SHOGUN
4.2.0
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Base class Distribution from which all methods implementing a distribution are derived.
Distributions are based on some general feature object and have to implement interfaces to
train() - for learning a distribution get_num_model_parameters() - for the total number of model parameters get_log_model_parameter() - for the n-th model parameter (logarithmic) get_log_derivative() - for the partial derivative wrt. to the n-th model parameter get_log_likelihood_example() - for the likelihood for the n-th example
This way methods building on CDistribution, might enumerate over all possible model parameters and obtain the parameter vector and the gradient. This is used to compute e.g. the TOP and Fisher Kernel (cf. CPluginEstimate, CHistogramKernel, CTOPFeatures and CFKFeatures ).
Definition at line 44 of file Distribution.h.
Public Member Functions | |
CDistribution () | |
virtual | ~CDistribution () |
virtual bool | train (CFeatures *data=NULL)=0 |
virtual int32_t | get_num_model_parameters ()=0 |
virtual int32_t | get_num_relevant_model_parameters () |
virtual float64_t | get_log_model_parameter (int32_t num_param)=0 |
virtual float64_t | get_log_derivative (int32_t num_param, int32_t num_example)=0 |
virtual float64_t | get_log_likelihood_example (int32_t num_example)=0 |
virtual float64_t | get_log_likelihood_sample () |
virtual SGVector< float64_t > | get_log_likelihood () |
virtual float64_t | get_model_parameter (int32_t num_param) |
virtual float64_t | get_derivative (int32_t num_param, int32_t num_example) |
virtual float64_t | get_likelihood_example (int32_t num_example) |
virtual SGVector< float64_t > | get_likelihood_for_all_examples () |
virtual void | set_features (CFeatures *f) |
virtual CFeatures * | get_features () |
virtual void | set_pseudo_count (float64_t pseudo) |
virtual float64_t | get_pseudo_count () |
virtual float64_t | update_params_em (float64_t *alpha_k, int32_t len) |
virtual CSGObject * | shallow_copy () const |
virtual CSGObject * | deep_copy () const |
virtual const char * | get_name () const =0 |
virtual bool | is_generic (EPrimitiveType *generic) const |
template<class T > | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
template<> | |
void | set_generic () |
void | unset_generic () |
virtual void | print_serializable (const char *prefix="") |
virtual bool | save_serializable (CSerializableFile *file, const char *prefix="") |
virtual bool | load_serializable (CSerializableFile *file, const char *prefix="") |
void | set_global_io (SGIO *io) |
SGIO * | get_global_io () |
void | set_global_parallel (Parallel *parallel) |
Parallel * | get_global_parallel () |
void | set_global_version (Version *version) |
Version * | get_global_version () |
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) |
bool | has (const std::string &name) const |
template<typename T > | |
bool | has (const Tag< T > &tag) const |
template<typename T , typename U = void> | |
bool | has (const std::string &name) const |
template<typename T > | |
void | set (const Tag< T > &_tag, const T &value) |
template<typename T , typename U = void> | |
void | set (const std::string &name, const T &value) |
template<typename T > | |
T | get (const Tag< T > &_tag) const |
template<typename T , typename U = void> | |
T | get (const std::string &name) const |
virtual void | update_parameter_hash () |
virtual bool | parameter_hash_changed () |
virtual bool | equals (CSGObject *other, float64_t accuracy=0.0, bool tolerant=false) |
virtual CSGObject * | clone () |
Static Public Member Functions | |
static CDistribution * | obtain_from_generic (CSGObject *object) |
Public Attributes | |
SGIO * | io |
Parallel * | parallel |
Version * | version |
Parameter * | m_parameters |
Parameter * | m_model_selection_parameters |
Parameter * | m_gradient_parameters |
uint32_t | m_hash |
Protected Member Functions | |
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) |
template<typename T > | |
void | register_param (Tag< T > &_tag, const T &value) |
template<typename T > | |
void | register_param (const std::string &name, const T &value) |
Protected Attributes | |
CFeatures * | features |
float64_t | pseudo_count |
CDistribution | ( | ) |
default constructor
Definition at line 17 of file Distribution.cpp.
