SHOGUN  3.2.1
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CDistance Class Reference

Detailed Description

Class Distance, a base class for all the distances used in the Shogun toolbox.

The distance (or metric) is a function \( d: X \times X \to R \) satisfying (for all \( x,y,z \in X\)) conditions below:

Currently distance inherited from the CDistance class should be symmetric.

The simplest example of a distance function is the Euclidean distance:

See Also
CEuclideanDistance

In the means of Shogun toolbox the distance function is defined on the 'space' of CFeatures.

Definition at line 83 of file Distance.h.

Inheritance diagram for CDistance:
Inheritance graph
[legend]

Public Member Functions

 CDistance ()
 CDistance (CFeatures *lhs, CFeatures *rhs)
virtual ~CDistance ()
virtual float64_t distance (int32_t idx_a, int32_t idx_b)
virtual float64_t distance_upper_bounded (int32_t idx_a, int32_t idx_b, float64_t upper_bound)
SGMatrix< float64_tget_distance_matrix ()
template<class T >
SGMatrix< T > get_distance_matrix ()
int32_t compute_row_start (int64_t offs, int32_t n, bool symmetric)
virtual bool init (CFeatures *lhs, CFeatures *rhs)
virtual void cleanup ()=0
void load (CFile *loader)
void save (CFile *writer)
CFeaturesget_lhs ()
CFeaturesget_rhs ()
CFeaturesreplace_rhs (CFeatures *rhs)
CFeaturesreplace_lhs (CFeatures *lhs)
virtual void remove_lhs_and_rhs ()
virtual void remove_lhs ()
 takes all necessary steps if the lhs is removed from distance matrix
virtual void remove_rhs ()
 takes all necessary steps if the rhs is removed from distance matrix
virtual EDistanceType get_distance_type ()=0
virtual EFeatureType get_feature_type ()=0
virtual EFeatureClass get_feature_class ()=0
bool get_precompute_matrix ()
virtual void set_precompute_matrix (bool flag)
virtual int32_t get_num_vec_lhs ()
virtual int32_t get_num_vec_rhs ()
virtual bool has_features ()
bool lhs_equals_rhs ()
virtual CSGObjectshallow_copy () const
virtual CSGObjectdeep_copy () const
virtual const char * get_name () const =0
virtual bool is_generic (EPrimitiveType *generic) const
template<class T >
void set_generic ()
void unset_generic ()
virtual void print_serializable (const char *prefix="")
virtual bool save_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
virtual bool load_serializable (CSerializableFile *file, const char *prefix="", int32_t param_version=Version::get_version_parameter())
DynArray< TParameter * > * load_file_parameters (const SGParamInfo *param_info, int32_t file_version, CSerializableFile *file, const char *prefix="")
DynArray< TParameter * > * load_all_file_parameters (int32_t file_version, int32_t current_version, CSerializableFile *file, const char *prefix="")
void map_parameters (DynArray< TParameter * > *param_base, int32_t &base_version, DynArray< const SGParamInfo * > *target_param_infos)
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 ()
SGStringList< char > get_modelsel_names ()
void print_modsel_params ()
char * get_modsel_param_descr (const char *param_name)
index_t get_modsel_param_index (const char *param_name)
void build_gradient_parameter_dictionary (CMap< TParameter *, CSGObject * > *dict)
virtual void update_parameter_hash ()
virtual bool parameter_hash_changed ()
virtual bool equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false)
virtual CSGObjectclone ()

Static Public Member Functions

template<class T >
static void * get_distance_matrix_helper (void *p)

Public Attributes

SGIOio
Parallelparallel
Versionversion
Parameterm_parameters
Parameterm_model_selection_parameters
Parameterm_gradient_parameters
ParameterMapm_parameter_map
uint32_t m_hash

Protected Member Functions

virtual float64_t compute (int32_t idx_a, int32_t idx_b)=0
void do_precompute_matrix ()
 matrix precomputation
virtual TParametermigrate (DynArray< TParameter * > *param_base, const SGParamInfo *target)
virtual void one_to_one_migration_prepare (DynArray< TParameter * > *param_base, const SGParamInfo *target, TParameter *&replacement, TParameter *&to_migrate, char *old_name=NULL)
virtual void load_serializable_pre () throw (ShogunException)
virtual void load_serializable_post () throw (ShogunException)
virtual void save_serializable_pre () throw (ShogunException)
virtual void save_serializable_post () throw (ShogunException)

Static Protected Member Functions

static void * run_distance_thread (void *p)
 run distance thread

Protected Attributes

float32_tprecomputed_matrix
bool precompute_matrix
CFeatureslhs
 feature vectors to occur on the left hand side
CFeaturesrhs
 feature vectors to occur on the right hand side
int32_t num_lhs
int32_t num_rhs

Constructor & Destructor Documentation

CDistance ( )

default constructor

Definition at line 58 of file Distance.cpp.

CDistance ( CFeatures lhs,
CFeatures rhs 
)

init distance

Parameters
lhsfeatures of left-hand side
rhsfeatures of right-hand side
Returns
if init was successful

Definition at line 64 of file Distance.cpp.

~CDistance ( )
virtual

Definition at line 70 of file Distance.cpp.

Member Function Documentation

void build_gradient_parameter_dictionary ( CMap< TParameter *, CSGObject * > *  dict)
inherited

Builds a dictionary of all parameters in SGObject as well of those of SGObjects that are parameters of this object. Dictionary maps parameters to the objects that own them.

Parameters
dictdictionary of parameters to be built.

Definition at line 1189 of file SGObject.cpp.

virtual void cleanup ( )
pure virtual
CSGObject * clone ( )
virtualinherited

Creates a clone of the current object. This is done via recursively traversing all parameters, which corresponds to a deep copy. Calling equals on the cloned object always returns true although none of the memory of both objects overlaps.

Returns
an identical copy of the given object, which is disjoint in memory. NULL if the clone fails. Note that the returned object is SG_REF'ed

Definition at line 1306 of file SGObject.cpp.

virtual float64_t compute ( int32_t  idx_a,
int32_t  idx_b 
)
protectedpure virtual
int32_t compute_row_start ( int64_t  offs,
int32_t  n,
bool  symmetric 
)

compute row start offset for parallel kernel matrix computation

Parameters
offsoffset
nnumber of columns
symmetricwhether matrix is symmetric

Definition at line 146 of file Distance.h.

CSGObject * deep_copy ( ) const
virtualinherited

A deep copy. All the instance variables will also be copied.

Definition at line 146 of file SGObject.cpp.

float64_t distance ( int32_t  idx_a,
int32_t  idx_b 
)
virtual

get distance function for lhs feature vector a and rhs feature vector b

Parameters
idx_afeature vector a at idx_a
idx_bfeature vector b at idx_b
Returns
distance value

Definition at line 189 of file Distance.cpp.

virtual float64_t distance_upper_bounded ( int32_t  idx_a,
int32_t  idx_b,
float64_t  upper_bound 
)
virtual

get distance function for lhs feature vector a and rhs feature vector b. The computation of the distance stops if the intermediate result is larger than upper_bound. This is useful to use with John Langford's Cover Tree and it is ONLY implemented for Euclidean distance

Parameters
idx_afeature vector a at idx_a
idx_bfeature vector b at idx_b
upper_boundvalue above which the computation halts
Returns
distance value or upper_bound

Reimplemented in CEuclideanDistance.

Definition at line 120 of file Distance.h.

void do_precompute_matrix ( )
protected

matrix precomputation

Definition at line 227 of file Distance.cpp.

bool equals ( CSGObject other,
float64_t  accuracy = 0.0,
bool  tolerant = false 
)
virtualinherited

Recursively compares the current SGObject to another one. Compares all registered numerical parameters, recursion upon complex (SGObject) parameters. Does not compare pointers!

May be overwritten but please do with care! Should not be necessary in most cases.

Parameters
otherobject to compare with
accuracyaccuracy to use for comparison (optional)
tolerantallows linient check on float equality (within accuracy)
Returns
true if all parameters were equal, false if not

Definition at line 1210 of file SGObject.cpp.

template SGMatrix< float32_t > get_distance_matrix< float32_t > ( )

get distance matrix

Returns
computed distance matrix (needs to be cleaned up)

Definition at line 129 of file Distance.h.

SGMatrix<T> get_distance_matrix ( )

get distance matrix (templated)

Returns
the distance matrix
template void * get_distance_matrix_helper< float32_t > ( void *  p)
static

helper for computing the kernel matrix in a parallel way

Parameters
pthread parameters

Definition at line 266 of file Distance.cpp.

virtual EDistanceType get_distance_type ( )
pure virtual
virtual EFeatureClass get_feature_class ( )
pure virtual

get feature class the distance can deal with

abstract base method

Returns
feature class

Implemented in CCustomDistance, CKernelDistance, CSparseDistance< ST >, CSparseDistance< float64_t >, CStringDistance< ST >, CStringDistance< uint16_t >, CDenseDistance< ST >, and CDenseDistance< float64_t >.

virtual EFeatureType get_feature_type ( )
pure virtual
SGIO * get_global_io ( )
inherited

get the io object

Returns
io object

Definition at line 183 of file SGObject.cpp.

Parallel * get_global_parallel ( )
inherited

get the parallel object

Returns
parallel object

Definition at line 224 of file SGObject.cpp.

Version * get_global_version ( )
inherited

get the version object

Returns
version object

Definition at line 237 of file SGObject.cpp.

CFeatures* get_lhs ( )

get left-hand side features used in distance matrix

Returns
left-hand side features

Definition at line 197 of file Distance.h.

SGStringList< char > get_modelsel_names ( )
inherited
Returns
vector of names of all parameters which are registered for model selection

Definition at line 1081 of file SGObject.cpp.

char * get_modsel_param_descr ( const char *  param_name)
inherited

Returns description of a given parameter string, if it exists. SG_ERROR otherwise

Parameters
param_namename of the parameter
Returns
description of the parameter

Definition at line 1105 of file SGObject.cpp.

index_t get_modsel_param_index ( const char *  param_name)
inherited

Returns index of model selection parameter with provided index

Parameters
param_namename of model selection parameter
Returns
index of model selection parameter with provided name, -1 if there is no such

Definition at line 1118 of file SGObject.cpp.

virtual const char* get_name ( ) const
pure virtualinherited

Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.

Returns
name of the SGSerializable

Implements SGRefObject.

Implemented in CMath, CHMM, CStringFeatures< ST >, CStringFeatures< T >, CStringFeatures< uint8_t >, CStringFeatures< char >, CStringFeatures< uint16_t >, CSVMLight, CTrie< Trie >, CTrie< DNATrie >, CTrie< POIMTrie >, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, CMultitaskKernelTreeNormalizer, CList, CDynProg, CLibSVMFile, CDenseFeatures< ST >, CDenseFeatures< uint32_t >, CDenseFeatures< float64_t >, CDenseFeatures< T >, CDenseFeatures< uint16_t >, CFile, CStatistics, CSparseFeatures< ST >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CSpecificityMeasure, CPrecisionMeasure, CPlif, CRecallMeasure, CUAIFile, CDynamicObjectArray, CCrossCorrelationMeasure, CF1Measure, CCSVFile, CLaRank, CBinaryFile, CWRACCMeasure, CProtobufFile, CRBM, CTaxonomy, CBALMeasure, CBitString, CStreamingVwFeatures, CStreamingSparseFeatures< T >, CMultitaskKernelPlifNormalizer, CErrorRateMeasure, CWDSVMOcas, CMachine, CNeuralLayer, CAccuracyMeasure, CStreamingFile, CQuadraticTimeMMD, CRandom, CStreamingMMD, CMultitaskKernelMaskNormalizer, CMemoryMappedFile< T >, CMemoryMappedFile< ST >, CAlphabet, CMKL, CStreamingDenseFeatures< T >, CLMNNStatistics, CStructuredModel, CStreamingDenseFeatures< float64_t >, CStreamingDenseFeatures< float32_t >, CCombinedDotFeatures, CFeatureSelection< ST >, CFeatureSelection< float64_t >, CGUIStructure, CCache< T >, CCache< SGSparseVectorEntry< ST > >, CCache< uint32_t >, CCache< ST >, CCache< SGSparseVectorEntry< float64_t > >, CCache< float64_t >, CCache< uint8_t >, CCache< KERNELCACHE_ELEM >, CCache< char >, CCache< uint16_t >, CCache< SGSparseVectorEntry< T > >, CMultitaskKernelMaskPairNormalizer, CSVM, CMultitaskKernelNormalizer, CNeuralNetwork, CGUIClassifier, CGaussian, CGUIFeatures, CGMM, CBinaryStream< T >, CHashedWDFeaturesTransposed, CLinearHMM, CSimpleFile< T >, CParameterCombination, CDeepBeliefNetwork, CStreamingStringFeatures< T >, CNeuralLinearLayer, CMulticlassSVM, CStateModel, CRandomKitchenSinksDotFeatures, COnlineLinearMachine, CVwParser, CPluginEstimate, CVowpalWabbit, CBinnedDotFeatures, CSVMOcas, CNeuralConvolutionalLayer, CSVRLight, CHashedWDFeatures, CPlifMatrix, CCrossValidation, CImplicitWeightedSpecFeatures, CCombinedFeatures, CSparseMatrixOperator< T >, CSNPFeatures, CWDFeatures, CKMeans, CCrossValidationMulticlassStorage, CHashedDenseFeatures< ST >, CIOBuffer, CLossFunction, CTwoStateModel, CPCA, CHMSVMModel, CDeepAutoencoder, CLeastAngleRegression, CGUIKernel, CKNN, CRandomFourierGaussPreproc, CMKLMulticlass, CHashedSparseFeatures< ST >, CAutoencoder, CHypothesisTest, CExplicitSpecFeatures, CModelSelectionParameters, CLibLinearMTL, CNOCCO, CPositionalPWM, CHashedDocDotFeatures, CGUIHMM, COnlineSVMSGD, CIntegration, CJacobiEllipticFunctions, CLibLinear, CLDA, CZeroMeanCenterKernelNormalizer, CSparsePolyFeatures, CHashedMultilabelModel, CSqrtDiagKernelNormalizer, CHuberLoss, CScatterKernelNormalizer, CCplex, CFisherLDA, CHSIC, CRationalApproximation, CStochasticProximityEmbedding, CLatentModel, CGMNPLib, CMulticlassMachine, CDixonQTestRejectionStrategy, CTableFactorType, CSVMSGD, CVwCacheReader, CLBPPyrDotFeatures, CRidgeKernelNormalizer, CDependenceMaximization, CLinearMachine, CGraphCut, CMulticlassSOLabels, CSerializableAsciiFile, CSGDQN, CSNPStringKernel, CTime, CMatrixFeatures< ST >, CWeightedCommWordStringKernel, CHingeLoss, CTwoSampleTest, CSquaredLoss, CAbsoluteDeviationLoss, CExponentialLoss, CQPBSVMLib, CCustomKernel, CMulticlassLabels, CHash, CLinearTimeMMD, CFactor, CPlifArray, CStreamingVwFile, CStreamingHashedDocDotFeatures, CKernelIndependenceTest, CCustomDistance, CWeightedDegreeStringKernel, CKernelRidgeRegression, CBaggingMachine, CQDA, CNeuralLayers, CNeuralLogisticLayer, CNeuralRectifiedLinearLayer, CHierarchicalMultilabelModel, CTOPFeatures, CDiceKernelNormalizer, CMultitaskKernelMklNormalizer, CTask, CGaussianProcessClassification, CVwEnvironment, CBinaryLabels, CMultilabelModel, CMultilabelSOLabels, CDomainAdaptationSVMLinear, CCHAIDTree, CKernelTwoSampleTest, CWeightedDegreePositionStringKernel, CMAPInferImpl, CBesselKernel, CTanimotoKernelNormalizer, CCircularBuffer, CMCLDA, CGaussianDistribution, CStreamingHashedDenseFeatures< ST >, CStreamingHashedSparseFeatures< ST >, CAvgDiagKernelNormalizer, CVarianceKernelNormalizer, CMulticlassModel, COnlineLibLinear, CIndexFeatures, CCARTree, CStreamingAsciiFile, CHierarchical, CIndependenceTest, CFKFeatures, CSpectrumMismatchRBFKernel, COperatorFunction< T >, CMultilabelCLRModel, COperatorFunction< float64_t >, CCombinedKernel, CSparseSpatialSampleStringKernel, CVwRegressor, CHashedDocConverter, CFactorGraphLabels, CKLInferenceMethod, CSubsequenceStringKernel, CDotKernel, CGaussianKernel, CCommWordStringKernel, CSet< T >, CDataGenerator, CNeuralInputLayer, CSequenceLabels, CNode, CContingencyTableEvaluation, CPolyFeatures, CDenseMatrixOperator< T >, CLibSVR, CDenseMatrixOperator< float64_t >, CChi2Kernel, CPyramidChi2, CSignal, CSalzbergWordStringKernel, CStructuredLabels, CSquaredHingeLoss, CLPBoost, CNewtonSVM, CKLApproxDiagonalInferenceMethod, CVwLearner, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CIterativeLinearSolver< T, ST >, CIterativeLinearSolver< float64_t, float64_t >, CIterativeLinearSolver< complex128_t, float64_t >, CIterativeLinearSolver< T, T >, CCommUlongStringKernel, CCompressor, CHomogeneousKernelMap, CSVMLin, CHistogram, CGaussianShiftKernel, CGCArray< T >, CIndexBlockTree, CMultiLaplacianInferenceMethod, CNeuralSoftmaxLayer, CLocallyLinearEmbedding, CMahalanobisDistance, CAttributeFeatures, CRandomFourierDotFeatures, CFirstElementKernelNormalizer, CMap< K, T >, CLogLoss, CLogLossMargin, CSmoothHingeLoss, CScatterSVM, CMap< TParameter *, CSGObject * >, CMap< TParameter *, SGVector< float64_t > >, CGNPPLib, CVwNativeCacheReader, CDistanceKernel, CLatentLabels, CMultilabelLabels, CKLLowerTriangularInferenceMethod, CSingleLaplacianInferenceMethodWithLBFGS, CSoftMaxLikelihood, CMMDKernelSelection, CSpectrumRBFKernel, CLogDetEstimator, CSegmentLoss, CKernelDistance, CStreamingFileFromFeatures, CLinearRidgeRegression, CDomainAdaptationSVM, CPolyMatchStringKernel, CSimpleLocalityImprovedStringKernel, CKernelSelection, CStreamingVwCacheFile, COligoStringKernel, CKLDualInferenceMethod, CEigenSolver, CLPM, CCircularKernel, CConstKernel, CDiagKernel, CSphericalKernel, CLogitDVGLikelihood, CC45ClassifierTree, CMultitaskClusteredLogisticRegression, CEmbeddingConverter, CEuclideanDistance, CWeightedMajorityVote, CMulticlassOVREvaluation, CPolyKernel, CPolyMatchWordStringKernel, CLanczosEigenSolver, CID3ClassifierTree, CNearestCentroid, CMultidimensionalScaling, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CANOVAKernel, CProductKernel, CSparseKernel< ST >, CGaussianMatchStringKernel, CRandomForest, CKernelPCA, CFixedDegreeStringKernel, CStringKernel< ST >, CTensorProductPairKernel, CTraceSampler, CGaussianNaiveBayes, CMulticlassOneVsRestStrategy, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CKernelDensity, CParser, CTStudentKernel, CWaveletKernel, CGaussianProcessRegression, MKLMulticlassGradient, CDiffusionMaps, CMinkowskiMetric, CExponentialKernel, CLaplacianEigenmaps, CAttenuatedEuclideanDistance, CCauchyKernel, CLogKernel, CPowerKernel, CRationalQuadraticKernel, CDistantSegmentsKernel, CWaveKernel, CLaplacianInferenceBase, CKernelMachine, CBAHSIC, CLocalityImprovedStringKernel, CMatchWordStringKernel, CRegulatoryModulesStringKernel, CDistanceMachine, CStructuredOutputMachine, CKernelDependenceMaximization, CAUCKernel, CHistogramIntersectionKernel, CSigmoidKernel, CGaussianProcessMachine, CInverseMultiQuadricKernel, CFFDiag, CJADiag, CJADiagOrth, CLibLinearRegression, CMMDKernelSelectionCombOpt, CLocalAlignmentStringKernel, CLabelsFactory, CJediDiag, CQDiag, CUWedge, CTreeMachineNode< T >, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< id3TreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< CARTreeNodeData >, CTreeMachineNode< C45TreeNodeData >, CTreeMachineNode< CHAIDTreeNodeData >, CTreeMachineNode< NbodyTreeNodeData >, CMulticlassAccuracy, CGaussianARDKernel, CGaussianShortRealKernel, CMultiquadricKernel, CExactInferenceMethod, CPerceptron, CICAConverter, CSplineKernel, CDelimiterTokenizer, CDualVariationalGaussianLikelihood, CLogitVGPiecewiseBoundLikelihood, CLogRationalApproximationIndividual, CDimensionReductionPreprocessor, CGHMM, CHistogramWordStringKernel, CMatrixOperator< T >, CTaskTree, CMatrixOperator< float64_t >, CProbabilityDistribution, CConstMean, CStochasticGBMachine, CLinearOperator< RetType, OperandType >, CCGMShiftedFamilySolver, CIterativeShiftedLinearFamilySolver< T, ST >, CLogRationalApproximationCGM, CTreeMachine< T >, CMMDKernelSelectionCombMaxL2, CMultitaskL12LogisticRegression, CMultitaskROCEvaluation, CLinearOperator< SGVector< complex128_t >, SGVector< complex128_t > >, CLinearOperator< SGVector< T >, SGVector< T > >, CLinearOperator< SGVector< float64_t >, SGVector< float64_t > >, CIterativeShiftedLinearFamilySolver< float64_t, complex128_t >, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CCanberraMetric, CCosineDistance, CManhattanMetric, CLineReader, CJensenShannonKernel, CLinearKernel, CNumericalVGLikelihood, CLinearStructuredOutputMachine, CDualLibQPBMSOSVM, CGeodesicMetric, CJensenMetric, CTanimotoDistance, CIdentityKernelNormalizer, CLinearStringKernel, CFITCInferenceMethod, CDecompressString< ST >, CGUIConverter, CNGramTokenizer, CStudentsTVGLikelihood, CMMDKernelSelectionMedian, MKLMulticlassGLPK, CChiSquareDistance, CHammingWordDistance, CLogitVGLikelihood, CProbitVGLikelihood, CRandomSearchModelSelection, CGUILabels, CAveragedPerceptron, CSOBI, CKernelLocallyLinearEmbedding, CSparseDistance< ST >, CCrossValidationResult, CLatentFeatures, CBinaryTreeMachineNode< T >, CMMDKernelSelectionOpt, CSparseDistance< float64_t >, CFFSep, CBrayCurtisDistance, CChebyshewMetric, CFactorGraphFeatures, CRegressionLabels, CJobResultAggregator, CMulticlassOneVsOneStrategy, CNbodyTree, CSparsePreprocessor< ST >, CLeastSquaresRegression, MKLMulticlassOptimizationBase, CVwNativeCacheWriter, CJediSep, CUWedgeSep, CSparseEuclideanDistance, CRealFileFeatures, CLinearARDKernel, CSingleLaplacianInferenceMethod, CDenseMatrixExactLog, CPNorm, CSparseMultilabel, CGUIPluginEstimate, CVwAdaptiveLearner, CStringDistance< ST >, CStructuredAccuracy, CLinearLatentMachine, CMulticlassStrategy, CRescaleFeatures, CStringDistance< uint16_t >, CVwNonAdaptiveLearner, CWeightedDegreeRBFKernel, CIndependentJob, CECOCRandomSparseEncoder, CLogPlusOne, CGradientCriterion, CLatentSVM, CEPInferenceMethod, CGMNPSVM, CNormOne, CMixtureModel, CFactorGraphObservation, CScalarResult< T >, CDirectLinearSolverComplex, CIndividualJobResultAggregator, CMAPInference, CMultitaskTraceLogisticRegression, CLibSVM, CStringFileFeatures< ST >, CLinearMulticlassMachine, CRationalApproximationCGMJob, CBallTree, CKDTree, CStringPreprocessor< ST >, CSumOne, CMultitaskLogisticRegression, CStringPreprocessor< uint16_t >, CStringPreprocessor< uint64_t >, CFastICA, CCanberraWordDistance, CManhattanWordDistance, CCrossValidationOutput, CRationalApproximationIndividualJob, CECOCDiscriminantEncoder, CRandomCARTree, CSortWordString, CResultSet, CTaskGroup, CGUIDistance, CStoreVectorAggregator< T >, CConjugateOrthogonalCGSolver, CPruneVarSubMean, CCCSOSVM, CIntronList, CRealNumber, CStoreVectorAggregator< complex128_t >, CJade, CIndexBlock, CIndexBlockGroup, CGradientModelSelection, CSortUlongString, CSequence, CGUIPreprocessor, CFeatureBlockLogisticRegression, CMeanSquaredError, CMeanSquaredLogError, CLatentSOSVM, CStudentsTLikelihood, CDenseExactLogJob, CMulticlassLibLinear, CMeanAbsoluteError, CDummyFeatures, CListElement, CIsomap, CDenseDistance< ST >, CRealDistance, CIndependentComputationEngine, CVectorResult< T >, CKernelStructuredOutputMachine, CLMNN, CThresholdRejectionStrategy, CMMDKernelSelectionMax, CDenseDistance< float64_t >, CSVMLightOneClass, CLinearLocalTangentSpaceAlignment, CNeighborhoodPreservingEmbedding, CEMBase< T >, CEMMixtureModel, CClusteringAccuracy, CClusteringMutualInformation, CMultilabelAccuracy, CMeanShiftDataGenerator, CVwConditionalProbabilityTree, CEMBase< MixModelData >, CHessianLocallyLinearEmbedding, CCustomMahalanobisDistance, CCombinationRule, CStoreScalarAggregator< T >, CConjugateGradientSolver, CMMDKernelSelectionComb, CFactorGraphModel, CMultitaskLeastSquaresRegression, CLocalTangentSpaceAlignment, CSubsetStack, CGaussianLikelihood, CGridSearchModelSelection, CStochasticSOSVM, CMultitaskLinearMachine, CMajorityVote, CMeanRule, CDirectEigenSolver, CLinearSolver< T, ST >, CLinearSolver< float64_t, float64_t >, CLinearSolver< complex128_t, float64_t >, CLinearSolver< T, T >, CLocalityPreservingProjections, CGradientEvaluation, CSerialComputationEngine, CECOCEncoder, CMulticlassLibSVM, CMKLRegression, CFactorDataSource, CFactorGraph, CTaskRelation, CGaussianBlobsDataGenerator, CIndexBlockRelation, CKernelMeanMatching, CLibSVMOneClass, CROCEvaluation, CKernelMulticlassMachine, CNormalSampler, CBalancedConditionalProbabilityTree, CFactorType, CSOSVMHelper, CDomainAdaptationMulticlassLibLinear, CMKLOneClass, CGPBTSVM, CMPDSVM, CGradientResult, CECOCIHDDecoder, CMulticlassTreeGuidedLogisticRegression, CConditionalProbabilityTree, CRelaxedTree, CFWSOSVM, CMKLClassification, CSubset, CDirectSparseLinearSolver, CECOCRandomDenseEncoder, CMulticlassLogisticRegression, CMulticlassOCAS, CShareBoost, CGNPPSVM, CStratifiedCrossValidationSplitting, CPRCEvaluation, CProbitLikelihood, CSparseInverseCovariance, CCrossValidationSplitting, CDisjointSet, CDenseSubsetFeatures< ST >, CECOCForestEncoder, CGUIMath, CGUITime, CLogitLikelihood, CTDistributedStochasticNeighborEmbedding, CCrossValidationPrintOutput, CJobResult, CECOCDecoder, CFactorAnalysis, CManifoldSculpting, CCrossValidationMKLStorage, SerializableAsciiReader00, CNativeMulticlassMachine, CFunction, CECOCAEDDecoder, CECOCEDDecoder, CECOCStrategy, CData, CZeroMean, CConverter, CLOOCrossValidationSplitting, CBaseMulticlassMachine, CECOCSimpleDecoder, CECOCLLBDecoder, CStructuredData, CECOCHDDecoder, CECOCOVOEncoder, CECOCOVREncoder, CRandomConditionalProbabilityTree, and CRejectionStrategy.

virtual int32_t get_num_vec_lhs ( )
virtual

get number of vectors of lhs features

Returns
number of vectors of left-hand side

Reimplemented in CCustomDistance.

Definition at line 285 of file Distance.h.

virtual int32_t get_num_vec_rhs ( )
virtual

get number of vectors of rhs features

Returns
number of vectors of right-hand side

Reimplemented in CCustomDistance.

Definition at line 294 of file Distance.h.

bool get_precompute_matrix ( )

FIXME: precompute matrix should be dropped, handling should be via customdistance

Returns
if precompute_matrix

Definition at line 263 of file Distance.h.

CFeatures* get_rhs ( )

get right-hand side features used in distance matrix

Returns
right-hand side features

Definition at line 203 of file Distance.h.

virtual bool has_features ( )
virtual

test whether features have been assigned to lhs and rhs

Returns
true if features are assigned

Reimplemented in CCustomDistance.

Definition at line 303 of file Distance.h.

bool init ( CFeatures lhs,
CFeatures rhs 
)
virtual
bool is_generic ( EPrimitiveType *  generic) const
virtualinherited

If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.

Parameters
genericset to the type of the generic if returning TRUE
Returns
TRUE if a class template.

Definition at line 243 of file SGObject.cpp.

bool lhs_equals_rhs ( )

test whether features on lhs and rhs are the same

Returns
true if features are the same

Definition at line 312 of file Distance.h.

void load ( CFile loader)

load the kernel matrix

Parameters
loaderFile object via which to load data

Definition at line 107 of file Distance.cpp.

DynArray< TParameter * > * load_all_file_parameters ( int32_t  file_version,
int32_t  current_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

maps all parameters of this instance to the provided file version and loads all parameter data from the file into an array, which is sorted (basically calls load_file_parameter(...) for all parameters and puts all results into a sorted array)

Parameters
file_versionparameter version of the file
current_versionversion from which mapping begins (you want to use Version::get_version_parameter() for this in most cases)
filefile to load from
prefixprefix for members
Returns
(sorted) array of created TParameter instances with file data

Definition at line 650 of file SGObject.cpp.

DynArray< TParameter * > * load_file_parameters ( const SGParamInfo param_info,
int32_t  file_version,
CSerializableFile file,
const char *  prefix = "" 
)
inherited

loads some specified parameters from a file with a specified version The provided parameter info has a version which is recursively mapped until the file parameter version is reached. Note that there may be possibly multiple parameters in the mapping, therefore, a set of TParameter instances is returned

Parameters
param_infoinformation of parameter
file_versionparameter version of the file, must be <= provided parameter version
filefile to load from
prefixprefix for members
Returns
new array with TParameter instances with the attached data

Definition at line 491 of file SGObject.cpp.

bool load_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
virtualinherited

Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!

Parameters
filewhere to load from
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 320 of file SGObject.cpp.

void load_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_POST is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.

Definition at line 1008 of file SGObject.cpp.

void load_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::LOAD_SERIALIZABLE_PRE is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 1003 of file SGObject.cpp.

void map_parameters ( DynArray< TParameter * > *  param_base,
int32_t &  base_version,
DynArray< const SGParamInfo * > *  target_param_infos 
)
inherited

Takes a set of TParameter instances (base) with a certain version and a set of target parameter infos and recursively maps the base level wise to the current version using CSGObject::migrate(...). The base is replaced. After this call, the base version containing parameters should be of same version/type as the initial target parameter infos. Note for this to work, the migrate methods and all the internal parameter mappings have to match

Parameters
param_baseset of TParameter instances that are mapped to the provided target parameter infos
base_versionversion of the parameter base
target_param_infosset of SGParamInfo instances that specify the target parameter base

Definition at line 688 of file SGObject.cpp.

TParameter * migrate ( DynArray< TParameter * > *  param_base,
const SGParamInfo target 
)
protectedvirtualinherited

creates a new TParameter instance, which contains migrated data from the version that is provided. The provided parameter data base is used for migration, this base is a collection of all parameter data of the previous version. Migration is done FROM the data in param_base TO the provided param info Migration is always one version step. Method has to be implemented in subclasses, if no match is found, base method has to be called.

If there is an element in the param_base which equals the target, a copy of the element is returned. This represents the case when nothing has changed and therefore, the migrate method is not overloaded in a subclass

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
Returns
a new TParameter instance with migrated data from the base of the type which is specified by the target parameter

Definition at line 895 of file SGObject.cpp.

void one_to_one_migration_prepare ( DynArray< TParameter * > *  param_base,
const SGParamInfo target,
TParameter *&  replacement,
TParameter *&  to_migrate,
char *  old_name = NULL 
)
protectedvirtualinherited

This method prepares everything for a one-to-one parameter migration. One to one here means that only ONE element of the parameter base is needed for the migration (the one with the same name as the target). Data is allocated for the target (in the type as provided in the target SGParamInfo), and a corresponding new TParameter instance is written to replacement. The to_migrate pointer points to the single needed TParameter instance needed for migration. If a name change happened, the old name may be specified by old_name. In addition, the m_delete_data flag of to_migrate is set to true. So if you want to migrate data, the only thing to do after this call is converting the data in the m_parameter fields. If unsure how to use - have a look into an example for this. (base_migration_type_conversion.cpp for example)

Parameters
param_baseset of TParameter instances to use for migration
targetparameter info for the resulting TParameter
replacement(used as output) here the TParameter instance which is returned by migration is created into
to_migratethe only source that is used for migration
old_namewith this parameter, a name change may be specified

Definition at line 835 of file SGObject.cpp.

bool parameter_hash_changed ( )
virtualinherited
Returns
whether parameter combination has changed since last update

Definition at line 209 of file SGObject.cpp.

void print_modsel_params ( )
inherited

prints all parameter registered for model selection and their type

Definition at line 1057 of file SGObject.cpp.

void print_serializable ( const char *  prefix = "")
virtualinherited

prints registered parameters out

Parameters
prefixprefix for members

Definition at line 255 of file SGObject.cpp.

void remove_lhs ( )
virtual

takes all necessary steps if the lhs is removed from distance matrix

Definition at line 130 of file Distance.cpp.

void remove_lhs_and_rhs ( )
virtual

remove lhs and rhs from distance

Definition at line 119 of file Distance.cpp.

void remove_rhs ( )
virtual

takes all necessary steps if the rhs is removed from distance matrix

takes all necessary steps if the rhs is removed from distance

Definition at line 138 of file Distance.cpp.

CFeatures * replace_lhs ( CFeatures lhs)

replace left-hand side features used in distance matrix

make sure to check that your distance can deal with the supplied features (!)

Parameters
lhsfeatures of right-hand side
Returns
replaced left-hand side features

Definition at line 167 of file Distance.cpp.

CFeatures * replace_rhs ( CFeatures rhs)

replace right-hand side features used in distance matrix

make sure to check that your distance can deal with the supplied features (!)

Parameters
rhsfeatures of right-hand side
Returns
replaced right-hand side features

Definition at line 145 of file Distance.cpp.

static void* run_distance_thread ( void *  p)
staticprotected

run distance thread

void save ( CFile writer)

save kernel matrix

Parameters
writerFile object via which to save data

Definition at line 113 of file Distance.cpp.

bool save_serializable ( CSerializableFile file,
const char *  prefix = "",
int32_t  param_version = Version::get_version_parameter() 
)
virtualinherited

Save this object to file.

Parameters
filewhere to save the object; will be closed during returning if PREFIX is an empty string.
prefixprefix for members
param_version(optional) a parameter version different to (this is mainly for testing, better do not use)
Returns
TRUE if done, otherwise FALSE

Definition at line 261 of file SGObject.cpp.

void save_serializable_post ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to post-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_POST is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel.

Definition at line 1018 of file SGObject.cpp.

void save_serializable_pre ( ) throw (ShogunException)
protectedvirtualinherited

Can (optionally) be overridden to pre-initialize some member variables which are not PARAMETER::ADD'ed. Make sure that at first the overridden method BASE_CLASS::SAVE_SERIALIZABLE_PRE is called.

Exceptions
ShogunExceptionwill be thrown if an error occurs.

Reimplemented in CKernel, CDynamicArray< T >, CDynamicArray< float64_t >, CDynamicArray< float32_t >, CDynamicArray< int32_t >, CDynamicArray< char >, CDynamicArray< bool >, and CDynamicObjectArray.

Definition at line 1013 of file SGObject.cpp.

void set_generic< complex128_t > ( )
inherited

set generic type to T

Definition at line 38 of file SGObject.cpp.

void set_global_io ( SGIO io)
inherited

set the io object

Parameters
ioio object to use

Definition at line 176 of file SGObject.cpp.

void set_global_parallel ( Parallel parallel)
inherited

set the parallel object

Parameters
parallelparallel object to use

Definition at line 189 of file SGObject.cpp.

void set_global_version ( Version version)
inherited

set the version object

Parameters
versionversion object to use

Definition at line 230 of file SGObject.cpp.

virtual void set_precompute_matrix ( bool  flag)
virtual

FIXME: precompute matrix should be dropped, handling should be via customdistance

Parameters
flagif precompute_matrix

Definition at line 270 of file Distance.h.

CSGObject * shallow_copy ( ) const
virtualinherited

A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.

Reimplemented in CGaussianKernel.

Definition at line 140 of file SGObject.cpp.

void unset_generic ( )
inherited

unset generic type

this has to be called in classes specializing a template class

Definition at line 250 of file SGObject.cpp.

void update_parameter_hash ( )
virtualinherited

Updates the hash of current parameter combination

Definition at line 196 of file SGObject.cpp.

Member Data Documentation

SGIO* io
inherited

io

Definition at line 457 of file SGObject.h.

CFeatures* lhs
protected

feature vectors to occur on the left hand side

Definition at line 345 of file Distance.h.

Parameter* m_gradient_parameters
inherited

parameters wrt which we can compute gradients

Definition at line 472 of file SGObject.h.

uint32_t m_hash
inherited

Hash of parameter values

Definition at line 478 of file SGObject.h.

Parameter* m_model_selection_parameters
inherited

model selection parameters

Definition at line 469 of file SGObject.h.

ParameterMap* m_parameter_map
inherited

map for different parameter versions

Definition at line 475 of file SGObject.h.

Parameter* m_parameters
inherited

parameters

Definition at line 466 of file SGObject.h.

int32_t num_lhs
protected

number of feature vectors on the left hand side

Definition at line 350 of file Distance.h.

int32_t num_rhs
protected

number of feature vectors on the right hand side

Definition at line 352 of file Distance.h.

Parallel* parallel
inherited

parallel

Definition at line 460 of file SGObject.h.

bool precompute_matrix
protected

FIXME: precompute matrix should be dropped, handling should be via customdistance

Definition at line 342 of file Distance.h.

float32_t* precomputed_matrix
protected

FIXME: precompute matrix should be dropped, handling should be via customdistance

Definition at line 337 of file Distance.h.

CFeatures* rhs
protected

feature vectors to occur on the right hand side

Definition at line 347 of file Distance.h.

Version* version
inherited

version

Definition at line 463 of file SGObject.h.


The documentation for this class was generated from the following files:

SHOGUN Machine Learning Toolbox - Documentation