Classes | Typedefs | Enumerations | Functions | Variables

shogun Namespace Reference

all of classes and functions are contained in the shogun namespace More...

Classes

class  DynArray
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  Parallel
 Class Parallel provides helper functions for multithreading. More...
struct  TParameter
class  Parameter
class  CSGObject
 Class SGObject is the base class of all shogun objects. More...
class  Version
 Class Version provides version information. More...
class  CClassifier
 A generic classifier interface. More...
class  CDistanceMachine
 A generic DistanceMachine interface. More...
class  CKernelMachine
 A generic KernelMachine interface. More...
class  CKernelPerceptron
 Class KernelPerceptron - currently unfinished implementation of a Kernel Perceptron. More...
class  CKNN
 Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
class  CLDA
class  CLinearClassifier
 Class LinearClassifier is a generic interface for all kinds of linear classifiers. More...
class  CLPBoost
class  CLPM
class  CMKL
 Multiple Kernel Learning. More...
class  CMKLClassification
 Multiple Kernel Learning for two-class-classification. More...
class  CMKLMultiClass
 MKLMultiClass is a class for L1-norm multiclass MKL. More...
class  MKLMultiClassGLPK
 MKLMultiClassGLPK is a helper class for MKLMultiClass. More...
class  MKLMultiClassGradient
 MKLMultiClassGradient is a helper class for MKLMultiClass. More...
class  MKLMultiClassOptimizationBase
 MKLMultiClassOptimizationBase is a helper class for MKLMultiClass. More...
class  CMKLOneClass
 Multiple Kernel Learning for one-class-classification. More...
class  CPerceptron
 Class Perceptron implements the standard linear (online) perceptron. More...
class  CPluginEstimate
 class PluginEstimate More...
class  CSubGradientLPM
class  CCPLEXSVM
class  CDomainAdaptationSVM
 class DomainAdaptiveSVM More...
class  CDomainAdaptationSVMLinear
 class DomainAdaptiveSVMLinear 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  CGNPPLib
 class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP). More...
class  CGNPPSVM
 class GNPPSVM More...
class  CGPBTSVM
 class GPBTSVM More...
class  CLaRank
class  CLibLinear
 class to implement LibLinear More...
struct  libqp_state_T
class  CLibSVM
 LibSVM. More...
class  CLibSVMMultiClass
 class LibSVMMultiClass More...
class  CLibSVMOneClass
 class LibSVMOneClass More...
class  CMPDSVM
 class MPDSVM More...
class  CMultiClassSVM
 class MultiClassSVM More...
class  CQPBSVMLib
 class QPBSVMLib More...
class  CScatterSVM
 ScatterSVM - Multiclass SVM. More...
class  CSubGradientSVM
 class SubGradientSVM More...
class  CSVM
 A generic Support Vector Machine Interface. More...
class  CSVMLight
class  CSVMLightOneClass
class  CSVMLin
 class SVMLin More...
class  CSVMOcas
 class SVMOcas More...
class  CSVMSGD
 class SVMSGD More...
class  CWDSVMOcas
 class WDSVMOcas More...
class  CHierarchical
 Agglomerative hierarchical single linkage clustering. More...
class  CKMeans
 KMeans clustering, partitions the data into k (a-priori specified) clusters. More...
class  CBrayCurtisDistance
 class Bray-Curtis distance More...
class  CCanberraMetric
 class CanberraMetric More...
class  CCanberraWordDistance
 class CanberraWordDistance More...
class  CChebyshewMetric
 class ChebyshewMetric More...
class  CChiSquareDistance
 class ChiSquareDistance More...
class  CCosineDistance
 class CosineDistance More...
class  CCustomDistance
 The Custom Distance allows for custom user provided distance matrices. More...
class  CDistance
 class Distance More...
class  CEuclidianDistance
 class EuclidianDistance More...
class  CGeodesicMetric
 class GeodesicMetric More...
class  CHammingWordDistance
 class HammingWordDistance More...
class  CJensenMetric
 class JensenMetric More...
class  CKernelDistance
 The Kernel distance takes a distance as input. More...
class  CManhattanMetric
 class ManhattanMetric More...
class  CManhattanWordDistance
 class ManhattanWordDistance More...
class  CMinkowskiMetric
 class MinkowskiMetric More...
class  CRealDistance
 class RealDistance More...
class  CSimpleDistance
 template class SimpleDistance More...
class  CSparseDistance
 template class SparseDistance More...
class  CSparseEuclidianDistance
 class SparseEucldianDistance More...
class  CStringDistance
 template class StringDistance More...
class  CTanimotoDistance
 class Tanimoto coefficient More...
class  CDistribution
 Base class Distribution from which all methods implementing a distribution are derived. More...
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...
class  CHistogram
 Class Histogram computes a histogram over all 16bit unsigned integers in the features. More...
class  Model
 class Model More...
class  CHMM
 Hidden Markov Model. More...
class  CLinearHMM
 The class LinearHMM is for learning Higher Order Markov chains. More...
class  CPerformanceMeasures
 Class to implement various performance measures. More...
class  CAlphabet
 The class Alphabet implements an alphabet and alphabet utility functions. More...
class  CAttributeFeatures
 Implements attributed features, that is in the simplest case a number of (attribute, value) pairs. More...
class  CCombinedDotFeatures
 Features that allow stacking of a number of DotFeatures. More...
class  CCombinedFeatures
 The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object. More...
class  CDotFeatures
 Features that support dot products among other operations. More...
class  CDummyFeatures
 The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any). More...
class  CExplicitSpecFeatures
 Features that compute the Spectrum Kernel feature space explicitly. More...
class  CFeatures
 The class Features is the base class of all feature objects. More...
class  CFKFeatures
 The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models. More...
class  CHashedWDFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CHashedWDFeaturesTransposed
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CImplicitWeightedSpecFeatures
 Features that compute the Weighted Spectrum Kernel feature space explicitly. More...
class  CLabels
 The class Labels models labels, i.e. class assignments of objects. More...
class  CLBPPyrDotFeatures
 implement DotFeatures for the polynomial kernel More...
class  CPolyFeatures
 implement DotFeatures for the polynomial kernel More...
class  CRealFileFeatures
 The class RealFileFeatures implements a dense double-precision floating point matrix from a file. More...
class  CSimpleFeatures
 The class SimpleFeatures implements dense feature matrices. More...
class  CSNPFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CSparseFeatures
 Template class SparseFeatures implements sparse matrices. More...
class  CSparsePolyFeatures
 implement DotFeatures for the polynomial kernel More...
struct  SSKDoubleFeature
struct  SSKTripleFeature
class  CStringFeatures
 Template class StringFeatures implements a list of strings. More...
class  CStringFileFeatures
 File based string features. More...
class  CTOPFeatures
 The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models. More...
class  CWDFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CSignalModel
 class SignalModel More...
class  CTrainPredMaster
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...
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...
class  CChi2Kernel
 The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms. More...
class  CCombinedKernel
 The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination. 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  CConstKernel
 The Constant Kernel returns a constant for all elements. More...
class  CCustomKernel
 The Custom Kernel allows for custom user provided kernel matrices. More...
class  CDiagKernel
 The Diagonal Kernel returns a constant for the diagonal and zero otherwise. More...
class  CDiceKernelNormalizer
 DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient). More...
class  CDistanceKernel
 The Distance kernel takes a distance as input. More...
class  CDotKernel
 Template class DotKernel is the base class for kernels working on DotFeatures. More...
class  CFirstElementKernelNormalizer
 Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. $ c=k({\bf x},{\bf x})$. 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  CGaussianKernel
 The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures. More...
class  CGaussianMatchStringKernel
 The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length. More...
class  CGaussianShiftKernel
 An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel. More...
class  CGaussianShortRealKernel
 The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features. More...
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...
class  CHistogramWordStringKernel
 The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains. More...
class  CIdentityKernelNormalizer
 Identity Kernel Normalization, i.e. no normalization is applied. More...
struct  K_THREAD_PARAM
class  CKernel
 The Kernel base class. More...
class  CKernelNormalizer
 The class Kernel Normalizer defines a function to post-process kernel values. More...
class  CLinearKernel
 Computes the standard linear kernel on CDotFeatures. 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  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...
class  CMultitaskKernelTreeNormalizer
 The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy. More...
class  COligoStringKernel
 This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004. More...
class  CPolyKernel
 Computes the standard polynomial kernel on CDotFeatures. 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  CPyramidChi2
 Pyramid Kernel over Chi2 matched histograms. 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  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...
class  CSalzbergWordStringKernel
 The SalzbergWordString kernel implements the Salzberg kernel. More...
class  CScatterKernelNormalizer
class  CSigmoidKernel
 The standard Sigmoid kernel computed on dense real valued features. 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...
class  CSparseKernel
 Template class SparseKernel, is the base class of kernels working on sparse features. More...
struct  SSKFeatures
class  CSparseSpatialSampleStringKernel
 Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> More...
struct  joint_list_struct
class  CSpectrumMismatchRBFKernel
class  CSpectrumRBFKernel
class  CSqrtDiagKernelNormalizer
 SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements. More...
class  CStringKernel
 Template class StringKernel, is the base class of all String Kernels. More...
class  CTanimotoKernelNormalizer
 TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ). More...
class  CTensorProductPairKernel
 Computes the Tensor Product Pair Kernel (TPPK). More...
class  CVarianceKernelNormalizer
 VarianceKernelNormalizer divides by the ``variance''. 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 $\beta_k$) 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  CWeightedDegreeRBFKernel
class  CWeightedDegreeStringKernel
 The Weighted Degree String kernel. More...
class  CZeroMeanCenterKernelNormalizer
 ZeroMeanCenterKernelNormalizer centers the kernel in feature space. More...
class  CArray
 Template class Array implements a dense one dimensional array. More...
class  CArray2
 Template class Array2 implements a dense two dimensional array. More...
class  CArray3
 Template class Array3 implements a dense three dimensional array. More...
class  CAsciiFile
 A Ascii File access class. More...
class  CBinaryFile
 A Binary file access class. More...
class  CBinaryStream
 memory mapped emulation via binary streams (files) 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
class  CCplex
struct  TString
struct  TSparseEntry
struct  TSparse
struct  TSGDataType
class  CDynamicArray
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  CDynamicArrayPtr
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  CDynInt
 integer type of dynamic size More...
class  CFile
 A File access base class. More...
class  CGCArray
class  CHash
 Collection of Hashing Functions. More...
class  CIndirectObject
 an array class that accesses elements indirectly via an index array. More...
class  IO
 Class IO, used to do input output operations throughout shogun. 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  CMath
 Class which collects generic mathematical functions. More...
class  CMemoryMappedFile
 memory mapped file More...
class  CSerializableAsciiFile
class  SerializableAsciiReader00
class  CSerializableFile
class  CSet
 Template Set class. 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  CSimpleFile
 Template class SimpleFile to read and write from files. More...
class  CTime
 Class Time that implements a stopwatch based on either cpu time or wall clock time. More...
class  CTrie
class  CDecompressString
 Preprocessor that decompresses compressed strings. 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  CNormDerivativeLem3
 Preprocessor NormDerivativeLem3, performs the normalization used in Lemma3 in Jaakola Hausslers Fischer Kernel paper currently not implemented More...
class  CNormOne
 Preprocessor NormOne, normalizes vectors to have norm 1. More...
class  CPCACut
class  CPreProc
 Class PreProc defines a preprocessor interface. More...
class  CPruneVarSubMean
 Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance. More...
class  CSimplePreProc
 Template class SimplePreProc, base class for preprocessors (cf. CPreProc) that apply to CSimpleFeatures (i.e. rectangular dense matrices). 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  CSparsePreProc
 Template class SparsePreProc, base class for preprocessors (cf. CPreProc) that apply to CSparseFeatures. More...
class  CStringPreProc
 Template class StringPreProc, base class for preprocessors (cf. CPreProc) that apply to CStringFeatures (i.e. strings of variable length). More...
class  CKRR
class  CLibSVR
 Class LibSVR, performs support vector regression using LibSVM. More...
class  CMKLRegression
 Multiple Kernel Learning for regression. More...
class  CSVRLight
struct  segment_loss_struct
 segment loss More...
class  CDynProg
 Dynamic Programming Class. More...
class  CIntronList
 class IntronList 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...

