%Pattern Recognition Tools (PRTOOLS 3.1.7)% Version 1 February 2002 (after PRSD course)%%Datasets and Mappings%---------------------%dataset     Define and retrieve dataset from datamatrix and labels%datasets    List information on datasets (just help, no command)%getlab      Retrieve object labels from datasets and mappings%getfeat     Retrieve feature labels from dataset%getlablist  Retrieve names of classes%classsizes  Retrieves sizes of classes%getprob     Retrieves class prior probabilities%genlab      Generate dataset labels%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%%Data Generation %---------------%gauss       Generation of multivariate Gaussian distributed data%gencirc     Generation of a one-class circular dataset%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 Higleyman 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%gendatt     Generation of testset from given dataset%prdata      Read data from file%seldat      Select classes / features / objects from dataset%%Linear and Quadratic Classifiers %--------------------------------%klclc       Linear classifier by KL expansion of common cov matrix%kljlc       Linear classifier by KL 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%perlc       Perceptron linear classifier%persc       Perceptron linear classifier%pfsvc       Pseudo-Fisher support vector classifier%qdc         Normal densities based quadratic (multi-class) classifier%udc         Uncorrelated normal densities based quadratic classifier%quadrc      Quadratic classifier%polyc       Add polynomial features and run arbitrary classifier%%classc      Converts a mapping into a classifier%classd      General classification routine for trained classifiers%testd       General error estimation routine for trained classifiers%%Other Classifiers %-----------------%knnc        k-nearest neighbour classifier (find k, build classifier)%knn_map     k-nearest neighbour mapping%testk       Error estimation for k-nearest neighbour rule%%parzenc     Parzen density based classifier%parzenml    Optimization of smoothing parameter in Parzen density estimation.%parzen_map  Parzen mapping%testp       Error estimation for Parzen classifier%%edicon      Edit and condense training sets%%treec       Construct binary decision tree classifier%tree_map    Classification with binary decision tree%%bpxnc       Train feed forward neural network classifier by backpropagation%lmnc        Train feed forward neural network by Levenberg-Marquardt rule%neurc       Automatic Neural Network Classifier (using lmnc)%rbnc        Train radial basis neural network classifier%rnnc        Random neural network classifier%%subsc       Subspace Classifier%svc         Support vector classifier%%Normal Density Based Classification%-----------------------------------%distmaha    Mahalanobis distance%normal_map  Normal density mapping%meancov     Estimation of means and covariance matrices from multiclass data%nbayesc     Bayes classifier for given 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%testn       Error estimate of discriminant on normal distributions%%Feature Selection%-----------------%feateval    Evaluation of a feature set%featrank    Ranking of individual feature permormances%featselb    Backward feature selection%featself    Forward 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%featselm    Feature selection map, general routine for feature selection%%Classifiers and tests (general)%-------------------------------------%classim     Classify image using a given classifier%classc      Convert mapping to classifier%classd      General classification routine for trained classifiers%cleval      Classifier evaluation (learning curve)%clevalb     Classifier evaluation (learning curve), bootstrap version%clevalf     Classifier evaluation (feature size curve)%confmat     Computation of confusion matrix%crossval    Crossvalidation %cnormc      Normalisation of classifiers%mclassc     Computation of multi-class classifier from 2-class discriminants%reject      Compute error-reject trade-off curve%roc         Receiver-operator curve%testd       General error estimation routine for trained classifiers%%Mappings%--------%classs      Linear mapping by classical scaling%cmapm       Compute some special maps%featselm    Feature selection map, general routine for feature selection%fisherm     Fisher mapping%invsigm     Inverse sigmoid map%klm         Decorrelation and Karhunen Loeve mapping (PCA)%klms        Scaled version of klm, useful for prewhitening%lmnm        Levenberg-Marquardt neural net diabolo mapping%maf         Maximum autocorrelation mapping (ICA)%mds         Non-linear mapping by multi-dimensional scaling (Sammon)%nlfisherm   Nonlinear Fisher mapping%normm       Object normalization map%pca         Principal Component Analysis%proxm       Proximity mapping and kernel construction%reducm      Reduce to minimal space mapping%scalem      Compute scaling data%sigm        Simoid mapping%spatm       Augment image dataset with spatial label information%subsm       Subspace mapping%svm         Support vector mapping, useful for kernel PCA%%Classifier combiners%--------------------%baggingc    Bootstrapping and aggregation of classifiers%majorc      Majority 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%parsc       Parse classifier or map%rsubc       Random Subspace Classifier%%Image operations%----------------%classim     Classify image using a given classifier%dataim      Image operation on dataset images%data2im     Convert dataset to image%getimheight Retrieve image height of images in datasets%dataimsize  Retrieve image size of 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%image       Display images stored in dataset%show        Display of dataset images and mapping eigen-images%spatm       Augment image dataset with spatial label information%%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%%Plotting%--------%gridsize    Set gridsize of scatterd, plotd and plotm plots%plotd       Plot discriminant function for two features%plot2       Plot 2d function%plotf       Plot feature distribution%plotm       Plot mapping%plotdg      Plot dendrgram (see hclust)%scatterd    Scatterplot%%Examples%--------%prex1       Classifiers and scatter plot%prex2       Plot learning curves of classifiers%prex3       Multi-class classifier plot%prex4       Classifier combining%prex5       Use of images and eigenfaces%prex6       Multi-band image segmentation%prex7       Independent Component Analysis of multi-band image%% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands