_src_criteria Directory Reference

Files
	classifier.hpp
	Defines interface for classifier implementations.
	classifier_extern.hpp
	Defines Classifier_Extern — bridge delegating train/test to a user-supplied external command (persistent process, line protocol).
	classifier_knn.hpp
	Implements k-Nearest Neighbor classifier.
	classifier_lda.hpp
	Defines Classifier_LDA — shared-covariance Gaussian (linear) discriminant for wrapper-based feature selection.
	classifier_linear_svm.hpp
	Defines Classifier_Linear_SVM — dependency-free linear SVM (dual coordinate descent) for wrapper-based feature selection.
	classifier_logreg.hpp
	Implements multinomial logistic regression classifier (dependency-free, deterministic; regularized damped-Newton training on internally standardized data).
	classifier_multinom_naivebayes.hpp
	Implements Naive-like Bayes classifier based on multinomial model.
	classifier_normal_bayes.hpp
	Implements Bayes classifier based on normal (gaussian) model.
	classifier_rbf_svm.hpp
	Defines Classifier_RBF_SVM — dependency-free RBF kernel SVM (kernel dual coordinate descent) for wrapper-based feature selection.
	classifier_rf.hpp
	Defines Classifier_Random_Forest — dependency-free, deterministic-by-design Random Forest for wrapper-based feature selection.
	classifier_svm.hpp
	Wraps external Support Vector Machine implementation (in LibSVM) to serve as FST4 classifier.
	criterion.hpp
	Defines interface for feature selection criterion classes.
	criterion_extern_value.hpp
	External-tool criterion: a user-supplied command computes the criterion value over the VALUE line protocol.
	criterion_multinom.hpp
	Defines interface for feature selection criterion implementations based on multinomial model.
	criterion_multinom_bhattacharyya.hpp
	Implements Bhattacharyya distance based on multinomial model to serve as feature selection criterion.
	criterion_negative.hpp
	Criterion_Negative returns the negative of another Criterion's result.
	criterion_normal.hpp
	Defines interface for feature selection criterion implementations based on normal model.
	criterion_normal_bhattacharyya.hpp
	Implements Bhattacharyya distance based on normal (gaussian) model to serve as feature selection criterion.
	criterion_normal_divergence.hpp
	Implements Divergence (distance) based on normal (gaussian) model to serve as feature selection criterion.
	criterion_normal_gmahalanobis.hpp
	Implements Generalized Mahalanobis distance based on normal (gaussian) model to serve as feature selection criterion.
	criterion_subsetsize.hpp
	Criterion_Subset_Size returns the (positive) subset size - rates larger subsets higher; wrap in Criterion_Negative to favor smaller subsets.
	criterion_sumofweights.hpp
	Criterion_Sum_Of_Weights returns sum of pre-specified feature weights for features in the evaluated subset.
	criterion_wrapper.hpp
	Criterion_Wrapper adapts Classifier objects to serve as feature selection criterion.
	criterion_wrapper_bias_estimate.hpp
	Wrapper Bias Evaluator - returns classifier bias estimate (difference between accuracy on training and validation data parts).
	dense_train_tools.hpp
	Shared dense-training helpers: standardized-design build and Cholesky factor/solve.
	distance.hpp
	Defines interface for distance evaluators (to be used primarily in Classifier_kNN).
	distance_euclid.hpp
	Implements Euclidean (L_2) distance.
	distance_L1.hpp
	Implements L_1 distance.
	distance_Lp.hpp
	Implements L_{p} distance, p equals numerator/denominator (template parameters).
	extern_bridge.hpp
	Persistent-child-process line-protocol plumbing shared by the external-tool bridges (Classifier_Extern, Criterion_Extern_Value).
	indexed_matrix.hpp
	Matrix representation and operations, allows operation in selected subspace.
	indexed_vector.hpp
	Vector representation and operations, allows operation in selected subspace.
	model.hpp
	Defines interface for data model implementations.
	model_multinom.hpp
	Implements multinomial model.
	model_normal.hpp
	Implements normal (gaussian) model.
	regressor.hpp
	Defines interface for regressor implementations.
	regressor_ridge.hpp
	Defines Regressor_Ridge — closed-form L2-regularized linear regression for wrapper-based feature selection.
	regressor_weighted_neighbours.hpp
	Experimental regressor, produces weighted average of the predicted variable over neighbours, weighted by distance.
	wrapper_metrics.hpp
	Wrapper criterion metrics beyond accuracy (balanced accuracy, F1, AUC, class weights, regression metrics).