Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

NFST	The Feature Selection Toolbox 4 library: every library component – data access, criteria and classifiers, search algorithms, result trackers and the global infrastructure – lives in this namespace
CBranch_And_Bound_Predictor	Defines Interface for Implementations of Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
CBranch_And_Bound_Predictor_Averaging	Averaging Prediction Mechanism in Fast Branch and Bound & Branch and Bound with Partial Prediction
CCandidate_Evaluator	Abstract candidate-evaluation strategy (sequential-inline vs. thread-pooled)
CCandidate_Evaluator_Sequential	Implements sequential in-line evaluation of a criterion on a set of subset candidates
CCandidate_Evaluator_Threaded	Implements concurrent evaluation of a criterion on a set of subset candidates
CThreadLocal	Thread-local storage of current subset candidate, criterion clone, and tracker clone
CCandidateResult	Temporary storage of subset candidate evaluation results
CClassifier	Abstract class, defines interface for classifier implementations (mainly to be used in wrappers)
CClassifier_Extern	Bridge classifier delegating training and prediction to a user-supplied external command
CClassifier_kNN	Implements k-Nearest Neighbor classifier
CNeighbour	Holds information on distance and data-class membership of neighbors processed in Classifier_kNN
CClassifier_LDA	Linear (Fisher/Gaussian shared-covariance) discriminant classifier
CClassifier_LIBSVM	Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST4 classifier
CParameterSet	Nested class to hold parameter candidates in the course of optimize_parameters() run
CClassifier_Linear_SVM	Dependency-free linear SVM trained by dual coordinate descent (the method of Hsieh et al., ICML 2008, as popularized by LIBLINEAR — implemented from scratch, no external code)
CClassifier_Logistic_Regression	Implements multinomial (softmax) logistic regression classifier
CClassifier_Multinomial_NaiveBayes	Implements Naive-like Bayes classifier based on multinomial model
CClassifier_Normal_Bayes	Implements Bayes classifier based on normal (gaussian) model
CClassifier_Random_Forest	Dependency-free Random Forest classifier (CART trees, Gini impurity, bagging, per-node feature subsampling), deterministic by design
CNode
CClassifier_RBF_SVM	Dependency-free RBF kernel SVM trained by kernel dual coordinate descent (implemented from scratch, no LibSVM code; C-SVC with the RBF kernel only, deliberately NOT a LibSVM replacement: no nu-variants, no SVR, no one-class, no probability estimates, no shrinking)
CClonable	Abstract class, defines interface for classes that may need to get cloned
CClonable_Impl	CRTP mixin implementing the Clonable protocol for a concrete class
CCriterion	Abstract class, defines interface for feature selection criterion classes
CCriterion_Extern_Value	External-tool criterion: a user-supplied command computes the criterion value itself - unlike wrapper-extern, which exchanges labels and computes accuracy on the FST4 side
CCriterion_Filter	Abstract class, defines interface for classes implementing "independent" feature selection criteria
CCriterion_Multinomial	Abstract class, defines interface for feature selection criterion implementations based on multinomial model
CCriterion_Multinomial_Bhattacharyya	Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion
CCriterion_Multinomial_MI	Implements individual Mutual Information based on multinomial model to serve as feature selection criterion in Best Individual Feature setting (feature ranking)
CCriterion_Negative	Returns the negative of another Criterion's result
CCriterion_Normal	Abstract class, defines interface for feature selection criterion implementations based on normal model
CCriterion_Normal_Bhattacharyya	Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion
CCriterion_Normal_Divergence	Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion
CCriterion_Normal_GMahalanobis	Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion
CCriterion_Subset_Size	Returns the (positive) subset size sub->get_d() - since FST4 searches maximize, this rates LARGER subsets higher. Wrap in Criterion_Negative to favor smaller subsets instead
CCriterion_Sum_Of_Weights	Returns sum of pre-specified feature weights for features in the evaluated subset
CCriterion_Wrapper	Wraps Classifier objects to serve as feature selection criterion
CCriterion_Wrapper_Bias_Estimate	Wrapper Bias Evaluator - returns bias estimate (difference of classifier accuracy on training and validation data parts)
CData_Accessor	Abstract class, defines interface for data access implementations
CBlock	One consecutive block of patterns as yielded by Block_Range/blocks()
CBlock_Range	RAII range over consecutive data blocks of one DataPart in the current split
Citerator	Forward iterator over Block_Range; drives the wrapped getFirstBlock()/getNextBlock() cursor at the range's owned loopdepth
CData_Accessor_Splitting	Partly-abstract class, defines support for data splitting
CDataSplit	Data splitting support structure; holds one set of intervals (train, test) per each splitting depth and (data)class
CData_Accessor_Splitting_Mem	Implements data access to data cached entirely in memory, concrete file type support is delegated to derived classes
CData_Accessor_Splitting_MemARFF	Implements data access to data cached entirely in memory, read once from a ARFF file
CData_Accessor_Splitting_MemCSV	Implements data access to data cached entirely in memory, read once from a CSV (delimited text) file; see Data_File_CSV for the accepted dialects and options
CData_Accessor_Splitting_MemTRN	Implements data access to data cached entirely in memory, read once from a TRN file
CData_Accessor_Universe	Data-free accessor over an abstract feature universe of N features (the fst4 CLI's --features N entry; the companion of criterion_extern_value.hpp in its data-free mode)
CData_File_ARFF	ARFF data format filter
CSkippedColumn	One @ATTRIBUTE column that was recognized but could not be loaded (STRING/DATE/unknown type), for the load-summary report
CData_File_ARFF_Feature	ARFF data format filter - representation of an attribute (one @ATTRIBUTE column)
CData_File_ARFF_Record	ARFF data format filter - representation of a record (one @DATA row)
CData_File_CSV	CSV (delimited text) data format filter — the simplest spreadsheet-to-FST4 path
CData_File_CSV_Options	CSV loader options
CData_File_TRN	TRN data format filter
CData_Interval	Support structure to hold data interval (referring into data held by data accessor)
CData_Intervaller	Container to hold list of Data_Interval; implements nested interval reduction
CData_Scaler	Abstract class, defines interface for data scaling implementations (for use in data accessors)
CData_Scaler_to01	Implements data normalization (of all feature values) to interval [0,1]
CData_Scaler_void	Void data scaler, to bypass data normalization
CData_Scaler_white	Implements data whitening (as result, each feature mean value is 0 with normalized stddev)
CData_Splitter	Abstract class, defines interface for data splitting implementations (for use in data accessors)
CTClassSplitter	Data splitting support structure; holds one set of intervals (train, test) per each (data)class
CData_Splitter_5050	Implements train/test data splitting: first 50% of original data for training, second 50% for testing, each (data)class split separately
CData_Splitter_CV	Implements train/test data splitting: by means of k-fold cross-validation
CData_Splitter_Holdout	Implements train/test data splitting: use initial x% of original data for training, the rest for testing, each (data)class split separately
CData_Splitter_Leave1Out	Implements train/test data splitting: by means of leave-one-out
CData_Splitter_RandomRandom	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
CData_Splitter_Resub	Implements train/test data splitting: use all data both for training and then once more for testing
CData_Splitter_TrainRandom_TestFixed	Implements train/test data splitting: equal to Data_Splitter_RandomRandom except that the same test data is returned in each loop
CDistance	Abstract class, defines interface for distance evaluators to be used in Classifier_kNN
CDistance_Euclid	Implements Euclidean (L_2) distance
CDistance_L1	Implements L_1 (city-block / Manhattan) distance
CDistance_Lp	Implements L_{p} distance, p equals numerator/denominator (template parameters)
CExtern_Bridge	Persistent-child-process line-protocol plumbing shared by the external-tool bridges: Classifier_Extern (TRAIN/TEST label protocol) and Criterion_Extern_Value (VALUE protocol)
Cfst_error	FST4 specific error passing class
CIndexed_Matrix	Matrix representation and operations, allows operation in selected subspace
CIndexed_Vector	Vector representation and operations, allows operation in selected subspace. Also serves as the helper structure for passing matrix-operation intermediate data between higher-level matrix objects and functions
CMetric_Spec	Configuration of the wrapper metric; immutable during search, copied by criterion clones (thread-safe, RNG-free)
CModel	Abstract class, defines interface for data model implementations
CModel_Multinomial	Multinomial ("bag of words") model: per-class term-count sums and the Laplace-smoothed term probabilities P(term|class) derived from them
CModel_Normal	Implements normal (gaussian) model
Cmsgbuf	Message accumulator for Search::report() - collects one progress message exactly as an std::ostringstream would, for delivery either to an attached Search_Observer or to the search's output stream
CRegressor	Abstract class, defines interface for Regressor implementations (mainly to be used in wrappers)
CRegressor_Ridge	Closed-form ridge regression (L2-regularized least squares) for wrapper-based feature selection
CRegressor_Weighted_Neighbours	Implements experimental regressor that produces weighted average of the predicted variable over neighbours, weighted by distance
CResult_Tracker	Abstract class, defines interface for classes that enable collecting multiple results
CResultRec	Structure to hold [subset,criterion value] temporary solutions in the course of search
CResult_Tracker_Dupless	Collects multiple results, avoiding duplicates
CResult_Tracker_Feature_Stats	Collects evaluated subsets to eventually provide Dependency-Aware Feature Ranking coefficients
CFeatureStat	Structure to gather feature occurrence statistics over probe subset evaluations
CSubSizeStat	Structure to gather probe subset cardinality statistics
CPairStat	Structure to hold pairwise feature statistics (2x2 presence-cell contrasts over probe subset evaluations)
CResult_Tracker_Regularizer	Collects multiple results. Enables eventual selection of alternative solution based on secondary criterion
CResult_Tracker_Stability_Evaluator	Collects multiple results to evaluate various stability measures on the collection
CResult_Weighter_Sampled	Online per-feature statistics for budgeted (sampled) sequential steps — discounted batch-standardized DAF contrast
CFeatureStat	Per-feature accumulators, one instance per feature index, updated by flush_batch(). m_is/m_isnot are adaptive-rate running means of the batch z-score, one side for evaluations in which the feature was selected ('is') and one for evaluations in which it was not ('isnot'); score() in STATS_ZCONTRAST mode returns their difference. raw_sum_is/raw_cnt_is accumulate the STATS_RAWMEAN statistic instead. n_is/n_isnot count how many evaluations landed on each side — also the exploration counts exposed via count_is()/count_isnot()
CBatchEntry	One buffered, not-yet-flushed criterion evaluation: its raw value, the cardinality of the evaluated subset, and the list of selected feature indices — everything flush_batch() needs to z-standardize the batch and fold it into the accumulators
CSearch	Abstract class, defines interface for search method implementations
CSearch_Branch_And_Bound	Implements Branch and Bound template method as basis for more advanced B&B implementations
CNode	Structure representing search tree node
CSearch_Branch_And_Bound_Basic	Implements Branch and Bound Basic method, i.e., with randomized node ordering
CSearch_Branch_And_Bound_Fast	Implements Fast Branch and Bound, i.e., B&B with full utilization of prediction mechanism
CSearch_Branch_And_Bound_Improved	Implements Improved Branch and Bound (IBB) method, i.e., with fully computed node ordering
CSearch_Branch_And_Bound_Improved_Threaded	DEPRECATED shim: threaded Improved Branch and Bound (IBB) method
CSearch_Branch_And_Bound_Partial_Prediction	Implements Branch and Bound with Partial Prediction (BBPP) method, i.e., with predicted node ordering
CSearch_DOS	Implements Dynamic_Oscillating_Search
CSearch_Exhaustive	Implements exhaustive (optimal) search yielding optimal feature subset with respect to chosen criterion
CSearch_Exhaustive_Threaded	DEPRECATED shim: threaded version of exhaustive (optimal) search
CSearch_Monte_Carlo	Repeatedly samples random subsets to eventually yield the one with highest criterion value
CSearch_Monte_Carlo_Threaded	DEPRECATED shim: threaded version of randomized search that repeatedly samples random subsets
CSearch_Observer	Observer interface for search progress reporting
CSearch_OS	Implements Oscillating_Search
CSearch_Ranking	Abstract base of the feature-ranking methods: scores features one by one, then turns the ranking into a feature subset
COneFeature	Structure to hold [feature,criterion value] pair while ranking features
CSearch_Ranking_BIF	Implements Best Individual Features, i.e., individual feature ranking
CSearch_Ranking_BIF_Threaded	DEPRECATED shim: threaded Best Individual Features, i.e., individual feature ranking
CSearch_Sequential	Abstract class, defines interface for sequential search method implementations, provides common implementation of the "sequential step" operation
CSearch_SFFS	Implements Sequential_Forward_Floating_Search and Sequential_Backward_Floating_Search
COneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
CSearch_SFRS	Implements Sequential_Forward_Retreating_Search and Sequential_Backward_Retreating_Search
COneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
CSearch_SFS	Implements Sequential_Forward_Selection and Sequential_Backward_Selection
COneSubset	Structure to hold [subset,criterion value] temporary solutions in the course of search
CSequential_Step	Abstract class, defines interface for implementations of one selection step in sequential search type of methods
CSequential_Step_Ensemble	Implements voting ensemble selection step in sequential search type of methods to possibly improve robustness and stability of feature selection result
CSubsetCandidate	Nested class to hold feature/subset candidate info in the course of the ensemble voting process
CSequential_Step_Hybrid	Implements hybrid selection step in sequential search type of methods
CSubsetCandidate	Nested class to hold [Subset,criterion value] pair in the course of hybrid feature candidate evaluation in Sequential_Step_Hybrid
CSequential_Step_Sampled	Implements the budgeted (sampled) sequential selection step: forward steps evaluate only a sampled batch of candidates, backward steps sweep in full unless a backward budget is set
CSequential_Step_Straight	Implements standard sequential selection step in sequential search type of methods
CSequential_Step_Straight_Threaded	DEPRECATED shim: threaded version of sequential selection step in sequential search type of methods
CStopWatch	Simple stopwatch to measure run time of search algorithms
CSubset	Stores the info on currently selected features, enables generating permutations, etc
CFeature_Range	Read-only forward range over feature indices
Citerator	Forward iterator over raw feature indices matching a Kind, skipping non-matching ones
Csyncout	Serializes textual reporting from concurrent threads
CThreadPool	Implements thread scheduler that assigns jobs up to maximum number of threads allowed
CThreadsArray	Structure to keep status of threads in the pool
Nfst4_cli	Internals of the fst4 command-line tool: its type aliases, the option record, the run pipeline with its result types, the CLI-specific observers and adapters, and the output emitters
CCriterion_Counting
CNullBuf	Swallows all output (used with --verbosity silent)
CStdin_Data_File
CObserver_JSON	--progress json: emits one JSON line per improved solution to the progress stream; free-text progress is dropped
CObserver_Curve
CConfig	All fst4 CLI settings in one place: one field per command-line flag (plus a few derived/internal fields such as data_resolved), populated by CLI11 in add_options() and consumed by the factory functions and run_pipeline() below. Unless noted, string fields default to "" (flag omitted / feature off); concrete defaults live in the Config() constructor below
CPipeline	Everything the run phase needs, assembled by the factories below
CRunResult
CValidationEntry	One classifier's/regressor's holdout validation outcome (--validate)
CTopEntry	One entry of the dupless tracker's --top-k best distinct subsets
CRegEntry	One --regularize-margins sweep point: the best subset within margin of the maximum by the secondary criterion
CTrialEntry	One --repeat-over-splits trial's search outcome
CStabilityEntry	One named stability/similarity measure computed by the stabileval tracker (see the FST4_STAB macro)
CDafEntry	One --daf-rank ranked feature
CPairDafEntry	One --daf-pairs pairwise (2-feature) DAF statistic
CSweepEntry	One --regr-multipliers sweep point (demo11reg)
CCurvePoint	One ROC/PR point at a given decision threshold (see extract_curves())
CClassCurve	One class's ROC and PR curves (see extract_classifier_curves())
CCurveEntry	One curve_over_d point: the best subset of a given size plus its holdout re-validation (the overfitting curve)
CCompareResult	Everything produced by a --compare-config second scenario (the demo55 protocol)
CSvg_Panel