Here are the classes, structs, unions and interfaces with brief descriptions:

CAccuracyMeasure | Class AccuracyMeasure used to measure accuracy of 2-class classifier |

CAlphabet | The class Alphabet implements an alphabet and alphabet utility functions |

CANOVAKernel | ANOVA (ANalysis Of VAriances) kernel |

CArray< T > | Template class Array implements a dense one dimensional array |

CArray2< T > | Template class Array2 implements a dense two dimensional array |

CArray3< T > | Template class Array3 implements a dense three dimensional array |

CAsciiFile | A Ascii File access class |

CAttenuatedEuclidianDistance | Class AttenuatedEuclidianDistance |

CAttributeFeatures | Implements attributed features, that is in the simplest case a number of (attribute, value) pairs |

CAUCKernel | The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training |

CAveragedPerceptron | Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron |

CAvgDiagKernelNormalizer | Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor) |

CBALMeasure | Class BALMeasure used to measure balanced error of 2-class classifier |

CBesselKernel | Class Bessel kernel |

CBinaryClassEvaluation | The class TwoClassEvaluation, a base class used to evaluate binary classification labels |

CBinaryFile | A Binary file access class |

CBinaryStream< T > | Memory mapped emulation via binary streams (files) |

CBitString | String class embedding a string in a compact bit representation |

CBrayCurtisDistance | Class Bray-Curtis distance |

CCache< T > | Template class Cache implements a simple cache |

CCanberraMetric | Class CanberraMetric |

CCanberraWordDistance | Class CanberraWordDistance |

CCauchyKernel | Cauchy kernel |

CChebyshewMetric | Class ChebyshewMetric |

CChi2Kernel | The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms |

CChiSquareDistance | Class ChiSquareDistance |

CCircularKernel | Circular kernel |

CCombinedDotFeatures | Features that allow stacking of a number of DotFeatures |

CCombinedFeatures | The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object |

CCombinedKernel | The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination |

CCommUlongStringKernel | The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers |

CCommWordStringKernel | The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers |

CCompressor | Compression library for compressing and decompressing buffers using one of the standard compression algorithms, LZO, GZIP, BZIP2 or LZMA |

CConstKernel | The Constant Kernel returns a constant for all elements |

CContingencyTableEvaluation | The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates |

CConverter | Class Converter used to convert data |

CCosineDistance | Class CosineDistance |

CCplex | Class CCplex to encapsulate access to the commercial cplex general purpose optimizer |

CCPLEXSVM | CplexSVM a SVM solver implementation based on cplex (unfinished) |

CCrossCorrelationMeasure | Class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier |

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 |

CCustomDistance | The Custom Distance allows for custom user provided distance matrices |

CCustomKernel | The Custom Kernel allows for custom user provided kernel matrices |

CDecompressString< ST > | Preprocessor that decompresses compressed strings |

CDiagKernel | The Diagonal Kernel returns a constant for the diagonal and zero otherwise |

CDiceKernelNormalizer | DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) |

CDiffusionMaps | CDiffusionMaps used to preprocess given data using diffusion maps dimensionality reduction technique |

CDimensionReductionPreprocessor | Class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices) |

CDistance | Class Distance, a base class for all the distances used in the Shogun toolbox |

CDistanceKernel | The Distance kernel takes a distance as input |

CDistanceMachine | A generic DistanceMachine interface |

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 |

CDistribution | Base class Distribution from which all methods implementing a distribution are derived |

CDomainAdaptationSVM | Class DomainAdaptationSVM |

CDomainAdaptationSVMLinear | Class DomainAdaptationSVMLinear |

CDotFeatures | Features that support dot products among other operations |

CDotKernel | Template class DotKernel is the base class for kernels working on DotFeatures |

CDummyFeatures | The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any) |

CDynamicArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |

CDynamicObjectArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |

CDynInt< T, sz > | Integer type of dynamic size |

CDynProg | Dynamic Programming Class |

CEmbeddingConverter | Class EmbeddingConverter used to create embeddings of features, e.g. construct dense numeric embedding of string features |

CErrorRateMeasure | Class ErrorRateMeasure used to measure error rate of 2-class classifier |

CEuclidianDistance | Class EuclidianDistance |

CEvaluation | Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression |

CExplicitSpecFeatures | Features that compute the Spectrum Kernel feature space explicitly |

CExponentialKernel | The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures |

CF1Measure | Class F1Measure used to measure F1 score of 2-class classifier |

CFeatures | The class Features is the base class of all feature objects |

CFile | A File access base class |

CFirstElementKernelNormalizer | Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. |

