PRTools website |

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 |

datafile, | Define datafile from set of files in directory |

createdatafile, | Save datafile, store intermediate result as raw datafile |

savedatafile, | Save datafile, store intermediate result as mature datafile |

filtm, | Mapping for arbitrary processing of a datafile |

prdatafiles, | Overview and download of standard 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 |

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 |

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 |

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 |

linearr, | Linear regression |

ridger, | Ridge regression |

lassor, | LASSO |

svmr, | Support vector regression |

ksmoothr, | Kernel smoother |

knnr, | k-nearest neighbor regression |

pinvr, | Pseudo-inverse regression |

plsr, | Partial least squares regression |

plsm, | Partial least squares mapping |

gpr, | Gaussian Process regression |

testr, | Mean squared regression error |

rsquared, | R^2-statistic |

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 |

classim, | Classify image using a given classifier |

doublem, | Convert datafile images into double |

filtim, | Image operation on objects in datafiles/datasets |

spatm, | Augment image dataset with spatial label information |

im_box, | Bounding box |

im_center, | Center image |

im_fft, | FFT transform (and more) |

im_gauss, | Gaussian filtering by Matlab |

im_gray, | Multi-band to gray-value conversion |

im_hist_equalize, | Histogram equalization |

im_invert, | Invert image |

im_label, | Labeling binary images |

im_norm, | Normalize images w.r.t. mean and variance |

im_resize, | Resize images |

im_rotate, | Rotate images |

im_scale, | Scale images |

im_select_blob, | Select largest blob |

im_stretch, | Contrast stretching of images |

im_threshold, | Threshold images |

im_unif, | Uniform filtering |

histm, | Convert images to histograms |

im_harris, | Find Harris points in images |

im_moments, | Computes moments as features from object images |

im_mean, | Computes center of gravity |

im_measure, | Computes some measurements |

im_profile, | Computes image profiles |

im_skel_meas, | Skeleton measurements |

im_stat, | Compute some simple statistics |

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 |

gridsize, | Set gridsize used in the PRTools plot commands |

plotc, | Plot discriminant function for two features |

plote, | Plot error curves |

plotf, | Plot feature distribution |

plotm, | Plot mapping |

ploto, | Plot object functions |

plotr, | Plot regression functions |

plotdg, | Plot dendrgram (see hclust) |

scatterd, | Scatterplot |

scatterdui, | Scatterplot scatterplot with feature selection |

scatterr, | Scatter regression dataset |

cdats, | Support routine for checking datasets |

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 |

issequential, | Test on sequential mapping |

isstacked, | Test on stacked mapping |

isparallel, | Test on parallel mapping |

issym, | Test on symmetric matrix |

isvaldset, | Test on valid dataset |

isvaldfile, | Test on valid datafile |

matchlablist, | Match entries of label lists |

labcmp, | Compare two label lists and find the differences |

nlabcmp, | Compare two label lists and count the differences |

testdatasize, | Check datasize and convert datafile to dataset |

define_mapping, | Define empty mapping |

mapping_task, | Check mapping task |

trained_mapping, | Defined trained mapping |

trained_classifier, | Define trained classifier |

setdefaults, | Substitute defaults |

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 |