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
	parameter struct More...
class	Parameter
	Parameter class. More...
class	SGParamInfo
	Class that holds informations about a certain parameter of an CSGObject. Contains name, type, etc. This is used for mapping types that have changed in different versions of shogun. Instances of this class may be compared to each other. Ordering is based on name, equalness is based on all attributes. More...
class	ParameterMapElement
	Class to hold instances of a parameter map. Each element contains a key and a value, which are of type SGParamInfo. May be compared to each other based on their keys. More...
class	ParameterMap
	Implements a map of ParameterMapElement instances. More...
class	CSGObject
	Class SGObject is the base class of all shogun objects. More...
class	Version
	Class Version provides version information. More...
class	CAveragedPerceptron
	Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron. More...
class	CGaussianNaiveBayes
	Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier. More...
class	CKNN
	Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
class	CLDA
	Class LDA implements regularized Linear Discriminant Analysis. More...
class	CLPBoost
	Class LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
class	CLPM
	Class LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
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 SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
class	CCPLEXSVM
	CplexSVM a SVM solver implementation based on cplex (unfinished). More...
class	CDomainAdaptationSVM
	class DomainAdaptationSVM More...
class	CDomainAdaptationSVMLinear
	class DomainAdaptationSVMLinear 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
	the LaRank multiclass SVM machine More...
class	CLibLinear
	class to implement LibLinear More...
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	COnlineLibLinear
	Class implementing a purely online version of LibLinear, using the L2R_L1LOSS_SVC_DUAL solver only. More...
class	COnlineSVMSGD
	class OnlineSVMSGD More...
class	CQPBSVMLib
	class QPBSVMLib More...
class	CScatterSVM
	ScatterSVM - Multiclass SVM. More...
class	CSGDQN
	class SGDQN More...
class	CSubGradientSVM
	class SubGradientSVM More...
class	CSVM
	A generic Support Vector Machine Interface. More...
class	CSVMLight
	class SVMlight More...
class	CSVMLightOneClass
	Trains a one class C SVM. More...
class	CSVMLin
	class SVMLin More...
class	CSVMOcas
	class SVMOcas More...
class	CSVMSGD
	class SVMSGD More...
class	CWDSVMOcas
	class WDSVMOcas More...
class	CVwCacheReader
	Base class from which all cache readers for VW should be derived. More...
class	CVwCacheWriter
	CVwCacheWriter is the base class for all VW cache creating classes. More...
class	CVwNativeCacheReader
	Class CVwNativeCacheReader reads from a cache exactly as that which has been produced by VW's default cache format. More...
class	CVwNativeCacheWriter
	Class CVwNativeCacheWriter writes a cache exactly as that which would be produced by VW's default cache format. More...
class	CVwAdaptiveLearner
	VwAdaptiveLearner uses an adaptive subgradient technique to update weights. More...
class	CVwNonAdaptiveLearner
	VwNonAdaptiveLearner uses a standard gradient descent weight update rule. More...
class	CVowpalWabbit
	Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit. More...
class	VwFeature
	One feature in VW. More...
class	VwExample
	Example class for VW. More...
class	VwLabel
	Class VwLabel holds a label object used by VW. More...
class	CVwEnvironment
	Class CVwEnvironment is the environment used by VW. More...
class	CVwLearner
	Base class for all VW learners. More...
class	CVwParser
	CVwParser is the object which provides the functions to parse examples from buffered input. More...
class	CVwRegressor
	Regressor used by VW. More...
class	CGMM
	Gaussian Mixture Model interface. More...
class	CHierarchical
	Agglomerative hierarchical single linkage clustering. More...
class	CKMeans
	KMeans clustering, partitions the data into k (a-priori specified) clusters. More...
class	CAttenuatedEuclidianDistance
	class AttenuatedEuclidianDistance 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, a base class for all the distances used in the Shogun toolbox. 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	CGaussian
	Gaussian distribution interface. 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	CPositionalPWM
	Positional PWM. More...
class	CBinaryClassEvaluation
	The class TwoClassEvaluation a base class used to evaluate 2-class classification. More...
class	CContingencyTableEvaluation
	The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates. More...
class	CAccuracyMeasure
	class AccuracyMeasure used to measure accuracy of 2-class classifier. More...
class	CErrorRateMeasure
	class ErrorRateMeasure used to measure error rate of 2-class classifier. More...
class	CBALMeasure
	class BALMeasure used to measure balanced error of 2-class classifier. More...
class	CWRACCMeasure
	class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier. More...
class	CF1Measure
	class F1Measure used to measure F1 score of 2-class classifier. More...
class	CCrossCorrelationMeasure
	class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier. More...
class	CRecallMeasure
	class RecallMeasure used to measure recall of 2-class classifier. More...
class	CPrecisionMeasure
	class PrecisionMeasure used to measure precision of 2-class classifier. More...
class	CSpecificityMeasure
	class SpecificityMeasure used to measure specificity of 2-class classifier. More...
struct	CrossValidationResult
	type to encapsulate the results of an evaluation run. May contain confidence interval (if conf_int_alpha!=0). m_conf_int_alpha is the probability for an error, i.e. the value does not lie in the confidence interval. More...
class	CCrossValidation
	base class for cross-validation evaluation. Given a learning machine, a splitting strategy, an evaluation criterium, features and correspnding labels, this provides an interface for cross-validation. Results may be retrieved using the evaluate method. A number of repetitions may be specified for obtaining more accurate results. The arithmetic mean of different runs is returned along with confidence intervals, if a p-value is specified. Default number of runs is one, confidence interval combutation is disabled. More...
class	CEvaluation
	The class Evaluation a main class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression. More...
class	CMeanSquaredError
	the class MeanSquaredError used to compute error of regression model. More...
class	CMulticlassAccuracy
	The class MulticlassAccuracy used to compute accuracy of multiclass classification. More...
class	CPRCEvaluation
	The class PRCEvalution used to evaluate PRC (Precision Recall Curve) graph of binary classifier. This class also has an capability of calculating auPRC (area under PRC). More...
class	CROCEvaluation
	The class ROCEvalution used to evaluate ROC (Receiver Operator Characteristic) graph of binary classifier. This class also has an capability of calculating auROC (area under ROC). More...
class	CSplittingStrategy
	Abstract base class for all splitting types. Takes a CLabels instance and generates a desired number of subsets which are being accessed by their indices via the method generate_subset_indices(...). More...
class	CStratifiedCrossValidationSplitting
	Implementation of stratified cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference) in which the label ratio is equal (at most one difference) to the label ratio of the specified labels. 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...
class	CStreamingDotFeatures
	Streaming features that support dot products among other operations. More...
class	CStreamingFeatures
	Streaming features are features which are used for online algorithms. More...
class	CStreamingSimpleFeatures
	This class implements streaming features with dense feature vectors. More...
class	CStreamingSparseFeatures
	This class implements streaming features with sparse feature vectors. The vector is represented as an SGSparseVector<T>. Each entry is of type SGSparseVectorEntry<T> with members `feat_index' and `entry'. More...
class	CStreamingStringFeatures
	This class implements streaming features as strings. More...
class	CStreamingVwFeatures
	This class implements streaming features for use with VW. More...
class	CStringFeatures
	Template class StringFeatures implements a list of strings. More...
class	CStringFileFeatures
	File based string features. More...
class	CSubset
	class for adding subset support to a class. Provides an interface for getting/setting subset_matrices and index conversion. Do not inherit from this class, use it as variable. Write wrappers for all get/set functions. 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	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	CFile
	A File access base class. More...
class	CInputParser
	Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples. More...
class	CIOBuffer
	An I/O buffer class. More...
class	CMemoryMappedFile
	memory mapped file More...
class	Example
	Class Example is the container type for the vector+label combination. More...
class	CParseBuffer
	Class CParseBuffer implements a ring of examples of a defined size. The ring stores objects of the Example type. More...
class	CSerializableAsciiFile
	serializable ascii file More...
class	SerializableAsciiReader00
	Serializable ascii reader. More...
class	CSerializableFile
	serializable file More...
class	SGIO
	Class SGIO, used to do input output operations throughout shogun. More...
struct	substring
	struct Substring, specified by start position and end position. More...
class	CSimpleFile
	Template class SimpleFile to read and write from files. More...
class	CStreamingAsciiFile
	Class StreamingAsciiFile to read vector-by-vector from ASCII files. More...
class	CStreamingFile
	A Streaming File access class. More...
class	CStreamingFileFromFeatures
	Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object. More...
class	CStreamingFileFromSimpleFeatures
	Class CStreamingFileFromSimpleFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CSimpleFeatures object. More...
class	CStreamingFileFromSparseFeatures
	Class CStreamingFileFromSparseFeatures is derived from CStreamingFile and provides an input source for the online framework. It uses an existing CSparseFeatures object to generate online examples. More...
class	CStreamingFileFromStringFeatures
	Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object. More...
class	CStreamingVwCacheFile
	Class StreamingVwCacheFile to read vector-by-vector from VW cache files. More...
class	CStreamingVwFile
	Class StreamingVwFile to read vector-by-vector from Vowpal Wabbit data files. It reads the example and label into one object of VwExample type. More...
class	CANOVAKernel
	ANOVA (ANalysis Of VAriances) kernel. More...
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	CBesselKernel
	the class Bessel kernel More...
class	CCauchyKernel
	Cauchy kernel. More...
class	CChi2Kernel
	The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms. More...
class	CCircularKernel
	Circular kernel. 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	CDistantSegmentsKernel
	The distant segments kernel is a string kernel, which counts the number of substrings, so-called segments, at a certain distance from each other. More...
class	CDotKernel
	Template class DotKernel is the base class for kernels working on DotFeatures. More...
class	CExponentialKernel
	The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures. 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...
class	CInverseMultiQuadricKernel
	InverseMultiQuadricKernel. More...
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	CLogKernel
	Log kernel. 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	CMultiquadricKernel
	MultiquadricKernel. 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	CPowerKernel
	Power kernel. More...
class	CPyramidChi2
	Pyramid Kernel over Chi2 matched histograms. More...
class	CRationalQuadraticKernel
	Rational Quadratic kernel. 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
	the scatter kernel normalizer More...
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
	SSKFeatures. More...
class	CSparseSpatialSampleStringKernel
	Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> More...
class	CSpectrumMismatchRBFKernel
	spectrum mismatch rbf kernel More...
class	CSpectrumRBFKernel
	spectrum rbf kernel More...
class	CSphericalKernel
	Spherical kernel. More...
class	CSplineKernel
	Computes the Spline Kernel function which is the cubic polynomial. More...
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	CTStudentKernel
	Generalized T-Student kernel. More...
class	CVarianceKernelNormalizer
	VarianceKernelNormalizer divides by the ``variance''. More...
class	CWaveKernel
	Wave kernel. More...
class	CWaveletKernel
	the class WaveletKernel 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
	weighted degree RBF kernel More...
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	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
	Compression library for compressing and decompressing buffers using one of the standard compression algorithms, LZO, GZIP, BZIP2 or LZMA. More...
class	SGVector
	shogun vector More...
class	SGMatrix
	shogun matrix More...
class	SGNDArray
	shogun n-dimensional array More...
struct	SGString
	shogun string More...
struct	SGStringList
	template class SGStringList More...
struct	SGSparseVectorEntry
	template class SGSparseVectorEntry More...
struct	SGSparseVector
	template class SGSparseVector More...
class	SGSparseMatrix
	template class SGSparseMatrix More...
struct	TSGDataType
	Datatypes that shogun supports. More...
class	CDynamicArray
	Template Dynamic array class that creates an array that can be used like a list or an array. More...
class	CDynamicObjectArray
	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	CGCArray
	Template class GCArray implements a garbage collecting static array. More...
class	CHash
	Collection of Hashing Functions. More...
class	CHashSet
	the class HashSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class	CIndirectObject
	an array class that accesses elements indirectly via an index array. 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	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	CTime
	Class Time that implements a stopwatch based on either cpu time or wall clock time. More...
class	CTrie
	Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored. More...
class	v_array
	Class v_array is a templated class used to store variable length arrays. Memory locations are stored as 'extents', i.e., address of the first memory location and address after the last member. More...
class	CHingeLoss
	CHingeLoss implements the hinge loss function. More...
class	CLogLoss
	CLogLoss implements the logarithmic loss function. More...
class	CLogLossMargin
	Class CLogLossMargin implements a margin-based log-likelihood loss function. More...
class	CLossFunction
	Class CLossFunction is the base class of all loss functions. More...
class	CSmoothHingeLoss
	CSmoothHingeLoss implements the smooth hinge loss function. More...
class	CSquaredHingeLoss
	Class CSquaredHingeLoss implements a squared hinge loss function. More...
class	CSquaredLoss
	CSquaredLoss implements the squared loss function. More...
class	CDistanceMachine
	A generic DistanceMachine interface. More...
class	CKernelMachine
	A generic KernelMachine interface. More...
class	CLinearMachine
	Class LinearMachine is a generic interface for all kinds of linear machines like classifiers. More...
class	CMachine
	A generic learning machine interface. More...
class	COnlineLinearMachine
	Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms. More...
class	CCplex
	Class CCplex to encapsulate access to the commercial cplex general purpose optimizer. More...
class	CLoss
	Class which collects generic mathematical functions. More...
class	CMath
	Class which collects generic mathematical functions. More...
class	CStatistics
	Class that contains certain functions related to statistics, such as the student's t distribution. More...
class	CGridSearchModelSelection
	Model selection class which searches for the best model by a grid- search. See CModelSelection for details. More...
class	CModelSelection
	Abstract base class for model selection. Takes a parameter tree which specifies parameters for model selection, and a cross-validation instance and searches for the best combination of parameters in the abstract method select_model(), which has to be implemented in concrete sub-classes. More...
class	CModelSelectionParameters
	Class to select parameters and their ranges for model selection. The structure is organized as a tree with different kinds of nodes, depending on the values of its member variables of name and CSGObject. More...
class	CParameterCombination
	class that holds ONE combination of parameters for a learning machine. The structure is organized as a tree. Every node may hold a name or an instance of a Parameter class. Nodes may have children. The nodes are organized in such way, that every parameter of a model for model selection has one node and sub-parameters are stored in sub-nodes. Using a tree of this class, parameters of models may easily be set. There are these types of nodes: More...
class	CDecompressString
	Preprocessor that decompresses compressed strings. More...
class	CDimensionReductionPreprocessor
	the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices). More...
class	CHessianLocallyLinearEmbedding
	the class HessianLocallyLinearEmbedding used to preprocess data using Hessian Locally Linear Embedding algorithm described in More...
class	CIsomap
	the class Isomap used to preprocess data using K-Isomap algorithm as described in More...
class	CKernelLocallyLinearEmbedding
	the class KernelLocallyLinearEmbedding used to preprocess data using kernel extension of Locally Linear Embedding algorithm as described in More...
class	CKernelPCA
	Preprocessor KernelPCA performs kernel principal component analysis. More...
class	CLaplacianEigenmaps
	the class LaplacianEigenmaps used to preprocess data using Laplacian Eigenmaps algorithm as described in: More...
class	CLocallyLinearEmbedding
	the class LocallyLinearEmbedding used to preprocess data using Locally Linear Embedding algorithm described in More...
class	CLocalTangentSpaceAlignment
	the class LocalTangentSpaceAlignment used to preprocess data using Local Tangent Space Alignment (LTSA) algorithm as described in: 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	CMultidimensionalScaling
	the class Multidimensionalscaling is used to perform multidimensional scaling (capable of landmark approximation if requested). More...
class	CNormOne
	Preprocessor NormOne, normalizes vectors to have norm 1. More...
class	CPCA
	Preprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold. More...
class	CPreprocessor
	Class Preprocessor defines a preprocessor interface. More...
class	CPruneVarSubMean
	Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance. More...
class	CRandomFourierGaussPreproc
	Preprocessor CRandomFourierGaussPreproc implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007 after preprocessing the features using them in a linear kernel approximates a gaussian kernel. More...
class	CSimplePreprocessor
	Template class SimplePreprocessor, base class for preprocessors (cf. CPreprocessor) 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	CSparsePreprocessor
	Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures. More...
class	CStringPreprocessor
	Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length). More...
class	CKRR
	Class KRR implements Kernel Ridge Regression - a regularized least square method for classification and regression. More...
class	CLibSVR
	Class LibSVR, performs support vector regression using LibSVM. More...
class	CMKLRegression
	Multiple Kernel Learning for regression. More...
class	CSVRLight
	Class SVRLight, performs support vector regression using SVMLight. 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...
class	CGUIClassifier
	UI classifier. More...
class	CGUIDistance
	UI distance. More...
class	CGUIFeatures
	UI features. More...
class	CGUIHMM
	UI HMM (Hidden Markov Model). More...
class	CGUIKernel
	UI kernel. More...
class	CGUILabels
	UI labels. More...
class	CGUIMath
	UI math. More...
class	CGUIPluginEstimate
	UI estimate. More...
class	CGUIPreprocessor
	UI preprocessor. More...
class	CGUIStructure
	UI structure. More...
class	CGUITime
	UI time. More...
Typedefs
typedef uint32_t(*	hash_func_t )(substring, uint32_t)
	Hash function typedef, takes a substring and seed as parameters.
typedef uint32_t	vw_size_t
	vw_size_t typedef to work across platforms
typedef int32_t(CVwParser::*	parse_func )(CIOBuffer , VwExample &)
	Parse function typedef. Takes an IOBuffer and VwExample as arguments.
typedef float64_t	KERNELCACHE_ELEM
typedef int64_t	KERNELCACHE_IDX
typedef int32_t	index_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
convenience typedefs

