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模式识别工具箱,希望对大家有用!

%Data Description Toolbox (version 0.8)
%
%Dataset construction
%--------------------
%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 around target class
%gendatgrid     create a grid dataset around a 2D dataset
%gendatout      create outlier data in a hypersphere around target
%               the data
%
%Data preprocessing
%------------------
%kwhiten        rescale data to unit variance in kernel space
%
%One-class classifiers
%---------------------
%random_dd      description which randomly assigns labels
%gauss_dd       data description using normal density
%mog_dd         mixture of Gaussians data description
%parzen_dd      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
%
%nndd           nearest neighbor bases data description
%svdd           Support vector data description
%range_svdd     SVDD over a range of scales (useful for ROC computation).
%lpdd           linear programming data description
%
%Error computation.
%-----------------
%dd_error       false positive and negative fraction of classifier
%dd_roc         computation of the Receiver-Operating Characterisic curve 
%dd_auc         error under the ROC curve
%dd_delta_aic   AIC error for density estimators
%
%Support functions.
%-----------------
%is_ocset       true if dataset is one-class dataset
%is_occ         true if classifier is one-class classifier
%find_target    gives the indices of target and outlier objs from a dataset
%dist2dens      map distance to posterior probabilities
%threshold      give percentiles for a sample
%setthres       set the threshold for a one-class classifier
%randsph        create outlier data uniformly in a unit hypersphere
%makegriddat    auxiliary function for constructing grid data
%plotg          plot a 2D grid of function values
%plotw          plot a 2D real-valued output of classifier w
%relabel        relabel a dataset
%ddistm         distance matrix for different types of distances
%lpdistm        L-p distances
%center         center the kernel matrix in kernel space
%
%Examples
%--------
%dd_example     show performance of nndd and svdd
%
% Copyright: D. Tax, R.P.W. Duin, [e-mail]
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands