space Alignment (LTSA) algorithm. More...
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class | CLaplacianEigenmaps |
| class LaplacianEigenmaps (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using Laplacian Eigenmaps algorithm as described in: More...
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class | CLinearLocalTangentSpaceAlignment |
| class LinearLocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
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class | CLocalityPreservingProjections |
| class LocalityPreservingProjections (part of the Efficient Dimensionality Reduction Toolkit) used to compute embeddings of data using Locality Preserving Projections method as described in More...
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class | CLocallyLinearEmbedding |
| class LocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Locally Linear Embedding algorithm described in More...
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class | CLocalTangentSpaceAlignment |
| class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in: More...
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class | CMultidimensionalScaling |
| class Multidimensionalscaling (part of the Efficient Dimensionality Reduction Toolkit) is used to perform multidimensional scaling (capable of landmark approximation if requested). More...
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class | CNeighborhoodPreservingEmbedding |
| NeighborhoodPreservingEmbedding (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
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class | CStochasticProximityEmbedding |
| class StochasticProximityEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using the Stochastic Proximity algorithm. More...
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class | CAttenuatedEuclideanDistance |
| class AttenuatedEuclideanDistance More...
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class | CBrayCurtisDistance |
| class Bray-Curtis distance More...
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class | CCanberraMetric |
| class CanberraMetric More...
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class | CCanberraWordDistance |
| class CanberraWordDistance More...
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class | CChebyshewMetric |
| class ChebyshewMetric More...
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class | CChiSquareDistance |
| class ChiSquareDistance More...
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class | CCosineDistance |
| class CosineDistance More...
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class | CCustomDistance |
| The Custom Distance allows for custom user provided distance matrices. More...
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class | CDenseDistance |
| template class DenseDistance More...
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class | CDistance |
| Class Distance, a base class for all the distances used in the Shogun toolbox. More...
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class | CEuclideanDistance |
| class EuclideanDistance More...
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class | CGeodesicMetric |
| class GeodesicMetric More...
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class | CHammingWordDistance |
| class HammingWordDistance More...
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class | CJensenMetric |
| class JensenMetric More...
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class | CKernelDistance |
| The Kernel distance takes a distance as input. More...
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class | CMahalanobisDistance |
| class MahalanobisDistance More...
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class | CManhattanMetric |
| class ManhattanMetric More...
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class | CManhattanWordDistance |
| class ManhattanWordDistance More...
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class | CMinkowskiMetric |
| class MinkowskiMetric More...
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class | CRealDistance |
| class RealDistance More...
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class | CSparseDistance |
| template class SparseDistance More...
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class | CSparseEuclideanDistance |
| class SparseEucldeanDistance More...
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class | CStringDistance |
| template class StringDistance More...
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class | CTanimotoDistance |
| class Tanimoto coefficient More...
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class | CDistribution |
| Base class Distribution from which all methods implementing a distribution are derived. More...
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class | CGaussian |
| Gaussian distribution interface. More...
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class | CGHMM |
| class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM). More...
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class | CHistogram |
| Class Histogram computes a histogram over all 16bit unsigned integers in the features. More...
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class | Model |
| class Model More...
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class | CHMM |
| Hidden Markov Model. More...
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class | CLinearHMM |
| The class LinearHMM is for learning Higher Order Markov chains. More...
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class | CPositionalPWM |
| Positional PWM. More...
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class | CBinaryClassEvaluation |
| The class TwoClassEvaluation, a base class used to evaluate binary classification labels. More...
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class | CClusteringAccuracy |
| clustering accuracy More...
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class | CClusteringEvaluation |
| The base class used to evaluate clustering. More...
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class | CClusteringMutualInformation |
| clustering (normalized) mutual information More...
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class | CContingencyTableEvaluation |
| The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates. More...
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class | CAccuracyMeasure |
| class AccuracyMeasure used to measure accuracy of 2-class classifier. More...
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class | CErrorRateMeasure |
| class ErrorRateMeasure used to measure error rate of 2-class classifier. More...
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class | CBALMeasure |
| class BALMeasure used to measure balanced error of 2-class classifier. More...
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class | CWRACCMeasure |
| class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier. More...
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class | CF1Measure |
| class F1Measure used to measure F1 score of 2-class classifier. More...
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class | CCrossCorrelationMeasure |
| class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier. More...
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class | CRecallMeasure |
| class RecallMeasure used to measure recall of 2-class classifier. More...
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class | CPrecisionMeasure |
| class PrecisionMeasure used to measure precision of 2-class classifier. More...
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class | CSpecificityMeasure |
| class SpecificityMeasure used to measure specificity of 2-class classifier. More...
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class | CCrossValidationResult |
| type to encapsulate the results of an evaluation run. May contain confidence interval (if conf_int_alpha!=0). m_conf_int_alpha is the probability for an error, i.e. the value does not lie in the confidence interval. More...
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class | CCrossValidation |
| base class for cross-validation evaluation. Given a learning machine, a splitting strategy, an evaluation criterium, features and correspnding labels, this provides an interface for cross-validation. Results may be retrieved using the evaluate method. A number of repetitions may be specified for obtaining more accurate results. The arithmetic mean of different runs is returned along with confidence intervals, if a p-value is specified. Default number of runs is one, confidence interval combutation is disabled. More...
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class | CCrossValidationMKLStorage |
| Class for storing MKL weights in every fold of cross-validation. More...
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class | CCrossValidationMulticlassStorage |
| Class for storing multiclass evaluation information in every fold of cross-validation. More...
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class | CCrossValidationOutput |
| Class for managing individual folds in cross-validation. More...
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class | CCrossValidationPrintOutput |
| Class for outputting cross-validation intermediate results to the standard output. Simply prints all messages it gets. More...
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class | CCrossValidationSplitting |
| Implementation of normal cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference) More...
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class | CDifferentiableFunction |
| DifferentiableFunction. More...
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class | CEvaluation |
| Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression. More...
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class | CEvaluationResult |
| EvaluationResult is the abstract class that contains the result generated by the MachineEvaluation class. More...
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class | CGradientCriterion |
| CGradientCriterion Simple class which specifies the direction of gradient search. Does not provide any label evaluation measure, however. More...
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class | CGradientEvaluation |
| GradientEvaluation evaluates a machine using its associated differentiable function for the function value and its gradient with respect to parameters. More...
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class | CGradientResult |
| GradientResult is a container class that returns results from GradientEvaluation. It contains the function value as well as its gradient. More...
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class | CMachineEvaluation |
| Machine Evaluation is an abstract class that evaluates a machine according to some criterion. More...
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class | CMeanAbsoluteError |
| Class MeanAbsoluteError used to compute an error of regression model. More...
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class | CMeanSquaredError |
| Class MeanSquaredError used to compute an error of regression model. More...
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class | CMeanSquaredLogError |
| Class CMeanSquaredLogError used to compute an error of regression model. More...
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class | CMulticlassAccuracy |
| The class MulticlassAccuracy used to compute accuracy of multiclass classification. More...
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class | CMulticlassOVREvaluation |
| The class MulticlassOVREvaluation used to compute evaluation parameters of multiclass classification via binary OvR decomposition and given binary evaluation technique. More...
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class | CPRCEvaluation |
| Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC). More...
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class | CROCEvaluation |
| Class ROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC). More...
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class | CSplittingStrategy |
| Abstract base class for all splitting types. Takes a CLabels instance and generates a desired number of subsets which are being accessed by their indices via the method generate_subset_indices(...). More...
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class | CStratifiedCrossValidationSplitting |
| Implementation of stratified cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference) in which the label ratio is equal (at most one difference) to the label ratio of the specified labels. Do not use for regression since it may be impossible to distribute nice in that case. More...
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class | CStructuredAccuracy |
| class CStructuredAccuracy used to compute accuracy of structured classification More...
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class | CAlphabet |
| The class Alphabet implements an alphabet and alphabet utility functions. More...
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class | CAttributeFeatures |
| Implements attributed features, that is in the simplest case a number of (attribute, value) pairs. More...
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class | CBinnedDotFeatures |
| The class BinnedDotFeatures contains a 0-1 conversion of features into bins. More...
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class | CCombinedDotFeatures |
| Features that allow stacking of a number of DotFeatures. More...
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class | CCombinedFeatures |
| The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object. More...
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class | CDataGenerator |
| Class that is able to generate various data samples, which may be used for examples in SHOGUN. More...
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class | CDenseFeatures |
| The class DenseFeatures implements dense feature matrices. More...
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class | CDenseSubsetFeatures |
class | CDotFeatures |
| Features that support dot products among other operations. More...
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class | CDummyFeatures |
| The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any). More...
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class | CExplicitSpecFeatures |
| Features that compute the Spectrum Kernel feature space explicitly. More...
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class | CFeatures |
| The class Features is the base class of all feature objects. More...
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class | CFKFeatures |
| The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models. More...
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class | CHashedWDFeatures |
| Features that compute the Weighted Degreee Kernel feature space explicitly. More...
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class | CHashedWDFeaturesTransposed |
| Features that compute the Weighted Degreee Kernel feature space explicitly. More...
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class | CImplicitWeightedSpecFeatures |
| Features that compute the Weighted Spectrum Kernel feature space explicitly. More...
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class | CLatentFeatures |
| Latent Features class The class if for representing features for latent learning, e.g. LatentSVM. It's basically a very generic way of storing features of any (user-defined) form based on CData. More...
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class | CLBPPyrDotFeatures |
| implement DotFeatures for the polynomial kernel More...
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class | CMatrixFeatures |
| Class CMatrixFeatures used to represent data whose feature vectors are better represented with matrices rather than with unidimensional arrays or vectors. Optionally, it can be restricted that all the feature vectors have the same number of features. Set the attribute num_features different to zero to use this restriction. Allow feature vectors with different number of features by setting num_features equal to zero (default behaviour). More...
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class | CPolyFeatures |
| implement DotFeatures for the polynomial kernel More...
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class | CRealFileFeatures |
| The class RealFileFeatures implements a dense double-precision floating point matrix from a file. More...
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class | CSNPFeatures |
| Features that compute the Weighted Degreee Kernel feature space explicitly. More...
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class | CSparseFeatures |
| Template class SparseFeatures implements sparse matrices. More...
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class | CSparsePolyFeatures |
| implement DotFeatures for the polynomial kernel More...
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class | CStreamingDenseFeatures |
| This class implements streaming features with dense feature vectors. More...
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class | CStreamingDotFeatures |
| Streaming features that support dot products among other operations. More...
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class | CStreamingFeatures |
| Streaming features are features which are used for online algorithms. More...
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class | CStreamingSparseFeatures |
| This class implements streaming features with sparse feature vectors. The vector is represented as an SGSparseVector<T>. Each entry is of type SGSparseVectorEntry<T> with members `feat_index' and `entry'. More...
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class | CStreamingStringFeatures |
| This class implements streaming features as strings. More...
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class | CStreamingVwFeatures |
| This class implements streaming features for use with VW. More...
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class | CStringFeatures |
| Template class StringFeatures implements a list of strings. More...
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class | CStringFileFeatures |
| File based string features. More...
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class | CSubset |
| Wrapper class for an index subset which is used by SubsetStack. More...
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class | CSubsetStack |
| class to add subset support to another class. A CSubsetStackStack instance should be added and wrapper methods to all interfaces should be added. More...
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class | CTOPFeatures |
| The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models. More...
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class | CWDFeatures |
| Features that compute the Weighted Degreee Kernel feature space explicitly. More...
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class | CAsciiFile |
| A Ascii File access class. More...
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class | CBinaryFile |
| A Binary file access class. More...
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class | CBinaryStream |
| memory mapped emulation via binary streams (files) More...
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class | CFile |
| A File access base class. More...
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class | CIOBuffer |
| An I/O buffer class. More...
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class | CMemoryMappedFile |
| memory mapped file More...
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class | CSerializableAsciiFile |
| serializable ascii file More...
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class | SerializableAsciiReader00 |
| Serializable ascii reader. More...
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class | CSerializableFile |
| serializable file More...
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class | SGIO |
| Class SGIO, used to do input output operations throughout shogun. More...
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struct | substring |
| struct Substring, specified by start position and end position. More...
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class | CSimpleFile |
| Template class SimpleFile to read and write from files. More...
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class | CInputParser |
| Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples. More...
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class | Example |
| Class Example is the container type for the vector+label combination. More...
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class | CParseBuffer |
| Class CParseBuffer implements a ring of examples of a defined size. The ring stores objects of the Example type. More...
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class | CStreamingAsciiFile |
| Class StreamingAsciiFile to read vector-by-vector from ASCII files. More...
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class | CStreamingFile |
| A Streaming File access class. More...
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class | CStreamingFileFromDenseFeatures |
| Class CStreamingFileFromDenseFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CDenseFeatures object. More...
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class | CStreamingFileFromFeatures |
| Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object. More...
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class | CStreamingFileFromSparseFeatures |
| Class CStreamingFileFromSparseFeatures is derived from CStreamingFile and provides an input source for the online framework. It uses an existing CSparseFeatures object to generate online examples. More...
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class | CStreamingFileFromStringFeatures |
| Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object. More...
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class | CStreamingVwCacheFile |
| Class StreamingVwCacheFile to read vector-by-vector from VW cache files. More...
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class | CStreamingVwFile |
| Class StreamingVwFile to read vector-by-vector from Vowpal Wabbit data files. It reads the example and label into one object of VwExample type. More...
