% Pattern Recognition Tools% Version 4.0.14 04-Mar-2005%%Datasets and Mappings (just most important routines)%---------------------%dataset     Define and retrieve dataset from datamatrix and labels%datasets    List information on datasets (just help, no command)%classsizes  Retrieve sizes of classes%gencirc     Generation of a one-class circular dataset%genclass    Generate class frequency distribution%genlab      Generate dataset labels%getlab      Retrieve object labels from datasets and mappings%getnlab     Retrieve nummeric object labels from dataset%getfeat     Retrieve feature labels from datasets and mappings%get         Get fields from datasets or mappings%setdata     Change data in dataset%setlabels   Change labels of dataset or mapping%mapping     Define and retrieve mapping and classifier from data%mappings    List information on mappings (just help, no command)%renumlab    Convert labels to numbers%matchlab    Match different labelings%primport    Convert old datasets to present PRTools definition%remclass    Remove a class from a dataset%seldat      Retrieve part of a dataset%%Data Generation (more in prdatasets)%---------------%circles3d   Create a dataset containing 2 circles in 3 dimensions%lines5d     Create a dataset containing 3 lines in 5 dimensions%gauss       Generation of multivariate Gaussian distributed data%gendat      Generation of classes from given data set%gendatb     Generation of banana shaped classes%gendatc     Generation of circular classes%gendatd     Generation of two difficult classes%gendath     Generation of Highleyman classes%gendatk     Nearest neighbour data generation%gendatl     Generation of Lithuanian classes%gendatm     Generation of 8 2d classes%gendatp     Parzen density data generation%gendats     Generation of two Gaussian distributed classes%prdata      Read data from file%seldat      Select classes / features / objects from dataset%prdataset   Read existing dataset from file%prdatasets  Overview of all datasets and data generators%%Linear and Quadratic Classifiers %--------------------------------%klldc       Linear classifier based on KL expansion of common cov matrix%pcldc       Linear classifier based on PCA expansion on the joint data%loglc       Logistic linear classifier%fisherc     Minimum least square linear classifier%ldc         Normal densities based linear (muli-class) classifier%nmc         Nearest mean linear classifier%nmsc        Scaled nearest mean linear classifier%quadrc      Quadratic classifier%qdc         Normal densities based quadratic (multi-class) classifier%udc         Uncorrelated normal densities based quadratic classifier%polyc       Add polynomial features and run arbitrary classifier%subsc       Subspace classifier%%classc      Converts a mapping into a classifier%labeld      Find labels of objects by classification%logdens     Convert density estimates to log-densities for more accuracy%testc       General error estimation routine for trained classifiers%%Other Classifiers %-----------------%knnc        k-nearest neighbour classifier (find k, build classifier)%knn_map     Map a dataset on a K-NN classifier, back end routine%testk       Error estimation for k-nearest neighbour rule%edicon      Edit and condense training sets%%parzenc     Parzen classifier%parzendc    Parzen density based classifier%parzen_map  Map a dataset on a Parzen classfier, back end routine%testp       Error estimation for Parzen classifier%%treec       Construct binary decision tree classifier%tree_map    Map a dataset on a decision tree, back end routine%naivebc     Naive Bayes classifier%bpxnc       Feed forward neural network classifier by backpropagation%lmnc        Feed forward neural network by Levenberg-Marquardt rule%neurc       Automatic neural network classifier%perlc       Linear perceptron %rbnc        Radial basis neural network classifier%rnnc        Random neural network classifier%ffnc        Feed-forward neural net classifier back-end routine%%fdsc        Feature based dissimilarity space classifier%%svc         Support vector classifier%svo         Support vector optimizer%svc_nu      Support vector classifier%svo_nu      Support vector optimizer%%Normal Density Based Classification%-----------------------------------%distmaha    Mahalanobis distance%meancov     Estimation of means and covariance matrices from multiclass data%nbayesc     Bayes classifier for given normal densities%normal_map  Back-end routine for computing normal densities%ldc         Normal densities based linear (muli-class) classifier%qdc         Normal densities based quadratic (multi-class) classifier%udc         Uncorrelated normal densities based quadratic classifier%mogc        Mixture of gaussians classification%testn       Error estimate of discriminant on normal distributions%%Feature Selection%-----------------%feateval    Evaluation of a feature set%featrank    Ranking of individual feature permormances%featsel     Feature Selection%featselb    Backward feature selection%featself    Forward feature selection%featsellr   Plus-l-takeaway-r feature selection%featseli    Feature selection on individual performance%featselm    Feature selection map, general routine for feature selection%featselo    Branch and bound feature selection%featselp    Floating forward feature selection%%Classifiers and tests (general)%-------------------------------%bayesc      Bayes classifier by combining density estimates%classim     Classify image using a given classifier%classc      Convert mapping to classifier%labeld      Find labels of objects by classification%cleval      Classifier evaluation (learning curve)%clevalb     Classifier evaluation (learning curve), bootstrap version%clevalf     Classifier evaluation (feature size curve)%clevals     Classifier evaluation (feature /learning curve), bootstrap%confmat     