Typedefs

typedef float64_t KERNELCACHE_ELEM
typedef int64_t KERNELCACHE_IDX
typedef int32_t index_t
typedef CDynInt< uint64_t, 3 > uint192_t
typedef CDynInt< uint64_t, 3 > uint256_t
typedef CDynInt< uint64_t, 3 > uint512_t
typedef CDynInt< uint64_t, 3 > uint1024_t
HMM specific types

typedef float64_t T_ALPHA_BETA_TABLE
 type for alpha/beta caching table
typedef uint8_t T_STATES
typedef T_STATESP_STATES

Enumerations

enum  EClassifierType {
  CT_NONE = 0, CT_LIGHT = 10, CT_LIGHTONECLASS = 11, CT_LIBSVM = 20,
  CT_LIBSVMONECLASS = 30, CT_LIBSVMMULTICLASS = 40, CT_MPD = 50, CT_GPBT = 60,
  CT_CPLEXSVM = 70, CT_PERCEPTRON = 80, CT_KERNELPERCEPTRON = 90, CT_LDA = 100,
  CT_LPM = 110, CT_LPBOOST = 120, CT_KNN = 130, CT_SVMLIN = 140,
  CT_KRR = 150, CT_GNPPSVM = 160, CT_GMNPSVM = 170, CT_SUBGRADIENTSVM = 180,
  CT_SUBGRADIENTLPM = 190, CT_SVMPERF = 200, CT_LIBSVR = 210, CT_SVRLIGHT = 220,
  CT_LIBLINEAR = 230, CT_KMEANS = 240, CT_HIERARCHICAL = 250, CT_SVMOCAS = 260,
  CT_WDSVMOCAS = 270, CT_SVMSGD = 280, CT_MKLMULTICLASS = 290, CT_MKLCLASSIFICATION = 300,
  CT_MKLONECLASS = 310, CT_MKLREGRESSION = 320, CT_SCATTERSVM = 330, CT_DASVM = 340,
  CT_LARANK = 350, CT_DASVMLINEAR = 360
}
enum  ESolverType {
  ST_AUTO = 0, ST_CPLEX = 1, ST_GLPK = 2, ST_NEWTON = 3,
  ST_DIRECT = 4, ST_ELASTICNET = 5
}
enum  LIBLINEAR_SOLVER_TYPE {
  L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL,
  MCSVM_CS, L1R_L2LOSS_SVC, L1R_LR
}
enum  LIBSVM_SOLVER_TYPE { LIBSVM_C_SVC = 1, LIBSVM_NU_SVC = 2 }
enum  EMultiClassSVM { ONE_VS_REST, ONE_VS_ONE }
enum  E_QPB_SOLVER {
  QPB_SOLVER_SCA, QPB_SOLVER_SCAS, QPB_SOLVER_SCAMV, QPB_SOLVER_PRLOQO,
  QPB_SOLVER_CPLEX, QPB_SOLVER_GS, QPB_SOLVER_GRADDESC
}
enum  SCATTER_TYPE { NO_BIAS_LIBSVM, NO_BIAS_SVMLIGHT, TEST_RULE1, TEST_RULE2 }
enum  E_SVM_TYPE { SVM_OCAS = 0, SVM_BMRM = 1 }
enum  EDistanceType {
  D_UNKNOWN = 0, D_MINKOWSKI = 10, D_MANHATTAN = 20, D_CANBERRA = 30,
  D_CHEBYSHEW = 40, D_GEODESIC = 50, D_JENSEN = 60, D_MANHATTANWORD = 70,
  D_HAMMINGWORD = 80, D_CANBERRAWORD = 90, D_SPARSEEUCLIDIAN = 100, D_EUCLIDIAN = 110,
  D_CHISQUARE = 120, D_TANIMOTO = 130, D_COSINE = 140, D_BRAYCURTIS = 150,
  D_CUSTOM = 160
}
enum  BaumWelchViterbiType {
  BW_NORMAL, BW_TRANS, BW_DEFINED, VIT_NORMAL,
  VIT_DEFINED
}
enum  EAlphabet {
  DNA = 0, RAWDNA = 1, RNA = 2, PROTEIN = 3,
  BINARY = 4, ALPHANUM = 5, CUBE = 6, RAWBYTE = 7,
  IUPAC_NUCLEIC_ACID = 8, IUPAC_AMINO_ACID = 9, NONE = 10, DIGIT = 11,
  DIGIT2 = 12, RAWDIGIT = 13, RAWDIGIT2 = 14, UNKNOWN = 15,
  SNP = 16, RAWSNP = 17
}
 

Alphabet of charfeatures/observations.

