| 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) |
| classnames, | Retrieve names of classes |
| 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 |
| setfeatlab, | Set feature labels in dataset |
| getfeatlab, | Get feature labels in dataset |
| getfeat, | Retrieve feature labels from datasets and mappings |
| setdat, | Change data in dataset for classifier output |
| setdata, | Change data in dataset or mapping |
| getdata, | Retrieve data from dataset or mapping |
| setlabels, | Change labels of dataset or mapping |
| getlables, | Retrieve labels from a dataset |
| setprior, | Reset class prior probabilities of dataset |
| getprior, | Retrieve class prior probabilities from dataset |
| addlabels, | Add additional labelling |
| changelablist, | Change current active labeling |
| misval, | Fix missing values in a dataset |
| 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 |
| prarff, | Convert ARFF file (WEKA) to PRTools dataset |
| remclass, | Remove a class from a dataset |
| seldat, | Retrieve a part of a dataset |
| selclass, | Retrieve a class from a dataset |
| circles3d, | Create a dataset containing 2 circles in 3 dimensions | | lines5d, | Create a dataset containing 3 lines in 5 dimensions |
| gendat, | Random sampling of datasets for training and testing |
| gensubsets, | Generation of a consistent series of subsets of a dataset |
| gendatgauss, | Generation of multivariate Gaussian distributed data |
| 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 |
| spirals, | Generation of a two-class spiral dataset |
| getwindows, | Get pixel feature vectors around given pixels in image dataset |
| prdataset, | Read existing dataset from file |
| prdatasets, | Overview and download of standard datasets |
| 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 |
| 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 |
| dtc, | Decision tree classifier, rewritten, also for nominal features |
| randomforestc, | Breiman's random forest 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 |
| bagc, | Feature set classifier, e.g. for multiple-instance learning |
| fdsc, | Feature based dissimilarity space classifier |
| mdsc, | Manhatten distance feature based dissimilarity space classifier |
| vpc, | Voted perceptron classifier |
| drbmc, | Discriminative restricted Boltzmann machine classifier |
| libsvc, | Support vector classifier by LIBSVM |
| nulibsvc, | Support vector classifier by LIBSVM |
| 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 |
| 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 |
| featselv, | Selection of varying features |
| 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 |
| loso, | Leave_one_set_out crossvalidation |
| 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) |
| shiftop, | Shift operating point of classifier |
| testc, | General error estimation routine for trained classifiers |
| testd, | Error of dataset applied to given classifier |
| testauc, | Estimate error as area under the ROC |
| affine, | Construct affine (linear) mapping from parameters | | bhatm, | Two-class Bhattacharryya mapping |
| cmapm, | Compute some special maps |
| datasetm, | Mapping conversion dataset |
| disnorm, | Normalization of a dissimilarity matrix |
| featselm, | Feature selection map, general routine for feature selection |
| fisherm, | Fisher mapping |
| chernoffm, | Chernoff 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 |
| parzenml, | Optimization of smoothing parameter in Parzen density estimation. |
| pca, | Principal Component Analysis |
| pcaklm, | Backend routine for PC and KL mappings |
| proxm, | Proximity mapping and kernel construction |
| reducm, | Reduce to minimal space mapping |
| remoutl, | Remove outliers |
| rejectm, | Creates rejecting mapping |
| scalem, | Compute scaling data |
| sigm, | Simoid mapping |
| spatm, | Augment image dataset with spatial label information |
| userkernel, | User supplied kernel definition |
| 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 |
| averagec, | Combining linear classifiers by averaging coefficients | | baggingc, | Bootstrapping and aggregation of classifiers |
| dcsc, | Dynamic Classifier Selecting Combiner |
| modselc, | Model Selection Combiner (Static selection) |
| rsscc, | Random subspace combining classifier |
| votec, | Voting classifier combiner |
| wvotec, | Weighted voting classifier combiner |
| maxc, | Maximum classifier combiner |
| minc, | Minimum classifier combiner |
| meanc, | Mean classifier combiner |
| medianc, | Median classifier combiner |
| mlrc, | Muli-response linear regression combiner |
| naivebcc, | Naive Bayes classifier combiner |
| perc, | Percentile combiner |
| prodc, | Product classifier combiner |
| traincc, | Train combining classifier |
| fixedcc, | Fixed combiner construction, back end |
| parsc, | Parse classifier or map |
| rejectc, | Creates reject version of exisiting classifier |
| parallel, | Parallel combining of classifiers |
| bagcc, | Feature set combining classifier |
| stacked, | Stacked combining of classifiers |
| sequential, | Sequential combining of classifiers |
| data2im, | Convert dataset to image | | getobjsize, | Retrieve image size of feature images in datasets |
| getfeatsize, | Retrieve image size of object images in datasets |
| obj2feat, | Transform object images to feature images in dataset |
| feat2obj, | Transform feature images to object images in dataset |
| im2feat, | Convert image to feature in dataset |
| im2obj, | Convert image to object in dataset |
| imsize, | Retrieve size of specific image in datafile |
| im_patch, | Find / generate patches in object images |
| band2obj, | Convert image bands to objects in dataset |
| bandsel, | Select image bands in dataset or datafile |
| selectim, | Select image in multi-band object image dataset/datafile |
| show, | Display objects in datasets, datafiles and mappings |
| im_dbr, | Image Database Retrieval GUI |
| prex_cleval, | learning curves | | prex_combining, | classifier combining |
| prex_confmat, | confusion matrix, scatterplot and gridsize |
| prex_datafile, | datafile usage |
| 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 |
| prex_soft, | Soft label example |
| prex_regr, | Regression example |
| prdownload, | low level routine for retrieving missing data |
| prglobal, | set / list all globals and settings |
| prversion, | returns version information on PRTools |
| prwaitbar, | report PRTools progress by single waitbar |
| prwarning, | control PRTools warning level |
| prmemory, | controol PRTools large dataset handling |
| prtver, | prtools version back end |
| typp, | list prtools routine nicely |