% Data Description Toolbox% Version 1.6.3 3-Jun-2008%%Dataset construction%--------------------%isocset        true if dataset is one-class dataset%gendatoc       generate a one-class dataset from two data matrices%oc_set         change normal classif. problem to one-class problem%target_class   extracts the target class from an one-class dataset%make_outliers  create outlier data in a box around target class%gendatgrid     create a grid dataset around a 2D dataset%gendatout      create outlier data in a hypersphere around the%               target data%gendatoutg     create outlier data normally distributed around the%               target data%gendatouts     create outlier data in the data PCA subspace in a%               hypersphere around the target data%dd_crossval    cross-validation dataset creation%dd_label       put the classification labels in the same dataset%%Data preprocessing%------------------%myproxm        replacement for proxm.m%kwhiten        rescale data to unit variance in kernel space%gower          compute the Gower similarities%%One-class classifiers%---------------------%random_dd      description which randomly assigns labels%stump_dd       threshold the first feature%gauss_dd       data description using normal density%rob_gauss_dd   robustified gaussian distribution%mcd_gauss_dd   Minimum Covariance Determinant gaussian%mog_dd         mixture of Gaussians data description%mog_extend     extend a Mixture of Gaussians data description%parzen_dd      Parzen density data description%nparzen_dd     Naive Parzen density data description%%autoenc_dd     auto-encoder neural network data description%kcenter_dd     k-center data description%kmeans_dd      k-means data description%pca_dd         principal component data description%som_dd         Self-Organizing Map data description%mst_dd         minimum spanning tree data description%%nndd           nearest neighbor based data description%knndd          K-nearest neighbor data description%ball_dd        L_p-ball data description%lpball_dd      extended L_p-ball data description%svdd           Support vector data description%incsvdd        Incremental Support vector data description%ksvdd          SVDD on general kernel matrices%lpdd           linear programming data description%mpm_dd         minimax probability machine data description%%dkcenter_dd    distance k-center data description%dnndd          distance nearest neighbor based data description%dknndd         distance K-nearest neighbor data description%dlpdd          distance-linear programming data description%%isocc          true if classifier is one-class classifier%%AUC optimizers%--------------%rankboostc     Rank-boosting algorithm%auclpm         AUC linear programming mapping%%Classifier postprocessing/optimization/combining.%--------------------------------------%consistent_occ optimize the hyperparameter using consistency%optim_auc      optimize the hyperparameter by maximizing AUC%dd_normc       normalize oc-classifier output%multic         construct a multi-class classifier from OCC's%%Error computation.%-----------------%dd_error       false positive and negative fraction of classifier%dd_f1          F1 score computation%dd_eer         equal error rate%dd_roc         computation of the Receiver-Operating Characterisic curve %dd_auc         error under the ROC curve%dd_costc       cost curve%dd_delta_aic   AIC error for density estimators%dd_fp          compute false positives for given false negative%               fraction%simpleroc      basic ROC curve computation%dd_setfn       set the threshold for a false negative rate%%Plot functions.%--------------%plotroc        plot an ROC curve%plotcostc      plot the cost curve%plotg          plot a 2D grid of function values%plotw          plot a 2D real-valued output of classifier w%askerplot      plot the FP and FN fraction wrt the thresholds%plot_mst       plot the minimum spanning tree%%Support functions.%-----------------%istarget       true if an object is target%find_target    gives the indices of target and outlier objs from a dataset%getoclab       returns numeric labels (+1/-1)%dist2dens      map distance to posterior probabilities%dd_threshold   give percentiles for a sample%randsph        create outlier data uniformly in a unit hypersphere%makegriddat    auxiliary function for constructing grid data%relabel        relabel a dataset%dd_kernel      general kernel definitions%center         center the kernel matrix in kernel space%gausspdf       multi-variate Gaussian prob.dens.function%mahaldist      Mahalanobis distance%sqeucldistm    square Euclidean distance%mog_init       initialize a Mixture of Gaussians%mog_P          probability density of Mixture of Gaussians%mog_update     update a MoG using EM%mogEMupdate    EM procedure to optimize Mixture of Gaussians%mogEMextend    smartly extend a MoG and apply EM%mykmeans       own implementation of the k-means clustering algorithm%getfeattype    find the nominal and continuous features%knn_optk       optimization of k for the knndd using leave-one-out%volsphere      compute the volume of a hypersphere%scale_range    compute a reasonable range of scales for a dataset%nndist_range   compute the average nearest neighbor distance%inckernel      kernel definitions for the incsvdd%Wstartup       startup function incsvdd%Wadd/Wremove   add/remove one object to an incsvdd%Wstore         store the structure in an incsvdd%plotroc_update support function for plotroc%roc_hull       convex hull over a ROC curve%lpball_dist    lp-distance to a center%lpball_vol     volume of a lpball%lpdist         fast lp-distance between two datasets%dd_message     printf with colors%%Examples%--------%dd_ex1         show performance of nndd and svdd%dd_ex2         show the performances of a list of classifiers%dd_ex3         shows the use of the svdd and ksvdd%dd_ex4         optimizes a hyperparameter using consistent_occ%dd_ex5         shows the construction of lpdd from dlpdd%dd_ex6         shows the different Mixture of Gaussians classifiers%dd_ex7         shows the combination of one-class classifiers%dd_ex8         shows the interactive adjustment of the operating point%dd_ex9         shows the use of dd_crossval%dd_ex10        shows the use of the incremental SVDD%dd_ex11        the construction of a multi-class classifier using OCCs%% Copyright: D.M.J. Tax, D.M.J.Tax@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands