plateform stringclasses 1
value | repo_name stringlengths 13 113 | name stringlengths 3 74 | ext stringclasses 1
value | path stringlengths 12 229 | size int64 23 843k | source_encoding stringclasses 9
values | md5 stringlengths 32 32 | text stringlengths 23 843k |
|---|---|---|---|---|---|---|---|---|
github | SASVDDwt/sa_svdd-master | compute_kernel.m | .m | sa_svdd-master/matlab/prtools/compute_kernel.m | 482 | utf_8 | f9bf0b63db5b6cd18107d12acf6d5881 |
function K = compute_kernel(a,s,kernel)
% compute a kernel matrix for the objects a w.r.t. the support objects s
% given a kernel description
if isstr(kernel) % routine supplied to compute kernel
K = feval(kernel,a,s);
elseif iscell(kernel)
K = feval(kernel{1},a,s,kernel{2:end});
elseif ismapping(kernel)
... |
github | SASVDDwt/sa_svdd-master | im_moments.m | .m | sa_svdd-master/matlab/prtools/im_moments.m | 10,359 | utf_8 | 77d5855d9bbefee75cf21b68423dbbe2 | %IM_MOMENTS PRTools routine for computing central moments of object images
%
% M = IM_MOMENTS(A,TYPE,MOMENTS)
% M = A*IM_MOMENTS([],TYPE,MOMENTS)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% TYPE Desired type of moments
% MOMENTS Desired moments
%
% OUTPUT
% M ... |
github | SASVDDwt/sa_svdd-master | svc.m | .m | sa_svdd-master/matlab/prtools/svc.m | 5,366 | utf_8 | 49f50a22b24bcddcbf2a5d5cd22c886b | %SVC Support Vector Classifier
%
% [W,J] = SVC(A,KERNEL,C)
% [W,J] = SVC(A,TYPE,PAR,C)
% W = A*SVC([],KERNEL,C)
% W = A*SVC([],TYPE,PAR,C)
%
% INPUT
% A Dataset
% KERNEL - Untrained mapping to compute kernel by A*(A*KERNEL) during
% training, or B*(A*KERNEL) during testing with dat... |
github | SASVDDwt/sa_svdd-master | im_bpropagation.m | .m | sa_svdd-master/matlab/prtools/im_bpropagation.m | 2,387 | utf_8 | c75c17355488eceb9ea5baddea69c6ff | %IM_BPROPAGATION Binary propagation of images stored in a dataset (DIP_Image)
%
% B = IM_BPROPAGATION(A1,A2,N,CONNECTIVITY,EDGE_CONDITION)
%
% INPUT
% A1 Dataset with binary object images dataset (possibly multi-band)
% to be treated as seed for the propagation
% A2 Dataset with binary obje... |
github | SASVDDwt/sa_svdd-master | im_unif.m | .m | sa_svdd-master/matlab/prtools/im_unif.m | 1,204 | utf_8 | 7f05e468c2eabcdf937dabf7598b1738 | %IM_UNIF Uniform filter of images stored in a dataset/datafile
%
% B = IM_UNIF(A,SX,SY)
% B = A*IM_UNIF([],SX,SY)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% SX Desired horizontal width for filter, default SX = 3
% SY Desired vertical width for filter, default SY = SX
%
% OU... |
github | SASVDDwt/sa_svdd-master | newfig.m | .m | sa_svdd-master/matlab/prtools/newfig.m | 942 | utf_8 | 88e9e3d9057179f2294f97a98ab73c79 | %NEWFIG Create new figure on given position
%
% NEWFIG(FIGURE_NUMBER,FIGURE_PER_ROW)
%
% INPUT
% FIGURE_NUMBER Number of the figure
% FIGURE_PER_ROW Figures per row (default: 4)
%
% OUTPUT
%
% DESCRIPTION
% Creates figure number FIGURE_NUMBER and places it on the screen,
% such that (when sufficient figures are ... |
github | SASVDDwt/sa_svdd-master | scalem.m | .m | sa_svdd-master/matlab/prtools/scalem.m | 3,466 | utf_8 | a9719e0efaeece0544951f60275173d2 | %SCALEM Compute scaling map
%
% W = SCALEM(A,T)
%
% INPUT
% A Dataset
% T Type of s caling (optional; default: the class priors weighted mean of A is shifted to the origin)
%
% OUTPUT
% W Scaling mapping
%
% DESCRIPTION
% Computes a scaling map W, whose type depends on the parameter T:
%
% [], 'c-mean' - T... |
github | SASVDDwt/sa_svdd-master | mclassm.m | .m | sa_svdd-master/matlab/prtools/mclassm.m | 2,458 | utf_8 | dfa95c18e9a18bd2606c311f0d9acf3e | %MCLASSM Computation of a combined, multi-class based mapping
%
% W = MCLASSM(A,MAPPING,MODE,PAR)
%
% INPUT
% A Dataset
% MAPPING Untrained mapping
% MODE Combining mode (optional; default: 'weight')
% PAR Parameter needed for the combining
%
% OUTPUT
% W Combined mapping
%
% DESCRIPTION
... |
github | SASVDDwt/sa_svdd-master | cdats.m | .m | sa_svdd-master/matlab/prtools/cdats.m | 1,889 | utf_8 | 1ca0168c3ca6ef553647ba3c33752ba6 | %CDATS Support routine for checking datasets
%
% [B,C,LABLIST,P] = CDATS(A,REDUCE)
%
% INPUT
% A Dataset or double
% REDUCE 0/1, Reduce A to labeled samples (1) or not (0, default), optional.
