 shogun | All of classes and functions are contained in the shogun namespace |
  DynArray | Template Dynamic array class that creates an array that can be used like a list or an array |
  Parallel | Class Parallel provides helper functions for multithreading |
  TParameter | Parameter struct |
  Parameter | Parameter 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 |
  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 |
  ParameterMap | Implements a map of ParameterMapElement instances |
  CSGObject | Class SGObject is the base class of all shogun objects |
  Version | Class Version provides version information |
  CAveragedPerceptron | Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron |
  CGaussianNaiveBayes | Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier |
  CKNN | Class KNN, an implementation of the standard k-nearest neigbor classifier |
  CLDA | Class LDA implements regularized Linear Discriminant Analysis |
  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 |
  CMKL | Multiple Kernel Learning |
  CMKLClassification | Multiple Kernel Learning for two-class-classification |
  CMKLMultiClass | MKLMultiClass is a class for L1-norm multiclass MKL |
  MKLMultiClassGLPK | MKLMultiClassGLPK is a helper class for MKLMultiClass |
  MKLMultiClassGradient | MKLMultiClassGradient is a helper class for MKLMultiClass |
  MKLMultiClassOptimizationBase | MKLMultiClassOptimizationBase is a helper class for MKLMultiClass |
  CMKLOneClass | Multiple Kernel Learning for one-class-classification |
  CPerceptron | Class Perceptron implements the standard linear (online) perceptron |
  CPluginEstimate | Class PluginEstimate |
  CSubGradientLPM | Class SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a norm regularizer |
  CCPLEXSVM | CplexSVM a SVM solver implementation based on cplex (unfinished) |
  CDomainAdaptationSVMLinear | Class DomainAdaptationSVMLinear |
  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 |
  CLaRank | LaRank multiclass SVM machine |
  CLibLinear | Class to implement LibLinear |
  CLibSVM | LibSVM |
  CLibSVMMultiClass | Class LibSVMMultiClass |
  CLibSVMOneClass | Class LibSVMOneClass |
  CMPDSVM | Class MPDSVM |
  CMultiClassSVM | Class MultiClassSVM |
  COnlineLibLinear | Class implementing a purely online version of LibLinear, using the L2R_L1LOSS_SVC_DUAL solver only |
  COnlineSVMSGD | Class OnlineSVMSGD |
  CQPBSVMLib | Class QPBSVMLib |
  CScatterSVM | ScatterSVM - Multiclass SVM |
  CSGDQN | Class SGDQN |
  CSubGradientSVM | Class SubGradientSVM |
  CSVM | A generic Support Vector Machine Interface |
  CSVMLin | Class SVMLin |
  CSVMOcas | Class SVMOcas |
  CSVMSGD | Class SVMSGD |
  CWDSVMOcas | Class WDSVMOcas |
  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 |
  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 |
  CVwAdaptiveLearner | VwAdaptiveLearner uses an adaptive subgradient technique to update weights |
  CVwNonAdaptiveLearner | VwNonAdaptiveLearner uses a standard gradient descent weight update rule |
  CVowpalWabbit | Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit |
  VwFeature | One feature in VW |
  VwExample | Example class for VW |
  VwLabel | Class VwLabel holds a label object used by VW |
  CVwEnvironment | Class CVwEnvironment is the environment used by VW |
  CVwLearner | Base class for all VW learners |
  CVwParser | CVwParser is the object which provides the functions to parse examples from buffered input |
  CVwRegressor | Regressor used by VW |
  CGMM | Gaussian Mixture Model interface |
  CHierarchical | Agglomerative hierarchical single linkage clustering |
  CKMeans | KMeans clustering, partitions the data into k (a-priori specified) clusters |
  CConverter | Class Converter used to convert data |
  CDiffusionMaps | CDiffusionMaps used to preprocess given data using diffusion maps dimensionality reduction technique |
  CEmbeddingConverter | Class EmbeddingConverter used to create embeddings of features, e.g. construct dense numeric embedding of string features |
  CHessianLocallyLinearEmbedding | Class HessianLocallyLinearEmbedding used to preprocess data using Hessian Locally Linear Embedding algorithm described in |
  CIsomap | Class Isomap used to preprocess data using K-Isomap algorithm as described in |
  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 |
  CLaplacianEigenmaps | Class LaplacianEigenmaps used to preprocess data using Laplacian Eigenmaps algorithm as described in: |
  CLinearLocalTangentSpaceAlignment | LinearLocalTangentSpaceAlignment converter used to construct embeddings as described in: |
  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: |
  CMultidimensionalScaling | Class Multidimensionalscaling is used to perform multidimensional scaling (capable of landmark approximation if requested) |
  CNeighborhoodPreservingEmbedding | NeighborhoodPreservingEmbedding converter used to construct embeddings as described in: |
  CAttenuatedEuclidianDistance | Class AttenuatedEuclidianDistance |
  CBrayCurtisDistance | Class Bray-Curtis distance |
  CCanberraMetric | Class CanberraMetric |
  CCanberraWordDistance | Class CanberraWordDistance |
  CChebyshewMetric | Class ChebyshewMetric |
  CChiSquareDistance | Class ChiSquareDistance |
  CCosineDistance | Class CosineDistance |
  CCustomDistance | The Custom Distance allows for custom user provided distance matrices |
  CDistance | Class Distance, a base class for all the distances used in the Shogun toolbox |
  CEuclidianDistance | Class EuclidianDistance |
  CGeodesicMetric | Class GeodesicMetric |
  CHammingWordDistance | Class HammingWordDistance |
  CJensenMetric | Class JensenMetric |
  CKernelDistance | The Kernel distance takes a distance as input |
  CManhattanMetric | Class ManhattanMetric |
  CManhattanWordDistance | Class ManhattanWordDistance |
  CMinkowskiMetric | Class MinkowskiMetric |
  CRealDistance | Class RealDistance |
  CSimpleDistance | Template class SimpleDistance |
  CSparseDistance | Template class SparseDistance |
  CSparseEuclidianDistance | Class SparseEucldianDistance |
  CStringDistance | Template class StringDistance |
  CTanimotoDistance | Class Tanimoto coefficient |
  CDistribution | Base class Distribution from which all methods implementing a distribution are derived |
  CGaussian | Gaussian distribution interface |
  CGHMM | Class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM) |
  CHistogram | Class Histogram computes a histogram over all 16bit unsigned integers in the features |
  Model | Class Model |
  CHMM | Hidden Markov Model |
  CLinearHMM | The class LinearHMM is for learning Higher Order Markov chains |
  CPositionalPWM | Positional PWM |
  CBinaryClassEvaluation | The class TwoClassEvaluation, a base class used to evaluate binary classification labels |
  CContingencyTableEvaluation | The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates |
  CAccuracyMeasure | Class AccuracyMeasure used to measure accuracy of 2-class classifier |
  CErrorRateMeasure | Class ErrorRateMeasure used to measure error rate of 2-class classifier |
  CBALMeasure | Class BALMeasure used to measure balanced error of 2-class classifier |
  CWRACCMeasure | Class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier |
  CF1Measure | Class F1Measure used to measure F1 score of 2-class classifier |
  CCrossCorrelationMeasure | Class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier |
  CRecallMeasure | Class RecallMeasure used to measure recall of 2-class classifier |
  CPrecisionMeasure | Class PrecisionMeasure used to measure precision of 2-class classifier |
  CSpecificityMeasure | Class SpecificityMeasure used to measure specificity of 2-class classifier |
  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 |
  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 |
  CEvaluation | Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression |
  CMeanAbsoluteError | Class MeanAbsoluteError used to compute an error of regression model |
  CMeanSquaredError | Class MeanSquaredError used to compute an error of regression model |
  CMulticlassAccuracy | The class MulticlassAccuracy used to compute accuracy of multiclass classification |
  CPRCEvaluation | Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC) |
  CROCEvaluation | Class ROCEvalution used to evaluate ROC (Receiver Operator Characteristic) and an area under ROC curve (auROC) |
  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(...) |
  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 |
  CAlphabet | The class Alphabet implements an alphabet and alphabet utility functions |
  CAttributeFeatures | Implements attributed features, that is in the simplest case a number of (attribute, value) pairs |
  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 |
  CDotFeatures | Features that support dot products among other operations |
  CDummyFeatures | The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any) |
  CExplicitSpecFeatures | Features that compute the Spectrum Kernel feature space explicitly |
  CFeatures | The class Features is the base class of all feature objects |
  CFKFeatures | The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models |
  CHashedWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
  CHashedWDFeaturesTransposed | Features that compute the Weighted Degreee Kernel feature space explicitly |
  CImplicitWeightedSpecFeatures | Features that compute the Weighted Spectrum Kernel feature space explicitly |
  CLabels | The class Labels models labels, i.e. class assignments of objects |
  CLBPPyrDotFeatures | Implement DotFeatures for the polynomial kernel |
  CPolyFeatures | Implement DotFeatures for the polynomial kernel |
  CRealFileFeatures | The class RealFileFeatures implements a dense double-precision floating point matrix from a file |
  CSimpleFeatures | The class SimpleFeatures implements dense feature matrices |
  CSNPFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
  CSparseFeatures | Template class SparseFeatures implements sparse matrices |
  CSparsePolyFeatures | Implement DotFeatures for the polynomial kernel |
  CStreamingDotFeatures | Streaming features that support dot products among other operations |
  CStreamingFeatures | Streaming features are features which are used for online algorithms |
  CStreamingSimpleFeatures | This class implements streaming features with dense feature vectors |
  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' |
  CStreamingStringFeatures | This class implements streaming features as strings |
  CStreamingVwFeatures | This class implements streaming features for use with VW |
  CStringFeatures | Template class StringFeatures implements a list of strings |
  CStringFileFeatures | File based string features |
  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 |
  CTOPFeatures | The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models |
  CWDFeatures | Features that compute the Weighted Degreee Kernel feature space explicitly |
  CAsciiFile | A Ascii File access class |
  CBinaryFile | A Binary file access class |
  CBinaryStream | Memory mapped emulation via binary streams (files) |
  CFile | A File access base class |
  CInputParser | Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples |
  CIOBuffer | An I/O buffer class |
  CMemoryMappedFile | Memory mapped file |
  Example | Class Example is the container type for the vector+label combination |
  CParseBuffer | Class CParseBuffer implements a ring of examples of a defined size. The ring stores objects of the Example type |
  CSerializableAsciiFile | Serializable ascii file |
  SerializableAsciiReader00 | Serializable ascii reader |
  CSerializableFile | Serializable file |
   TSerializableReader | Serializable reader |
  SGIO | Class SGIO, used to do input output operations throughout shogun |
  substring | Struct Substring, specified by start position and end position |
  CSimpleFile | Template class SimpleFile to read and write from files |
  CStreamingAsciiFile | Class StreamingAsciiFile to read vector-by-vector from ASCII files |
  CStreamingFile | A Streaming File access class |
  CStreamingFileFromFeatures | Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object |
  CStreamingFileFromSimpleFeatures | Class CStreamingFileFromSimpleFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CSimpleFeatures object |
  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 |
  CStreamingFileFromStringFeatures | Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object |
  CStreamingVwCacheFile | Class StreamingVwCacheFile to read vector-by-vector from VW cache files |
  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 |
  CANOVAKernel | ANOVA (ANalysis Of VAriances) kernel |
  CAUCKernel | The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training |
  CAvgDiagKernelNormalizer | Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor) |
  CBesselKernel | Class Bessel kernel |
  CCauchyKernel | Cauchy kernel |
  CChi2Kernel | The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms |
  CCircularKernel | Circular kernel |
  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 |
  CConstKernel | The Constant Kernel returns a constant for all elements |
  CCustomKernel | The Custom Kernel allows for custom user provided kernel matrices |
  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) |
  CDistanceKernel | The Distance kernel takes a distance as input |
  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 |
  CDotKernel | Template class DotKernel is the base class for kernels working on DotFeatures |
  CExponentialKernel | The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures |
  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 |
  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 |
  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 |
  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 |
  CIdentityKernelNormalizer | Identity Kernel Normalization, i.e. no normalization is applied |
  CInverseMultiQuadricKernel | InverseMultiQuadricKernel |
  CKernel | The Kernel base class |
  CKernelNormalizer | The class Kernel Normalizer defines a function to post-process kernel values |
  CLinearKernel | Computes the standard linear kernel on CDotFeatures |
  CLinearStringKernel | Computes the standard linear kernel on dense char valued features |
  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 |
  CLogKernel | Log kernel |
  CMatchWordStringKernel | The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet |
  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 |
  CNode | A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks |
  CTaxonomy | CTaxonomy is used to describe hierarchical structure between tasks |
  CMultitaskKernelTreeNormalizer | The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy |
  COligoStringKernel | This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004 |
  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 |
  CPowerKernel | Power kernel |
  CPyramidChi2 | Pyramid Kernel over Chi2 matched histograms |
  CRationalQuadraticKernel | Rational Quadratic kernel |
  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) |
  CSalzbergWordStringKernel | The SalzbergWordString kernel implements the Salzberg kernel |
  CScatterKernelNormalizer | Scatter kernel normalizer |
  CSigmoidKernel | The standard Sigmoid kernel computed on dense real valued features |
  CSimpleLocalityImprovedStringKernel | SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel |
  CSNPStringKernel | The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length |
  CSparseKernel | Template class SparseKernel, is the base class of kernels working on sparse features |
  SSKFeatures | SSKFeatures |
  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 |
  CSpectrumMismatchRBFKernel | Spectrum mismatch rbf kernel |
  CSpectrumRBFKernel | Spectrum rbf kernel |
  CSphericalKernel | Spherical kernel |
  CSplineKernel | Computes the Spline Kernel function which is the cubic polynomial |
  CSqrtDiagKernelNormalizer | SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements |
  CStringKernel | Template class StringKernel, is the base class of all String Kernels |
  CTanimotoKernelNormalizer | TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) |
  CTensorProductPairKernel | Computes the Tensor Product Pair Kernel (TPPK) |
  CTStudentKernel | Generalized T-Student kernel |
  CVarianceKernelNormalizer | VarianceKernelNormalizer divides by the ``variance'' |
  CWaveKernel | Wave kernel |
  CWaveletKernel | Class WaveletKernel |