More...
enum  EFeatureType {
  F_UNKNOWN = 0, F_BOOL = 5, F_CHAR = 10, F_BYTE = 20,
  F_SHORT = 30, F_WORD = 40, F_INT = 50, F_UINT = 60,
  F_LONG = 70, F_ULONG = 80, F_SHORTREAL = 90, F_DREAL = 100,
  F_LONGREAL = 110, F_ANY = 1000
}
 

shogun feature type

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enum  EFeatureClass {
  C_UNKNOWN = 0, C_SIMPLE = 10, C_SPARSE = 20, C_STRING = 30,
  C_COMBINED = 40, C_COMBINED_DOT = 60, C_WD = 70, C_SPEC = 80,
  C_WEIGHTEDSPEC = 90, C_POLY = 100, C_ANY = 1000
}
 

shogun feature class

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enum  EFeatureProperty { FP_NONE = 0, FP_DOT = 1 }
 

shogun feature properties

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enum  EOptimizationType { FASTBUTMEMHUNGRY, SLOWBUTMEMEFFICIENT }
enum  EKernelType {
  K_UNKNOWN = 0, K_LINEAR = 10, K_POLY = 20, K_GAUSSIAN = 30,
  K_GAUSSIANSHIFT = 32, K_GAUSSIANMATCH = 33, K_HISTOGRAM = 40, K_SALZBERG = 41,
  K_LOCALITYIMPROVED = 50, K_SIMPLELOCALITYIMPROVED = 60, K_FIXEDDEGREE = 70, K_WEIGHTEDDEGREE = 80,
  K_WEIGHTEDDEGREEPOS = 81, K_WEIGHTEDDEGREERBF = 82, K_WEIGHTEDCOMMWORDSTRING = 90, K_POLYMATCH = 100,
  K_ALIGNMENT = 110, K_COMMWORDSTRING = 120, K_COMMULONGSTRING = 121, K_SPECTRUMMISMATCHRBF = 122,
  K_COMBINED = 140, K_AUC = 150, K_CUSTOM = 160, K_SIGMOID = 170,
  K_CHI2 = 180, K_DIAG = 190, K_CONST = 200, K_DISTANCE = 220,
  K_LOCALALIGNMENT = 230, K_PYRAMIDCHI2 = 240, K_OLIGO = 250, K_MATCHWORD = 260,
  K_TPPK = 270, K_REGULATORYMODULES = 280, K_SPARSESPATIALSAMPLE = 290, K_HISTOGRAMINTERSECTION = 300
}
enum  EKernelProperty { KP_NONE = 0, KP_LINADD = 1, KP_KERNCOMBINATION = 2, KP_BATCHEVALUATION = 4 }
enum  ENormalizerType { N_REGULAR = 0, N_MULTITASK = 1 }
enum  EWDKernType {
  E_WD = 0, E_EXTERNAL = 1, E_BLOCK_CONST = 2, E_BLOCK_LINEAR = 3,
  E_BLOCK_SQPOLY = 4, E_BLOCK_CUBICPOLY = 5, E_BLOCK_EXP = 6, E_BLOCK_LOG = 7
}
enum  E_COMPRESSION_TYPE {
  UNCOMPRESSED, LZO, GZIP, BZIP2,
  LZMA
}
enum  E_PROB_TYPE { E_LINEAR, E_QP }
enum  EContainerType { CT_SCALAR, CT_VECTOR, CT_MATRIX }
enum  EStructType { ST_NONE, ST_STRING, ST_SPARSE }
enum  EPrimitiveType {
  PT_BOOL, PT_CHAR, PT_INT8, PT_UINT8,
  PT_INT16, PT_UINT16, PT_INT32, PT_UINT32,
  PT_INT64, PT_UINT64, PT_FLOAT32, PT_FLOAT64,
  PT_FLOATMAX, PT_SGOBJECT
}
enum  EMessageType {
  MSG_GCDEBUG, MSG_DEBUG, MSG_INFO, MSG_NOTICE,
  MSG_WARN, MSG_ERROR, MSG_CRITICAL, MSG_ALERT,
  MSG_EMERGENCY, MSG_MESSAGEONLY
}
enum  EPreProcType {
  P_UNKNOWN = 0, P_NORMONE = 10, P_LOGPLUSONE = 20, P_SORTWORDSTRING = 30,
  P_SORTULONGSTRING = 40, P_SORTWORD = 50, P_PRUNEVARSUBMEAN = 60, P_DECOMPRESSCHARSTRING = 70,
  P_DECOMPRESSBYTESTRING = 80, P_DECOMPRESSWORDSTRING = 90, P_DECOMPRESSULONGSTRING = 100
}
enum  ERegressionType { RT_NONE = 0, RT_LIGHT = 10, RT_LIBSVM = 20 }
enum  ETransformType {
  T_LINEAR, T_LOG, T_LOG_PLUS1, T_LOG_PLUS3,
  T_LINEAR_PLUS3
}

Functions

CSGObjectnew_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 exit_shogun ()
void set_global_io (IO *io)
IOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
void set_global_math (CMath *math)
CMathget_global_math ()
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 void * xmalloc (int32_t n)
static void * xrealloc (void *ptr, int32_t n)
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_tlarank_kcache_query_row (larank_kcache_t *self, int32_t i, int32_t len)
static const float64_tget_col (uint32_t i)
static float64_t get_time ()
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)
void * sqdist_thread_func (void *P)
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)
bool read_real_valued_sparse (TSparse< float64_t > *&matrix, int32_t &num_feat, int32_t &num_vec)
bool write_real_valued_sparse (const TSparse< 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 (TString< char > *&strings, int32_t &num_str, int32_t &max_string_len)
bool write_char_valued_strings (const TString< char > *strings, int32_t num_str)

Variables

IOsg_io = NULL
Parallelsg_parallel = NULL
Versionsg_version = NULL
CMathsg_math = NULL
void(* sg_print_message )(FILE *target, const char *str) = NULL
 function called to print normal messages
void(* sg_print_warning )(FILE *target, const char *str) = NULL
 function called to print warning messages
void(* sg_print_error )(FILE *target, const char *str) = NULL
 function called to print error messages
void(* sg_cancel_computations )(bool &delayed, bool &immediately) = NULL
 function called to cancel things
uint32_t Randnext
static const uint32_t QPSolverMaxIter = 10000000
static float64_tH
static uint32_t BufSize

Detailed Description

all of classes and functions are contained in the shogun namespace


Typedef Documentation

typedef int32_t index_t

Definition at line 22 of file DataType.h.