%
% OUTPUT
% B Dataset
% C Number of classes
% LABLIST Label list of A
% P Priors
%
%... |
github | SASVDDwt/sa_svdd-master | proxm.m | .m | sa_svdd-master/matlab/prtools/proxm.m | 5,494 | utf_8 | 7af1d72202749867bdcd74f23e02fec0 | %PROXM Proximity mapping
%
% W = PROXM(A,TYPE,P,WEIGHTS)
% W = A*PROXM([],TYPE,P,WEIGHTS)
%
% INPUT
% A Dataset
% TYPE Type of the proximity (optional; default: 'distance')
% P Parameter of the proximity (optional; default: 1)
% WEIGHTS Weights (optional; default: all 1)
%
% OUTPUT
% W Pro... |
github | SASVDDwt/sa_svdd-master | iscolumn.m | .m | sa_svdd-master/matlab/prtools/iscolumn.m | 844 | utf_8 | 9151df7ffa8e00b864db611c1f8ed057 | %ISCOLUMN Checks whether the argument is a column array
%
% [OK,Y] = ISCOLUMN(X)
%
% INPUT
% X Array: an array of entities such as numbers, strings or cells
%
% OUTPUT
% OK 1 if X is a column array and 0, otherwise
% Y X or X' to ensure that Y is a column array
%
% DESCRIPTION
% Returns 1 if X is a column a... |
github | SASVDDwt/sa_svdd-master | createdatafile.m | .m | sa_svdd-master/matlab/prtools/createdatafile.m | 8,214 | utf_8 | 8986b9e332522ad4a5b2849faa07ac83 | %CREATEDATAFILE Create datafile on disk
%
% B = CREATEDATAFILE(A,DIR,ROOT,TYPE,CMD,FMT)
%
% INPUT
% A Datafile
% DIR Name of datafile, default: name of A
% ROOT Root directory in which datafile should be created
% default: present directory
% TYPE Datafile type ('raw' o... |
github | SASVDDwt/sa_svdd-master | chernoffm.m | .m | sa_svdd-master/matlab/prtools/chernoffm.m | 2,904 | utf_8 | caa081625bd5017362d1495949d6425c | %CHERNOFFM Suboptimal discrimination linear mapping (Chernoff mapping)
%
% W = CHERNOFFM(A,N,R)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
% (default: min(C,K)-1, where K is the number of features in A)
% R Regularization variable, 0 <= r <= 1, default i... |
github | SASVDDwt/sa_svdd-master | band2obj.m | .m | sa_svdd-master/matlab/prtools/band2obj.m | 1,323 | utf_8 | ed1e3b216d5c017457e633cade393af9 | %BAND2OBJ Mapping image bands to objects
%
% B = BAND2OBJ(A,N)
% W = BAND2OBJ([],N)
% B = A*W
%
% INPUT
% A Dataset or datafile with multiband image objects.
% N Number of successive bands to be combined in an object.
% The number of image bands in A should be multiple of N.
% Default N = 1.
%
... |
github | SASVDDwt/sa_svdd-master | labcmp.m | .m | sa_svdd-master/matlab/prtools/labcmp.m | 1,131 | utf_8 | d87dd411597e3fa02281d08bc37d9a2b | %LABCMP Compare label sets
%
% [JNE,JEQ] = LABCMP(LABELS1,LABELS2)
%
% INPUT
% LABELS1 - list of labels (strings or numeric)
% LABELS2 - list of labels (strings or numeric)
%
% OUTPUT
% JNE - Indices of non-matching labels
% JEQ - indices of matching labels
%
% DESCRIPTION
% The comparison of two label ... |
github | SASVDDwt/sa_svdd-master | testd.m | .m | sa_svdd-master/matlab/prtools/testd.m | 373 | utf_8 | fdb624852133ec80a72cc42eb94d00a4 | %TESTD Replaced by TESTC
function [errors,class_errors] = testd(a,w)
global TESTD_REPLACED_BY_TESTC
if isempty(TESTD_REPLACED_BY_TESTC)
disp([newline 'TESTD has been replaced by TESTC, please use it'])
TESTD_REPLACED_BY_TESTC = 1;
end
if nargin == 0
errors = testc;
elseif nargin == 1
[errors,class_errors] = tes... |
github | SASVDDwt/sa_svdd-master | im_stat.m | .m | sa_svdd-master/matlab/prtools/im_stat.m | 2,355 | utf_8 | c9d64e86fc06111f67c8b0dae44e983c | %IM_STAT Computation of some image statistics
%
% B = IM_STAT(A,STAT)
% B = A*IM_STAT([],STAT)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% STAT String cell array or series of statistics
%
% OUTPUT
% B Dataset with statistics replacing images (possibly multi-band)
%
%... |
github | SASVDDwt/sa_svdd-master | cnormc.m | .m | sa_svdd-master/matlab/prtools/cnormc.m | 2,908 | utf_8 | 7ee0f99114bc42ee3c3c0daa4d88d4d2 | %CNORMC Classifier normalisation for ML posterior probabilities
%
% W = CNORMC(W,A)
%
% INPUT
% W Classifier mapping
% A Labeled dataset
%
% OUTPUT
% W Scaled classifier mapping
%
% DESCRIPTION
% The mapping W is scaled such that the likelihood of the posterior
% probabilities of the samples in A, estimated b... |
github | SASVDDwt/sa_svdd-master | immoments.m | .m | sa_svdd-master/matlab/prtools/immoments.m | 10,023 | utf_8 | fc13c8334c4890f218ea857fa073eb18 | %IMMOMENTS PRTools routine for computing central moments of object images
%
% M = IMMOMENTS(A,TYPE,MOMENTS)
%
% INPUT
% A Dataset with object images dataset
% TYPE Desired type of moments
% MOMENTS Desired moments
%
% OUTPUT
% M Dataset with moments replacing images
%
% DESCRIPTION
% Comput... |
github | SASVDDwt/sa_svdd-master | treec.m | .m | sa_svdd-master/matlab/prtools/treec.m | 16,965 | utf_8 | c73b63bacd9644aaa83f58e1a11c41ad | %TREEC Build a decision tree classifier
%
% W = TREEC(A,CRIT,PRUNE,T)
%
% Computation of a decision tree classifier out of a dataset A using
% a binary splitting criterion CRIT:
% INFCRIT - information gain
% MAXCRIT - purity (default)
% FISHCRIT - Fisher criterion
%
% Pruning is defined by prune:
% ... |
github | SASVDDwt/sa_svdd-master | getfeat.m | .m | sa_svdd-master/matlab/prtools/getfeat.m | 855 | utf_8 | d0e5811fcde6732e0e08c00c43e50989 | %GETFEAT Get feature labels of a dataset or a mapping
%
% LABELS = GETFEAT(A)
% LABELS = GETFEAT(W)
%
% INPUT
% A,W Dataset or mapping
%
% OUTPUT
% LABELS Label vector with feature labels
%
% DESCRIPTION
% Returns the labels of the features in the dataset A or the labels
% assigned by the mapping W.
