shogun Namespace Reference

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

Namespaces
	detail
	linalg

Classes
struct	_IterInfo
	struct that contains current state of the iteration for iterative linear solvers More...
class	AdaDeltaUpdater
	The class implements the AdaDelta method. \[ \begin{array}{l} g_\theta=(1-\lambda){(\frac{ \partial f(\cdot) }{\partial \theta })}^2+\lambda g_\theta\\ d_\theta=\alpha\frac{\sqrt{s_\theta+\epsilon}}{\sqrt{g_\theta+\epsilon}}\frac{ \partial f(\cdot) }{\partial \theta }\\ s_\theta=(1-\lambda){(d_\theta)}^2+\lambda s_\theta \end{array} \] . More...
class	AdaGradUpdater
	The class implements the AdaGrad method. More...
class	AdamUpdater
	The class implements the Adam method. More...
class	AdaptMomentumCorrection
	This implements the adaptive momentum correction method. More...
class	Any
	Allows to store objects of arbitrary types by using a BaseAnyPolicy and provides a type agnostic API. See its usage in CSGObject::Self, CSGObject::set(), CSGObject::get() and CSGObject::has(). More...
class	BaseAnyPolicy
	An interface for a policy to store a value. Value can be any data like primitive data-types, shogun objects, etc. Policy defines how to handle this data. It works with a provided memory region and is able to set value, clear it and return the type-name as string. More...
class	BaseTag
	Base class for all tags. This class stores name and not the type information for a shogun object. It can be used as an identifier for a shogun object where type information is not known. One application of this can be found in CSGObject::set_param_with_btag(). More...
struct	C45TreeNodeData
	structure to store data of a node of C4.5 tree. This can be used as a template type in TreeMachineNode class. Ex: C4.5 algorithm uses nodes of type CTreeMachineNode<C45TreeNodeData> More...
class	CAbsoluteDeviationLoss
	CAbsoluteDeviationLoss implements the absolute deviation loss function. \(L(y_i,f(x_i)) = \mod{y_i-f(x_i)}\). More...
class	CAccuracyMeasure
	class AccuracyMeasure used to measure accuracy of 2-class classifier. More...
class	CAlphabet
	The class Alphabet implements an alphabet and alphabet utility functions. More...
class	CANOVAKernel
	ANOVA (ANalysis Of VAriances) kernel. More...
class	CApproxJointDiagonalizer
	Class ApproxJointDiagonalizer defines an Approximate Joint Diagonalizer (AJD) interface. More...
struct	CARTreeNodeData
	structure to store data of a node of CART. This can be used as a template type in TreeMachineNode class. CART algorithm uses nodes of type CTreeMachineNode<CARTreeNodeData> More...
class	CAttenuatedEuclideanDistance
	class AttenuatedEuclideanDistance More...
class	CAttributeFeatures
	Implements attributed features, that is in the simplest case a number of (attribute, value) pairs. 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	CAutoencoder
	Represents a single layer neural autoencoder. More...
class	CAveragedPerceptron
	Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron. 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	CBaggingMachine
	: Bagging algorithm i.e. bootstrap aggregating More...
class	CBAHSIC
	Class CBAHSIC, that extends CKernelDependenceMaximization and uses HSIC [1] to compute dependence measures for feature selection using a backward elimination approach as described in [1]. This class serves as a convenience class that initializes the CDependenceMaximization::m_estimator with an instance of CHSIC and allows only shogun::BACKWARD_ELIMINATION algorithm to use which is set internally. Therefore, trying to use other algorithms by set_algorithm() will not work. Plese see the class documentation of CHSIC and [2] for more details on mathematical description of HSIC. More...
class	CBalancedConditionalProbabilityTree
class	CBallTree
	This class implements Ball tree. The ball tree is contructed using the top-down approach. cf. ftp://ftp.icsi.berkeley.edu/pub/techreports/1989/tr-89-063.pdf. More...
class	CBALMeasure
	class BALMeasure used to measure balanced error of 2-class classifier. More...
class	CBaseMulticlassMachine
class	CBesselKernel
	the class Bessel kernel More...
class	CBinaryClassEvaluation
	The class TwoClassEvaluation, a base class used to evaluate binary classification labels. More...
class	CBinaryFile
	A Binary file access class. More...
class	CBinaryLabels
	Binary Labels for binary classification. More...
class	CBinaryStream
	memory mapped emulation via binary streams (files) More...
class	CBinaryTreeMachineNode
	The node of the tree structure forming a TreeMachine The node contains pointer to its parent and pointers to its 2 children: left child and right child. The node also contains data which can be of any type and has to be specified using template specifier. More...
class	CBinnedDotFeatures
	The class BinnedDotFeatures contains a 0-1 conversion of features into bins. More...
class	CBitString
	a string class embedding a string in a compact bit representation More...
class	CBrayCurtisDistance
	class Bray-Curtis distance More...
class	CC45ClassifierTree
	Class C45ClassifierTree implements the C4.5 algorithm for decision tree learning. The algorithm steps are briefy explained below : . More...
class	CCache
	Template class Cache implements a simple cache. More...
class	CCanberraMetric
	class CanberraMetric More...
class	CCanberraWordDistance
	class CanberraWordDistance More...
class	CCARTree
	This class implements the Classification And Regression Trees algorithm by Breiman et al for decision tree learning. A CART tree is a binary decision tree that is constructed by splitting a node into two child nodes repeatedly, beginning with the root node that contains the whole dataset. TREE GROWING PROCESS : During the tree growing process, we recursively split a node into left child and right child so that the resulting nodes are "purest". We do this until any of the stopping criteria is met. To find the best split, we scan through all possible splits in all predictive attributes. The best split is one that maximises some splitting criterion. For classification tasks, ie. when the dependent attribute is categorical, the Gini index is used. For regression tasks, ie. when the dependent variable is continuous, least squares deviation is used. The algorithm uses two stopping criteria : if node becomes completely "pure", ie. all its members have identical dependent variable, or all of them have identical predictive attributes (independent variables). . More...
class	CCauchyKernel
	Cauchy kernel. More...
class	CCCSOSVM
	CCSOSVM. More...
class	CCGMShiftedFamilySolver
	class that uses conjugate gradient method for solving a shifted linear system family where the linear opeator is real valued and symmetric positive definite, the vector is real valued, but the shifts are complex More...
class	CCHAIDTree
	This class implements the CHAID algorithm proposed by Kass (1980) for decision tree learning. CHAID consists of three steps: merging, splitting and stopping. A tree is grown by repeatedly using these three steps on each node starting from the root node. CHAID accepts nominal or ordinal categorical predictors only. If predictors are continuous, they have to be transformed into ordinal predictors before tree growing. CONVERTING CONTINUOUS PREDICTORS TO ORDINAL : Continuous predictors are converted to ordinal by binning. The number of bins (K) has to be supplied by the user. Given K, a predictor is split in such a way that all the bins get the same number (more or less) of distinct predictor values. The maximum feature value in each bin is used as a breakpoint. MERGING : During the merging step, allowable pairs of categories of a predictor are evaluated for similarity. If the similarity of a pair is above a threshold, the categories constituting the pair are merged into a single category. The process is repeated until there is no pair left having high similarity between its categories. Similarity between categories is evaluated using the p_value SPLITTING : The splitting step selects which predictor to be used to best split the node. Selection is accomplished by comparing the adjusted p_value associated with each predictor. The predictor that has the smallest adjusted p_value is chosen for splitting the node. STOPPING : The tree growing process stops if any of the following conditions is satisfied : . More...
class	CChebyshewMetric
	class ChebyshewMetric More...
class	CChi2Kernel
	The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms. More...
class	CChiSquareDistance
	class ChiSquareDistance More...
class	CCircularBuffer
	Implementation of circular buffer This buffer has logical structure such as queue (FIFO). But this queue is cyclic: tape, ends of which are connected, just instead tape there is block of physical memory. So, if you push big block of data it can be situated both at the end and the begin of buffer's memory. More...
class	CCircularKernel
	Circular kernel. More...
class	CClusteringAccuracy
	clustering accuracy More...
class	CClusteringEvaluation
	The base class used to evaluate clustering. More...
class	CClusteringMutualInformation
	clustering (normalized) mutual information More...
class	CCombinationRule
	CombinationRule abstract class The CombinationRule defines an interface to how to combine the classification or regression outputs of an ensemble of Machines. 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	CCombinedKernel
	The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination. More...
class	CCommUlongStringKernel
	The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers. More...
class	CCommWordStringKernel
	The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers. More...
class	CCompressor
	Compression library for compressing and decompressing buffers using one of the standard compression algorithms: More...
class	CConditionalProbabilityTree
class	CConjugateGradientSolver
	class that uses conjugate gradient method of solving a linear system involving a real valued linear operator and vector. Useful for large sparse systems involving sparse symmetric and positive-definite matrices. More...
class	CConjugateOrthogonalCGSolver
	class that uses conjugate orthogonal conjugate gradient method of solving a linear system involving a complex valued linear operator and vector. Useful for large sparse systems involving sparse symmetric matrices that are not Herimitian. More...
class	CConstKernel
	The Constant Kernel returns a constant for all elements. More...
class	CConstMean
	The Const mean function class. More...
class	CContingencyTableEvaluation
	The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates. More...
class	CConverter
	class Converter used to convert data More...
class	CConvolutionalFeatureMap
	Handles convolution and gradient calculation for a single feature map in a convolutional neural network. More...
class	CCosineDistance
	class CosineDistance More...
class	CCplex
	Class CCplex to encapsulate access to the commercial cplex general purpose optimizer. More...
class	CCPLEXSVM
	CplexSVM a SVM solver implementation based on cplex (unfinished). More...
class	CCrossCorrelationMeasure
	class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier. More...
class	CCrossValidation
	base class for cross-validation evaluation. Given a learning machine, a splitting strategy, an evaluation criterion, features and corresponding 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 and standard deviation of different runs is returned. Default number of runs is one. 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	CCrossValidationResult
	type to encapsulate the results of an evaluation run. 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	CCSVFile
	Class CSVFile used to read data from comma-separated values (CSV) files. See http://en.wikipedia.org/wiki/Comma-separated_values. More...
class	CCustomDistance
	The Custom Distance allows for custom user provided distance matrices. More...
class	CCustomKernel
	The Custom Kernel allows for custom user provided kernel matrices. More...
class	CCustomMahalanobisDistance
	Class CustomMahalanobisDistance used to compute the distance between feature vectors \( \vec{x_i} \) and \( \vec{x_j} \) as \( (\vec{x_i} - \vec{x_j})^T \mathbf{M} (\vec{x_i} - \vec{x_j}) \), given the matrix \( \mathbf{M} \) which will be referred to as Mahalanobis matrix. More...
class	CData
	dummy data holder More...
class	CDataGenerator
	Class that is able to generate various data samples, which may be used for examples in SHOGUN. More...
class	CDecompressString
	Preprocessor that decompresses compressed strings. More...
class	CDeepAutoencoder
	Represents a muti-layer autoencoder. More...
class	CDeepBeliefNetwork
	A Deep Belief Network. More...
class	CDelimiterTokenizer
	The class CDelimiterTokenizer is used to tokenize a SGVector<char> into tokens using custom chars as delimiters. One can set the delimiters to use by setting to 1 the appropriate index of the public field delimiters. Eg. to set as delimiter the character ':', one should do: tokenizer->delimiters[':'] = 1;. More...
class	CDenseDistance
	template class DenseDistance More...
class	CDenseExactLogJob
	Class that represents the job of applying the log of a CDenseMatrixOperator on a real vector. More...
singleton	CDenseFeatures
	The class DenseFeatures implements dense feature matrices. More...
class	CDenseLabels
	Dense integer or floating point labels. More...
class	CDenseMatrixExactLog
	Class that generates jobs for computing logarithm of a dense matrix linear operator. More...
singleton	CDenseMatrixOperator
	Class that represents a dense-matrix linear operator. It computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n}\rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in\mathbb{C}^{n}\) being the vector. The result is a vector \(y\in\mathbb{C}^{m}\). More...
class	CDensePreprocessor
	Template class DensePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CDenseFeatures (i.e. rectangular dense matrices) More...
class	CDenseSubSamplesFeatures
class	CDenseSubsetFeatures
class	CDependenceMaximization
	Class CDependenceMaximization, base class for all feature selection preprocessors which select a subset of features that shows maximum dependence between the features and the labels. This is done via an implementation of CIndependenceTest, m_estimator inside compute_measures() (see class documentation of CFeatureSelection), which performs a statistical test for a given feature \(\mathbf{X}_i\) from the set of features \(\mathbf{X}\), and the labels \(\mathbf{Y}\). The test checks \[ \textbf{H}_0 : P\left(\mathbf{X}\setminus \mathbf{X}_i, \mathbf{Y}\right) =P\left(\mathbf{X}\setminus \mathbf{X}_i\right)P\left(\mathbf{Y}\right) \] The test statistic is then used as a measure which signifies the independence between the rest of the features and the labels - higher the value of the test statistic, greater the dependency between the rest of the features and the class labels, and therefore lesser significant the current feature becomes. Therefore, highest scoring features are removed. The removal policy thus can only be shogun::N_LARGEST and shogun::PERCENTILE_LARGEST and it can be set via set_policy() call. remove_feats() method handles the removal of features based on the specified policy. More...
class	CDiagKernel
	The Diagonal Kernel returns a constant for the diagonal and zero otherwise. More...
class	CDiceKernelNormalizer
	DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient) More...
class	CDifferentiableFunction
	An abstract class that describes a differentiable function used for GradientEvaluation. More...
class	CDiffusionMaps
	class DiffusionMaps used to preprocess given data using Diffusion Maps dimensionality reduction technique as described in More...
class	CDimensionReductionPreprocessor
	the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices). More...
class	CDirectEigenSolver
	Class that computes eigenvalues of a real valued, self-adjoint dense matrix linear operator using Eigen3. More...
class	CDirectLinearSolverComplex
	Class that provides a solve method for complex dense-matrix linear systems. More...
class	CDirectSparseLinearSolver
	Class that provides a solve method for real sparse-matrix linear systems using LLT. More...
class	CDiscreteDistribution
	This is the base interface class for all discrete distributions. More...
class	CDisjointSet
	Class CDisjointSet data structure for linking graph nodes It's easy to identify connected graph, acyclic graph, roots of forest etc. please refer to http://en.wikipedia.org/wiki/Disjoint-set_data_structure. More...
class	CDistance
	Class Distance, a base class for all the distances used in the Shogun toolbox. More...
class	CDistanceKernel
	The Distance kernel takes a distance as input. More...
class	CDistanceMachine
	A generic DistanceMachine interface. 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	CDistribution
	Base class Distribution from which all methods implementing a distribution are derived. 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	CDomainAdaptationMulticlassLibLinear
	domain adaptation multiclass LibLinear wrapper Source domain is assumed to b More...
class	CDomainAdaptationSVM
	class DomainAdaptationSVM More...
class	CDomainAdaptationSVMLinear
	class DomainAdaptationSVMLinear More...
class	CDotFeatures
	Features that support dot products among other operations. More...
class	CDotKernel
	Template class DotKernel is the base class for kernels working on DotFeatures. More...
class	CDualVariationalGaussianLikelihood
	Class that models dual variational likelihood. More...
class	CDummyFeatures
	The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any). More...
singleton	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	CDynProg
	Dynamic Programming Class. 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	CEigenSolver
	Abstract base class that provides an abstract compute method for computing eigenvalues of a real valued, self-adjoint linear operator. It also provides method for getting min and max eigenvalues. More...
class	CEMBase
	This is the base class for Expectation Maximization (EM). EM for various purposes can be derived from this base class. This is a template class having a template member called data which can be used to store all parameters used and results calculated by the expectation and maximization steps of EM. 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	CEMMixtureModel
	This is the implementation of EM specialized for Mixture models. More...
class	CEPInferenceMethod
	Class of the Expectation Propagation (EP) posterior approximation inference method. More...
class	CErrorRateMeasure
	class ErrorRateMeasure used to measure error rate of 2-class classifier. More...
class	CEuclideanDistance
	class EuclideanDistance 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
	Abstract class that contains the result generated by the MachineEvaluation class. More...
class	CExactInferenceMethod
	The Gaussian exact form inference method class. More...
class	CExplicitSpecFeatures
	Features that compute the Spectrum Kernel feature space explicitly. More...
class	CExponentialARDKernel
	Exponential Kernel with Automatic Relevance Detection computed on CDotFeatures. More...
class	CExponentialKernel
	The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures. More...
class	CExponentialLoss
	CExponentialLoss implements the exponential loss function. \(L(y_i,f(x_i)) = \exp^{-y_if(x_i)}\). More...
class	CF1Measure
	class F1Measure used to measure F1 score of 2-class classifier. More...
class	CFactor
	Class CFactor A factor is defined on a clique in the factor graph. Each factor can have its own data, either dense, sparse or shared data. Note that currently this class is table factor oriented. More...
class	CFactorAnalysis
	The Factor Analysis class is used to embed data using Factor Analysis algorithm. More...
class	CFactorDataSource
	Class CFactorDataSource Source for factor data. In some cases, the same data can be shared by many factors. More...
class	CFactorGraph
	Class CFactorGraph a factor graph is a structured input in general. More...
class	CFactorGraphDataGenerator
	Class CFactorGraphDataGenerator Create factor graph data for multiple unit tests. More...
class	CFactorGraphFeatures
	CFactorGraphFeatures maintains an array of factor graphs, each graph is a sample, i.e. an instance of structured input. More...
class	CFactorGraphLabels
	Class FactorGraphLabels used e.g. in the application of Structured Output (SO) learning with the FactorGraphModel. Each of the labels is represented by a graph. Each label is of type CFactorGraphObservation and all of them are stored in a CDynamicObjectArray. More...
class	CFactorGraphModel
	CFactorGraphModel defines a model in terms of CFactorGraph and CMAPInference, where parameters are associated with factor types, in the model. There is a mapping vector records the locations of local factor parameters in the global parameter vector. More...
class	CFactorGraphObservation
	Class CFactorGraphObservation is used as the structured output. More...
class	CFactorType
	Class CFactorType defines the way of factor parameterization. More...
class	CFastICA
	class FastICA More...
class	CFeatures
	The class Features is the base class of all feature objects. More...
class	CFeatureSelection
	Template class CFeatureSelection, base class for all feature selection preprocessors which select a subset of features (dimensions in the feature matrix) to achieve a specified number of dimensions, m_target_dim from a given set of features. This class showcases all feature selection algorithms via a generic interface. Supported algorithms are specified by the enum EFeatureSelectionAlgorithm which can be set via set_algorithm() call. Supported wrapper algorithms are. More...
class	CFFDiag
	Class FFDiag. More...
class	CFFSep
	class FFSep More...
class	CFile
	A File access base class. 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	CFisherLDA
	Preprocessor FisherLDA attempts to model the difference between the classes of data by performing linear discriminant analysis on input feature vectors/matrices. When the init method in FisherLDA is called with proper feature matrix X(say N number of vectors and D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods, this creates a transformation whose outputs are the reduced T-Dimensional & class-specific distribution (where T<= number of unique classes-1). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the specified number of eigenvectors which maximizes the ratio of between class matrix to within class matrix. More...
