Classes | Typedefs | Enumerations | Functions | Variables

shogun Namespace Reference

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

Classes

class  DynArray
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  Parallel
 Class Parallel provides helper functions for multithreading. More...
struct  TParameter
 parameter struct More...
class  Parameter
 Parameter class. More...
class  SGParamInfo
 Class that holds informations about a certain parameter of an CSGObject. Contains name, type, etc. This is used for mapping types that have changed in different versions of shogun. Instances of this class may be compared to each other. Ordering is based on name, equalness is based on all attributes. More...
class  ParameterMapElement
 Class to hold instances of a parameter map. Each element contains a key and a set of values, which each are of type SGParamInfo. May be compared to each other based on their keys. More...
class  ParameterMap
 Implements a map of ParameterMapElement instances Maps one key to a set of values. More...
class  CSGObject
 Class SGObject is the base class of all shogun objects. More...
class  Version
 Class Version provides version information. More...
class  CAveragedPerceptron
 Class Averaged Perceptron implements the standard linear (online) algorithm. Averaged perceptron is the simple extension of Perceptron. More...
class  CFeatureBlockLogisticRegression
 class FeatureBlockLogisticRegression, a linear binary logistic loss classifier for problems with complex feature relations. Currently two feature relations are supported - feature group (done via CIndexBlockGroup) and feature tree (done via CIndexTree). Handling of feature relations is done via L1/Lq (for groups) and L1/L2 (for trees) regularization. More...
class  CLDA
 Class LDA implements regularized Linear Discriminant Analysis. More...
class  CLPBoost
 Class LPBoost trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
class  CLPM
 Class LPM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
class  CMKL
 Multiple Kernel Learning. More...
class  CMKLClassification
 Multiple Kernel Learning for two-class-classification. More...
class  CMKLMulticlass
 MKLMulticlass is a class for L1-norm multiclass MKL. More...
class  MKLMulticlassGLPK
 MKLMulticlassGLPK is a helper class for MKLMulticlass. More...
class  MKLMulticlassGradient
 MKLMulticlassGradient is a helper class for MKLMulticlass. More...
class  MKLMulticlassOptimizationBase
 MKLMulticlassOptimizationBase is a helper class for MKLMulticlass. More...
class  CMKLOneClass
 Multiple Kernel Learning for one-class-classification. More...
class  CNearestCentroid
 Class NearestCentroid, an implementation of Nearest Shrunk Centroid classifier. More...
class  CPerceptron
 Class Perceptron implements the standard linear (online) perceptron. More...
class  CPluginEstimate
 class PluginEstimate More...
class  CSubGradientLPM
 Class SubGradientSVM trains a linear classifier called Linear Programming Machine, i.e. a SVM using a $\ell_1$ norm regularizer. More...
class  CCPLEXSVM
 CplexSVM a SVM solver implementation based on cplex (unfinished). More...
class  CGNPPLib
 class GNPPLib, a Library of solvers for Generalized Nearest Point Problem (GNPP). More...
class  CGNPPSVM
 class GNPPSVM More...
class  CGPBTSVM
 class GPBTSVM More...
class  CLibLinear
 class to implement LibLinear More...
class  CLibSVM
 LibSVM. More...
class  CLibSVMOneClass
 class LibSVMOneClass More...
class  CMPDSVM
 class MPDSVM More...
class  CNewtonSVM
 NewtonSVM, In this Implementation linear SVM is trained in its primal form using Newton-like iterations. This Implementation is ported from the Olivier Chapelles fast newton based SVM solver, Which could be found here :http://mloss.org/software/view/30/ For further information on this implementation of SVM refer to this paper: http://www.kyb.mpg.de/publications/attachments/neco_%5B0%5D.pdf. More...
class  COnlineLibLinear
 Class implementing a purely online version of LibLinear, using the L2R_L1LOSS_SVC_DUAL solver only. More...
class  COnlineSVMSGD
 class OnlineSVMSGD More...
class  CQPBSVMLib
 class QPBSVMLib More...
class  CSGDQN
 class SGDQN More...
class  CSubGradientSVM
 class SubGradientSVM More...
class  CSVM
 A generic Support Vector Machine Interface. More...
class  CSVMLight
 class SVMlight More...
class  CSVMLightOneClass
 Trains a one class C SVM. More...
class  CSVMLin
 class SVMLin More...
class  CSVMOcas
 class SVMOcas More...
class  CSVMSGD
 class SVMSGD More...
class  CWDSVMOcas
 class WDSVMOcas More...
class  CVwCacheReader
 Base class from which all cache readers for VW should be derived. More...
class  CVwCacheWriter
 CVwCacheWriter is the base class for all VW cache creating classes. More...
class  CVwNativeCacheReader
 Class CVwNativeCacheReader reads from a cache exactly as that which has been produced by VW's default cache format. More...
class  CVwNativeCacheWriter
 Class CVwNativeCacheWriter writes a cache exactly as that which would be produced by VW's default cache format. More...
class  CVwAdaptiveLearner
 VwAdaptiveLearner uses an adaptive subgradient technique to update weights. More...
class  CVwNonAdaptiveLearner
 VwNonAdaptiveLearner uses a standard gradient descent weight update rule. More...
class  CVowpalWabbit
 Class CVowpalWabbit is the implementation of the online learning algorithm used in Vowpal Wabbit. More...
class  VwFeature
 One feature in VW. More...
class  VwExample
 Example class for VW. More...
class  VwLabel
 Class VwLabel holds a label object used by VW. More...
class  CVwEnvironment
 Class CVwEnvironment is the environment used by VW. More...
class  CVwLearner
 Base class for all VW learners. More...
class  CVwParser
 CVwParser is the object which provides the functions to parse examples from buffered input. More...
class  CVwRegressor
 Regressor used by VW. More...
class  CGMM
 Gaussian Mixture Model interface. More...
class  CHierarchical
 Agglomerative hierarchical single linkage clustering. More...
class  CKMeans
 KMeans clustering, partitions the data into k (a-priori specified) clusters. More...
class  CConverter
 class Converter used to convert data More...
class  CDiffusionMaps
 class DiffusionMaps (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess given data using Diffusion Maps dimensionality reduction technique as described in More...
class  CEmbeddingConverter
 class EmbeddingConverter (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of features, e.g. construct dense numeric embedding of string features More...
class  CHessianLocallyLinearEmbedding
 class HessianLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to preprocess data using Hessian Locally Linear Embedding algorithm as described in More...
class  CIsomap
 class Isomap (part of the Efficient Dimension Reduction Toolkit) used to embed data using Isomap algorithm as described in More...
class  CKernelLocallyLinearEmbedding
 class KernelLocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using kernel formulation of Locally Linear Embedding algorithm as described in More...
class  CKernelLocalTangentSpaceAlignment
 class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using kernel extension of the Local Tangent Space Alignment (LTSA) algorithm. More...
class  CLaplacianEigenmaps
 class LaplacianEigenmaps (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using Laplacian Eigenmaps algorithm as described in: More...
class  CLinearLocalTangentSpaceAlignment
 class LinearLocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
class  CLocalityPreservingProjections
 class LocalityPreservingProjections (part of the Efficient Dimensionality Reduction Toolkit) used to compute embeddings of data using Locality Preserving Projections method as described in More...
class  CLocallyLinearEmbedding
 class LocallyLinearEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Locally Linear Embedding algorithm described in More...
class  CLocalTangentSpaceAlignment
 class LocalTangentSpaceAlignment (part of the Efficient Dimensionality Reduction Toolkit) used to embed data using Local Tangent Space Alignment (LTSA) algorithm as described in: More...
class  CMultidimensionalScaling
 class Multidimensionalscaling (part of the Efficient Dimensionality Reduction Toolkit) is used to perform multidimensional scaling (capable of landmark approximation if requested). More...
class  CNeighborhoodPreservingEmbedding
 NeighborhoodPreservingEmbedding (part of the Efficient Dimensionality Reduction Toolkit) converter used to construct embeddings as described in: More...
class  CStochasticProximityEmbedding
 class StochasticProximityEmbedding (part of the Efficient Dimensionality Reduction Toolkit) used to construct embeddings of data using the Stochastic Proximity algorithm. More...
class  CAttenuatedEuclideanDistance
 class AttenuatedEuclideanDistance More...
class  CBrayCurtisDistance
 class Bray-Curtis distance More...
class  CCanberraMetric
 class CanberraMetric More...
class  CCanberraWordDistance
 class CanberraWordDistance More...
class  CChebyshewMetric
 class ChebyshewMetric More...
class  CChiSquareDistance
 class ChiSquareDistance More...
class  CCosineDistance
 class CosineDistance More...
class  CCustomDistance
 The Custom Distance allows for custom user provided distance matrices. More...
class  CDenseDistance
 template class DenseDistance More...
class  CDistance
 Class Distance, a base class for all the distances used in the Shogun toolbox. More...
class  CEuclideanDistance
 class EuclideanDistance More...
class  CGeodesicMetric
 class GeodesicMetric More...
class  CHammingWordDistance
 class HammingWordDistance More...
class  CJensenMetric
 class JensenMetric More...
class  CKernelDistance
 The Kernel distance takes a distance as input. More...
class  CMahalanobisDistance
 class MahalanobisDistance More...
class  CManhattanMetric
 class ManhattanMetric More...
class  CManhattanWordDistance
 class ManhattanWordDistance More...
class  CMinkowskiMetric
 class MinkowskiMetric More...
class  CRealDistance
 class RealDistance More...
class  CSparseDistance
 template class SparseDistance More...
class  CSparseEuclideanDistance
 class SparseEucldeanDistance More...
class  CStringDistance
 template class StringDistance More...
class  CTanimotoDistance
 class Tanimoto coefficient More...
class  CDistribution
 Base class Distribution from which all methods implementing a distribution are derived. More...
class  CGaussian
 Gaussian distribution interface. More...
class  CGHMM
 class GHMM - this class is non-functional and was meant to implement a Generalize Hidden Markov Model (aka Semi Hidden Markov HMM). More...
class  CHistogram
 Class Histogram computes a histogram over all 16bit unsigned integers in the features. More...
class  Model
 class Model More...
class  CHMM
 Hidden Markov Model. More...
class  CLinearHMM
 The class LinearHMM is for learning Higher Order Markov chains. More...
class  CPositionalPWM
 Positional PWM. More...
class  CBinaryClassEvaluation
 The class TwoClassEvaluation, a base class used to evaluate binary classification labels. More...
class  CClusteringAccuracy
 clustering accuracy More...
class  CClusteringEvaluation
 The base class used to evaluate clustering. More...
class  CClusteringMutualInformation
 clustering (normalized) mutual information More...
class  CContingencyTableEvaluation
 The class ContingencyTableEvaluation a base class used to evaluate 2-class classification with TP, FP, TN, FN rates. More...
class  CAccuracyMeasure
 class AccuracyMeasure used to measure accuracy of 2-class classifier. More...
class  CErrorRateMeasure
 class ErrorRateMeasure used to measure error rate of 2-class classifier. More...
class  CBALMeasure
 class BALMeasure used to measure balanced error of 2-class classifier. More...
class  CWRACCMeasure
 class WRACCMeasure used to measure weighted relative accuracy of 2-class classifier. More...
class  CF1Measure
 class F1Measure used to measure F1 score of 2-class classifier. More...
class  CCrossCorrelationMeasure
 class CrossCorrelationMeasure used to measure cross correlation coefficient of 2-class classifier. More...
class  CRecallMeasure
 class RecallMeasure used to measure recall of 2-class classifier. More...
class  CPrecisionMeasure
 class PrecisionMeasure used to measure precision of 2-class classifier. More...
class  CSpecificityMeasure
 class SpecificityMeasure used to measure specificity of 2-class classifier. More...
class  CCrossValidationResult
 type to encapsulate the results of an evaluation run. May contain confidence interval (if conf_int_alpha!=0). m_conf_int_alpha is the probability for an error, i.e. the value does not lie in the confidence interval. More...
class  CCrossValidation
 base class for cross-validation evaluation. Given a learning machine, a splitting strategy, an evaluation criterium, features and correspnding labels, this provides an interface for cross-validation. Results may be retrieved using the evaluate method. A number of repetitions may be specified for obtaining more accurate results. The arithmetic mean of different runs is returned along with confidence intervals, if a p-value is specified. Default number of runs is one, confidence interval combutation is disabled. More...
class  CCrossValidationMKLStorage
 Class for storing MKL weights in every fold of cross-validation. More...
class  CCrossValidationMulticlassStorage
 Class for storing multiclass evaluation information in every fold of cross-validation. More...
class  CCrossValidationOutput
 Class for managing individual folds in cross-validation. More...
class  CCrossValidationPrintOutput
 Class for outputting cross-validation intermediate results to the standard output. Simply prints all messages it gets. More...
class  CCrossValidationSplitting
 Implementation of normal cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference). More...
class  CDifferentiableFunction
 DifferentiableFunction. More...
class  CEvaluation
 Class Evaluation, a base class for other classes used to evaluate labels, e.g. accuracy of classification or mean squared error of regression. More...
class  CEvaluationResult
 EvaluationResult is the abstract class that contains the result generated by the MachineEvaluation class. More...
class  CGradientCriterion
 CGradientCriterion Simple class which specifies the direction of gradient search. Does not provide any label evaluation measure, however. More...
class  CGradientEvaluation
 GradientEvaluation evaluates a machine using its associated differentiable function for the function value and its gradient with respect to parameters. More...
class  CGradientResult
 GradientResult is a container class that returns results from GradientEvaluation. It contains the function value as well as its gradient. More...
class  CMachineEvaluation
 Machine Evaluation is an abstract class that evaluates a machine according to some criterion. More...
class  CMeanAbsoluteError
 Class MeanAbsoluteError used to compute an error of regression model. More...
class  CMeanSquaredError
 Class MeanSquaredError used to compute an error of regression model. More...
class  CMeanSquaredLogError
 Class CMeanSquaredLogError used to compute an error of regression model. More...
class  CMulticlassAccuracy
 The class MulticlassAccuracy used to compute accuracy of multiclass classification. More...
class  CMulticlassOVREvaluation
 The class MulticlassOVREvaluation used to compute evaluation parameters of multiclass classification via binary OvR decomposition and given binary evaluation technique. More...
class  CPRCEvaluation
 Class PRCEvaluation used to evaluate PRC (Precision Recall Curve) and an area under PRC curve (auPRC). More...
class  CROCEvaluation
 Class ROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC). More...
class  CSplittingStrategy
 Abstract base class for all splitting types. Takes a CLabels instance and generates a desired number of subsets which are being accessed by their indices via the method generate_subset_indices(...). More...
class  CStratifiedCrossValidationSplitting
 Implementation of stratified cross-validation on the base of CSplittingStrategy. Produces subset index sets of equal size (at most one difference) in which the label ratio is equal (at most one difference) to the label ratio of the specified labels. Do not use for regression since it may be impossible to distribute nice in that case. More...
class  CStructuredAccuracy
 class CStructuredAccuracy used to compute accuracy of structured classification More...
class  CAlphabet
 The class Alphabet implements an alphabet and alphabet utility functions. More...
class  CAttributeFeatures
 Implements attributed features, that is in the simplest case a number of (attribute, value) pairs. More...
class  CBinnedDotFeatures
 The class BinnedDotFeatures contains a 0-1 conversion of features into bins. More...
class  CCombinedDotFeatures
 Features that allow stacking of a number of DotFeatures. More...
class  CCombinedFeatures
 The class CombinedFeatures is used to combine a number of of feature objects into a single CombinedFeatures object. More...
class  CDataGenerator
 Class that is able to generate various data samples, which may be used for examples in SHOGUN. More...
class  CDenseFeatures
 The class DenseFeatures implements dense feature matrices. More...
class  CDenseSubsetFeatures
class  CDotFeatures
 Features that support dot products among other operations. More...
class  CDummyFeatures
 The class DummyFeatures implements features that only know the number of feature objects (but don't actually contain any). More...
class  CExplicitSpecFeatures
 Features that compute the Spectrum Kernel feature space explicitly. More...
class  CFeatures
 The class Features is the base class of all feature objects. More...
class  CFKFeatures
 The class FKFeatures implements Fischer kernel features obtained from two Hidden Markov models. More...
class  CHashedWDFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CHashedWDFeaturesTransposed
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CImplicitWeightedSpecFeatures
 Features that compute the Weighted Spectrum Kernel feature space explicitly. More...
class  CLatentFeatures
 Latent Features class The class if for representing features for latent learning, e.g. LatentSVM. It's basically a very generic way of storing features of any (user-defined) form based on CData. More...
class  CLBPPyrDotFeatures
 implement DotFeatures for the polynomial kernel More...
class  CMatrixFeatures
 Class CMatrixFeatures used to represent data whose feature vectors are better represented with matrices rather than with unidimensional arrays or vectors. Optionally, it can be restricted that all the feature vectors have the same number of features. Set the attribute num_features different to zero to use this restriction. Allow feature vectors with different number of features by setting num_features equal to zero (default behaviour). More...
class  CPolyFeatures
 implement DotFeatures for the polynomial kernel More...
class  CRealFileFeatures
 The class RealFileFeatures implements a dense double-precision floating point matrix from a file. More...
class  CSNPFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CSparseFeatures
 Template class SparseFeatures implements sparse matrices. More...
class  CSparsePolyFeatures
 implement DotFeatures for the polynomial kernel More...
class  CMeanShiftDataGenerator
 Class to generate dense features data via the streaming features interface. The core are pairs of methods to a) set the data model and parameters, and b) to generate a data vector using these model parameters Both methods are automatically called when calling get_next_example() This allows to treat generated data as a stream via the standard streaming features interface. More...
class  CStreamingDenseFeatures
 This class implements streaming features with dense feature vectors. More...
class  CStreamingDotFeatures
 Streaming features that support dot products among other operations. More...
class  CStreamingFeatures
 Streaming features are features which are used for online algorithms. More...
class  CStreamingSparseFeatures
 This class implements streaming features with sparse feature vectors. The vector is represented as an SGSparseVector<T>. Each entry is of type SGSparseVectorEntry<T> with members `feat_index' and `entry'. More...
class  CStreamingStringFeatures
 This class implements streaming features as strings. More...
class  CStreamingVwFeatures
 This class implements streaming features for use with VW. More...
class  CStringFeatures
 Template class StringFeatures implements a list of strings. More...
class  CStringFileFeatures
 File based string features. More...
class  CSubset
 Wrapper class for an index subset which is used by SubsetStack. More...
class  CSubsetStack
 class to add subset support to another class. A CSubsetStackStack instance should be added and wrapper methods to all interfaces should be added. More...
class  CTOPFeatures
 The class TOPFeatures implements TOP kernel features obtained from two Hidden Markov models. More...
class  CWDFeatures
 Features that compute the Weighted Degreee Kernel feature space explicitly. More...
class  CAsciiFile
 A Ascii File access class. More...
class  CBinaryFile
 A Binary file access class. More...
class  CBinaryStream
 memory mapped emulation via binary streams (files) More...
class  CFile
 A File access base class. More...
class  CIOBuffer
 An I/O buffer class. More...
class  CMemoryMappedFile
 memory mapped file More...
class  CSerializableAsciiFile
 serializable ascii file More...
class  SerializableAsciiReader00
 Serializable ascii reader. More...
class  CSerializableFile
 serializable file More...
class  SGIO
 Class SGIO, used to do input output operations throughout shogun. More...
struct  substring
 struct Substring, specified by start position and end position. More...
class  CSimpleFile
 Template class SimpleFile to read and write from files. More...
class  CInputParser
 Class CInputParser is a templated class used to maintain the reading/parsing/providing of examples. More...
class  Example
 Class Example is the container type for the vector+label combination. More...
class  CParseBuffer
 Class CParseBuffer implements a ring of examples of a defined size. The ring stores objects of the Example type. More...
class  CStreamingAsciiFile
 Class StreamingAsciiFile to read vector-by-vector from ASCII files. More...
class  CStreamingFile
 A Streaming File access class. More...
class  CStreamingFileFromDenseFeatures
 Class CStreamingFileFromDenseFeatures is a derived class of CStreamingFile which creates an input source for the online framework from a CDenseFeatures object. More...
class  CStreamingFileFromFeatures
 Class StreamingFileFromFeatures to read vector-by-vector from a CFeatures object. More...
class  CStreamingFileFromSparseFeatures
 Class CStreamingFileFromSparseFeatures is derived from CStreamingFile and provides an input source for the online framework. It uses an existing CSparseFeatures object to generate online examples. More...
class  CStreamingFileFromStringFeatures
 Class CStreamingFileFromStringFeatures is derived from CStreamingFile and provides an input source for the online framework from a CStringFeatures object. More...
class  CStreamingVwCacheFile
 Class StreamingVwCacheFile to read vector-by-vector from VW cache files. More...
class  CStreamingVwFile
 Class StreamingVwFile to read vector-by-vector from Vowpal Wabbit data files. It reads the example and label into one object of VwExample type. More...
class  CANOVAKernel
 ANOVA (ANalysis Of VAriances) kernel. More...
class  CAUCKernel
 The AUC kernel can be used to maximize the area under the receiver operator characteristic curve (AUC) instead of margin in SVM training. More...
class  CBesselKernel
 the class Bessel kernel More...
class  CCauchyKernel
 Cauchy kernel. More...
class  CChi2Kernel
 The Chi2 kernel operating on realvalued vectors computes the chi-squared distance between sets of histograms. More...
class  CCircularKernel
 Circular kernel. More...
class  CCombinedKernel
 The Combined kernel is used to combine a number of kernels into a single CombinedKernel object by linear combination. More...
class  CConstKernel
 The Constant Kernel returns a constant for all elements. More...
class  CCustomKernel
 The Custom Kernel allows for custom user provided kernel matrices. More...
class  CDiagKernel
 The Diagonal Kernel returns a constant for the diagonal and zero otherwise. More...
class  CDistanceKernel
 The Distance kernel takes a distance as input. More...
class  CDotKernel
 Template class DotKernel is the base class for kernels working on DotFeatures. More...
class  CExponentialKernel
 The Exponential Kernel, closely related to the Gaussian Kernel computed on CDotFeatures. More...
class  CGaussianARDKernel
 Gaussian Kernel with Automatic Relevance Detection. More...
class  CGaussianKernel
 The well known Gaussian kernel (swiss army knife for SVMs) computed on CDotFeatures. More...
class  CGaussianShiftKernel
 An experimental kernel inspired by the WeightedDegreePositionStringKernel and the Gaussian kernel. More...
class  CGaussianShortRealKernel
 The well known Gaussian kernel (swiss army knife for SVMs) on dense short-real valued features. More...
class  CHistogramIntersectionKernel
 The HistogramIntersection kernel operating on realvalued vectors computes the histogram intersection distance between sets of histograms. Note: the current implementation assumes positive values for the histograms, and input vectors should sum to 1. More...
class  CInverseMultiQuadricKernel
 InverseMultiQuadricKernel. More...
class  CJensenShannonKernel
 The Jensen-Shannon kernel operating on real-valued vectors computes the Jensen-Shannon distance between the features. Often used in computer vision. More...
class  CKernel
 The Kernel base class. More...
class  CLinearARDKernel
 Linear Kernel with Automatic Relevance Detection. More...
class  CLinearKernel
 Computes the standard linear kernel on CDotFeatures. More...
class  CLogKernel
 Log kernel. More...
class  CMultiquadricKernel
 MultiquadricKernel. More...
class  CAvgDiagKernelNormalizer
 Normalize the kernel by either a constant or the average value of the diagonal elements (depending on argument c of the constructor). More...
class  CDiceKernelNormalizer
 DiceKernelNormalizer performs kernel normalization inspired by the Dice coefficient (see http://en.wikipedia.org/wiki/Dice's_coefficient). More...
class  CFirstElementKernelNormalizer
 Normalize the kernel by a constant obtained from the first element of the kernel matrix, i.e. $ c=k({\bf x},{\bf x})$. More...
class  CIdentityKernelNormalizer
 Identity Kernel Normalization, i.e. no normalization is applied. More...
class  CKernelNormalizer
 The class Kernel Normalizer defines a function to post-process kernel values. More...
class  CRidgeKernelNormalizer
 Normalize the kernel by adding a constant term to its diagonal. This aids kernels to become positive definite (even though they are not - often caused by numerical problems). More...
class  CScatterKernelNormalizer
 the scatter kernel normalizer More...
class  CSqrtDiagKernelNormalizer
 SqrtDiagKernelNormalizer divides by the Square Root of the product of the diagonal elements. More...
class  CTanimotoKernelNormalizer
 TanimotoKernelNormalizer performs kernel normalization inspired by the Tanimoto coefficient (see http://en.wikipedia.org/wiki/Jaccard_index ). More...
class  CVarianceKernelNormalizer
 VarianceKernelNormalizer divides by the ``variance''. More...
class  CZeroMeanCenterKernelNormalizer
 ZeroMeanCenterKernelNormalizer centers the kernel in feature space. More...
class  CPolyKernel
 Computes the standard polynomial kernel on CDotFeatures. More...
class  CPowerKernel
 Power kernel. More...
class  CProductKernel
 The Product kernel is used to combine a number of kernels into a single ProductKernel object by element multiplication. More...
class  CPyramidChi2
 Pyramid Kernel over Chi2 matched histograms. More...
class  CRationalQuadraticKernel
 Rational Quadratic kernel. More...
class  CSigmoidKernel
 The standard Sigmoid kernel computed on dense real valued features. More...
class  CSparseKernel
 Template class SparseKernel, is the base class of kernels working on sparse features. More...
class  CSphericalKernel
 Spherical kernel. More...
class  CSplineKernel
 Computes the Spline Kernel function which is the cubic polynomial. More...
class  CCommUlongStringKernel
 The CommUlongString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 64bit integers. More...
class  CCommWordStringKernel
 The CommWordString kernel may be used to compute the spectrum kernel from strings that have been mapped into unsigned 16bit integers. More...
class  CDistantSegmentsKernel
 The distant segments kernel is a string kernel, which counts the number of substrings, so-called segments, at a certain distance from each other. More...
class  CFixedDegreeStringKernel
 The FixedDegree String kernel takes as input two strings of same size and counts the number of matches of length d. More...
class  CGaussianMatchStringKernel
 The class GaussianMatchStringKernel computes a variant of the Gaussian kernel on strings of same length. More...
class  CHistogramWordStringKernel
 The HistogramWordString computes the TOP kernel on inhomogeneous Markov Chains. More...
class  CLinearStringKernel
 Computes the standard linear kernel on dense char valued features. More...
class  CLocalAlignmentStringKernel
 The LocalAlignmentString kernel compares two sequences through all possible local alignments between the two sequences. More...
class  CLocalityImprovedStringKernel
 The LocalityImprovedString kernel is inspired by the polynomial kernel. Comparing neighboring characters it puts emphasize on local features. More...
class  CMatchWordStringKernel
 The class MatchWordStringKernel computes a variant of the polynomial kernel on strings of same length converted to a word alphabet. More...
class  COligoStringKernel
 This class offers access to the Oligo Kernel introduced by Meinicke et al. in 2004. More...
class  CPolyMatchStringKernel
 The class PolyMatchStringKernel computes a variant of the polynomial kernel on strings of same length. More...
class  CPolyMatchWordStringKernel
 The class PolyMatchWordStringKernel computes a variant of the polynomial kernel on word-features. More...
class  CRegulatoryModulesStringKernel
 The Regulaty Modules kernel, based on the WD kernel, as published in Schultheiss et al., Bioinformatics (2009) on regulatory sequences. More...
class  CSalzbergWordStringKernel
 The SalzbergWordString kernel implements the Salzberg kernel. More...
class  CSimpleLocalityImprovedStringKernel
 SimpleLocalityImprovedString kernel, is a ``simplified'' and better performing version of the Locality improved kernel. More...
class  CSNPStringKernel
 The class SNPStringKernel computes a variant of the polynomial kernel on strings of same length. More...
struct  SSKFeatures
 SSKFeatures. More...
class  CSparseSpatialSampleStringKernel
 Sparse Spatial Sample String Kernel by Pavel Kuksa <pkuksa@cs.rutgers.edu> and Vladimir Pavlovic <vladimir@cs.rutgers.edu> More...
class  CSpectrumMismatchRBFKernel
 spectrum mismatch rbf kernel More...
class  CSpectrumRBFKernel
 spectrum rbf kernel More...
class  CStringKernel
 Template class StringKernel, is the base class of all String Kernels. More...
class  CWeightedCommWordStringKernel
 The WeightedCommWordString kernel may be used to compute the weighted spectrum kernel (i.e. a spectrum kernel for 1 to K-mers, where each k-mer length is weighted by some coefficient $\beta_k$) from strings that have been mapped into unsigned 16bit integers. More...
class  CWeightedDegreePositionStringKernel
 The Weighted Degree Position String kernel (Weighted Degree kernel with shifts). More...
class  CWeightedDegreeStringKernel
 The Weighted Degree String kernel. More...
class  CTensorProductPairKernel
 Computes the Tensor Product Pair Kernel (TPPK). More...
class  CTStudentKernel
 Generalized T-Student kernel. More...
class  CWaveKernel
 Wave kernel. More...
class  CWaveletKernel
 the class WaveletKernel More...
class  CWeightedDegreeRBFKernel
 weighted degree RBF kernel More...
class  CBinaryLabels
 Binary Labels for binary classification. More...
class  CDenseLabels
 Dense integer or floating point labels. More...
class  CLabels
 The class Labels models labels, i.e. class assignments of objects. More...
class  CLatentLabels
 abstract class for latent labels As latent labels always depends on the given application, this class only defines the API that the user has to implement for latent labels. More...
class  CMulticlassLabels
 Multiclass Labels for multi-class classification. More...
class  CMulticlassMultipleOutputLabels
 Multiclass Labels for multi-class classification with multiple labels. More...
class  CRegressionLabels
 Real Labels are real-valued labels. More...
class  CStructuredLabels
 Base class of the labels used in Structured Output (SO) problems. More...
class  CLatentModel
 Abstract class CLatentModel It represents the application specific model and contains most of the application dependent logic to solve latent variable based problems. More...
class  CLatentSOSVM
 class Latent Structured Output SVM, an structured output based machine for classification problems with latent variables. More...
class  CLatentSVM
 LatentSVM class Latent SVM implementation based on [1]. For optimization this implementation uses SVMOcas. More...
class  CBitString
 a string class embedding a string in a compact bit representation More...
class  CCache
 Template class Cache implements a simple cache. More...
class  CCompressor
 Compression library for compressing and decompressing buffers using one of the standard compression algorithms, LZO, GZIP, BZIP2 or LZMA. More...
class  CoverTree
class  CData
 dummy data holder More...
struct  TSGDataType
 Datatypes that shogun supports. More...
class  CDynamicArray
 Template Dynamic array class that creates an array that can be used like a list or an array. More...
class  CDynamicObjectArray
 Dynamic array class for CSGObject pointers that creates an array that can be used like a list or an array. More...
class  CDynInt
 integer type of dynamic size More...
class  func_wrapper
class  CGCArray
 Template class GCArray implements a garbage collecting static array. More...
class  CHash
 Collection of Hashing Functions. More...
class  CIndexBlock
 class IndexBlock used to represent contiguous indices of one group (e.g. block of related features) More...
class  CIndexBlockGroup
 class IndexBlockGroup used to represent group-based feature relation. More...
class  CIndexBlockRelation
 class IndexBlockRelation More...
class  CIndexBlockTree
 class IndexBlockTree used to represent tree guided feature relation. More...
class  CIndirectObject
 an array class that accesses elements indirectly via an index array. More...
class  v_array
 Class v_array taken directly from JL's implementation. More...
class  CJLCoverTreePoint
 Class Point to use with John Langford's CoverTree. This class must have some assoficated functions defined (distance, parse_points and print, see below) so it can be used with the CoverTree implementation. More...
class  CListElement
 Class ListElement, defines how an element of the the list looks like. More...
class  CList
 Class List implements a doubly connected list for low-level-objects. More...
class  CMap
 the class CMap, a map based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class  CSet
 the class CSet, a set based on the hash-table. w: http://en.wikipedia.org/wiki/Hash_table More...
class  SGMatrix
 shogun matrix More...
class  SGMatrixList
 shogun matrix list More...
class  SGNDArray
 shogun n-dimensional array More...
class  SGReferencedData
 shogun reference count managed data More...
class  SGSparseMatrix
 template class SGSparseMatrix More...
struct  SGSparseVectorEntry
 template class SGSparseVectorEntry More...
class  SGSparseVector
 template class SGSparseVector The assumtion is that the stored SGSparseVectorEntry<T>* vector is ordered by SGSparseVectorEntry.feat_index in non-decreasing order. This has to be assured by the user of the class. More...
class  SGString
 shogun string More...
struct  SGStringList
 template class SGStringList More...
class  SGVector
 shogun vector More...
class  ShogunException
 Class ShogunException defines an exception which is thrown whenever an error inside of shogun occurs. More...
class  CSignal
 Class Signal implements signal handling to e.g. allow ctrl+c to cancel a long running process. More...
class  CStructuredData
 Base class of the components of StructuredLabels. More...
class  CTime
 Class Time that implements a stopwatch based on either cpu time or wall clock time. More...
class  CTrie
 Template class Trie implements a suffix trie, i.e. a tree in which all suffixes up to a certain length are stored. More...
class  CHingeLoss
 CHingeLoss implements the hinge loss function. More...
class  CLogLoss
 CLogLoss implements the logarithmic loss function. More...
class  CLogLossMargin
 Class CLogLossMargin implements a margin-based log-likelihood loss function. More...
class  CLossFunction
 Class CLossFunction is the base class of all loss functions. More...
class  CSmoothHingeLoss
 CSmoothHingeLoss implements the smooth hinge loss function. More...
class  CSquaredHingeLoss
 Class CSquaredHingeLoss implements a squared hinge loss function. More...
class  CSquaredLoss
 CSquaredLoss implements the squared loss function. More...
class  CBaseMulticlassMachine
class  CDistanceMachine
 A generic DistanceMachine interface. More...
class  CKernelMachine
 A generic KernelMachine interface. More...
class  CKernelMulticlassMachine
 generic kernel multiclass More...
class  CKernelStructuredOutputMachine
class  CLinearLatentMachine
 abstract implementaion of Linear Machine with latent variable This is the base implementation of all linear machines with latent variable. More...
class  CLinearMachine
 Class LinearMachine is a generic interface for all kinds of linear machines like classifiers. More...
class  CLinearMulticlassMachine
 generic linear multiclass machine More...
class  CLinearStructuredOutputMachine
class  CMachine
 A generic learning machine interface. More...
class  CMulticlassMachine
 experimental abstract generic multiclass machine class More...
class  CNativeMulticlassMachine
 experimental abstract native multiclass machine class More...
class  COnlineLinearMachine
 Class OnlineLinearMachine is a generic interface for linear machines like classifiers which work through online algorithms. More...
class  CStructuredOutputMachine
class  CCplex
 Class CCplex to encapsulate access to the commercial cplex general purpose optimizer. More...
class  CLoss
 Class which collects generic mathematical functions. More...
class  CMath
 Class which collects generic mathematical functions. More...
class  Munkres
 Munkres. More...
class  CSparseInverseCovariance
 used to estimate inverse covariance matrix using graphical lasso More...
class  CStatistics
 Class that contains certain functions related to statistics, such as probability/cumulative distribution functions, different statistics, etc. More...
class  CGradientModelSelection
 Model selection class which searches for the best model by a gradient- search. More...
class  CGridSearchModelSelection
 Model selection class which searches for the best model by a grid- search. See CModelSelection for details. More...
class  CModelSelection
 Abstract base class for model selection. Takes a parameter tree which specifies parameters for model selection, and a cross-validation instance and searches for the best combination of parameters in the abstract method select_model(), which has to be implemented in concrete sub-classes. More...
class  CModelSelectionParameters
 Class to select parameters and their ranges for model selection. The structure is organized as a tree with different kinds of nodes, depending on the values of its member variables of name and CSGObject. More...
class  CParameterCombination
 class that holds ONE combination of parameters for a learning machine. The structure is organized as a tree. Every node may hold a name or an instance of a Parameter class. Nodes may have children. The nodes are organized in such way, that every parameter of a model for model selection has one node and sub-parameters are stored in sub-nodes. Using a tree of this class, parameters of models may easily be set. There are these types of nodes: More...
class  CRandomSearchModelSelection
 Model selection class which searches for the best model by a random search. See CModelSelection for details. More...
class  CConjugateIndex
 conjugate index classifier. Described in: More...
class  CECOCAEDDecoder
class  CECOCDecoder
class  CECOCDiscriminantEncoder
class  CECOCEDDecoder
class  CECOCEncoder
 ECOCEncoder produce an ECOC codebook. More...
class  CECOCForestEncoder
class  CECOCHDDecoder
class  CECOCIHDDecoder
class  CECOCLLBDecoder
class  CECOCOVOEncoder
class  CECOCOVREncoder
class  CECOCRandomDenseEncoder
class  CECOCRandomSparseEncoder
class  CECOCSimpleDecoder
class  CECOCStrategy
class  CECOCUtil
class  CGaussianNaiveBayes
 Class GaussianNaiveBayes, a Gaussian Naive Bayes classifier. More...
class  CGMNPLib
 class GMNPLib Library of solvers for Generalized Minimal Norm Problem (GMNP). More...
class  CGMNPSVM
 Class GMNPSVM implements a one vs. rest MultiClass SVM. More...
class  CKNN
 Class KNN, an implementation of the standard k-nearest neigbor classifier. More...
class  CLaRank
 the LaRank multiclass SVM machine More...
class  CMulticlassLibLinear
 multiclass LibLinear wrapper. Uses Crammer-Singer formulation and gradient descent optimization algorithm implemented in the LibLinear library. Regularized bias support is added using stacking bias 'feature' to hyperplanes normal vectors. More...
class  CMulticlassLibSVM
 class LibSVMMultiClass. Does one vs one classification. More...
class  CMulticlassLogisticRegression
 multiclass logistic regression More...
class  CMulticlassOCAS
 multiclass OCAS wrapper More...
class  CMulticlassOneVsOneStrategy
 multiclass one vs one strategy used to train generic multiclass machines for K-class problems with building voting-based ensemble of K*(K-1) binary classifiers More...
class  CMulticlassOneVsRestStrategy
 multiclass one vs rest strategy used to train generic multiclass machines for K-class problems with building ensemble of K binary classifiers More...
class  CMulticlassStrategy
 class MulticlassStrategy used to construct generic multiclass classifiers with ensembles of binary classifiers More...
class  CMulticlassSVM
 class MultiClassSVM More...
class  CMulticlassTreeGuidedLogisticRegression
 multiclass tree guided logistic regression More...
class  CQDA
 Class QDA implements Quadratic Discriminant Analysis. More...
class  CRejectionStrategy
 base rejection strategy class More...
class  CThresholdRejectionStrategy
 threshold based rejection strategy More...
class  CDixonQTestRejectionStrategy
 simplified version of Dixon's Q test outlier based rejection strategy. Statistic values are taken from http://www.vias.org/tmdatanaleng/cc_outlier_tests_dixon.html More...
class  CScatterSVM
 ScatterSVM - Multiclass SVM. More...
class  CShareBoost
class  ShareBoostOptimizer
class  CBalancedConditionalProbabilityTree
class  CConditionalProbabilityTree
struct  ConditionalProbabilityTreeNodeData
 struct to store data of node of conditional probability tree More...
class  CRandomConditionalProbabilityTree
class  CRelaxedTree
struct  RelaxedTreeNodeData
class  RelaxedTreeUtil
class  CTreeMachine
 class TreeMachine, a base class for tree based multiclass classifiers More...
class  CTreeMachineNode
struct  VwConditionalProbabilityTreeNodeData
class  CVwConditionalProbabilityTree
struct  tag_callback_data
struct  tag_iteration_data
struct  lbfgs_parameter_t
class  CDecompressString
 Preprocessor that decompresses compressed strings. More...
class  CDensePreprocessor
 Template class DensePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CDenseFeatures (i.e. rectangular dense matrices). More...
class  CDimensionReductionPreprocessor
 the class DimensionReductionPreprocessor, a base class for preprocessors used to lower the dimensionality of given simple features (dense matrices). More...
class  CHomogeneousKernelMap
 Preprocessor HomogeneousKernelMap performs homogeneous kernel maps as described in. More...
class  CKernelPCA
 Preprocessor KernelPCA performs kernel principal component analysis. More...
class  CLogPlusOne
 Preprocessor LogPlusOne does what the name says, it adds one to a dense real valued vector and takes the logarithm of each component of it. More...
class  CNormOne
 Preprocessor NormOne, normalizes vectors to have norm 1. More...
class  CPCA
 Preprocessor PCACut performs principial component analysis on the input vectors and keeps only the n eigenvectors with eigenvalues above a certain threshold. More...
class  CPNorm
 Preprocessor PNorm, normalizes vectors to have p-norm. More...
class  CPreprocessor
 Class Preprocessor defines a preprocessor interface. More...
class  CPruneVarSubMean
 Preprocessor PruneVarSubMean will substract the mean and remove features that have zero variance. More...
class  CRandomFourierGaussPreproc
 Preprocessor CRandomFourierGaussPreproc implements Random Fourier Features for the Gauss kernel a la Ali Rahimi and Ben Recht Nips2007 after preprocessing the features using them in a linear kernel approximates a gaussian kernel. More...
class  CSortUlongString
 Preprocessor SortUlongString, sorts the indivual strings in ascending order. More...
class  CSortWordString
 Preprocessor SortWordString, sorts the indivual strings in ascending order. More...
class  CSparsePreprocessor
 Template class SparsePreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CSparseFeatures. More...
class  CStringPreprocessor
 Template class StringPreprocessor, base class for preprocessors (cf. CPreprocessor) that apply to CStringFeatures (i.e. strings of variable length). More...
class  CSumOne
 Preprocessor SumOne, normalizes vectors to have sum 1. More...
class  CGaussianProcessRegression
 Class GaussianProcessRegression implements Gaussian Process Regression.vInstead of a distribution over weights, the GP specifies a distribution over functions. More...
class  CExactInferenceMethod
 The Gaussian Exact Form Inference Method. More...
class  CFITCInferenceMethod
 The Fully Independent Conditional Training Inference Method. More...
class  CGaussianLikelihood
 This is the class that models a Gaussian Likelihood. More...
class  CInferenceMethod
 The Inference Method base class. More...
class  Psi_line
class  CLaplacianInferenceMethod
 The Laplace Approximation Inference Method. More...
class  CLikelihoodModel
 The Likelihood Model base class. More...
class  CMeanFunction
 Mean Function base class. More...
class  CStudentsTLikelihood
 This is the class that models a likelihood model with a Student's T Distribution. The parameters include degrees of freedom as well as a sigma scale parameter. More...
class  CZeroMean
 Zero Mean Function. More...
class  CKernelRidgeRegression
 Class KernelRidgeRegression implements Kernel Ridge Regression - a regularized least square method for classification and regression. More...
class  CLeastAngleRegression
 Class for Least Angle Regression, can be used to solve LASSO. More...
class  CLeastSquaresRegression
 class to perform Least Squares Regression More...
class  CLinearRidgeRegression
 Class LinearRidgeRegression implements Ridge Regression - a regularized least square method for classification and regression. More...
class  CLibLinearRegression
 LibLinear for regression. More...
class  CLibSVR
 Class LibSVR, performs support vector regression using LibSVM. More...
class  CMKLRegression
 Multiple Kernel Learning for regression. More...
class  CSVRLight
 Class SVRLight, performs support vector regression using SVMLight. More...
class  CHSIC
 This class implements the Hilbert Schmidtd Independence Criterion based independence test as described in [1]. More...
class  CKernelIndependenceTestStatistic
 Independence test base class. Provides an interface for performing an independence test. Given samples $Z=\{(x_i,y_i)\}_{i=1}^m$ from the joint distribution $\textbf{P}_x\textbf{P}_y$, does the joint distribution factorize as $\textbf{P}_{xy}=\textbf{P}_x\textbf{P}_y$? The null- hypothesis says yes, i.e. no independence, the alternative hypothesis says yes. More...
class  CKernelMeanMatching
 Kernel Mean Matching. More...
class  CKernelTwoSampleTestStatistic
 Two sample test base class. Provides an interface for performing a two-sample test, i.e. Given samples from two distributions $p$ and $q$, the null-hypothesis is: $H_0: p=q$, the alternative hypothesis: $H_1: p\neq q$. More...
class  CLinearTimeMMD
 This class implements the linear time Maximum Mean Statistic as described in [1]. This statistic is in particular suitable for streaming data. Therefore, only streaming features may be passed. To process other feature types, construct streaming features from these (see constructor documentations). A blocksize has to be specified that determines how many examples are processed at once. This should be set as large as available memory allows to ensure faster computations. More...
class  CQuadraticTimeMMD
 This class implements the quadratic time Maximum Mean Statistic as described in [1]. The MMD is the distance of two probability distributions $p$ and $q$ in a RKHS

\[ \text{MMD}}[\mathcal{F},p,q]^2=\textbf{E}_{x,x'}\left[ k(x,x')\right]- 2\textbf{E}_{x,y}\left[ k(x,y)\right] +\textbf{E}_{y,y'}\left[ k(y,y')\right]=||\mu_p - \mu_q||^2_\mathcal{F} \]

. More...

class  CTestStatistic
 Test statistic base class. Provides an interface for statistical tests via three methods: compute_statistic(), compute_p_value() and compute_threshold(). The second computes a p-value for the statistic computed by the first method. The p-value represents the position of the statistic in the null-distribution, i.e. the distribution of the statistic population given the null-hypothesis is true. (1-position = p-value). The third method, compute_threshold(), computes a threshold for a given test level which is needed to reject the null-hypothesis. More...
class  CTwoDistributionsTestStatistic
 Provides an interface for performing statistical tests on two sets of samples from two distributions. Instances of these tests are the classical two-sample test and the independence test. This class may be used as base class for both. More...
class  CDualLibQPBMSOSVM
 Class DualLibQPBMSOSVM that uses Bundle Methods for Regularized Risk Minimization algorithms for structured output (SO) problems [1] presented in [2]. More...
class  CDynProg
 Dynamic Programming Class. More...
class  CSequence
 Class CSequence to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). More...
class  CHMSVMLabels
 Class CHMSVMLabels to be used in the application of Structured Output (SO) learning to Hidden Markov Support Vector Machines (HM-SVM). Each of the labels is represented by a sequence of integers. Each label is of type CSequence and all of them are stored in a CDynamicObjectArray. More...
class  CHMSVMModel
 Class CHMSVMModel that represents the application specific model and contains the application dependent logic to solve Hidden Markov Support Vector Machines (HM-SVM) type of problems within a generic SO framework. More...
class  CIntronList
 class IntronList More...
struct  bmrm_return_value_T
struct  bmrm_ll
class  CMulticlassModel
 Class CMulticlassModel that represents the application specific model and contains the application dependent logic to solve multiclass classification within a generic SO framework. More...
struct  CRealNumber
 Class CRealNumber to be used in the application of Structured Output (SO) learning to multiclass classification. Even though it is likely that it does not make sense to consider real numbers as structured data, it has been made in this way because the basic type to use in structured labels needs to inherit from CStructuredData. More...
class  CMulticlassSOLabels
 Class CMulticlassSOLabels to be used in the application of Structured Output (SO) learning to multiclass classification. Each of the labels is represented by a real number and it is required that the values of the labels are in the set {0, 1, ..., num_classes-1}. Each label is of type CRealNumber and all of them are stored in a CDynamicObjectArray. More...
class  CPlif
 class Plif More...
class  CPlifArray
 class PlifArray More...
class  CPlifBase
 class PlifBase More...
class  CPlifMatrix
 store plif arrays for all transitions in the model More...
class  CSegmentLoss
 class IntronList More...
class  CStateModel
 class CStateModel base, abstract class for the internal state representation used in the CHMSVMModel. More...
struct  TMultipleCPinfo
struct  CResultSet
class  CStructuredModel
 Class CStructuredModel that represents the application specific model and contains most of the application dependent logic to solve structured output (SO) problems. The idea of this class is to be instantiated giving pointers to the functions that are dependent on the application, i.e. the combined feature representation $\Psi(\bold{x},\bold{y})$ and the argmax function $ {\arg\max} _{\bold{y} \neq \bold{y}_i} \left \langle { \bold{w}, \Psi(\bold{x}_i,\bold{y}) } \right \rangle $. See: MulticlassModel.h and .cpp for an example of these functions implemented. More...
class  CTwoStateModel
 class CTwoStateModel class for the internal two-state representation used in the CHMSVMModel. More...
class  CDomainAdaptationMulticlassLibLinear
 domain adaptation multiclass LibLinear wrapper Source domain is assumed to b More...
class  CDomainAdaptationSVM
 class DomainAdaptationSVM More...
class  CDomainAdaptationSVMLinear
 class DomainAdaptationSVMLinear More...
class  MappedSparseMatrix
 mapped sparse matrix for representing graph relations of tasks More...
class  CLibLinearMTL
 class to implement LibLinear More...
class  CMultitaskClusteredLogisticRegression
 class MultitaskClusteredLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. Assumes task in group are related with a clustered structure. More...
class  CMultitaskCompositeMachine
 class MultitaskCompositeMachine used to solve multitask binary classification problems with separate training of given binary classifier on each task More...
class  CMultitaskKernelMaskNormalizer
 The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class  CMultitaskKernelMaskPairNormalizer
 The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class  CMultitaskKernelMklNormalizer
 Base-class for parameterized Kernel Normalizers. More...
class  CMultitaskKernelNormalizer
 The MultitaskKernel allows Multitask Learning via a modified kernel function. More...
class  CMultitaskKernelPlifNormalizer
 The MultitaskKernel allows learning a piece-wise linear function (PLIF) via MKL. More...
class  CNode
 A CNode is an element of a CTaxonomy, which is used to describe hierarchical structure between tasks. More...
class  CTaxonomy
 CTaxonomy is used to describe hierarchical structure between tasks. More...
class  CMultitaskKernelTreeNormalizer
 The MultitaskKernel allows Multitask Learning via a modified kernel function based on taxonomy. More...
class  CMultitaskL12LogisticRegression
 class MultitaskL12LogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
class  CMultitaskLeastSquaresRegression
 class Multitask Least Squares Regression, a machine to solve regression problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees. More...
class  CMultitaskLinearMachine
 class MultitaskLinearMachine, a base class for linear multitask classifiers More...
class  CMultitaskLogisticRegression
 class Multitask Logistic Regression used to solve classification problems with a few tasks related via group or tree. Based on L1/Lq regression for groups and L1/L2 for trees. More...
class  CMultitaskROCEvaluation
 Class MultitaskROCEvalution used to evaluate ROC (Receiver Operating Characteristic) and an area under ROC curve (auROC) of each task separately. More...
class  CMultitaskTraceLogisticRegression
 class MultitaskTraceLogisticRegression, a classifier for multitask problems. Supports only task group relations. Based on solver ported from the MALSAR library. More...
class  CTask
 class Task used to represent tasks in multitask learning. Essentially it represent a set of feature vector indices. More...
class  CTaskGroup
 class TaskGroup used to represent a group of tasks. Tasks in group do not overlap. More...
class  CTaskRelation
 used to represent tasks in multitask learning More...
class  CTaskTree
 class TaskTree used to represent a tree of tasks. Tree is constructed via task with subtasks (and subtasks of subtasks ..) passed to the TaskTree. More...
class  CGUIClassifier
 UI classifier. More...
class  CGUIConverter
 UI converter. More...
class  CGUIDistance
 UI distance. More...
class  CGUIFeatures
 UI features. More...
class  CGUIHMM
 UI HMM (Hidden Markov Model). More...
class  CGUIKernel
 UI kernel. More...
class  CGUILabels
 UI labels. More...
class  CGUIMath
 UI math. More...
class  CGUIPluginEstimate
 UI estimate. More...
class  CGUIPreprocessor
 UI preprocessor. More...
class  CGUIStructure
 UI structure. More...
class  CGUITime
 UI time. More...

Typedefs

typedef uint32_t(* hash_func_t )(substring, uint32_t)
 Hash function typedef, takes a substring and seed as parameters.
typedef uint32_t vw_size_t
 vw_size_t typedef to work across platforms
typedef int32_t(CVwParser::* parse_func )(CIOBuffer *, VwExample *&)
 Parse function typedef. Takes an IOBuffer and VwExample as arguments.
typedef float64_t KERNELCACHE_ELEM
typedef int64_t KERNELCACHE_IDX
typedef int32_t index_t
typedef double DoubleOfDouble (double)
typedef struct tag_callback_data callback_data_t
typedef struct tag_iteration_data iteration_data_t
typedef int32_t(* line_search_proc )(int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param)
typedef float64_t(* lbfgs_evaluate_t )(void *instance, const float64_t *x, float64_t *g, const int n, const float64_t step)
typedef int(* lbfgs_progress_t )(void *instance, const float64_t *x, const float64_t *g, const float64_t fx, const float64_t xnorm, const float64_t gnorm, const float64_t step, int n, int k, int ls)
HMM specific types

typedef float64_t T_ALPHA_BETA_TABLE
 type for alpha/beta caching table
typedef uint8_t T_STATES
typedef T_STATESP_STATES
convenience typedefs

typedef CDynInt< uint64_t, 3 > uint192_t
 192 bit integer constructed out of 3 64bit uint64_t's
typedef CDynInt< uint64_t, 4 > uint256_t
 256 bit integer constructed out of 4 64bit uint64_t's
typedef CDynInt< uint64_t, 8 > uint512_t
 512 bit integer constructed out of 8 64bit uint64_t's
typedef CDynInt< uint64_t, 16 > uint1024_t
 1024 bit integer constructed out of 16 64bit uint64_t's

Enumerations

enum  EModelSelectionAvailability { MS_NOT_AVAILABLE = 0, MS_AVAILABLE }
enum  LIBLINEAR_SOLVER_TYPE {
  L2R_LR, L2R_L2LOSS_SVC_DUAL, L2R_L2LOSS_SVC, L2R_L1LOSS_SVC_DUAL,
  L1R_L2LOSS_SVC, L1R_LR, L2R_LR_DUAL
}
enum  EVwCacheType { C_NATIVE = 0, C_PROTOBUF = 1 }
enum  E_VW_PARSER_TYPE { T_VW = 1, T_SVMLIGHT = 2, T_DENSE = 3 }
 

The type of input to parse.

More...
enum  ESPEStrategy { SPE_GLOBAL, SPE_LOCAL }
enum  EDistanceType {
  D_UNKNOWN = 0, D_MINKOWSKI = 10, D_MANHATTAN = 20, D_CANBERRA = 30,
  D_CHEBYSHEW = 40, D_GEODESIC = 50, D_JENSEN = 60, D_MANHATTANWORD = 70,
  D_HAMMINGWORD = 80, D_CANBERRAWORD = 90, D_SPARSEEUCLIDEAN = 100, D_EUCLIDEAN = 110,
  D_CHISQUARE = 120, D_TANIMOTO = 130, D_COSINE = 140, D_BRAYCURTIS = 150,
  D_CUSTOM = 160, D_ATTENUATEDEUCLIDEAN = 170, D_MAHALANOBIS = 180, D_DIRECTOR = 190
}
enum  ECovType { FULL, DIAG, SPHERICAL }
enum  BaumWelchViterbiType {
  BW_NORMAL, BW_TRANS, BW_DEFINED, VIT_NORMAL,
  VIT_DEFINED
}
enum  EContingencyTableMeasureType {
  ACCURACY = 0, ERROR_RATE = 10, BAL = 20, WRACC = 30,
  F1 = 40, CROSS_CORRELATION = 50, RECALL = 60, PRECISION = 70,
  SPECIFICITY = 80, CUSTOM = 999
}
enum  EEvaluationDirection { ED_MINIMIZE, ED_MAXIMIZE }
enum  EEvaluationResultType { CROSSVALIDATION_RESULT, GRADIENTEVALUATION_RESULT }
enum  EAlphabet {
  DNA = 0, RAWDNA = 1, RNA = 2, PROTEIN = 3,
  BINARY = 4, ALPHANUM = 5, CUBE = 6, RAWBYTE = 7,
  IUPAC_NUCLEIC_ACID = 8, IUPAC_AMINO_ACID = 9, NONE = 10, DIGIT = 11,
  DIGIT2 = 12, RAWDIGIT = 13, RAWDIGIT2 = 14, UNKNOWN = 15,
  SNP = 16, RAWSNP = 17
}
 

Alphabet of charfeatures/observations.

More...
enum  EFeatureType {
  F_UNKNOWN = 0, F_BOOL = 5, F_CHAR = 10, F_BYTE = 20,
  F_SHORT = 30, F_WORD = 40, F_INT = 50, F_UINT = 60,
  F_LONG = 70, F_ULONG = 80, F_SHORTREAL = 90, F_DREAL = 100,
  F_LONGREAL = 110, F_ANY = 1000
}
 

shogun feature type

More...
enum  EFeatureClass {
  C_UNKNOWN = 0, C_DENSE = 10, C_SPARSE = 20, C_STRING = 30,
  C_COMBINED = 40, C_COMBINED_DOT = 60, C_WD = 70, C_SPEC = 80,
  C_WEIGHTEDSPEC = 90, C_POLY = 100, C_STREAMING_DENSE = 110, C_STREAMING_SPARSE = 120,
  C_STREAMING_STRING = 130, C_STREAMING_VW = 140, C_BINNED_DOT = 150, C_DIRECTOR_DOT = 160,
  C_LATENT = 170, C_ANY = 1000
}
 

shogun feature class

More...
enum  EFeatureProperty { FP_NONE = 0, FP_DOT = 1, FP_STREAMING_DOT = 2 }
 

shogun feature properties

More...
enum  EMessageType {
  MSG_GCDEBUG, MSG_DEBUG, MSG_INFO, MSG_NOTICE,
  MSG_WARN, MSG_ERROR, MSG_CRITICAL, MSG_ALERT,
  MSG_EMERGENCY, MSG_MESSAGEONLY
}
enum  E_EXAMPLE_TYPE { E_LABELLED = 1, E_UNLABELLED = 2 }
enum  E_IS_EXAMPLE_USED { E_EMPTY = 1, E_NOT_USED = 2, E_USED = 3 }
enum  EOptimizationType { FASTBUTMEMHUNGRY, SLOWBUTMEMEFFICIENT }
enum  EKernelType {
  K_UNKNOWN = 0, K_LINEAR = 10, K_POLY = 20, K_GAUSSIAN = 30,
  K_GAUSSIANSHIFT = 32, K_GAUSSIANMATCH = 33, K_HISTOGRAM = 40, K_SALZBERG = 41,
  K_LOCALITYIMPROVED = 50, K_SIMPLELOCALITYIMPROVED = 60, K_FIXEDDEGREE = 70, K_WEIGHTEDDEGREE = 80,
  K_WEIGHTEDDEGREEPOS = 81, K_WEIGHTEDDEGREERBF = 82, K_WEIGHTEDCOMMWORDSTRING = 90, K_POLYMATCH = 100,
  K_ALIGNMENT = 110, K_COMMWORDSTRING = 120, K_COMMULONGSTRING = 121, K_SPECTRUMRBF = 122,
  K_SPECTRUMMISMATCHRBF = 123, K_COMBINED = 140, K_AUC = 150, K_CUSTOM = 160,
  K_SIGMOID = 170, K_CHI2 = 180, K_DIAG = 190, K_CONST = 200,
  K_DISTANCE = 220, K_LOCALALIGNMENT = 230, K_PYRAMIDCHI2 = 240, K_OLIGO = 250,
  K_MATCHWORD = 260, K_TPPK = 270, K_REGULATORYMODULES = 280, K_SPARSESPATIALSAMPLE = 290,
  K_HISTOGRAMINTERSECTION = 300, K_WAVELET = 310, K_WAVE = 320, K_CAUCHY = 330,
  K_TSTUDENT = 340, K_RATIONAL_QUADRATIC = 350, K_MULTIQUADRIC = 360, K_EXPONENTIAL = 370,
  K_SPHERICAL = 380, K_SPLINE = 390, K_ANOVA = 400, K_POWER = 410,
  K_LOG = 420, K_CIRCULAR = 430, K_INVERSEMULTIQUADRIC = 440, K_DISTANTSEGMENTS = 450,
  K_BESSEL = 460, K_JENSENSHANNON = 470, K_DIRECTOR = 480, K_PRODUCT = 490,
  K_LINEARARD = 500, K_GAUSSIANARD = 510, K_STREAMING = 520
}
enum  EKernelProperty { KP_NONE = 0, KP_LINADD = 1, KP_KERNCOMBINATION = 2, KP_BATCHEVALUATION = 4 }
enum  ENormalizerType { N_REGULAR = 0, N_MULTITASK = 1 }
enum  EWDKernType {
  E_WD = 0, E_EXTERNAL = 1, E_BLOCK_CONST = 2, E_BLOCK_LINEAR = 3,
  E_BLOCK_SQPOLY = 4, E_BLOCK_CUBICPOLY = 5, E_BLOCK_EXP = 6, E_BLOCK_LOG = 7
}
enum  E_COMPRESSION_TYPE {
  UNCOMPRESSED, LZO, GZIP, BZIP2,
  LZMA, SNAPPY
}
enum  EFeaturesContainer { FC_LHS = 0, FC_RHS = 1 }
enum  EStructuredDataType { SDT_UNKNOWN = 0, SDT_REAL = 1, SDT_SEQUENCE = 2 }
enum  ELossType {
  L_HINGELOSS = 0, L_SMOOTHHINGELOSS = 10, L_SQUAREDHINGELOSS = 20, L_SQUAREDLOSS = 30,
  L_LOGLOSS = 100, L_LOGLOSSMARGIN = 110
}
 

shogun loss type

More...
enum  EMachineType {
  CT_NONE = 0, CT_LIGHT = 10, CT_LIGHTONECLASS = 11, CT_LIBSVM = 20,
  CT_LIBSVMONECLASS = 30, CT_LIBSVMMULTICLASS = 40, CT_MPD = 50, CT_GPBT = 60,
  CT_CPLEXSVM = 70, CT_PERCEPTRON = 80, CT_KERNELPERCEPTRON = 90, CT_LDA = 100,
  CT_LPM = 110, CT_LPBOOST = 120, CT_KNN = 130, CT_SVMLIN = 140,
  CT_KERNELRIDGEREGRESSION = 150, CT_GNPPSVM = 160, CT_GMNPSVM = 170, CT_SUBGRADIENTSVM = 180,
  CT_SUBGRADIENTLPM = 190, CT_SVMPERF = 200, CT_LIBSVR = 210, CT_SVRLIGHT = 220,
  CT_LIBLINEAR = 230, CT_KMEANS = 240, CT_HIERARCHICAL = 250, CT_SVMOCAS = 260,
  CT_WDSVMOCAS = 270, CT_SVMSGD = 280, CT_MKLMULTICLASS = 290, CT_MKLCLASSIFICATION = 300,
  CT_MKLONECLASS = 310, CT_MKLREGRESSION = 320, CT_SCATTERSVM = 330, CT_DASVM = 340,
  CT_LARANK = 350, CT_DASVMLINEAR = 360, CT_GAUSSIANNAIVEBAYES = 370, CT_AVERAGEDPERCEPTRON = 380,
  CT_SGDQN = 390, CT_CONJUGATEINDEX = 400, CT_LINEARRIDGEREGRESSION = 410, CT_LEASTSQUARESREGRESSION = 420,
  CT_QDA = 430, CT_NEWTONSVM = 440, CT_GAUSSIANPROCESSREGRESSION = 450, CT_LARS = 460,
  CT_MULTICLASS = 470, CT_DIRECTORLINEAR = 480, CT_DIRECTORKERNEL = 490
}
enum  ESolverType {
  ST_AUTO = 0, ST_CPLEX = 1, ST_GLPK = 2, ST_NEWTON = 3,
  ST_DIRECT = 4, ST_ELASTICNET = 5, ST_BLOCK_NORM = 6
}
enum  EProblemType {
  PT_BINARY = 0, PT_REGRESSION = 1, PT_MULTICLASS = 2, PT_STRUCTURED = 3,
  PT_LATENT = 4
}
enum  E_PROB_TYPE { E_LINEAR, E_QP }
enum  ERangeType { R_LINEAR, R_EXP, R_LOG }
enum  EMSParamType {
  MSPT_NONE = 0, MSPT_FLOAT64, MSPT_INT32, MSPT_FLOAT64_VECTOR,
  MSPT_INT32_VECTOR, MSPT_FLOAT64_SGVECTOR, MSPT_INT32_SGVECTOR
}
enum  SCATTER_TYPE { NO_BIAS_LIBSVM, NO_BIAS_SVMLIGHT, TEST_RULE1, TEST_RULE2 }
enum  {
  LBFGS_SUCCESS = 0, LBFGS_CONVERGENCE = 0, LBFGS_STOP, LBFGS_ALREADY_MINIMIZED,
  LBFGSERR_UNKNOWNERROR = -1024, LBFGSERR_LOGICERROR, LBFGSERR_OUTOFMEMORY, LBFGSERR_CANCELED,
  LBFGSERR_INVALID_N, LBFGSERR_INVALID_N_SSE, LBFGSERR_INVALID_X_SSE, LBFGSERR_INVALID_EPSILON,
  LBFGSERR_INVALID_TESTPERIOD, LBFGSERR_INVALID_DELTA, LBFGSERR_INVALID_LINESEARCH, LBFGSERR_INVALID_MINSTEP,
  LBFGSERR_INVALID_MAXSTEP, LBFGSERR_INVALID_FTOL, LBFGSERR_INVALID_WOLFE, LBFGSERR_INVALID_GTOL,
  LBFGSERR_INVALID_XTOL, LBFGSERR_INVALID_MAXLINESEARCH, LBFGSERR_INVALID_ORTHANTWISE, LBFGSERR_INVALID_ORTHANTWISE_START,
  LBFGSERR_INVALID_ORTHANTWISE_END, LBFGSERR_OUTOFINTERVAL, LBFGSERR_INCORRECT_TMINMAX, LBFGSERR_ROUNDING_ERROR,
  LBFGSERR_MINIMUMSTEP, LBFGSERR_MAXIMUMSTEP, LBFGSERR_MAXIMUMLINESEARCH, LBFGSERR_MAXIMUMITERATION,
  LBFGSERR_WIDTHTOOSMALL, LBFGSERR_INVALIDPARAMETERS, LBFGSERR_INCREASEGRADIENT
}
enum  {
  LBFGS_LINESEARCH_DEFAULT = 0, LBFGS_LINESEARCH_MORETHUENTE = 0, LBFGS_LINESEARCH_BACKTRACKING_ARMIJO = 1, LBFGS_LINESEARCH_BACKTRACKING = 2,
  LBFGS_LINESEARCH_BACKTRACKING_WOLFE = 2, LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 3
}
enum  HomogeneousKernelType { HomogeneousKernelIntersection = 0, HomogeneousKernelChi2, HomogeneousKernelJS }
 

Type of kernel.

More...
enum  HomogeneousKernelMapWindowType { HomogeneousKernelMapWindowUniform = 0, HomogeneousKernelMapWindowRectangular = 1 }
 

Type of spectral windowing function.

More...
enum  EPCAMode { THRESHOLD, VARIANCE_EXPLAINED, FIXED_NUMBER }
enum  EPreprocessorType {
  P_UNKNOWN = 0, P_NORMONE = 10, P_LOGPLUSONE = 20, P_SORTWORDSTRING = 30,
  P_SORTULONGSTRING = 40, P_SORTWORD = 50, P_PRUNEVARSUBMEAN = 60, P_DECOMPRESSSTRING = 70,
  P_DECOMPRESSCHARSTRING = 80, P_DECOMPRESSBYTESTRING = 90, P_DECOMPRESSWORDSTRING = 100, P_DECOMPRESSULONGSTRING = 110,
  P_RANDOMFOURIERGAUSS = 120, P_PCA = 130, P_KERNELPCA = 140, P_NORMDERIVATIVELEM3 = 150,
  P_DIMENSIONREDUCTIONPREPROCESSOR = 160, P_SUMONE = 170, P_HOMOGENEOUSKERNELMAP = 180, P_PNORM = 190
}
enum  ELikelihoodModelType { LT_NONE = 0, LT_GAUSSIAN = 10, LT_STUDENTST = 20 }
enum  ETrainingType { PINV = 1, GS = 2 }
enum  ERegressionType { RT_NONE = 0, RT_LIGHT = 10, RT_LIBSVM = 20 }
 

type of regressor

More...
enum  LIBLINEAR_REGRESSION_TYPE { L2R_L2LOSS_SVR, L2R_L1LOSS_SVR_DUAL, L2R_L2LOSS_SVR_DUAL }
enum  EQuadraticMMDType { BIASED, UNBIASED }
enum  ENullApproximationMethod {
  BOOTSTRAP, MMD2_SPECTRUM, MMD2_GAMMA, MMD1_GAUSSIAN,
  HSIC_GAMMA
}
enum  ESolver { BMRM = 1, PPBMRM = 2, P3BMRM = 3 }
enum  ETransformType {
  T_LINEAR, T_LOG, T_LOG_PLUS1, T_LOG_PLUS3,
  T_LINEAR_PLUS3
}
enum  EStateModelType { SMT_UNKNOWN = 0, SMT_TWO_STATE = 1 }

Functions

CSGObjectnew_sgserializable (const char *sgserializable_name, EPrimitiveType generic)
void init_shogun (void(*print_message)(FILE *target, const char *str), void(*print_warning)(FILE *target, const char *str), void(*print_error)(FILE *target, const char *str), void(*cancel_computations)(bool &delayed, bool &immediately))
void sg_global_print_default (FILE *target, const char *str)
void init_shogun_with_defaults ()
void exit_shogun ()
void set_global_io (SGIO *io)
SGIOget_global_io ()
void set_global_parallel (Parallel *parallel)
Parallelget_global_parallel ()
void set_global_version (Version *version)
Versionget_global_version ()
void set_global_math (CMath *math)
CMathget_global_math ()
float32_t sd_offset_add (float32_t *weights, vw_size_t mask, VwFeature *begin, VwFeature *end, vw_size_t offset)
float32_t sd_offset_truncadd (float32_t *weights, vw_size_t mask, VwFeature *begin, VwFeature *end, vw_size_t offset, float32_t gravity)
float32_t one_pf_quad_predict (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask)
float32_t one_pf_quad_predict_trunc (float32_t *weights, VwFeature &f, v_array< VwFeature > &cross_features, vw_size_t mask, float32_t gravity)
float32_t real_weight (float32_t w, float32_t gravity)
void * sqdist_thread_func (void *P)
bool read_real_valued_sparse (SGSparseVector< float64_t > *&matrix, int32_t &num_feat, int32_t &num_vec)
bool write_real_valued_sparse (const SGSparseVector< float64_t > *matrix, int32_t num_feat, int32_t num_vec)
bool read_real_valued_dense (float64_t *&matrix, int32_t &num_feat, int32_t &num_vec)
bool write_real_valued_dense (const float64_t *matrix, int32_t num_feat, int32_t num_vec)
bool read_char_valued_strings (SGString< char > *&strings, int32_t &num_str, int32_t &max_string_len)
bool write_char_valued_strings (const SGString< char > *strings, int32_t num_str)
char * c_string_of_substring (substring s)
void print_substring (substring s)
float32_t float_of_substring (substring s)
float64_t double_of_substring (substring s)
int32_t int_of_substring (substring s)
uint32_t ulong_of_substring (substring s)
uint32_t ss_length (substring s)
double glomin (double a, double b, double c, double m, double e, double t, func_base &f, double &x)
double local_min (double a, double b, double t, func_base &f, double &x)
double local_min_rc (double &a, double &b, int &status, double value)
double r8_abs (double x)
double r8_epsilon ()
double r8_max (double x, double y)
double r8_sign (double x)
void timestamp ()
double zero (double a, double b, double t, func_base &f)
void zero_rc (double a, double b, double t, double &arg, int &status, double value)
double glomin (double a, double b, double c, double m, double e, double t, double f(double x), double &x)
double local_min (double a, double b, double t, double f(double x), double &x)
double zero (double a, double b, double t, double f(double x))
int32_t InnerProjector (int32_t method, int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t l, float64_t u, float64_t *x, float64_t &lambda)
int32_t gvpm (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
int32_t FletcherAlg2A (int32_t Projector, int32_t n, float32_t *vecA, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
int32_t gpm_solver (int32_t Solver, int32_t Projector, int32_t n, float32_t *A, float64_t *b, float64_t c, float64_t e, int32_t *iy, float64_t *x, float64_t tol, int32_t *ls, int32_t *proj)
float64_t ProjectR (float64_t *x, int32_t n, float64_t lambda, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u)
int32_t ProjectDai (int32_t n, int32_t *a, float64_t b, float64_t *c, float64_t l, float64_t u, float64_t *x, float64_t &lam_ext)
float64_t quick_select (float64_t *arr, int32_t n)
int32_t Pardalos (int32_t n, int32_t *iy, float64_t e, float64_t *qk, float64_t low, float64_t up, float64_t *x)
static const float64_tget_col (uint32_t i)
static float64_t get_time ()
ocas_return_value_T svm_ocas_solver_nnw (float64_t C, uint32_t nData, uint32_t num_nnw, uint32_t *nnw_idx, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, int(*add_pw_constr)(uint32_t, uint32_t, void *), void(*clip_neg_W)(uint32_t, uint32_t *, void *), void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
ocas_return_value_T svm_ocas_solver (float64_t C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
ocas_return_value_T svm_ocas_solver_difC (float64_t *C, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
static void findactive (float64_t *Theta, float64_t *SortedA, uint32_t *nSortedA, float64_t *A, float64_t *B, int n, int(*sort)(float64_t *, float64_t *, uint32_t))
ocas_return_value_T msvm_ocas_solver (float64_t C, float64_t *data_y, uint32_t nY, uint32_t nData, float64_t TolRel, float64_t TolAbs, float64_t QPBound, float64_t MaxTime, uint32_t _BufSize, uint8_t Method, void(*compute_W)(float64_t *, float64_t *, float64_t *, uint32_t, void *), float64_t(*update_W)(float64_t, void *), int(*add_new_cut)(float64_t *, uint32_t *, uint32_t, void *), int(*compute_output)(float64_t *, void *), int(*sort)(float64_t *, float64_t *, uint32_t), void(*ocas_print)(ocas_return_value_T), void *user_data)
libqp_state_T libqp_splx_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *b, uint32_t *I, uint8_t *S, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolAbs, float64_t TolRel, float64_t QP_TH, void(*print_state)(libqp_state_T state))
libqp_state_T libqp_gsmo_solver (const float64_t *(*get_col)(uint32_t), float64_t *diag_H, float64_t *f, float64_t *a, float64_t b, float64_t *LB, float64_t *UB, float64_t *x, uint32_t n, uint32_t MaxIter, float64_t TolKKT, void(*print_state)(libqp_state_T state))
void nrerror (char error_text[])
bool choldc (float64_t *a, int32_t n, float64_t *p)
void cholsb (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
void chol_forward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
void chol_backward (float64_t a[], int32_t n, float64_t p[], float64_t b[], float64_t x[])
bool solve_reduced (int32_t n, int32_t m, float64_t h_x[], float64_t h_y[], float64_t a[], float64_t x_x[], float64_t x_y[], float64_t c_x[], float64_t c_y[], float64_t workspace[], int32_t step)
void matrix_vector (int32_t n, float64_t m[], float64_t x[], float64_t y[])
int32_t pr_loqo (int32_t n, int32_t m, float64_t c[], float64_t h_x[], float64_t a[], float64_t b[], float64_t l[], float64_t u[], float64_t primal[], float64_t dual[], int32_t verb, float64_t sigfig_max, int32_t counter_max, float64_t margin, float64_t bound, int32_t restart)
void ssl_train (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t CGLS (const struct data *Data, const struct options *Options, const struct vector_int *Subset, struct vector_double *Weights, struct vector_double *Outputs)
int32_t L2_SVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini)
float64_t line_search (float64_t *w, float64_t *w_bar, float64_t lambda, float64_t *o, float64_t *o_bar, float64_t *Y, float64_t *C, int32_t d, int32_t l)
int32_t TSVM_MFN (const struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t switch_labels (float64_t *Y, float64_t *o, int32_t *JU, int32_t u, int32_t S)
int32_t DA_S3VM (struct data *Data, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs)
int32_t optimize_w (const struct data *Data, const float64_t *p, struct options *Options, struct vector_double *Weights, struct vector_double *Outputs, int32_t ini)
void optimize_p (const float64_t *g, int32_t u, float64_t T, float64_t r, float64_t *p)
float64_t transductive_cost (float64_t normWeights, float64_t *Y, float64_t *Outputs, int32_t m, float64_t lambda, float64_t lambda_u)
float64_t entropy (const float64_t *p, int32_t u)
float64_t KL (const float64_t *p, const float64_t *q, int32_t u)
float64_t norm_square (const vector_double *A)
void initialize (struct vector_double *A, int32_t k, float64_t a)
void initialize (struct vector_int *A, int32_t k)
void GetLabeledData (struct data *D, const struct data *Data)
template<class T >
void push (v_array< T > &v, const T &new_ele)
template<class T >
void alloc (v_array< T > &v, int length)
template<class T >
v_array< T > pop (v_array< v_array< T > > &stack)
float distance (CJLCoverTreePoint p1, CJLCoverTreePoint p2, float64_t upper_bound)
v_array< CJLCoverTreePointparse_points (CDistance *distance, EFeaturesContainer fc)
void print (CJLCoverTreePoint &p)
malsar_result_t malsar_clustered (CDotFeatures *features, double *y, double rho1, double rho2, const malsar_options &options)
malsar_result_t malsar_joint_feature_learning (CDotFeatures *features, double *y, double rho1, double rho2, const malsar_options &options)
malsar_result_t malsar_low_rank (CDotFeatures *features, double *y, double rho, const malsar_options &options)
slep_result_t slep_mc_plain_lr (CDotFeatures *features, CMulticlassLabels *labels, float64_t z, const slep_options &options)
slep_result_t slep_mc_tree_lr (CDotFeatures *features, CMulticlassLabels *labels, float64_t z, const slep_options &options)
double compute_regularizer (double *w, double lambda, double lambda2, int n_vecs, int n_feats, int n_blocks, const slep_options &options)
double compute_lambda (double *ATx, double z, CDotFeatures *features, double *y, int n_vecs, int n_feats, int n_blocks, const slep_options &options)
void projection (double *w, double *v, int n_feats, int n_blocks, double lambda, double lambda2, double L, double *z, double *z0, const slep_options &options)
double search_point_gradient_and_objective (CDotFeatures *features, double *ATx, double *As, double *sc, double *y, int n_vecs, int n_feats, int n_tasks, double *g, double *gc, const slep_options &options)
slep_result_t slep_solver (CDotFeatures *features, double *y, double z, const slep_options &options)
void arpack_dsxupd (double *matrix, double *rhs, bool is_rhs_diag, int n, int nev, const char *which, bool use_superlu, int mode, bool pos, bool cov, double shift, double tolerance, double *eigenvalues, double *eigenvectors, int &status)
void wrap_dsyev (char jobz, char uplo, int n, double *a, int lda, double *w, int *info)
void wrap_dgesvd (char jobu, char jobvt, int m, int n, double *a, int lda, double *sing, double *u, int ldu, double *vt, int ldvt, int *info)
void wrap_dgeqrf (int m, int n, double *a, int lda, double *tau, int *info)
void wrap_dorgqr (int m, int n, int k, double *a, int lda, double *tau, int *info)
void wrap_dsyevr (char jobz, char uplo, int n, double *a, int lda, int il, int iu, double *eigenvalues, double *eigenvectors, int *info)
void wrap_dsygvx (int itype, char jobz, char uplo, int n, double *a, int lda, double *b, int ldb, int il, int iu, double *eigenvalues, double *eigenvectors, int *info)
template<class T >
SGVector< T > create_range_array (T min, T max, ERangeType type, T step, T type_base)
static larank_kcache_t * larank_kcache_create (CKernel *kernelfunc)
static void xtruncate (larank_kcache_t *self, int32_t k, int32_t nlen)
static void xpurge (larank_kcache_t *self)
static void larank_kcache_set_maximum_size (larank_kcache_t *self, int64_t entries)
static void larank_kcache_destroy (larank_kcache_t *self)
static void xminsize (larank_kcache_t *self, int32_t n)
static int32_t * larank_kcache_r2i (larank_kcache_t *self, int32_t n)
static void xextend (larank_kcache_t *self, int32_t k, int32_t nlen)
static void xswap (larank_kcache_t *self, int32_t i1, int32_t i2, int32_t r1, int32_t r2)
static void larank_kcache_swap_rr (larank_kcache_t *self, int32_t r1, int32_t r2)
static void larank_kcache_swap_ri (larank_kcache_t *self, int32_t r1, int32_t i2)
static float64_t xquery (larank_kcache_t *self, int32_t i, int32_t j)
static float64_t larank_kcache_query (larank_kcache_t *self, int32_t i, int32_t j)
static void larank_kcache_set_buddy (larank_kcache_t *self, larank_kcache_t *buddy)
static float32_tlarank_kcache_query_row (larank_kcache_t *self, int32_t i, int32_t len)
static int32_t line_search_backtracking (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param)
static int32_t line_search_backtracking_owlqn (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wp, callback_data_t *cd, const lbfgs_parameter_t *param)
static int32_t line_search_morethuente (int32_t n, float64_t *x, float64_t *f, float64_t *g, float64_t *s, float64_t *stp, const float64_t *xp, const float64_t *gp, float64_t *wa, callback_data_t *cd, const lbfgs_parameter_t *param)
static int32_t update_trial_interval (float64_t *x, float64_t *fx, float64_t *dx, float64_t *y, float64_t *fy, float64_t *dy, float64_t *t, float64_t *ft, float64_t *dt, const float64_t tmin, const float64_t tmax, int32_t *brackt)
static float64_t owlqn_x1norm (const float64_t *x, const int32_t start, const int32_t n)
static void owlqn_pseudo_gradient (float64_t *pg, const float64_t *x, const float64_t *g, const int32_t n, const float64_t c, const int32_t start, const int32_t end)
static void owlqn_project (float64_t *d, const float64_t *sign, const int32_t start, const int32_t end)
void lbfgs_parameter_init (lbfgs_parameter_t *param)
int32_t lbfgs (int32_t n, float64_t *x, float64_t *ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void *instance, lbfgs_parameter_t *_param)
int lbfgs (int n, float64_t *x, float64_t *ptr_fx, lbfgs_evaluate_t proc_evaluate, lbfgs_progress_t proc_progress, void *instance, lbfgs_parameter_t *param)
void add_cutting_plane (bmrm_ll **tail, bool *map, float64_t *A, uint32_t free_idx, float64_t *cp_data, uint32_t dim)
void remove_cutting_plane (bmrm_ll **head, bmrm_ll **tail, bool *map, float64_t *icp)
bmrm_return_value_T svm_bmrm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)
float64_tget_cutting_plane (bmrm_ll *ptr)
uint32_t find_free_idx (bool *map, uint32_t size)
bmrm_return_value_T svm_p3bm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, uint32_t cp_models, bool verbose)
bmrm_return_value_T svm_ppbm_solver (CStructuredModel *model, float64_t *W, float64_t TolRel, float64_t TolAbs, float64_t _lambda, uint32_t _BufSize, bool cleanICP, uint32_t cleanAfter, float64_t K, uint32_t Tmax, bool verbose)

Variables

Parallelsg_parallel = NULL
SGIOsg_io = NULL
Versionsg_version = NULL
CMathsg_math = NULL
void(* sg_print_message )(FILE *target, const char *str) = NULL
 function called to print normal messages
void(* sg_print_warning )(FILE *target, const char *str) = NULL
 function called to print warning messages
void(* sg_print_error )(FILE *target, const char *str) = NULL
 function called to print error messages
void(* sg_cancel_computations )(bool &delayed, bool &immediately) = NULL
 function called to cancel things
const int32_t quadratic_constant = 27942141
 Constant used while hashing/accessing quadratic features.
const int32_t constant_hash = 11650396
 Constant used to access the constant feature.
const uint32_t hash_base = 97562527
 Seed for hash.
const int32_t LOGSUM_TBL = 10000
uint32_t Randnext
static const uint32_t QPSolverMaxIter = 10000000
static float64_tH
static uint32_t BufSize
static double * H_diag_matrix
static int H_diag_matrix_ld
static const float64_t Q_test_statistic_values [10][8]
static const lbfgs_parameter_t _defparam
static const float64_t epsilon = 0.0
static float64_tH2

Detailed Description

all of classes and functions are contained in the shogun namespace


Typedef Documentation

Definition at line 85 of file lbfgs.cpp.

typedef double DoubleOfDouble(double)

Definition at line 1464 of file brent.cpp.

typedef uint32_t(* hash_func_t)(substring, uint32_t)

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

Definition at line 21 of file vw_constants.h.

typedef int32_t index_t

index

Definition at line 26 of file DataType.h.

Definition at line 93 of file lbfgs.cpp.

kernel cache element

Definition at line 32 of file Kernel.h.

typedef int64_t KERNELCACHE_IDX

kernel cache index

Definition at line 43 of file Kernel.h.

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

Definition at line 104 of file lbfgs.cpp.

typedef T_STATES * P_STATES

Definition at line 66 of file HMM.h.

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

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

Definition at line 20 of file StreamingVwFile.h.

type for alpha/beta caching table

Definition at line 37 of file HMM.h.

typedef uint16_t T_STATES

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

Definition at line 64 of file HMM.h.

typedef CDynInt<uint64_t,16> uint1024_t

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

Definition at line 574 of file DynInt.h.

typedef CDynInt<uint64_t,3> uint192_t

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

Definition at line 565 of file DynInt.h.

typedef CDynInt<uint64_t,4> uint256_t

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

Definition at line 568 of file DynInt.h.

typedef CDynInt<uint64_t,8> uint512_t

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

Definition at line 571 of file DynInt.h.

typedef uint32_t vw_size_t

vw_size_t typedef to work across platforms

Definition at line 24 of file vw_constants.h.


Enumeration Type Documentation

Training type

Enumerator:
BW_NORMAL 

standard baum welch

BW_TRANS 

baum welch only for specified transitions

BW_DEFINED 

baum welch only for defined transitions/observations

VIT_NORMAL 

standard viterbi

VIT_DEFINED 

viterbi only for defined transitions/observations

Definition at line 71 of file HMM.h.

compression type

Enumerator:
UNCOMPRESSED 
LZO 
GZIP 
BZIP2 
LZMA 
SNAPPY 

Definition at line 26 of file Compressor.h.

Type of example, either E_LABELLED or E_UNLABELLED

Enumerator:
E_LABELLED 
E_UNLABELLED 

Definition at line 26 of file InputParser.h.

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

Enumerator:
E_EMPTY 
E_NOT_USED 
E_USED 

Definition at line 23 of file ParseBuffer.h.

Enumerator:
E_LINEAR 
E_QP 

Definition at line 29 of file Cplex.h.

The type of input to parse.

Enumerator:
T_VW 
T_SVMLIGHT 
T_DENSE 

Definition at line 28 of file VwParser.h.

enum EAlphabet

Alphabet of charfeatures/observations.

Enumerator:
DNA 

DNA - letters A,C,G,T.

RAWDNA 

RAWDNA - letters 0,1,2,3.

RNA 

RNA - letters A,C,G,U.

PROTEIN 

PROTEIN - letters A-Z.

BINARY 
ALPHANUM 

ALPHANUM - [0-9A-Z].

CUBE 

CUBE - [1-6].

RAWBYTE 

RAW BYTE - [0-255].

IUPAC_NUCLEIC_ACID 

IUPAC_NUCLEIC_ACID.

IUPAC_AMINO_ACID 

IUPAC_AMINO_ACID.

NONE 

NONE - type has no alphabet.

DIGIT 

DIGIT - letters 0-9.

DIGIT2 

DIGIT2 - letters 0-2.

RAWDIGIT 

RAWDIGIT - 0-9.

RAWDIGIT2 

RAWDIGIT2 - 0-2.

UNKNOWN 

unknown alphabet

SNP 

SNP - letters A,C,G,T,0.

RAWSNP 

RAWSNP - letters 0,1,2,3,4.

Definition at line 20 of file Alphabet.h.

type of measure

Enumerator:
ACCURACY 
ERROR_RATE 
BAL 
WRACC 
F1 
CROSS_CORRELATION 
RECALL 
PRECISION 
SPECIFICITY 
CUSTOM 

Definition at line 25 of file ContingencyTableEvaluation.h.

enum ECovType

Covariance type

Enumerator:
FULL 

full covariance

DIAG 

diagonal covariance

SPHERICAL 

spherical covariance

Definition at line 29 of file Gaussian.h.

type of distance

Enumerator:
D_UNKNOWN 
D_MINKOWSKI 
D_MANHATTAN 
D_CANBERRA 
D_CHEBYSHEW 
D_GEODESIC 
D_JENSEN 
D_MANHATTANWORD 
D_HAMMINGWORD 
D_CANBERRAWORD 
D_SPARSEEUCLIDEAN 
D_EUCLIDEAN 
D_CHISQUARE 
D_TANIMOTO 
D_COSINE 
D_BRAYCURTIS 
D_CUSTOM 
D_ATTENUATEDEUCLIDEAN 
D_MAHALANOBIS 
D_DIRECTOR 

Definition at line 31 of file Distance.h.

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

Enumerator:
ED_MINIMIZE 
ED_MAXIMIZE 

Definition at line 24 of file Evaluation.h.

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.

shogun feature class

Enumerator:
C_UNKNOWN 
C_DENSE 
C_SPARSE 
C_STRING 
C_COMBINED 
C_COMBINED_DOT 
C_WD 
C_SPEC 
C_WEIGHTEDSPEC 
C_POLY 
C_STREAMING_DENSE 
C_STREAMING_SPARSE 
C_STREAMING_STRING 
C_STREAMING_VW 
C_BINNED_DOT 
C_DIRECTOR_DOT 
C_LATENT 
C_ANY 

Definition at line 35 of file FeatureTypes.h.

shogun feature properties

Enumerator:
FP_NONE 
FP_DOT 
FP_STREAMING_DOT 

Definition at line 58 of file FeatureTypes.h.

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.

shogun feature type

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

Definition at line 16 of file FeatureTypes.h.

kernel property

Enumerator:
KP_NONE 
KP_LINADD 
KP_KERNCOMBINATION 
KP_BATCHEVALUATION 

Definition at line 118 of file Kernel.h.

kernel type

Enumerator:
K_UNKNOWN 
K_LINEAR 
K_POLY 
K_GAUSSIAN 
K_GAUSSIANSHIFT 
K_GAUSSIANMATCH 
K_HISTOGRAM 
K_SALZBERG 
K_LOCALITYIMPROVED 
K_SIMPLELOCALITYIMPROVED 
K_FIXEDDEGREE 
K_WEIGHTEDDEGREE 
K_WEIGHTEDDEGREEPOS 
K_WEIGHTEDDEGREERBF 
K_WEIGHTEDCOMMWORDSTRING 
K_POLYMATCH 
K_ALIGNMENT 
K_COMMWORDSTRING 
K_COMMULONGSTRING 
K_SPECTRUMRBF 
K_SPECTRUMMISMATCHRBF 
K_COMBINED 
K_AUC 
K_CUSTOM 
K_SIGMOID 
K_CHI2 
K_DIAG 
K_CONST 
K_DISTANCE 
K_LOCALALIGNMENT 
K_PYRAMIDCHI2 
K_OLIGO 
K_MATCHWORD 
K_TPPK 
K_REGULATORYMODULES 
K_SPARSESPATIALSAMPLE 
K_HISTOGRAMINTERSECTION 
K_WAVELET 
K_WAVE 
K_CAUCHY 
K_TSTUDENT 
K_RATIONAL_QUADRATIC 
K_MULTIQUADRIC 
K_EXPONENTIAL 
K_SPHERICAL 
K_SPLINE 
K_ANOVA 
K_POWER 
K_LOG 
K_CIRCULAR 
K_INVERSEMULTIQUADRIC 
K_DISTANTSEGMENTS 
K_BESSEL 
K_JENSENSHANNON 
K_DIRECTOR 
K_PRODUCT 
K_LINEARARD 
K_GAUSSIANARD 
K_STREAMING 

Definition at line 54 of file Kernel.h.

Type of likelihood model

Enumerator:
LT_NONE 
LT_GAUSSIAN 
LT_STUDENTST 

Definition at line 22 of file LikelihoodModel.h.

enum ELossType

shogun loss type

Enumerator:
L_HINGELOSS 
L_SMOOTHHINGELOSS 
L_SQUAREDHINGELOSS 
L_SQUAREDLOSS 
L_LOGLOSS 
L_LOGLOSSMARGIN 

Definition at line 27 of file LossFunction.h.

classifier type

Enumerator:
CT_NONE 
CT_LIGHT 
CT_LIGHTONECLASS 
CT_LIBSVM 
CT_LIBSVMONECLASS 
CT_LIBSVMMULTICLASS 
CT_MPD 
CT_GPBT 
CT_CPLEXSVM 
CT_PERCEPTRON 
CT_KERNELPERCEPTRON 
CT_LDA 
CT_LPM 
CT_LPBOOST 
CT_KNN 
CT_SVMLIN 
CT_KERNELRIDGEREGRESSION 
CT_GNPPSVM 
CT_GMNPSVM 
CT_SUBGRADIENTSVM 
CT_SUBGRADIENTLPM 
CT_SVMPERF 
CT_LIBSVR 
CT_SVRLIGHT 
CT_LIBLINEAR 
CT_KMEANS 
CT_HIERARCHICAL 
CT_SVMOCAS 
CT_WDSVMOCAS 
CT_SVMSGD 
CT_MKLMULTICLASS 
CT_MKLCLASSIFICATION 
CT_MKLONECLASS 
CT_MKLREGRESSION 
CT_SCATTERSVM 
CT_DASVM 
CT_LARANK 
CT_DASVMLINEAR 
CT_GAUSSIANNAIVEBAYES 
CT_AVERAGEDPERCEPTRON 
CT_SGDQN 
CT_CONJUGATEINDEX 
CT_LINEARRIDGEREGRESSION 
CT_LEASTSQUARESREGRESSION 
CT_QDA 
CT_NEWTONSVM 
CT_GAUSSIANPROCESSREGRESSION 
CT_LARS 
CT_MULTICLASS 
CT_DIRECTORLINEAR 
CT_DIRECTORKERNEL 

Definition at line 33 of file Machine.h.

The io libs output [DEBUG] etc in front of every message 'higher' messages filter output depending on the loglevel, i.e. CRITICAL messages will print all MSG_CRITICAL TO MSG_EMERGENCY messages.

Enumerator:
MSG_GCDEBUG 
MSG_DEBUG 
MSG_INFO 
MSG_NOTICE 
MSG_WARN 
MSG_ERROR 
MSG_CRITICAL 
MSG_ALERT 
MSG_EMERGENCY 
MSG_MESSAGEONLY 

Definition at line 42 of file SGIO.h.

model selection availability

Enumerator:
MS_NOT_AVAILABLE 
MS_AVAILABLE 

Definition at line 72 of file SGObject.h.

value type of a model selection parameter node

Enumerator:
MSPT_NONE 

no type

MSPT_FLOAT64 
MSPT_INT32 
MSPT_FLOAT64_VECTOR 
MSPT_INT32_VECTOR 
MSPT_FLOAT64_SGVECTOR 
MSPT_INT32_SGVECTOR 

Definition at line 29 of file ModelSelectionParameters.h.

normalizer type

Enumerator:
N_REGULAR 
N_MULTITASK 

Definition at line 21 of file KernelNormalizer.h.

enum for different method to approximate null-distibution

Enumerator:
BOOTSTRAP 
MMD2_SPECTRUM 
MMD2_GAMMA 
MMD1_GAUSSIAN 
HSIC_GAMMA 

Definition at line 19 of file TestStatistic.h.

optimization type

Enumerator:
FASTBUTMEMHUNGRY 
SLOWBUTMEMEFFICIENT 

Definition at line 47 of file Kernel.h.

enum EPCAMode

mode of pca

Enumerator:
THRESHOLD 

cut by threshold

VARIANCE_EXPLAINED 

variance explained

FIXED_NUMBER 

keep fixed number of features

Definition at line 26 of file PCA.h.

enumeration of possible preprocessor types used by Shogun UI

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

Enumerator:
P_UNKNOWN 
P_NORMONE 
P_LOGPLUSONE 
P_SORTWORDSTRING 
P_SORTULONGSTRING 
P_SORTWORD 
P_PRUNEVARSUBMEAN 
P_DECOMPRESSSTRING 
P_DECOMPRESSCHARSTRING 
P_DECOMPRESSBYTESTRING 
P_DECOMPRESSWORDSTRING 
P_DECOMPRESSULONGSTRING 
P_RANDOMFOURIERGAUSS 
P_PCA 
P_KERNELPCA 
P_NORMDERIVATIVELEM3 
P_DIMENSIONREDUCTIONPREPROCESSOR 
P_SUMONE 
P_HOMOGENEOUSKERNELMAP 
P_PNORM 

Definition at line 30 of file Preprocessor.h.

problem type

Enumerator:
PT_BINARY 
PT_REGRESSION 
PT_MULTICLASS 
PT_STRUCTURED 
PT_LATENT 

Definition at line 101 of file Machine.h.

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

Enumerator:
BIASED 
UNBIASED 

Definition at line 22 of file QuadraticTimeMMD.h.

enum ERangeType

type of range

Enumerator:
R_LINEAR 
R_EXP 
R_LOG 

Definition at line 23 of file ModelSelectionParameters.h.

type of regressor

Enumerator:
RT_NONE 
RT_LIGHT 
RT_LIBSVM 

Definition at line 17 of file Regression.h.

enum ESolver

Enum Training method selection

Enumerator:
BMRM 

Standard BMRM algorithm.

PPBMRM 

Proximal Point BMRM (BMRM with prox-term)

P3BMRM 

Proximal Point P-BMRM (multiple cutting plane models)

Definition at line 25 of file DualLibQPBMSOSVM.h.

solver type

Enumerator:
ST_AUTO 
ST_CPLEX 
ST_GLPK 
ST_NEWTON 
ST_DIRECT 
ST_ELASTICNET 
ST_BLOCK_NORM 

Definition at line 89 of file Machine.h.

Stochastic Proximity Embedding (SPE) strategy

Enumerator:
SPE_GLOBAL 
SPE_LOCAL 

Definition at line 23 of file StochasticProximityEmbedding.h.

state model type

Enumerator:
SMT_UNKNOWN 
SMT_TWO_STATE 

Definition at line 18 of file StateModelTypes.h.

structured data type

Enumerator:
SDT_UNKNOWN 
SDT_REAL 
SDT_SEQUENCE 

Definition at line 17 of file StructuredDataTypes.h.

which training method to use for KRR

Enumerator:
PINV 

via pseudo inverse

GS 

or gauss-seidel iterative method

Definition at line 26 of file KernelRidgeRegression.h.

Ways to transform inputs

Enumerator:
T_LINEAR 

Linear.

T_LOG 

Logarithmic.

T_LOG_PLUS1 

Logarithmic (log(1+x)).

T_LOG_PLUS3 

Logarithmic (log(3+x)).

T_LINEAR_PLUS3 

Linear (3+x).

Definition at line 22 of file Plif.h.

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

Enumerator:
C_NATIVE 
C_PROTOBUF 

Definition at line 29 of file VwCacheReader.h.

WD kernel type

Enumerator:
E_WD 
E_EXTERNAL 
E_BLOCK_CONST 
E_BLOCK_LINEAR 
E_BLOCK_SQPOLY 
E_BLOCK_CUBICPOLY 
E_BLOCK_EXP 
E_BLOCK_LOG 

Definition at line 25 of file WeightedDegreeStringKernel.h.

Type of spectral windowing function.

Enumerator:
HomogeneousKernelMapWindowUniform 

uniform window

HomogeneousKernelMapWindowRectangular 

rectangular window

Definition at line 30 of file HomogeneousKernelMap.h.

Type of kernel.

Enumerator:
HomogeneousKernelIntersection 

intersection kernel

HomogeneousKernelChi2 

Chi2 kernel

HomogeneousKernelJS 

Jensen-Shannon kernel

Definition at line 23 of file HomogeneousKernelMap.h.

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.

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.

scatter svm variant

Enumerator:
NO_BIAS_LIBSVM 

no bias w/ libsvm

NO_BIAS_SVMLIGHT 

no bias w/ svmlight

TEST_RULE1 

training with bias using test rule 1

TEST_RULE2 

training with bias using test rule 2

Definition at line 25 of file ScatterSVM.h.


Function Documentation

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

Add cutting plane

Parameters:
tail Pointer to the last CP entry
map Pointer to map storing info about CP physical memory
A CP physical memory
free_idx Index to physical memory where the CP data will be stored
cp_data CP data
dim Dimension of CP data
void shogun::alloc ( v_array< T > &  v,
int  length 
)

Used to modify the capacity of the vector

Parameters:
v vector
length the new length of the vector

Definition at line 78 of file JLCoverTreePoint.h.

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

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

Parameters:
matrix symmetric real matrix of size n*n (will be modified if mode==3)
rhs if is_rhs_diag is true - array of size n representing right hand side diagonal matrix, else array of size n*n representing right hand side full matrix should be NULL if non-general eigenproblem to be solved
is_rhs_diag true if rhs is diagonal and represented by array of size N
n size of matrix
nev number of eigenpairs to compute (nev<=n)
which eigenvalue finding strategy. Possible values:

  • "LM": nev Largest Magnitude eigenvalues
  • "SM": nev Smallest Magnitude eigenvalues
  • "LA": nev Largest Algebraic eigenvalues (if mode==3 eigenvalues from the right of shift)
  • "SA": nev Smallest Algebraic eigenvalues (if mode==3 eigenvalues from the left of shift)
  • "BE": half of nev from each end of the spectrum, i.e. nev2 smallest and nev2 largest eigenvalues. If nev is odd, one more largest eigenvalue will be computed
use_superlu if superlu should be used (works efficiently only for sparse matrices)
mode shift-mode of IRLM. Possible values:

  • 1: regular mode
  • 3: shift-invert mode
pos true if matrix is positive definite (Cholesky factorization is used in this case instead of LUP factorization))
cov true whether matrix A should be considered as A^T A
shift shift for shift-invert (3) mode of IRLM. In this mode routine will compute eigenvalues near provided shift
tolerance tolerance with eigenvalues should be computed (zero means machine precision)
eigenvalues array of size nev to hold computed eigenvalues
eigenvectors array of size nev*n to hold computed eigenvectors
status on output -1 if computation failed
char* shogun::c_string_of_substring ( substring  s  ) 

Return a C string from the substring

Parameters:
s substring
Returns:
new C string representation

Definition at line 472 of file SGIO.h.

int32_t shogun::CGLS ( const struct data *  Data,
const struct options *  Options,
const struct vector_int *  Subset,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

Definition at line 79 of file ssl.cpp.

void shogun::chol_backward ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 162 of file pr_loqo.cpp.

void shogun::chol_forward ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 149 of file pr_loqo.cpp.

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

Definition at line 54 of file pr_loqo.cpp.

void shogun::cholsb ( float64_t  a[],
int32_t  n,
float64_t  p[],
float64_t  b[],
float64_t  x[] 
)

Definition at line 125 of file pr_loqo.cpp.

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

Definition at line 106 of file slep_solver.cpp.

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

Definition at line 23 of file slep_solver.cpp.

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

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

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

Definition at line 223 of file ModelSelectionParameters.h.

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

Definition at line 570 of file ssl.cpp.

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

Functions declared out of the class definition to respect JLCoverTree structure

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

Definition at line 138 of file JLCoverTreePoint.h.

float64_t shogun::double_of_substring ( substring  s  ) 

Return value of substring as double

Parameters:
s substring
Returns:
substring as double

Definition at line 512 of file SGIO.h.

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

Definition at line 1031 of file ssl.cpp.

void exit_shogun (  ) 

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

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

Get index of free slot for new cutting plane

Parameters:
map Pointer to map storing info about CP physical memory
size Size of the CP buffer
Returns:
Index of unoccupied memory field in CP physical memory

Definition at line 145 of file libbmrm.h.

static void shogun::findactive ( float64_t Theta,
float64_t SortedA,
uint32_t *  nSortedA,
float64_t A,
float64_t B,
int  n,
int(*)(float64_t *, float64_t *, uint32_t)  sort 
) [static]

Definition at line 1411 of file libocas.cpp.

int32_t shogun::FletcherAlg2A ( int32_t  Projector,
int32_t  n,
float32_t vecA,
float64_t b,
float64_t  c,
float64_t  e,
int32_t *  iy,
float64_t x,
float64_t  tol,
int32_t *  ls,
int32_t *  proj 
)

Definition at line 448 of file gpm.cpp.

float32_t shogun::float_of_substring ( substring  s  ) 

Get value of substring as float (if possible)

Parameters:
s substring
Returns:
float32_t value of substring

Definition at line 497 of file SGIO.h.

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

Definition at line 33 of file libppbm.cpp.

float64_t* shogun::get_cutting_plane ( bmrm_ll *  ptr  ) 

Get cutting plane

Parameters:
ptr Pointer to some CP entry
Returns:
Pointer to cutting plane at given entry

Definition at line 137 of file libbmrm.h.

SGIO * get_global_io (  ) 

get the global io object

Returns:
io object
CMath * get_global_math (  ) 

get the global math object

Returns:
math object
Parallel * get_global_parallel (  ) 

get the global parallel object

Returns:
parallel object
Version * get_global_version (  ) 

get the global version object

Returns:
version object
static float64_t shogun::get_time (  )  [static]

Definition at line 47 of file libocas.cpp.

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

Definition at line 1097 of file ssl.cpp.

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

Definition at line 13 of file brent.cpp.

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

Definition at line 1479 of file brent.cpp.

int32_t gpm_solver ( int32_t  Solver,
int32_t  Projector,
int32_t  n,
float32_t A,
float64_t b,
float64_t  c,
float64_t  e,
int32_t *  iy,
float64_t x,
float64_t  tol,
int32_t *  ls = 0,
int32_t *  proj = 0 
)

gpm solver

Parameters:
Solver 
Projector 
n 
A 
b 
c 
e 
iy 
x 
tol 
ls 
proj 
int32_t shogun::gvpm ( int32_t  Projector,
int32_t  n,
float32_t vecA,
float64_t b,
float64_t  c,
float64_t  e,
int32_t *  iy,
float64_t x,
float64_t  tol,
int32_t *  ls,
int32_t *  proj 
)

Definition at line 110 of file gpm.cpp.

void init_shogun ( void(*)(FILE *target, const char *str)  print_message = NULL,
void(*)(FILE *target, const char *str)  print_warning = NULL,
void(*)(FILE *target, const char *str)  print_error = NULL,
void(*)(bool &delayed, bool &immediately)  cancel_computations = NULL 
)

This function must be called before libshogun is used. Usually shogun does not provide any output messages (neither debugging nor error; apart from exceptions). This function allows one to specify customized output callback functions and a callback function to check for exceptions:

Parameters:
print_message function pointer to print a message
print_warning function pointer to print a warning message
print_error function pointer to print an error message (this will be printed before shogun throws an exception)
cancel_computations function pointer to check for exception
void init_shogun_with_defaults (  ) 

init shogun with defaults

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

Definition at line 1087 of file ssl.cpp.

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

Definition at line 1077 of file ssl.cpp.

int32_t InnerProjector ( int32_t  method,
int32_t  n,
int32_t *  iy,
float64_t  e,
float64_t qk,
float64_t  l,
float64_t  u,
float64_t x,
float64_t lambda 
)

Definition at line 1221 of file gpm.cpp.

int32_t shogun::int_of_substring ( substring  s  ) 

Integer value of substring

Parameters:
s substring
Returns:
int value of substring

Definition at line 527 of file SGIO.h.

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

Definition at line 1044 of file ssl.cpp.

int32_t shogun::L2_SVM_MFN ( const struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs,
int32_t  ini 
)

Definition at line 198 of file ssl.cpp.

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

Definition at line 65 of file LaRank.cpp.

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

Definition at line 133 of file LaRank.cpp.

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

Definition at line 324 of file LaRank.cpp.

static float32_t* shogun::larank_kcache_query_row ( larank_kcache_t *  self,
int32_t  i,
int32_t  len 
) [static]

Definition at line 356 of file LaRank.cpp.

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

Definition at line 197 of file LaRank.cpp.

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

Definition at line 333 of file LaRank.cpp.

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

Definition at line 125 of file LaRank.cpp.

static void shogun::larank_kcache_swap_ri ( larank_kcache_t *  self,
int32_t  r1,
int32_t  i2 
) [static]

Definition at line 290 of file LaRank.cpp.

static void shogun::larank_kcache_swap_rr ( larank_kcache_t *  self,
int32_t  r1,
int32_t  r2 
) [static]

Definition at line 284 of file LaRank.cpp.

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

Definition at line 204 of file lbfgs.cpp.

libqp_state_T libqp_gsmo_solver ( const float64_t *(*)(uint32_t)  get_col,
float64_t diag_H,
float64_t f,
float64_t a,
float64_t  b,
float64_t LB,
float64_t UB,
float64_t x,
uint32_t  n,
uint32_t  MaxIter,
float64_t  TolKKT,
void(*)(libqp_state_T state)  print_state 
)

Generalized SMO algorithm

Definition at line 73 of file libqp_gsmo.cpp.

libqp_state_T libqp_splx_solver ( const float64_t *(*)(uint32_t)  get_col,
float64_t diag_H,
float64_t f,
float64_t b,
uint32_t *  I,
uint8_t *  S,
float64_t x,
uint32_t  n,
uint32_t  MaxIter,
float64_t  TolAbs,
float64_t  TolRel,
float64_t  QP_TH,
void(*)(libqp_state_T state)  print_state 
)

QP solver for tasks with simplex constraints

Definition at line 83 of file libqp_splx.cpp.

float64_t shogun::line_search ( float64_t w,
float64_t w_bar,
float64_t  lambda,
float64_t o,
float64_t o_bar,
float64_t Y,
float64_t C,
int32_t  d,
int32_t  l 
)

Definition at line 359 of file ssl.cpp.

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

Definition at line 595 of file lbfgs.cpp.

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

Definition at line 688 of file lbfgs.cpp.

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

Definition at line 762 of file lbfgs.cpp.

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

Definition at line 296 of file brent.cpp.

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

Definition at line 1487 of file brent.cpp.

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

Definition at line 538 of file brent.cpp.

malsar_result_t malsar_clustered ( CDotFeatures *  features,
double *  y,
double  rho1,
double  rho2,
const malsar_options &  options 
)
malsar_result_t malsar_joint_feature_learning ( CDotFeatures *  features,
double *  y,
double  rho1,
double  rho2,
const malsar_options &  options 
)
malsar_result_t malsar_low_rank ( CDotFeatures *  features,
double *  y,
double  rho,
const malsar_options &  options 
)
void shogun::matrix_vector ( int32_t  n,
float64_t  m[],
float64_t  x[],
float64_t  y[] 
)

Definition at line 263 of file pr_loqo.cpp.

ocas_return_value_T shogun::msvm_ocas_solver ( float64_t  C,
float64_t data_y,
uint32_t  nY,
uint32_t  nData,
float64_t  TolRel,
float64_t  TolAbs,
float64_t  QPBound,
float64_t  MaxTime,
uint32_t  _BufSize,
uint8_t  Method,
void(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 1468 of file libocas.cpp.

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

new shogun serializable

Parameters:
sgserializable_name 
generic 

Definition at line 1609 of file class_list.cpp.

float64_t shogun::norm_square ( const vector_double *  A  ) 

Definition at line 1066 of file ssl.cpp.

void shogun::nrerror ( char  error_text[]  ) 

Definition at line 38 of file pr_loqo.cpp.

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

Get the prediction contribution from one feature.

Parameters:
weights weights
f feature
cross_features paired features
mask mask
Returns:
prediction from one feature
float32_t one_pf_quad_predict_trunc ( float32_t weights,
VwFeature &  f,
v_array< VwFeature > &  cross_features,
vw_size_t  mask,
float32_t  gravity 
)

Get the prediction contribution from one feature.

Weights are taken as truncated weights.

Parameters:
weights weights
f feature
cross_features paired features
mask mask
gravity weight threshold value
Returns:
prediction from one feature
void shogun::optimize_p ( const float64_t g,
int32_t  u,
float64_t  T,
float64_t  r,
float64_t p 
)

Definition at line 932 of file ssl.cpp.

int32_t shogun::optimize_w ( const struct data *  Data,
const float64_t p,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs,
int32_t  ini 
)

Definition at line 653 of file ssl.cpp.

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

Definition at line 1309 of file lbfgs.cpp.

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

Definition at line 1266 of file lbfgs.cpp.

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

Definition at line 1250 of file lbfgs.cpp.

int32_t shogun::Pardalos ( int32_t  n,
int32_t *  iy,
float64_t  e,
float64_t qk,
float64_t  low,
float64_t  up,
float64_t x 
)

Definition at line 1052 of file gpm.cpp.

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

Fills up a v_array of CJLCoverTreePoint objects

Definition at line 183 of file JLCoverTreePoint.h.

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

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

Parameters:
stack of vectors
Returns:
the adequate vector according to the previous conditions

Definition at line 94 of file JLCoverTreePoint.h.

int32_t pr_loqo ( int32_t  n,
int32_t  m,
float64_t  c[],
float64_t  h_x[],
float64_t  a[],
float64_t  b[],
float64_t  l[],
float64_t  u[],
float64_t  primal[],
float64_t  dual[],
int32_t  verb,
float64_t  sigfig_max,
int32_t  counter_max,
float64_t  margin,
float64_t  bound,
int32_t  restart 
)

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

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

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

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

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

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

void shogun::print ( CJLCoverTreePoint &  p  ) 

Print the information of the CoverTree point

Definition at line 207 of file JLCoverTreePoint.h.

void shogun::print_substring ( substring  s  ) 

Print the substring

Parameters:
s substring

Definition at line 484 of file SGIO.h.

int32_t shogun::ProjectDai ( int32_t  n,
int32_t *  a,
float64_t  b,
float64_t c,
float64_t  l,
float64_t  u,
float64_t x,
float64_t lam_ext 
)

Definition at line 860 of file gpm.cpp.

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

Definition at line 277 of file slep_solver.cpp.

float64_t shogun::ProjectR ( float64_t x,
int32_t  n,
float64_t  lambda,
int32_t *  a,
float64_t  b,
float64_t c,
float64_t  l,
float64_t  u 
)

Definition at line 832 of file gpm.cpp.

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

Insert a new element at the end of the vector

Parameters:
v vector
new_ele element to insert

Definition at line 61 of file JLCoverTreePoint.h.

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

Definition at line 992 of file gpm.cpp.

double shogun::r8_abs ( double  x  ) 

Definition at line 834 of file brent.cpp.

double shogun::r8_epsilon (  ) 

Definition at line 875 of file brent.cpp.

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

Definition at line 921 of file brent.cpp.

double shogun::r8_sign ( double  x  ) 

Definition at line 962 of file brent.cpp.

bool shogun::read_char_valued_strings ( SGString< char > *&  strings,
int32_t &  num_str,
int32_t &  max_string_len 
)

read char string features, simple ascii format e.g. foo bar ACGTACGTATCT

two strings

Parameters:
strings strings to read into
num_str number of strings
max_string_len length of longest string
Returns:
if reading was successful
bool shogun::read_real_valued_dense ( float64_t *&  matrix,
int32_t &  num_feat,
int32_t &  num_vec 
)

read dense real valued features, simple ascii format e.g. 1.0 1.1 0.2 2.3 3.5 5

a matrix that consists of 3 vectors with each of 2d

Parameters:
matrix matrix to read into
num_feat number of features for each vector
num_vec number of vectors in matrix
Returns:
if reading was successful
bool shogun::read_real_valued_sparse ( SGSparseVector< float64_t > *&  matrix,
int32_t &  num_feat,
int32_t &  num_vec 
)

read sparse real valued features in svm light format e.g. -1 1:10.0 2:100.2 1000:1.3 with -1 == (optional) label and dim 1 - value 10.0 dim 2 - value 100.2 dim 1000 - value 1.3

Parameters:
matrix matrix to read into
num_feat number of features for each vector
num_vec number of vectors in matrix
Returns:
if reading was successful
float32_t shogun::real_weight ( float32_t  w,
float32_t  gravity 
)

Get the truncated weight value

Parameters:
w weight
gravity threshold for the weight
Returns:
truncated weight

Definition at line 33 of file vw_math.h.

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

Remove cutting plane at given index

Parameters:
head Pointer to the first CP entry
tail Pointer to the last CP entry
map Pointer to map storing info about CP physical memory
icp Pointer to inactive CP that should be removed
float32_t sd_offset_add ( float32_t weights,
vw_size_t  mask,
VwFeature *  begin,
VwFeature *  end,
vw_size_t  offset 
)

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

Parameters:
weights weight vector
mask mask
begin first feature of the vector
end last feature of the vector
offset index offset
Returns:
dot product
float32_t sd_offset_truncadd ( float32_t weights,
vw_size_t  mask,
VwFeature *  begin,
VwFeature *  end,
vw_size_t  offset,
float32_t  gravity 
)

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

Weights are taken as the truncated weights.

Parameters:
weights weights
mask mask
begin first feature of the vector
end last feature of the vector
offset index offset
gravity weight threshold value
Returns:
dot product
double shogun::search_point_gradient_and_objective ( CDotFeatures *  features,
double *  ATx,
double *  As,
double *  sc,
double *  y,
int  n_vecs,
int  n_feats,
int  n_tasks,
double *  g,
double *  gc,
const slep_options &  options 
)

Definition at line 313 of file slep_solver.cpp.

void set_global_io ( SGIO *  io  ) 

set the global io object

Parameters:
io io object to use
void set_global_math ( CMath *  math  ) 

set the global math object

Parameters:
math math object to use
void set_global_parallel ( Parallel *  parallel  ) 

set the global parallel object

Parameters:
parallel parallel object to use
void set_global_version ( Version *  version  ) 

set the global version object

Parameters:
version version object to use
void shogun::sg_global_print_default ( FILE *  target,
const char *  str 
)

Definition at line 72 of file init.cpp.

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

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

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

(n_vecs*n_classes);

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

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

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

(n_vecs*n_classes);

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

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

Based on accelerated projected gradient method.

Supports two types of losses: logistic and least squares.

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

bool shogun::solve_reduced ( int32_t  n,
int32_t  m,
float64_t  h_x[],
float64_t  h_y[],
float64_t  a[],
float64_t  x_x[],
float64_t  x_y[],
float64_t  c_x[],
float64_t  c_y[],
float64_t  workspace[],
int32_t  step 
)

Definition at line 200 of file pr_loqo.cpp.

void* shogun::sqdist_thread_func ( void *  P  ) 

Definition at line 144 of file KMeans.cpp.

uint32_t shogun::ss_length ( substring  s  ) 

Length of substring

Parameters:
s substring
Returns:
length of substring

Definition at line 551 of file SGIO.h.

void shogun::ssl_train ( struct data *  Data,
struct options *  Options,
struct vector_double *  Weights,
struct vector_double *  Outputs 
)

Definition at line 33 of file ssl.cpp.

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

Standard BMRM Solver for Structured Output Learning

Parameters:
model Pointer to user defined CStructuredModel
W Weight vector
TolRel Relative tolerance
TolAbs Absolute tolerance
_lambda Regularization constant
_BufSize Size of the CP buffer (i.e. maximal number of iterations)
cleanICP Flag that enables/disables inactive cutting plane removal feature
cleanAfter Number of iterations that should be cutting plane inactive for to be removed
K Parameter K
Tmax Parameter Tmax
verbose Flag that enables/disables screen output
Returns:
Structure with BMRM algorithm result
ocas_return_value_T shogun::svm_ocas_solver ( float64_t  C,
uint32_t  nData,
float64_t  TolRel,
float64_t  TolAbs,
float64_t  QPBound,
float64_t  MaxTime,
uint32_t  _BufSize,
uint8_t  Method,
void(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 530 of file libocas.cpp.

ocas_return_value_T shogun::svm_ocas_solver_difC ( float64_t C,
uint32_t  nData,
float64_t  TolRel,
float64_t  TolAbs,
float64_t  QPBound,
float64_t  MaxTime,
uint32_t  _BufSize,
uint8_t  Method,
void(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 959 of file libocas.cpp.

ocas_return_value_T shogun::svm_ocas_solver_nnw ( float64_t  C,
uint32_t  nData,
uint32_t  num_nnw,
uint32_t *  nnw_idx,
float64_t  TolRel,
float64_t  TolAbs,
float64_t  QPBound,
float64_t  MaxTime,
uint32_t  _BufSize,
uint8_t  Method,
int(*)(uint32_t, uint32_t, void *)  add_pw_constr,
void(*)(uint32_t, uint32_t *, void *)  clip_neg_W,
void(*)(float64_t *, float64_t *, float64_t *, uint32_t, void *)  compute_W,
float64_t(*)(float64_t, void *)  update_W,
int(*)(float64_t *, uint32_t *, uint32_t, uint32_t, void *)  add_new_cut,
int(*)(float64_t *, void *)  compute_output,
int(*)(float64_t *, float64_t *, uint32_t)  sort,
void(*)(ocas_return_value_T)  ocas_print,
void *  user_data 
)

Definition at line 63 of file libocas.cpp.

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

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

Parameters:
model Pointer to user defined CStructuredModel
W Weight vector
TolRel Relative tolerance
TolAbs Absolute tolerance
_lambda Regularization constant
_BufSize Size of the CP buffer (i.e. maximal number of iterations)
cleanICP Flag that enables/disables inactive cutting plane removal feature
cleanAfter Number of iterations that should be cutting plane inactive for to be removed
K Parameter K
Tmax Parameter Tmax
cp_models Count of cutting plane models to be used
verbose Flag that enables/disables screen output
Returns:
Structure with BMRM algorithm result
bmrm_return_value_T svm_ppbm_solver ( CStructuredModel *  model,
float64_t W,
float64_t  TolRel,
float64_t  TolAbs,
float64_t  _lambda,
uint32_t  _BufSize,
bool  cleanICP,
uint32_t  cleanAfter,
float64_t  K,
uint32_t  Tmax,
bool  verbose 
)

Proximal Point BMRM Solver for Structured Output Learning

Parameters:
model Pointer to user defined CStructuredModel
W Weight vector
TolRel Relative tolerance
TolAbs Absolute tolerance
_lambda Regularization constant
_BufSize Size of the CP buffer (i.e. maximal number of iterations)
cleanICP Flag that enables/disables inactive cutting plane removal feature
cleanAfter Number of iterations that should be cutting plane inactive for to be removed
K Parameter K
Tmax Parameter Tmax
verbose Flag that enables/disables screen output
Returns:
Structure with BMRM algorithm result
int32_t shogun::switch_labels ( float64_t Y,
float64_t o,
int32_t *  JU,
int32_t  u,
int32_t  S 
)

Definition at line 525 of file ssl.cpp.

void shogun::timestamp (  ) 

Definition at line 1003 of file brent.cpp.

float64_t shogun::transductive_cost ( float64_t  normWeights,
float64_t Y,
float64_t Outputs,
int32_t  m,
float64_t  lambda,
float64_t  lambda_u 
)

Definition at line 1011 of file ssl.cpp.

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

FIXME Clear(Data_Labeled);

Definition at line 437 of file ssl.cpp.

uint32_t shogun::ulong_of_substring ( substring  s  ) 

Unsigned long value of substring

Parameters:
s substring
Returns:
unsigned long value of substring

Definition at line 541 of file SGIO.h.

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

Update a safeguarded trial value and interval for line search.

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

Parameters:
x The pointer to the value of one endpoint.
fx The pointer to the value of f(x).
dx The pointer to the value of f'(x).
y The pointer to the value of another endpoint.
fy The pointer to the value of f(y).
dy The pointer to the value of f'(y).
t The pointer to the value of the trial value, t.
ft The pointer to the value of f(t).
dt The pointer to the value of f'(t).
tmin The minimum value for the trial value, t.
tmax The maximum value for the trial value, t.
brackt The pointer to the predicate if the trial value is bracketed.
Return values:
int32_t Status value. Zero indicates a normal termination.
See also:
Jorge J. More and David J. Thuente. Line search algorithm with guaranteed sufficient decrease. ACM Transactions on Mathematical Software (TOMS), Vol 20, No 3, pp. 286-307, 1994.

Definition at line 1077 of file lbfgs.cpp.

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

Definition at line 268 of file lapack.cpp.

void shogun::wrap_dgesvd ( char  jobu,
char  jobvt,
int  m,
int  n,
double *  a,
int  lda,
double *  sing,
double *  u,
int  ldu,
double *  vt,
int  ldvt,
int *  info 
)

Definition at line 250 of file lapack.cpp.

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

Definition at line 286 of file lapack.cpp.

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

Definition at line 232 of file lapack.cpp.

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

Definition at line 304 of file lapack.cpp.

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

Definition at line 339 of file lapack.cpp.

bool shogun::write_char_valued_strings ( const SGString< char > *  strings,
int32_t  num_str 
)

write char string features, simple ascii format

Parameters:
strings strings to write
num_str number of strings
Returns:
if writing was successful
bool shogun::write_real_valued_dense ( const float64_t matrix,
int32_t  num_feat,
int32_t  num_vec 
)

write dense real valued features, simple ascii format

Parameters:
matrix matrix to write
num_feat number of features for each vector
num_vec number of vectros in matrix
Returns:
if writing was successful
bool shogun::write_real_valued_sparse ( const SGSparseVector< float64_t > *  matrix,
int32_t  num_feat,
int32_t  num_vec 
)

write sparse real valued features in svm light format

Parameters:
matrix matrix to write
num_feat number of features for each vector
num_vec number of vectros in matrix
Returns:
if writing was successful
static void shogun::xextend ( larank_kcache_t *  self,
int32_t  k,
int32_t  nlen 
) [static]

Definition at line 203 of file LaRank.cpp.

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

Definition at line 166 of file LaRank.cpp.

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

Definition at line 111 of file LaRank.cpp.

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

Definition at line 296 of file LaRank.cpp.

static void shogun::xswap ( larank_kcache_t *  self,
int32_t  i1,
int32_t  i2,
int32_t  r1,
int32_t  r2 
) [static]

Definition at line 222 of file LaRank.cpp.

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

Definition at line 85 of file LaRank.cpp.

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

Definition at line 1495 of file brent.cpp.

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

Definition at line 1050 of file brent.cpp.

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

Definition at line 1229 of file brent.cpp.


Variable Documentation

const lbfgs_parameter_t _defparam [static]
Initial value:
 {
    6, 1e-5, 0, 1e-5,
    0, LBFGS_LINESEARCH_DEFAULT, 40,
    1e-20, 1e20, 1e-4, 0.9, 0.9, 1.0e-16,
    0.0, 0, -1,
}

Definition at line 95 of file lbfgs.cpp.

static uint32_t BufSize [static]

Definition at line 34 of file libocas.cpp.

const int32_t constant_hash = 11650396

Constant used to access the constant feature.

Definition at line 30 of file vw_constants.h.

static const float64_t epsilon = 0.0 [static]

Definition at line 25 of file libbmrm.cpp.

static float64_t * H [static]

Definition at line 33 of file libocas.cpp.

static float64_t * H2 [static]

Definition at line 27 of file libp3bm.cpp.

double* H_diag_matrix [static]

Definition at line 24 of file malsar_clustered.cpp.

int H_diag_matrix_ld [static]

Definition at line 25 of file malsar_clustered.cpp.

const uint32_t hash_base = 97562527

Seed for hash.

Definition at line 33 of file vw_constants.h.

const int32_t LOGSUM_TBL = 10000

span of the logsum table

Definition at line 21 of file LocalAlignmentStringKernel.h.

const float64_t Q_test_statistic_values[10][8] [static]
Initial value:
 
{
    
    {0.713,0.683,0.637,0.597,0.551,0.477,0.409,0.325},
    {0.627,0.604,0.568,0.538,0.503,0.450,0.401,0.339},
    {0.539,0.517,0.484,0.456,0.425,0.376,0.332,0.278},
    {0.490,0.469,0.438,0.412,0.382,0.337,0.295,0.246},
    {0.460,0.439,0.410,0.384,0.355,0.312,0.272,0.226},
    {0.437,0.417,0.388,0.363,0.336,0.294,0.256,0.211},
    {0.422,0.403,0.374,0.349,0.321,0.280,0.244,0.201},
    {0.408,0.389,0.360,0.337,0.310,0.270,0.234,0.192},
    {0.397,0.377,0.350,0.326,0.300,0.261,0.226,0.185},
    {0.387,0.368,0.341,0.317,0.292,0.253,0.219,0.179}
}

Definition at line 78 of file RejectionStrategy.h.

static const uint32_t QPSolverMaxIter = 10000000 [static]

Definition at line 31 of file libocas.cpp.

const int32_t quadratic_constant = 27942141

Constant used while hashing/accessing quadratic features.

Definition at line 27 of file vw_constants.h.

uint32_t Randnext
void(* sg_cancel_computations)(bool &delayed, bool &immediately) = NULL

function called to cancel things

Definition at line 39 of file init.cpp.

SGIO * sg_io = NULL

shogun IO

Definition at line 25 of file init.cpp.

CMath * sg_math = NULL

Definition at line 27 of file init.cpp.

Definition at line 24 of file init.cpp.

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

function called to print error messages

Definition at line 36 of file init.cpp.

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

function called to print normal messages

Definition at line 30 of file init.cpp.

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

function called to print warning messages

Definition at line 33 of file init.cpp.

Version * sg_version = NULL

Definition at line 26 of file init.cpp.

 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines

SHOGUN Machine Learning Toolbox - Documentation