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virtual |
destructor
Definition at line 23 of file Distribution.cpp.
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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.
dict | dictionary of parameters to be built. |
Definition at line 630 of file SGObject.cpp.
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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.
Definition at line 747 of file SGObject.cpp.
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virtualinherited |
A deep copy. All the instance variables will also be copied.
Definition at line 231 of file SGObject.cpp.
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.
other | object to compare with |
accuracy | accuracy to use for comparison (optional) |
tolerant | allows linient check on float equality (within accuracy) |
Definition at line 651 of file SGObject.cpp.
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inherited |
Getter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
Definition at line 367 of file SGObject.h.
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inherited |
Getter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
Definition at line 388 of file SGObject.h.
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virtual |
get partial derivative of likelihood function
num_param | partial derivative against which param |
num_example | which example |
Definition at line 134 of file Distribution.h.
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virtual |
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inherited |
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inherited |
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inherited |
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virtual |
compute likelihood for example
num_example | which example |
Reimplemented in CGMM, and CLinearHMM.
Definition at line 145 of file Distribution.h.
compute likelihood for all vectors in sample
Definition at line 65 of file Distribution.cpp.
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pure virtual |
get partial derivative of likelihood function (logarithmic)
abstract base method
num_param | derivative against which param |
num_example | which example |
Implemented in CHMM, CGMM, CKernelDensity, CLinearHMM, CGaussian, CMixtureModel, CDiscreteDistribution, CHistogram, and CPositionalPWM.
compute log likelihood for each example
Definition at line 39 of file Distribution.cpp.
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pure virtual |
compute log likelihood for example
abstract base method
num_example | which example |
Implemented in CHMM, CGMM, CKernelDensity, CLinearHMM, CGaussian, CMixtureModel, CDiscreteDistribution, CHistogram, and CPositionalPWM.
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virtual |
compute log likelihood for whole sample
Definition at line 28 of file Distribution.cpp.
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pure virtual |
get model parameter (logarithmic)
abstract base method
Implemented in CHMM, CLinearHMM, CGMM, CKernelDensity, CGaussian, CMixtureModel, CDiscreteDistribution, CHistogram, and CPositionalPWM.
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virtual |
get model parameter
num_param | which param |
Definition at line 123 of file Distribution.h.
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inherited |
Definition at line 531 of file SGObject.cpp.
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inherited |
Returns description of a given parameter string, if it exists. SG_ERROR otherwise
param_name | name of the parameter |
Definition at line 555 of file SGObject.cpp.
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inherited |
Returns index of model selection parameter with provided index
param_name | name of model selection parameter |
Definition at line 568 of file SGObject.cpp.
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pure virtualinherited |
Returns the name of the SGSerializable instance. It MUST BE the CLASS NAME without the prefixed `C'.
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, CDenseFeatures< ST >, CDenseFeatures< uint32_t >, CDenseFeatures< float64_t >, CDenseFeatures< T >, CDenseFeatures< uint16_t >, CFile, CSparseFeatures< ST >, CSparseFeatures< float64_t >, CSparseFeatures< T >, CSpecificityMeasure, CPrecisionMeasure, CPlif, CRecallMeasure, CDynamicObjectArray, CSingleFITCLaplaceNewtonOptimizer, CCrossCorrelationMeasure, CLaRank, CCSVFile, CF1Measure, CBinaryFile, CProtobufFile, CWRACCMeasure, CRBM, CBALMeasure, CBitString, CStreamingVwFeatures, CLibSVMFile, CStreamingSparseFeatures< T >, CErrorRateMeasure, CNeuralLayer, CMultitaskKernelPlifNormalizer, CMachine, CAccuracyMeasure, CStreamingFile, CStatistics, CQuadraticTimeMMD, CRandom, CStreamingMMD, CMemoryMappedFile< T >, CMultitaskKernelMaskNormalizer, CMemoryMappedFile< ST >, CAlphabet, CStructuredModel, CStreamingDenseFeatures< T >, CLMNNStatistics, CStreamingDenseFeatures< float64_t >, CStreamingDenseFeatures< float32_t >, CCombinedDotFeatures, CSingleLaplaceNewtonOptimizer, CFeatureSelection< ST >, CFeatureSelection< float64_t >, CGUIStructure, CMKL, CCache< T >, CCache< uint32_t >, CCache< ST >, CCache< float64_t >, CCache< uint8_t >, CCache< KERNELCACHE_ELEM >, CCache< char >, CCache< uint16_t >, CCache< shogun::SGSparseVectorEntry< T > >, CCache< shogun::SGSparseVectorEntry< float64_t > >, CCache< shogun::SGSparseVectorEntry< ST > >, CMultitaskKernelMaskPairNormalizer, CSVM, CNeuralNetwork, CMultitaskKernelNormalizer, CGUIClassifier, CGUIFeatures, CGaussian, CGMM, CHashedWDFeaturesTransposed, CBinaryStream< T >, CLinearHMM, CSimpleFile< T >, CStreamingStringFeatures< T >, CParameterCombination, CDeepBeliefNetwork, CNeuralLinearLayer, CStateModel, CMulticlassSVM, CNeuralConvolutionalLayer, CRandomKitchenSinksDotFeatures, COnlineLinearMachine, CVwParser, CPluginEstimate, CVowpalWabbit, CBinnedDotFeatures, CSVRLight, CPlifMatrix, CHashedWDFeatures, CImplicitWeightedSpecFeatures, CCombinedFeatures, CLeastAngleRegression, CSparseMatrixOperator< T >, CSNPFeatures, CKNN, CWDFeatures, CCrossValidationMulticlassStorage, CHashedDenseFeatures< ST >, CIOBuffer, CUAIFile, CTwoStateModel, CLossFunction, CHMSVMModel, CDeepAutoencoder, CPCA, CGUIKernel, CHashedSparseFeatures< ST >, CRandomFourierGaussPreproc, CMKLMulticlass, CAutoencoder, CHypothesisTest, CExplicitSpecFeatures, CCrossValidation, CLibLinearMTL, CLinearMachine, CModelSelectionParameters, CPositionalPWM, CNOCCO, CHashedDocDotFeatures, CGUIHMM, COnlineSVMSGD, CLibLinear, CIntegration, CJacobiEllipticFunctions, CLDA, CZeroMeanCenterKernelNormalizer, CSparsePolyFeatures, CHashedMultilabelModel, CSqrtDiagKernelNormalizer, CHuberLoss, CQDA, CCplex, CScatterKernelNormalizer, CKMeansBase, CFisherLDA, CHSIC, CLatentModel, CRationalApproximation, CStochasticProximityEmbedding, CTableFactorType, CSVMSGD, CMulticlassMachine, CDixonQTestRejectionStrategy, CGMNPLib, CVwCacheReader, CLBPPyrDotFeatures, CRidgeKernelNormalizer, CDependenceMaximization, CMulticlassSOLabels, CGraphCut, CSerializableAsciiFile, CKLDualInferenceMethod, CNeuralLayers, CSGDQN, CSNPStringKernel, CTime, CMatrixFeatures< ST >, CWeightedCommWordStringKernel, CHingeLoss, CTwoSampleTest, CSquaredLoss, CAbsoluteDeviationLoss, CExponentialLoss, CCustomKernel, CMulticlassLabels, CHash, CFactor, CPlifArray, CLinearTimeMMD, CStreamingHashedDocDotFeatures, CKernelIndependenceTest, CCustomDistance, CStreamingVwFile, CWeightedDegreeStringKernel, CBaggingMachine, CNeuralLogisticLayer, CNeuralRectifiedLinearLayer, CTOPFeatures, CDiceKernelNormalizer, CHierarchicalMultilabelModel, CMultitaskKernelMklNormalizer, CTask, CVwEnvironment, CGaussianKernel, CBinaryLabels, CMultilabelModel, CMultilabelSOLabels, CDomainAdaptationSVMLinear, CGaussianProcessClassification, CDotKernel, CCHAIDTree, CKernelTwoSampleTest, CMAPInferImpl, CWeightedDegreePositionStringKernel, CTanimotoKernelNormalizer, CCircularBuffer, CGaussianDistribution, CStreamingHashedDenseFeatures< ST >, CStreamingHashedSparseFeatures< ST >, CBesselKernel, CAvgDiagKernelNormalizer, CVarianceKernelNormalizer, CMCLDA, CMulticlassModel, COnlineLibLinear, CIndexFeatures, CShiftInvariantKernel, CCARTree, CKernelRidgeRegression, CStreamingAsciiFile, CIndependenceTest, CHierarchical, CEuclideanDistance, CFKFeatures, CCombinedKernel, CSparseSpatialSampleStringKernel, CSpectrumMismatchRBFKernel, COperatorFunction< T >, CMultilabelCLRModel, COperatorFunction< float64_t >, CVwRegressor, CHashedDocConverter, CFactorGraphLabels, CCommWordStringKernel, CSubsequenceStringKernel, CSet< T >, CKLInference, CKRRNystrom, CDataGenerator, CSparseInference, CNeuralInputLayer, CSequenceLabels, CPolyFeatures, CNode, CContingencyTableEvaluation, CChi2Kernel, CPyramidChi2, CSignal, CLibSVR, CPeriodicKernel, CSalzbergWordStringKernel, CStructuredLabels, CSquaredHingeLoss, CDenseMatrixOperator< T >, CDenseMatrixOperator< float64_t >, CNewtonSVM, CLPBoost, CVwLearner, CIndexBlockTree, CKLDiagonalInferenceMethod, CCommUlongStringKernel, CCompressor, CExactInferenceMethod, CKLCholeskyInferenceMethod, CKLCovarianceInferenceMethod, CIterativeLinearSolver< T, ST >, CIterativeLinearSolver< float64_t, float64_t >, CIterativeLinearSolver< complex128_t, float64_t >, CIterativeLinearSolver< T, T >, CSVMLin, CHistogram, CGaussianShiftKernel, CGCArray< T >, CNeuralSoftmaxLayer, CHomogeneousKernelMap, CMahalanobisDistance, CAttributeFeatures, CRandomFourierDotFeatures, CFirstElementKernelNormalizer, CMap< K, T >, CWrappedObjectArray, CLogLoss, CLogLossMargin, CSmoothHingeLoss, CMultiLaplaceInferenceMethod, CSingleFITCInference, CMap< shogun::TParameter *, shogun::SGVector< float64_t > >, CMap< shogun::TParameter *, shogun::CSGObject * >, CMap< std::string, T >, CVwNativeCacheReader, CLocallyLinearEmbedding, CDistanceKernel, CLatentLabels, CSoftMaxLikelihood, CScatterSVM, AdamUpdater, FirstOrderStochasticMinimizer, CSpectrumRBFKernel, CMultilabelLabels, CKLLowerTriangularInference, NesterovMomentumCorrection, CMMDKernelSelection, CSegmentLoss, CKernelDistance, CLogDetEstimator, CLinearRidgeRegression, CGNPPLib, CStreamingFileFromFeatures, CPolyMatchStringKernel, CWrappedBasic< T >, CNeuralLeakyRectifiedLinearLayer, CDomainAdaptationSVM, COligoStringKernel, CSimpleLocalityImprovedStringKernel, CKernelSelection, CStreamingVwCacheFile, CCircularKernel, CConstKernel, CDiagKernel, CExponentialARDKernel, CSphericalKernel, CEigenSolver, CC45ClassifierTree, CLPM, CEmbeddingConverter, CWeightedMajorityVote, CMulticlassOVREvaluation, CPolyKernel, CPolyMatchWordStringKernel, CLogitDVGLikelihood, CSingleFITCLaplaceInferenceMethod, CID3ClassifierTree, CANOVAKernel, CProductKernel, CSparseKernel< ST >, CGaussianMatchStringKernel, CRandomForest, CKernelPCA, CNearestCentroid, CMultidimensionalScaling, CStreamingFileFromDenseFeatures< T >, CStreamingFileFromSparseFeatures< T >, CStreamingFileFromStringFeatures< T >, CFixedDegreeStringKernel, CStringKernel< ST >, CTensorProductPairKernel, CLanczosEigenSolver, CGaussianNaiveBayes, CStringKernel< uint16_t >, CStringKernel< char >, CStringKernel< uint64_t >, CKernelDensity, CParser, CTStudentKernel, CWaveletKernel, CTraceSampler, CMulticlassOneVsRestStrategy, CMinkowskiMetric, CExponentialKernel, CDiffusionMaps, CAttenuatedEuclideanDistance, CCauchyKernel, CLogKernel, CPowerKernel, CRationalQuadraticKernel, CWaveKernel, CEPInferenceMethod, CGaussianProcessRegression, CGEMPLP, CLaplacianEigenmaps, CDistantSegmentsKernel, CLocalityImprovedStringKernel, CMatchWordStringKernel, CRegulatoryModulesStringKernel, CLaplaceInference, CKernelMachine, CBAHSIC, MKLMulticlassGradient, CAUCKernel, CHistogramIntersectionKernel, CSigmoidKernel, CDistanceMachine, CStructuredOutputMachine, CKernelDependenceMaximization, CGaussianARDKernel, CInverseMultiQuadricKernel, CGaussianProcessMachine, CLabelsFactory, CFITCInferenceMethod, CVarDTCInferenceMethod, CFFDiag, CJADiag, CJADiagOrth, CTreeMachineNode< T >, CLibLinearRegression, CTreeMachineNode< ConditionalProbabilityTreeNodeData >, CTreeMachineNode< RelaxedTreeNodeData >, CTreeMachineNode< id3TreeNodeData >, CTreeMachineNode< VwConditionalProbabilityTreeNodeData >, CTreeMachineNode< CARTreeNodeData >, CTreeMachineNode< C45TreeNodeData >, CTreeMachineNode< CHAIDTreeNodeData >, CTreeMachineNode< NbodyTreeNodeData >, CMulticlassAccuracy, CGaussianShortRealKernel, CMultiquadricKernel, CLocalAlignmentStringKernel, CStudentsTLikelihood, CJediDiag, CQDiag, CUWedge, AdaDeltaUpdater, RmsPropUpdater, CSplineKernel, CDelimiterTokenizer, CWrappedSGMatrix< T >, CWrappedSGVector< T >, CSingleSparseInference, StandardMomentumCorrection, CDimensionReductionPreprocessor, CPerceptron, CICAConverter, CHistogramWordStringKernel, CDualVariationalGaussianLikelihood, CLogitVGPiecewiseBoundLikelihood, CStudentsTVGLikelihood, FirstOrderMinimizer, CTaskTree, CProbabilityDistribution, CConstMean, CStochasticGBMachine, CMatrixOperator< T >, CLogRationalApproximationIndividual, CTreeMachine< T >, CMultitaskROCEvaluation, CTreeMachine< ConditionalProbabilityTreeNodeData >, CTreeMachine< RelaxedTreeNodeData >, CTreeMachine< id3TreeNodeData >, CTreeMachine< VwConditionalProbabilityTreeNodeData >, CTreeMachine< CARTreeNodeData >, CTreeMachine< C45TreeNodeData >, CTreeMachine< CHAIDTreeNodeData >, CTreeMachine< NbodyTreeNodeData >, CCanberraMetric, CCosineDistance, CManhattanMetric, CJensenShannonKernel, CLinearKernel, CGaussianLikelihood, CIterativeShiftedLinearFamilySolver< T, ST >, CIterativeShiftedLinearFamilySolver< float64_t, complex128_t >, CGeodesicMetric, CJensenMetric, CTanimotoDistance, CLineReader, CIdentityKernelNormalizer, CLinearStringKernel, CNumericalVGLikelihood, CLinearStructuredOutputMachine, CCGMShiftedFamilySolver, CLogRationalApproximationCGM, L1Penalty, SMIDASMinimizer, SVRGMinimizer, CDecompressString< ST >, CGUIConverter, CKMeans, CNGramTokenizer, CMMDKernelSelectionMedian, CIsomap, CChiSquareDistance, CHammingWordDistance, CRandomSearchModelSelection, CGUILabels, MKLMulticlassGLPK, CSparseDistance< ST >, CLatentFeatures, CLogitVGLikelihood, CProbitVGLikelihood, CBinaryTreeMachineNode< T >, CMMDKernelSelectionOpt, CSparseDistance< float64_t >, CAveragedPerceptron, CSOBI, CKernelLocallyLinearEmbedding, CBrayCurtisDistance, CChebyshewMetric, CFactorGraphFeatures, CRegressionLabels, CNbodyTree, SMDMinimizer, CSparsePreprocessor< ST >, CLeastSquaresRegression, MKLMulticlassOptimizationBase, CVwNativeCacheWriter, CFFSep, CSparseEuclideanDistance, CRealFileFeatures, CJobResultAggregator, CGaussianARDSparseKernel, CKLDualInferenceMethodMinimizer, CMulticlassOneVsOneStrategy, AdaptMomentumCorrection, CGUIPluginEstimate, CVwAdaptiveLearner, CJediSep, CUWedgeSep, CStringDistance< ST >, CSingleLaplaceInferenceMethod, CLinearLatentMachine, AdaGradUpdater, CPNorm, CRescaleFeatures, CSparseMultilabel, CStringDistance< uint16_t >, CVwNonAdaptiveLearner, CStructuredAccuracy, CWeightedDegreeRBFKernel, CDenseMatrixExactLog, CECOCRandomSparseEncoder, CMulticlassStrategy, ElasticNetPenalty, InverseScalingLearningRate, L1PenaltyForTG, CGradientCriterion, CIndependentJob, CProbitLikelihood, CGMNPSVM, SGDMinimizer, CLogPlusOne, CMAPInference, CMixtureModel, CFactorGraphObservation, CNormOne, CLibSVM, CDenseSubSamplesFeatures< ST >, CStringFileFeatures< ST >, CGaussianCompactKernel, CScalarResult< T >, CLogitLikelihood, CBallTree, CKDTree, CStringPreprocessor< ST >, CStringPreprocessor< uint16_t >, CStringPreprocessor< uint64_t >, CFactorAnalysis, CCanberraWordDistance, CManhattanWordDistance, CCrossValidationOutput, CLinearMulticlassMachine, CDirectLinearSolverComplex, CIndividualJobResultAggregator, CECOCDiscriminantEncoder, CRandomCARTree, GradientDescendUpdater, CSumOne, CResultSet, CTaskGroup, CGUIDistance, CFastICA, CRationalApproximationCGMJob, L2Penalty, PNormMappingFunction, CSortWordString, CCCSOSVM, CIntronList, CRealNumber, CStoreVectorAggregator< T >, CIndexBlock, CIndexBlockGroup, CZeroMean, CRationalApproximationIndividualJob, CLBFGSMinimizer, CPruneVarSubMean, CSequence, CGUIPreprocessor, CStoreVectorAggregator< complex128_t >, CJade, CMeanSquaredError, CMeanSquaredLogError, CConjugateOrthogonalCGSolver, ConstLearningRate, CSortUlongString, CMeanAbsoluteError, CDummyFeatures, CListElement, CMulticlassLibLinear, CDenseDistance< ST >, CRealDistance, CCrossValidationResult, CStringMap< T >, CDenseExactLogJob, CMMDKernelSelectionMax, CDenseDistance< float64_t >, CSVMLightOneClass, CEMBase< T >, CEMMixtureModel, CIndependentComputationEngine, CVectorResult< T >, CKernelStructuredOutputMachine, CLMNN, CThresholdRejectionStrategy, CVwConditionalProbabilityTree, CEMBase< MixModelData >, CLinearLocalTangentSpaceAlignment, CNeighborhoodPreservingEmbedding, CCombinationRule, CClusteringAccuracy, CClusteringMutualInformation, CMultilabelAccuracy, CMeanShiftDataGenerator, CFactorGraphModel, CHessianLocallyLinearEmbedding, CCustomMahalanobisDistance, CSubsetStack, CStoreScalarAggregator< T >, CGridSearchModelSelection, CStochasticSOSVM, CKMeansMiniBatch, CLocalTangentSpaceAlignment, CMajorityVote, CLinearOperator< T >, CConjugateGradientSolver, CLinearOperator< float64_t >, CLinearOperator< complex128_t >, CMeanRule, CGradientEvaluation, CLinearSolver< T, ST >, CMulticlassLibSVM, CMKLRegression, CFactorDataSource, CFactorGraph, CTaskRelation, CLinearSolver< float64_t, float64_t >, CLinearSolver< complex128_t, float64_t >, CLinearSolver< T, T >, CLocalityPreservingProjections, CSerialComputationEngine, CIndexBlockRelation, CDirectEigenSolver, CECOCEncoder, CGradientResult, CROCEvaluation, CGaussianBlobsDataGenerator, CBalancedConditionalProbabilityTree, CFactorType, CSOSVMHelper, CMKLOneClass, CLibSVMOneClass, CMPDSVM, CKernelMulticlassMachine, CNormalSampler, CECOCIHDDecoder, CConditionalProbabilityTree, CRelaxedTree, CFWSOSVM, CDomainAdaptationMulticlassLibLinear, CMKLClassification, CSubset, CECOCRandomDenseEncoder, CShareBoost, CGNPPSVM, CFactorGraphDataGenerator, CPRCEvaluation, CStratifiedCrossValidationSplitting, CDirectSparseLinearSolver, CDisjointSet, CCrossValidationSplitting, CDenseSubsetFeatures< ST >, CECOCForestEncoder, CGUIMath, CGUITime, CCrossValidationPrintOutput, CTDistributedStochasticNeighborEmbedding, CCrossValidationMKLStorage, SerializableAsciiReader00, CJobResult, CFunction, CECOCAEDDecoder, CECOCDecoder, CECOCEDDecoder, CManifoldSculpting, CData, CNativeMulticlassMachine, CECOCStrategy, CConverter, CBaseMulticlassMachine, CECOCSimpleDecoder, CLOOCrossValidationSplitting, CECOCLLBDecoder, CStructuredData, CECOCHDDecoder, CRandomConditionalProbabilityTree, CECOCOVOEncoder, CECOCOVREncoder, and CRejectionStrategy.
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pure virtual |
get number of parameters in model
abstract base method
Implemented in CHMM, CLinearHMM, CGMM, CKernelDensity, CGaussian, CMixtureModel, CDiscreteDistribution, CHistogram, and CPositionalPWM.
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virtual |
get number of parameters in model that are relevant, i.e. > ALMOST_NEG_INFTY
Definition at line 52 of file Distribution.cpp.
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virtual |
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inherited |
Checks if object has a class parameter identified by a name.
name | name of the parameter |
Definition at line 289 of file SGObject.h.
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inherited |
Checks if object has a class parameter identified by a Tag.
tag | tag of the parameter containing name and type information |
Definition at line 301 of file SGObject.h.
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inherited |
Checks if a type exists for a class parameter identified by a name.
name | name of the parameter |
Definition at line 312 of file SGObject.h.
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virtualinherited |
If the SGSerializable is a class template then TRUE will be returned and GENERIC is set to the type of the generic.
generic | set to the type of the generic if returning TRUE |
Definition at line 329 of file SGObject.cpp.
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virtualinherited |
Load this object from file. If it will fail (returning FALSE) then this object will contain inconsistent data and should not be used!
file | where to load from |
prefix | prefix for members |
Definition at line 402 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel, CWeightedDegreePositionStringKernel, CList, CAlphabet, CLinearHMM, CGaussianKernel, CInverseMultiQuadricKernel, CCircularKernel, and CExponentialKernel.
Definition at line 459 of file SGObject.cpp.
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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.
ShogunException | will 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 454 of file SGObject.cpp.
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static |
obtain from generic
object | generic object |
Definition at line 85 of file Distribution.cpp.
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virtualinherited |
Definition at line 295 of file SGObject.cpp.
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inherited |
prints all parameter registered for model selection and their type
Definition at line 507 of file SGObject.cpp.
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virtualinherited |
prints registered parameters out
prefix | prefix for members |
Definition at line 341 of file SGObject.cpp.