class	CFITCInferenceMethod
	The Fully Independent Conditional Training inference method class. 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	CFKFeatures
	The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models. More...
class	CFunction
	Class of a function of one variable. More...
class	CFWSOSVM
	Class CFWSOSVM solves SOSVM using Frank-Wolfe algorithm [1]. More...
class	CGaussian
	Gaussian distribution interface. More...
class	CGaussianARDKernel
	Gaussian Kernel with Automatic Relevance Detection computed on CDotFeatures. More...
class	CGaussianARDSparseKernel
	Gaussian Kernel with Automatic Relevance Detection with supporting Sparse inference. More...
class	CGaussianBlobsDataGenerator
class	CGaussianCompactKernel
	The compact version as given in Bart Hamers' thesis Kernel Models for Large Scale Applications (Eq. 4.10) is computed as. More...
class	CGaussianDistribution
	Dense version of the well-known Gaussian probability distribution, defined as \[ \mathcal{N}_x(\mu,\Sigma)= \frac{1}{\sqrt{|2\pi\Sigma|}} \exp\left(-\frac{1}{2}(x-\mu)^T\Sigma^{-1}(x-\mu)\right) \] . More...
class	CGaussianKernel
	The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures. More...
class	CGaussianLikelihood
	Class that models Gaussian likelihood. More...
class	CGaussianMatchStringKernel
	The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length. More...
class	CGaussianNaiveBayes
	Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier. More...
class	CGaussianProcessClassification
	Class GaussianProcessClassification implements binary and multiclass classification based on Gaussian Processes. More...
class	CGaussianProcessMachine
	A base class for Gaussian Processes. More...
class	CGaussianProcessRegression
	Class GaussianProcessRegression implements regression based on Gaussian Processes. 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	CGCArray
	Template class GCArray implements a garbage collecting static array. More...
class	CGEMPLP
class	CGeodesicMetric
	class GeodesicMetric More...
class	CGMM
	Gaussian Mixture Model interface. More...
class	CGMNPLib
	class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP). More...
class	CGMNPSVM
	Class GMNPSVM implements a one vs. rest MultiClass SVM. More...
class	CGNPPLib
	class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP). More...
class	CGNPPSVM
	class GNPPSVM More...
class	CGradientCriterion
	Simple class which specifies the direction of gradient search. More...
class	CGradientEvaluation
	Class evaluates a machine using its associated differentiable function for the function value and its gradient with respect to parameters. More...
class	CGradientResult
	Container class that returns results from GradientEvaluation. It contains the function value as well as its gradient. More...
class	CGraphCut
class	CGridSearchModelSelection
	Model selection class which searches for the best model by a grid- search. See CModelSelection for details. 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...
struct	CHAIDTreeNodeData
	structure to store data of a node of CHAID. This can be used as a template type in TreeMachineNode class. CHAID algorithm uses nodes of type CTreeMachineNode<CHAIDTreeNodeData> More...
class	CHammingWordDistance
	class HammingWordDistance More...
class	CHash
	Collection of Hashing Functions. More...
class	CHashedDenseFeatures
	This class is identical to the CDenseFeatures class except that it hashes each dimension to a new feature space. More...
class	CHashedDocConverter
	This class can be used to convert a document collection contained in a CStringFeatures<char> object where each document is stored as a single vector into a hashed Bag-of-Words representation. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]. More...
class	CHashedDocDotFeatures
	This class can be used to provide on-the-fly vectorization of a document collection. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]. More...
class	CHashedMultilabelModel
	Class CHashedMultilabelModel represents application specific model and contains application dependent logic for solving multilabel classification with feature hashing within a generic SO framework. We hash the feature of each class with a separate seed and put them in the same feature space (exploded feature space). More...
class	CHashedSparseFeatures
	This class is identical to the CDenseFeatures class except that it hashes each dimension to a new feature space. 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	CHessianLocallyLinearEmbedding
	class HessianLocallyLinearEmbedding used to preprocess data using Hessian Locally Linear Embedding algorithm as described in More...
class	CHierarchical
	Agglomerative hierarchical single linkage clustering. More...
class	CHierarchicalMultilabelModel
	Class CHierarchicalMultilabelModel represents application specific model and contains application dependent logic for solving hierarchical multilabel classification[1] within a generic SO framework. More...
class	CHingeLoss
	CHingeLoss implements the hinge loss function. More...
class	CHistogram
	Class Histogram computes a histogram over all 16bit unsigned integers in the features. More...
class	CHistogramIntersectionKernel
	The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1. More...
class	CHistogramWordStringKernel
	The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains. More...
class	CHMM
	Hidden Markov Model. 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	CHomogeneousKernelMap
	Preprocessor HomogeneousKernelMap performs homogeneous kernel maps as described in. More...
class	CHSIC
	This class implements the Hilbert Schmidtd Independence Criterion based independence test as described in [1]. More...
class	CHuberLoss
	CHuberLoss implements the Huber loss function. It behaves like SquaredLoss function at values below Huber delta and like absolute deviation at values greater than the delta. More...
class	CHypothesisTest
	Hypothesis test base class. Provides an interface for statistical hypothesis testing 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	CICAConverter
	class ICAConverter Base class for ICA algorithms More...
class	CID3ClassifierTree
	class ID3ClassifierTree, implements classifier tree for discrete feature values using the ID3 algorithm. The training algorithm implemented is as follows : More...
class	CIdentityKernelNormalizer
	Identity Kernel Normalization, i.e. no normalization is applied. More...
class	CImplicitWeightedSpecFeatures
	Features that compute the Weighted Spectrum Kernel feature space explicitly. More...
class	CIndependenceTest
	Provides an interface for performing the independence test. Given samples \(Z=\{(x_i,y_i)\}_{i=1}^m\) from the joint distribution \(\textbf{P}_{xy}\), does the joint distribution factorize as \(\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y\), i.e. product of the marginals? The null-hypothesis says yes, i.e. no dependence, the alternative hypothesis says no. More...
class	CIndependentComputationEngine
	Abstract base class for solving multiple independent instances of CIndependentJob. It has one method, submit_job, which may add the job to an internal queue and might block if there is yet not space in the queue. After jobs are submitted, it might not yet be ready. wait_for_all waits until all jobs are completed, which must be called to guarantee that all jobs are finished. More...
class	CIndependentJob
	Abstract base for general computation jobs to be registered in CIndependentComputationEngine. compute method produces a job result and submits it to the internal JobResultAggregator. Each set of jobs that form a result will share the same job result aggregator. 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	CIndexFeatures
	The class IndexFeatures implements features that contain the index of the features. This features used in the CCustomKernel::init to make the subset of the kernel matrix. Initial CIndexFeature of row_idx and col_idx, pass them to the CCustomKernel::init(row_idx, col_idx), then use CCustomKernel::get_kernel_matrix() will get the sub kernel matrix specified by the row_idx and col_idx. More...
class	CIndirectObject
	an array class that accesses elements indirectly via an index array. More...
class	CIndividualJobResultAggregator
	Class that aggregates vector job results in each submit_result call of jobs generated from rational approximation of linear operator function times a vector. finalize extracts the imaginary part of that aggregation, applies the linear operator to the aggregation, performs a dot product with the sample vector, multiplies with the constant multiplier (see CRationalApproximation) and stores the result as CScalarResult. More...
class	CInference
	The Inference Method base class. More...
class	CIntegration
	Class that contains certain methods related to numerical integration. More...
class	CIntronList
	class IntronList More...
class	CInverseMultiQuadricKernel
	InverseMultiQuadricKernel. More...
class	CIOBuffer
	An I/O buffer class. More...
class	CIsomap
	The Isomap class is used to embed data using Isomap algorithm as described in: More...
class	CIterativeLinearSolver
	abstract template base for all iterative linear solvers such as conjugate gradient (CG) solvers. provides interface for setting the iteration limit, relative/absolute tolerence. solve method is abstract. More...
class	CIterativeShiftedLinearFamilySolver
	abstract template base for CG based solvers to the solution of shifted linear systems of the form \((A+\sigma)x=b\) for several values of \(\sigma\) simultaneously, using only as many matrix-vector operations as the solution of a single system requires. This class adds another interface to the basic iterative linear solver that takes the shifts, \(\sigma\), and also weights, \(\alpha\), and returns the summation \(\sum_{i} \alpha_{i}x_{i}\), where \(x_{i}\) is the solution of the system \((A+\sigma_{i})x_{i}=b\). More...
class	CJacobiEllipticFunctions
	Class that contains methods for computing Jacobi elliptic functions related to complex analysis. These functions are inverse of the elliptic integral of first kind, i.e. \[ u(k,m)=\int_{0}^{k}\frac{dt}{\sqrt{(1-t^{2})(1-m^{2}t^{2})}} =\int_{0}^{\varphi}\frac{d\theta}{\sqrt{(1-m^{2}sin^{2}\theta)}} \] where \(k=sin\varphi\), \(t=sin\theta\) and parameter \(m, 0\le m \le 1\) is called modulus. Three main Jacobi elliptic functions are defined as \(sn(u,m)=k=sin\theta\), \(cn(u,m)=cos\theta=\sqrt{1-sn(u,m)^{2}}\) and \(dn(u,m)=\sqrt{1-m^{2}sn(u,m)^{2}}\). For \(k=1\), i.e. \(\varphi=\frac{\pi}{2}\), \(u(1,m)=K(m)\) is known as the complete elliptic integral of first kind. Similarly, \(u(1,m'))= K'(m')\), \(m'=\sqrt{1-m^{2}}\) is called the complementary complete elliptic integral of first kind. Jacobi functions are double periodic with quardratic periods \(K\) and \(K'\). More...
class	CJade
	class Jade More...
class	CJADiag
	Class JADiag. More...
class	CJADiagOrth
	Class JADiagOrth. More...
class	CJediDiag
	Class Jedi. More...
class	CJediSep
	class JediSep More...
class	CJensenMetric
	class JensenMetric 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	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	CJobResult
	Base class that stores the result of an independent job. More...
class	CJobResultAggregator
	Abstract base class that provides an interface for computing an aggeregation of the job results of independent computation jobs as they are submitted and also for finalizing the aggregation. More...
class	CKDTree
	This class implements KD-Tree. cf. http://www.autonlab.org/autonweb/14665/version/2/part/5/data/moore-tutorial.pdf. More...
class	CKernel
	The Kernel base class. More...
class	CKernelDensity
	This class implements the kernel density estimation technique. Kernel density estimation is a non-parametric way to estimate an unknown pdf. The pdf at a query point given finite training samples is calculated using the following formula : \ \(pdf(x')= \frac{1}{nh} \sum_{i=1}^n K(\frac{||x-x_i||}{h})\) \ K() in the above formula is called the kernel function and is controlled by the parameter h called kernel bandwidth. Presently, this class supports only Gaussian kernel which can be used with either Euclidean distance or Manhattan distance. This class makes use of 2 tree structures KD-tree and Ball tree for fast calculation. KD-trees are faster than ball trees at lower dimensions. In case of high dimensional data, ball tree tends to out-perform KD-tree. By default, the class used is Ball tree. More...
class	CKernelDependenceMaximization
	Class CKernelDependenceMaximization, that uses an implementation of CKernelIndependenceTest to compute dependence measures for feature selection. Different kernels are used for labels and data. For the sake of computational convenience, the precompute() method is overridden to precompute the kernel for labels and save as an instance of CCustomKernel. More...
class	CKernelDistance
	The Kernel distance takes a distance as input. More...
class	CKernelIndependenceTest
	Kernel 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 \(\textbf{P}_{xy}\), does the joint distribution factorize as \(\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y\), i.e. product of the marginals? More...
class	CKernelLocallyLinearEmbedding
	class KernelLocallyLinearEmbedding used to construct embeddings of data using kernel formulation of Locally Linear Embedding algorithm as described in More...
class	CKernelMachine
	A generic KernelMachine interface. More...
class	CKernelMulticlassMachine
	generic kernel multiclass More...
class	CKernelNormalizer
	The class Kernel Normalizer defines a function to post-process kernel values. More...
class	CKernelPCA
	Preprocessor KernelPCA performs kernel principal component analysis. More...
class	CKernelRidgeRegression
	Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression. More...
class	CKernelSelection
	Base class for kernel selection for kernel two-sample test statistic implementations (e.g. MMD). Provides abstract methods for selecting kernels and computing criteria or kernel weights for the implemented method. In order to implement new methods for kernel selection, simply write a new implementation of this class. More...
class	CKernelStructuredOutputMachine
class	CKernelTwoSampleTest
	Kernel two sample test base class. Provides an interface for performing a two-sample test using a kernel, i.e. Given samples from two distributions \(p\) and \(q\), the null-hypothesis is: \(H_0: p=q\), the alternative hypothesis: \(H_1: p\neq q\). More...
class	CKLCholeskyInferenceMethod
	The KL approximation inference method class. More...
class	CKLCovarianceInferenceMethod
	The KL approximation inference method class. More...
class	CKLDiagonalInferenceMethod
	The KL approximation inference method class. More...
class	CKLDualInferenceMethod
	The dual KL approximation inference method class. More...
class	CKLDualInferenceMethodMinimizer
	Build-in minimizer for KLDualInference. More...
class	CKLInference
	The KL approximation inference method class. More...
class	CKLLowerTriangularInference
	The KL approximation inference method class. More...
class	CKMeans
	KMeans clustering, partitions the data into k (a-priori specified) clusters. More...
class	CKMeansBase
class	CKMeansMiniBatch
class	CKNN
	Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
class	CKNNHeap
	This class implements a specialized version of max heap structure. This heap specializes in storing the least k values seen so far along with the indices (or id) of the entities with which the values are associated. On calling the push method, it is automatically checked, if the new value supplied, is among the least k distances seen so far. Also, in case the heap is full already, the max among the stored values is automatically thrown out as the new value finds its proper place in the heap. More...
class	CKRRNystrom
	Class KRRNystrom implements the Nyström method for kernel ridge regression, using a low-rank approximation to the kernel matrix. More...
class	CLabels
	The class Labels models labels, i.e. class assignments of objects. More...
class	CLabelsFactory
	The helper class to specialize base class instances of labels. More...
class	CLanczosEigenSolver
	Class that computes eigenvalues of a real valued, self-adjoint linear operator using Lanczos algorithm. More...
class	CLaplaceInference
	The Laplace approximation inference method base class. More...
class	CLaplacianEigenmaps
	class LaplacianEigenmaps used to construct embeddings of data using Laplacian Eigenmaps algorithm as described in: More...
class	CLaRank
	the LaRank multiclass SVM machine This implementation uses LaRank algorithm from Bordes, Antoine, et al., 2007. "Solving multiclass support vector machines with LaRank." 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	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	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	CLBFGSMinimizer
	The class wraps the Shogun's C-style LBFGS minimizer. More...
class	CLBPPyrDotFeatures
	Implements Local Binary Patterns with Scale Pyramids as dot features for a set of images. Expects the images to be loaded in a CDenseFeatures object. More...
class	CLDA
	Class LDA implements regularized Linear Discriminant Analysis. 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	CLibLinear
	This class provides an interface to the LibLinear library for large- scale linear learning focusing on SVM [1]. This is the classification interface. For regression, see CLibLinearRegression. There is also an online version, see COnlineLibLinear. More...
class	CLibLinearMTL
	class to implement LibLinear More...
class	CLibLinearRegression
	This class provides an interface to the LibLinear library for large- scale linear learning focusing on SVM [1]. This is the regression interface. For classification, see CLibLinear. More...
class	CLibSVM
	LibSVM. More...
class	CLibSVMFile
	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 More...
class	CLibSVMOneClass
	class LibSVMOneClass More...
class	CLibSVR
	Class LibSVR, performs support vector regression using LibSVM. More...
class	CLikelihoodModel
	The Likelihood model base class. More...
class	CLinearHMM
	The class LinearHMM is for learning Higher Order Markov chains. More...
class	CLinearKernel
	Computes the standard linear kernel on CDotFeatures. More...
class	CLinearLatentMachine
	abstract implementaion of Linear Machine with latent variable This is the base implementation of all linear machines with latent variable. More...
class	CLinearLocalTangentSpaceAlignment
	class LinearLocalTangentSpaceAlignment converter used to construct embeddings as described in: 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...
singleton	CLinearOperator
	Abstract template base class that represents a linear operator, e.g. a matrix. More...
class	CLinearRidgeRegression
	Class LinearRidgeRegression implements Ridge Regression - a regularized least square method for classification and regression. More...
singleton	CLinearSolver
	Abstract template base class that provides an abstract solve method for linear systems, that takes a linear operator \(A\), a vector \(b\), solves the system \(Ax=b\) and returns the vector \(x\). More...
class	CLinearStringKernel
	Computes the standard linear kernel on dense char valued features. More...
class	CLinearStructuredOutputMachine
class	CLinearTimeMMD
	This class implements the linear time Maximum Mean Statistic as described in [1] for streaming data (see CStreamingMMD for description). More...
class	CLineReader
	Class for buffered reading from a ascii file. More...
class	CList
	Class List implements a doubly connected list for low-level-objects. More...
class	CListElement
	Class ListElement, defines how an element of the the list looks like. More...
class	CLMNN
	Class LMNN that implements the distance metric learning technique Large Margin Nearest Neighbour (LMNN) described in. More...
class	CLMNNStatistics
	Class LMNNStatistics used to give access to intermediate results obtained training LMNN. 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	CLocalityPreservingProjections
	class LocalityPreservingProjections used to compute embeddings of data using Locality Preserving Projections method as described in More...
class	CLocallyLinearEmbedding
	class LocallyLinearEmbedding used to embed data using Locally Linear Embedding algorithm described in More...
class	CLocalTangentSpaceAlignment
	class LocalTangentSpaceAlignment used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in: More...
class	CLock
	Class Lock used for synchronization in concurrent programs. More...
class	CLogDetEstimator
	Class to create unbiased estimators of \(log(\left|C\right|)= trace(log(C))\). For each estimate, it samples trace vectors (one by one) and calls submit_jobs of COperatorFunction, stores the resulting job result aggregator instances, calls wait_for_all of CIndependentComputationEngine to ensure that the job result aggregators are all up to date. Then simply computes running averages over the estimates. More...
class	CLogitDVGLikelihood
	Class that models dual variational logit likelihood. More...
class	CLogitLikelihood
	Class that models Logit likelihood. More...
class	CLogitVGLikelihood
	Class that models Logit likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] . More...
class	CLogitVGPiecewiseBoundLikelihood
	Class that models Logit likelihood and uses variational piecewise bound to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] where \[ p(y_i|f_i) = \frac{exp(y_i*f_i)}{1+exp(f_i)}, y_i \in \{0,1\} \] . More...
class	CLogKernel
	Log kernel. More...
class	CLogLoss
	CLogLoss implements the logarithmic loss function. More...
class	CLogLossMargin
	Class CLogLossMargin implements a margin-based log-likelihood loss function. 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	CLogRationalApproximationCGM
	Implementaion of rational approximation of a operator-function times vector where the operator function is log of a linear operator. Each complex system generated from the shifts due to rational approximation of opertor- log times vector expression are solved at once with a shifted linear-family solver by the computation engine. generate_jobs generates one job per sample. More...
class	CLogRationalApproximationIndividual
	Implementaion of rational approximation of a operator-function times vector where the operator function is log of a dense-matrix. Each complex system generated from the shifts due to rational approximation of opertor- log times vector expression are solved individually with a complex linear solver by the computation engine. generate_jobs generates num_shifts number of jobs per trace sample. More...
class	CLOOCrossValidationSplitting
	Implementation of Leave one out cross-validation on the base of CCrossValidationSplitting. Produces subset index sets consisting of one element,for each label. More...
class	CLoss
	Class which collects generic mathematical functions. More...
class	CLossFunction
	Class CLossFunction is the base class of all loss functions. 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	CMachine
	A generic learning machine interface. More...
class	CMachineEvaluation
	Machine Evaluation is an abstract class that evaluates a machine according to some criterion. More...
class	CMahalanobisDistance
	class MahalanobisDistance More...
class	CMajorityVote
	CMajorityVote is a CWeightedMajorityVote combiner, where each Machine's weight in the ensemble is 1.0. More...
class	CManhattanMetric
	class ManhattanMetric More...
class	CManhattanWordDistance
	class ManhattanWordDistance More...
class	CManifoldSculpting
	class CManifoldSculpting used to embed data using manifold sculpting embedding algorithm. More...
class	CMap
	the class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class	CMAPInference
	Class CMAPInference performs MAP inference on a factor graph. Briefly, given a factor graph model, with features \(\bold{x}\), the prediction is obtained by \( {\arg\max} _{\bold{y}} P(\bold{Y} = \bold{y} | \bold{x}; \bold{w}) \). More...
class	CMAPInferImpl
	Class CMAPInferImpl abstract class of MAP inference implementation. 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	CMath
	Class which collects generic mathematical functions. 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	CMatrixOperations
	The helper class is used for Laplace and KL methods. More...
singleton	CMatrixOperator
	Abstract base class that represents a matrix linear operator. It provides an interface to computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n} \rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in \mathbb{C}^{n}\) being the vector. The result is a vector \(y\in \mathbb{C}^{m}\). More...
class	CMCLDA
	Class MCLDA implements multiclass Linear Discriminant Analysis. More...
class	CMeanAbsoluteError
	Class MeanAbsoluteError used to compute an error of regression model. More...
class	CMeanFunction
	An abstract class of the mean function. More...
class	CMeanRule
	CMeanRule simply averages the outputs of the Machines in the ensemble. More...
class	CMeanShiftDataGenerator
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	CMemoryMappedFile
	memory mapped file More...
class	CMinkowskiMetric
	class MinkowskiMetric More...
class	CMixtureModel
	This is the generic class for mixture models. The final distribution is a mixture of various simple distributions supplied by the user. 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	CMKLOneClass
	Multiple Kernel Learning for one-class-classification. More...
class	CMKLRegression
	Multiple Kernel Learning for regression. More...
class	CMMDKernelSelection
	Base class for kernel selection for MMD-based two-sample test statistic implementations. Provides abstract methods for selecting kernels and computing criteria or kernel weights for the implemented method. In order to implement new methods for kernel selection, simply write a new implementation of this class. More...
class	CMMDKernelSelectionMax
	Kernel selection class that selects the single kernel that maximises the MMD statistic. Works for CQuadraticTimeMMD and CLinearTimeMMD. This leads to a heuristic that is better than the standard median heuristic for Gaussian kernels. However, it comes with no guarantees. More...
class	CMMDKernelSelectionMedian
	Implements MMD kernel selection for a number of Gaussian baseline kernels via selecting the one with a bandwidth parameter that is closest to the median of all pairwise distances in the underlying data. Therefore, it only works for data to which a GaussianKernel can be applied, which are grouped under the class CDotFeatures in SHOGUN. More...
class	CMMDKernelSelectionOpt
	Implements optimal kernel selection for single kernels. Given a number of baseline kernels, this method selects the one that minimizes the type II error for a given type I error for a two-sample test. This only works for the CLinearTimeMMD statistic. More...
class	CModelSelection
	Abstract base class for model selection. 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	CMPDSVM
	class MPDSVM More...
class	CMulticlassAccuracy
	The class MulticlassAccuracy used to compute accuracy of multiclass classification. More...
class	CMulticlassLabels
	Multiclass Labels for multi-class classification. 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	CMulticlassMachine
	experimental abstract generic multiclass machine class More...
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...
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 multiclass probabilistic outputs can be obtained by using the heuristics described in [1] 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	CMulticlassOVREvaluation
	The class MulticlassOVREvaluation used to compute evaluation parameters of multiclass classification via binary OvR decomposition and given binary evaluation technique. 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	CMulticlassStrategy
	class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary classifiers More...
class	CMulticlassSVM
	class MultiClassSVM More...
class	CMultidimensionalScaling
	class Multidimensionalscaling is used to perform multidimensional scaling (capable of landmark approximation if requested). More...
class	CMultilabelAccuracy
	Class CMultilabelAccuracy used to compute accuracy of multilabel classification. More...
class	CMultilabelCLRModel
	Class MultilabelCLRModel represents application specific model and contains application dependent logic for solving multi-label classification using Calibrated Label Ranking (CLR) [1] method within a generic SO framework. More...
class	CMultilabelLabels
	Multilabel Labels for multi-label classification. More...
class	CMultilabelModel
	Class CMultilabelModel represents application specific model and contains application dependent logic for solving multilabel classification within a generic SO framework. More...
class	CMultilabelSOLabels
	Class CMultilabelSOLabels used in the application of Structured Output (SO) learning to Multilabel Classification. Labels are subsets of {0, 1, ..., num_classes-1}. Each of the label if of type CSparseMultilabel and all of them are stored in a CDynamicObjectArray. More...
class	CMultiLaplaceInferenceMethod
	The Laplace approximation inference method class for multi classification. More...
class	CMultiquadricKernel
	MultiquadricKernel. More...
class	CMultitaskKernelMaskNormalizer
	The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class	CMultitaskKernelMaskPairNormalizer
	The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class	CMultitaskKernelMklNormalizer
	Base-class for parameterized Kernel Normalizers. More...
class	CMultitaskKernelNormalizer
	The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class	CMultitaskKernelPlifNormalizer
	The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL. More...
class	CMultitaskKernelTreeNormalizer
	The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy. 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	CNativeMulticlassMachine
	experimental abstract native multiclass machine class More...
class	CNbodyTree
	This class implements genaralized tree for N-body problems like k-NN, kernel density estimation, 2 point correlation. More...
class	CNearestCentroid
	Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier. More...
class	CNeighborhoodPreservingEmbedding
	NeighborhoodPreservingEmbedding converter used to construct embeddings as described in: More...
class	CNeuralConvolutionalLayer
	Main component in convolutional neural networks More...
class	CNeuralInputLayer
	Represents an input layer. The layer can be either connected to all the input features that a network receives (default) or connected to just a small part of those features. More...
class	CNeuralLayer
	Base class for neural network layers. More...
class	CNeuralLayers
	A class to construct neural layers. More...
class	CNeuralLeakyRectifiedLinearLayer
	Neural layer with leaky rectified linear neurons. More...
class	CNeuralLinearLayer
	Neural layer with linear neurons, with an identity activation function. can be used as a hidden layer or an output layer. More...
class	CNeuralLogisticLayer
	Neural layer with linear neurons, with a logistic activation function. can be used as a hidden layer or an output layer. More...
class	CNeuralNetwork
	A generic multi-layer neural network. More...
class	CNeuralRectifiedLinearLayer
	Neural layer with rectified linear neurons. More...
class	CNeuralSoftmaxLayer
	Neural layer with linear neurons, with a softmax activation function. can be only be used as an output layer. Cross entropy error measure is used. 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	CNGramTokenizer
	The class CNGramTokenizer is used to tokenize a SGVector<char> into n-grams. More...
class	CNOCCO
	This class implements the NOrmalized Cross Covariance Operator (NOCCO) based independence test as described in [1]. More...
class	CNode
	A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks. More...
class	CNormalSampler
	Class that provides a sample method for Gaussian samples. More...
class	CNormOne
	Preprocessor NormOne, normalizes vectors to have norm 1. More...
class	CNumericalVGLikelihood
	Class that models likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] . More...
class	COligoStringKernel
	This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004. More...
struct	ConditionalProbabilityTreeNodeData
	struct to store data of node of conditional probability tree More...
class	COnlineLibLinear
	Class implementing a purely online version of CLibLinear, using the L2R_L1LOSS_SVC_DUAL solver only. More...
class	COnlineLinearMachine
	Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms. More...
class	COnlineSVMSGD
	class OnlineSVMSGD More...
class	ConstLearningRate
	This implements the const learning rate class for a descent-based minimizer. More...
class	COperatorFunction
	Abstract template base class for computing \(s^{T} f(C) s\) for a linear operator C and a vector s. submit_jobs method creates a bunch of jobs needed to solve for this particular \(s\) and attaches one unique job aggregator to each of them, then submits them all to the computation engine. 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	CParser
	Class for reading from a string. More...
class	CPCA
	Preprocessor PCA performs principial component analysis on input feature vectors/matrices. When the init method in PCA is called with proper feature matrix X (with say N number of vectors and D feature dimension), a transformation matrix is computed and stored internally. This transformation matrix is then used to transform all D-dimensional feature vectors or feature matrices (with D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods. This tranformation outputs the T-Dimensional approximation of all these input vectors and matrices (where T<=min(D,N)). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the eigenvectors of the covariance matrix(XX') having top T eigenvalues. More...
class	CPerceptron
	Class Perceptron implements the standard linear (online) perceptron. More...
class	CPeriodicKernel
	The periodic kernel as described in The Kernel Cookbook by David Duvenaud: http://people.seas.harvard.edu/~dduvenaud/cookbook/. 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	CPluginEstimate
	class PluginEstimate More...
class	CPNorm
	Preprocessor PNorm, normalizes vectors to have p-norm. More...
class	CPolyFeatures
	implement DotFeatures for the polynomial kernel More...
class	CPolyKernel
	Computes the standard polynomial kernel on CDotFeatures. More...
class	CPolyMatchStringKernel
	The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length. More...
class	CPolyMatchWordStringKernel
	The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features. More...
class	CPositionalPWM
	Positional PWM. More...
class	CPowerKernel
	Power kernel. More...
class	CPRCEvaluation
	Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC). More...
class	CPrecisionMeasure
	class PrecisionMeasure used to measure precision of 2-class classifier. More...
class	CPreprocessor
	Class Preprocessor defines a preprocessor interface. More...
class	CProbabilityDistribution
	A base class for representing n-dimensional probability distribution over the real numbers (64bit) for which various statistics can be computed and which can be sampled. More...
class	CProbitLikelihood
	Class that models Probit likelihood. More...
class	CProbitVGLikelihood
	Class that models Probit likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] . More...
class	CProductKernel
	The Product kernel is used to combine a number of kernels into a single ProductKernel object by element multiplication. More...
class	CProtobufFile
	Class for work with binary file in protobuf format. More...
class	CPruneVarSubMean
	Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance. More...
class	CPyramidChi2
	Pyramid Kernel over Chi2 matched histograms. More...
class	CQDA
	Class QDA implements Quadratic Discriminant Analysis. More...
class	CQDiag
	Class QDiag. 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 \(p\) and \(q\) in a RKHS which we denote by \[ \hat{\eta_k}=\text{MMD}[\mathcal{F},p,q]^2=\textbf{E}_{x,x'} \left[ k(x,x')\right]-2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]=||\mu_p - \mu_q||^2_\mathcal{F} \] . More...
class	CRandom
	: Pseudo random number geneartor More...
class	CRandomCARTree
	This class implements randomized CART algorithm used in the tree growing process of candidate trees in Random Forests algorithm. The tree growing process is different from the original CART algorithm because of the input attributes which are considered for each node split. In randomized CART, a few (fixed number) attributes are randomly chosen from all available attributes while deciding the best split. This is unlike the original CART where all available attributes are considered while deciding the best split. More...
class	CRandomConditionalProbabilityTree
class	CRandomForest
	This class implements the Random Forests algorithm. In Random Forests algorithm, we train a number of randomized CART trees (see class CRandomCARTree) using the supplied training data. The number of trees to be trained is a parameter (called number of bags) controlled by the user. Test feature vectors are classified/regressed by combining the outputs of all these trained candidate trees using a combination rule (see class CCombinationRule). The feature for calculating out-of-box error is also provided to help determine the appropriate number of bags. The evaluatin criteria for calculating this out-of-box error is specified by the user (see class CEvaluation). More...
class	CRandomFourierDotFeatures
	This class implements the random fourier features for the DotFeatures framework. Basically upon the object creation it computes the random coefficients, namely w and b, that are needed for this method and then every time a vector is required it is computed based on the following formula z(x) = sqrt(2/D) * cos(w'*x + b), where D is the number of samples that are used. 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	CRandomKitchenSinksDotFeatures
	class that implements the Random Kitchen Sinks (RKS) for the DotFeatures as mentioned in http://books.nips.cc/papers/files/nips21/NIPS2008_0885.pdf. More...
class	CRandomSearchModelSelection
	Model selection class which searches for the best model by a random search. See CModelSelection for details. More...
class	CRationalApproximation
	Abstract base class of the rational approximation of a function of a linear operator (A) times vector (v) using Cauchy's integral formula - \[f(\text{A})\text{v}=\oint_{\Gamma}f(z)(z\text{I}-\text{A})^{-1} \text{v}dz\] Computes eigenvalues of linear operator and uses Jacobi elliptic functions and conformal maps [2] for quadrature rule for discretizing the contour integral and computes complex shifts, weights and constant multiplier of the rational approximation of the above expression as \[f(\text{A})\text{v}\approx \eta\text{A}\Im-\left(\sum_{l=1}^{N}\alpha_{l} (\text{A}-\sigma_{l}\text{I})^{-1}\text{v}\right)\] where \(\alpha_{l},\sigma_{l}\in\mathbb{C}\) are respectively the shifts and weights of the linear systems generated from the rational approximation, and \(\eta\in\mathbb{R}\) is the constant multiplier, equals to \(\frac{-8K(\lambda_{m}\lambda_{M})^{\frac{1}{4}}}{k\pi N}\). More...
class	CRationalApproximationCGMJob
	Implementation of independent jobs that solves one whole family of shifted systems in rational approximation of linear operator function times a vector using CG-M linear solver. compute calls submit_results of the aggregator with CScalarResult (see CRationalApproximation) More...
class	CRationalApproximationIndividualJob
	Implementation of independent job that solves one of the family of shifted systems in rational approximation of linear operator function times a vector using a direct linear solver. The shift is moved inside the operator. compute calls submit_results of the aggregator with CVectorResult which is the solution vector for that shift multiplied by complex weight (See CRationalApproximation) More...
class	CRationalQuadraticKernel
	Rational Quadratic kernel. More...
class	CRBM
	A Restricted Boltzmann Machine. More...
class	CRealDistance
	class RealDistance More...
class	CRealFileFeatures
	The class RealFileFeatures implements a dense double-precision floating point matrix from a file. 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	CRecallMeasure
	class RecallMeasure used to measure recall of 2-class classifier. More...
class	CRegressionLabels
	Real Labels are real-valued labels. 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	CRejectionStrategy
	base rejection strategy class More...
class	CRelaxedTree
class	CRescaleFeatures
	Preprocessor RescaleFeautres is rescaling the range of features to make the features independent of each other and aims to scale the range in [0, 1] or [-1, 1]. More...
struct	CResultSet
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	CROCEvaluation
	Class ROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC). More...
class	CSalzbergWordStringKernel
	The SalzbergWordString kernel implements the Salzberg kernel. More...
class	CScalarResult
	Base class that stores the result of an independent job when the result is a scalar. More...
class	CScatterKernelNormalizer
	the scatter kernel normalizer More...
class	CScatterSVM
	ScatterSVM - Multiclass SVM. More...
class	CSegmentLoss
	class IntronList 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	CSequenceLabels
	Class CSequenceLabels used e.g. 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	CSerialComputationEngine
	Class that computes multiple independent instances of computation jobs sequentially. More...
class	CSerializableAsciiFile
	serializable ascii file More...
class	CSerializableFile
	serializable file More...
class	CSet
	the class CSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class	CSGDQN
	class SGDQN More...
class	CSGObject
	Class SGObject is the base class of all shogun objects. More...
class	CShareBoost
class	CShiftInvariantKernel
	Base class for the family of kernel functions that only depend on the difference of the inputs, i.e. whose values does not change if the inputs are shifted by the same amount. More precisely, \[ k(\mathbf{x}, \mathbf{x'}) = k(\mathbf{x-x'}) \] For example, Gaussian (RBF) kernel is a shfit invariant kernel. More...
class	CSigmoidKernel
	The standard Sigmoid kernel computed on dense real valued features. More...
class	CSignal
	Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process. More...
class	CSimpleFile
	Template class SimpleFile to read and write from files. More...
class	CSimpleLocalityImprovedStringKernel
	SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel. More...
class	CSingleFITCInference
	The Fully Independent Conditional Training inference base class for Laplace and regression for 1-D labels (1D regression and binary classification) More...
class	CSingleFITCLaplaceInferenceMethod
	The FITC approximation inference method class for regression and binary Classification. Note that the number of inducing points (m) is usually far less than the number of input points (n). (the time complexity is computed based on the assumption m < n) More...
class	CSingleFITCLaplaceNewtonOptimizer
	The build-in minimizer for SingleFITCLaplaceInference. More...
class	CSingleLaplaceInferenceMethod
	The SingleLaplace approximation inference method class for regression and binary Classification. More...
class	CSingleLaplaceNewtonOptimizer
	The build-in minimizer for SingleLaplaceInference. More...
class	CSingleSparseInference
	The sparse inference base class for classification and regression for 1-D labels (1D regression and binary classification) More...
class	CSmoothHingeLoss
	CSmoothHingeLoss implements the smooth hinge loss function. More...
class	CSNPFeatures
	Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class	CSNPStringKernel
	The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length. More...
class	CSOBI
	class SOBI More...
class	CSoftMaxLikelihood
	Class that models Soft-Max likelihood. 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	CSOSVMHelper
	class CSOSVMHelper contains helper functions to compute primal objectives, dual objectives, average training losses, duality gaps etc. These values will be recorded to check convergence. This class is inspired by the matlab implementation of the block coordinate Frank-Wolfe SOSVM solver [1]. More...