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class | CANOVAKernel |
| ANOVA (ANalysis Of VAriances) kernel. More...
|
class | CAUCKernel |
| The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training. More...
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class | CBesselKernel |
| the class Bessel kernel More...
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class | CCauchyKernel |
| Cauchy kernel. More...
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class | CChi2Kernel |
| The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms. More...
|
class | CCircularKernel |
| Circular kernel. More...
|
class | CCombinedKernel |
| The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination. More...
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class | CConstKernel |
| The Constant Kernel returns a constant for all elements. More...
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class | CCustomKernel |
| The Custom Kernel allows for custom user provided kernel matrices. More...
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class | CDiagKernel |
| The Diagonal Kernel returns a constant for the diagonal and zero otherwise. More...
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class | CDistanceKernel |
| The Distance kernel takes a distance as input. More...
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class | CDotKernel |
| Template class DotKernel is the base class for kernels working on DotFeatures. More...
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class | CExponentialKernel |
| The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures. More...
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class | CGaussianARDKernel |
| Gaussian Kernel with Automatic Relevance Detection. More...
|
class | CGaussianKernel |
| The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures. More...
|
class | CGaussianShiftKernel |
| An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel. More...
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class | CGaussianShortRealKernel |
| The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features. More...
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class | CHistogramIntersectionKernel |
| The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1. More...
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class | CInverseMultiQuadricKernel |
| InverseMultiQuadricKernel. More...
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class | CJensenShannonKernel |
| The Jensen-Shannon kernel operating on real-valued vectors computes the Jensen-Shannon distance between the features. Often used in computer vision. More...
|
class | CKernel |
| The Kernel base class. More...
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class | CLinearARDKernel |
| Linear Kernel with Automatic Relevance Detection. More...
|
class | CLinearKernel |
| Computes the standard linear kernel on CDotFeatures. More...
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class | CLogKernel |
| Log kernel. More...
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class | CMultiquadricKernel |
| MultiquadricKernel. More...
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class | CAvgDiagKernelNormalizer |
| Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor). More...
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class | CDiceKernelNormalizer |
| DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) More...
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class | CFirstElementKernelNormalizer |
| Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. . More...
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class | CIdentityKernelNormalizer |
| Identity Kernel Normalization, i.e. no normalization is applied. More...
|
class | CKernelNormalizer |
| The class Kernel Normalizer defines a function to post-process kernel values. More...
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class | CRidgeKernelNormalizer |
| Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems). More...
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class | CScatterKernelNormalizer |
| the scatter kernel normalizer More...
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class | CSqrtDiagKernelNormalizer |
| SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements. More...
|
class | CTanimotoKernelNormalizer |
| TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) More...
|
class | CVarianceKernelNormalizer |
| VarianceKernelNormalizer divides by the ``variance''. More...
|
class | CZeroMeanCenterKernelNormalizer |
| ZeroMeanCenterKernelNormalizer centers the kernel in feature space. More...
|
class | CPolyKernel |
| Computes the standard polynomial kernel on CDotFeatures. More...
|
class | CPowerKernel |
| Power kernel. More...
|
class | CProductKernel |
| The Product kernel is used to combine a number of kernels into a single ProductKernel object by element multiplication. More...
|
class | CPyramidChi2 |
| Pyramid Kernel over Chi2 matched histograms. More...
|
class | CRationalQuadraticKernel |
| Rational Quadratic kernel. More...
|
class | CSigmoidKernel |
| The standard Sigmoid kernel computed on dense real valued features. More...
|
class | CSparseKernel |
| Template class SparseKernel, is the base class of kernels working on sparse features. More...
|
class | CSphericalKernel |
| Spherical kernel. More...
|
class | CSplineKernel |
| Computes the Spline Kernel function which is the cubic polynomial. More...
|
class | CCommUlongStringKernel |
| The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers. More...
|
class | CCommWordStringKernel |
| The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers. More...
|
class | CDistantSegmentsKernel |
| The distant segments kernel is a string kernel, which counts the number of substrings, so-called segments, at a certain distance from each other. More...
|
class | CFixedDegreeStringKernel |
| The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d. More...
|
class | CGaussianMatchStringKernel |
| The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length. More...
|
class | CHistogramWordStringKernel |
| The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains. More...
|
class | CLinearStringKernel |
| Computes the standard linear kernel on dense char valued features. More...
|
class | CLocalAlignmentStringKernel |
| The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences. More...
|
class | CLocalityImprovedStringKernel |
| The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features. More...
|
class | CMatchWordStringKernel |
| The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet. More...
|
class | COligoStringKernel |
| This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004. More...
|
class | CPolyMatchStringKernel |
| The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length. More...
|
class | CPolyMatchWordStringKernel |
| The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features. More...
|
class | CRegulatoryModulesStringKernel |
| The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences. More...
|
class | CSalzbergWordStringKernel |
| The SalzbergWordString kernel implements the Salzberg kernel. More...
|
class | CSimpleLocalityImprovedStringKernel |
| SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel. More...
|
class | CSNPStringKernel |
| The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length. More...
|
struct | SSKFeatures |
| SSKFeatures. More...
|
class | CSparseSpatialSampleStringKernel |
| Sparse Spatial Sample String Kernel by Pavel Kuksa pkuks.nosp@m.a@cs.nosp@m..rutg.nosp@m.ers..nosp@m.edu and Vladimir Pavlovic vladi.nosp@m.mir@.nosp@m.cs.ru.nosp@m.tger.nosp@m.s.edu More...
|
class | CSpectrumMismatchRBFKernel |
| spectrum mismatch rbf kernel More...
|
class | CSpectrumRBFKernel |
| spectrum rbf kernel More...
|
class | CStringKernel |
| Template class StringKernel, is the base class of all String Kernels. More...
|
class | CWeightedCommWordStringKernel |
| The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient ) from strings that have been mapped into unsigned 16bit integers. More...
|
class | CWeightedDegreePositionStringKernel |
| The Weighted Degree Position String kernel (Weighted Degree kernel with shifts). More...
|
class | CWeightedDegreeStringKernel |
| The Weighted Degree String kernel. More...
|
class | CTensorProductPairKernel |
| Computes the Tensor Product Pair Kernel (TPPK). More...
|
class | CTStudentKernel |
| Generalized T-Student kernel. More...
|
class | CWaveKernel |
| Wave kernel. More...
|
class | CWaveletKernel |
| the class WaveletKernel More...
|
class | CWeightedDegreeRBFKernel |
| weighted degree RBF kernel More...
|
class | CBinaryLabels |
| Binary Labels for binary classification. More...
|
class | CDenseLabels |
| Dense integer or floating point labels. More...
|
class | CLabels |
| The class Labels models labels, i.e. class assignments of objects. More...
|
class | CLatentLabels |
| abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels. More...
|
class | CMulticlassLabels |
| Multiclass Labels for multi-class classification. More...
|
class | CMulticlassMultipleOutputLabels |
| Multiclass Labels for multi-class classification with multiple labels. More...
|
class | CRegressionLabels |
| Real Labels are real-valued labels. More...
|
class | CStructuredLabels |
| Base class of the labels used in Structured Output (SO) problems. More...
|
class | CLatentModel |
| Abstract class CLatentModel It represents the application specific model and contains most of the application dependent logic to solve latent variable based problems. More...
|
class | CLatentSOSVM |
| class Latent Structured Output SVM, an structured output based machine for classification problems with latent variables. More...
|
class | CLatentSVM |
| LatentSVM class Latent SVM implementation based on [1]. For optimization this implementation uses SVMOcas. More...
|
class | CBitString |
| a string class embedding a string in a compact bit representation More...
|
class | CCache |
| Template class Cache implements a simple cache. More...
|
class | CCompressor |
| Compression library for compressing and decompressing buffers using one of the standard compression algorithms, LZO, GZIP, BZIP2 or LZMA. More...
|
class | CoverTree |
class | CData |
| dummy data holder More...
|
struct | TSGDataType |
| Datatypes that shogun supports. More...
|
class | CDynamicArray |
| Template Dynamic array class that creates an array that can be used like a list or an array. More...
|
class | CDynamicObjectArray |
| Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an array. More...
|
class | CDynInt |
| integer type of dynamic size More...
|
class | func_wrapper |
class | CGCArray |
| Template class GCArray implements a garbage collecting static array. More...
|
class | CHash |
| Collection of Hashing Functions. More...
|
class | CIndexBlock |
| class IndexBlock used to represent contiguous indices of one group (e.g. block of related features) More...
|
class | CIndexBlockGroup |
| class IndexBlockGroup used to represent group-based feature relation. More...
|
class | CIndexBlockRelation |
| class IndexBlockRelation More...
|
class | CIndexBlockTree |
| class IndexBlockTree used to represent tree guided feature relation. More...
|
class | CIndirectObject |
| an array class that accesses elements indirectly via an index array. More...
|
class | v_array |
| Class v_array taken directly from JL's implementation. More...
|
class | CJLCoverTreePoint |
| Class Point to use with John Langford's CoverTree. This class must have some assoficated functions defined (distance, parse_points and print, see below) so it can be used with the CoverTree implementation. More...
|
class | CListElement |
| Class ListElement, defines how an element of the the list looks like. More...
|
class | CList |
| Class List implements a doubly connected list for low-level-objects. More...
|
class | CMap |
| the class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
|
class | CSet |
| the class CSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
|
class | SGMatrix |
| shogun matrix More...
|
class | SGMatrixList |
| shogun matrix list More...
|
class | SGNDArray |
| shogun n-dimensional array More...
|
class | SGReferencedData |
| shogun reference count managed data More...
|
class | SGSparseMatrix |
| template class SGSparseMatrix More...
|
struct | SGSparseVectorEntry |
| template class SGSparseVectorEntry More...
|
class | SGSparseVector |
| template class SGSparseVector More...
|
class | SGString |
| shogun string More...
|
struct | SGStringList |
| template class SGStringList More...
|
class | SGVector |
| shogun vector More...
|
class | ShogunException |
| Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs. More...
|
class | CSignal |
| Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process. More...
|
class | CStructuredData |
| Base class of the components of StructuredLabels. More...
|
class | CTime |
| Class Time that implements a stopwatch based on either cpu time or wall clock time. More...
|
class | CTrie |
| Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored. More...
|
class | CHingeLoss |
| CHingeLoss implements the hinge loss function. More...
|
class | CLogLoss |
| CLogLoss implements the logarithmic loss function. More...
|
class | CLogLossMargin |
| Class CLogLossMargin implements a margin-based log-likelihood loss function. More...
|
class | CLossFunction |
| Class CLossFunction is the base class of all loss functions. More...
|
class | CSmoothHingeLoss |
| CSmoothHingeLoss implements the smooth hinge loss function. More...
|
class | CSquaredHingeLoss |
| Class CSquaredHingeLoss implements a squared hinge loss function. More...
|
class | CSquaredLoss |
| CSquaredLoss implements the squared loss function. More...
|
class | CBaseMulticlassMachine |
class | CDistanceMachine |
| A generic DistanceMachine interface. More...
|
class | CKernelMachine |
| A generic KernelMachine interface. More...
|
class | CKernelMulticlassMachine |
| generic kernel multiclass More...
|
class | CKernelStructuredOutputMachine |
class | CLinearLatentMachine |
| abstract implementaion of Linear Machine with latent variable This is the base implementation of all linear machines with latent variable. More...
|
class | CLinearMachine |
| Class LinearMachine is a generic interface for all kinds of linear machines like classifiers. More...
|
class | CLinearMulticlassMachine |
| generic linear multiclass machine More...
|
class | CLinearStructuredOutputMachine |
class | CMachine |
| A generic learning machine interface. More...
|
class | CMulticlassMachine |
| experimental abstract generic multiclass machine class More...
|
class | CNativeMulticlassMachine |
| experimental abstract native multiclass machine class More...
|
class | COnlineLinearMachine |
| Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms. More...
|
class | CStructuredOutputMachine |
class | CCplex |
| Class CCplex to encapsulate access to the commercial cplex general purpose optimizer. More...
|
class | CLoss |
| Class which collects generic mathematical functions. More...
|
class | CMath |
| Class which collects generic mathematical functions. More...
|
class | Munkres |
| Munkres. More...
|
class | CSparseInverseCovariance |
| used to estimate inverse covariance matrix using graphical lasso More...
|
class | CStatistics |
| Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc. More...
|
class | CGradientModelSelection |
| Model selection class which searches for the best model by a gradient- search. More...
|
class | CGridSearchModelSelection |
| Model selection class which searches for the best model by a grid- search. See CModelSelection for details. More...
|
class | CModelSelection |
| Abstract base class for model selection. Takes a parameter tree which specifies parameters for model selection, and a cross-validation instance and searches for the best combination of parameters in the abstract method select_model(), which has to be implemented in concrete sub-classes. More...
|
class | CModelSelectionParameters |
| Class to select parameters and their ranges for model selection. The structure is organized as a tree with different kinds of nodes, depending on the values of its member variables of name and CSGObject. More...
|
class | CParameterCombination |
| class that holds ONE combination of parameters for a learning machine. The structure is organized as a tree. Every node may hold a name or an instance of a Parameter class. Nodes may have children. The nodes are organized in such way, that every parameter of a model for model selection has one node and sub-parameters are stored in sub-nodes. Using a tree of this class, parameters of models may easily be set. There are these types of nodes: More...
|
class | CRandomSearchModelSelection |
| Model selection class which searches for the best model by a random search. See CModelSelection for details. More...
|
class | CConjugateIndex |
| conjugate index classifier. Described in: More...
|
class | CECOCAEDDecoder |
class | CECOCDecoder |
class | CECOCDiscriminantEncoder |
class | CECOCEDDecoder |
class | CECOCEncoder |
| ECOCEncoder produce an ECOC codebook. More...
|
class | CECOCForestEncoder |
class | CECOCHDDecoder |
class | CECOCIHDDecoder |
class | CECOCLLBDecoder |
class | CECOCOVOEncoder |
class | CECOCOVREncoder |
class | CECOCRandomDenseEncoder |
class | CECOCRandomSparseEncoder |
class | CECOCSimpleDecoder |
class | CECOCStrategy |
class | CECOCUtil |
class | CGaussianNaiveBayes |
| Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier. More...
|
class | CGMNPLib |
| class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP). More...
|
class | CGMNPSVM |
| Class GMNPSVM implements a one vs. rest MultiClass SVM. More...
|
class | CKNN |
| Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
|
class | CLaRank |
| the LaRank multiclass SVM machine More...
|
class | CMulticlassLibLinear |
| multiclass LibLinear wrapper. Uses Crammer-Singer formulation and gradient descent optimization algorithm implemented in the LibLinear library. Regularized bias support is added using stacking bias 'feature' to hyperplanes normal vectors. More...
|
class | CMulticlassLibSVM |
| class LibSVMMultiClass. Does one vs one classification. More...
|
class | CMulticlassOCAS |
| multiclass OCAS wrapper More...
|
class | CMulticlassOneVsOneStrategy |
| multiclass one vs one strategy used to train generic multiclass machines for K-class problems with building voting-based ensemble of K*(K-1) binary classifiers More...
|
class | CMulticlassOneVsRestStrategy |
| multiclass one vs rest strategy used to train generic multiclass machines for K-class problems with building ensemble of K binary classifiers More...
|
class | CMulticlassStrategy |
| class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary classifiers More...
|
class | CMulticlassSVM |
| class MultiClassSVM More...
|
class | CMulticlassTreeGuidedLogisticRegression |
| multiclass tree guided logistic regression More...
|
class | CQDA |
| Class QDA implements Quadratic Discriminant Analysis. More...
|
class | CRejectionStrategy |
| base rejection strategy class More...
|
class | CThresholdRejectionStrategy |
| threshold based rejection strategy More...
|
class | CDixonQTestRejectionStrategy |
| simplified version of Dixon's Q test outlier based rejection strategy. Statistic values are taken from http://www.vias.org/tmdatanaleng/cc_outlier_tests_dixon.html More...
|
class | CScatterSVM |
| ScatterSVM - Multiclass SVM. More...
|
class | CShareBoost |
class | ShareBoostOptimizer |
class | CBalancedConditionalProbabilityTree |
class | CConditionalProbabilityTree |
struct | ConditionalProbabilityTreeNodeData |
| struct to store data of node of conditional probability tree More...
|
class | CRandomConditionalProbabilityTree |
class | CRelaxedTree |
struct | RelaxedTreeNodeData |
class | RelaxedTreeUtil |
class | CTreeMachine |
| class TreeMachine, a base class for tree based multiclass classifiers More...
|
class | CTreeMachineNode |
struct | VwConditionalProbabilityTreeNodeData |
class | CVwConditionalProbabilityTree |
struct | tag_callback_data |
struct | tag_iteration_data |
struct | lbfgs_parameter_t |
class | CDecompressString |
| Preprocessor that decompresses compressed strings. More...
|
class | CDensePreprocessor |
| Template class DensePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CDenseFeatures (i.e. rectangular dense matrices) More...
|
class | CDimensionReductionPreprocessor |
| the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices). More...
|
class | CHomogeneousKernelMap |
| Preprocessor HomogeneousKernelMap performs homogeneous kernel maps as described in. More...
|
class | CKernelPCA |
| Preprocessor KernelPCA performs kernel principal component analysis. More...
|
class | CLogPlusOne |
| Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it. More...
|
class | CNormOne |
| Preprocessor NormOne, normalizes vectors to have norm 1. More...
|
class | CPCA |
| Preprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold. More...
|
class | CPNorm |
| Preprocessor PNorm, normalizes vectors to have p-norm. More...
|
class | CPreprocessor |
| Class Preprocessor defines a preprocessor interface. More...
|
class | CPruneVarSubMean |
| Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance. More...
|
class | CRandomFourierGaussPreproc |
| Preprocessor CRandomFourierGaussPreproc implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007 after preprocessing the features using them in a linear kernel approximates a gaussian kernel. More...
|
class | CSortUlongString |
| Preprocessor SortUlongString, sorts the indivual strings in ascending order. More...
|
class | CSortWordString |
| Preprocessor SortWordString, sorts the indivual strings in ascending order. More...
|
class | CSparsePreprocessor |
| Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures. More...
|
class | CStringPreprocessor |
| Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length). More...
|
class | CSumOne |
| Preprocessor SumOne, normalizes vectors to have sum 1. More...
|
class | CGaussianProcessRegression |
| Class GaussianProcessRegression implements Gaussian Process Regression.vInstead of a distribution over weights, the GP specifies a distribution over functions. More...
|
class | CExactInferenceMethod |
| The Gaussian Exact Form Inference Method. More...
|
class | CFITCInferenceMethod |
| The Fully Independent Conditional Training Inference Method. More...
|
class | CGaussianLikelihood |
| This is the class that models a Gaussian Likelihood. More...
|
class | CInferenceMethod |
| The Inference Method base class. More...
|
class | Psi_line |
class | CLaplacianInferenceMethod |
| The Laplace Approximation Inference Method. More...
|
class | CLikelihoodModel |
| The Likelihood Model base class. More...
|
class | CMeanFunction |
| Mean Function base class. More...
|
class | CStudentsTLikelihood |
| This is the class that models a likelihood model with a Student's T Distribution. The parameters include degrees of freedom as well as a sigma scale parameter. More...
|
class | CZeroMean |
| Zero Mean Function. More...
|
class | CKernelRidgeRegression |
| Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression. More...
|
class | CLeastAngleRegression |
| Class for Least Angle Regression, can be used to solve LASSO. More...
|
class | CLeastSquaresRegression |
| class to perform Least Squares Regression More...
|
class | CLinearRidgeRegression |
| Class LinearRidgeRegression implements Ridge Regression - a regularized least square method for classification and regression. More...
|
class | CLibLinearRegression |
| LibLinear for regression. More...
|
class | CLibSVR |
| Class LibSVR, performs support vector regression using LibSVM. More...
|
class | CMKLRegression |
| Multiple Kernel Learning for regression. More...
|
class | CSVRLight |
| Class SVRLight, performs support vector regression using SVMLight. More...
|
class | CHSIC |
| This class implements the Hilbert Schmidtd Independence Criterion based independence test as described in [1]. More...
|
class | CKernelIndependenceTestStatistic |
| Independence test base class. Provides an interface for performing an independence test. Given samples from the joint distribution , does the joint distribution factorize as ? The null- hypothesis says yes, i.e. no independence, the alternative hypothesis says yes. More...
|
class | CKernelMeanMatching |
| Kernel Mean Matching. More...
|
class | CKernelTwoSampleTestStatistic |
| Two sample test base class. Provides an interface for performing a two-sample test, i.e. Given samples from two distributions and , the null-hypothesis is: , the alternative hypothesis: . More...
|
class | CLinearTimeMMD |
| This class implements the linear time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions and in a RKHS.
. More...
|
class | CQuadraticTimeMMD |
| This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions and in a RKHS
. More...
|
class | CTestStatistic |
| Test statistic base class. Provides an interface for statistical tests via three methods: compute_statistic(), compute_p_value() and compute_threshold(). The second computes a p-value for the statistic computed by the first method. The p-value represents the position of the statistic in the null-distribution, i.e. the distribution of the statistic population given the null-hypothesis is true. (1-position = p-value). The third method, compute_threshold(), computes a threshold for a given test level which is needed to reject the null-hypothesis. More...
|
class | CTwoDistributionsTestStatistic |
| Provides an interface for performing statistical tests on two sets of samples from two distributions. Instances of these tests are the classical two-sample test and the independence test. This class may be used as base class for both. More...
|
class | CDualLibQPBMSOSVM |
| Class DualLibQPBMSOSVM that uses Bundle Methods for Regularized Risk Minimization algorithms for structured output (SO) problems [1] presented in [2]. More...
|
class | CDynProg |
| Dynamic Programming Class. More...
|
class | CSequence |
| Class CSequence to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). More...
|
class | CHMSVMLabels |
| Class CHMSVMLabels to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). Each of the labels is represented by a sequence of integers. Each label is of type CSequence and all of them are stored in a CDynamicObjectArray. More...
|
class | CHMSVMModel |
| Class CHMSVMModel that represents the application specific model and contains the application dependent logic to solve Hidden Markov Support Vector Machines (HM-SVM) type of problems within a generic SO framework. More...
|
class | CIntronList |
| class IntronList More...
|
struct | bmrm_return_value_T |
struct | bmrm_ll |
class | CMulticlassModel |
| Class CMulticlassModel that represents the application specific model and contains the application dependent logic to solve multiclass classification within a generic SO framework. More...
|
struct | CRealNumber |
| Class CRealNumber to be used in the application of Structured Output (SO) learning to multiclass classification. Even though it is likely that it does not make sense to consider real numbers as structured data, it has been made in this way because the basic type to use in structured labels needs to inherit from CStructuredData. More...
|
class | CMulticlassSOLabels |
| Class CMulticlassSOLabels to be used in the application of Structured Output (SO) learning to multiclass classification. Each of the labels is represented by a real number and it is required that the values of the labels are in the set {0, 1, ..., num_classes-1}. Each label is of type CRealNumber and all of them are stored in a CDynamicObjectArray. More...
|
class | CPlif |
| class Plif More...
|
class | CPlifArray |
| class PlifArray More...
|
class | CPlifBase |
| class PlifBase More...
|
class | CPlifMatrix |
| store plif arrays for all transitions in the model More...
|
class | CSegmentLoss |
| class IntronList More...
|
class | CStateModel |
| class CStateModel base, abstract class for the internal state representation used in the CHMSVMModel. More...
|
struct | TMultipleCPinfo |
struct | CResultSet |
class | CStructuredModel |
| Class CStructuredModel that represents the application specific model and contains most of the application dependent logic to solve structured output (SO) problems. The idea of this class is to be instantiated giving pointers to the functions that are dependent on the application, i.e. the combined feature representation and the argmax function . See: MulticlassModel.h and .cpp for an example of these functions implemented. More...
|
class | CTwoStateModel |
| class CTwoStateModel class for the internal two-state representation used in the CHMSVMModel. More...
|
class | CDomainAdaptationMulticlassLibLinear |
| domain adaptation multiclass LibLinear wrapper Source domain is assumed to b More...
|
class | CDomainAdaptationSVM |
| class DomainAdaptationSVM More...
|
class | CDomainAdaptationSVMLinear |
| class DomainAdaptationSVMLinear More...
|
class | MappedSparseMatrix |
| mapped sparse matrix for representing graph relations of tasks More...
|
class | CLibLinearMTL |
| class to implement LibLinear More...
|
class | CMultitaskClusteredLogisticRegression |
| class MultitaskClusteredLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. Assumes task in group are related with a clustered structure. More...
|
class | CMultitaskCompositeMachine |
| class MultitaskCompositeMachine used to solve multitask binary classification problems with separate training of given binary classifier on each task More...
|
class | CMultitaskKernelMaskNormalizer |
| The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
|
class | CMultitaskKernelMaskPairNormalizer |
| The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
|
class | CMultitaskKernelMklNormalizer |
| Base-class for parameterized Kernel Normalizers. More...
|
class | CMultitaskKernelNormalizer |
| The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
|
class | CMultitaskKernelPlifNormalizer |
| The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL. More...
|
class | CNode |
| A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks. More...
|
class | CTaxonomy |
| CTaxonomy is used to describe hierarchical structure between tasks. More...
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class | CMultitaskKernelTreeNormalizer |
| The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy. More...
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class | CMultitaskL12LogisticRegression |
| class MultitaskL12LogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
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class | CMultitaskLeastSquaresRegression |
| class Multitask Least Squares Regression, a machine to solve regression problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees More...
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class | CMultitaskLinearMachine |
| class MultitaskLinearMachine, a base class for linear multitask classifiers More...
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class | CMultitaskLogisticRegression |
| class Multitask Logistic Regression used to solve classification problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees. More...
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class | CMultitaskROCEvaluation |
| Class MultitaskROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC) of each task separately. More...
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class | CMultitaskTraceLogisticRegression |
| class MultitaskTraceLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
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class | CTask |
| class Task used to represent tasks in multitask learning. Essentially it represent a set of feature vector indices. More...
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class | CTaskGroup |
| class TaskGroup used to represent a group of tasks. Tasks in group do not overlap. More...
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class | CTaskRelation |
| used to represent tasks in multitask learning More...
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class | CTaskTree |
| class TaskTree used to represent a tree of tasks. Tree is constructed via task with subtasks (and subtasks of subtasks ..) passed to the TaskTree. More...
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class | CGUIClassifier |
| UI classifier. More...
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class | CGUIConverter |
| UI converter. More...
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class | CGUIDistance |
| UI distance. More...