  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 |
  CZeroMeanCenterKernelNormalizer | ZeroMeanCenterKernelNormalizer centers the kernel in feature space |
  CArray | Template class Array implements a dense one dimensional array |
  CArray2 | Template class Array2 implements a dense two dimensional array |
  CArray3 | Template class Array3 implements a dense three dimensional array |
  CBitString | String class embedding a string in a compact bit representation |
  CCache | Template class Cache implements a simple cache |
  CCompressor | Compression library for compressing and decompressing buffers using one of the standard compression algorithms, LZO, GZIP, BZIP2 or LZMA |
  SGVector | Shogun vector |
  SGMatrix | Shogun matrix |
  SGNDArray | Shogun n-dimensional array |
  SGString | Shogun string |
  SGStringList | Template class SGStringList |
  SGSparseVectorEntry | Template class SGSparseVectorEntry |
  SGSparseVector | Template class SGSparseVector |
  SGSparseMatrix | Template class SGSparseMatrix |
  TSGDataType | Datatypes that shogun supports |
  CDynamicArray | Template Dynamic array class that creates an array that can be used like a list or an array |
  CDynamicObjectArray | Template Dynamic array class that creates an array that can be used like a list or an array |
  CDynInt | Integer type of dynamic size |
  CGCArray | Template class GCArray implements a garbage collecting static array |
  CHash | Collection of Hashing Functions |
  CHashSet | Class HashSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table |
  CIndirectObject | Array class that accesses elements indirectly via an index array |
  CListElement | Class ListElement, defines how an element of the the list looks like |
  CList | Class List implements a doubly connected list for low-level-objects |
  CSet | Template Set class |
  ShogunException | Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs |
  CSignal | Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process |
  CTime | Class Time that implements a stopwatch based on either cpu time or wall clock time |
  CTrie | Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored |
  v_array | 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 |
  CHingeLoss | CHingeLoss implements the hinge loss function |
  CLogLoss | CLogLoss implements the logarithmic loss function |
  CLogLossMargin | Class CLogLossMargin implements a margin-based log-likelihood loss function |
  CLossFunction | Class CLossFunction is the base class of all loss functions |
  CSmoothHingeLoss | CSmoothHingeLoss implements the smooth hinge loss function |
  CSquaredHingeLoss | Class CSquaredHingeLoss implements a squared hinge loss function |
  CSquaredLoss | CSquaredLoss implements the squared loss function |
  CDistanceMachine | A generic DistanceMachine interface |
  CKernelMachine | A generic KernelMachine interface |
  CLinearMachine | Class LinearMachine is a generic interface for all kinds of linear machines like classifiers |
  CMachine | A generic learning machine interface |
  COnlineLinearMachine | Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms |
  CCplex | Class CCplex to encapsulate access to the commercial cplex general purpose optimizer |
  CLoss | Class which collects generic mathematical functions |
  CMath | Class which collects generic mathematical functions |
  CStatistics | Class that contains certain functions related to statistics, such as the student's t distribution |
  CGridSearchModelSelection | Model selection class which searches for the best model by a grid- search. See CModelSelection for details |
  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 |
  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: |
  CDecompressString | Preprocessor that decompresses compressed strings |
  CDimensionReductionPreprocessor | Class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices) |
  CKernelPCA | Preprocessor KernelPCA performs kernel principal component analysis |
  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 |
  CNormOne | Preprocessor NormOne, normalizes vectors to have norm 1 |
  CPCA | Preprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold |
  CPreprocessor | Class Preprocessor defines a preprocessor interface |
  CPruneVarSubMean | Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance |
  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 |
  CSimplePreprocessor | Template class SimplePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSimpleFeatures (i.e. rectangular dense matrices) |
  CSortUlongString | Preprocessor SortUlongString, sorts the indivual strings in ascending order |
  CSortWordString | Preprocessor SortWordString, sorts the indivual strings in ascending order |
  CSparsePreprocessor | Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures |
  CStringPreprocessor | Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length) |
  CKRR | Class KRR implements Kernel Ridge Regression - a regularized least square method for classification and regression |
  CLibSVR | Class LibSVR, performs support vector regression using LibSVM |
  CMKLRegression | Multiple Kernel Learning for regression |
  CDynProg | Dynamic Programming Class |
  CIntronList | Class IntronList |
  CPlif | Class Plif |
  CPlifArray | Class PlifArray |
  CPlifBase | Class PlifBase |
  CPlifMatrix | Store plif arrays for all transitions in the model |
  CSegmentLoss | Class IntronList |
  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 |
 CSyntaxHighLight | Syntax highlight |
 CTron | Class Tron |