class	CSparseDistance
	template class SparseDistance More...
class	CSparseEuclideanDistance
	class SparseEucldeanDistance More...
singleton	CSparseFeatures
	Template class SparseFeatures implements sparse matrices. More...
class	CSparseInference
	The Fully Independent Conditional Training inference base class. More...
class	CSparseKernel
	Template class SparseKernel, is the base class of kernels working on sparse features. More...
class	CSparseMatrixOperator
	Class that represents a sparse-matrix linear operator. It computes matrix-vector product \(Ax\) in its apply method, \(A\in\mathbb{C}^{m\times n},A:\mathbb{C}^{n}\rightarrow \mathbb{C}^{m}\) being the matrix operator and \(x\in\mathbb{C}^{n}\) being the vector. The result is a vector \(y\in\mathbb{C}^{m}\). More...
class	CSparseMultilabel
	Class CSparseMultilabel to be used in the application of Structured Output (SO) learning to Multilabel classification. More...
class	CSparsePolyFeatures
	implement DotFeatures for the polynomial kernel More...
class	CSparsePreprocessor
	Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures. More...
class	CSparseSpatialSampleStringKernel
	Sparse Spatial Sample String Kernel by Pavel Kuksa pkuks.nosp@m.a@cs.nosp@m..rutg.nosp@m.ers..nosp@m.edu and Vladimir Pavlovic vladi.nosp@m.mir@.nosp@m.cs.ru.nosp@m.tger.nosp@m.s.edu More...
class	CSpecificityMeasure
	class SpecificityMeasure used to measure specificity of 2-class classifier. More...
class	CSpectrumMismatchRBFKernel
	spectrum mismatch rbf kernel More...
class	CSpectrumRBFKernel
	spectrum rbf kernel More...
class	CSphericalKernel
	Spherical kernel. More...
class	CSplineKernel
	Computes the Spline Kernel function which is the cubic polynomial. More...
class	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	CSqrtDiagKernelNormalizer
	SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements. More...
class	CSquaredHingeLoss
	Class CSquaredHingeLoss implements a squared hinge loss function. More...
class	CSquaredLoss
	CSquaredLoss implements the squared loss function. More...
class	CStateModel
	class CStateModel base, abstract class for the internal state representation used in the CHMSVMModel. More...
class	CStatistics
	Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc. More...
class	CStochasticGBMachine
	This class implements the stochastic gradient boosting algorithm for ensemble learning invented by Jerome H. Friedman. This class works with a variety of loss functions like squared loss, exponential loss, Huber loss etc which can be accessed through Shogun's CLossFunction interface (cf. http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CLossFunction.html). Additionally, it can create an ensemble of any regressor class derived from the CMachine class (cf. http://www.shogun-toolbox.org/doc/en/latest/classshogun_1_1CMachine.html). For one dimensional optimization, this class uses the backtracking linesearch accessed via Shogun's L-BFGS class. A concise description of the algorithm implemented can be found in the following link : http://en.wikipedia.org/wiki/Gradient_boosting#Algorithm. More...
class	CStochasticProximityEmbedding
	class StochasticProximityEmbedding used to construct embeddings of data using the Stochastic Proximity algorithm. More...
class	CStochasticSOSVM
	Class CStochasticSOSVM solves SOSVM using stochastic subgradient descent on the SVM primal problem [1], which is equivalent to SGD or Pegasos [2]. This class is inspired by the matlab SGD implementation in [3]. More...
singleton	CStoreScalarAggregator
	Template class that aggregates scalar job results in each submit_result call, finalize then transforms current aggregation into a CScalarResult. More...
class	CStoreVectorAggregator
	Abstract template class that aggregates vector job results in each submit_result call, finalize is abstract. 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	CStreamingAsciiFile
	Class StreamingAsciiFile to read vector-by-vector from ASCII files. 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	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	CStreamingHashedDenseFeatures
	This class acts as an alternative to the CStreamingDenseFeatures class and their difference is that the current example in this class is hashed into a smaller dimension dim. More...
class	CStreamingHashedDocDotFeatures
	This class implements streaming features for a document collection. Like in the standard Bag-of-Words representation, this class considers each document as a collection of tokens, which are then hashed into a new feature space of a specified dimension. This class is very flexible and allows the user to specify the tokenizer used to tokenize each document, specify whether the results should be normalized with regards to the sqrt of the document size, as well as to specify whether he wants to combine different tokens. The latter implements a k-skip n-grams approach, meaning that you can combine up to n tokens, while skipping up to k. Eg. for the tokens ["a", "b", "c", "d"], with n_grams = 2 and skips = 2, one would get the following combinations : ["a", "ab", "ac" (skipped 1), "ad" (skipped 2), "b", "bc", "bd" (skipped 1), "c", "cd", "d"]. More...
class	CStreamingHashedSparseFeatures
	This class acts as an alternative to the CStreamingSparseFeatures class and their difference is that the current example in this class is hashed into a smaller dimension dim. More...
class	CStreamingMMD
	Abstract base class that provides an interface for performing kernel two-sample test on streaming data using Maximum Mean Discrepancy (MMD) as the test statistic. The MMD is the distance of two probability distributions \(p\) and \(q\) in a RKHS (see [1] for formal description). 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	CStreamingVwCacheFile
	Class StreamingVwCacheFile to read vector-by-vector from VW cache files. More...
class	CStreamingVwFeatures
	This class implements streaming features for use with VW. 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	CStringDistance
	template class StringDistance More...
singleton	CStringFeatures
	Template class StringFeatures implements a list of strings. More...
class	CStringFileFeatures
	File based string features. More...
class	CStringKernel
	Template class StringKernel, is the base class of all String Kernels. More...
class	CStringMap
	The class is a customized map for the optimization framework. More...
class	CStringPreprocessor
	Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length). More...
class	CStructuredAccuracy
	class CStructuredAccuracy used to compute accuracy of structured classification More...
class	CStructuredData
	Base class of the components of StructuredLabels. More...
class	CStructuredLabels
	Base class of the labels used in Structured Output (SO) problems. More...
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	CStructuredOutputMachine
class	CStudentsTLikelihood
	Class that models a Student's-t likelihood. More...
class	CStudentsTVGLikelihood
	Class that models Student's T likelihood and uses numerical integration to approximate the following variational expection of log likelihood \[ \sum_{{i=1}^n}{E_{q(f_i|{\mu}_i,{\sigma}^2_i)}[logP(y_i|f_i)]} \] . More...
class	CSubsequenceStringKernel
	class SubsequenceStringKernel that implements String Subsequence Kernel (SSK) discussed by Lodhi et. al.[1]. A subsequence is any ordered sequence of \(n\) characters occurring in the text, though not necessarily contiguous. More formally, string \(u\) is a subsequence of string \(s\), iff there exists indices \(\mathbf{i}=(i_{1},\dots,i_{|u|})\), with \(1\le i_{1} \le \cdots \le i_{|u|} \le |s|\), such that \(u_{j}=s_{i_{j}}\) for \(j=1,\dots,|u|\), written as \(u=s[\mathbf{i}]\). The feature mapping \(\phi\) in this scenario is given by \[ \phi_{u}(s)=\sum_{\mathbf{i}:u=s[\mathbf{i}]}\lambda^{l(\mathbf{i})} \] for some \(lambda\le 1\), where \(l(\mathbf{i})\) is the length of the subsequence in \(s\), given by \(i_{|u|}-i_{1}+1\). The kernel here is an inner product in the feature space generated by all subsequences of length \(n\). \[ K_{n}(s,t)=\sum_{u\in\Sigma^{n}}\langle \phi_{u}(s), \phi_{u}(t)\rangle = \sum_{u\in\Sigma^{n}}\sum_{\mathbf{i}:u=s[\mathbf{i}]} \sum_{\mathbf{j}:u=t[\mathbf{j}]}\lambda^{l(\mathbf{i})+l(\mathbf{j})} \] Since the subsequences are weighted by the exponentially decaying factor \(\lambda\) of their full length in the text, more weight is given to those occurrences that are nearly contiguous. A direct computation is infeasible since the dimension of the feature space grows exponentially with \(n\). The paper describes an efficient computation approach using a dynamic programming technique. 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	CSumOne
	Preprocessor SumOne, normalizes vectors to have sum 1. 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	CSVMSGD
	class SVMSGD More...
class	CSVRLight
	Class SVRLight, performs support vector regression using SVMLight. More...
class	CTableFactorType
	Class CTableFactorType the way that store assignments of variables and energies in a table or a multi-array. More...
class	CTanimotoDistance
	class Tanimoto coefficient More...
class	CTanimotoKernelNormalizer
	TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ) 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	CTaxonomy
	CTaxonomy is used to describe hierarchical structure between tasks. More...
class	CTDistributedStochasticNeighborEmbedding
	class CTDistributedStochasticNeighborEmbedding used to embed data using t-distributed stochastic neighbor embedding algorithm: http://jmlr.csail.mit.edu/papers/volume9/vandermaaten08a/vandermaaten08a.pdf. More...
class	CTensorProductPairKernel
	Computes the Tensor Product Pair Kernel (TPPK). More...
class	CThresholdRejectionStrategy
	threshold based rejection strategy More...
class	CTime
	Class Time that implements a stopwatch based on either cpu time or wall clock time. More...
class	CTokenizer
	The class CTokenizer acts as a base class in order to implement tokenizers. Sub-classes must implement the methods has_next(), next_token_idx() and get_copy(). More...
class	CTOPFeatures
	The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models. More...
class	CTraceSampler
	Abstract template base class that provides an interface for sampling the trace of a linear operator using an abstract sample method. More...
class	CTreeMachine
	class TreeMachine, a base class for tree based multiclass classifiers. This class is derived from CBaseMulticlassMachine and stores the root node (of class type CTreeMachineNode) to the tree structure More...
class	CTreeMachineNode
	The node of the tree structure forming a TreeMachine The node contains a pointer to its parent and a vector of pointers to its children. A node of this class can have only one parent but any number of children.The node also contains data which can be of any type and has to be specified using template specifier. 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	CTStudentKernel
	Generalized T-Student kernel. More...
class	CTwoSampleTest
	Provides an interface for performing the classical two-sample test i.e. Given samples from two distributions \(p\) and \(q\), the null-hypothesis is: \(H_0: p=q\), the alternative hypothesis: \(H_1: p\neq q\). More...
class	CTwoStateModel
	class CTwoStateModel class for the internal two-state representation used in the CHMSVMModel. More...
class	CUAIFile
	Class UAIFILE used to read data from UAI files. See http://graphmod.ics.uci.edu/uai08/FileFormat for more details. More...
class	CUWedge
	Class UWedge. More...
class	CUWedgeSep
	class UWedgeSep More...
class	CVarDTCInferenceMethod
	The inference method class based on the Titsias' variational bound. For more details, see Titsias, Michalis K. "Variational learning of inducing variables in sparse Gaussian processes." International Conference on Artificial Intelligence and Statistics. 2009. More...
class	CVarianceKernelNormalizer
	VarianceKernelNormalizer divides by the ``variance''. More...
class	CVariationalGaussianLikelihood
	The variational Gaussian Likelihood base class. The variational distribution is Gaussian. More...
class	CVariationalLikelihood
	The Variational Likelihood base class. More...
class	CVectorResult
	Base class that stores the result of an independent job when the result is a vector. More...
class	CVowpalWabbit
	Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit. More...
class	CVwAdaptiveLearner
	VwAdaptiveLearner uses an adaptive subgradient technique to update weights. 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	CVwConditionalProbabilityTree
class	CVwEnvironment
	Class CVwEnvironment is the environment used by VW. More...
class	CVwLearner
	Base class for all VW learners. 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	CVwNonAdaptiveLearner
	VwNonAdaptiveLearner uses a standard gradient descent weight update rule. 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	CWaveKernel
	Wave kernel. More...
class	CWaveletKernel
	the class WaveletKernel More...
class	CWDFeatures
	Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class	CWeightedCommWordStringKernel
	The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient \(\beta_k\)) from strings that have been mapped into unsigned 16bit integers. More...
class	CWeightedDegreePositionStringKernel
	The Weighted Degree Position String kernel (Weighted Degree kernel with shifts). More...
class	CWeightedDegreeRBFKernel
	weighted degree RBF kernel More...
class	CWeightedDegreeStringKernel
	The Weighted Degree String kernel. More...
class	CWeightedMajorityVote
	Weighted Majority Vote implementation. More...
class	CWRACCMeasure
	class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier. More...
class	CWrappedBasic
	Simple wrapper class that allows to store any Shogun basic parameter (i.e. float64_t, int64_t, char, etc) in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors. More...
class	CWrappedObjectArray
	Specialization of CDynamicObjectArray that adds methods to append wrapped elements to make them serializable. Objects are wrapped through the classes CWrappedBasic, CWrappedSGVector, CWrappedSGMatrix. More...
class	CWrappedSGMatrix
	Simple wrapper class that allows to store any Shogun SGMatrix<T> in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors. More...
class	CWrappedSGVector
	Simple wrapper class that allows to store any Shogun SGVector<T> in a CSGObject, and therefore to make it serializable. Using a template argument that is not a Shogun parameter will cause a compile error when trying to register the passed value as a parameter in the constructors. More...
class	CZeroMean
	The zero mean function class. More...
class	CZeroMeanCenterKernelNormalizer
	ZeroMeanCenterKernelNormalizer centers the kernel in feature space. More...
class	DescendCorrection
	This is a base class for descend based correction method. More...
class	DescendUpdater
	This is a base class for descend update. More...
class	DescendUpdaterWithCorrection
	This is a base class for descend update with descend based correction. More...
singleton	DynArray
	Template Dynamic array class that creates an array that can be used like a list or an array. More...
class	EigenSparseUtil
	This class contains some utilities for Eigen3 Sparse Matrix integration with shogun. Currently it provides a method for converting SGSparseMatrix to Eigen3 SparseMatrix. More...
class	ElasticNetPenalty
	The is the base class for ElasticNet penalty/regularization within the FirstOrderMinimizer framework. More...
class	FirstOrderBoundConstraintsCostFunction
	The first order cost function base class with bound constrains. More...
class	FirstOrderCostFunction
	The first order cost function base class. More...
class	FirstOrderMinimizer
	The first order minimizer base class. More...
class	FirstOrderSAGCostFunction
	The class is about a stochastic cost function for stochastic average minimizers. More...
class	FirstOrderStochasticCostFunction
	The first order stochastic cost function base class. More...
class	FirstOrderStochasticMinimizer
	The base class for stochastic first-order gradient-based minimizers. More...
struct	GCEdge
	Graph cuts edge. More...
struct	GCNode
	Graph cuts node. More...
struct	GCNodePtr
	Graph guts node pointer. More...
class	GradientDescendUpdater
	The class implements the gradient descend method. More...
struct	id3TreeNodeData
	structure to store data of a node of id3 tree. This can be used as a template type in TreeMachineNode class. Ex: id3 algorithm uses nodes of type CTreeMachineNode<id3TreeNodeData> More...
class	InverseScalingLearningRate
	The implements the inverse scaling learning rate. More...
class	IterativeSolverIterator
	template class that is used as an iterator for an iterative linear solver. In the iteration of solving phase, each solver initializes the iteration with a maximum number of iteration limit, and relative/ absolute tolerence. They then call begin with the residual vector and continue until its end returns true, i.e. either it has converged or iteration count reached maximum limit. More...
struct	K_THREAD_PARAM
class	L1Penalty
	The is the base class for L1 penalty/regularization within the FirstOrderMinimizer framework. More...
class	L1PenaltyForTG
	The is the base class for L1 penalty/regularization within the FirstOrderMinimizer framework. More...
class	L2Penalty
	The class implements L2 penalty/regularization within the FirstOrderMinimizer framework. More...
struct	lbfgs_parameter_t
class	LearningRate
	The base class about learning rate for descent-based minimizers. More...
class	MappedSparseMatrix
	mapped sparse matrix for representing graph relations of tasks More...
class	MappingFunction
	The base mapping function for mirror descend. More...
class	Maybe
	Holder that represents an object that can be either present or absent. Quite simllar to std::optional introduced in C++14, but provides a way to pass the reason of absence (e.g. "incorrect parameter"). More...
class	Minimizer
	The minimizer base class. More...
struct	MixModelData
	This structure is used for storing data required for using the generic Expectation Maximization (EM) implemented by the template class CEMBase for mixture models like gaussian mixture model, multinomial mixture model etc. The EM specialized for mixture models is implemented by the class CEMMixtureModel which uses this MixModelData structure. 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	Model
	class Model More...
class	MomentumCorrection
	This is a base class for momentum correction methods. More...
class	Munkres
	Munkres. More...
struct	NbodyTreeNodeData
	structure to store data of a node of N-Body tree. This can be used as a template type in TreeMachineNode class. N-Body tree building algorithm uses nodes of type CBinaryTreeMachineNode<NbodyTreeNodeData> More...
class	NesterovMomentumCorrection
	This implements the Nesterov's Accelerated Gradient (NAG) correction. More...
class	Nothing
class	Parallel
	Class Parallel provides helper functions for multithreading. More...
class	Parameter
	Parameter class. More...
class	Penalty
	The base class for penalty/regularization used in minimization. More...
class	PNormMappingFunction
	This implements the P-norm mapping/projection function. More...
class	PointerValueAnyPolicy
	This is one concrete implementation of policy that uses void pointers to store values. More...
class	ProximalPenalty
	The base class for sparse penalty/regularization used in minimization. More...
class	RefCount
struct	RelaxedTreeNodeData
class	RelaxedTreeUtil
class	RmsPropUpdater
	The class implements the RmsProp method. More...
class	SerializableAsciiReader00
	Serializable ascii reader. More...
class	SGDMinimizer
	The class implements the stochastic gradient descend (SGD) minimizer. More...
class	SGIO
	Class SGIO, used to do input output operations throughout shogun. More...
singleton	SGMatrix
	shogun matrix More...
singleton	SGMatrixList
	shogun matrix list More...
class	SGNDArray
	shogun n-dimensional array More...
class	SGReferencedData
	shogun reference count managed data More...
singleton	SGSparseMatrix
	template class SGSparseMatrix More...
singleton	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...
struct	SGSparseVectorEntry
	template class SGSparseVectorEntry More...
singleton	SGString
	shogun string More...
class	SGStringList
	template class SGStringList More...
singleton	SGVector
	shogun vector More...
class	ShareBoostOptimizer
class	ShogunException
	Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs. More...
class	SMDMinimizer
	The class implements the stochastic mirror descend (SMD) minimizer. More...
class	SMIDASMinimizer
	The class implements the Stochastic MIrror Descent mAde Sparse (SMIDAS) minimizer. More...
class	SparsePenalty
	The base class for sparse penalty/regularization used in minimization. More...
struct	SparsityStructure
	Struct that represents the sparsity structure of the Sparse Matrix in CRS. Implementation has been adapted from Krylstat (https://github.com/ Froskekongen/KRYLSTAT) library (c) Erlend Aune erlen.nosp@m.da@m.nosp@m.ath.n.nosp@m.tnu..nosp@m.no under GPL2+. More...
struct	SSKFeatures
	SSKFeatures. More...
class	StandardMomentumCorrection
	This implements the plain momentum correction. More...
struct	substring
	struct Substring, specified by start position and end position. More...
class	SVRGMinimizer
	The class implements the stochastic variance reduced gradient (SVRG) minimizer. More...
class	Tag
	Acts as an identifier for a shogun object. It contains type information and name of the object. Generally used to CSGObject::set() and CSGObject::get() parameters of a class. More...
struct	tag_callback_data
struct	tag_iteration_data
struct	TMultipleCPinfo
struct	TParameter
	parameter struct More...
struct	TSGDataType
	Datatypes that shogun supports. More...
class	Unique
class	v_array
	Class v_array taken directly from JL's implementation. More...
class	Version
	Class Version provides version information. More...
struct	VwConditionalProbabilityTreeNodeData
class	VwExample
	Example class for VW. More...
class	VwFeature
	One feature in VW. More...
class	VwLabel
	Class VwLabel holds a label object used by VW. More...

Typedefs
typedef std::map< BaseTag, Any >	ParametersMap
typedef uint32_t(*	hash_func_t )(substring, uint32_t)
	Hash function typedef, takes a substring and seed as parameters. More...
typedef uint32_t	vw_size_t
	vw_size_t typedef to work across platforms More...
typedef int32_t(CVwParser::*	parse_func )(CIOBuffer *, VwExample *&)
	Parse function typedef. Takes an IOBuffer and VwExample as arguments. More...
typedef float64_t	KERNELCACHE_ELEM
typedef int64_t	KERNELCACHE_IDX
typedef struct shogun::_IterInfo	IterInfo
	struct that contains current state of the iteration for iterative linear solvers More...
typedef CBinaryTreeMachineNode < VwConditionalProbabilityTreeNodeData >	bnode_t
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)
typedef float64_t(*	lbfgs_adjust_step_t )(void *instance, const float64_t *x, const float64_t *d, const int n, const float64_t step)
HMM specific types
typedef float64_t	T_ALPHA_BETA_TABLE
	type for alpha/beta caching table More...
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 More...
typedef CDynInt< uint64_t, 4 >	uint256_t
	256 bit integer constructed out of 4 64bit uint64_t's More...
typedef CDynInt< uint64_t, 8 >	uint512_t
	512 bit integer constructed out of 8 64bit uint64_t's More...
typedef CDynInt< uint64_t, 16 >	uint1024_t
	1024 bit integer constructed out of 16 64bit uint64_t's More...

Enumerations
enum	EModelSelectionAvailability { MS_NOT_AVAILABLE =0, MS_AVAILABLE =1 }
enum	EGradientAvailability { GRADIENT_NOT_AVAILABLE =0, GRADIENT_AVAILABLE =1 }
enum	ELDAMethod { AUTO_LDA = 10, SVD_LDA = 20, FLD_LDA = 30 }
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,   D_CUSTOMMAHALANOBIS = 200 }
enum	ECovType { FULL, DIAG, SPHERICAL }
enum	BaumWelchViterbiType {   BW_NORMAL, BW_TRANS, BW_DEFINED, VIT_NORMAL,   VIT_DEFINED }
enum	EEvaluationMode { EM_KDTREE_SINGLE, EM_BALLTREE_SINGLE, EM_KDTREE_DUAL, EM_BALLTREE_DUAL }
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 =0, ED_MAXIMIZE =1 }
enum	EEvaluationResultType { CROSSVALIDATION_RESULT =0, GRADIENTEVALUATION_RESULT =1 }
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_MATRIX = 180, C_FACTOR_GRAPH = 190, C_INDEX = 200,   C_SUB_SAMPLES_DENSE =300, C_ANY = 1000 }
	shogun feature class More...
enum	EFeatureProperty { FP_NONE = 0, FP_DOT = 1, FP_STREAMING_DOT = 2 }
	shogun feature properties More...
enum	KernelName { GAUSSIAN, NOT_SPECIFIED }
enum	EMessageType {   MSG_GCDEBUG =0, MSG_DEBUG =1, MSG_INFO =2, MSG_NOTICE =3,   MSG_WARN =4, MSG_ERROR =5, MSG_CRITICAL =6, MSG_ALERT =7,   MSG_EMERGENCY =8, MSG_MESSAGEONLY =9 }
enum	EMessageLocation { MSG_NONE =0, MSG_FUNCTION =1, MSG_LINE_AND_FILE =2 }
enum	EARDKernelType { KT_SCALAR =10, KT_DIAG =20, KT_FULL =30 }
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_GAUSSIANCOMPACT = 34, 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_EXPONENTIALARD = 500, K_GAUSSIANARD = 510, K_GAUSSIANARDSPARSE = 511,   K_STREAMING = 520, K_PERIODIC = 530 }
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, SDT_FACTOR_GRAPH = 3,   SDT_SPARSE_MULTILABEL = 4 }
enum	ELossType {   L_HINGELOSS = 0, L_SMOOTHHINGELOSS = 10, L_SQUAREDHINGELOSS = 20, L_SQUAREDLOSS = 30,   L_EXPONENTIALLOSS = 40, L_ABSOLUTEDEVIATIONLOSS = 50, L_HUBERLOSS = 60, L_LOGLOSS = 100,   L_LOGLOSSMARGIN = 110 }
	shogun loss type More...
enum	EInferenceType {   INF_NONE =0, INF_EXACT =10, INF_SPARSE =20, INF_FITC_REGRESSION =21,   INF_FITC_LAPLACE_SINGLE =22, INF_LAPLACE =30, INF_LAPLACE_SINGLE =31, INF_LAPLACE_MULTIPLE =32,   INF_EP =40, INF_KL =50, INF_KL_DIAGONAL =51, INF_KL_CHOLESKY =52,   INF_KL_COVARIANCE =53, INF_KL_DUAL =54, INF_KL_SPARSE_REGRESSION =55 }
enum	ELikelihoodModelType {   LT_NONE =0, LT_GAUSSIAN =10, LT_STUDENTST =20, LT_LOGIT =30,   LT_PROBIT =40, LT_SOFTMAX =50 }
enum	EMCSamplerType { MC_Probability, MC_Mean, MC_Variance }
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_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, CT_LIBQPSOSVM = 500, CT_PRIMALMOSEKSOSVM = 510, CT_CCSOSVM = 520,   CT_GAUSSIANPROCESSBINARY = 530, CT_GAUSSIANPROCESSMULTICLASS = 540, CT_STOCHASTICSOSVM = 550, CT_NEURALNETWORK = 560,   CT_BAGGING = 570, CT_FWSOSVM = 580, CT_BCFWSOSVM = 590, CT_GAUSSIANPROCESSCLASS }
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, PT_CLASS = 5 }
enum	EStructRiskType {   N_SLACK_MARGIN_RESCALING = 0, N_SLACK_SLACK_RESCALING = 1, ONE_SLACK_MARGIN_RESCALING = 2, ONE_SLACK_SLACK_RESCALING = 3,   CUSTOMIZED_RISK = 4 }
enum	E_PROB_TYPE { E_LINEAR, E_QP }
enum	EDirectSolverType {   DS_LLT =0, DS_QR_NOPERM =1, DS_QR_COLPERM =2, DS_QR_FULLPERM =3,   DS_SVD =4 }
enum	EOperatorFunction { OF_SQRT =0, OF_LOG =1, OF_POLY =2, OF_UNDEFINED =3 }
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	KNN_SOLVER { KNN_BRUTE, KNN_KDTREE, KNN_COVER_TREE }
enum	EProbHeuristicType {   PROB_HEURIS_NONE = 0, OVA_NORM = 1, OVA_SOFTMAX = 2, OVO_PRICE = 3,   OVO_HASTIE = 4, OVO_HAMAMURA = 5 }
enum	SCATTER_TYPE { NO_BIAS_LIBSVM, NO_BIAS_SVMLIGHT, TEST_RULE1, TEST_RULE2 }
enum	EAENoiseType { AENT_NONE =0, AENT_DROPOUT =1, AENT_GAUSSIAN =2 }
	Determines the noise type for denoising autoencoders. More...
enum	EConvMapActivationFunction { CMAF_IDENTITY = 0, CMAF_LOGISTIC = 1, CMAF_RECTIFIED_LINEAR = 2 }
	Determines the activation function for neurons in a convolutional feature map. More...
enum	EInitializationMode { NORMAL =0, RE_NORMAL =1 }
enum	ENLAutoencoderPosition { NLAP_NONE =0, NLAP_ENCODING =1, NLAP_DECODING =2 }
enum	ENNOptimizationMethod { NNOM_GRADIENT_DESCENT =0, NNOM_LBFGS =1 }
enum	ERBMMonitoringMethod { RBMMM_RECONSTRUCTION_ERROR =0, RBMMM_PSEUDO_LIKELIHOOD =1 }
enum	ERBMVisibleUnitType { RBMVUT_BINARY =0, RBMVUT_GAUSSIAN =1, RBMVUT_SOFTMAX =2 }
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, LBFGSERR_INVALID_VALUE }
enum	{   LBFGS_LINESEARCH_DEFAULT = MORETHUENTE, LBFGS_LINESEARCH_MORETHUENTE = MORETHUENTE, LBFGS_LINESEARCH_BACKTRACKING_ARMIJO = BACKTRACKING_ARMIJO, LBFGS_LINESEARCH_BACKTRACKING = BACKTRACKING_WOLFE,   LBFGS_LINESEARCH_BACKTRACKING_WOLFE = BACKTRACKING_WOLFE, LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE = BACKTRACKING_STRONG_WOLFE }
enum	ELBFGSLineSearch { MORETHUENTE =0, BACKTRACKING_ARMIJO =1, BACKTRACKING_WOLFE =2, BACKTRACKING_STRONG_WOLFE =3 }
enum	EFeatureSelectionAlgorithm { BACKWARD_ELIMINATION, FORWARD_SELECTION }
enum	EFeatureRemovalPolicy { N_SMALLEST, PERCENTILE_SMALLEST, N_LARGEST, PERCENTILE_LARGEST }
enum	EFLDAMethod { AUTO_FLDA = 10, CANVAR_FLDA = 20, CLASSIC_FLDA = 30 }
enum	HomogeneousKernelType { HomogeneousKernelIntersection = 0, HomogeneousKernelChi2, HomogeneousKernelJS }
	Type of kernel. More...
enum	HomogeneousKernelMapWindowType { HomogeneousKernelMapWindowUniform = 0, HomogeneousKernelMapWindowRectangular = 1 }
	Type of spectral windowing function. More...
enum	EPCAMethod { AUTO = 10, SVD = 20, EVD = 30 }
enum	EPCAMode { THRESHOLD, VARIANCE_EXPLAINED, FIXED_NUMBER }
enum	EPCAMemoryMode { MEM_REALLOCATE, MEM_IN_PLACE }
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,   P_RESCALEFEATURES = 200, P_FISHERLDA = 210, P_BAHSIC = 220 }
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	EStatisticType { S_LINEAR_TIME_MMD, S_QUADRATIC_TIME_MMD, S_HSIC, S_NOCCO }
enum	ENullApproximationMethod {   PERMUTATION, MMD2_SPECTRUM_DEPRECATED, MMD2_SPECTRUM, MMD2_GAMMA,   MMD1_GAUSSIAN, HSIC_GAMMA }
enum	EQuadraticMMDType {   BIASED, BIASED_DEPRECATED, UNBIASED, UNBIASED_DEPRECATED,   INCOMPLETE }
enum	EQPType { MOSEK =1, SVMLIGHT =2 }
enum	ETerminalType { SOURCE = 0, SINK = 1 }
enum	EMAPInferType {   TREE_MAX_PROD = 0, LOOPY_MAX_PROD = 1, LP_RELAXATION = 2, TRWS_MAX_PROD = 3,   GRAPH_CUT = 4, GEMPLP = 5 }
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 ()
void	set_global_rand (CRandom *rand)
CRandom *	get_global_rand ()
void	init_from_env ()
template<typename T >
static Maybe< T >	Just (const T &value)
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)
template<typename T >
std::string	demangledType ()
bool	operator== (const Any &lhs, const Any &rhs)
bool	operator!= (const Any &lhs, const Any &rhs)
template<typename T >
Any	erase_type (const T &v)
template<typename T >
T	recall_type (const Any &any)
bool	operator== (const BaseTag &first, const BaseTag &second)
bool	operator!= (const BaseTag &first, const BaseTag &second)
bool	operator< (const BaseTag &first, const BaseTag &second)
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)
void *	sg_malloc (size_t size)
void *	sg_calloc (size_t num, size_t size)
void	sg_free (void *ptr)
void *	sg_realloc (void *ptr, size_t size)
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)
void	wrap_dstemr (char jobz, char range, int n, double *diag, double *subdiag, double vl, double vu, int il, int iu, int *m, double *w, double *z__, int ldz, int nzc, int *isuppz, int tryrac, 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, lbfgs_adjust_step_t proc_adjust_step)
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, lbfgs_adjust_step_t proc_adjust_step=NULL)

Variables
Parallel *	sg_parallel =NULL
SGIO *	sg_io =NULL
Version *	sg_version =NULL
CMath *	sg_math =NULL
CRandom *	sg_rand =NULL
void(*	sg_print_message )(FILE *target, const char *str) = NULL
	function called to print normal messages More...
void(*	sg_print_warning )(FILE *target, const char *str) = NULL
	function called to print warning messages More...
void(*	sg_print_error )(FILE *target, const char *str) = NULL
	function called to print error messages More...
void(*	sg_cancel_computations )(bool &delayed, bool &immediately) =NULL
	function called to cancel things More...
const int32_t	quadratic_constant = 27942141
	Constant used while hashing/accessing quadratic features. More...
const int32_t	constant_hash = 11650396
	Constant used to access the constant feature. More...
const uint32_t	hash_base = 97562527
	Seed for hash. More...
const int32_t	LOGSUM_TBL =10000
static const float64_t	Q_test_statistic_values [10][8]
static const lbfgs_parameter_t	_defparam

Detailed Description

all of classes and functions are contained in the shogun namespace

Typedef Documentation

typedef CBinaryTreeMachineNode<VwConditionalProbabilityTreeNodeData> bnode_t

define shortcut for the node type

Definition at line 37 of file VwConditionalProbabilityTree.h.

typedef struct tag_callback_data callback_data_t

Definition at line 89 of file lbfgs.cpp.

typedef uint32_t(* hash_func_t)(substring, uint32_t)

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

Definition at line 23 of file vw_constants.h.

typedef struct tag_iteration_data iteration_data_t

Definition at line 97 of file lbfgs.cpp.

typedef struct shogun::_IterInfo IterInfo

struct that contains current state of the iteration for iterative linear solvers

typedef float64_t KERNELCACHE_ELEM

kernel cache element

Definition at line 35 of file Kernel.h.

typedef int64_t KERNELCACHE_IDX

kernel cache index

Definition at line 46 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 108 of file lbfgs.cpp.

typedef T_STATES * P_STATES

Definition at line 64 of file HMM.h.

typedef std::map<BaseTag, Any> ParametersMap

Definition at line 42 of file SGObject.cpp.

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

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

Definition at line 22 of file StreamingVwFile.h.

typedef float64_t T_ALPHA_BETA_TABLE

type for alpha/beta caching table

Definition at line 35 of file HMM.h.

typedef uint8_t T_STATES

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

Definition at line 62 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 576 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 567 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 570 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 573 of file DynInt.h.

typedef uint32_t vw_size_t

vw_size_t typedef to work across platforms

Definition at line 26 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 69 of file HMM.h.

enum E_COMPRESSION_TYPE

compression type

Enumerator
UNCOMPRESSED
LZO
GZIP
BZIP2
LZMA
SNAPPY

Definition at line 21 of file Compressor.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 30 of file VwParser.h.

enum EAENoiseType

Determines the noise type for denoising autoencoders.

Enumerator
AENT_NONE	No noise is applied
AENT_DROPOUT	Each neuron in the input layer is randomly set to zero with some probability
AENT_GAUSSIAN	Gaussian noise is added to neurons in the input layer

Definition at line 47 of file Autoencoder.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 23 of file Alphabet.h.

enum EARDKernelType

ExponentialARD type

Enumerator
KT_SCALAR
KT_DIAG
KT_FULL

Definition at line 24 of file ExponentialARDKernel.h.

enum EContingencyTableMeasureType

type of measure

Enumerator
ACCURACY
ERROR_RATE
BAL
WRACC
F1
CROSS_CORRELATION
RECALL
PRECISION
SPECIFICITY
CUSTOM

Definition at line 27 of file ContingencyTableEvaluation.h.

enum EConvMapActivationFunction

Determines the activation function for neurons in a convolutional feature map.

Enumerator
CMAF_IDENTITY	Identity activation function: \( f(x) = x \)
CMAF_LOGISTIC	Logistic activation function: \( f(x) = \frac{1}{1+exp(-x)} \)
CMAF_RECTIFIED_LINEAR	Rectified linear activation function: \( f(x) = max(x,0) \)

Definition at line 46 of file ConvolutionalFeatureMap.h.

enum ECovType

Covariance type

Enumerator
FULL	full covariance
DIAG	diagonal covariance
SPHERICAL	spherical covariance

Definition at line 32 of file Gaussian.h.

enum EDirectSolverType

solver type for direct solvers

Enumerator
DS_LLT
DS_QR_NOPERM
DS_QR_COLPERM
DS_QR_FULLPERM
DS_SVD

Definition at line 21 of file DirectLinearSolverComplex.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
D_CUSTOMMAHALANOBIS

Definition at line 32 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 29 of file Evaluation.h.

enum EEvaluationMode

Evaluation method of KDE, see CKernelDensity

Enumerator
EM_KDTREE_SINGLE
EM_BALLTREE_SINGLE
EM_KDTREE_DUAL
EM_BALLTREE_DUAL

Definition at line 43 of file KernelDensity.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_MATRIX
C_FACTOR_GRAPH
C_INDEX
C_SUB_SAMPLES_DENSE
C_ANY

Definition at line 38 of file FeatureTypes.h.

enum EFeatureProperty

shogun feature properties

Enumerator
FP_NONE
FP_DOT
FP_STREAMING_DOT

Definition at line 65 of file FeatureTypes.h.

enum EFeatureRemovalPolicy

Enum for feature removal policy in feature selection algorithms. See class documentation of CFeatureSelection for their descriptions.

Enumerator
N_SMALLEST
PERCENTILE_SMALLEST
N_LARGEST
PERCENTILE_LARGEST

Definition at line 56 of file FeatureSelection.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 EFeatureSelectionAlgorithm

Enum for feature selection algorithms. See class documentation of CFeatureSelection for their descriptions.

Enumerator
BACKWARD_ELIMINATION
FORWARD_SELECTION

Definition at line 47 of file FeatureSelection.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 19 of file FeatureTypes.h.

enum EFLDAMethod

Matrix decomposition method for Fisher LDA

Enumerator
AUTO_FLDA	if N>D then CLASSIC_FLDA is chosen automatically else CANVAR_FLDA is chosen (D-dimensions N-number of vectors)
CANVAR_FLDA	Cannonical Variable based FLDA.
CLASSIC_FLDA	Classical Fisher Linear Discriminant Analysis.

Definition at line 48 of file FisherLDA.h.

enum EGradientAvailability

gradient availability

Enumerator
GRADIENT_NOT_AVAILABLE
GRADIENT_AVAILABLE

Definition at line 97 of file SGObject.h.

enum EInferenceType

inference type

Enumerator
INF_NONE
INF_EXACT
INF_SPARSE
INF_FITC_REGRESSION
INF_FITC_LAPLACE_SINGLE
INF_LAPLACE
INF_LAPLACE_SINGLE
INF_LAPLACE_MULTIPLE
INF_EP
INF_KL
INF_KL_DIAGONAL
INF_KL_CHOLESKY
INF_KL_COVARIANCE
INF_KL_DUAL
INF_KL_SPARSE_REGRESSION

Definition at line 53 of file Inference.h.

enum EInitializationMode

Specifies the type of initialization of parameters/weights for this layer.

Enumerator
NORMAL	Specify the standard deviation of the Gaussian Distribution at the time of initialization of the network
RE_NORMAL	Sets standard deviation to \[ \sqrt{\frac{2}{input\_width\times input\_height\times input\_channels}} \]

Definition at line 46 of file NeuralConvolutionalLayer.h.

enum EKernelProperty

kernel property

Enumerator
KP_NONE
KP_LINADD
KP_KERNCOMBINATION
KP_BATCHEVALUATION

Definition at line 124 of file Kernel.h.

enum EKernelType

kernel type

Enumerator
K_UNKNOWN
K_LINEAR
K_POLY
K_GAUSSIAN
K_GAUSSIANSHIFT
K_GAUSSIANMATCH
K_GAUSSIANCOMPACT
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_EXPONENTIALARD
K_GAUSSIANARD
K_GAUSSIANARDSPARSE
K_STREAMING
K_PERIODIC

Definition at line 57 of file Kernel.h.

enum ELBFGSLineSearch

linesearch method

Enumerator
MORETHUENTE
BACKTRACKING_ARMIJO
BACKTRACKING_WOLFE
BACKTRACKING_STRONG_WOLFE

Definition at line 13 of file lbfgscommon.h.

enum ELDAMethod

Method for solving LDA

Enumerator
AUTO_LDA	if N>D then FLD_LDA is chosen automatically else SVD_LDA is chosen (D-dimensions N-number of vectors)
SVD_LDA	Singular Value Decomposition based LDA.
FLD_LDA	Fisher two class discrimiant based LDA.

Definition at line 26 of file LDA.h.

enum ELikelihoodModelType

type of likelihood model

Enumerator
LT_NONE
LT_GAUSSIAN
LT_STUDENTST
LT_LOGIT
LT_PROBIT
LT_SOFTMAX

Definition at line 47 of file LikelihoodModel.h.

enum ELossType

shogun loss type

Enumerator
L_HINGELOSS
L_SMOOTHHINGELOSS
L_SQUAREDHINGELOSS
L_SQUAREDLOSS
L_EXPONENTIALLOSS
L_ABSOLUTEDEVIATIONLOSS
L_HUBERLOSS
L_LOGLOSS
L_LOGLOSSMARGIN

Definition at line 28 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_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
CT_LIBQPSOSVM
CT_PRIMALMOSEKSOSVM
CT_CCSOSVM
CT_GAUSSIANPROCESSBINARY
CT_GAUSSIANPROCESSMULTICLASS
CT_STOCHASTICSOSVM
CT_NEURALNETWORK
CT_BAGGING
CT_FWSOSVM
CT_BCFWSOSVM
CT_GAUSSIANPROCESSCLASS

Definition at line 33 of file Machine.h.

enum EMAPInferType

the following inference methods are acceptable: Tree Max Product, Loopy Max Product, LP Relaxation, Sequential Tree Reweighted Max Product (TRW-S), Graph cuts

Enumerator
TREE_MAX_PROD
LOOPY_MAX_PROD
LP_RELAXATION
TRWS_MAX_PROD
GRAPH_CUT
GEMPLP

Definition at line 29 of file MAPInference.h.

enum EMCSamplerType

mc sampler type

Enumerator
MC_Probability
MC_Mean
MC_Variance

Definition at line 54 of file SoftMaxLikelihood.h.

enum EMessageLocation

The io functions can optionally prepend the function name or the line from where the print occurred.

