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PRTools manual



Pattern Recognition Tools , Version 4.2.3 27-Nov-2012

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)
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

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
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

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
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

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
featselv, Selection of varying features

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
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

Classifier combiners

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

Handling images in datasets and datafiles

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

Operations on images in datasets and datafiles

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

Feature extraction from images in datasets and datafiles

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

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


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

Various tests and support routines

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