PRTools

PRTOOLS

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

Datafiles

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

Mappings

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

Regression

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

Plotting

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

Examples

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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