%
% Note... |
github | SASVDDwt/sa_svdd-master | im_hist_equalize.m | .m | sa_svdd-master/matlab/prtools/im_hist_equalize.m | 888 | utf_8 | f3f11d9287d170ff8356974bbc4a06aa | %IM_HIST_EQUALIZE Histogram equalization of images stored in a dataset
% (DIP_Image)
%
% B = IM_HIST_EQUALIZE(A)
% B = A*IM_HIST_EQUALIZE
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
%
% OUTPUT
% B Dataset with filtered images
%
% SEE ALSO
% DATASETS, DATAFIL... |
github | SASVDDwt/sa_svdd-master | isvaldset.m | .m | sa_svdd-master/matlab/prtools/isvaldset.m | 1,457 | utf_8 | 33fb0411958c9895b7c1e2abc2645c1a | %ISVALDSET Test whether the argument is a valid dataset
%
% N = ISVALDSET(A);
% N = ISVALDSET(A,M);
% N = ISVALDSET(A,M,C);
%
% INPUT
% A Input argument, to be tested on dataset
% M Minimum number of objects per class in A
% C Minimum number of classes in A
%
% OUTPUT
% N 1/0 if A is / isn't a v... |
github | SASVDDwt/sa_svdd-master | im_label.m | .m | sa_svdd-master/matlab/prtools/im_label.m | 1,355 | utf_8 | 7dbcb468c8d8516bcb86e204f0003626 | %IM_LABEL Labeling of binary images stored in a dataset (DIP_Image)
%
% B = IM_LABEL(A,CONNECTIVITY,MIN_SIZE,MAX_SIZE)
% B = A*IM_LABEL([],CONNECTIVITY,MIN_SIZE,MAX_SIZE)
%
% INPUT
% A Dataset with binary object images dataset (possibly multi-band)
% N Number of iterations (default 1)
% CONNECTIVITY... |
github | SASVDDwt/sa_svdd-master | parzenmls.m | .m | sa_svdd-master/matlab/prtools/parzenmls.m | 3,182 | utf_8 | dfedca9ffa4ae3de38b0349c7a7b8a90 | %PARZENML Optimum smoothing parameter in Parzen density estimation.
% Soft label version
%
% H = PARZENML(A,FID)
%
% INPUT
% A input dataset
% FID File ID to write progress to (default [], see PRPROGRESS)
%
% OUTPUT
% H scalar smoothing parameter
%
% DESCRIPTION
% Maximum likelihood estimation ... |
github | SASVDDwt/sa_svdd-master | im_gaussf.m | .m | sa_svdd-master/matlab/prtools/im_gaussf.m | 1,311 | utf_8 | 4cc0d1ee779b18cb90b369721be95fa6 | %IM_GAUSSF Gaussian filter of images stored in a dataset (DIPImage)
%
% B = IM_GAUSSF(A,S)
% B = A*IM_GAUSSF([],S)
%
% INPUT
% A Dataset with object images dataset (possibly multi-band)
% S Desired standard deviation for filter, default S = 1
%
% OUTPUT
% B Dataset with Gaussian filtered imag... |
github | SASVDDwt/sa_svdd-master | featself.m | .m | sa_svdd-master/matlab/prtools/featself.m | 2,272 | utf_8 | 9ae66ecdd6ab7bfbe60e1d20c6df8412 | %FEATSELF Forward feature selection for classification
%
% [W,R] = FEATSELF(A,CRIT,K,T,FID)
% [W,R] = FEATSELF(A,CRIT,K,N,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', i.e. the 1-Nearest Neighbor error)
% K Number of features to select (def... |
github | SASVDDwt/sa_svdd-master | featseli.m | .m | sa_svdd-master/matlab/prtools/featseli.m | 2,709 | utf_8 | 4b51b093c93693618924936b36758367 | %FEATSELI Individual feature selection for classification
%
% [W,R] = FEATSELI(A,CRIT,K,T)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', i.e. the 1-Nearest Neighbor error)
% K Number of features to select (default: sort all features)
% T Tu... |
github | SASVDDwt/sa_svdd-master | typp.m | .m | sa_svdd-master/matlab/prtools/typp.m | 1,043 | utf_8 | 8d294b9b1229f66b0f727b0811f2242d | %TYPP list M-File of PRTools
% TYPE foo.bar lists the ascii file called 'foo.bar'.
%
% TYPE foo lists the ascii file called 'foo.m'.
%
% If files called foo and foo.m both exist, then
% TYPE foo lists the file 'foo', and
% TYPE foo.m list the file 'foo.m'.
%
% TYPE FILENAME lists the contents... |
github | SASVDDwt/sa_svdd-master | meanc.m | .m | sa_svdd-master/matlab/prtools/meanc.m | 1,668 | utf_8 | 3314b58c9af19d2ddf34a40652decacc | %MEANC Mean combining classifier
%
% W = MEANC(V)
% W = V*MEANC
%
% INPUT
% V Set of classifiers (optional)
%
% OUTPUT
% W Mean combiner
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes and W is the mean combiner: it selects the class with the mean of
% the ... |
github | SASVDDwt/sa_svdd-master | adaboostc.m | .m | sa_svdd-master/matlab/prtools/adaboostc.m | 3,741 | utf_8 | 0d07407d9961f9b1e330489dcc94c8bc | %ADABOOSTC
%
% [W,V,ALF] = ADABOOSTC(A,CLASSF,N,RULE,VERBOSE);
%
% INPUT
% A Dataset
% CLASSF Untrained weak classifier
% N Number of classifiers to be trained
% RULE Combining rule (default: weighted voting)
% VERBOSE Suppress progress report if 0 (default 1)
%
% OUTPUT
% W Combined ... |
github | SASVDDwt/sa_svdd-master | cmapm.m | .m | sa_svdd-master/matlab/prtools/cmapm.m | 5,587 | utf_8 | fdab88072a7718ae4aba1acccde27dfb | %CMAPM Compute some special maps
%
% INPUT
% Various
%
% OUTPUT
% W Mapping
%
% DESCRIPTION
% CMAPM computes some special data-independent maps for scaling, selecting or
% rotating K-dimensional feature spaces.
%
% W = CMAPM(K,N) Selects the features listed in the vector N
% W = CMAPM(K,P) ... |
github | SASVDDwt/sa_svdd-master | knnm.m | .m | sa_svdd-master/matlab/prtools/knnm.m | 2,385 | utf_8 | 3c12f6c27efbcf938a922e17d38d0fc7 | %KNNM K-Nearest Neighbour based density estimate
%
% W = KNNM(A,KNN)
%
% D = B*W
%
% INPUT
% A Dataset
% KNN Number of nearest neighbours
%
% OUTPUT
% W Density estimate
%
% DESCRIPTION
% A density estimator is constructed based on the k-Nearest Neighbour rule
% using the labeled objects in A. A... |
github | SASVDDwt/sa_svdd-master | polyc.m | .m | sa_svdd-master/matlab/prtools/polyc.m | 2,421 | utf_8 | 044939bf2b4794fc73056fb724c2ac1f | %POLYC Polynomial Classification
%
% W = polyc(A,CLASSF,N,S)
%
% INPUT
% A Dataset
% CLASSF Untrained classifier (optional; default: FISHERC)
% N Degree of polynomial (optional; default: 1)
% S 1/0, 1 indicates that 2nd order combination terms should be used
% (optional; default... |
github | SASVDDwt/sa_svdd-master | confmat.m | .m | sa_svdd-master/matlab/prtools/confmat.m | 6,584 | utf_8 | 26acd26b29d4f6a1fe1593ce69cde93b | %CONFMAT Construct confusion matrix
%
% [C,NE,LABLIST] = CONFMAT(LAB1,LAB2,METHOD,FID)
%
% INPUT
% LAB1 Set of labels
% LAB2 Set of labels
% METHOD 'count' (default) to count number of co-occurences in
% LAB1 and LAB2, 'disagreement' to count relative
% non-co-occurrence... |
github | SASVDDwt/sa_svdd-master | reorderclasses.m | .m | sa_svdd-master/matlab/prtools/reorderclasses.m | 3,660 | utf_8 | 0f7203be264e07c19eb91ce888d8c812 | %REORDERCLASSES Reorder the lablist
%
% X = REORDERCLASSES(X,LABLIST)
% X = REORDERCLASSES(X,I)
%
% INPUT
% X (labeled) dataset
% LABLIST correctly ordered lablist
% I permutation vector
%
% OUTPUT
% X dataset with reordered classes
%
% DESCRIPTION
% Change the order of... |
github | SASVDDwt/sa_svdd-master | bayesc.m | .m | sa_svdd-master/matlab/prtools/bayesc.m | 2,610 | utf_8 | a2b6e4daf57226d20987035d9c4ae2f7 | %BAYESC Bayes classifier
%
% W = BAYESC(WA,WB, ... ,P,LABLIST)
%
% INPUT
% WA, WB, ... Trained mappings for supplying class density estimates
% P Vector with class prior probabilities
% Default: equal priors
% LABLIST List of class names (labels)
%
% OUTPUT
% W Bayes clas... |
github | SASVDDwt/sa_svdd-master | traincc.m | .m | sa_svdd-master/matlab/prtools/traincc.m | 1,509 | utf_8 | 759b566e65fe22063d9831d7231bdf02 | %TRAINCC Train combining classifier if needed
%
% W = TRAINCC(A,W,CCLASSF)
%
% INPUT
% A Training dataset
% W A set of classifiers to be combined
% CCLASSF Combining classifier
%
% OUTPUT
% B Combined classifier mapping
%
% DESCRIPTION
% The combining classifier CCLASSF is trained ... |
github | SASVDDwt/sa_svdd-master | plotr.m | .m | sa_svdd-master/matlab/prtools/plotr.m | 1,058 | utf_8 | e922a00d48d26055bff39c4243bfad90 | %PLOTR Plot regression
%
% PLOTR(W)
% PLOTR(W,CLR)
%
% Plot the regression function W, optionally using plot string CLR.
% This plot string can be anything that is defined in plot.m.
% For the best results (concerning the definition of the axis for
% instance) it is wise to first scatter the regression data using... |
github | SASVDDwt/sa_svdd-master | matchcost.m | .m | sa_svdd-master/matlab/prtools/matchcost.m | 1,233 | utf_8 | 7f0b9c4e4003b50b936ad26092ce9e5c | % Matchcost
% Copyright: D.M.J. Tax, duin@ph.tn.tudelft.nl
% Faculty of Applied Sciences, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
% $Id: matchcost.m,v 1.2 2006/03/08 22:06:58 duin Exp $
function [cost,lablist] = matchcost(orglablist,cost,lablist)
prtrace(mfilename,2);
k ... |
github | SASVDDwt/sa_svdd-master | featsel.m | .m | sa_svdd-master/matlab/prtools/featsel.m | 1,464 | utf_8 | a8864e8c57717ff7703f063fdc3f81cf | %FEATSEL Selection of known features
%
% W = FEATSEL(K,J)
%
% INPUT
% K Input dimensionality
% J Index vector of features to be selected
%
% OUTPUT
% W Mapping performing the feature selection
%
% DESCRIPTION
% This is a simple support routine that writes feature selection
% in terms of a mapping. If A... |
github | SASVDDwt/sa_svdd-master | gaussm.m | .m | sa_svdd-master/matlab/prtools/gaussm.m | 2,709 | utf_8 | 5a026f02800a67f37a2c04d957f43487 | %GAUSSM Mixture of Gaussians density estimate
%
% W = GAUSSM(A,K,R,S,M)
% W = A*GAUSSM([],K,R,S,M);
%
% INPUT
% A Dataset
% K Number of Gaussians to use (default: 1)
% R,S,M Regularization parameters, 0 <= R,S <= 1, see QDC
%
% OUTPUT
% W Mixture of Gaussians density estimate
%
% DESCRIPTION
% Est... |
github | SASVDDwt/sa_svdd-master | maxc.m | .m | sa_svdd-master/matlab/prtools/maxc.m | 1,900 | utf_8 | 27971a25a4f95b74f121b8713b358fb2 | %MAXC Maximum combining classifier
%
% W = MAXC(V)
% W = V*MAXC
%
% INPUT
% V Stacked set of classifiers
%
% OUTPUT
% W Combined classifier using max-rule
%
% DESCRIPTION
% If V = [V1,V2,V3, ... ] is a set of classifiers trained on the same
% classes, then W is the maximum combiner: it selects the class tha... |
github | SASVDDwt/sa_svdd-master | kernelc.m | .m | sa_svdd-master/matlab/prtools/kernelc.m | 2,478 | utf_8 | 9b6875d44cc7a61ba7499ea9471b3635 | %KERNELC Arbitrary kernel/dissimilarity based classifier
%
% W = KERNELC(A,KERNEL,CLASSF)
% W = A*KERNELC([],KERNEL,CLASSF)
%
% INPUT
% A Dateset used for training
% KERNEL - untrained mapping to compute kernel by A*(A*KERNEL) for
% training CLASSF or B*(A*KERNEL) for testing with dataset B, ... |
github | SASVDDwt/sa_svdd-master | ismapping.m | .m | sa_svdd-master/matlab/prtools/ismapping.m | 493 | utf_8 | 9616ca5d38d4cc8ea4813f5dcdc0ecac | %ISMAPPING Test whether the argument is a mapping
%
% N = ISMAPPING(W);
%
% INPUT
% W Input argument
%
% OUTPUT
% N 1/0 if W is/isn't a mapping object
%
% DESCRIPTION
% True (1) if W is a mapping object and false (0), otherwise.
%
% SEE ALSO
% ISDATASET, ISFEATIM, ISDATAIM
% $Id: ismapping.m,v 1.2 2006/03/08 2... |
github | SASVDDwt/sa_svdd-master | pls_train.m | .m | sa_svdd-master/matlab/prtools/pls_train.m | 9,369 | utf_8 | 2fe689e20602ca0e8dcaff1bcd156675 | %pls_train Partial Least Squares (training)
%
% [B,XRes,YRes,Options] = pls_train(X,Y)
% [B,XRes,YRes,Options] = pls_train(X,Y,Options)
%
% INPUT
% X [N -by- d_X] the training (input) data matrix, N samples, d_X variables
% Y [N -by- d_Y] the training (output) data matrix, N samples, d_Y variables
%
% Optio... |
github | SASVDDwt/sa_svdd-master | featselo.m | .m | sa_svdd-master/matlab/prtools/featselo.m | 4,261 | utf_8 | 90601db66be58d6919ced75ad363e913 | %FEATSELO Branch and bound feature selection
%
% W = featselo(A,CRIT,K,T,FID)
%
% INPUT
% A input dataset
% CRIT string name of the criterion or untrained mapping
% (optional, def= 'NN' 1-Nearest Neighbor error)
% K numner of features to select (optional, def: K=2)
% T validation set ... |
github | SASVDDwt/sa_svdd-master | prwaitbar.m | .m | sa_svdd-master/matlab/prtools/prwaitbar.m | 9,418 | utf_8 | 6f5b32110360b6c6f588cd82ff2e3ea1 | %PRWAITBAR Report PRTools progress by single waitbar
%
% H = PRWAITBAR(N,M,TEXT)
% H = PRWAITBAR(N,TEXT,FLAG)
% S = PRWAITBAR
%
% INPUT
% N Integer, total number of steps in loop
% M Integer, progress in number of steps in loop
% TEXT Text to be displayed in waitbar
% FLAG Flag (0/1)
%
% OUT... |
github | SASVDDwt/sa_svdd-master | neurc.m | .m | sa_svdd-master/matlab/prtools/neurc.m | 4,133 | utf_8 | b5c828f2c19833cfaf7febcd6f54c3bb | %NEURC Automatic neural network classifier
%
% W = NEURC (A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of units
% Default: 0.2 x size smallest class in A.
