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PRTools Guide



Pattern Recognition Tools (PRTools Guide)
Version 5.4.1 18-Mar-2018

Datasets and Mappings (just most important routines)

prdataset Define dataset from datamatrix and labels
datasets List information on datasets (just help, no command)
prdatafile Define dataset from directory of object files
datafiles List information on datafiles (just help, no command)
cat2dset Create categorical dataset
cat2feat Conversion of categarical data to features
cat2real Conversion categorical features to real by one-hot encoding
classnames Retrieve names of classes
classsizes Retrieve sizes of classes
cell2dset Create dataset from cell array
dset2cell Convert dataset to cell array
feat2lab Label dataset by one of its features and remove this feature
feattypes Determine feature types in dataset
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
getlabels 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)
prmapping 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
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
gendatg Generation of Gaussian circle and blob
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
gendatmm Generation of 4 multi-modal 2d classes
gendatp Parzen density data generation
gendatr Generate regression dataset from data and target values
gendats Generation of two Gaussian distributed classes
gendatu Generation of uniform circle and blob
gendatv Generation of a very large dataset
gendatw Sample dataset by given weigths
gentrunk Generation of Trunk's example
genmdat Generation of a multi-dimensional dataset
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


prdatafile 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

fisherc Minimum least square linear classifier
ldc Normal densities based linear (muli-class) classifier
loglc Logistic linear classifier
logmlc Logistic Multi-Class 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
statslinc*Linear classifier from the Stats toolbox
classc Converts a mapping into a classifier
labeld Find labels of objects by classification
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)
fnnc fast nearest neighbor classifier
testk Error estimation for k-nearest neighbour rule
edicon Edit and condense training sets
statsknnc*k-nearest neighbour classifier from the Stats toolbox

weakc Weak classifier
stumpc Decision stump classifier
adaboostc ADABoost classifier

parzenc Parzen classifier
parzendc Parzen density based classifier
testp Error estimation for Parzen classifier

treec Construct binary decision tree classifier
dtc Decision tree classifier, rewritten, also for nominal features
statsdtc*Decision tree classifier from the Stats toolbox
randomforestc Breiman's random forest classifier
naivebc Naive Bayes classifier
statsnbc*Naive Bayes classifier from the Stats toolbox
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
pklibsvc*Radial basis LIBSVM using the Parzen kernel
rblibsvc*Radial basis LIBSVM with optimised kernel
nulibsvc*Support vector classifier by LIBSVM
svc Support vector classifier
nusvc Support vector classifier
pksvc Radial basis SV classifier using the Parzen kernel
rbsvc Radial basis SV classifier
statssvc*Support vector classifier (Stats toolbox)
pkstatssvc*Radial basis Parzen kernel SV classifier (Stats toolbox)
rbstatssvc*Radial basis optimised kernel SV classifier (Stats toolbox)
kernelc General kernel/dissimilarity based classification
onec fallback routine for degenerated training sets

Normal Density Based Classification

distmaha Mahalanobis distance
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
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
prcrossval 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
prroc 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
copulam Compute copula mapping
datasetm Mapping conversion dataset
disnorm Normalisation 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 datafiles/datasets, object by object
mapm Arbitrary mapping operation on doubles and 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
mapsd Train mapping between two representations
mclassm Computation of mapping from multi-class dataset
prmap General routine for computing and executing mappings
mappingtools Macro defining some mappings
nlfisherm Nonlinear Fisher mapping
normm Object normalisation map
parzenm Parzen density estimation
parzenml Optimisation of smoothing parameter in Parzen density estimation.
pcam 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
tsnem tSNE mapping
sammonm Multi-dimensional scaling by Sammon mapping
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)
prplotsom 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
rfishercc Fisher combining of randomly generated classifiers
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 equalisation
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. Trains the bin positions
im_hist Convert images to histograms for fixed bin positions
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 - maximisation clustering
proxm Proximity mapping and kernel construction
hclust Hierarchical clustering
kcentres k-centres clustering
prkmeans 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
scattern Simple, unannotated scatterplot, no axes.
scatterr Scatter regression dataset
inpoly Select objects in a scatterplot by drawing a polygon

Various tests and support routines (many low-level routines not listed)

cdats Support routine for checking datasets
concatm Concatenate cell array of mappings or datasets ({} --> [])
cleandset Clean dataset for degenerate training sets
iscomdset Test on compatible datasets
isdataim Test on image dataset
isdataset Test on dataset
isfeatim Test on feature image dataset
ismapping Test on mapping
ismisval Test dataset on missing values
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
mapex Train and execute mapping on the same dataset
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
shiftargin Conditional shift of input arguments
prload Load prtools4 mat-files and convert to prtools5
prtools4to5 Convert prtools4 directory to prtools5


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_mds Multi-dimensional scaling and visualisation
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 datasets
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 control PRTools large dataset handling
prtime control maximum run time of some optimisations
prtver prtools version back end
typp list prtools routine nicely

* classifiers that are not availble under Octave

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