Definition at line 36 of file Kernel.h.

typedef int64_t KERNELCACHE_IDX

Definition at line 44 of file Kernel.h.

typedef T_STATES * P_STATES

Definition at line 66 of file HMM.h.

type for alpha/beta caching table

Definition at line 37 of file HMM.h.

typedef uint8_t T_STATES

type that is used for states. Probably uint8_t is enough if you have at most 256 states, however uint16_t/long/... is also possible although you might quickly run into memory problems

Definition at line 64 of file HMM.h.

typedef CDynInt<uint64_t,3> uint1024_t

Definition at line 565 of file DynInt.h.

typedef CDynInt<uint64_t,3> uint192_t

convenience typedefs

Definition at line 562 of file DynInt.h.

typedef CDynInt<uint64_t,3> uint256_t

Definition at line 563 of file DynInt.h.

typedef CDynInt<uint64_t,3> uint512_t

Definition at line 564 of file DynInt.h.


Enumeration Type Documentation

Enumerator:
BW_NORMAL 
BW_TRANS 
BW_DEFINED 
VIT_NORMAL 
VIT_DEFINED 

Definition at line 70 of file HMM.h.

Enumerator:
UNCOMPRESSED 
LZO 
GZIP 
BZIP2 
LZMA 

Definition at line 25 of file Compressor.h.

Enumerator:
E_LINEAR 
E_QP 

Definition at line 29 of file Cplex.h.

Enumerator:
QPB_SOLVER_SCA 
QPB_SOLVER_SCAS 
QPB_SOLVER_SCAMV 
QPB_SOLVER_PRLOQO 
QPB_SOLVER_CPLEX 
QPB_SOLVER_GS 
QPB_SOLVER_GRADDESC 

Definition at line 30 of file QPBSVMLib.h.

enum E_SVM_TYPE
Enumerator:
SVM_OCAS 
SVM_BMRM 

Definition at line 23 of file SVMOcas.h.

enum EAlphabet

Alphabet of charfeatures/observations.

Enumerator:
DNA 

DNA - letters A,C,G,T.

RAWDNA 

RAWDNA - letters 0,1,2,3.

RNA 

RNA - letters A,C,G,U.

PROTEIN 

PROTEIN - letters a-z.

BINARY 
ALPHANUM 

ALPHANUM - [0-9a-z].

CUBE 

CUBE - [1-6].

RAWBYTE 

RAW BYTE - [0-255].

IUPAC_NUCLEIC_ACID 

IUPAC_NUCLEIC_ACID.

IUPAC_AMINO_ACID 

IUPAC_AMINO_ACID.

NONE 

NONE - type has no alphabet.

DIGIT 

DIGIT - letters 0-9.

DIGIT2 

DIGIT2 - letters 0-2.

RAWDIGIT 

RAWDIGIT - 0-9.

RAWDIGIT2 

RAWDIGIT2 - 0-2.

UNKNOWN 

unknown alphabet

SNP 

SNP - letters A,C,G,T,0.

RAWSNP 

RAWSNP - letters 0,1,2,3,4.

Definition at line 21 of file Alphabet.h.

Enumerator:
CT_NONE 
CT_LIGHT 
CT_LIGHTONECLASS 
CT_LIBSVM 
CT_LIBSVMONECLASS 
CT_LIBSVMMULTICLASS 
CT_MPD 
CT_GPBT 
CT_CPLEXSVM 
CT_PERCEPTRON 
CT_KERNELPERCEPTRON 
CT_LDA 
CT_LPM 
CT_LPBOOST 
CT_KNN 
CT_SVMLIN 
CT_KRR 
CT_GNPPSVM 
CT_GMNPSVM 
CT_SUBGRADIENTSVM 
CT_SUBGRADIENTLPM 
CT_SVMPERF 
CT_LIBSVR 
CT_SVRLIGHT 
CT_LIBLINEAR 
CT_KMEANS 
CT_HIERARCHICAL 
CT_SVMOCAS 
CT_WDSVMOCAS 
CT_SVMSGD 
CT_MKLMULTICLASS 
CT_MKLCLASSIFICATION 
CT_MKLONECLASS 
CT_MKLREGRESSION 
CT_SCATTERSVM 
CT_DASVM 
CT_LARANK 
CT_DASVMLINEAR 

Definition at line 27 of file Classifier.h.

Enumerator:
CT_SCALAR 
CT_VECTOR 
CT_MATRIX 

Definition at line 49 of file DataType.h.

Enumerator:
D_UNKNOWN 
D_MINKOWSKI 
D_MANHATTAN 
D_CANBERRA 
D_CHEBYSHEW 
D_GEODESIC 
D_JENSEN 
D_MANHATTANWORD 
D_HAMMINGWORD 
D_CANBERRAWORD 
D_SPARSEEUCLIDIAN 
D_EUCLIDIAN 
D_CHISQUARE 
D_TANIMOTO 
D_COSINE 
D_BRAYCURTIS 
D_CUSTOM 

Definition at line 31 of file Distance.h.

shogun feature class

Enumerator:
C_UNKNOWN 
C_SIMPLE 
C_SPARSE 
C_STRING 
C_COMBINED 
C_COMBINED_DOT 
C_WD 
C_SPEC 
C_WEIGHTEDSPEC 
C_POLY 
C_ANY 

Definition at line 35 of file FeatureTypes.h.

shogun feature properties

Enumerator:
FP_NONE 
FP_DOT 

Definition at line 51 of file FeatureTypes.h.

shogun feature type

Enumerator:
F_UNKNOWN 
F_BOOL 
F_CHAR 
F_BYTE 
F_SHORT 
F_WORD 
F_INT 
F_UINT 
F_LONG 
F_ULONG 
F_SHORTREAL 
F_DREAL 
F_LONGREAL 
F_ANY 

Definition at line 16 of file FeatureTypes.h.

Enumerator:
KP_NONE 
KP_LINADD 
KP_KERNCOMBINATION 
KP_BATCHEVALUATION 

Definition at line 93 of file Kernel.h.

Enumerator:
K_UNKNOWN 
K_LINEAR 
K_POLY 
K_GAUSSIAN 
K_GAUSSIANSHIFT 
K_GAUSSIANMATCH 
K_HISTOGRAM 
K_SALZBERG 
K_LOCALITYIMPROVED 
K_SIMPLELOCALITYIMPROVED 
K_FIXEDDEGREE 
K_WEIGHTEDDEGREE 
K_WEIGHTEDDEGREEPOS 
K_WEIGHTEDDEGREERBF 
K_WEIGHTEDCOMMWORDSTRING 
K_POLYMATCH 
K_ALIGNMENT 
K_COMMWORDSTRING 
K_COMMULONGSTRING 
K_SPECTRUMMISMATCHRBF 
K_COMBINED 
K_AUC 
K_CUSTOM 
K_SIGMOID 
K_CHI2 
K_DIAG 
K_CONST 
K_DISTANCE 
K_LOCALALIGNMENT 
K_PYRAMIDCHI2 
K_OLIGO 
K_MATCHWORD 
K_TPPK 
K_REGULATORYMODULES 
K_SPARSESPATIALSAMPLE 
K_HISTOGRAMINTERSECTION 

Definition at line 53 of file Kernel.h.

The io libs output [DEBUG] etc in front of every message 'higher' messages filter output depending on the loglevel, i.e. CRITICAL messages will print all MSG_CRITICAL TO MSG_EMERGENCY messages.

Enumerator:
MSG_GCDEBUG 
MSG_DEBUG 
MSG_INFO 
MSG_NOTICE 
MSG_WARN 
MSG_ERROR 
MSG_CRITICAL 
MSG_ALERT 
MSG_EMERGENCY 
MSG_MESSAGEONLY 

Definition at line 41 of file io.h.

Enumerator:
ONE_VS_REST 
ONE_VS_ONE 

Definition at line 21 of file MultiClassSVM.h.

normalizer type

Enumerator:
N_REGULAR 
N_MULTITASK 

Definition at line 21 of file KernelNormalizer.h.

Enumerator:
FASTBUTMEMHUNGRY 
SLOWBUTMEMEFFICIENT 

Definition at line 47 of file Kernel.h.

Enumerator:
P_UNKNOWN 
P_NORMONE 
P_LOGPLUSONE 
P_SORTWORDSTRING 
P_SORTULONGSTRING 
P_SORTWORD 
P_PRUNEVARSUBMEAN 
P_DECOMPRESSCHARSTRING 
P_DECOMPRESSBYTESTRING 
P_DECOMPRESSWORDSTRING 
P_DECOMPRESSULONGSTRING 

Definition at line 26 of file PreProc.h.

Enumerator:
PT_BOOL 
PT_CHAR 
PT_INT8 
PT_UINT8 
PT_INT16 
PT_UINT16 
PT_INT32 
PT_UINT32 
PT_INT64 
PT_UINT64 
PT_FLOAT32 
PT_FLOAT64 
PT_FLOATMAX 
PT_SGOBJECT 

Definition at line 57 of file DataType.h.

Enumerator:
RT_NONE 
RT_LIGHT 
RT_LIBSVM 

Definition at line 16 of file Regression.h.

Enumerator:
ST_AUTO 
ST_CPLEX 
ST_GLPK 
ST_NEWTON 
ST_DIRECT 
ST_ELASTICNET 

Definition at line 69 of file Classifier.h.

Enumerator:
ST_NONE 
ST_STRING 
ST_SPARSE 

Definition at line 53 of file DataType.h.

Enumerator:
T_LINEAR 
T_LOG 
T_LOG_PLUS1 
T_LOG_PLUS3 
T_LINEAR_PLUS3 

Definition at line 21 of file Plif.h.

Enumerator:
E_WD 
E_EXTERNAL 
E_BLOCK_CONST 
E_BLOCK_LINEAR 
E_BLOCK_SQPOLY 
E_BLOCK_CUBICPOLY 
E_BLOCK_EXP 
E_BLOCK_LOG 

Definition at line 24 of file WeightedDegreeStringKernel.h.

liblinar solver type

Enumerator:
L2R_LR 

L2 regularized linear logistic regression.

L2R_L2LOSS_SVC_DUAL 

L2 regularized SVM with L2-loss using dual coordinate descent.

L2R_L2LOSS_SVC 

L2 regularized SVM with L2-loss using newton in the primal.

L2R_L1LOSS_SVC_DUAL 

L2 regularized linear SVM with L1-loss using dual coordinate descent.

MCSVM_CS 

linear multi-class svm by Crammer and Singer

L1R_L2LOSS_SVC 

L1 regularized SVM with L2-loss using dual coordinate descent.

L1R_LR 

L1 regularized logistic regression.

Definition at line 25 of file LibLinear.h.

Enumerator:
LIBSVM_C_SVC 
LIBSVM_NU_SVC 

Definition at line 20 of file LibSVM.h.

scatter svm variant

Enumerator:
NO_BIAS_LIBSVM 

no bias w/ libsvm

NO_BIAS_SVMLIGHT 

no bias w/ svmlight

TEST_RULE1 

training with bias using test rule 1

TEST_RULE2 

training with bias using test rule 2

Definition at line 25 of file ScatterSVM.h.


Function Documentation

int32_t shogun::CGLS ( const struct data *  Data,
const struct options *  Options,
const struct vector_int *  Subset,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

Definition at line 76 of file ssl.cpp.

void shogun::chol_backward ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 169 of file pr_loqo.cpp.

void shogun::chol_forward ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 156 of file pr_loqo.cpp.

bool shogun::choldc ( float64_t a,
int32_t  n,
float64_t p 
)

Definition at line 61 of file pr_loqo.cpp.

void shogun::cholsb ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 132 of file pr_loqo.cpp.

int32_t shogun::DA_S3VM ( struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

Definition at line 567 of file ssl.cpp.

float64_t shogun::entropy ( const float64_t p,
int32_t  u 
)

Definition at line 1028 of file ssl.cpp.

void exit_shogun (  ) 

This function must be called when one stops using libshogun. It will perform a number of cleanups

static void shogun::findactive ( float64_t Theta,
float64_t SortedA,
uint32_t *  nSortedA,
float64_t A,
float64_t B,
int  n,
int(*)(float64_t *, float64_t *, uint32_t)  sort 
) [static]

Definition at line 937 of file libocas.cpp.

int32_t shogun::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 
)

Definition at line 448 of file gpm.cpp.

static const float64_t* shogun::get_col ( uint32_t  i  )  [static]

Definition at line 38 of file libocas.cpp.

IO * get_global_io (  ) 

get the global io object

Returns:
io object
CMath * get_global_math (  ) 

get the global math object

Returns:
math object
Parallel * get_global_parallel (  ) 

get the global parallel object

Returns:
parallel object
Version * get_global_version (  ) 

get the global version object

Returns:
version object
static float64_t shogun::get_time (  )  [static]

Definition at line 46 of file libocas.cpp.

void shogun::GetLabeledData ( struct data *  D,
const struct data *  Data 
)

Definition at line 1094 of file ssl.cpp.

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 
)
int32_t shogun::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 
)

Definition at line 110 of file gpm.cpp.

void init_shogun ( void(*)(FILE *target, const char *str)  print_message = NULL,
void(*)(FILE *target, const char *str)  print_warning = NULL,
void(*)(FILE *target, const char *str)  print_error = NULL,
void(*)(bool &delayed, bool &immediately)  cancel_computations = NULL 
)

This function must be called before libshogun is used. Usually shogun does not provide any output messages (neither debugging nor error; apart from exceptions). This function allows one to specify customized output callback functions and a callback function to check for exceptions:

Parameters:
print_message function pointer to print a message
print_warning function pointer to print a warning message
print_error function pointer to print an error message (this will be printed before shogun throws an exception)
cancel_computations function pointer to check for exception
void shogun::initialize ( struct vector_int *  A,
int32_t  k 
)

Definition at line 1084 of file ssl.cpp.

void shogun::initialize ( struct vector_double *  A,
int32_t  k,
float64_t  a 
)

Definition at line 1074 of file ssl.cpp.

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 
)

Definition at line 1221 of file gpm.cpp.

float64_t shogun::KL ( const float64_t p,
const float64_t q,
int32_t  u 
)

Definition at line 1041 of file ssl.cpp.

int32_t shogun::L2_SVM_MFN ( const struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs,
int32_t  ini 
)

Definition at line 195 of file ssl.cpp.

static larank_kcache_t* shogun::larank_kcache_create ( CKernel kernelfunc  )  [static]

Definition at line 82 of file LaRank.cpp.

static void shogun::larank_kcache_destroy ( larank_kcache_t *  self  )  [static]

Definition at line 151 of file LaRank.cpp.

static float64_t shogun::larank_kcache_query ( larank_kcache_t *  self,
int32_t  i,
int32_t  j 
) [static]

Definition at line 342 of file LaRank.cpp.

static float32_t* shogun::larank_kcache_query_row ( larank_kcache_t *  self,
int32_t  i,
int32_t  len 
) [static]

Definition at line 374 of file LaRank.cpp.

static int32_t* shogun::larank_kcache_r2i ( larank_kcache_t *  self,
int32_t  n 
) [static]

Definition at line 215 of file LaRank.cpp.

static void shogun::larank_kcache_set_buddy ( larank_kcache_t *  self,
larank_kcache_t *  buddy 
) [static]

Definition at line 351 of file LaRank.cpp.

static void shogun::larank_kcache_set_maximum_size ( larank_kcache_t *  self,
int64_t  entries 
) [static]

Definition at line 143 of file LaRank.cpp.

static void shogun::larank_kcache_swap_ri ( larank_kcache_t *  self,
int32_t  r1,
int32_t  i2 
) [static]

Definition at line 308 of file LaRank.cpp.

static void shogun::larank_kcache_swap_rr ( larank_kcache_t *  self,
int32_t  r1,
int32_t  r2 
) [static]

Definition at line 302 of file LaRank.cpp.

libqp_state_T shogun::libqp_gsmo_solver ( const float64_t *(*)(uint32_t)  get_col,
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(*)(libqp_state_T state)  print_state 
)
libqp_state_T libqp_splx_solver ( const float64_t *(*)(uint32_t)  get_col,
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(*)(libqp_state_T state)  print_state 
)

Definition at line 83 of file libqp_splx.cpp.

float64_t shogun::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 
)

Definition at line 356 of file ssl.cpp.

void shogun::matrix_vector ( int32_t  n,
float64_t  m[],
float64_t  x[],
float64_t  y[] 
)

Definition at line 270 of file pr_loqo.cpp.

ocas_return_value_T shogun::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(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 994 of file libocas.cpp.

CSGObject* shogun::new_sgserializable ( const char *  sgserializable_name,
EPrimitiveType  generic 
)
float64_t shogun::norm_square ( const vector_double *  A  ) 

Definition at line 1063 of file ssl.cpp.

void shogun::nrerror ( char  error_text[]  ) 

Definition at line 45 of file pr_loqo.cpp.

void shogun::optimize_p ( const float64_t g,
int32_t  u,
float64_t  T,
float64_t  r,
float64_t p 
)

Definition at line 929 of file ssl.cpp.

int32_t shogun::optimize_w ( const struct data *  Data,
const float64_t p,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs,
int32_t  ini 
)

Definition at line 650 of file ssl.cpp.

int32_t shogun::Pardalos ( int32_t  n,
int32_t *  iy,
float64_t  e,
float64_t qk,
float64_t  low,
float64_t  up,
float64_t x 
)

Definition at line 1052 of file gpm.cpp.

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 
)
int32_t shogun::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 
)

Definition at line 860 of file gpm.cpp.

float64_t shogun::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 
)

Definition at line 832 of file gpm.cpp.

float64_t shogun::quick_select ( float64_t arr,
int32_t  n 
)

Definition at line 992 of file gpm.cpp.

bool shogun::read_char_valued_strings ( TString< char > *&  strings,
int32_t &  num_str,
int32_t &  max_string_len 
)

read char string features, simple ascii format e.g. foo bar ACGTACGTATCT

two strings

Parameters:
strings strings to read into
num_str number of strings
max_string_len length of longest string
Returns:
if reading was successful
bool shogun::read_real_valued_dense ( float64_t *&  matrix,
int32_t &  num_feat,
int32_t &  num_vec 
)

read dense real valued features, simple ascii format e.g. 1.0 1.1 0.2 2.3 3.5 5

a matrix that consists of 3 vectors with each of 2d

Parameters:
matrix matrix to read into
num_feat number of features for each vector
num_vec number of vectors in matrix
Returns:
if reading was successful
bool shogun::read_real_valued_sparse ( TSparse< float64_t > *&  matrix,
int32_t &  num_feat,
int32_t &  num_vec 
)

read sparse real valued features in svm light format e.g. -1 1:10.0 2:100.2 1000:1.3 with -1 == (optional) label and dim 1 - value 10.0 dim 2 - value 100.2 dim 1000 - value 1.3

Parameters:
matrix matrix to read into
num_feat number of features for each vector
num_vec number of vectors in matrix
Returns:
if reading was successful
void set_global_io ( IO *  io  ) 

set the global io object

Parameters:
io io object to use
void set_global_math ( CMath *  math  ) 

set the global math object

Parameters:
math math object to use
void set_global_parallel ( Parallel *  parallel  ) 

set the global parallel object

Parameters:
parallel parallel object to use
void set_global_version ( Version *  version  ) 

set the global version object

Parameters:
version version object to use
bool shogun::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 
)

Definition at line 207 of file pr_loqo.cpp.

void* shogun::sqdist_thread_func ( void *  P  ) 

Definition at line 100 of file KMeans.cpp.

void shogun::ssl_train ( struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

Definition at line 33 of file ssl.cpp.

ocas_return_value_T shogun::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(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 58 of file libocas.cpp.

ocas_return_value_T shogun::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(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 485 of file libocas.cpp.

int32_t shogun::switch_labels ( float64_t Y,
float64_t o,
int32_t *  JU,
int32_t  u,
int32_t  S 
)

Definition at line 522 of file ssl.cpp.

float64_t shogun::transductive_cost ( float64_t  normWeights,
float64_t Y,
float64_t Outputs,
int32_t  m,
float64_t  lambda,
float64_t  lambda_u 
)

Definition at line 1008 of file ssl.cpp.

int32_t shogun::TSVM_MFN ( const struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

FIXME Clear(Data_Labeled);

Definition at line 434 of file ssl.cpp.

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_dsyev ( char  jobz,
char  uplo,
int  n,
double *  a,
int  lda,
double *  w,
int *  info 
)
bool shogun::write_char_valued_strings ( const TString< char > *  strings,
int32_t  num_str 
)

write char string features, simple ascii format

Parameters:
strings strings to write
num_str number of strings
Returns:
if writing was successful
bool shogun::write_real_valued_dense ( const float64_t matrix,
int32_t  num_feat,
int32_t  num_vec 
)

write dense real valued features, simple ascii format

Parameters:
matrix matrix to write
num_feat number of features for each vector
num_vec number of vectros in matrix
Returns:
if writing was successful
bool shogun::write_real_valued_sparse ( const TSparse< float64_t > *  matrix,
int32_t  num_feat,
int32_t  num_vec 
)

write sparse real valued features in svm light format

Parameters:
matrix matrix to write
num_feat number of features for each vector
num_vec number of vectros in matrix
Returns:
if writing was successful
static void shogun::xextend ( larank_kcache_t *  self,
int32_t  k,
int32_t  nlen 
) [static]

Definition at line 221 of file LaRank.cpp.

static void* shogun::xmalloc ( int32_t  n  )  [static]

Definition at line 63 of file LaRank.cpp.

static void shogun::xminsize ( larank_kcache_t *  self,
int32_t  n 
) [static]

Definition at line 184 of file LaRank.cpp.

static void shogun::xpurge ( larank_kcache_t *  self  )  [static]

Definition at line 129 of file LaRank.cpp.

static float64_t shogun::xquery ( larank_kcache_t *  self,
int32_t  i,
int32_t  j 
) [static]

Definition at line 314 of file LaRank.cpp.

static void* shogun::xrealloc ( void *  ptr,
int32_t  n 
) [static]

Definition at line 71 of file LaRank.cpp.

static void shogun::xswap ( larank_kcache_t *  self,
int32_t  i1,
int32_t  i2,
int32_t  r1,
int32_t  r2 
) [static]

Definition at line 240 of file LaRank.cpp.

static void shogun::xtruncate ( larank_kcache_t *  self,
int32_t  k,
int32_t  nlen 
) [static]

Definition at line 103 of file LaRank.cpp.


Variable Documentation

uint32_t BufSize [static]

Definition at line 33 of file libocas.cpp.

float64_t* H [static]

Definition at line 32 of file libocas.cpp.

const uint32_t QPSolverMaxIter = 10000000 [static]

Definition at line 30 of file libocas.cpp.

uint32_t Randnext
void(* sg_cancel_computations)(bool &delayed, bool &immediately) = NULL

function called to cancel things

Definition at line 38 of file init.cpp.

IO * sg_io = NULL

Definition at line 21 of file init.cpp.

CMath * sg_math = NULL

Definition at line 23 of file init.cpp.

Definition at line 20 of file init.cpp.

void(* sg_print_error)(FILE *target, const char *str) = NULL

function called to print error messages

Definition at line 35 of file init.cpp.

void(* sg_print_message)(FILE *target, const char *str) = NULL

function called to print normal messages

Definition at line 29 of file init.cpp.

void(* sg_print_warning)(FILE *target, const char *str) = NULL

function called to print warning messages

Definition at line 32 of file init.cpp.

Version * sg_version = NULL

Definition at line 22 of file init.cpp.

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SHOGUN Machine Learning Toolbox - Documentation