%
% OUTPUT
% W Trained feed-forward neural network mapping
%
% DESCRIPTION
% Automatically trained feed-forward neural network classifie... |
github | SASVDDwt/sa_svdd-master | scatterd.m | .m | sa_svdd-master/matlab/prtools/scatterd.m | 9,629 | utf_8 | e0b78b87c978ae3cc9e9cf9e61b835c0 | %SCATTERD Display scatterplot
%
% H = SCATTERD(A)
% H = SCATTERD(A,DIM,S,CMAP,FONTSIZE,'label','both','legend','gridded')
%
% INPUT
% A Dataset or matrix
% DIM Number of dimensions: 1,2 or 3 (optional; default: 2)
% S String specifying the colors and markers (optional)
% CMAP Matrix with a color... |
github | SASVDDwt/sa_svdd-master | featselp.m | .m | sa_svdd-master/matlab/prtools/featselp.m | 5,618 | utf_8 | 84891ada17da0c783f5b2892938bada2 | %FEATSELP Pudil's floating feature selection (forward)
%
% [W,R] = FEATSELP(A,CRIT,K,T,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or untrained mapping
% (default: 'NN', 1-Nearest Neighbor error)
% K Number of features to select (default: K = 0, select optimal set)
% T T... |
github | SASVDDwt/sa_svdd-master | parzenml3.m | .m | sa_svdd-master/matlab/prtools/parzenml3.m | 3,240 | utf_8 | 45a4ba9c574b18917e09346cfaac44e7 | %PARZENML Optimum smoothing parameter in Parzen density estimation.
%
% H = PARZENML(A,FID)
%
% INPUT
% A input dataset
% FID File ID to write progress to (default [], see PRPROGRESS)
%
% OUTPUT
% H scalar smoothing parameter
%
% DESCRIPTION
% Maximum likelihood estimation for the smoothing parameter H... |
github | SASVDDwt/sa_svdd-master | labelim.m | .m | sa_svdd-master/matlab/prtools/labelim.m | 1,675 | utf_8 | 54421f3d90d172ac5de0915a11706f21 | %LABELIM Construct image of object (pixel) labels
%
% IM = LABELIM(A)
% IM = A*LABELIM
%
% INPUT
% A Dataset containing images stored as features
%
% OUTPUT
% IM Image containing the labels of the objects
%
% DESCRIPTION
% For a dataset A containing images stored as features, where each pixel
% corresponds to a... |
github | SASVDDwt/sa_svdd-master | parzen_map.m | .m | sa_svdd-master/matlab/prtools/parzen_map.m | 3,716 | utf_8 | d8c30b56a555094142a25709bf69bcce | %PARZEN_MAP Map a dataset on a Parzen densities based classifier
%
% F = PARZEN_MAP(A,W)
%
% INPUT
% A Dataset
% W Trained Parzen classifier mapping (default: PARZENC(A))
%
% OUTPUT
% F Mapped dataset
%
% DESCRIPTION
% Maps the dataset A by the Parzen density based classfier W. F*sigm are the
% posterior... |
github | SASVDDwt/sa_svdd-master | fisherm.m | .m | sa_svdd-master/matlab/prtools/fisherm.m | 3,331 | utf_8 | f729ad07dd78730a8680ff448b9c31b4 | %FISHERM Optimal discrimination linear mapping (Fisher mapping, LDA)
%
% W = FISHERM(A,N,ALF)
%
% INPUT
% A Dataset
% N Number of dimensions to map to, N < C, where C is the number of classes
% (default: min(C,K)-1, where K is the number of features in A)
% ALF Preserved variance in the pre-whitening ste... |
github | SASVDDwt/sa_svdd-master | im_box.m | .m | sa_svdd-master/matlab/prtools/im_box.m | 2,442 | utf_8 | 7c6a217eee999a1b8862f11bcc96bb4a | %IM_BOX Find rectangular image in datafile enclosing a blob (0/1 image)
%
% B = IM_BOX(A)
% B = A*IM_BOX
%
% If A is a 0/1 image then B is the same image with all empty (0) border
% columns and rows removed.
%
% B = IM_BOX(A,N)
%
% If A is a 0/1 image then B is the same image, but having in each direction
% N emp... |
github | SASVDDwt/sa_svdd-master | im_patch.m | .m | sa_svdd-master/matlab/prtools/im_patch.m | 5,355 | utf_8 | 6b3e86851911260925c95750824d5c8b | %IM_PATCH Generate patches from images
%
% B = IM_PATCH(A,PSIZE,PNUM,TYPE)
% B = IM_PATCH(A,PSIZE,COORD,'user')
% W = IM_PATCH([],PSIZE,PNUM,TYPE)
% B = A*W
%
% INPUT
% A Dataset or datafile with (multi-band) object images dataset
% PSIZE 2-dimensional patch size. If PSIZE is 1-dimensional square
... |
github | SASVDDwt/sa_svdd-master | pca.m | .m | sa_svdd-master/matlab/prtools/pca.m | 1,888 | utf_8 | 4f05b0416c32e4724340e266115a23e8 | %PCA Principal component analysis (PCA or MCA on overall covariance matrix)
%
% [W,FRAC] = PCA(A,N)
% [W,N] = PCA(A,FRAC)
%
% INPUT
% A Dataset
% N or FRAC Number of dimensions (>= 1) or fraction of variance (< 1)
% to retain; if > 0, perform PCA; otherwise MCA. Default: N = inf.
... |
github | SASVDDwt/sa_svdd-master | im_threshold.m | .m | sa_svdd-master/matlab/prtools/im_threshold.m | 2,565 | utf_8 | c0695bb34cf81afd570191ccd7a06f38 | %IM_THRESHOLD Threshold images stored in a dataset (DIP_Image)
%
% B = IM_THRESHOLD(A,TYPE,PAR,INV)
% B = A*IM_THRESHOLD([],TYPE,PAR,INV)
%
% INPUT
% A Dataset with object images (possibly multi-band)
% TYPE Type of procedure, see below
% PAR Related parameter
% INV If INV = 1, result inver... |
github | SASVDDwt/sa_svdd-master | cbands.m | .m | sa_svdd-master/matlab/prtools/cbands.m | 601 | utf_8 | cb9a639d7853ee5ce672bf3aa93ea566 | %CBANDS 12000 objects with 30 features in 24 classes
%
% A = CBANDS
% A = CBANDS(M,N)
%
% Load the dataset in A, select the objects and features according to the
% index vectors M and N.
%
% See also DATASETS, PRDATASETS
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
% Faculty of Applied Sciences, Delft University of... |
github | SASVDDwt/sa_svdd-master | nmc.m | .m | sa_svdd-master/matlab/prtools/nmc.m | 2,082 | utf_8 | d558c2bb6bea7917ab6f3684c3637ebe | %NMC Nearest Mean Classifier
%
% W = NMC(A)
% W = A*NMC
%
% INPUT
% A Dataset
%
% OUTPUT
% W Nearest Mean Classifier
%
% DESCRIPTION
% Computation of the nearest mean classifier between the classes in the
% dataset A. The use of soft labels is supported. Prior probabilities are
% not used.
%
% The di... |
github | SASVDDwt/sa_svdd-master | gendats.m | .m | sa_svdd-master/matlab/prtools/gendats.m | 2,027 | utf_8 | c58923f6c7eac290187cae1e5d282b72 | %GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]).
% K Dimensionality of the dataset to be generated (default: 2).
% D Distance between class means in t... |
github | SASVDDwt/sa_svdd-master | ffnc.m | .m | sa_svdd-master/matlab/prtools/ffnc.m | 9,416 | utf_8 | 7864662e5e58dd99fb8be940cef800a7 | %FFNC Feed-forward neural net classifier back-end
%
% [W,HIST] = FFNC (ALG,A,UNITS,ITER,W_INI,T,FID)
%
% INPUT
% ALG Training algorithm: 'bpxnc' for back-propagation (default), 'lmnc'
% for Levenberg-Marquardt
% A Training dataset
% UNITS Array indicating number of units in each hidden layer (defau... |
github | SASVDDwt/sa_svdd-master | im_resize.m | .m | sa_svdd-master/matlab/prtools/im_resize.m | 1,902 | utf_8 | 72cdd2d12cda40aeefe8efe3fe0e2b12 | %IM_RESIZE Mapping for resizing object images in datasets and datafiles
%
% B = IM_RESIZE(A,SIZE,METHOD)
% B = A*IM_RESIZE([],SIZE,METHOD)
%
% INPUT
% A Dataset or datafile
% SIZE Desired size
% METHOD Method, see IMRESIZE
%
% OUTPUT
% B Dataset or datafile
%
% DESCRIPTION
% The objects stored as ... |
github | SASVDDwt/sa_svdd-master | remoutl.m | .m | sa_svdd-master/matlab/prtools/remoutl.m | 1,593 | utf_8 | 30de9687b8f002b3fc42aa90be9ee9dd | %REMOUTL Remove outliers from a dataset
%
% B = REMOUTL(A,T,P)
% B = A*REMOUTL([],T,P)
%
% INPUT
% A Dataset
% T Threshold for outlier detection (default 3)
% P Fraction of distances passing T (default 0.10)
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% Outliers in A are removed, other objects are copied to B. Cla... |
github | SASVDDwt/sa_svdd-master | histm.m | .m | sa_svdd-master/matlab/prtools/histm.m | 3,620 | utf_8 | aaba14d14184321525906d5a10530805 | %HISTM Histogramming: mapping of dataset (datafile) to histogram
%
% W = HISTM(A,N)
% W = A*HISTM([],N)
% C = B*W
%
% C = HISTM(B,X)
% C = B*HISTM([],X)
%
%
%
% INPUT
% A Dataset or datafile for defining histogram bins (training)
% N Scalar defining number of histogram bins (default 10)
%
% ... |
github | SASVDDwt/sa_svdd-master | costm.m | .m | sa_svdd-master/matlab/prtools/costm.m | 2,895 | utf_8 | 0944fce6461afc5c2b21b6f8e50d1ba5 | %COSTM Cost mapping, classification using costs
%
% Y = COSTM(X,C,LABLIST)
% W = COSTM([],C,LABLIST)
%
% DESCRIPTION
% Maps the classifier output X (assumed to be posterior probability
% estimates) to the cost-outputs, defined by the cost-matrix C:
%
% C(i,j) = cost of misclassifying an object from class i as cl... |