CFixedDegreeStringKernel | The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d |

CFKFeatures | The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models |

CGaussian | Gaussian distribution interface |

CGaussianKernel | The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures |

CGaussianMatchStringKernel | The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length |

CGaussianNaiveBayes | Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier |

CGaussianShiftKernel | An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel |

CGaussianShortRealKernel | The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features |

CGCArray< T > | Template class GCArray implements a garbage collecting static array |

CGeodesicMetric | Class GeodesicMetric |

CGHMM | Class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM) |

CGMM | Gaussian Mixture Model interface |

CGMNPLib | Class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP) |

CGMNPSVM | Class GMNPSVM implements a one vs. rest MultiClass SVM |

CGNPPLib | Class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP) |

CGNPPSVM | Class GNPPSVM |

CGPBTSVM | Class GPBTSVM |

CGridSearchModelSelection | Model selection class which searches for the best model by a grid- search. See CModelSelection for details |

CGUIClassifier | UI classifier |

CGUIDistance | UI distance |

CGUIFeatures | UI features |

CGUIHMM | UI HMM (Hidden Markov Model) |

CGUIKernel | UI kernel |

CGUILabels | UI labels |

CGUIMath | UI math |

CGUIPluginEstimate | UI estimate |

CGUIPreprocessor | UI preprocessor |

CGUIStructure | UI structure |

CGUITime | UI time |

CHammingWordDistance | Class HammingWordDistance |

CHash | Collection of Hashing Functions |

CHashedWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CHashedWDFeaturesTransposed | Features that compute the Weighted Degreee Kernel feature space explicitly |

CHashSet | Class HashSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table |

CHessianLocallyLinearEmbedding | Class HessianLocallyLinearEmbedding used to preprocess data using Hessian Locally Linear Embedding algorithm described in |

CHierarchical | Agglomerative hierarchical single linkage clustering |

CHingeLoss | CHingeLoss implements the hinge loss function |

CHistogram | Class Histogram computes a histogram over all 16bit unsigned integers in the features |

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 |

CHistogramWordStringKernel | The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains |

CHMM | Hidden Markov Model |

CIdentityKernelNormalizer | Identity Kernel Normalization, i.e. no normalization is applied |

CImplicitWeightedSpecFeatures | Features that compute the Weighted Spectrum Kernel feature space explicitly |

CIndirectObject< T, P > | Array class that accesses elements indirectly via an index array |

CInputParser< T > | Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples |

CIntronList | Class IntronList |

CInverseMultiQuadricKernel | InverseMultiQuadricKernel |

CIOBuffer | An I/O buffer class |

CIsomap | Class Isomap used to preprocess data using K-Isomap algorithm as described in |

CJensenMetric | Class JensenMetric |

CKernel | The Kernel base class |

CKernelDistance | The Kernel distance takes a distance as input |

CKernelLocallyLinearEmbedding | Class KernelLocallyLinearEmbedding used to preprocess data using kernel extension of Locally Linear Embedding algorithm as described in |

CKernelLocalTangentSpaceAlignment | Class LocalTangentSpaceAlignment used to preprocess data using kernel extension of the Local Tangent Space Alignment (LTSA) algorithm |

CKernelMachine | A generic KernelMachine interface |

CKernelNormalizer | The class Kernel Normalizer defines a function to post-process kernel values |

CKernelPCA | Preprocessor KernelPCA performs kernel principal component analysis |

CKMeans | KMeans clustering, partitions the data into k (a-priori specified) clusters |

CKNN | Class KNN, an implementation of the standard k-nearest neigbor classifier |

CKRR | Class KRR implements Kernel Ridge Regression - a regularized least square method for classification and regression |

CLabels | The class Labels models labels, i.e. class assignments of objects |

CLaplacianEigenmaps | Class LaplacianEigenmaps used to preprocess data using Laplacian Eigenmaps algorithm as described in: |

CLaRank | LaRank multiclass SVM machine |

CLBPPyrDotFeatures | Implement DotFeatures for the polynomial kernel |

CLDA | Class LDA implements regularized Linear Discriminant Analysis |

CLibLinear | Class to implement LibLinear |

CLibSVM | LibSVM |

CLibSVMMultiClass | Class LibSVMMultiClass |

CLibSVMOneClass | Class LibSVMOneClass |

CLibSVR | Class LibSVR, performs support vector regression using LibSVM |

CLinearHMM | The class LinearHMM is for learning Higher Order Markov chains |

CLinearKernel | Computes the standard linear kernel on CDotFeatures |

CLinearLocalTangentSpaceAlignment | LinearLocalTangentSpaceAlignment converter used to construct embeddings as described in: |