Computation of confusion matrix%costm       Cost mapping, classification using costs%crossval    Crossvalidation %cnormc      Normalisation of classifiers%disperror   Display error matrix with information on classifiers and datasets%labelim     Construct image of labeled pixels%logdens     Convert density estimates to log-densities for more accuracy%mclassc     Computation of multi-class classifier from 2-class discriminants%reject      Compute error-reject trade-off curve%roc         Receiver-operator curve%testc       General error estimation routine for trained classifiers%%Mappings%--------%affine      Construct affine (linear) mapping from parameters%bhatm       Two-class Bhattacharryya mapping%cmapm       Compute some special maps%featselm    Feature selection map, general routine for feature selection%fisherm     Fisher mapping%invsigm     Inverse sigmoid map%gaussm      Mixture of Gaussians density estimation%kernelm     PCA based kernel mapping%klm         Decorrelation and Karhunen Loeve mapping (PCA)%klms        Scaled version of klm, useful for prewhitening%knnm        k-Nearest neighbor density estimation%mclassm     Computation of mapping from multi-class dataset%map         General routine for computing and executing mappings%nlfisherm   Nonlinear Fisher mapping%normm       Object normalization map%parzenm     Parzen density estimation%parzenml    Optimization of smoothing parameter in Parzen density estimation.%pca         Principal Component Analysis%pcaklm      Back en routine for PC and KL mappings%proxm       Proximity mapping and kernel construction%reducm      Reduce to minimal space mapping%remoutl     Remove outliers%scalem      Compute scaling data%sigm        Simoid mapping%spatm       Augment image dataset with spatial label information%%gtm         Fit a Generative Topographic Mapping (GTM) by EM%plotgtm     Plot a Generative Topographic Mapping in 2D%som         Simple routine computing a Self-Organizing Map (SOM)%plotsom     Plot a Self-Organizing Map in 2D%%Classifier combiners%--------------------%averagec    Combining linear classifiers by averaging coefficients%baggingc    Bootstrapping and aggregation of classifiers%votec       Voting classifier combiner (Voting)%maxc        Maximum classifier combiner%minc        Minimum classifier combiner%meanc       Mean classifier combiner%medianc     Median classifier combiner%prodc       Product classifier combiner%traincc     Train combining classifier%fixedcc     Fixed combiner construction, back end%parsc       Parse classifier or map%parallel    Parallel combining of classifiers%stacked     Stacked combining of classifiers%sequential  Sequential combining of classifiers%%Image operations%----------------%classim     Classify image using a given classifier%dataim      Image operation on dataset images%data2im     Convert dataset to image%getobjsize  Retrieve image size of feature images in datasets%getfeatsize Retrieve image size of object images in datasets%datfilt     Filter dataset image%datgauss    Filter dataset image by Gaussian filter%datunif     Filter dataset image by uniform filter%im2obj      Convert image to object in dataset%im2feat     Convert image to feature in dataset%immoments   Computes moments as features from object images%spatm       Augment image dataset with spatial label information%show        Display objects in datasets and mappings%%Clustering and distances%------------------------%distm       Distance matrix between two data sets.%emclust     Expectation - maximization clustering%proxm       Proximity mapping and kernel construction%hclust      Hierarchical clustering%kcentres    k-centres clustering%kmeans      k-means clustering%modeseek    Clustering by modeseeking%%mds         Non-linear mapping by multi-dimensional scaling (Sammon)%mds_cs      Linear mapping by classical scaling%mds_init    Initialisation of multi-dimensional scaling%mds_stress  Dissimilarity of distance matrices%%Plotting%--------%gridsize    Set gridsize used in the PRTools plot commands%plotc       Plot discriminant function for two features%plotr       Plot error curves%plotf       Plot feature distribution%plotm       Plot mapping%ploto       Plot object functions%plotdg      Plot dendrgram (see hclust)%scatterd    Scatterplot%scatterdui  Scatterplot scatterplot with feature selection%%Various tests and support routines%----------------------------------%cdats       Support routine for checking datasets%iscolumn    Test on column array%iscomdset   Test on compatible datasets%isdataim    Test on image dataset%isdataset   Test on dataset%isfeatim    Test on feature image dataset%ismapping   Test on mapping%isobjim     Test on object image dataset%isparallel  Test on parallel mapping%isstacked   Test on stacked mapping%issym.m     Test on symmetric matrix%isvaldset   Test on valid dataset%matchlablist Match entries of label lists%newfig      Control of figures on the screen%newline     Generate a new line in the command window%nlabcmp     Compare two label lists and count the differences%%Examples%--------%prex_cleval     learning curves%prex_combining  classifier combining%prex_confmat    confusion matrix, scatterplot and gridsize%prex_datasets   standard datasets%prex_density    Various density plots%prex_eigenfaces Use of images and eigenfaces%prex_matchlab   K-means clustering and matching labels%prex_mcplot     Multi-class classifier plot%prex_plotc      Dataset scatter and classifier plot%prex_som        Training a SelfOrganizing Maps%prex_spatm      Spatial smoothing of image classification%prex_cost       Cost matrices and rejection%prex_logdens    Density based classifier improvement%%prversion   returns version information on PRTools%prtver      prtools version back end%typp        list prtools routine nicely% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands