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 set of values, which each are of type SGParamInfo. May be compared to each other based on their keys. More...
class	ParameterMap
	Implements a map of ParameterMapElement instances Maps one key to a set of values. 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	CFeatureBlockLogisticRegression
	class FeatureBlockLogisticRegression, a linear binary logistic loss classifier for problems with complex feature relations. Currently two feature relations are supported - feature group (done via CIndexBlockGroup) and feature tree (done via CIndexTree). Handling of feature relations is done via L1/Lq (for groups) and L1/L2 (for trees) regularization. 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	CNearestCentroid
	Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier. 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	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	CLibLinear
	class to implement LibLinear More...
class	CLibSVM
	LibSVM. More...
class	CLibSVMOneClass
	class LibSVMOneClass More...
class	CMPDSVM
	class MPDSVM More...
class	CNewtonSVM
	NewtonSVM, In this Implementation linear SVM is trained in its primal form using Newton-like iterations. This Implementation is ported from the Olivier Chapelles fast newton based SVM solver, Which could be found here :http://mloss.org/software/view/30/ For further information on this implementation of SVM refer to this paper: http://www.kyb.mpg.de/publications/attachments/neco_%5B0%5D.pdf. 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	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	CConverter
	class Converter used to convert data More...
class	CDiffusionMaps
	class DiffusionMaps (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess given data using Diffusion Maps dimensionality reduction technique as described in More...
class	CEmbeddingConverter
	class EmbeddingConverter (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of features, e.g. construct dense numeric embedding of string features More...
class	CHessianLocallyLinearEmbedding
	class HessianLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess data using Hessian Locally Linear Embedding algorithm as described in More...
class	CIsomap
	class Isomap (part of the Efficient Dimension Reduction Toolkit) used to embed data using Isomap algorithm as described in More...
class	CKernelLocallyLinearEmbedding
	class KernelLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using kernel formulation of Locally Linear Embedding algorithm as described in More...
class	CKernelLocalTangentSpaceAlignment
	class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using kernel extension of the Local Tangent Space Alignment (LTSA) algorithm. More...
class	CLaplacianEigenmaps
	class LaplacianEigenmaps (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using Laplacian Eigenmaps algorithm as described in: More...
class	CLinearLocalTangentSpaceAlignment
	class LinearLocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
class	CLocalityPreservingProjections
	class LocalityPreservingProjections (part of the Efficient Dimensionality Reduction Toolkit) used to compute embeddings of data using Locality Preserving Projections method as described in More...
class	CLocallyLinearEmbedding
	class LocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Locally Linear Embedding algorithm described in More...
class	CLocalTangentSpaceAlignment
	class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in: More...
class	CMultidimensionalScaling
	class Multidimensionalscaling (part of the Efficient Dimensionality Reduction Toolkit) is used to perform multidimensional scaling (capable of landmark approximation if requested). More...
class	CNeighborhoodPreservingEmbedding
	NeighborhoodPreservingEmbedding (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
class	CStochasticProximityEmbedding
	class StochasticProximityEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using the Stochastic Proximity algorithm. More...
class	CAttenuatedEuclideanDistance
	class AttenuatedEuclideanDistance 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	CDenseDistance
	template class DenseDistance More...
class	CDistance
	Class Distance, a base class for all the distances used in the Shogun toolbox. More...
class	CEuclideanDistance
	class EuclideanDistance 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	CMahalanobisDistance
	class MahalanobisDistance More...
class	CManhattanMetric
	class ManhattanMetric More...
class	CManhattanWordDistance
	class ManhattanWordDistance More...
class	CMinkowskiMetric
	class MinkowskiMetric More...
class	CRealDistance
	class RealDistance More...
class	CSparseDistance
	template class SparseDistance More...
class	CSparseEuclideanDistance
	class SparseEucldeanDistance 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 binary classification labels. More...
class	CClusteringAccuracy
	clustering accuracy More...
class	CClusteringEvaluation
	The base class used to evaluate clustering. More...
class	CClusteringMutualInformation
	clustering (normalized) mutual information 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...
class	CCrossValidationResult
	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	CCrossValidationMKLStorage
	Class for storing MKL weights in every fold of cross-validation. More...
class	CCrossValidationMulticlassStorage
	Class for storing multiclass evaluation information in every fold of cross-validation. More...
class	CCrossValidationOutput
	Class for managing individual folds in cross-validation. More...
class	CCrossValidationPrintOutput
	Class for outputting cross-validation intermediate results to the standard output. Simply prints all messages it gets. More...
class	CCrossValidationSplitting
	Implementation of normal cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference). More...
class	CDifferentiableFunction
	DifferentiableFunction. More...
class	CEvaluation
	Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression. More...
class	CEvaluationResult
	EvaluationResult is the abstract class that contains the result generated by the MachineEvaluation class. More...
class	CGradientCriterion
	CGradientCriterion Simple class which specifies the direction of gradient search. Does not provide any label evaluation measure, however. More...
class	CGradientEvaluation
	GradientEvaluation evaluates a machine using its associated differentiable function for the function value and its gradient with respect to parameters. More...
class	CGradientResult
	GradientResult is a container class that returns results from GradientEvaluation. It contains the function value as well as its gradient. More...
class	CMachineEvaluation
	Machine Evaluation is an abstract class that evaluates a machine according to some criterion. More...
class	CMeanAbsoluteError
	Class MeanAbsoluteError used to compute an error of regression model. More...
class	CMeanSquaredError
	Class MeanSquaredError used to compute an error of regression model. More...
class	CMeanSquaredLogError
	Class CMeanSquaredLogError used to compute an error of regression model. More...
class	CMulticlassAccuracy
	The class MulticlassAccuracy used to compute accuracy of multiclass classification. More...
class	CMulticlassOVREvaluation
	The class MulticlassOVREvaluation used to compute evaluation parameters of multiclass classification via binary OvR decomposition and given binary evaluation technique. More...
class	CPRCEvaluation
	Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC). More...
class	CROCEvaluation
	Class ROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC). 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. Do not use for regression since it may be impossible to distribute nice in that case. More...
class	CStructuredAccuracy
	class CStructuredAccuracy used to compute accuracy of structured classification 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	CBinnedDotFeatures
	The class BinnedDotFeatures contains a 0-1 conversion of features into bins. 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	CDataGenerator
	Class that is able to generate various data samples, which may be used for examples in SHOGUN. More...
class	CDenseFeatures
	The class DenseFeatures implements dense feature matrices. More...
class	CDenseSubsetFeatures
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	CLatentFeatures
	Latent Features class The class if for representing features for latent learning, e.g. LatentSVM. It's basically a very generic way of storing features of any (user-defined) form based on CData. More...
class	CLBPPyrDotFeatures
	implement DotFeatures for the polynomial kernel More...
class	CMatrixFeatures
	Class CMatrixFeatures used to represent data whose feature vectors are better represented with matrices rather than with unidimensional arrays or vectors. Optionally, it can be restricted that all the feature vectors have the same number of features. Set the attribute num_features different to zero to use this restriction. Allow feature vectors with different number of features by setting num_features equal to zero (default behaviour). 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	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	CMeanShiftDataGenerator
	Class to generate dense features data via the streaming features interface. The core are pairs of methods to a) set the data model and parameters, and b) to generate a data vector using these model parameters Both methods are automatically called when calling get_next_example() This allows to treat generated data as a stream via the standard streaming features interface. More...
class	CStreamingDenseFeatures
	This class implements streaming features with dense feature vectors. 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	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
	Wrapper class for an index subset which is used by SubsetStack. More...
class	CSubsetStack
	class to add subset support to another class. A CSubsetStackStack instance should be added and wrapper methods to all interfaces should be added. 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	CIOBuffer
	An I/O buffer class. More...
class	CMemoryMappedFile
	memory mapped file 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	CInputParser
	Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples. 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	CStreamingAsciiFile
	Class StreamingAsciiFile to read vector-by-vector from ASCII files. More...
class	CStreamingFile
	A Streaming File access class. More...
class	CStreamingFileFromDenseFeatures
	Class CStreamingFileFromDenseFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CDenseFeatures object. More...
class	CStreamingFileFromFeatures
	Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures 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	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	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	CDistanceKernel
	The Distance kernel takes a distance as input. More...
class	CDotKernel
	Template class DotKernel is the base class for kernels working on DotFeatures. More...
class	CExponentialKernel
	The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures. More...
class	CGaussianARDKernel
	Gaussian Kernel with Automatic Relevance Detection. More...
class	CGaussianKernel
	The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures. 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	CInverseMultiQuadricKernel
	InverseMultiQuadricKernel. More...
class	CJensenShannonKernel
	The Jensen-Shannon kernel operating on real-valued vectors computes the Jensen-Shannon distance between the features. Often used in computer vision. More...
class	CKernel
	The Kernel base class. More...
class	CLinearARDKernel
	Linear Kernel with Automatic Relevance Detection. More...
class	CLinearKernel
	Computes the standard linear kernel on CDotFeatures. More...
class	CLogKernel
	Log kernel. More...
class	CMultiquadricKernel
	MultiquadricKernel. 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	CDiceKernelNormalizer
	DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient). 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	CIdentityKernelNormalizer
	Identity Kernel Normalization, i.e. no normalization is applied. More...
class	CKernelNormalizer
	The class Kernel Normalizer defines a function to post-process kernel values. 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	CScatterKernelNormalizer
	the scatter kernel normalizer More...
class	CSqrtDiagKernelNormalizer
	SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements. More...
class	CTanimotoKernelNormalizer
	TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ). More...
class	CVarianceKernelNormalizer
	VarianceKernelNormalizer divides by the ``variance''. More...
class	CZeroMeanCenterKernelNormalizer
	ZeroMeanCenterKernelNormalizer centers the kernel in feature space. More...
class	CPolyKernel
	Computes the standard polynomial kernel on CDotFeatures. More...
class	CPowerKernel
	Power kernel. More...
class	CProductKernel
	The Product kernel is used to combine a number of kernels into a single ProductKernel object by element multiplication. More...
class	CPyramidChi2
	Pyramid Kernel over Chi2 matched histograms. More...
class	CRationalQuadraticKernel
	Rational Quadratic kernel. More...
class	CSigmoidKernel
	The standard Sigmoid kernel computed on dense real valued features. More...
class	CSparseKernel
	Template class SparseKernel, is the base class of kernels working on sparse features. More...
class	CSphericalKernel
	Spherical kernel. More...
class	CSplineKernel
	Computes the Spline Kernel function which is the cubic polynomial. 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	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	CFixedDegreeStringKernel
	The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d. More...
class	CGaussianMatchStringKernel
	The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length. More...
class	CHistogramWordStringKernel
	The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains. More...
class	CLinearStringKernel
	Computes the standard linear kernel on dense char valued features. More...
class	CLocalAlignmentStringKernel
	The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences. More...
class	CLocalityImprovedStringKernel
	The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features. More...
class	CMatchWordStringKernel
	The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet. More...
class	COligoStringKernel
	This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004. 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	CRegulatoryModulesStringKernel
	The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences. More...
class	CSalzbergWordStringKernel
	The SalzbergWordString kernel implements the Salzberg kernel. 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...
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	CStringKernel
	Template class StringKernel, is the base class of all String Kernels. 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	CWeightedDegreeStringKernel
	The Weighted Degree String kernel. More...
class	CTensorProductPairKernel
	Computes the Tensor Product Pair Kernel (TPPK). More...
class	CTStudentKernel
	Generalized T-Student kernel. More...
class	CWaveKernel
	Wave kernel. More...
class	CWaveletKernel
	the class WaveletKernel More...
class	CWeightedDegreeRBFKernel
	weighted degree RBF kernel More...
class	CBinaryLabels
	Binary Labels for binary classification. More...
class	CDenseLabels
	Dense integer or floating point labels. More...
class	CLabels
	The class Labels models labels, i.e. class assignments of objects. More...
class	CLatentLabels
	abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels. More...
class	CMulticlassLabels
	Multiclass Labels for multi-class classification. More...
class	CMulticlassMultipleOutputLabels
	Multiclass Labels for multi-class classification with multiple labels. More...
class	CRegressionLabels
	Real Labels are real-valued labels. More...
class	CStructuredLabels
	Base class of the labels used in Structured Output (SO) problems. More...
class	CLatentModel
	Abstract class CLatentModel It represents the application specific model and contains most of the application dependent logic to solve latent variable based problems. More...
class	CLatentSOSVM
	class Latent Structured Output SVM, an structured output based machine for classification problems with latent variables. More...
class	CLatentSVM
	LatentSVM class Latent SVM implementation based on [1]. For optimization this implementation uses SVMOcas. 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	CoverTree
class	CData
	dummy data holder 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
	Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an array. More...
class	CDynInt
	integer type of dynamic size More...
class	func_wrapper
class	CGCArray
	Template class GCArray implements a garbage collecting static array. More...
class	CHash
	Collection of Hashing Functions. More...
class	CIndexBlock
	class IndexBlock used to represent contiguous indices of one group (e.g. block of related features) More...
class	CIndexBlockGroup
	class IndexBlockGroup used to represent group-based feature relation. More...
class	CIndexBlockRelation
	class IndexBlockRelation More...
class	CIndexBlockTree
	class IndexBlockTree used to represent tree guided feature relation. More...
class	CIndirectObject
	an array class that accesses elements indirectly via an index array. More...
class	v_array
	Class v_array taken directly from JL's implementation. More...
class	CJLCoverTreePoint
	Class Point to use with John Langford's CoverTree. This class must have some assoficated functions defined (distance, parse_points and print, see below) so it can be used with the CoverTree implementation. 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	CMap
	the class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class	CSet
	the class CSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class	SGMatrix
	shogun matrix More...
class	SGMatrixList
	shogun matrix list More...
class	SGNDArray
	shogun n-dimensional array More...
class	SGReferencedData
	shogun reference count managed data More...
class	SGSparseMatrix
	template class SGSparseMatrix More...
struct	SGSparseVectorEntry
	template class SGSparseVectorEntry More...
class	SGSparseVector
	template class SGSparseVector The assumtion is that the stored SGSparseVectorEntry<T>* vector is ordered by SGSparseVectorEntry.feat_index in non-decreasing order. This has to be assured by the user of the class. More...
class	SGString
	shogun string More...
struct	SGStringList
	template class SGStringList More...
class	SGVector
	shogun vector 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	CStructuredData
	Base class of the components of StructuredLabels. 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	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	CBaseMulticlassMachine
class	CDistanceMachine
	A generic DistanceMachine interface. More...
class	CKernelMachine
	A generic KernelMachine interface. More...
class	CKernelMulticlassMachine
	generic kernel multiclass More...
class	CKernelStructuredOutputMachine
class	CLinearLatentMachine
	abstract implementaion of Linear Machine with latent variable This is the base implementation of all linear machines with latent variable. More...
class	CLinearMachine
	Class LinearMachine is a generic interface for all kinds of linear machines like classifiers. More...
class	CLinearMulticlassMachine
	generic linear multiclass machine More...
class	CLinearStructuredOutputMachine
class	CMachine
	A generic learning machine interface. More...
class	CMulticlassMachine
	experimental abstract generic multiclass machine class More...
class	CNativeMulticlassMachine
	experimental abstract native multiclass machine class More...
class	COnlineLinearMachine
	Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms. More...
class	CStructuredOutputMachine
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	Munkres
	Munkres. More...
class	CSparseInverseCovariance
	used to estimate inverse covariance matrix using graphical lasso More...
class	CStatistics
	Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc. More...
class	CGradientModelSelection
	Model selection class which searches for the best model by a gradient- search. 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	CRandomSearchModelSelection
	Model selection class which searches for the best model by a random search. See CModelSelection for details. More...
class	CConjugateIndex
	conjugate index classifier. Described in: More...
class	CECOCAEDDecoder
class	CECOCDecoder
class	CECOCDiscriminantEncoder
class	CECOCEDDecoder
class	CECOCEncoder
	ECOCEncoder produce an ECOC codebook. More...
class	CECOCForestEncoder
class	CECOCHDDecoder
class	CECOCIHDDecoder
class	CECOCLLBDecoder
class	CECOCOVOEncoder
class	CECOCOVREncoder
class	CECOCRandomDenseEncoder
class	CECOCRandomSparseEncoder
class	CECOCSimpleDecoder
class	CECOCStrategy
class	CECOCUtil
class	CGaussianNaiveBayes
	Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier. 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	CKNN
	Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
class	CLaRank
	the LaRank multiclass SVM machine More...
class	CMulticlassLibLinear
	multiclass LibLinear wrapper. Uses Crammer-Singer formulation and gradient descent optimization algorithm implemented in the LibLinear library. Regularized bias support is added using stacking bias 'feature' to hyperplanes normal vectors. More...
class	CMulticlassLibSVM
	class LibSVMMultiClass. Does one vs one classification. More...
class	CMulticlassLogisticRegression
	multiclass logistic regression More...
class	CMulticlassOCAS
	multiclass OCAS wrapper More...
class	CMulticlassOneVsOneStrategy
	multiclass one vs one strategy used to train generic multiclass machines for K-class problems with building voting-based ensemble of K*(K-1) binary classifiers More...
class	CMulticlassOneVsRestStrategy
	multiclass one vs rest strategy used to train generic multiclass machines for K-class problems with building ensemble of K binary classifiers More...
class	CMulticlassStrategy
	class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary classifiers More...
class	CMulticlassSVM
	class MultiClassSVM More...
class	CMulticlassTreeGuidedLogisticRegression
	multiclass tree guided logistic regression More...
class	CQDA
	Class QDA implements Quadratic Discriminant Analysis. More...
class	CRejectionStrategy
	base rejection strategy class More...
class	CThresholdRejectionStrategy
	threshold based rejection strategy More...
class	CDixonQTestRejectionStrategy
	simplified version of Dixon's Q test outlier based rejection strategy. Statistic values are taken from http://www.vias.org/tmdatanaleng/cc_outlier_tests_dixon.html More...
class	CScatterSVM
	ScatterSVM - Multiclass SVM. More...
class	CShareBoost
class	ShareBoostOptimizer
class	CBalancedConditionalProbabilityTree
class	CConditionalProbabilityTree
struct	ConditionalProbabilityTreeNodeData
	struct to store data of node of conditional probability tree More...
class	CRandomConditionalProbabilityTree
class	CRelaxedTree
struct	RelaxedTreeNodeData
class	RelaxedTreeUtil
class	CTreeMachine
	class TreeMachine, a base class for tree based multiclass classifiers More...
class	CTreeMachineNode
struct	VwConditionalProbabilityTreeNodeData
class	CVwConditionalProbabilityTree
struct	tag_callback_data
struct	tag_iteration_data
struct	lbfgs_parameter_t
class	CDecompressString
	Preprocessor that decompresses compressed strings. More...
class	CDensePreprocessor
	Template class DensePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CDenseFeatures (i.e. rectangular dense matrices). More...
class	CDimensionReductionPreprocessor
	the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices). More...
class	CHomogeneousKernelMap
	Preprocessor HomogeneousKernelMap performs homogeneous kernel maps as described in. More...
class	CKernelPCA
	Preprocessor KernelPCA performs kernel principal component analysis. 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	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	CPNorm
	Preprocessor PNorm, normalizes vectors to have p-norm. 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	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	CSumOne
	Preprocessor SumOne, normalizes vectors to have sum 1. More...
class	CGaussianProcessRegression
	Class GaussianProcessRegression implements Gaussian Process Regression.vInstead of a distribution over weights, the GP specifies a distribution over functions. More...
class	CExactInferenceMethod
	The Gaussian Exact Form Inference Method. More...
class	CFITCInferenceMethod
	The Fully Independent Conditional Training Inference Method. More...
class	CGaussianLikelihood
	This is the class that models a Gaussian Likelihood. More...
class	CInferenceMethod
	The Inference Method base class. More...
class	Psi_line
class	CLaplacianInferenceMethod
	The Laplace Approximation Inference Method. More...
class	CLikelihoodModel
	The Likelihood Model base class. More...
class	CMeanFunction
	Mean Function base class. More...
class	CStudentsTLikelihood
	This is the class that models a likelihood model with a Student's T Distribution. The parameters include degrees of freedom as well as a sigma scale parameter. More...
class	CZeroMean
	Zero Mean Function. More...
class	CKernelRidgeRegression
	Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression. More...
class	CLeastAngleRegression
	Class for Least Angle Regression, can be used to solve LASSO. More...
class	CLeastSquaresRegression
	class to perform Least Squares Regression More...
class	CLinearRidgeRegression
	Class LinearRidgeRegression implements Ridge Regression - a regularized least square method for classification and regression. More...
class	CLibLinearRegression
	LibLinear for 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	CHSIC
	This class implements the Hilbert Schmidtd Independence Criterion based independence test as described in [1]. More...
class	CKernelIndependenceTestStatistic
	Independence test base class. Provides an interface for performing an independence test. Given samples $Z=\{(x_i,y_i)\}_{i=1}^m$ from the joint distribution $\textbf{P}_x\textbf{P}_y$ , does the joint distribution factorize as $\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y$ ? The null- hypothesis says yes, i.e. no independence, the alternative hypothesis says yes. More...
class	CKernelMeanMatching
	Kernel Mean Matching. More...
class	CKernelTwoSampleTestStatistic
	Two sample test base class. Provides an interface for performing a two-sample test, i.e. Given samples from two distributions and , the null-hypothesis is: , the alternative hypothesis: $H_1: p\neq q$ . More...
class	CLinearTimeMMD
	This class implements the linear time Maximum Mean Statistic as described in [1]. This statistic is in particular suitable for streaming data. Therefore, only streaming features may be passed. To process other feature types, construct streaming features from these (see constructor documentations). A blocksize has to be specified that determines how many examples are processed at once. This should be set as large as available memory allows to ensure faster computations. More...
class	CQuadraticTimeMMD
	This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions and in a RKHS $\text{MMD}}[\mathcal{F},p,q]^2=\textbf{E}_{x,x'}\left[ k(x,x')\right]- 2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]=\|\|\mu_p - \mu_q\|\|^2_\mathcal{F}$ . More...
class	CTestStatistic
	Test statistic base class. Provides an interface for statistical tests via three methods: compute_statistic(), compute_p_value() and compute_threshold(). The second computes a p-value for the statistic computed by the first method. The p-value represents the position of the statistic in the null-distribution, i.e. the distribution of the statistic population given the null-hypothesis is true. (1-position = p-value). The third method, compute_threshold(), computes a threshold for a given test level which is needed to reject the null-hypothesis. More...
class	CTwoDistributionsTestStatistic
	Provides an interface for performing statistical tests on two sets of samples from two distributions. Instances of these tests are the classical two-sample test and the independence test. This class may be used as base class for both. More...
class	CDualLibQPBMSOSVM
	Class DualLibQPBMSOSVM that uses Bundle Methods for Regularized Risk Minimization algorithms for structured output (SO) problems [1] presented in [2]. More...
class	CDynProg
	Dynamic Programming Class. More...
class	CSequence
	Class CSequence to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). More...
class	CHMSVMLabels
	Class CHMSVMLabels to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). Each of the labels is represented by a sequence of integers. Each label is of type CSequence and all of them are stored in a CDynamicObjectArray. More...
class	CHMSVMModel
	Class CHMSVMModel that represents the application specific model and contains the application dependent logic to solve Hidden Markov Support Vector Machines (HM-SVM) type of problems within a generic SO framework. More...
class	CIntronList
	class IntronList More...
struct	bmrm_return_value_T
struct	bmrm_ll
class	CMulticlassModel
	Class CMulticlassModel that represents the application specific model and contains the application dependent logic to solve multiclass classification within a generic SO framework. More...
struct	CRealNumber
	Class CRealNumber to be used in the application of Structured Output (SO) learning to multiclass classification. Even though it is likely that it does not make sense to consider real numbers as structured data, it has been made in this way because the basic type to use in structured labels needs to inherit from CStructuredData. More...
class	CMulticlassSOLabels
	Class CMulticlassSOLabels to be used in the application of Structured Output (SO) learning to multiclass classification. Each of the labels is represented by a real number and it is required that the values of the labels are in the set {0, 1, ..., num_classes-1}. Each label is of type CRealNumber and all of them are stored in a CDynamicObjectArray. 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	CStateModel
	class CStateModel base, abstract class for the internal state representation used in the CHMSVMModel. More...
struct	TMultipleCPinfo
struct	CResultSet
class	CStructuredModel
	Class CStructuredModel that represents the application specific model and contains most of the application dependent logic to solve structured output (SO) problems. The idea of this class is to be instantiated giving pointers to the functions that are dependent on the application, i.e. the combined feature representation $\Psi(\bold{x},\bold{y})$ and the argmax function ${\arg\max} _{\bold{y} \neq \bold{y}_i} \left \langle { \bold{w}, \Psi(\bold{x}_i,\bold{y}) } \right \rangle$ . See: MulticlassModel.h and .cpp for an example of these functions implemented. More...
class	CTwoStateModel
	class CTwoStateModel class for the internal two-state representation used in the CHMSVMModel. More...
class	CDomainAdaptationMulticlassLibLinear
	domain adaptation multiclass LibLinear wrapper Source domain is assumed to b More...
class	CDomainAdaptationSVM
	class DomainAdaptationSVM More...
class	CDomainAdaptationSVMLinear
	class DomainAdaptationSVMLinear More...
class	MappedSparseMatrix
	mapped sparse matrix for representing graph relations of tasks More...
class	CLibLinearMTL
	class to implement LibLinear More...
class	CMultitaskClusteredLogisticRegression
	class MultitaskClusteredLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. Assumes task in group are related with a clustered structure. More...
class	CMultitaskCompositeMachine
	class MultitaskCompositeMachine used to solve multitask binary classification problems with separate training of given binary classifier on each task 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	CMultitaskL12LogisticRegression
	class MultitaskL12LogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
class	CMultitaskLeastSquaresRegression
	class Multitask Least Squares Regression, a machine to solve regression problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees. More...
class	CMultitaskLinearMachine
	class MultitaskLinearMachine, a base class for linear multitask classifiers More...
class	CMultitaskLogisticRegression
	class Multitask Logistic Regression used to solve classification problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees. More...
class	CMultitaskROCEvaluation
	Class MultitaskROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC) of each task separately. More...
class	CMultitaskTraceLogisticRegression
	class MultitaskTraceLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
class	CTask
	class Task used to represent tasks in multitask learning. Essentially it represent a set of feature vector indices. More...
class	CTaskGroup
	class TaskGroup used to represent a group of tasks. Tasks in group do not overlap. More...
class	CTaskRelation
	used to represent tasks in multitask learning More...
class	CTaskTree
	class TaskTree used to represent a tree of tasks. Tree is constructed via task with subtasks (and subtasks of subtasks ..) passed to the TaskTree. More...
class	CGUIClassifier
	UI classifier. More...
class	CGUIConverter
	UI converter. 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
typedef double	DoubleOfDouble (double)
typedef struct tag_callback_data	callback_data_t
typedef struct tag_iteration_data	iteration_data_t
typedef int32_t(*	line_search_proc )(int32_t n, float64_t x, float64_t f, float64_t g, float64_t s, float64_t stp, const float64_t xp, const float64_t gp, float64_t wa, callback_data_t cd, const lbfgs_parameter_t param)
typedef float64_t(*	lbfgs_evaluate_t )(void instance, const float64_t x, float64_t *g, const int n, const float64_t step)
typedef int(*	lbfgs_progress_t )(void instance, const float64_t x, const float64_t *g, const float64_t fx, const float64_t xnorm, const float64_t gnorm, const float64_t step, int n, int k, int ls)
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, L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL }
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	ESPEStrategy { SPE_GLOBAL, SPE_LOCAL }
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_SPARSEEUCLIDEAN = 100, D_EUCLIDEAN = 110, D_CHISQUARE = 120, D_TANIMOTO = 130, D_COSINE = 140, D_BRAYCURTIS = 150, D_CUSTOM = 160, D_ATTENUATEDEUCLIDEAN = 170, D_MAHALANOBIS = 180, D_DIRECTOR = 190 }
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, CUSTOM = 999 }
enum	EEvaluationDirection { ED_MINIMIZE, ED_MAXIMIZE }
enum	EEvaluationResultType { CROSSVALIDATION_RESULT, GRADIENTEVALUATION_RESULT }
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_DENSE = 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_DENSE = 110, C_STREAMING_SPARSE = 120, C_STREAMING_STRING = 130, C_STREAMING_VW = 140, C_BINNED_DOT = 150, C_DIRECTOR_DOT = 160, C_LATENT = 170, C_ANY = 1000 }
	shogun feature class More...
enum	EFeatureProperty { FP_NONE = 0, FP_DOT = 1, FP_STREAMING_DOT = 2 }
	shogun feature properties More...
enum	EMessageType { MSG_GCDEBUG, MSG_DEBUG, MSG_INFO, MSG_NOTICE, MSG_WARN, MSG_ERROR, MSG_CRITICAL, MSG_ALERT, MSG_EMERGENCY, MSG_MESSAGEONLY }
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	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, K_JENSENSHANNON = 470, K_DIRECTOR = 480, K_PRODUCT = 490, K_LINEARARD = 500, K_GAUSSIANARD = 510, K_STREAMING = 520 }
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	EFeaturesContainer { FC_LHS = 0, FC_RHS = 1 }
enum	EStructuredDataType { SDT_UNKNOWN = 0, SDT_REAL = 1, SDT_SEQUENCE = 2 }
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	EMachineType { 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_KERNELRIDGEREGRESSION = 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, CT_CONJUGATEINDEX = 400, CT_LINEARRIDGEREGRESSION = 410, CT_LEASTSQUARESREGRESSION = 420, CT_QDA = 430, CT_NEWTONSVM = 440, CT_GAUSSIANPROCESSREGRESSION = 450, CT_LARS = 460, CT_MULTICLASS = 470, CT_DIRECTORLINEAR = 480, CT_DIRECTORKERNEL = 490 }
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	EProblemType { PT_BINARY = 0, PT_REGRESSION = 1, PT_MULTICLASS = 2, PT_STRUCTURED = 3, PT_LATENT = 4 }
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, MSPT_FLOAT64_VECTOR, MSPT_INT32_VECTOR, MSPT_FLOAT64_SGVECTOR, MSPT_INT32_SGVECTOR }
enum	SCATTER_TYPE { NO_BIAS_LIBSVM, NO_BIAS_SVMLIGHT, TEST_RULE1, TEST_RULE2 }
enum	{ LBFGS_SUCCESS = 0, LBFGS_CONVERGENCE = 0, LBFGS_STOP, LBFGS_ALREADY_MINIMIZED, LBFGSERR_UNKNOWNERROR = -1024, LBFGSERR_LOGICERROR, LBFGSERR_OUTOFMEMORY, LBFGSERR_CANCELED, LBFGSERR_INVALID_N, LBFGSERR_INVALID_N_SSE, LBFGSERR_INVALID_X_SSE, LBFGSERR_INVALID_EPSILON, LBFGSERR_INVALID_TESTPERIOD, LBFGSERR_INVALID_DELTA, LBFGSERR_INVALID_LINESEARCH, LBFGSERR_INVALID_MINSTEP, LBFGSERR_INVALID_MAXSTEP, LBFGSERR_INVALID_FTOL, LBFGSERR_INVALID_WOLFE, LBFGSERR_INVALID_GTOL, LBFGSERR_INVALID_XTOL, LBFGSERR_INVALID_MAXLINESEARCH, LBFGSERR_INVALID_ORTHANTWISE, LBFGSERR_INVALID_ORTHANTWISE_START, LBFGSERR_INVALID_ORTHANTWISE_END, LBFGSERR_OUTOFINTERVAL, LBFGSERR_INCORRECT_TMINMAX, LBFGSERR_ROUNDING_ERROR, LBFGSERR_MINIMUMSTEP, LBFGSERR_MAXIMUMSTEP, LBFGSERR_MAXIMUMLINESEARCH, LBFGSERR_MAXIMUMITERATION, LBFGSERR_WIDTHTOOSMALL, LBFGSERR_INVALIDPARAMETERS, LBFGSERR_INCREASEGRADIENT }
enum	{ LBFGS_LINESEARCH_DEFAULT = 0, LBFGS_LINESEARCH_MORETHUENTE = 0, LBFGS_LINESEARCH_BACKTRACKING_ARMIJO = 1, LBFGS_LINESEARCH_BACKTRACKING = 2, LBFGS_LINESEARCH_BACKTRACKING_WOLFE = 2, LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 3 }
enum	HomogeneousKernelType { HomogeneousKernelIntersection = 0, HomogeneousKernelChi2, HomogeneousKernelJS }
	Type of kernel. More...
enum	HomogeneousKernelMapWindowType { HomogeneousKernelMapWindowUniform = 0, HomogeneousKernelMapWindowRectangular = 1 }
	Type of spectral windowing function. More...
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_SUMONE = 170, P_HOMOGENEOUSKERNELMAP = 180, P_PNORM = 190 }
enum	ELikelihoodModelType { LT_NONE = 0, LT_GAUSSIAN = 10, LT_STUDENTST = 20 }
enum	ETrainingType { PINV = 1, GS = 2 }
enum	ERegressionType { RT_NONE = 0, RT_LIGHT = 10, RT_LIBSVM = 20 }
	type of regressor More...
enum	LIBLINEAR_REGRESSION_TYPE { L2R_L2LOSS_SVR, L2R_L1LOSS_SVR_DUAL, L2R_L2LOSS_SVR_DUAL }
enum	EQuadraticMMDType { BIASED, UNBIASED }
enum	ENullApproximationMethod { BOOTSTRAP, MMD2_SPECTRUM, MMD2_GAMMA, MMD1_GAUSSIAN, HSIC_GAMMA }
enum	ESolver { BMRM = 1, PPBMRM = 2, P3BMRM = 3 }
enum	ETransformType { T_LINEAR, T_LOG, T_LOG_PLUS1, T_LOG_PLUS3, T_LINEAR_PLUS3 }
enum	EStateModelType { SMT_UNKNOWN = 0, SMT_TWO_STATE = 1 }
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 ()
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)
double	glomin (double a, double b, double c, double m, double e, double t, func_base &f, double &x)
double	local_min (double a, double b, double t, func_base &f, double &x)
double	local_min_rc (double &a, double &b, int &status, double value)
double	r8_abs (double x)
double	r8_epsilon ()
double	r8_max (double x, double y)
double	r8_sign (double x)
void	timestamp ()
double	zero (double a, double b, double t, func_base &f)
void	zero_rc (double a, double b, double t, double &arg, int &status, double value)
double	glomin (double a, double b, double c, double m, double e, double t, double f(double x), double &x)
double	local_min (double a, double b, double t, double f(double x), double &x)
double	zero (double a, double b, double t, double f(double x))
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 const float64_t *	get_col (uint32_t i)
static float64_t	get_time ()
ocas_return_value_T	svm_ocas_solver_nnw (float64_t C, uint32_t nData, uint32_t num_nnw, uint32_t nnw_idx, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, int(add_pw_constr)(uint32_t, uint32_t, void ), void(clip_neg_W)(uint32_t, uint32_t , void ), 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 (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)
template<class T >
void	push (v_array< T > &v, const T &new_ele)
template<class T >
void	alloc (v_array< T > &v, int length)
template<class T >
v_array< T >	pop (v_array< v_array< T > > &stack)
float	distance (CJLCoverTreePoint p1, CJLCoverTreePoint p2, float64_t upper_bound)
v_array< CJLCoverTreePoint >	parse_points (CDistance *distance, EFeaturesContainer fc)
void	print (CJLCoverTreePoint &p)
malsar_result_t	malsar_clustered (CDotFeatures features, double y, double rho1, double rho2, const malsar_options &options)
malsar_result_t	malsar_joint_feature_learning (CDotFeatures features, double y, double rho1, double rho2, const malsar_options &options)
malsar_result_t	malsar_low_rank (CDotFeatures features, double y, double rho, const malsar_options &options)
slep_result_t	slep_mc_plain_lr (CDotFeatures features, CMulticlassLabels labels, float64_t z, const slep_options &options)
slep_result_t	slep_mc_tree_lr (CDotFeatures features, CMulticlassLabels labels, float64_t z, const slep_options &options)
double	compute_regularizer (double *w, double lambda, double lambda2, int n_vecs, int n_feats, int n_blocks, const slep_options &options)
double	compute_lambda (double ATx, double z, CDotFeatures features, double *y, int n_vecs, int n_feats, int n_blocks, const slep_options &options)
void	projection (double w, double v, int n_feats, int n_blocks, double lambda, double lambda2, double L, double z, double z0, const slep_options &options)
double	search_point_gradient_and_objective (CDotFeatures features, double ATx, double As, double sc, double y, int n_vecs, int n_feats, int n_tasks, double g, double *gc, const slep_options &options)
slep_result_t	slep_solver (CDotFeatures features, double y, double z, const slep_options &options)
void	arpack_dsxupd (double matrix, double rhs, bool is_rhs_diag, int n, int nev, const char which, bool use_superlu, int mode, bool pos, bool cov, double shift, double tolerance, double eigenvalues, double *eigenvectors, int &status)
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)
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 int32_t	line_search_backtracking (int32_t n, float64_t x, float64_t f, float64_t g, float64_t s, float64_t stp, const float64_t xp, const float64_t gp, float64_t wa, callback_data_t cd, const lbfgs_parameter_t param)
static int32_t	line_search_backtracking_owlqn (int32_t n, float64_t x, float64_t f, float64_t g, float64_t s, float64_t stp, const float64_t xp, const float64_t gp, float64_t wp, callback_data_t cd, const lbfgs_parameter_t param)
static int32_t	line_search_morethuente (int32_t n, float64_t x, float64_t f, float64_t g, float64_t s, float64_t stp, const float64_t xp, const float64_t gp, float64_t wa, callback_data_t cd, const lbfgs_parameter_t param)
static int32_t	update_trial_interval (float64_t x, float64_t fx, float64_t dx, float64_t y, float64_t fy, float64_t dy, float64_t t, float64_t ft, float64_t dt, const float64_t tmin, const float64_t tmax, int32_t brackt)
static float64_t	owlqn_x1norm (const float64_t *x, const int32_t start, const int32_t n)
static void	owlqn_pseudo_gradient (float64_t pg, const float64_t x, const float64_t *g, const int32_t n, const float64_t c, const int32_t start, const int32_t end)
static void	owlqn_project (float64_t d, const float64_t sign, const int32_t start, const int32_t end)
void	lbfgs_parameter_init (lbfgs_parameter_t *param)
int32_t	lbfgs (int32_t n, float64_t x, float64_t ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void instance, lbfgs_parameter_t _param)
int	lbfgs (int n, float64_t x, float64_t ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void instance, lbfgs_parameter_t param)
void	add_cutting_plane (bmrm_ll *tail, bool map, float64_t A, uint32_t free_idx, float64_t cp_data, uint32_t dim)
void	remove_cutting_plane (bmrm_ll head, bmrm_ll tail, bool map, float64_t icp)
bmrm_return_value_T	svm_bmrm_solver (CStructuredModel model, float64_t W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)
float64_t *	get_cutting_plane (bmrm_ll *ptr)
uint32_t	find_free_idx (bool *map, uint32_t size)
bmrm_return_value_T	svm_p3bm_solver (CStructuredModel model, float64_t W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, uint32_t cp_models, bool verbose)
bmrm_return_value_T	svm_ppbm_solver (CStructuredModel model, float64_t W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)
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
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.
const int32_t	LOGSUM_TBL = 10000
uint32_t	Randnext
static const uint32_t	QPSolverMaxIter = 10000000
static float64_t *	H
static uint32_t	BufSize
static double *	H_diag_matrix
static int	H_diag_matrix_ld
static const float64_t	Q_test_statistic_values [10][8]
static const lbfgs_parameter_t	_defparam
static const float64_t	epsilon = 0.0
static float64_t *	H2

Detailed Description

all of classes and functions are contained in the shogun namespace

Typedef Documentation

typedef struct tag_callback_data callback_data_t

Definition at line 85 of file lbfgs.cpp.

typedef double DoubleOfDouble(double)

Definition at line 1464 of file brent.cpp.

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 26 of file DataType.h.

typedef struct tag_iteration_data iteration_data_t

Definition at line 93 of file lbfgs.cpp.

typedef float64_t KERNELCACHE_ELEM

kernel cache element

Definition at line 32 of file Kernel.h.

typedef int64_t KERNELCACHE_IDX

kernel cache index

Definition at line 43 of file Kernel.h.

typedef int32_t(* line_search_proc)(int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param)

Definition at line 104 of file lbfgs.cpp.

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 uint16_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 20 of file Alphabet.h.

enum EContingencyTableMeasureType

type of measure

Enumerator:

ACCURACY
ERROR_RATE
BAL
WRACC
F1
CROSS_CORRELATION
RECALL
PRECISION
SPECIFICITY
CUSTOM

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_SPARSEEUCLIDEAN
D_EUCLIDEAN
D_CHISQUARE
D_TANIMOTO
D_COSINE
D_BRAYCURTIS
D_CUSTOM
D_ATTENUATEDEUCLIDEAN
D_MAHALANOBIS
D_DIRECTOR

Definition at line 31 of file Distance.h.

enum EEvaluationDirection

enum which used to define whether an evaluation measure has to be minimized or maximized

Enumerator:

ED_MINIMIZE
ED_MAXIMIZE

Definition at line 24 of file Evaluation.h.

enum EEvaluationResultType

Type of evaluation result. Currently this includes Cross Validation and Gradient Evaluation

Enumerator:

CROSSVALIDATION_RESULT
GRADIENTEVALUATION_RESULT

Definition at line 23 of file EvaluationResult.h.

enum EFeatureClass

shogun feature class

Enumerator:

C_UNKNOWN
C_DENSE
C_SPARSE
C_STRING
C_COMBINED
C_COMBINED_DOT
C_WD
C_SPEC
C_WEIGHTEDSPEC
C_POLY
C_STREAMING_DENSE
C_STREAMING_SPARSE
C_STREAMING_STRING
C_STREAMING_VW
C_BINNED_DOT
C_DIRECTOR_DOT
C_LATENT
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 58 of file FeatureTypes.h.

enum EFeaturesContainer

Type used to indicate where to find (either lhs or rhs) the coordinate information of this point in the CDistance object associated

Enumerator:

FC_LHS
FC_RHS

Definition at line 107 of file JLCoverTreePoint.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 118 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
K_JENSENSHANNON
K_DIRECTOR
K_PRODUCT
K_LINEARARD
K_GAUSSIANARD
K_STREAMING

Definition at line 54 of file Kernel.h.

enum ELikelihoodModelType

Type of likelihood model

Enumerator:

LT_NONE
LT_GAUSSIAN
LT_STUDENTST

Definition at line 22 of file LikelihoodModel.h.

enum ELossType

shogun loss type

Enumerator:

L_HINGELOSS
L_SMOOTHHINGELOSS
L_SQUAREDHINGELOSS
L_SQUAREDLOSS
L_LOGLOSS
L_LOGLOSSMARGIN

Definition at line 27 of file LossFunction.h.

enum EMachineType

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_KERNELRIDGEREGRESSION
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
CT_CONJUGATEINDEX
CT_LINEARRIDGEREGRESSION
CT_LEASTSQUARESREGRESSION
CT_QDA
CT_NEWTONSVM
CT_GAUSSIANPROCESSREGRESSION
CT_LARS
CT_MULTICLASS
CT_DIRECTORLINEAR
CT_DIRECTORKERNEL

Definition at line 33 of file Machine.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 72 of file SGObject.h.

enum EMSParamType

value type of a model selection parameter node

Enumerator:

MSPT_NONE	no type
MSPT_FLOAT64
MSPT_INT32
MSPT_FLOAT64_VECTOR
MSPT_INT32_VECTOR
MSPT_FLOAT64_SGVECTOR
MSPT_INT32_SGVECTOR

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 ENullApproximationMethod

enum for different method to approximate null-distibution

Enumerator:

BOOTSTRAP
MMD2_SPECTRUM
MMD2_GAMMA
MMD1_GAUSSIAN
HSIC_GAMMA

Definition at line 19 of file TestStatistic.h.

enum EOptimizationType

optimization type

Enumerator:

FASTBUTMEMHUNGRY
SLOWBUTMEMEFFICIENT

Definition at line 47 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 used by Shogun UI

Note to developer: any new preprocessor should be added here.

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_SUMONE
P_HOMOGENEOUSKERNELMAP
P_PNORM

Definition at line 30 of file Preprocessor.h.

enum EProblemType

problem type

Enumerator:

PT_BINARY
PT_REGRESSION
PT_MULTICLASS
PT_STRUCTURED
PT_LATENT

Definition at line 101 of file Machine.h.

enum EQuadraticMMDType

Enum to select which statistic type of quadratic time MMD should be computed

Enumerator:

BIASED
UNBIASED

Definition at line 22 of file QuadraticTimeMMD.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 ESolver

Enum Training method selection

Enumerator:

BMRM	Standard BMRM algorithm.
PPBMRM	Proximal Point BMRM (BMRM with prox-term)
P3BMRM	Proximal Point P-BMRM (multiple cutting plane models)

Definition at line 25 of file DualLibQPBMSOSVM.h.

enum ESolverType

solver type

Enumerator:

ST_AUTO
ST_CPLEX
ST_GLPK
ST_NEWTON
ST_DIRECT
ST_ELASTICNET
ST_BLOCK_NORM

Definition at line 89 of file Machine.h.

enum ESPEStrategy

Stochastic Proximity Embedding (SPE) strategy

Enumerator:

SPE_GLOBAL
SPE_LOCAL

Definition at line 23 of file StochasticProximityEmbedding.h.

enum EStateModelType

state model type

Enumerator:

SMT_UNKNOWN
SMT_TWO_STATE

Definition at line 18 of file StateModelTypes.h.

enum EStructuredDataType

structured data type

Enumerator:

SDT_UNKNOWN
SDT_REAL
SDT_SEQUENCE

Definition at line 17 of file StructuredDataTypes.h.

enum ETrainingType

which training method to use for KRR

Enumerator:

PINV	via pseudo inverse
GS	or gauss-seidel iterative method

Definition at line 26 of file KernelRidgeRegression.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 HomogeneousKernelMapWindowType

Type of spectral windowing function.

Enumerator:

HomogeneousKernelMapWindowUniform	uniform window
HomogeneousKernelMapWindowRectangular	rectangular window

Definition at line 30 of file HomogeneousKernelMap.h.

enum HomogeneousKernelType

Type of kernel.

Enumerator:

HomogeneousKernelIntersection	intersection kernel
HomogeneousKernelChi2	Chi2 kernel
HomogeneousKernelJS	Jensen-Shannon kernel

Definition at line 23 of file HomogeneousKernelMap.h.

enum LIBLINEAR_REGRESSION_TYPE

liblinar regression solver type

Enumerator:

L2R_L2LOSS_SVR	L2 regularized support vector regression with L2 epsilon tube loss.
L2R_L1LOSS_SVR_DUAL	L2 regularized support vector regression with L1 epsilon tube loss.
L2R_L2LOSS_SVR_DUAL	L2 regularized support vector regression with L2 epsilon tube loss (dual).

Definition at line 22 of file LibLinearRegression.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.
L1R_L2LOSS_SVC	L1 regularized SVM with L2-loss using dual coordinate descent.
L1R_LR	L1 regularized logistic regression.
L2R_LR_DUAL	L2 regularized linear logistic regression via dual.

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

void add_cutting_plane	(	bmrm_ll **	tail,
		bool *	map,
		float64_t *	A,
		uint32_t	free_idx,
		float64_t *	cp_data,
		uint32_t	dim
	)

Add cutting plane

Parameters:

	tail	Pointer to the last CP entry
	map	Pointer to map storing info about CP physical memory
	A	CP physical memory
	free_idx	Index to physical memory where the CP data will be stored
	cp_data	CP data
	dim	Dimension of CP data

void shogun::alloc	(	v_array< T > &	v,
		int	length
	)

Used to modify the capacity of the vector

Parameters:

	v	vector
	length	the new length of the vector

Definition at line 78 of file JLCoverTreePoint.h.

void shogun::arpack_dsxupd	(	double *	matrix,
		double *	rhs,
		bool	is_rhs_diag,
		int	n,
		int	nev,
		const char *	which,
		bool	use_superlu,
		int	mode,
		bool	pos,
		bool	cov,
		double	shift,
		double	tolerance,
		double *	eigenvalues,
		double *	eigenvectors,
		int &	status
	)

Wrapper for ARPACK's dsaupd/dseupd routines. These ARPACK routines are being used to compute specified number of eigenpairs (e.g. k largest eigenvalues). Underlying routines involve a variant of Arnoldi process called the IRAM (Implicitly Restarted Arnoldi Method) reduced to IRLM (Implicitly Restarted Lanczos Method). A strategy specifying which eigenpairs to compute should be provided as parameter.

Parameters:

	matrix	symmetric real matrix of size n*n (will be modified if mode==3)
	rhs	if is_rhs_diag is true - array of size n representing right hand side diagonal matrix, else array of size n*n representing right hand side full matrix should be NULL if non-general eigenproblem to be solved
	is_rhs_diag	true if rhs is diagonal and represented by array of size N
	n	size of matrix
	nev	number of eigenpairs to compute (nev<=n)
	which	eigenvalue finding strategy. Possible values: "LM": nev Largest Magnitude eigenvalues "SM": nev Smallest Magnitude eigenvalues "LA": nev Largest Algebraic eigenvalues (if mode==3 eigenvalues from the right of shift) "SA": nev Smallest Algebraic eigenvalues (if mode==3 eigenvalues from the left of shift) "BE": half of nev from each end of the spectrum, i.e. nev2 smallest and nev2 largest eigenvalues. If nev is odd, one more largest eigenvalue will be computed
	use_superlu	if superlu should be used (works efficiently only for sparse matrices)
	mode	shift-mode of IRLM. Possible values: 1: regular mode 3: shift-invert mode
	pos	true if matrix is positive definite (Cholesky factorization is used in this case instead of LUP factorization))
	cov	true whether matrix A should be considered as A^T A
	shift	shift for shift-invert (3) mode of IRLM. In this mode routine will compute eigenvalues near provided shift
	tolerance	tolerance with eigenvalues should be computed (zero means machine precision)
	eigenvalues	array of size nev to hold computed eigenvalues
	eigenvectors	array of size nev*n to hold computed eigenvectors
	status	on output -1 if computation failed

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 472 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 162 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 149 of file pr_loqo.cpp.

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

Definition at line 54 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 125 of file pr_loqo.cpp.

double shogun::compute_lambda	(	double *	ATx,
		double	z,
		CDotFeatures *	features,
		double *	y,
		int	n_vecs,
		int	n_feats,
		int	n_blocks,
		const slep_options &	options
	)

Definition at line 106 of file slep_solver.cpp.

double shogun::compute_regularizer	(	double *	w,
		double	lambda,
		double	lambda2,
		int	n_vecs,
		int	n_feats,
		int	n_blocks,
		const slep_options &	options
	)

Definition at line 23 of file slep_solver.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 223 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.

float shogun::distance	(	CJLCoverTreePoint	p1,
		CJLCoverTreePoint	p2,
		float64_t	upper_bound
	)

Functions declared out of the class definition to respect JLCoverTree structure

Call m_distance->distance() with the proper index order depending on the feature containers in m_distance for each of the points

Definition at line 138 of file JLCoverTreePoint.h.

float64_t shogun::double_of_substring ( substring s )

Return value of substring as double

Parameters:

s

substring

Returns:: substring as double

Definition at line 512 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

uint32_t shogun::find_free_idx	(	bool *	map,
		uint32_t	size
	)

Get index of free slot for new cutting plane

Parameters:

	map	Pointer to map storing info about CP physical memory
	size	Size of the CP buffer

Returns:: Index of unoccupied memory field in CP physical memory

Definition at line 145 of file libbmrm.h.

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 1411 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 497 of file SGIO.h.

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

Definition at line 33 of file libppbm.cpp.

float64_t* shogun::get_cutting_plane ( bmrm_ll * ptr )

Get cutting plane

Parameters:

ptr

Pointer to some CP entry

Returns:: Pointer to cutting plane at given entry

Definition at line 137 of file libbmrm.h.

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 47 of file libocas.cpp.

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

Definition at line 1097 of file ssl.cpp.

double shogun::glomin	(	double	a,
		double	b,
		double	c,
		double	m,
		double	e,
		double	t,
		func_base &	f,
		double &	x
	)

Definition at line 13 of file brent.cpp.

double shogun::glomin	(	double	a,
		double	b,
		double	c,
		double	m,
		double	e,
		double	t,
		double	fdouble x,
		double &	x
	)

Definition at line 1479 of file brent.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_int *	A,
		int32_t	k
	)

Definition at line 1087 of file ssl.cpp.

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

Definition at line 1077 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 527 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 65 of file LaRank.cpp.

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

Definition at line 133 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 324 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 356 of file LaRank.cpp.

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

Definition at line 197 of file LaRank.cpp.

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

Definition at line 333 of file LaRank.cpp.

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

Definition at line 125 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 290 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 284 of file LaRank.cpp.

int32_t shogun::lbfgs	(	int32_t	n,
		float64_t *	x,
		float64_t *	ptr_fx,
		lbfgs_evaluate_t	proc_evaluate,
		lbfgs_progress_t	proc_progress,
		void *	instance,
		lbfgs_parameter_t *	_param
	)

Definition at line 204 of file lbfgs.cpp.

libqp_state_T 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

Definition at line 73 of file libqp_gsmo.cpp.

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.

static int32_t line_search_backtracking	(	int32_t	n,
		float64_t *	x,
		float64_t *	f,
		float64_t *	g,
		float64_t *	s,
		float64_t *	stp,
		const float64_t *	xp,
		const float64_t *	gp,
		float64_t *	wa,
		callback_data_t *	cd,
		const lbfgs_parameter_t *	param
	)			`[static]`

Definition at line 595 of file lbfgs.cpp.

static int32_t line_search_backtracking_owlqn	(	int32_t	n,
		float64_t *	x,
		float64_t *	f,
		float64_t *	g,
		float64_t *	s,
		float64_t *	stp,
		const float64_t *	xp,
		const float64_t *	gp,
		float64_t *	wp,
		callback_data_t *	cd,
		const lbfgs_parameter_t *	param
	)			`[static]`

Definition at line 688 of file lbfgs.cpp.

static int32_t line_search_morethuente	(	int32_t	n,
		float64_t *	x,
		float64_t *	f,
		float64_t *	g,
		float64_t *	s,
		float64_t *	stp,
		const float64_t *	xp,
		const float64_t *	gp,
		float64_t *	wa,
		callback_data_t *	cd,
		const lbfgs_parameter_t *	param
	)			`[static]`

Definition at line 762 of file lbfgs.cpp.

double shogun::local_min	(	double	a,
		double	b,
		double	t,
		func_base &	f,
		double &	x
	)

Definition at line 296 of file brent.cpp.

double shogun::local_min	(	double	a,
		double	b,
		double	t,
		double	fdouble x,
		double &	x
	)

Definition at line 1487 of file brent.cpp.

double shogun::local_min_rc	(	double &	a,
		double &	b,
		int &	status,
		double	value
	)

Definition at line 538 of file brent.cpp.

malsar_result_t malsar_clustered	(	CDotFeatures *	features,
		double *	y,
		double	rho1,
		double	rho2,
		const malsar_options &	options
	)

malsar_result_t malsar_joint_feature_learning	(	CDotFeatures *	features,
		double *	y,
		double	rho1,
		double	rho2,
		const malsar_options &	options
	)

malsar_result_t malsar_low_rank	(	CDotFeatures *	features,
		double *	y,
		double	rho,
		const malsar_options &	options
	)

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

Definition at line 263 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 1468 of file libocas.cpp.

CSGObject * new_sgserializable	(	const char *	sgserializable_name,
		EPrimitiveType	generic
	)

new shogun serializable

Parameters:

	sgserializable_name
	generic

Definition at line 1609 of file class_list.cpp.

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 38 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.

static void owlqn_project	(	float64_t *	d,
		const float64_t *	sign,
		const int32_t	start,
		const int32_t	end
	)			`[static]`

Definition at line 1309 of file lbfgs.cpp.

static void owlqn_pseudo_gradient	(	float64_t *	pg,
		const float64_t *	x,
		const float64_t *	g,
		const int32_t	n,
		const float64_t	c,
		const int32_t	start,
		const int32_t	end
	)			`[static]`

Definition at line 1266 of file lbfgs.cpp.

static float64_t owlqn_x1norm	(	const float64_t *	x,
		const int32_t	start,
		const int32_t	n
	)			`[static]`

Definition at line 1250 of file lbfgs.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.

v_array< CJLCoverTreePoint > shogun::parse_points	(	CDistance *	distance,
		EFeaturesContainer	fc
	)

Fills up a v_array of CJLCoverTreePoint objects

Definition at line 183 of file JLCoverTreePoint.h.

v_array<T> shogun::pop ( v_array< v_array< T > > & stack )

Returns the vector previous to the pointed one in the stack of vectors and decrements the index of the stack. No memory is freed here. If there are no vectors stored in the stack, create and return a new empty vector

Parameters:

stack

of vectors

Returns:: the adequate vector according to the previous conditions

Definition at line 94 of file JLCoverTreePoint.h.

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 ( CJLCoverTreePoint & p )

Print the information of the CoverTree point

Definition at line 207 of file JLCoverTreePoint.h.

void shogun::print_substring ( substring s )

Print the substring

Parameters:

s

substring

Definition at line 484 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.

void shogun::projection	(	double *	w,
		double *	v,
		int	n_feats,
		int	n_blocks,
		double	lambda,
		double	lambda2,
		double	L,
		double *	z,
		double *	z0,
		const slep_options &	options
	)

Definition at line 277 of file slep_solver.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.

void shogun::push	(	v_array< T > &	v,
		const T &	new_ele
	)

Insert a new element at the end of the vector

Parameters:

	v	vector
	new_ele	element to insert

Definition at line 61 of file JLCoverTreePoint.h.

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

Definition at line 992 of file gpm.cpp.

double shogun::r8_abs ( double x )

Definition at line 834 of file brent.cpp.

double shogun::r8_epsilon ( )

Definition at line 875 of file brent.cpp.

double shogun::r8_max	(	double	x,
		double	y
	)

Definition at line 921 of file brent.cpp.

double shogun::r8_sign ( double x )

Definition at line 962 of file brent.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.

void remove_cutting_plane	(	bmrm_ll **	head,
		bmrm_ll **	tail,
		bool *	map,
		float64_t *	icp
	)

Remove cutting plane at given index

Parameters:

	head	Pointer to the first CP entry
	tail	Pointer to the last CP entry
	map	Pointer to map storing info about CP physical memory
	icp	Pointer to inactive CP that should be removed

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

double shogun::search_point_gradient_and_objective	(	CDotFeatures *	features,
		double *	ATx,
		double *	As,
		double *	sc,
		double *	y,
		int	n_vecs,
		int	n_feats,
		int	n_tasks,
		double *	g,
		double *	gc,
		const slep_options &	options
	)

Definition at line 313 of file slep_solver.cpp.

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.

slep_result_t slep_mc_plain_lr	(	CDotFeatures *	features,
		CMulticlassLabels *	labels,
		float64_t	z,
		const slep_options &	options
	)

Accelerated projected gradient solver for multiclass logistic regression problem with feature tree regularization.

Parameters:

	features	features to be used
	labels	labels to be used
	z	regularization ratio
	options	options of solver

(n_vecs*n_classes);

slep_result_t slep_mc_tree_lr	(	CDotFeatures *	features,
		CMulticlassLabels *	labels,
		float64_t	z,
		const slep_options &	options
	)

Accelerated projected gradient solver for multiclass logistic regression problem with feature tree regularization.

Parameters:

	features	features to be used
	labels	labels to be used
	z	regularization ratio
	options	options of solver

(n_vecs*n_classes);

slep_result_t slep_solver	(	CDotFeatures *	features,
		double *	y,
		double	z,
		const slep_options &	options
	)

Learning optimization task solver ported from the SLEP (Sparse LEarning Package) library.

Based on accelerated projected gradient method.

Supports two types of losses: logistic and least squares.

Supports multitask problems (task group [MULTITASK_GROUP] and task tree [MULTITASK_TREE] relations), problems with feature relations (feature group [FEATURE_GROUP] and feature tree [FEATURE_TREE]), basic regularized problems [PLAIN] and fused formulation.

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 200 of file pr_loqo.cpp.

void* shogun::sqdist_thread_func ( void * P )

Definition at line 144 of file KMeans.cpp.

uint32_t shogun::ss_length ( substring s )

Length of substring

Parameters:

s

substring

Returns:: length of substring

Definition at line 551 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.

bmrm_return_value_T svm_bmrm_solver	(	CStructuredModel *	model,
		float64_t *	W,
		float64_t	TolRel,
		float64_t	TolAbs,
		float64_t	_lambda,
		uint32_t	_BufSize,
		bool	cleanICP,
		uint32_t	cleanAfter,
		float64_t	K,
		uint32_t	Tmax,
		bool	verbose
	)

Standard BMRM Solver for Structured Output Learning

Parameters:

	model	Pointer to user defined CStructuredModel
	W	Weight vector
	TolRel	Relative tolerance
	TolAbs	Absolute tolerance
	_lambda	Regularization constant
	_BufSize	Size of the CP buffer (i.e. maximal number of iterations)
	cleanICP	Flag that enables/disables inactive cutting plane removal feature
	cleanAfter	Number of iterations that should be cutting plane inactive for to be removed
	K	Parameter K
	Tmax	Parameter Tmax
	verbose	Flag that enables/disables screen output

Returns:: Structure with BMRM algorithm result

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 530 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 959 of file libocas.cpp.

ocas_return_value_T shogun::svm_ocas_solver_nnw	(	float64_t	C,
		uint32_t	nData,
		uint32_t	num_nnw,
		uint32_t *	nnw_idx,
		float64_t	TolRel,
		float64_t	TolAbs,
		float64_t	QPBound,
		float64_t	MaxTime,
		uint32_t	_BufSize,
		uint8_t	Method,
		int()(uint32_t, uint32_t, void )	add_pw_constr,
		void()(uint32_t, uint32_t , void *)	clip_neg_W,
		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 63 of file libocas.cpp.

bmrm_return_value_T svm_p3bm_solver	(	CStructuredModel *	model,
		float64_t *	W,
		float64_t	TolRel,
		float64_t	TolAbs,
		float64_t	_lambda,
		uint32_t	_BufSize,
		bool	cleanICP,
		uint32_t	cleanAfter,
		float64_t	K,
		uint32_t	Tmax,
		uint32_t	cp_models,
		bool	verbose
	)

Proximal Point P-BMRM (multiple cutting plane models) Solver for Structured Output Learning

Parameters:

	model	Pointer to user defined CStructuredModel
	W	Weight vector
	TolRel	Relative tolerance
	TolAbs	Absolute tolerance
	_lambda	Regularization constant
	_BufSize	Size of the CP buffer (i.e. maximal number of iterations)
	cleanICP	Flag that enables/disables inactive cutting plane removal feature
	cleanAfter	Number of iterations that should be cutting plane inactive for to be removed
	K	Parameter K
	Tmax	Parameter Tmax
	cp_models	Count of cutting plane models to be used
	verbose	Flag that enables/disables screen output

Returns:: Structure with BMRM algorithm result

bmrm_return_value_T svm_ppbm_solver	(	CStructuredModel *	model,
		float64_t *	W,
		float64_t	TolRel,
		float64_t	TolAbs,
		float64_t	_lambda,
		uint32_t	_BufSize,
		bool	cleanICP,
		uint32_t	cleanAfter,
		float64_t	K,
		uint32_t	Tmax,
		bool	verbose
	)

Proximal Point BMRM Solver for Structured Output Learning

Parameters:

	model	Pointer to user defined CStructuredModel
	W	Weight vector
	TolRel	Relative tolerance
	TolAbs	Absolute tolerance
	_lambda	Regularization constant
	_BufSize	Size of the CP buffer (i.e. maximal number of iterations)
	cleanICP	Flag that enables/disables inactive cutting plane removal feature
	cleanAfter	Number of iterations that should be cutting plane inactive for to be removed
	K	Parameter K
	Tmax	Parameter Tmax
	verbose	Flag that enables/disables screen output

Returns:: Structure with BMRM algorithm result

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.

void shogun::timestamp ( )

Definition at line 1003 of file brent.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 541 of file SGIO.h.

static int32_t update_trial_interval	(	float64_t *	x,
		float64_t *	fx,
		float64_t *	dx,
		float64_t *	y,
		float64_t *	fy,
		float64_t *	dy,
		float64_t *	t,
		float64_t *	ft,
		float64_t *	dt,
		const float64_t	tmin,
		const float64_t	tmax,
		int32_t *	brackt
	)			`[static]`

Update a safeguarded trial value and interval for line search.

The parameter x represents the step with the least function value. The parameter t represents the current step. This function assumes that the derivative at the point of x in the direction of the step. If the bracket is set to true, the minimizer has been bracketed in an interval of uncertainty with endpoints between x and y.

Parameters:

	x	The pointer to the value of one endpoint.
	fx	The pointer to the value of f(x).
	dx	The pointer to the value of f'(x).
	y	The pointer to the value of another endpoint.
	fy	The pointer to the value of f(y).
	dy	The pointer to the value of f'(y).
	t	The pointer to the value of the trial value, t.
	ft	The pointer to the value of f(t).
	dt	The pointer to the value of f'(t).
	tmin	The minimum value for the trial value, t.
	tmax	The maximum value for the trial value, t.
	brackt	The pointer to the predicate if the trial value is bracketed.

Return values:

int32_t

Status value. Zero indicates a normal termination.

See also:: Jorge J. More and David J. Thuente. Line search algorithm with guaranteed sufficient decrease. ACM Transactions on Mathematical Software (TOMS), Vol 20, No 3, pp. 286-307, 1994.

Definition at line 1077 of file lbfgs.cpp.

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 250 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 232 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 339 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 203 of file LaRank.cpp.

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

Definition at line 166 of file LaRank.cpp.

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

Definition at line 111 of file LaRank.cpp.

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

Definition at line 296 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 222 of file LaRank.cpp.

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

Definition at line 85 of file LaRank.cpp.

double shogun::zero	(	double	a,
		double	b,
		double	t,
		double	fdouble x
	)

Definition at line 1495 of file brent.cpp.

double shogun::zero	(	double	a,
		double	b,
		double	t,
		func_base &	f
	)

Definition at line 1050 of file brent.cpp.

void shogun::zero_rc	(	double	a,
		double	b,
		double	t,
		double &	arg,
		int &	status,
		double	value
	)

Definition at line 1229 of file brent.cpp.

Variable Documentation

const lbfgs_parameter_t _defparam [static]

Initial value:

 {
    6, 1e-5, 0, 1e-5,
    0, LBFGS_LINESEARCH_DEFAULT, 40,
    1e-20, 1e20, 1e-4, 0.9, 0.9, 1.0e-16,
    0.0, 0, -1,
}

Definition at line 95 of file lbfgs.cpp.

static uint32_t BufSize [static]

Definition at line 34 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.

static const float64_t epsilon = 0.0 [static]

Definition at line 25 of file libbmrm.cpp.

static float64_t * H [static]

Definition at line 33 of file libocas.cpp.

static float64_t * H2 [static]

Definition at line 27 of file libp3bm.cpp.

double* H_diag_matrix [static]

Definition at line 24 of file malsar_clustered.cpp.

int H_diag_matrix_ld [static]

Definition at line 25 of file malsar_clustered.cpp.

const uint32_t hash_base = 97562527

Seed for hash.

Definition at line 33 of file vw_constants.h.

const int32_t LOGSUM_TBL = 10000

span of the logsum table

Definition at line 21 of file LocalAlignmentStringKernel.h.

const float64_t Q_test_statistic_values[10][8] [static]

Initial value:

 
{
    
    {0.713,0.683,0.637,0.597,0.551,0.477,0.409,0.325},
    {0.627,0.604,0.568,0.538,0.503,0.450,0.401,0.339},
    {0.539,0.517,0.484,0.456,0.425,0.376,0.332,0.278},
    {0.490,0.469,0.438,0.412,0.382,0.337,0.295,0.246},
    {0.460,0.439,0.410,0.384,0.355,0.312,0.272,0.226},
    {0.437,0.417,0.388,0.363,0.336,0.294,0.256,0.211},
    {0.422,0.403,0.374,0.349,0.321,0.280,0.244,0.201},
    {0.408,0.389,0.360,0.337,0.310,0.270,0.234,0.192},
    {0.397,0.377,0.350,0.326,0.300,0.261,0.226,0.185},
    {0.387,0.368,0.341,0.317,0.292,0.253,0.219,0.179}
}

Definition at line 78 of file RejectionStrategy.h.

static const uint32_t QPSolverMaxIter = 10000000 [static]

Definition at line 31 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.

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