Classes | Typedefs | Enumerations | Functions | Variables

shogun Namespace Reference

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

Classes

class  DynArray
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  Parallel
 Class Parallel provides helper functions for multithreading. More...
struct  TParameter
 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_STATESP_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

CSGObjectnew_sgserializable (const char *sgserializable_name, EPrimitiveType generic)
void init_shogun (void(*print_message)(FILE *target, const char *str), void(*print_warning)(FILE *target, const char *str), void(*print_error)(FILE *target, const char *str), void(*cancel_computations)(bool &delayed, bool &immediately))
void sg_global_print_default (FILE *target, const char *str)
void init_shogun_with_defaults ()
void exit_shogun ()
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
void set_global_math (CMath *math)
CMathget_global_math ()
int32_t InnerProjector (int32_t method, int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t l, float64_t u, float64_t *x, float64_t &lambda)
int32_t gvpm (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
int32_t FletcherAlg2A (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
int32_t gpm_solver (int32_t Solver, int32_t Projector, int32_t n, float32_t *A, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
float64_t ProjectR (float64_t *x, int32_t n, float64_t lambda, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u)
int32_t ProjectDai (int32_t n, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u, float64_t *x, float64_t &lam_ext)
float64_t quick_select (float64_t *arr, int32_t n)
int32_t Pardalos (int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t low, float64_t up, float64_t *x)
static larank_kcache_t * larank_kcache_create (CKernel *kernelfunc)
static void xtruncate (larank_kcache_t *self, int32_t k, int32_t nlen)
static void xpurge (larank_kcache_t *self)
static void larank_kcache_set_maximum_size (larank_kcache_t *self, int64_t entries)
static void larank_kcache_destroy (larank_kcache_t *self)
static void xminsize (larank_kcache_t *self, int32_t n)
static int32_t * larank_kcache_r2i (larank_kcache_t *self, int32_t n)
static void xextend (larank_kcache_t *self, int32_t k, int32_t nlen)
static void xswap (larank_kcache_t *self, int32_t i1, int32_t i2, int32_t r1, int32_t r2)
static void larank_kcache_swap_rr (larank_kcache_t *self, int32_t r1, int32_t r2)
static void larank_kcache_swap_ri (larank_kcache_t *self, int32_t r1, int32_t i2)
static float64_t xquery (larank_kcache_t *self, int32_t i, int32_t j)
static float64_t larank_kcache_query (larank_kcache_t *self, int32_t i, int32_t j)
static void larank_kcache_set_buddy (larank_kcache_t *self, larank_kcache_t *buddy)
static float32_tlarank_kcache_query_row (larank_kcache_t *self, int32_t i, int32_t len)
static const float64_tget_col (uint32_t i)
static float64_t get_time ()
ocas_return_value_T svm_ocas_solver (float64_t C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
ocas_return_value_T svm_ocas_solver_difC (float64_t *C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
static void findactive (float64_t *Theta, float64_t *SortedA, uint32_t *nSortedA, float64_t *A, float64_t *B, int n, int(*sort)(float64_t *, float64_t *, uint32_t))
ocas_return_value_T msvm_ocas_solver (float64_t C, float64_t *data_y, uint32_t nY, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
libqp_state_T libqp_splx_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *b, uint32_t *I, uint8_t *S, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolAbs, float64_t TolRel, float64_t QP_TH, void(*print_state)(libqp_state_T state))
libqp_state_T libqp_gsmo_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *a, float64_t b, float64_t *LB, float64_t *UB, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolKKT, void(*print_state)(libqp_state_T state))
void nrerror (char error_text[])
bool choldc (float64_t *a, int32_t n, float64_t *p)
void cholsb (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
void chol_forward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
void chol_backward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
bool solve_reduced (int32_t n, int32_t m, float64_t h_x[], float64_t h_y[], float64_t a[], float64_t x_x[], float64_t x_y[], float64_t c_x[], float64_t c_y[], float64_t workspace[], int32_t step)
void matrix_vector (int32_t n, float64_t m[], float64_t x[], float64_t y[])
int32_t pr_loqo (int32_t n, int32_t m, float64_t c[], float64_t h_x[], float64_t a[], float64_t b[], float64_t l[], float64_t u[], float64_t primal[], float64_t dual[], int32_t verb, float64_t sigfig_max, int32_t counter_max, float64_t margin, float64_t bound, int32_t restart)
void ssl_train (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t CGLS (const struct data *Data, const struct options *Options, const struct vector_int *Subset, struct vector_double *Weights, struct vector_double *Outputs)
int32_t L2_SVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini)
float64_t line_search (float64_t *w, float64_t *w_bar, float64_t lambda, float64_t *o, float64_t *o_bar, float64_t *Y, float64_t *C, int32_t d, int32_t l)
int32_t TSVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t switch_labels (float64_t *Y, float64_t *o, int32_t *JU, int32_t u, int32_t S)
int32_t DA_S3VM (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t optimize_w (const struct data *Data, const float64_t *p, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini)
void optimize_p (const float64_t *g, int32_t u, float64_t T, float64_t r, float64_t *p)
float64_t transductive_cost (float64_t normWeights, float64_t *Y, float64_t *Outputs, int32_t m, float64_t lambda, float64_t lambda_u)
float64_t entropy (const float64_t *p, int32_t u)
float64_t KL (const float64_t *p, const float64_t *q, int32_t u)
float64_t norm_square (const vector_double *A)
void initialize (struct vector_double *A, int32_t k, float64_t a)
void initialize (struct vector_int *A, int32_t k)
void GetLabeledData (struct data *D, const struct data *Data)
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

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

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.

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

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.

compression type

Enumerator:
UNCOMPRESSED 
LZO 
GZIP 
BZIP2 
LZMA 
SNAPPY 

Definition at line 26 of file Compressor.h.

Type of example, either E_LABELLED or E_UNLABELLED

Enumerator:
E_LABELLED 
E_UNLABELLED 

Definition at line 26 of file InputParser.h.

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.

Enumerator:
E_LINEAR 
E_QP 

Definition at line 29 of file Cplex.h.

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.

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.

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.

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 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.

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.

shogun feature properties

Enumerator:
FP_NONE 
FP_DOT 
FP_STREAMING_DOT 

Definition at line 55 of file FeatureTypes.h.

shogun feature type

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

Definition at line 16 of file FeatureTypes.h.

kernel property

Enumerator:
KP_NONE 
KP_LINADD 
KP_KERNCOMBINATION 
KP_BATCHEVALUATION 

Definition at line 117 of file Kernel.h.

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.

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.

model selection availability

Enumerator:
MS_NOT_AVAILABLE 
MS_AVAILABLE 

Definition at line 62 of file SGObject.h.

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.

normalizer type

Enumerator:
N_REGULAR 
N_MULTITASK 

Definition at line 21 of file KernelNormalizer.h.

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.

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.

type of regressor

Enumerator:
RT_NONE 
RT_LIGHT 
RT_LIBSVM 

Definition at line 17 of file Regression.h.

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.

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 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.

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.

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.

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 ( min,
max,
ERangeType  type,
step,
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.

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.

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