Class List

Here are the classes, structs, unions and interfaces with brief descriptions:
CAlphabetThe class Alphabet implements an alphabet and alphabet utility functions
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
CAttributeFeaturesImplements attributed features, that is in the simplest case a number of (attribute, value) pairs
CAUCKernelThe AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training
CAvgDiagKernelNormalizerNormalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor)
CBinaryStream< T >Memory mapped emulation via binary streams (files)
CBitStringString class embedding a string in a compact bit representation
CBrayCurtisDistanceClass Bray-Curtis distance
CCache< T >Template class Cache implements a simple cache
CCanberraMetricClass CanberraMetric
CCanberraWordDistanceClass CanberraWordDistance
CChebyshewMetricClass ChebyshewMetric
CChi2KernelThe Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms
CChiSquareDistanceClass ChiSquareDistance
CClassifierA generic classifier interface
CCombinedDotFeaturesFeatures that allow stacking of a number of DotFeatures
CCombinedFeaturesThe class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object
CCombinedKernelThe Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination
CCommUlongStringKernelThe CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers
CCommWordStringKernelThe CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers
CCompressor
CConstKernelThe Constant Kernel returns a constant for all elements
CCosineDistanceClass CosineDistance
CCplexClass CCplex to encapsulate access to the commercial cplex general purpose optimizer
CCPLEXSVMCplexSVM a SVM solver implementation based on cplex (unfinished)
CCustomKernelThe Custom Kernelallows for custom user provided kernel matrices
CDecompressString< ST >Preprocessor that decompresses compressed strings
CDiagKernelThe Diagonal Kernel returns a constant for the diagonal and zero otherwise
CDiceKernelNormalizerDiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient)
CDistanceClass Distance
CDistanceKernelThe Distance kernel takes a distance as input
CDistanceMachineA generic DistanceMachine interface
CDistributionBase class Distribution from which all methods implementing a distribution are derived
CDomainAdaptationSVMClass DomainAdaptiveSVM
CDotFeaturesFeatures that support dot products among other operations
CDummyFeaturesThe 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
CDynInt< T, sz >Integer type of dynamic size
CDynProgDynamic Programming Class
CEuclidianDistanceClass EuclidianDistance
CExplicitSpecFeaturesFeatures that compute the Spectrum Kernel feature space explicitly
CFeaturesThe class Features is the base class of all feature objects
CFileA File access class
CFirstElementKernelNormalizerNormalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. $ c=k({\bf x},{\bf x})$
CFixedDegreeStringKernelThe FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d
CFKFeaturesThe class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models
CGaussianKernelThe well known Gaussian kernel (swiss army knife for SVMs) on dense real valued features
CGaussianShiftKernelAn experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel
CGaussianShortRealKernelThe 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
CGeodesicMetricClass GeodesicMetric
CGHMMClass GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM)
CGMNPLibClass GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP)
CGMNPSVMClass GMNPSVM implements a one vs. rest MultiClass SVM
CGNPPLibClass GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP)
CGNPPSVMClass GNPPSVM
CGPBTSVMClass GPBTSVM
CHammingWordDistanceClass HammingWordDistance
CHashCollection of Hashing Functions
CHierarchicalAgglomerative hierarchical single linkage clustering
CHistogramClass Histogram computes a histogram over all 16bit unsigned integers in the features
CHistogramWordStringKernelThe HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains
CHMMHidden Markov Model
CIdentityKernelNormalizerIdentity Kernel Normalization, i.e. no normalization is applied
CImplicitWeightedSpecFeaturesFeatures that compute the Weighted Spectrum Kernel feature space explicitly
CIndirectObject< T, P >Array class that accesses elements indirectly via an index array
CIntronListClass IntronList
CIOClass IO, used to do input output operations throughout shogun
CJensenMetricClass JensenMetric
CKernelThe Kernel base class
CKernelMachineA generic KernelMachine interface
CKernelNormalizerThe class Kernel Normalizer defines a function to postprocess kernel values
CKernelPerceptronClass KernelPerceptron - currently unfinished implementation of a Kernel Perceptron
CKMeansKMeans clustering, partitions the data into k (a-priori specified) clusters
CKNNClass KNN, an implementation of the standard k-nearest neigbor classifier
CKRRClass KRR implements Kernel Ridge Regression - a regularized least square method for classification and regression
CLabelsThe class Labels models labels, i.e. class assignments of objects
CLaRank
CLDAClass LDA implements regularized Linear Discriminant Analysis
CLibLinearClass to implement LibLinear
CLibSVMLibSVM
CLibSVMMultiClassClass LibSVMMultiClass
CLibSVMOneClassClass LibSVMOneClass
CLibSVRClass LibSVR, performs support vector regression using LibSVM
CLinearByteKernelComputes the standard linear kernel on dense byte valued features
CLinearClassifierClass LinearClassifier is a generic interface for all kinds of linear classifiers
CLinearHMMThe class LinearHMM is for learning Higher Order Markov chains
CLinearKernelComputes the standard linear kernel on dense real valued features
CLinearStringKernelComputes the standard linear kernel on dense char valued features
CLinearWordKernelComputes the standard linear kernel on dense word (2-byte) valued features
CList< T >Class List implements a doubly connected list for low-level-objects
CListElement< T >Class ListElement, defines how an element of the the list looks like
CLocalAlignmentStringKernelThe LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences
CLocalityImprovedStringKernelThe LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features
CLogPlusOnePreprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it
CLPBoostClass LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer
CLPMClass LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer
CManhattanMetricClass ManhattanMetric
CManhattanWordDistanceClass ManhattanWordDistance
CMatchWordStringKernelThe class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet
CMathClass which collects generic mathematical functions
CMemoryMappedFile< T >Memory mapped file
CMinkowskiMetricClass MinkowskiMetric
CMKLMultiple Kernel Learning
CMKLClassificationMultiple Kernel Learning for two-class-classification
CMKLMultiClassMKLMultiClass is a class for L1-norm multiclass MKL
CMKLOneClassMultiple Kernel Learning for one-class-classification
CMKLRegressionMultiple Kernel Learning for regression
CModelClass Model
CMPDSVMClass MPDSVM
CMultiClassSVMClass MultiClassSVM
CMultitaskKernelNormalizerThe MultitaskKernel allows Multitask Learning via a modified kernel function
CNormDerivativeLem3Preprocessor NormDerivativeLem3, performs the normalization used in Lemma3 in Jaakola Hausslers Fischer Kernel paper currently not implemented
CNormOnePreprocessor NormOne, normalizes vectors to have norm 1
COligoStringKernelThis class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004
CParallelClass Parallel provides helper functions for multithreading
CPCACutPreprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold
CPerceptronClass Perceptron implements the standard linear (online) perceptron
CPerformanceMeasuresClass to implement various performance measures
CPlifClass Plif
CPlifArrayClass PlifArray
CPlifBaseClass PlifBase
CPlifMatrixStore plif arrays for all transitions in the model
CPluginEstimateClass PluginEstimate
CPolyFeaturesImplement DotFeatures for the polynomial kernel
CPolyKernelComputes the standard polynomial kernel on dense real valued features
CPolyMatchStringKernelThe class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length
CPolyMatchWordStringKernelThe class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features
CPreProcClass PreProc defines a preprocessor interface
CPruneVarSubMeanPreprocessor PruneVarSubMean will substract the mean and remove features that have zero variance
CPyramidChi2Pyramid Kernel over Chi2 matched histograms
CQPBSVMLibClass QPBSVMLib
CRealDistanceClass RealDistance
CRealFileFeaturesThe class RealFileFeatures implements a dense double-precision floating point matrix from a file
CRegulatoryModulesStringKernelThe Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences
CRidgeKernelNormalizerNormalize 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)
CSalzbergWordStringKernelThe SalzbergWordString kernel implements the Salzberg kernel
CScatterSVMScatterSVM - Multiclass SVM
CSegmentLossClass IntronList
CSet< T >Template Set class
CSGObjectClass SGObject is the base class of all shogun objects
CSigmoidKernelThe standard Sigmoid kernel computed on dense real valued features
CSignalClass 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
CSimpleKernel< ST >Template class SimpleKernel is the base class for kernels working on Simple features
CSimpleLocalityImprovedStringKernelSimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel
CSimplePreProc< ST >Template class SimplePreProc, base class for preprocessors (cf. CPreProc) that apply to CSimpleFeatures (i.e. rectangular dense matrices)
CSortUlongStringPreprocessor SortUlongString, sorts the indivual strings in ascending order
CSortWordStringPreprocessor SortWordString, sorts the indivual strings in ascending order
CSparseDistance< ST >Template class SparseDistance
CSparseEuclidianDistanceClass SparseEucldianDistance
CSparseFeatures< ST >Template class SparseFeatures implements sparse matrices
CSparseGaussianKernelThe well known Gaussian kernel (swiss army knife for SVMs) on sparse real valued features
CSparseKernel< ST >Template class SparseKernel, is the base class of kernels working on sparse features
CSparseLinearKernelComputes the standard linear kernel on sparse real valued features
CSparsePolyKernelComputes the standard polynomial kernel on sparse real valued features
CSparsePreProc< ST >Template class SparsePreProc, base class for preprocessors (cf. CPreProc) that apply to CSparseFeatures
CSqrtDiagKernelNormalizerSqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements
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
CStringPreProc< ST >Template class StringPreProc, base class for preprocessors (cf. CPreProc) that apply to CStringFeatures (i.e. strings of variable length)
CSubGradientLPMClass SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer
CSubGradientSVMClass SubGradientSVM
CSVMA generic Support Vector Machine Interface
CSVMLinClass SVMLin
CSVMOcasClass SVMOcas
CSVMSGDClass SVMSGD
CTanimotoDistanceClass Tanimoto coefficient
CTanimotoKernelNormalizerTanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index )
CTensorProductPairKernelComputes the Tensor Product Pair Kernel (TPPK)
CTimeClass Time that implements a stopwatch based on either cpu time or wall clock time
CTOPFeaturesThe 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
CTronClass Tron
CVarianceKernelNormalizerVarianceKernelNormalizer divides by the ``variance''
CVersionClass Version provides version information
CWDFeaturesFeatures that compute the Weighted Degreee Kernel feature space explicitly
CWDSVMOcasClass WDSVMOcas
CWeightedCommWordStringKernelThe 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 $\beta_k$) from strings that have been mapped into unsigned 16bit integers
CWeightedDegreePositionStringKernelThe Weighted Degree Position String kernel (Weighted Degree kernel with shifts)
CWeightedDegreeStringKernelThe Weighted Degree String kernel
K_THREAD_PARAM< T >
larank_kcache_s
LaRankOutput
LaRankPattern
LaRankPatterns
MKLMultiClassGLPKMKLMultiClassGLPK is a helper class for MKLMultiClass
ShogunExceptionClass ShogunException defines an exception which is thrown whenever an error inside of shogun occurs

SHOGUN Machine Learning Toolbox - Documentation