CLinearMachine | Class LinearMachine is a generic interface for all kinds of linear machines like classifiers |

CLinearStringKernel | Computes the standard linear kernel on dense char valued features |

CList | Class List implements a doubly connected list for low-level-objects |

CListElement | Class ListElement, defines how an element of the the list looks like |

CLocalAlignmentStringKernel | The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences |

CLocalityImprovedStringKernel | The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features |

CLocalityPreservingProjections | |

CLocallyLinearEmbedding | Class LocallyLinearEmbedding used to preprocess data using Locally Linear Embedding algorithm described in |

CLocalTangentSpaceAlignment | LocalTangentSpaceAlignment used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in: |

CLogKernel | Log kernel |

CLogLoss | CLogLoss implements the logarithmic loss function |

CLogLossMargin | Class CLogLossMargin implements a margin-based log-likelihood loss function |

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 |

CLoss | Class which collects generic mathematical functions |

CLossFunction | Class CLossFunction is the base class of all loss functions |

CLPBoost | Class LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer |

CLPM | Class LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer |

CMachine | A generic learning machine interface |

CManhattanMetric | Class ManhattanMetric |

CManhattanWordDistance | Class ManhattanWordDistance |

CMatchWordStringKernel | The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet |

CMath | Class which collects generic mathematical functions |

CMeanAbsoluteError | Class MeanAbsoluteError used to compute an error of regression model |

CMeanSquaredError | Class MeanSquaredError used to compute an error of regression model |

CMemoryMappedFile< T > | Memory mapped file |

CMinkowskiMetric | Class MinkowskiMetric |

CMKL | Multiple Kernel Learning |

CMKLClassification | Multiple Kernel Learning for two-class-classification |

CMKLMultiClass | MKLMultiClass is a class for L1-norm multiclass MKL |

CMKLOneClass | Multiple Kernel Learning for one-class-classification |

CMKLRegression | Multiple Kernel Learning for regression |

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 |

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 |

CMPDSVM | Class MPDSVM |

CMulticlassAccuracy | The class MulticlassAccuracy used to compute accuracy of multiclass classification |

CMultiClassSVM | Class MultiClassSVM |

CMultidimensionalScaling | Class Multidimensionalscaling is used to perform multidimensional scaling (capable of landmark approximation if requested) |

CMultiquadricKernel | MultiquadricKernel |

CMultitaskKernelMaskNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelMaskPairNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelMklNormalizer | Base-class for parameterized Kernel Normalizers |

CMultitaskKernelNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function |

CMultitaskKernelPlifNormalizer | The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL |

CMultitaskKernelTreeNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy |

CNeighborhoodPreservingEmbedding | NeighborhoodPreservingEmbedding converter used to construct embeddings as described in: |

CNode | A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks |

CNormOne | Preprocessor NormOne, normalizes vectors to have norm 1 |

COligoStringKernel | This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004 |

COnlineLibLinear | Class implementing a purely online version of LibLinear, using the L2R_L1LOSS_SVC_DUAL solver only |

COnlineLinearMachine | Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms |

COnlineSVMSGD | Class OnlineSVMSGD |

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: |

CParseBuffer< T > | Class CParseBuffer implements a ring of examples of a defined size. The ring stores objects of the Example type |

CPCA | Preprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold |

CPerceptron | Class Perceptron implements the standard linear (online) perceptron |

CPlif | Class Plif |

CPlifArray | Class PlifArray |

CPlifBase | Class PlifBase |

CPlifMatrix | Store plif arrays for all transitions in the model |

CPluginEstimate | Class PluginEstimate |

CPolyFeatures | Implement DotFeatures for the polynomial kernel |

CPolyKernel | Computes the standard polynomial kernel on CDotFeatures |

CPolyMatchStringKernel | The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length |

CPolyMatchWordStringKernel | The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features |

CPositionalPWM | Positional PWM |

CPowerKernel | Power kernel |

CPRCEvaluation | Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC) |

CPrecisionMeasure | Class PrecisionMeasure used to measure precision of 2-class classifier |

CPreprocessor | Class Preprocessor defines a preprocessor interface |

CPruneVarSubMean | Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance |

CPyramidChi2 | Pyramid Kernel over Chi2 matched histograms |

CQPBSVMLib | Class QPBSVMLib |

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 |

CRationalQuadraticKernel | Rational Quadratic kernel |

CRealDistance | Class RealDistance |

CRealFileFeatures | The class RealFileFeatures implements a dense double-precision floating point matrix from a file |

