% Pattern Recognition Tools% Version 4.1.3 10-Jun-2008%%Datasets and Mappings (just most important routines)%---------------------%dataset     Define dataset from datamatrix and labels%datasets    List information on datasets (just help, no command)%datafile    Define dataset from directory of object files %datafiles   List information on datafiles (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%addlabels   Add additional labelling%changelablist  Change current active labeling%multi_labeling List information on multi-labeling (help only)%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%gendati     Generation of random windows from images%gendatk     Nearest neighbour data generation%gendatl     Generation of Lithuanian classes%gendatm     Generation of 8 2d classes%gendatp     Parzen density data generation%gendatr     Generate regression dataset from data and target values%gendats     Generation of two Gaussian distributed classes%gendatw     Sample dataset by given weigths%gentrunk    Generation of Trunk's example%prdata      Read data from file%seldat      Select classes / features / objects from dataset%getwindows  Get pixel feature vectors around given pixels in image dataset%prdataset   Read existing dataset from file%prdatasets  Overview of all datasets and data generators%%Datafiles%---------%datafile    Define datafile from set of files in directory%savedatafile Save datafile, store intermediate result%filtm       Mapping for arbitrary processing of datafile%%Linear and Quadratic Classifiers (*operate on datasets and datafiles)%--------------------------------%fisherc     Minimum least square linear classifier%ldc         Normal densities based linear (muli-class) classifier%loglc       Logistic linear 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%klldc       Linear classifier based on KL expansion of common cov matrix%pcldc       Linear classifier based on PCA expansion on the joint data%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%rejectc     Creates reject version of exisiting classifier%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%%weakc       Weak classifier%stumpc      Decision stump classifier%adaboostc   ADABoost classifier%%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%nusvc       Support vector classifier%nusvo       Support vector optimizer%rbsvc       Radial basis SV classifier%kernelc     General kernel/dissimilarity based classification%%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%regoptc     Optimisation of regularisation and complexity parameters%reject      Compute error-reject trade-off curve%roc         Receiver-operator curve (ROC)%testc       General error estimation routine for trained classifiers%testauc     Estimate error as area under the ROC%%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%filtm       Arbitrary operation on objects in datafiles/datasets%gaussm      Mixture of Gaussians density estimation%kernelm     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