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protectedinherited |
Registers a class parameter which is identified by a tag. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 439 of file SGObject.h.
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protectedinherited |
Registers a class parameter which is identified by a name. This enables the parameter to be modified by set() and retrieved by get(). Parameters can be registered in the constructor of the class.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 452 of file SGObject.h.
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virtualinherited |
Save this object to file.
file | where to save the object; will be closed during returning if PREFIX is an empty string. |
prefix | prefix for members |
Definition at line 347 of file SGObject.cpp.
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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.
ShogunException | will be thrown if an error occurs. |
Reimplemented in CKernel.
Definition at line 469 of file SGObject.cpp.
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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.
ShogunException | will 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 464 of file SGObject.cpp.
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inherited |
Setter for a class parameter, identified by a Tag. Throws an exception if the class does not have such a parameter.
_tag | name and type information of parameter |
value | value of the parameter |
Definition at line 328 of file SGObject.h.
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inherited |
Setter for a class parameter, identified by a name. Throws an exception if the class does not have such a parameter.
name | name of the parameter |
value | value of the parameter along with type information |
Definition at line 354 of file SGObject.h.
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virtual |
|
inherited |
Definition at line 74 of file SGObject.cpp.
|
inherited |
Definition at line 79 of file SGObject.cpp.
|
inherited |
Definition at line 84 of file SGObject.cpp.
|
inherited |
Definition at line 89 of file SGObject.cpp.
|
inherited |
Definition at line 94 of file SGObject.cpp.
|
inherited |
Definition at line 99 of file SGObject.cpp.
|
inherited |
Definition at line 104 of file SGObject.cpp.
|
inherited |
Definition at line 109 of file SGObject.cpp.
|
inherited |
Definition at line 114 of file SGObject.cpp.
|
inherited |
Definition at line 119 of file SGObject.cpp.
|
inherited |
Definition at line 124 of file SGObject.cpp.
|
inherited |
Definition at line 129 of file SGObject.cpp.
|
inherited |
Definition at line 134 of file SGObject.cpp.
|
inherited |
Definition at line 139 of file SGObject.cpp.
|
inherited |
Definition at line 144 of file SGObject.cpp.
|
inherited |
set generic type to T
|
inherited |
|
inherited |
set the parallel object
parallel | parallel object to use |
Definition at line 274 of file SGObject.cpp.
|
inherited |
set the version object
version | version object to use |
Definition at line 316 of file SGObject.cpp.
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virtual |
|
virtualinherited |
A shallow copy. All the SGObject instance variables will be simply assigned and SG_REF-ed.
Reimplemented in CGaussianKernel.
Definition at line 225 of file SGObject.cpp.
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pure virtual |
learn distribution
data | training data (parameter can be avoided if distance or kernel-based classifiers are used and distance/kernels are initialized with train data) |
Implemented in CHMM, CKernelDensity, CGaussian, CLinearHMM, CMixtureModel, CGMM, CDiscreteDistribution, CHistogram, and CPositionalPWM.
|
inherited |
unset generic type
this has to be called in classes specializing a template class
Definition at line 336 of file SGObject.cpp.
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virtualinherited |
Updates the hash of current parameter combination
Definition at line 281 of file SGObject.cpp.
update parameters in the em maximization step for mixture model of which this distribution is a part
abstract base method
alpha_k | "belongingness" values of various data points |
len | length of alpha_k array |
Reimplemented in CGaussian.
Definition at line 78 of file Distribution.cpp.
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protected |
feature vectors
Definition at line 209 of file Distribution.h.
|
inherited |
io
Definition at line 537 of file SGObject.h.
|
inherited |
parameters wrt which we can compute gradients
Definition at line 552 of file SGObject.h.
|
inherited |
Hash of parameter values
Definition at line 555 of file SGObject.h.
|
inherited |
model selection parameters
Definition at line 549 of file SGObject.h.
|
inherited |
parameters
Definition at line 546 of file SGObject.h.
|
inherited |
parallel
Definition at line 540 of file SGObject.h.
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protected |
pseudo count
Definition at line 211 of file Distribution.h.
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inherited |
version
Definition at line 543 of file SGObject.h.