Enumerator
MSG_NONE
MSG_FUNCTION
MSG_LINE_AND_FILE

Definition at line 61 of file SGIO.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 44 of file SGIO.h.

enum EModelSelectionAvailability

model selection availability

Enumerator
MS_NOT_AVAILABLE
MS_AVAILABLE

Definition at line 91 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 32 of file ModelSelectionParameters.h.

enum ENLAutoencoderPosition

For autoencoders, specifies the position of the layer in the autoencoder, i.e an encoding layer or a decoding layer

Enumerator
NLAP_NONE	The layer is not a part of an autoencoder
NLAP_ENCODING	The layer is an encoding layer
NLAP_DECODING	The layer is a decoding layer

Definition at line 49 of file NeuralLayer.h.

enum ENNOptimizationMethod

optimization method for neural networks

Enumerator
NNOM_GRADIENT_DESCENT
NNOM_LBFGS

Definition at line 49 of file NeuralNetwork.h.

enum ENormalizerType

normalizer type

Enumerator
N_REGULAR
N_MULTITASK

Definition at line 23 of file KernelNormalizer.h.

enum ENullApproximationMethod

enum for different method to approximate null-distibution

Enumerator
PERMUTATION
MMD2_SPECTRUM_DEPRECATED
MMD2_SPECTRUM
MMD2_GAMMA
MMD1_GAUSSIAN
HSIC_GAMMA

Definition at line 51 of file HypothesisTest.h.

enum EOperatorFunction

linear operator function types

Enumerator
OF_SQRT
OF_LOG
OF_POLY
OF_UNDEFINED

Definition at line 23 of file OperatorFunction.h.

enum EOptimizationType

optimization type

Enumerator
FASTBUTMEMHUNGRY
SLOWBUTMEMEFFICIENT

Definition at line 50 of file Kernel.h.

enum EPCAMemoryMode

memory usage by PCA : In-place or through reallocation

Enumerator
MEM_REALLOCATE	The feature matrix replaced by new matrix with target dims. This requires memory for old matrix as well as new matrix at once for a short amount of time initially.
MEM_IN_PLACE	The feature matrix is modified in-place to generate the new matrix. Output matrix dimensions are changed to target dims, but actual matrix size remains same internally. Modifies initial data matrix

Definition at line 52 of file PCA.h.

enum EPCAMethod

Matrix decomposition method for PCA

Enumerator
AUTO	if N>D then EVD is chosen automatically else SVD is chosen (D-dimensions N-number of vectors)
SVD	SVD based PCA. Time complexity ~14dn^2 (d-dimensions n-number of vectors)
EVD	Eigenvalue decomposition of covariance matrix. Time complexity ~10d^3 (d-dimensions n-number of vectors)

Definition at line 26 of file PCA.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 41 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
P_RESCALEFEATURES
P_FISHERLDA
P_BAHSIC

Definition at line 32 of file Preprocessor.h.

enum EProbHeuristicType

multiclass prob output heuristics in [1] OVA_NORM: simple normalization of probabilites, eq.(6) OVA_SOFTMAX: normalizing using softmax function, eq.(7) OVO_PRICE: proposed by Price et al. see method 1 in [1] OVO_HASTIE: proposed by Hastie et al. see method 2 [9] in [1] OVO_HAMAMURA: proposed by Hamamura et al. see eq.(14) in [1]

[1] J. Milgram, M. Cheriet, R.Sabourin, "One Against One" or "One Against One": Which One is Better for Handwriting Recognition with SVMs?

Enumerator
PROB_HEURIS_NONE
OVA_NORM
OVA_SOFTMAX
OVO_PRICE
OVO_HASTIE
OVO_HAMAMURA

Definition at line 36 of file MulticlassStrategy.h.

enum EProblemType

problem type

Enumerator
PT_BINARY
PT_REGRESSION
PT_MULTICLASS
PT_STRUCTURED
PT_LATENT
PT_CLASS

Definition at line 110 of file Machine.h.

enum EQPType

Enum Training method selection

Enumerator
MOSEK	MOSEK.
SVMLIGHT	SVM^Light

Definition at line 29 of file CCSOSVM.h.

enum EQuadraticMMDType

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

Enumerator
BIASED
BIASED_DEPRECATED
UNBIASED
UNBIASED_DEPRECATED
INCOMPLETE

Definition at line 47 of file QuadraticTimeMMD.h.

enum ERangeType

type of range

Enumerator
R_LINEAR
R_EXP
R_LOG

Definition at line 26 of file ModelSelectionParameters.h.

enum ERBMMonitoringMethod

Enumerator
RBMMM_RECONSTRUCTION_ERROR
RBMMM_PSEUDO_LIKELIHOOD

Definition at line 48 of file RBM.h.

enum ERBMVisibleUnitType

Enumerator
RBMVUT_BINARY
RBMVUT_GAUSSIAN
RBMVUT_SOFTMAX

Definition at line 54 of file RBM.h.

enum ERegressionType

type of regressor

Enumerator
RT_NONE
RT_LIGHT
RT_LIBSVM

Definition at line 19 of file Regression.h.

enum ESolverType

solver type

Enumerator
ST_AUTO
ST_CPLEX
ST_GLPK
ST_NEWTON
ST_DIRECT
ST_ELASTICNET
ST_BLOCK_NORM

Definition at line 98 of file Machine.h.

enum ESPEStrategy

Stochastic Proximity Embedding (SPE) strategy

Enumerator
SPE_GLOBAL
SPE_LOCAL

Definition at line 22 of file StochasticProximityEmbedding.h.

enum EStateModelType

state model type

Enumerator
SMT_UNKNOWN
SMT_TWO_STATE

Definition at line 20 of file StateModelTypes.h.

enum EStatisticType

enum for different statistic types

Enumerator
S_LINEAR_TIME_MMD
S_QUADRATIC_TIME_MMD
S_HSIC
S_NOCCO

Definition at line 42 of file HypothesisTest.h.

enum EStructRiskType

The structured empirical risk types, corresponding to different training objectives [1].

[1] T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural SVMs, Machine Learning Journal, 2009.

Enumerator
N_SLACK_MARGIN_RESCALING
N_SLACK_SLACK_RESCALING
ONE_SLACK_MARGIN_RESCALING
ONE_SLACK_SLACK_RESCALING
CUSTOMIZED_RISK

Definition at line 35 of file StructuredOutputMachine.h.

enum EStructuredDataType

structured data type

Enumerator
SDT_UNKNOWN
SDT_REAL
SDT_SEQUENCE
SDT_FACTOR_GRAPH
SDT_SPARSE_MULTILABEL

Definition at line 20 of file StructuredDataTypes.h.

enum ETerminalType

Graph cuts terminal type

Enumerator
SOURCE	source terminal
SINK	sink terminal

Definition at line 32 of file GraphCut.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 25 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 27 of file WeightedDegreeStringKernel.h.

enum HomogeneousKernelMapWindowType

Type of spectral windowing function.

Enumerator
HomogeneousKernelMapWindowUniform	uniform window
HomogeneousKernelMapWindowRectangular	rectangular window

Definition at line 31 of file HomogeneousKernelMap.h.

enum HomogeneousKernelType

Type of kernel.

Enumerator
HomogeneousKernelIntersection	intersection kernel
HomogeneousKernelChi2	Chi2 kernel
HomogeneousKernelJS	Jensen-Shannon kernel

Definition at line 24 of file HomogeneousKernelMap.h.

enum KernelName

names of kernels that can be approximated currently

Enumerator
GAUSSIAN	approximate gaussian kernel expects one parameter to be specified : kernel width
NOT_SPECIFIED	not specified

Definition at line 26 of file RandomFourierDotFeatures.h.

enum KNN_SOLVER

Enumerator
KNN_BRUTE
KNN_KDTREE
KNN_COVER_TREE

Definition at line 27 of file KNN.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 24 of file ScatterSVM.h.

Function Documentation

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.

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 210 of file ModelSelectionParameters.h.

std::string shogun::demangledType

(

)

Converts compiler-dependent name of class to something human readable.

Returns

human readable name of class

Definition at line 50 of file any.h.

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.

Any shogun::erase_type

(

const T &

v

)

Erases value type i.e. converts it to Any For input object of any type, it returns an Any object which stores the input object's raw value. It saves the type information internally to be recalled later by using recall_type().

Parameters

v

value

Returns

Any object with the input value

Definition at line 296 of file any.h.

void exit_shogun

(

)

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

Definition at line 100 of file init.cpp.

SGIO * get_global_io

(

)

get the global io object

Returns

io object

Definition at line 131 of file init.cpp.

CMath * get_global_math

(

)

get the global math object

Returns

math object

Definition at line 170 of file init.cpp.

Parallel * get_global_parallel

(

)

get the global parallel object

Returns

parallel object

Definition at line 144 of file init.cpp.

CRandom * get_global_rand

(

)

get the global random object

Returns

random object

Definition at line 183 of file init.cpp.

Version * get_global_version

(

)

get the global version object

Returns

version object

Definition at line 157 of file init.cpp.

void init_from_env

(

)

Checks environment variables and modifies global objects

Definition at line 189 of file init.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

Definition at line 54 of file init.cpp.

void init_shogun_with_defaults

(

)

init shogun with defaults

Definition at line 94 of file init.cpp.

static Maybe<T> shogun::Just ( const T &  value )

static

Wraps provided value into Maybe with present value.

Doesn't throw any exception.

Parameters

value

the value to wrap

Definition at line 194 of file maybe.h.

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

static

Definition at line 68 of file LaRank.cpp.

static void shogun::larank_kcache_destroy ( larank_kcache_t *  self )

static

Definition at line 136 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 327 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 359 of file LaRank.cpp.

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

static

Definition at line 200 of file LaRank.cpp.

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

static

Definition at line 336 of file LaRank.cpp.

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

static

Definition at line 128 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 293 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 287 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,
		lbfgs_adjust_step_t	proc_adjust_step
	)

Definition at line 208 of file lbfgs.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 606 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 722 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 798 of file lbfgs.cpp.

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

new shogun serializable

Parameters

sgserializable_name
generic

Definition at line 2411 of file class_list.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

Definition at line 40 of file vw_math.cpp.

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

Definition at line 48 of file vw_math.cpp.

bool shogun::operator!=	(	const BaseTag &	first,
		const BaseTag &	second
	)

Inequality operator

Parameters

first	first BaseTag
second	secondBaseTag

Definition at line 120 of file basetag.h.

bool shogun::operator!=	(	const Any &	lhs,
		const Any &	rhs
	)

Inequality operator

Parameters

lhs	Any object on left hand side
rhs	Any object on right hand side

Returns

false if both are equal

Definition at line 272 of file any.h.

bool shogun::operator<	(	const BaseTag &	first,
		const BaseTag &	second
	)

Comparison operator

Parameters

first	first BaseTag
second	secondBaseTag

Definition at line 125 of file basetag.h.

bool shogun::operator==	(	const BaseTag &	first,
		const BaseTag &	second
	)

Equality operator

Parameters

first	first BaseTag
second	secondBaseTag

Definition at line 115 of file basetag.h.

bool shogun::operator==	(	const Any &	lhs,
		const Any &	rhs
	)

Equality operator

Parameters

lhs	Any object on left hand side
rhs	Any object on right hand side

Returns

true if both are equal

Definition at line 264 of file any.h.

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

static

Definition at line 1349 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 1306 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 1290 of file lbfgs.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.

void shogun::print

(

CJLCoverTreePoint &

p

)

Print the information of the CoverTree point

Definition at line 207 of file JLCoverTreePoint.h.

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.

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 35 of file vw_math.h.

T shogun::recall_type

(

const Any &

any

)

Tries to recall Any type, fails when type is wrong. Any stores type information of an object internally in a BaseAnyPolicy. This function returns type-casted value if the internal type information matches with the provided typename, otherwise throws std::logic_error.

Parameters

any	object of Any

Returns

type-casted value

Definition at line 310 of file any.h.

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

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

Parameters

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

Returns

dot product

Definition at line 20 of file vw_math.cpp.

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

Definition at line 28 of file vw_math.cpp.

void set_global_io

(

SGIO *

io

)

set the global io object

Parameters

io	io object to use

Definition at line 124 of file init.cpp.

void set_global_math

(

CMath *

math

)

set the global math object

Parameters

math	math object to use

Definition at line 163 of file init.cpp.

void set_global_parallel

(

Parallel *

parallel

)

set the global parallel object

Parameters

parallel

parallel object to use

Definition at line 137 of file init.cpp.

void set_global_rand

(

CRandom *

rand

)

set the global random object

Parameters

rand	random object to use

Definition at line 176 of file init.cpp.

void set_global_version

(

Version *

version

)

set the global version object

Parameters

version

version object to use

Definition at line 150 of file init.cpp.

void* shogun::sg_calloc	(	size_t	num,
		size_t	size
	)

Definition at line 227 of file memory.cpp.

void shogun::sg_free

(

void *

ptr

)

Definition at line 263 of file memory.cpp.

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

Definition at line 89 of file init.cpp.

void* shogun::sg_malloc

(

size_t

size

)

Definition at line 194 of file memory.cpp.

void* shogun::sg_realloc	(	void *	ptr,
		size_t	size
	)

Definition at line 279 of file memory.cpp.

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 1117 of file lbfgs.cpp.

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

Definition at line 271 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 253 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 289 of file lapack.cpp.

void shogun::wrap_dstemr	(	char	jobz,
		char	range,
		int	n,
		double *	diag,
		double *	subdiag,
		double	vl,
		double	vu,
		int	il,
		int	iu,
		int *	m,
		double *	w,
		double *	z__,
		int	ldz,
		int	nzc,
		int *	isuppz,
		int	tryrac,
		int *	info
	)

Definition at line 373 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 235 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 307 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 342 of file lapack.cpp.

static void shogun::xextend ( larank_kcache_t *  self, int32_t  k, int32_t  nlen  )

static

Definition at line 206 of file LaRank.cpp.

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

static

Definition at line 169 of file LaRank.cpp.

static void shogun::xpurge ( larank_kcache_t *  self )

static

Definition at line 114 of file LaRank.cpp.

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

static

Definition at line 299 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 225 of file LaRank.cpp.

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

static

Definition at line 88 of file LaRank.cpp.

Variable Documentation

const lbfgs_parameter_t _defparam

static

Initial value:

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

Definition at line 99 of file lbfgs.cpp.

const int32_t constant_hash = 11650396

Constant used to access the constant feature.

Definition at line 32 of file vw_constants.h.

const uint32_t hash_base = 97562527

Seed for hash.

Definition at line 35 of file vw_constants.h.

const int32_t LOGSUM_TBL =10000

span of the logsum table

Definition at line 23 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.

const int32_t quadratic_constant = 27942141

Constant used while hashing/accessing quadratic features.

Definition at line 29 of file vw_constants.h.

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

function called to cancel things

Definition at line 51 of file init.cpp.

SGIO * sg_io =NULL

shogun IO

Definition at line 36 of file init.cpp.

CMath* sg_math =NULL

Definition at line 38 of file init.cpp.

Parallel * sg_parallel =NULL

Definition at line 35 of file init.cpp.

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

function called to print error messages

Definition at line 48 of file init.cpp.

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

function called to print normal messages

Definition at line 42 of file init.cpp.

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

function called to print warning messages

Definition at line 45 of file init.cpp.

CRandom * sg_rand =NULL

random number generator

Definition at line 39 of file init.cpp.

Version * sg_version =NULL

Definition at line 37 of file init.cpp.