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. . 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 ) 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_STATES * | P_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 More... | |
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 More... | |
enum | EFeatureProperty { FP_NONE = 0, FP_DOT = 1 } |
shogun feature properties More... | |
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 | |
CSGObject * | new_sgserializable (const char *sgserializable_name, EPrimitiveType generic) |
void | init_shogun (void(*print_message)(FILE *target, const char *str), void(*print_warning)(FILE *target, const char *str), void(*print_error)(FILE *target, const char *str), void(*cancel_computations)(bool &delayed, bool &immediately)) |
void | exit_shogun () |
void | set_global_io (IO *io) |
IO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
void | set_global_math (CMath *math) |
CMath * | get_global_math () |
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_t * | larank_kcache_query_row (larank_kcache_t *self, int32_t i, int32_t len) |
static const float64_t * | get_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 | |
IO * | sg_io = NULL |
Parallel * | sg_parallel = NULL |
Version * | sg_version = NULL |
CMath * | sg_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_t * | H |
static uint32_t | BufSize |
all of classes and functions are contained in the shogun namespace
typedef int32_t index_t |
Definition at line 22 of file DataType.h.
typedef float64_t KERNELCACHE_ELEM |
typedef int64_t KERNELCACHE_IDX |
typedef float64_t T_ALPHA_BETA_TABLE |
typedef uint8_t T_STATES |
typedef CDynInt<uint64_t,3> uint1024_t |
enum BaumWelchViterbiType |
enum E_COMPRESSION_TYPE |
Definition at line 25 of file Compressor.h.
enum E_PROB_TYPE |
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 |
Definition at line 30 of file QPBSVMLib.h.
enum E_SVM_TYPE |
enum EAlphabet |
Alphabet of charfeatures/observations.
Definition at line 21 of file Alphabet.h.
enum EClassifierType |
Definition at line 27 of file Classifier.h.
enum EContainerType |
Definition at line 49 of file DataType.h.
enum EDistanceType |
Definition at line 31 of file Distance.h.
enum EFeatureClass |
shogun feature class
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.
enum EFeatureProperty |
enum EFeatureType |
shogun feature type
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.
enum EKernelProperty |
enum EKernelType |
enum EMessageType |
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.
enum EMultiClassSVM |
Definition at line 21 of file MultiClassSVM.h.
enum ENormalizerType |
enum EOptimizationType |
enum EPreProcType |
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 |
Definition at line 57 of file DataType.h.
enum ERegressionType |
Definition at line 16 of file Regression.h.
enum ESolverType |
Definition at line 69 of file Classifier.h.
enum EStructType |
Definition at line 53 of file DataType.h.
enum ETransformType |
enum EWDKernType |
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
Definition at line 25 of file LibLinear.h.
enum LIBSVM_SOLVER_TYPE |
enum SCATTER_TYPE |
scatter svm variant
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.
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 | |||
) |
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.
Definition at line 156 of file pr_loqo.cpp.
Definition at line 61 of file pr_loqo.cpp.
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 | |||
) |
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.
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
CMath * get_global_math | ( | ) |
get the global math object
Parallel * get_global_parallel | ( | ) |
get the global parallel object
Version * get_global_version | ( | ) |
get the global 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 | |||
) |
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 | |||
) |
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:
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 | |||
) |
void shogun::initialize | ( | struct vector_double * | A, | |
int32_t | k, | |||
float64_t | a | |||
) |
int32_t shogun::L2_SVM_MFN | ( | const struct data * | Data, | |
struct options * | Options, | |||
struct vector_double * | Weights, | |||
struct vector_double * | Outputs, | |||
int32_t | ini | |||
) |
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.
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 | |||
) |
void shogun::nrerror | ( | char | error_text[] | ) |
Definition at line 45 of file pr_loqo.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 | |||
) |
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 | |||
) |
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
strings | strings to read into | |
num_str | number of strings | |
max_string_len | length of longest string |
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
matrix | matrix to read into | |
num_feat | number of features for each vector | |
num_vec | number of vectors in matrix |
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
matrix | matrix to read into | |
num_feat | number of features for each vector | |
num_vec | number of vectors in matrix |
void set_global_io | ( | IO * | io | ) |
set the global io object
io | io object to use |
void set_global_math | ( | CMath * | math | ) |
set the global math object
math | math object to use |
void set_global_parallel | ( | Parallel * | parallel | ) |
set the global parallel object
parallel | parallel object to use |
void set_global_version | ( | Version * | version | ) |
set the global version object
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 | |||
) |
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::TSVM_MFN | ( | const struct data * | Data, | |
struct options * | Options, | |||
struct vector_double * | Weights, | |||
struct vector_double * | Outputs | |||
) |
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
strings | strings to write | |
num_str | number of strings |
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
matrix | matrix to write | |
num_feat | number of features for each vector | |
num_vec | number of vectros in matrix |
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
matrix | matrix to write | |
num_feat | number of features for each vector | |
num_vec | number of vectros in matrix |
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.
uint32_t BufSize [static] |
Definition at line 33 of file libocas.cpp.
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 |
Parallel * sg_parallel = NULL |
void(* sg_print_error)(FILE *target, const char *str) = NULL |
void(* sg_print_message)(FILE *target, const char *str) = NULL |
void(* sg_print_warning)(FILE *target, const char *str) = NULL |
Version * sg_version = NULL |