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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 | ApplyConceptsHelperFik |
| 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. More...
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| class | Factory |
| | This class' purpose is to generalise some of the setup that was shared by some applications. More...
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| class | FikSvm |
| class | KernelMatrix |
| | First setup of a KernelMatrix class, work in progress. 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 | PrecomputeTask |
| | PrecomputeTask: manages information about what to compute. 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 | TestBestFile |
| | This class tests some feature functionality. More...
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| class | TestSvm |
| class | TrainDataSrc |
| | Abstraction of training data. More...
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| 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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Functions |
| | ILOG_VAR_INIT (ApplyConcepts, Impala.Core.Training) |
| | ILOG_VAR_INIT (ApplyConceptsHelperFeatures, Impala.Core.Training) |
| | ILOG_VAR_INIT (ApplyConceptsHelperFik, Impala.Core.Training) |
| | ILOG_VAR_INIT (ApplyConceptsHelperKernels, Impala.Core.Training) |
| | ILOG_VAR_INIT (AveragePrecision, Impala.Core.Training) |
| int | BestFileDiff (Util::PropertySet *set1, Util::PropertySet *set2) |
| int | BestFileReferenceDiff (Util::PropertySet *set1, Util::PropertySet *set2) |
| | ILOG_VAR_INIT (ClassifierEvaluator, Impala.Core.Training) |
| template<class FType, class PrecomputeTaskT> |
| void | ComputeKernelMatrix (PrecomputeTaskT *pt, int slabWidth, bool GPU) |
| | Kernel matrix computation.
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| 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 (FikSvm, Impala.Core.Training) |
| | ILOG_VAR_INIT (KernelMatrix, Impala.Core.Training) |
| Matrix::DistributedAccess * | LoadDistributedAccess (String model, String kernel, Core::Database::RawDataSet *set, Core::Database::RawDataSet *set2, int startNode, int nodeCount) |
| 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) |
| | ILOG_VAR_INIT (PrecomputeTask, Impala.Core.Training) |
| | ILOG_VAR_INIT (Svm, Impala.Core.Training) |
| bool | Equals (const Svm *svm1, const Svm *svm2) |
| int | Diff (const Svm *svm1, const Svm *svm2) |
| | ILOG_VAR_INIT (SvmProblemBuilder, Impala.Core.Training) |
| | ILOG_CLASS_INIT (TestBestFile, Impala.Core.Training) |
| | CPPUNIT_TEST_SUITE_REGISTRATION (TestBestFile) |
| | CPPUNIT_TEST_SUITE_REGISTRATION (TestSvm) |
| | 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) |
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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