CRecallMeasure | Class RecallMeasure used to measure recall of 2-class classifier |

CRegulatoryModulesStringKernel | The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences |

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) |

CROCEvaluation | Class ROCEvalution used to evaluate ROC (Receiver Operator Characteristic) and an area under ROC curve (auROC) |

CrossValidationResult | 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 |

CSalzbergWordStringKernel | The SalzbergWordString kernel implements the Salzberg kernel |

CScatterKernelNormalizer | Scatter kernel normalizer |

CScatterSVM | ScatterSVM - Multiclass SVM |

CSegmentLoss | Class IntronList |

CSerializableAsciiFile | Serializable ascii file |

CSerializableFile | Serializable file |

CSet< T > | Template Set class |

CSGDQN | Class SGDQN |

CSGObject | Class SGObject is the base class of all shogun objects |

CSigmoidKernel | The standard Sigmoid kernel computed on dense real valued features |

CSignal | Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process |

CSimpleDistance< ST > | Template class SimpleDistance |

CSimpleFeatures< ST > | The class SimpleFeatures implements dense feature matrices |

CSimpleFile< T > | Template class SimpleFile to read and write from files |

CSimpleLocalityImprovedStringKernel | SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel |

CSimplePreprocessor< ST > | Template class SimplePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSimpleFeatures (i.e. rectangular dense matrices) |

CSmoothHingeLoss | CSmoothHingeLoss implements the smooth hinge loss function |

CSNPFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CSNPStringKernel | The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length |

CSortUlongString | Preprocessor SortUlongString, sorts the indivual strings in ascending order |

CSortWordString | Preprocessor SortWordString, sorts the indivual strings in ascending order |

CSparseDistance< ST > | Template class SparseDistance |

CSparseEuclidianDistance | Class SparseEucldianDistance |

CSparseFeatures< ST > | Template class SparseFeatures implements sparse matrices |

CSparseKernel< ST > | Template class SparseKernel, is the base class of kernels working on sparse features |

CSparsePolyFeatures | Implement DotFeatures for the polynomial kernel |

CSparsePreprocessor< ST > | Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures |

CSparseSpatialSampleStringKernel | Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> |

CSpecificityMeasure | Class SpecificityMeasure used to measure specificity of 2-class classifier |

CSpectrumMismatchRBFKernel | Spectrum mismatch rbf kernel |

CSpectrumRBFKernel | Spectrum rbf kernel |

CSphericalKernel | Spherical kernel |

CSplineKernel | Computes the Spline Kernel function which is the cubic polynomial |

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(...) |

CSqrtDiagKernelNormalizer | SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements |

CSquaredHingeLoss | Class CSquaredHingeLoss implements a squared hinge loss function |

CSquaredLoss | CSquaredLoss implements the squared loss function |

CStatistics | Class that contains certain functions related to statistics, such as the student's t distribution |

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 |

CStreamingAsciiFile | Class StreamingAsciiFile to read vector-by-vector from ASCII files |

CStreamingDotFeatures | Streaming features that support dot products among other operations |

CStreamingFeatures | Streaming features are features which are used for online algorithms |

CStreamingFile | A Streaming File access class |

CStreamingFileFromFeatures | Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object |

CStreamingFileFromSimpleFeatures< T > | Class CStreamingFileFromSimpleFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CSimpleFeatures object |

CStreamingFileFromSparseFeatures< T > | 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 |

CStreamingFileFromStringFeatures< T > | Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object |

CStreamingSimpleFeatures< T > | This class implements streaming features with dense feature vectors |

CStreamingSparseFeatures< T > | 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' |

CStreamingStringFeatures< T > | This class implements streaming features as strings |

CStreamingVwCacheFile | Class StreamingVwCacheFile to read vector-by-vector from VW cache files |

CStreamingVwFeatures | This class implements streaming features for use with VW |

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 |

CStringDistance< ST > | Template class StringDistance |

CStringFeatures< ST > | Template class StringFeatures implements a list of strings |

CStringFileFeatures< ST > | File based string features |

CStringKernel< ST > | Template class StringKernel, is the base class of all String Kernels |

CStringPreprocessor< ST > | Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length) |

CSubGradientLPM | Class SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer |

CSubGradientSVM | Class SubGradientSVM |

CSubset | Class for adding subset support to a class. Provides an interface for getting/setting subset_matrices and index conversion. Do not inherit from this class, use it as variable. Write wrappers for all get/set functions |

CSVM | A generic Support Vector Machine Interface |

CSVMLight | Class SVMlight |

CSVMLightOneClass | Trains a one class C SVM |

CSVMLin | Class SVMLin |

CSVMOcas | Class SVMOcas |

CSVMSGD | Class SVMSGD |

CSVRLight | Class SVRLight, performs support vector regression using SVMLight |

CSyntaxHighLight | Syntax highlight |

CTanimotoDistance | Class Tanimoto coefficient |

CTanimotoKernelNormalizer | TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) |

CTaxonomy | CTaxonomy is used to describe hierarchical structure between tasks |

CTensorProductPairKernel | Computes the Tensor Product Pair Kernel (TPPK) |

CTime | Class Time that implements a stopwatch based on either cpu time or wall clock time |

CTOPFeatures | The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models |

CTrie< Trie > | Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored |

CTron | Class Tron |

CTStudentKernel | Generalized T-Student kernel |

CVarianceKernelNormalizer | VarianceKernelNormalizer divides by the ``variance'' |

CVowpalWabbit | Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit |

CVwAdaptiveLearner | VwAdaptiveLearner uses an adaptive subgradient technique to update weights |

CVwCacheReader | Base class from which all cache readers for VW should be derived |

CVwCacheWriter | CVwCacheWriter is the base class for all VW cache creating classes |

CVwEnvironment | Class CVwEnvironment is the environment used by VW |

CVwLearner | Base class for all VW learners |

CVwNativeCacheReader | Class CVwNativeCacheReader reads from a cache exactly as that which has been produced by VW's default cache format |

CVwNativeCacheWriter | Class CVwNativeCacheWriter writes a cache exactly as that which would be produced by VW's default cache format |

CVwNonAdaptiveLearner | VwNonAdaptiveLearner uses a standard gradient descent weight update rule |

CVwParser | CVwParser is the object which provides the functions to parse examples from buffered input |

CVwRegressor | Regressor used by VW |

CWaveKernel | Wave kernel |

CWaveletKernel | Class WaveletKernel |

CWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |

CWDSVMOcas | Class WDSVMOcas |

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 |

CWeightedDegreePositionStringKernel | The Weighted Degree Position String kernel (Weighted Degree kernel with shifts) |

CWeightedDegreeRBFKernel | Weighted degree RBF kernel |

CWeightedDegreeStringKernel | The Weighted Degree String kernel |

CWRACCMeasure | Class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier |

CZeroMeanCenterKernelNormalizer | ZeroMeanCenterKernelNormalizer centers the kernel in feature space |

DynArray< T > | Template Dynamic array class that creates an array that can be used like a list or an array |

Example< T > | Class Example is the container type for the vector+label combination |

MKLMultiClassGLPK | MKLMultiClassGLPK is a helper class for MKLMultiClass |

MKLMultiClassGradient | MKLMultiClassGradient is a helper class for MKLMultiClass |

MKLMultiClassOptimizationBase | MKLMultiClassOptimizationBase is a helper class for MKLMultiClass |

Model | Class Model |

Parallel | Class Parallel provides helper functions for multithreading |

Parameter | Parameter class |

ParameterMap | Implements a map of ParameterMapElement instances |

ParameterMapElement | Class to hold instances of a parameter map. Each element contains a key and a value, which are of type SGParamInfo. May be compared to each other based on their keys |

SerializableAsciiReader00 | Serializable ascii reader |

SGIO | Class SGIO, used to do input output operations throughout shogun |

SGMatrix< T > | Shogun matrix |

SGNDArray< T > | Shogun n-dimensional array |

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 |

SGSparseMatrix< T > | Template class SGSparseMatrix |

SGSparseVector< T > | Template class SGSparseVector |

SGSparseVectorEntry< T > | Template class SGSparseVectorEntry |

SGString< T > | Shogun string |

SGStringList< T > | Template class SGStringList |

SGVector< T > | Shogun vector |

ShogunException | Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs |

SSKFeatures | SSKFeatures |

substring | Struct Substring, specified by start position and end position |

TParameter | Parameter struct |

CSerializableFile::TSerializableReader | Serializable reader |

TSGDataType | Datatypes that shogun supports |

v_array< T > | Class v_array is a templated class used to store variable length arrays. Memory locations are stored as 'extents', i.e., address of the first memory location and address after the last member |

Version | Class Version provides version information |

VwExample | Example class for VW |

VwFeature | One feature in VW |

VwLabel | Class VwLabel holds a label object used by VW |

SHOGUN Machine Learning Toolbox - Documentation