typedef CDynInt< uint64_t, 3 >	uint192_t
	192 bit integer constructed out of 3 64bit uint64_t's
typedef CDynInt< uint64_t, 4 >	uint256_t
	256 bit integer constructed out of 4 64bit uint64_t's
typedef CDynInt< uint64_t, 8 >	uint512_t
	512 bit integer constructed out of 8 64bit uint64_t's
typedef CDynInt< uint64_t, 16 >	uint1024_t
	1024 bit integer constructed out of 16 64bit uint64_t's
Enumerations
enum	EModelSelectionAvailability { MS_NOT_AVAILABLE = 0, MS_AVAILABLE }
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	SCATTER_TYPE { NO_BIAS_LIBSVM, NO_BIAS_SVMLIGHT, TEST_RULE1, TEST_RULE2 }
enum	EVwCacheType { C_NATIVE = 0, C_PROTOBUF = 1 }
enum	E_VW_PARSER_TYPE { T_VW = 1, T_SVMLIGHT = 2, T_DENSE = 3 }
	The type of input to parse. More...
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, D_ATTENUATEDEUCLIDIAN = 170 }
enum	ECovType { FULL, DIAG, SPHERICAL }
enum	BaumWelchViterbiType { BW_NORMAL, BW_TRANS, BW_DEFINED, VIT_NORMAL, VIT_DEFINED }
enum	EContingencyTableMeasureType { ACCURACY = 0, ERROR_RATE = 10, BAL = 20, WRACC = 30, F1 = 40, CROSS_CORRELATION = 50, RECALL = 60, PRECISION = 70, SPECIFICITY = 80 }
enum	EEvaluationDirection { ED_MINIMIZE, ED_MAXIMIZE }
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_STREAMING_SIMPLE = 110, C_STREAMING_SPARSE = 120, C_STREAMING_STRING = 130, C_STREAMING_VW = 140, C_ANY = 1000 }
	shogun feature class More...
enum	EFeatureProperty { FP_NONE = 0, FP_DOT = 1, FP_STREAMING_DOT = 2 }
	shogun feature properties More...
enum	E_EXAMPLE_TYPE { E_LABELLED = 1, E_UNLABELLED = 2 }
enum	E_IS_EXAMPLE_USED { E_EMPTY = 1, E_NOT_USED = 2, E_USED = 3 }
enum	EMessageType { MSG_GCDEBUG, MSG_DEBUG, MSG_INFO, MSG_NOTICE, MSG_WARN, MSG_ERROR, MSG_CRITICAL, MSG_ALERT, MSG_EMERGENCY, MSG_MESSAGEONLY }
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_SPECTRUMRBF = 122, K_SPECTRUMMISMATCHRBF = 123, 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, K_WAVELET = 310, K_WAVE = 320, K_CAUCHY = 330, K_TSTUDENT = 340, K_RATIONAL_QUADRATIC = 350, K_MULTIQUADRIC = 360, K_EXPONENTIAL = 370, K_SPHERICAL = 380, K_SPLINE = 390, K_ANOVA = 400, K_POWER = 410, K_LOG = 420, K_CIRCULAR = 430, K_INVERSEMULTIQUADRIC = 440, K_DISTANTSEGMENTS = 450, K_BESSEL = 460 }
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, SNAPPY }
enum	ELossType { L_HINGELOSS = 0, L_SMOOTHHINGELOSS = 10, L_SQUAREDHINGELOSS = 20, L_SQUAREDLOSS = 30, L_LOGLOSS = 100, L_LOGLOSSMARGIN = 110 }
	shogun loss type More...
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, CT_GAUSSIANNAIVEBAYES = 370, CT_AVERAGEDPERCEPTRON = 380, CT_SGDQN = 390 }
enum	ESolverType { ST_AUTO = 0, ST_CPLEX = 1, ST_GLPK = 2, ST_NEWTON = 3, ST_DIRECT = 4, ST_ELASTICNET = 5, ST_BLOCK_NORM = 6 }
enum	E_PROB_TYPE { E_LINEAR, E_QP }
enum	ERangeType { R_LINEAR, R_EXP, R_LOG }
enum	EMSParamType { MSPT_NONE = 0, MSPT_FLOAT64, MSPT_INT32 }
enum	EPCAMode { THRESHOLD, VARIANCE_EXPLAINED, FIXED_NUMBER }
enum	EPreprocessorType { P_UNKNOWN = 0, P_NORMONE = 10, P_LOGPLUSONE = 20, P_SORTWORDSTRING = 30, P_SORTULONGSTRING = 40, P_SORTWORD = 50, P_PRUNEVARSUBMEAN = 60, P_DECOMPRESSSTRING = 70, P_DECOMPRESSCHARSTRING = 80, P_DECOMPRESSBYTESTRING = 90, P_DECOMPRESSWORDSTRING = 100, P_DECOMPRESSULONGSTRING = 110, P_RANDOMFOURIERGAUSS = 120, P_PCA = 130, P_KERNELPCA = 140, P_NORMDERIVATIVELEM3 = 150, P_DIMENSIONREDUCTIONPREPROCESSOR = 160, P_MULTIDIMENSIONALSCALING = 170, P_LOCALLYLINEAREMBEDDING = 180, P_ISOMAP = 190, P_HESSIANLOCALLYLINEAREMBEDDING = 200, P_LOCALTANGENTSPACEALIGNMENT = 210, P_LAPLACIANEIGENMAPS = 220, P_KERNELLOCALLYLINEAREMBEDDING = 230 }
enum	ERegressionType { RT_NONE = 0, RT_LIGHT = 10, RT_LIBSVM = 20 }
	type of regressor More...
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	sg_global_print_default (FILE target, const char str)
void	init_shogun_with_defaults ()
void	exit_shogun ()
void	set_global_io (SGIO *io)
SGIO *	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 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)
float32_t	sd_offset_add (float32_t weights, vw_size_t mask, VwFeature begin, VwFeature *end, vw_size_t offset)
float32_t	sd_offset_truncadd (float32_t weights, vw_size_t mask, VwFeature begin, VwFeature *end, vw_size_t offset, float32_t gravity)
float32_t	one_pf_quad_predict (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask)
float32_t	one_pf_quad_predict_trunc (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask, float32_t gravity)
float32_t	real_weight (float32_t w, float32_t gravity)
void *	sqdist_thread_func (void *P)
bool	read_real_valued_sparse (SGSparseVector< float64_t > *&matrix, int32_t &num_feat, int32_t &num_vec)
bool	write_real_valued_sparse (const SGSparseVector< 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 (SGString< char > *&strings, int32_t &num_str, int32_t &max_string_len)
bool	write_char_valued_strings (const SGString< char > *strings, int32_t num_str)
char *	c_string_of_substring (substring s)
void	print_substring (substring s)
float32_t	float_of_substring (substring s)
float64_t	double_of_substring (substring s)
int32_t	int_of_substring (substring s)
uint32_t	ulong_of_substring (substring s)
uint32_t	ss_length (substring s)
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)
void	wrap_dgeqrf (int m, int n, double a, int lda, double tau, int *info)
void	wrap_dorgqr (int m, int n, int k, double a, int lda, double tau, int *info)
void	wrap_dsyevr (char jobz, char uplo, int n, double a, int lda, int il, int iu, double eigenvalues, double eigenvectors, int info)
void	wrap_dsygvx (int itype, char jobz, char uplo, int n, double a, int lda, double b, int ldb, int il, int iu, double eigenvalues, double eigenvectors, int *info)
template<class T >
SGVector< T >	create_range_array (T min, T max, ERangeType type, T step, T type_base)
Variables
Parallel *	sg_parallel = NULL
SGIO *	sg_io = 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
const int32_t	quadratic_constant = 27942141
	Constant used while hashing/accessing quadratic features.
const int32_t	constant_hash = 11650396
	Constant used to access the constant feature.
const uint32_t	hash_base = 97562527
	Seed for hash.

Detailed Description

all of classes and functions are contained in the shogun namespace

Typedef Documentation

typedef uint32_t(* hash_func_t)(substring, uint32_t)

Hash function typedef, takes a substring and seed as parameters.

Definition at line 21 of file vw_constants.h.

typedef int32_t index_t

index

Definition at line 22 of file DataType.h.

typedef float64_t KERNELCACHE_ELEM

kernel cache element

Definition at line 37 of file Kernel.h.

typedef int64_t KERNELCACHE_IDX

kernel cache index

Definition at line 48 of file Kernel.h.

typedef T_STATES * P_STATES

Definition at line 66 of file HMM.h.

typedef int32_t(CVwParser::* parse_func)(CIOBuffer *, VwExample *&)

Parse function typedef. Takes an IOBuffer and VwExample as arguments.

Definition at line 20 of file StreamingVwFile.h.

typedef float64_t T_ALPHA_BETA_TABLE

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,16> uint1024_t

1024 bit integer constructed out of 16 64bit uint64_t's

Definition at line 574 of file DynInt.h.

typedef CDynInt<uint64_t,3> uint192_t

192 bit integer constructed out of 3 64bit uint64_t's

Definition at line 565 of file DynInt.h.

typedef CDynInt<uint64_t,4> uint256_t

256 bit integer constructed out of 4 64bit uint64_t's

Definition at line 568 of file DynInt.h.

typedef CDynInt<uint64_t,8> uint512_t

512 bit integer constructed out of 8 64bit uint64_t's

Definition at line 571 of file DynInt.h.

typedef uint32_t vw_size_t

vw_size_t typedef to work across platforms

Definition at line 24 of file vw_constants.h.

Enumeration Type Documentation

enum BaumWelchViterbiType

Training type

Enumerator:

BW_NORMAL	standard baum welch
BW_TRANS	baum welch only for specified transitions
BW_DEFINED	baum welch only for defined transitions/observations
VIT_NORMAL	standard viterbi
VIT_DEFINED	viterbi only for defined transitions/observations

Definition at line 71 of file HMM.h.

enum E_COMPRESSION_TYPE

compression type

Enumerator:

UNCOMPRESSED
LZO
GZIP
BZIP2
LZMA
SNAPPY

Definition at line 26 of file Compressor.h.

enum E_EXAMPLE_TYPE

Type of example, either E_LABELLED or E_UNLABELLED

Enumerator:

E_LABELLED
E_UNLABELLED

Definition at line 26 of file InputParser.h.

enum E_IS_EXAMPLE_USED

Specifies whether location is empty, contains an unused example or a used example.

Enumerator:

E_EMPTY
E_NOT_USED
E_USED

Definition at line 23 of file ParseBuffer.h.

enum E_PROB_TYPE

Enumerator:

E_LINEAR
E_QP

Definition at line 29 of file Cplex.h.

enum E_VW_PARSER_TYPE

The type of input to parse.

Enumerator:

T_VW
T_SVMLIGHT
T_DENSE

Definition at line 28 of file VwParser.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.

enum EClassifierType

classifier type

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
CT_GAUSSIANNAIVEBAYES
CT_AVERAGEDPERCEPTRON
CT_SGDQN

Definition at line 28 of file Machine.h.

enum EContingencyTableMeasureType

type of measure

Enumerator:

ACCURACY
ERROR_RATE
BAL
WRACC
F1
CROSS_CORRELATION
RECALL
PRECISION
SPECIFICITY

Definition at line 25 of file ContingencyTableEvaluation.h.

enum ECovType

Covariance type

Enumerator:

FULL	full covariance
DIAG	diagonal covariance
SPHERICAL	spherical covariance

Definition at line 29 of file Gaussian.h.

enum EDistanceType

type of distance

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
D_ATTENUATEDEUCLIDIAN

Definition at line 33 of file Distance.h.

enum EEvaluationDirection

enum which is used to define whether an evaluation criterium has to be minimised or maximised

Enumerator:

ED_MINIMIZE
ED_MAXIMIZE

Definition at line 24 of file Evaluation.h.

enum EFeatureClass

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_STREAMING_SIMPLE
C_STREAMING_SPARSE
C_STREAMING_STRING
C_STREAMING_VW
C_ANY

Definition at line 35 of file FeatureTypes.h.

enum EFeatureProperty

shogun feature properties

Enumerator:

FP_NONE
FP_DOT
FP_STREAMING_DOT

Definition at line 55 of file FeatureTypes.h.

enum EFeatureType

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.

enum EKernelProperty

kernel property

Enumerator:

KP_NONE
KP_LINADD
KP_KERNCOMBINATION
KP_BATCHEVALUATION

Definition at line 117 of file Kernel.h.

enum EKernelType

kernel type

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_SPECTRUMRBF
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
K_WAVELET
K_WAVE
K_CAUCHY
K_TSTUDENT
K_RATIONAL_QUADRATIC
K_MULTIQUADRIC
K_EXPONENTIAL
K_SPHERICAL
K_SPLINE
K_ANOVA
K_POWER
K_LOG
K_CIRCULAR
K_INVERSEMULTIQUADRIC
K_DISTANTSEGMENTS
K_BESSEL

Definition at line 59 of file Kernel.h.

enum ELossType

shogun loss type

Enumerator:

L_HINGELOSS
L_SMOOTHHINGELOSS
L_SQUAREDHINGELOSS
L_SQUAREDLOSS
L_LOGLOSS
L_LOGLOSSMARGIN

Definition at line 26 of file LossFunction.h.

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.

Enumerator:

MSG_GCDEBUG
MSG_DEBUG
MSG_INFO
MSG_NOTICE
MSG_WARN
MSG_ERROR
MSG_CRITICAL
MSG_ALERT
MSG_EMERGENCY
MSG_MESSAGEONLY

Definition at line 42 of file SGIO.h.

enum EModelSelectionAvailability

model selection availability

Enumerator:

MS_NOT_AVAILABLE
MS_AVAILABLE

Definition at line 62 of file SGObject.h.

enum EMSParamType

value type of a model selection parameter node

Enumerator:

MSPT_NONE	no type
MSPT_FLOAT64
MSPT_INT32

Definition at line 29 of file ModelSelectionParameters.h.

enum ENormalizerType

normalizer type

Enumerator:

N_REGULAR
N_MULTITASK

Definition at line 21 of file KernelNormalizer.h.

enum EOptimizationType

optimization type

Enumerator:

FASTBUTMEMHUNGRY
SLOWBUTMEMEFFICIENT

Definition at line 52 of file Kernel.h.

enum EPCAMode

mode of pca

Enumerator:

THRESHOLD	cut by threshold
VARIANCE_EXPLAINED	variance explained
FIXED_NUMBER	keep fixed number of features

Definition at line 26 of file PCA.h.

enum EPreprocessorType

enumeration of possible preprocessor types

Enumerator:

P_UNKNOWN
P_NORMONE
P_LOGPLUSONE
P_SORTWORDSTRING
P_SORTULONGSTRING
P_SORTWORD
P_PRUNEVARSUBMEAN
P_DECOMPRESSSTRING
P_DECOMPRESSCHARSTRING
P_DECOMPRESSBYTESTRING
P_DECOMPRESSWORDSTRING
P_DECOMPRESSULONGSTRING
P_RANDOMFOURIERGAUSS
P_PCA
P_KERNELPCA
P_NORMDERIVATIVELEM3
P_DIMENSIONREDUCTIONPREPROCESSOR
P_MULTIDIMENSIONALSCALING
P_LOCALLYLINEAREMBEDDING
P_ISOMAP
P_HESSIANLOCALLYLINEAREMBEDDING
P_LOCALTANGENTSPACEALIGNMENT
P_LAPLACIANEIGENMAPS
P_KERNELLOCALLYLINEAREMBEDDING

Definition at line 30 of file Preprocessor.h.

enum ERangeType

type of range

Enumerator:

R_LINEAR
R_EXP
R_LOG

Definition at line 23 of file ModelSelectionParameters.h.

enum ERegressionType

type of regressor

Enumerator:

RT_NONE
RT_LIGHT
RT_LIBSVM

Definition at line 17 of file Regression.h.

enum ESolverType

solver type

Enumerator:

ST_AUTO
ST_CPLEX
ST_GLPK
ST_NEWTON
ST_DIRECT
ST_ELASTICNET
ST_BLOCK_NORM

Definition at line 75 of file Machine.h.

enum ETransformType

Ways to transform inputs

Enumerator:

T_LINEAR	Linear.
T_LOG	Logarithmic.
T_LOG_PLUS1	Logarithmic (log(1+x)).
T_LOG_PLUS3	Logarithmic (log(3+x)).
T_LINEAR_PLUS3	Linear (3+x).

Definition at line 22 of file Plif.h.

enum EVwCacheType

Enum EVwCacheType specifies the type of cache used, either C_NATIVE or C_PROTOBUF.

Enumerator:

C_NATIVE
C_PROTOBUF

Definition at line 29 of file VwCacheReader.h.

enum EWDKernType

WD kernel type

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 25 of file WeightedDegreeStringKernel.h.

enum LIBLINEAR_SOLVER_TYPE

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.

enum SCATTER_TYPE

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

char* shogun::c_string_of_substring ( substring s )

Return a C string from the substring

Parameters:

s

substring

Returns:: new C string representation

Definition at line 463 of file SGIO.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
	)

Definition at line 79 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.

SGVector<T> shogun::create_range_array	(	T	min,
		T	max,
		ERangeType	type,
		T	step,
		T	type_base
	)

Creates an array of values specified by the parameters. A minimum and a maximum is specified, step interval, and an ERangeType (s. above) of the range, which is used to fill an array with concrete values. For some range types, a base is required. All values are given by void pointers to them (type conversion is done via m_value_type variable).

Parameters:

	min	minimum of desired range. Requires min<max
	max	maximum of desired range. Requires min<max
	type	the way the values are created, see ERangeType
	step	increment instaval for the values
	type_base	base for EXP or LOG ranges

Definition at line 167 of file ModelSelectionParameters.h.

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

Definition at line 570 of file ssl.cpp.

float64_t shogun::double_of_substring ( substring s )

Return value of substring as double

Parameters:

s

substring

Returns:: substring as double

Definition at line 503 of file SGIO.h.

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

Definition at line 1031 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.

float32_t shogun::float_of_substring ( substring s )

Get value of substring as float (if possible)

Parameters:

s

substring

Returns:: float32_t value of substring

Definition at line 488 of file SGIO.h.

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

Definition at line 38 of file libocas.cpp.

SGIO * 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 1097 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 = `0`,
		int32_t *	proj = `0`
	)

gpm solver

Parameters:

	Solver
	Projector
	n
	A
	b
	c
	e
	iy
	x
	tol
	ls
	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 init_shogun_with_defaults ( )

init shogun with defaults

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

Definition at line 1077 of file ssl.cpp.

void shogun::initialize	(	struct vector_int *	A,
		int32_t	k
	)

Definition at line 1087 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.

int32_t shogun::int_of_substring ( substring s )

Integer value of substring

Parameters:

s

substring

Returns:: int value of substring

Definition at line 518 of file SGIO.h.

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

Definition at line 1044 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 198 of file ssl.cpp.

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

Definition at line 63 of file LaRank.cpp.

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

Definition at line 131 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 322 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 354 of file LaRank.cpp.

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

Definition at line 195 of file LaRank.cpp.

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

Definition at line 331 of file LaRank.cpp.

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

Definition at line 123 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 288 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 282 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
	)

Generalized SMO algorithm

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
	)

QP solver for tasks with simplex constraints

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 359 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
	)

new shogun serializable

Parameters:

	sgserializable_name
	generic

float64_t shogun::norm_square ( const vector_double * A )

Definition at line 1066 of file ssl.cpp.

void shogun::nrerror ( char error_text[] )

Definition at line 45 of file pr_loqo.cpp.

float32_t one_pf_quad_predict	(	float32_t *	weights,
		VwFeature &	f,
		v_array< VwFeature > &	cross_features,
		vw_size_t	mask
	)

Get the prediction contribution from one feature.

Parameters:

	weights	weights
	f	feature
	cross_features	paired features
	mask	mask

Returns:: prediction from one feature

float32_t one_pf_quad_predict_trunc	(	float32_t *	weights,
		VwFeature &	f,
		v_array< VwFeature > &	cross_features,
		vw_size_t	mask,
		float32_t	gravity
	)

Get the prediction contribution from one feature.

Weights are taken as truncated weights.

Parameters:

	weights	weights
	f	feature
	cross_features	paired features
	mask	mask
	gravity	weight threshold value

Returns:: prediction from one feature

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

Definition at line 932 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 653 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
	)

n : number of primal variables m : number of constraints (typically 1) h_x : dot product matrix (n.n) a : constraint matrix (n.m) b : constant term (m) l : lower bound (n) u : upper bound (m)

primal : workspace for primal variables, has to be of size 3 n

x = primal; n g = x + n; n t = g + n; n

dual : workspace for dual variables, has to be of size m + 2 n

y = dual; m z = y + m; n s = z + n; n

verb : verbosity level sigfig_max : number of significant digits counter_max: stopping criterion restart : 1 if restart desired

void shogun::print_substring ( substring s )

Print the substring

Parameters:

s

substring

Definition at line 475 of file SGIO.h.

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	(	SGString< 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	(	SGSparseVector< 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

float32_t shogun::real_weight	(	float32_t	w,
		float32_t	gravity
	)

Get the truncated weight value

Parameters:

	w	weight
	gravity	threshold for the weight

Returns:: truncated weight

Definition at line 33 of file vw_math.h.

float32_t sd_offset_add	(	float32_t *	weights,
		vw_size_t	mask,
		VwFeature *	begin,
		VwFeature *	end,
		vw_size_t	offset
	)

Dot product of feature vector with the weight vector with an offset added to the feature indices.

Parameters:

	weights	weight vector
	mask	mask
	begin	first feature of the vector
	end	last feature of the vector
	offset	index offset

Returns:: dot product

float32_t sd_offset_truncadd	(	float32_t *	weights,
		vw_size_t	mask,
		VwFeature *	begin,
		VwFeature *	end,
		vw_size_t	offset,
		float32_t	gravity
	)

Dot product of feature vector with the weight vector with an offset added to the feature indices.

Weights are taken as the truncated weights.

Parameters:

	weights	weights
	mask	mask
	begin	first feature of the vector
	end	last feature of the vector
	offset	index offset
	gravity	weight threshold value

Returns:: dot product

void set_global_io ( SGIO * 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

void shogun::sg_global_print_default	(	FILE *	target,
		const char *	str
	)

Definition at line 72 of file init.cpp.

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 101 of file KMeans.cpp.

uint32_t shogun::ss_length ( substring s )

Length of substring

Parameters:

s

substring

Returns:: length of substring

Definition at line 542 of file SGIO.h.

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 525 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 1011 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 437 of file ssl.cpp.

uint32_t shogun::ulong_of_substring ( substring s )

Unsigned long value of substring

Parameters:

s

substring

Returns:: unsigned long value of substring

Definition at line 532 of file SGIO.h.

void shogun::wrap_dgeqrf	(	int	m,
		int	n,
		double *	a,
		int	lda,
		double *	tau,
		int *	info
	)

Definition at line 268 of file lapack.cpp.

void shogun::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
	)

Definition at line 249 of file lapack.cpp.

void shogun::wrap_dorgqr	(	int	m,
		int	n,
		int	k,
		double *	a,
		int	lda,
		double *	tau,
		int *	info
	)

Definition at line 286 of file lapack.cpp.

void shogun::wrap_dsyev	(	char	jobz,
		char	uplo,
		int	n,
		double *	a,
		int	lda,
		double *	w,
		int *	info
	)

Definition at line 231 of file lapack.cpp.

void shogun::wrap_dsyevr	(	char	jobz,
		char	uplo,
		int	n,
		double *	a,
		int	lda,
		int	il,
		int	iu,
		double *	eigenvalues,
		double *	eigenvectors,
		int *	info
	)

Definition at line 304 of file lapack.cpp.

void shogun::wrap_dsygvx	(	int	itype,
		char	jobz,
		char	uplo,
		int	n,
		double *	a,
		int	lda,
		double *	b,
		int	ldb,
		int	il,
		int	iu,
		double *	eigenvalues,
		double *	eigenvectors,
		int *	info
	)

Definition at line 340 of file lapack.cpp.

bool shogun::write_char_valued_strings	(	const SGString< 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 SGSparseVector< 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 201 of file LaRank.cpp.

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

Definition at line 164 of file LaRank.cpp.

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

Definition at line 109 of file LaRank.cpp.

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

Definition at line 294 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 220 of file LaRank.cpp.

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

Definition at line 83 of file LaRank.cpp.

Variable Documentation

uint32_t BufSize [static]

Definition at line 33 of file libocas.cpp.

const int32_t constant_hash = 11650396

Constant used to access the constant feature.

Definition at line 30 of file vw_constants.h.

float64_t* H [static]

Definition at line 32 of file libocas.cpp.

const uint32_t hash_base = 97562527

Seed for hash.

Definition at line 33 of file vw_constants.h.

const uint32_t QPSolverMaxIter = 10000000 [static]

Definition at line 30 of file libocas.cpp.

const int32_t quadratic_constant = 27942141

Constant used while hashing/accessing quadratic features.

Definition at line 27 of file vw_constants.h.

uint32_t Randnext

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

function called to cancel things

Definition at line 39 of file init.cpp.

SGIO * sg_io = NULL

shogun IO

Definition at line 25 of file init.cpp.

CMath * sg_math = NULL

Definition at line 27 of file init.cpp.

Parallel * sg_parallel = NULL

Definition at line 24 of file init.cpp.

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

function called to print error messages

Definition at line 36 of file init.cpp.

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

function called to print normal messages

Definition at line 30 of file init.cpp.

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

function called to print warning messages

Definition at line 33 of file init.cpp.

Version * sg_version = NULL

Definition at line 26 of file init.cpp.

shogun Namespace Reference

Classes

Typedefs

Enumerations

Functions

Variables

Detailed Description

Typedef Documentation

Enumeration Type Documentation

Function Documentation

Variable Documentation