ECG-Kit 1.0
(20,086 bytes)
% Pattern Recognition Tools (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% Version 5.1.1 14-May-2014
%
%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)
%cat2data - Create categorical dataset
%classnames - Retrieve names of classes
%classsizes - Retrieve sizes of classes
%feat2lab - Label dataset by one of its features and remove this feature
%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
%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
%gendatv - Generation of a very large dataset
%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
%
%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 (*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
%statslinc - Linear classifier from the Stats toolbox
%
%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)
%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
%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
%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
%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 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
%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 normalization map
%parzenm - Parzen density estimation
%parzenml - Optimization 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
%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 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. 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 - maximization 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
%
%Various tests and support routines
%----------------------------------
%cdats - Support routine for checking datasets
%concatm - Concatenate cell array of mappings or 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
%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 - controol PRTools large dataset handling
%prtver - prtools version back end
%typp - list prtools routine nicely
%
%--- <a href="http://37steps.com/prtools">PRTools Guide</a> ---
% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
% Faculty EWI, Delft University of Technology
% P.O. Box 5031, 2600 GA Delft, The Netherlands