| FST::Branch_And_Bound_Predictor< RETURNTYPE, DIMTYPE > | Defines Interface for Implementations of Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction |
| FST::Branch_And_Bound_Predictor_Averaging< RETURNTYPE, DIMTYPE > | Averaging Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction |
| FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements concurrent evaluation of a criterion on a set of subset candidates |
| FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::CandidateResult | Temporary storage of subset candidate evaluation results |
| FST::Classifier< RETURNTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Abstract class, defines interface for classifier implementations (mainly to be used in wrappers) |
| FST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE > | Implements k-Nearest Neighbor classifier |
| FST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST3 classifier |
| FST::Classifier_Multinomial_NaiveBayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Naive-like Bayes classifier based on multinomial model |
| FST::Classifier_Normal_Bayes< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Bayes classifier based on normal (gaussian) model |
| FST::Clonable | Abstract class, defines interface for classes that may need to get cloned |
| FST::Criterion< RETURNTYPE, SUBSET > | Abstract class, defines interface for feature selection criterion classes |
| FST::Criterion_Filter< RETURNTYPE, SUBSET > | Abstract class, defines interface for classes implementing "independent" feature selection criteria |
| FST::Criterion_Multinomial< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Abstract class, defines interface for feature selection criterion implementations based on multinomial model |
| FST::Criterion_Multinomial_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion |
| FST::Criterion_Multinomial_MI< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements individual Mutual Information based on multinomial model to serve as feature selection criterion in Best Individual Feature setting (feature ranking) only |
| FST::Criterion_Negative< CRITERION, RETURNTYPE, SUBSET > | Returns the negative of another Criterion's result |
| FST::Criterion_Normal< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Abstract class, defines interface for feature selection criterion implementations based on normal model |
| FST::Criterion_Normal_Bhattacharyya< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion |
| FST::Criterion_Normal_Divergence< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion |
| FST::Criterion_Normal_GMahalanobis< RETURNTYPE, DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion |
| FST::Criterion_Subset_Size< RETURNTYPE, SUBSET > | Returns negative subset size - rates smaller subsets higher |
| FST::Criterion_Sum_Of_Weights< RETURNTYPE, DIMTYPE, SUBSET > | Returns sum of pre-specified feature weights for features in the evaluated subset |
| FST::Criterion_Wrapper< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR > | Wraps Classifier objects to serve as feature selection criterion |
| FST::Criterion_Wrapper_Bias_Estimate< RETURNTYPE, SUBSET, CLASSIFIER, DATAACCESSOR > | Wrapper Bias Evaluator - returns bias estimate (difference of classifier accuracy on training and validation data parts) |
| FST::Data_Accessor< DATATYPE, IDXTYPE > | Abstract class, defines interface for data access implementations |
| FST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER > | Partly-abstract class, defines support for data splitting |
| FST::Data_Accessor_Splitting_Mem< DATATYPE, IDXTYPE, INTERVALCONTAINER > | Implements data access to data cached entirely in memory, concrete file type support is delegated to derived classes |
| FST::Data_Accessor_Splitting_MemARFF< DATATYPE, IDXTYPE, INTERVALCONTAINER > | Implements data access to data cached entirely in memory, read once from a ARFF file |
| FST::Data_Accessor_Splitting_MemTRN< DATATYPE, IDXTYPE, INTERVALCONTAINER > | Implements data access to data cached entirely in memory, read once from a TRN file |
| FST::Data_File_ARFF | ARFF data format filter |
| FST::Data_File_ARFF_Feature | ARFF data format filter - representation of an attribute |
| FST::Data_File_ARFF_Record | ARFF data format filter - representation of a record |
| FST::Data_File_TRN< DATATYPE, IDXTYPE > | TRN data format filter |
| FST::Data_Interval< IDXTYPE > | Support structure to hold data interval (referring into data held by data accessor) |
| FST::Data_Intervaller< CONTAINER, IDXTYPE > | Container to hold list of Data_Interval; implements nested interval reduction |
| FST::Data_Scaler< DATATYPE > | Abstract class, defines interface for data scaling implementations (for use in data accessors) |
| FST::Data_Scaler_to01< DATATYPE > | Implements data normalization (of all feature values) to interval [0,1] |
| FST::Data_Scaler_void< DATATYPE > | Void data scaler, to bypass data normalization |
| FST::Data_Scaler_white< DATATYPE, IDXTYPE > | Implements data whitening (as result, each feature mean value is 0 with normalized stddev) |
| FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE > | Abstract class, defines interface for data splitting implementations (for use in data accessors) |
| FST::Data_Splitter_5050< INTERVALCONTAINER, IDXTYPE > | Implements train/test data splitting: first 50% of original data for training, second 50% for testing, each (data)class split separately |
| FST::Data_Splitter_CV< INTERVALCONTAINER, IDXTYPE > | Implements train/test data splitting: by means of k-fold cross-validation |
| FST::Data_Splitter_Holdout< INTERVALCONTAINER, IDXTYPE > | Implements train/test data splitting: use initial x% of original data for training, the rest for testing, each (data)class split separately |
| FST::Data_Splitter_Leave1Out< INTERVALCONTAINER, IDXTYPE > | Implements train/test data splitting: by means of leave-one-out |
| FST::Data_Splitter_RandomRandom< INTERVALCONTAINER, IDXTYPE, BINTYPE > | Implements train/test data splitting: use randomly chosen x% of data samples for training and another y% of data for testing, without overlaps, separately in each class |
| FST::Data_Splitter_Resub< INTERVALCONTAINER, IDXTYPE > | Implements train/test data splitting: use all data both for training and then once more for testing |
| FST::Data_Splitter_TrainRandom_TestFixed< INTERVALCONTAINER, IDXTYPE, BINTYPE > | Implements train/test data splitting: equal to Data_Splitter_RandomRandom except that the same test data is returned in each loop |
| FST::Data_Accessor_Splitting< DATATYPE, IDXTYPE, INTERVALCONTAINER >::DataSplit | Data splitting support structure; holds one set of intervals (train, test) per each splitting depth and (data)class |
| FST::Distance< DATATYPE, DIMTYPE, SUBSET > | Abstract class, defines interface for distance evaluators to be used in Classifier_kNN |
| FST::Distance_Euclid< DATATYPE, DIMTYPE, SUBSET > | Implements Euclidean (L_2) distance |
| FST::Distance_L1< DATATYPE, DIMTYPE, SUBSET > | Implements L_1 distance |
| FST::Distance_Lp< DATATYPE, REALTYPE, DIMTYPE, SUBSET, numerator, denominator > | Implements L_{p} distance, p equals numerator/denominator (template parameters) |
| FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::FeatureStat | Structure to gather feature occurence statistics over probe subset evaluations |
| FST::fst_error | FST3 specific error passing class |
| FST::Indexed_Matrix< DATATYPE, DIMTYPE, SUBSET > | Matrix representation and operations, allows operation in selected subspace |
| FST::Indexed_Vector< DATATYPE, DIMTYPE, SUBSET > | Vector representation and operations, allows operation in selected subspace |
| FST::Model< SUBSET, DATAACCESSOR > | Abstract class, defines interface for data model implementations |
| FST::Model_Multinomial< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements multinomial model |
| FST::Model_Normal< DATATYPE, REALTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR > | Implements normal (gaussian) model |
| FST::Classifier_kNN< RETURNTYPE, DATATYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR, DISTANCE >::Neighbour | Holds information on distance and data-class membership of neighbors processed in Classifier_kNN |
| FST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::Node | Structure representing search tree node |
| FST::Search_BIF< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::OneFeature | Structure to hold [feature,criterion value] pair while ranking features in Search_BIF |
| FST::Search_BIF_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::OneFeature | Structure to hold [feature,criterion value] pair while ranking features in Search_BIF_Threaded |
| FST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset | Structure to hold [subset,criterion value] temporary solutions in the course of search |
| FST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset | Structure to hold [subset,criterion value] temporary solutions in the course of search |
| FST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR >::OneSubset | Structure to hold [subset,criterion value] temporary solutions in the course of search |
| FST::Classifier_LIBSVM< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, DATAACCESSOR >::ParameterSet | Nested class to hold parameter candidates in the course of optimize_parameters() run |
| FST::Result_Tracker< RETURNTYPE, SUBSET > | Abstract class, defines interface for classes that enable collecting multiple results |
| FST::Result_Tracker_Dupless< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET > | Collects multiple results, avoiding duplicates |
| FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET > | Collects evaluated subsets to eventually provide Dependency-Aware Feature Ranking coefficients |
| FST::Result_Tracker_Regularizer< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET, CRITERION > | Collects multiple results. Enables eventual selection of alternative solution based on secondary criterion |
| FST::Result_Tracker_Stability_Evaluator< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET > | Collects multiple results to evaluate various stability measures on the colleciton |
| FST::Result_Tracker< RETURNTYPE, SUBSET >::ResultRec | Structure to hold [subset,criterion value] temporary solutions in the course of search |
| FST::Search< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Abstract class, defines interface for search method implementations |
| FST::Search_BIF< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements Best Individual Features, i.e., individual feature ranking |
| FST::Search_BIF_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements Best Individual Features, i.e., individual feature ranking |
| FST::Search_Branch_And_Bound< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements Branch and Bound template method as basis for more advanced B&B implementations |
| FST::Search_Branch_And_Bound_Basic< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements Branch and Bound Basic method, i.e., with randomized node ordering |
| FST::Search_Branch_And_Bound_Fast< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR > | Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism |
| FST::Search_Branch_And_Bound_Improved< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering |
| FST::Search_Branch_And_Bound_Improved_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements threaded Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering |
| FST::Search_Branch_And_Bound_Partial_Prediction< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, PREDICTOR > | Implements Branch and Bound with Partial Prediction (BBPP) method, i.e., with predicted node ordering |
| FST::Search_DOS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Implements Dynamic_Oscillating_Search |
| FST::Search_Exhaustive< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion |
| FST::Search_Exhaustive_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements threaded version of exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion |
| FST::Search_Monte_Carlo< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Repeatedly samples random subsets to eventually yield the one with highest criterion value |
| FST::Search_Monte_Carlo_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements threaded version of randomized search that repeatedly samples random subsets to eventually yield the one with highest criterion value |
| FST::Search_OS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Implements Oscillating_Search |
| FST::Search_Sequential< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Abstract class, defines interface for sequential search method implementations, provides common implementation of the "sequential step" operation |
| FST::Search_SFFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Implements Sequential_Forward_Floating_Search and Sequential_Backward_Floating_Search |
| FST::Search_SFRS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Implements Sequential_Forward_Retreating_Search and Sequential_Backward_Retreating_Search |
| FST::Search_SFS< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, EVALUATOR > | Implements Sequential_Forward_Selection and Sequential_Backward_Selection |
| FST::Sequential_Step< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Abstract class, defines interface for implementations of one selection step in sequential search type of methods |
| FST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements voting ensemble selection step in sequential search type of methods to possibly improve robustness and stability of feature selection result |
| FST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION > | Implements hybrid selection step in sequential search type of methods |
| FST::Sequential_Step_Straight< RETURNTYPE, DIMTYPE, SUBSET, CRITERION > | Implements standard sequential selection step in sequential search type of methods |
| FST::Sequential_Step_Straight_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads > | Implements threaded version of sequential selection step in sequential search type of methods |
| FST::StopWatch | Simple stopwatch to measure run time of search algorithms |
| FST::Subset< BINTYPE, DIMTYPE > | Stores the info on currently selected features, enables generating permutations, etc |
| FST::Sequential_Step_Ensemble< RETURNTYPE, DIMTYPE, SUBSET, CRITERION >::SubsetCandidate | Nested class to hold feature/subset candidate info in the course of the ensemble voting process |
| FST::Sequential_Step_Hybrid< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, FILTERCRITERION >::SubsetCandidate | Nested class to hold [Subset,criterion value] pair in the course of hybrid feature candidate evaluation in Sequential_Step_Hybrid |
| FST::Result_Tracker_Feature_Stats< RETURNTYPE, IDXTYPE, DIMTYPE, SUBSET >::SubSizeStat | Structure to gather probe subset cardinality statistics |
| FST::syncout | Synchronized output singleton class to enable concurrent textual reporting |
| FST::Data_Splitter< INTERVALCONTAINER, IDXTYPE >::TClassSplitter | Data splitting support structure; holds one set of intervals (train, test) per each (data)class |
| FST::Search_Exhaustive_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocal | Thread-local storage of current subset candidate, criterion clone, and tracker clone |
| FST::Candidate_Evaluator_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocal | Thread-local storage of current subset candidate, criterion clone, and tracker clone |
| FST::Search_Monte_Carlo_Threaded< RETURNTYPE, DIMTYPE, SUBSET, CRITERION, max_threads >::ThreadLocal | Thread-local storage of current subset candidate, criterion clone, and tracker clone |
| FST::ThreadPool< max_threads > | Implements thread scheduler that assigns jobs up to maximum number of threads allowed |
| FST::ThreadPool< max_threads >::ThreadsArray | Structure to keep status of threads in the pool |
1.6.1