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Classes |
| class | ApplyConcepts |
| | This class handles the logic of applying trained models to a dataset. More...
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| class | ApplyConceptsHelper |
| class | ApplyConceptsHelperFeatures |
| class | ApplyConceptsHelperKernels |
| class | AreaUnderRocCurve |
| | This class can be applied to a ScoreTable to compute the area under the ROC curve. More...
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| class | AveragePrecision |
| | This class can be applied to a ScoreTable to compute the average precision. More...
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| class | BalancedAveragePrecision |
| class | Classifier |
| | This is a baseclass for classifiers. More...
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| class | ClassifierEvaluator |
| | This class works together with ParameterSearcher (through baseclass ParameterEvaluator). More...
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| class | Evaluation |
| | Baseclass for evaluation of ranked lists (which are passed in as AnnotationTableBaseType). More...
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| class | Factory |
| class | Fisher |
| | Fisher is a well known Classifier, see internet for details. More...
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| class | MpoChi2Kernel |
| | this class implements a trait for the Chi2 kernel More...
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| class | LogisticRegression |
| | Not implemented, perhaps now that Taylan has made some matrix operations available, this classifier can also be implemented. More...
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| class | ParameterEvaluator |
| | Abstract base class for classes that the ParameterSearcher can work with. More...
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| class | ParameterSearcher |
| | This class searches a range of parameters for the best setting. More...
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| class | PrecisionAtN |
| | This class can be applied to a ScoreTable to compute the precision at a given N. More...
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| class | RecallAtN |
| | This class can be applied to a ScoreTable to compute the recall at a given N. More...
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| class | Svm |
| | SVM is a well known Classifier, see internet for details. More...
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| class | SvmProblemBuilder |
| | This class does the actual conversion from feature and annotation data to the data format used by SvmLib. More...
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| class | Tester |
| | This class tests the training functionality. More...
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| class | TesterIOHelper |
| class | TrainDataSrc |
| class | TrainDataSrcFeature |
| class | TrainDataSrcFeatureTable |
| class | TrainDataSrcKernelDistributed |
| | this class assumes some other cpus serve the distributed matrix. More...
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| class | TrainDataSrcKernelMatrix |
| | Wraps a kernel matrix (of type Matrix::Mat*). More...
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| class | TrainDataSrcKernelTable |
Functions |
| | ILOG_VAR_INIT (ApplyConcepts, Impala.Core.Training) |
| | ILOG_VAR_INIT (ApplyConceptsHelperFeatures, Impala.Core.Training) |
| | ILOG_VAR_INIT (ApplyConceptsHelperKernels, Impala.Core.Training) |
| | ILOG_VAR_INIT (AveragePrecision, Impala.Core.Training) |
| | ILOG_VAR_INIT (ClassifierEvaluator, Impala.Core.Training) |
| bool | Equals (const svm_parameter *p1, const svm_parameter *p2) |
| | All Equals functions compare the objects (they do not test for 0 pointers).
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| bool | EqualsForModel (const svm_parameter *p1, const svm_parameter *p2) |
| bool | Equals (const svm_node *sv1, const svm_node *sv2) |
| bool | Equals (const svm_problem *p1, const svm_problem *p2) |
| bool | Equals (const svm_model *m1, const svm_model *m2) |
| | ILOG_VAR_INIT (Factory, Impala.Core.Training) |
| | ILOG_VAR_INIT (Fisher, Impala.Core.Training) |
| double | Chi2Distance (const Vector::VectorTem< double > &v1, const Vector::VectorTem< double > &v2) |
| Classifier * | LoadClassifier (CString modelFileName, Util::Database *db) |
| | factory function for loading classifiers.
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| Evaluation * | MakeEvaluation (const std::string &description, Table::AnnotationTable *annotation) |
| | Factory function for Evaluation objects.
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| | ILOG_VAR_INIT (ParameterSearcher, Impala.Core.Training) |
| Matrix::Mat * | PrecomputeKernelMatrix (std::vector< Feature::FeatureTable * > features, std::vector< double > weights, Util::PropertySet *properties) |
| Matrix::Mat * | PrecomputeKernelMatrix (Feature::FeatureTable *features, Util::PropertySet *properties) |
| svm_problem * | ConvertToSvmProblem (const Core::Vector::VectorTem< double > *feature) |
| | ILOG_VAR_INIT (Svm, Impala.Core.Training) |
| bool | Equals (const Svm *svm1, const Svm *svm2) |
| svm_problem * | ReadSvmFile (const std::string &filename) |
| void | WriteSvmFile (const svm_problem *p, const std::string &filename) |
| | ILOG_VAR_INIT (SvmProblemBuilder, Impala.Core.Training) |
| | ILOG_VAR_INIT (Tester, Impala.Core.Training) |
| | ILOG_VAR_INIT (TesterIOHelper, Impala.Core.Training) |
| | ILOG_VAR_INIT (TrainDataSrc, Impala.Core.Training) |
| | ILOG_VAR_INIT (TrainDataSrcFeature, Impala.Core.Training) |
| | ILOG_VAR_INIT (TrainDataSrcFeatureTable, Impala.Core.Training) |
| | ILOG_VAR_INIT (TrainDataSrcKernelDistributed, Impala.Core.Training) |
| | ILOG_CLASS_INIT (TrainDataSrcKernelTable, Impala.Core.Training.Svm) |
Variables |
| const double | cPrecision = 0.000001 |
| | since svm lib saves in ascii with 6 decimal numbers, we can't expect higher precision than this
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| const double | cInvalid = 666 |