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class | CGUIFeatures |
| UI features. More...
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class | CGUIHMM |
| UI HMM (Hidden Markov Model) More...
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class | CGUIKernel |
| UI kernel. More...
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class | CGUILabels |
| UI labels. More...
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class | CGUIMath |
| UI math. More...
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class | CGUIPluginEstimate |
| UI estimate. More...
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class | CGUIPreprocessor |
| UI preprocessor. More...
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class | CGUIStructure |
| UI structure. More...
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class | CGUITime |
| UI time. More...
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Functions |
CSGObject * | new_sgserializable (const char *sgserializable_name, EPrimitiveType generic) |
void | init_shogun (void(*print_message)(FILE *target, const char *str), void(*print_warning)(FILE *target, const char *str), void(*print_error)(FILE *target, const char *str), void(*cancel_computations)(bool &delayed, bool &immediately)) |
void | sg_global_print_default (FILE *target, const char *str) |
void | init_shogun_with_defaults () |
void | exit_shogun () |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
void | set_global_math (CMath *math) |
CMath * | get_global_math () |
float32_t | sd_offset_add (float32_t *weights, vw_size_t mask, VwFeature *begin, VwFeature *end, vw_size_t offset) |
float32_t | sd_offset_truncadd (float32_t *weights, vw_size_t mask, VwFeature *begin, VwFeature *end, vw_size_t offset, float32_t gravity) |
float32_t | one_pf_quad_predict (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask) |
float32_t | one_pf_quad_predict_trunc (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask, float32_t gravity) |
float32_t | real_weight (float32_t w, float32_t gravity) |
void * | sqdist_thread_func (void *P) |
bool | read_real_valued_sparse (SGSparseVector< float64_t > *&matrix, int32_t &num_feat, int32_t &num_vec) |
bool | write_real_valued_sparse (const SGSparseVector< float64_t > *matrix, int32_t num_feat, int32_t num_vec) |
bool | read_real_valued_dense (float64_t *&matrix, int32_t &num_feat, int32_t &num_vec) |
bool | write_real_valued_dense (const float64_t *matrix, int32_t num_feat, int32_t num_vec) |
bool | read_char_valued_strings (SGString< char > *&strings, int32_t &num_str, int32_t &max_string_len) |
bool | write_char_valued_strings (const SGString< char > *strings, int32_t num_str) |
char * | c_string_of_substring (substring s) |
void | print_substring (substring s) |
float32_t | float_of_substring (substring s) |
float64_t | double_of_substring (substring s) |
int32_t | int_of_substring (substring s) |
uint32_t | ulong_of_substring (substring s) |
uint32_t | ss_length (substring s) |
double | glomin (double a, double b, double c, double m, double e, double t, func_base &f, double &x) |
double | local_min (double a, double b, double t, func_base &f, double &x) |
double | local_min_rc (double &a, double &b, int &status, double value) |
double | r8_abs (double x) |
double | r8_epsilon () |
double | r8_max (double x, double y) |
double | r8_sign (double x) |
void | timestamp () |
double | zero (double a, double b, double t, func_base &f) |
void | zero_rc (double a, double b, double t, double &arg, int &status, double value) |
double | glomin (double a, double b, double c, double m, double e, double t, double f(double x), double &x) |
double | local_min (double a, double b, double t, double f(double x), double &x) |
double | zero (double a, double b, double t, double f(double x)) |
int32_t | InnerProjector (int32_t method, int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t l, float64_t u, float64_t *x, float64_t &lambda) |
int32_t | gvpm (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj) |
int32_t | FletcherAlg2A (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj) |
int32_t | gpm_solver (int32_t Solver, int32_t Projector, int32_t n, float32_t *A, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj) |
float64_t | ProjectR (float64_t *x, int32_t n, float64_t lambda, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u) |
int32_t | ProjectDai (int32_t n, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u, float64_t *x, float64_t &lam_ext) |
float64_t | quick_select (float64_t *arr, int32_t n) |
int32_t | Pardalos (int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t low, float64_t up, float64_t *x) |
static const float64_t * | get_col (uint32_t i) |
static float64_t | get_time () |
ocas_return_value_T | svm_ocas_solver_nnw (float64_t C, uint32_t nData, uint32_t num_nnw, uint32_t *nnw_idx, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, int(*add_pw_constr)(uint32_t, uint32_t, void *), void(*clip_neg_W)(uint32_t, uint32_t *, void *), void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data) |
ocas_return_value_T | svm_ocas_solver (float64_t C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data) |
ocas_return_value_T | svm_ocas_solver_difC (float64_t *C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data) |
static void | findactive (float64_t *Theta, float64_t *SortedA, uint32_t *nSortedA, float64_t *A, float64_t *B, int n, int(*sort)(float64_t *, float64_t *, uint32_t)) |
ocas_return_value_T | msvm_ocas_solver (float64_t C, float64_t *data_y, uint32_t nY, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data) |
libqp_state_T | libqp_splx_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *b, uint32_t *I, uint8_t *S, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolAbs, float64_t TolRel, float64_t QP_TH, void(*print_state)(libqp_state_T state)) |
libqp_state_T | libqp_gsmo_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *a, float64_t b, float64_t *LB, float64_t *UB, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolKKT, void(*print_state)(libqp_state_T state)) |
void | nrerror (char error_text[]) |
bool | choldc (float64_t *a, int32_t n, float64_t *p) |
void | cholsb (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[]) |
void | chol_forward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[]) |
void | chol_backward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[]) |
bool | solve_reduced (int32_t n, int32_t m, float64_t h_x[], float64_t h_y[], float64_t a[], float64_t x_x[], float64_t x_y[], float64_t c_x[], float64_t c_y[], float64_t workspace[], int32_t step) |
void | matrix_vector (int32_t n, float64_t m[], float64_t x[], float64_t y[]) |
int32_t | pr_loqo (int32_t n, int32_t m, float64_t c[], float64_t h_x[], float64_t a[], float64_t b[], float64_t l[], float64_t u[], float64_t primal[], float64_t dual[], int32_t verb, float64_t sigfig_max, int32_t counter_max, float64_t margin, float64_t bound, int32_t restart) |
void | ssl_train (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs) |
int32_t | CGLS (const struct data *Data, const struct options *Options, const struct vector_int *Subset, struct vector_double *Weights, struct vector_double *Outputs) |
int32_t | L2_SVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini) |
float64_t | line_search (float64_t *w, float64_t *w_bar, float64_t lambda, float64_t *o, float64_t *o_bar, float64_t *Y, float64_t *C, int32_t d, int32_t l) |
int32_t | TSVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs) |
int32_t | switch_labels (float64_t *Y, float64_t *o, int32_t *JU, int32_t u, int32_t S) |
int32_t | DA_S3VM (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs) |
int32_t | optimize_w (const struct data *Data, const float64_t *p, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini) |
void | optimize_p (const float64_t *g, int32_t u, float64_t T, float64_t r, float64_t *p) |
float64_t | transductive_cost (float64_t normWeights, float64_t *Y, float64_t *Outputs, int32_t m, float64_t lambda, float64_t lambda_u) |
float64_t | entropy (const float64_t *p, int32_t u) |
float64_t | KL (const float64_t *p, const float64_t *q, int32_t u) |
float64_t | norm_square (const vector_double *A) |
void | initialize (struct vector_double *A, int32_t k, float64_t a) |
void | initialize (struct vector_int *A, int32_t k) |
void | GetLabeledData (struct data *D, const struct data *Data) |
template<class T > |
void | push (v_array< T > &v, const T &new_ele) |
template<class T > |
void | alloc (v_array< T > &v, int length) |
template<class T > |
v_array< T > | pop (v_array< v_array< T > > &stack) |
float | distance (CJLCoverTreePoint p1, CJLCoverTreePoint p2, float64_t upper_bound) |
v_array< CJLCoverTreePoint > | parse_points (CDistance *distance, EFeaturesContainer fc) |
void | print (CJLCoverTreePoint &p) |
static const double * | get_col (uint32_t j) |
malsar_result_t | malsar_clustered (CDotFeatures *features, double *y, double rho1, double rho2, const malsar_options &options) |
malsar_result_t | malsar_joint_feature_learning (CDotFeatures *features, double *y, double rho1, double rho2, const malsar_options &options) |
malsar_result_t | malsar_low_rank (CDotFeatures *features, double *y, double rho, const malsar_options &options) |
slep_result_t | slep_mc_tree_lr (CDotFeatures *features, CMulticlassLabels *labels, float64_t z, const slep_options &options) |
double | compute_regularizer (double *w, double lambda, double lambda2, int n_vecs, int n_feats, int n_blocks, const slep_options &options) |
double | compute_lambda (double *ATx, double z, CDotFeatures *features, double *y, int n_vecs, int n_feats, int n_blocks, const slep_options &options) |
void | projection (double *w, double *v, int n_feats, int n_blocks, double lambda, double lambda2, double L, double *z, double *z0, const slep_options &options) |
double | search_point_gradient_and_objective (CDotFeatures *features, double *ATx, double *As, double *sc, double *y, int n_vecs, int n_feats, int n_tasks, double *g, double *gc, const slep_options &options) |
slep_result_t | slep_solver (CDotFeatures *features, double *y, double z, const slep_options &options) |
void | wrap_dsyev (char jobz, char uplo, int n, double *a, int lda, double *w, int *info) |
void | wrap_dgesvd (char jobu, char jobvt, int m, int n, double *a, int lda, double *sing, double *u, int ldu, double *vt, int ldvt, int *info) |
void | wrap_dgeqrf (int m, int n, double *a, int lda, double *tau, int *info) |
void | wrap_dorgqr (int m, int n, int k, double *a, int lda, double *tau, int *info) |
void | wrap_dsyevr (char jobz, char uplo, int n, double *a, int lda, int il, int iu, double *eigenvalues, double *eigenvectors, int *info) |
void | wrap_dsygvx (int itype, char jobz, char uplo, int n, double *a, int lda, double *b, int ldb, int il, int iu, double *eigenvalues, double *eigenvectors, int *info) |
template<class T > |
SGVector< T > | create_range_array (T min, T max, ERangeType type, T step, T type_base) |
static larank_kcache_t * | larank_kcache_create (CKernel *kernelfunc) |
static void | xtruncate (larank_kcache_t *self, int32_t k, int32_t nlen) |
static void | xpurge (larank_kcache_t *self) |
static void | larank_kcache_set_maximum_size (larank_kcache_t *self, int64_t entries) |
static void | larank_kcache_destroy (larank_kcache_t *self) |
static void | xminsize (larank_kcache_t *self, int32_t n) |
static int32_t * | larank_kcache_r2i (larank_kcache_t *self, int32_t n) |
static void | xextend (larank_kcache_t *self, int32_t k, int32_t nlen) |
static void | xswap (larank_kcache_t *self, int32_t i1, int32_t i2, int32_t r1, int32_t r2) |
static void | larank_kcache_swap_rr (larank_kcache_t *self, int32_t r1, int32_t r2) |
static void | larank_kcache_swap_ri (larank_kcache_t *self, int32_t r1, int32_t i2) |
static float64_t | xquery (larank_kcache_t *self, int32_t i, int32_t j) |
static float64_t | larank_kcache_query (larank_kcache_t *self, int32_t i, int32_t j) |
static void | larank_kcache_set_buddy (larank_kcache_t *self, larank_kcache_t *buddy) |
static float32_t * | larank_kcache_query_row (larank_kcache_t *self, int32_t i, int32_t len) |
static int32_t | line_search_backtracking (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param) |
static int32_t | line_search_backtracking_owlqn (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wp, callback_data_t *cd, const lbfgs_parameter_t *param) |
static int32_t | line_search_morethuente (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param) |
static int32_t | update_trial_interval (float64_t *x, float64_t *fx, float64_t *dx, float64_t *y, float64_t *fy, float64_t *dy, float64_t *t, float64_t *ft, float64_t *dt, const float64_t tmin, const float64_t tmax, int32_t *brackt) |
static float64_t | owlqn_x1norm (const float64_t *x, const int32_t start, const int32_t n) |
static void | owlqn_pseudo_gradient (float64_t *pg, const float64_t *x, const float64_t *g, const int32_t n, const float64_t c, const int32_t start, const int32_t end) |
static void | owlqn_project (float64_t *d, const float64_t *sign, const int32_t start, const int32_t end) |
void | lbfgs_parameter_init (lbfgs_parameter_t *param) |
int32_t | lbfgs (int32_t n, float64_t *x, float64_t *ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void *instance, lbfgs_parameter_t *_param) |
int | lbfgs (int n, float64_t *x, float64_t *ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void *instance, lbfgs_parameter_t *param) |
void | add_cutting_plane (bmrm_ll **tail, bool *map, float64_t *A, uint32_t free_idx, float64_t *cp_data, uint32_t dim) |
void | remove_cutting_plane (bmrm_ll **head, bmrm_ll **tail, bool *map, float64_t *icp) |
static const float64_t * | get_col (uint32_t i) |
bmrm_return_value_T | svm_bmrm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose) |
float64_t * | get_cutting_plane (bmrm_ll *ptr) |
uint32_t | find_free_idx (bool *map, uint32_t size) |
static const float64_t * | get_col (uint32_t i) |
bmrm_return_value_T | svm_p3bm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, uint32_t cp_models, bool verbose) |
static const float64_t * | get_col (uint32_t i) |
bmrm_return_value_T | svm_ppbm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose) |