github | SASVDDwt/sa_svdd-master | remclass.m | .m | sa_svdd-master/matlab/prtools/remclass.m | 548 | utf_8 | 0fb9d6764c92862db572eb38a297da00 | %REMCLASS Remove small classes
%
% B = REMCLASS(A,N)
%
% INPUT
% A Dataset
% N Integer, maximum class size to be removed (optional; default 0)
%
% OUTPUT
% B Dataset
%
% DESCRIPTION
% Classes having N objects or less are removed. The corresponding objects
% are made unlabeled. Use SELDAT to remove unlabel... |
github | SASVDDwt/sa_svdd-master | clevals.m | .m | sa_svdd-master/matlab/prtools/clevals.m | 7,795 | utf_8 | 78938416bcf29c02dc21b55343c11a1c | %CLEVALS Classifier evaluation (feature size/learning curve), bootstrap possible
%
% E = CLEVALS(A,CLASSF,FEATSIZE,TRAINSIZES,NREPS,T)
%
% INPUT
% A Training dataset
% CLASSF Classifier to evaluate
% FEATSIZE Vector of feature sizes
% (default: 1:K, where K is the number of feature... |
github | SASVDDwt/sa_svdd-master | nlfisherm.m | .m | sa_svdd-master/matlab/prtools/nlfisherm.m | 3,143 | utf_8 | 91f426665fa815168041bffac501f9c1 | %NLFISHERM Non-linear Fisher Mapping according to Marco Loog
%
% W = NLFISHERM(A,N)
%
% INPUT
% A Dataset
% N Number of dimensions (optional; default: MIN(K,C)-1, where
% K is the dimensionality of A and C is the number of classes)
%
% OUTPUT
% W Non-linear Fisher mapping
%
% DESCRIPTION
% Finds... |
github | SASVDDwt/sa_svdd-master | rnnc.m | .m | sa_svdd-master/matlab/prtools/rnnc.m | 2,817 | utf_8 | 916bb7ca9fd194f6c83f71f944206b17 | %RNNC Random Neural Net classifier
%
% W = RNNC(A,N,S)
%
% INPUT
% A Input dataset
% N Number of neurons in the hidden layer
% S Standard deviation of weights in an input layer (default: 1)
%
% OUTPUT
% W Trained Random Neural Net classifier
%
% DESCRIPTION
% W is a feed-forward neural net with one hidde... |
github | SASVDDwt/sa_svdd-master | fdsc.m | .m | sa_svdd-master/matlab/prtools/fdsc.m | 3,059 | utf_8 | 67b6eee02c698ba1097339898c8bc48c | %FDSC Feature based Dissimilarity Space Classification (outdated)
%
% This routine is outdated, use KERNELC instead
%
% W = FDSC(A,R,FEATMAP,TYPE,P,CLASSF)
% W = A*FDSC([],R,FEATMAP,TYPE,P,CLASSF)
%
% INPUT
% A Dateset used for training
% R Dataset used for representation
% or a fraction ... |
github | SASVDDwt/sa_svdd-master | spatm.m | .m | sa_svdd-master/matlab/prtools/spatm.m | 2,054 | utf_8 | e46a002860322effbf2030ce585f464d | %SPATM Augment image dataset with spatial label information
%
% E = SPATM(D,S)
% E = D*SPATM([],S)
%
% INPUT
% D image dataset classified by a classifier
% S smoothing parameter (optional, default: sigma = 1.0)
%
% OUTPUT
% E augmented dataset with additional spatial information
%
% ... |
github | SASVDDwt/sa_svdd-master | matchlab.m | .m | sa_svdd-master/matlab/prtools/matchlab.m | 1,598 | utf_8 | 59187a89fb817d5f8116b6fa08d51c58 | %MATCHLAB Compare two labellings and rotate the labels for an optimal match
%
% LABELS = MATCHLAB(LAB1,LAB2)
%
% INPUT
% LAB1,LAB2 Label lists of the same objects
%
% OUTPUT
% LABELS A rotated version of LAB2, optimally matched with LAB1
%
% DESCRIPTION
% LAB1 and LAB2 are label lists for the same objects.... |
github | SASVDDwt/sa_svdd-master | sequential.m | .m | sa_svdd-master/matlab/prtools/sequential.m | 3,315 | utf_8 | 50904a5920af497ff9e0d22b163c04a7 | %SEQUENTIAL Sequential mapping
%
% V = SEQUENTIAL(W1,W2)
% B = SEQUENTIAL(A,W)
%
% INPUT
% W,W1,W2 Mappings
% A Dataset
%
% OUTPUT
% V Sequentially combined mapping
% B Dataset
%
% DESCRIPTION
% The two mappings W1 and W2 are combined into a single mapping V. Note
% that SEQUENTIAL(W... |
github | SASVDDwt/sa_svdd-master | gendatd.m | .m | sa_svdd-master/matlab/prtools/gendatd.m | 2,549 | utf_8 | 2490ae4a0fa5edd6119f172424195e52 | %GENDATD Generation of 'difficult' normally distributed classes
%
% A = GENDATD(N,K,D1,D2,LABTYPE)
%
% INPUT
% N Number of objects in each of the classes (default: [50 50])
% K Dimensionality of the dataset (default: 2)
% D1 Difference in mean in feature 1 (default: 3)
% D2 Differen... |
github | SASVDDwt/sa_svdd-master | showfigs.m | .m | sa_svdd-master/matlab/prtools/showfigs.m | 514 | utf_8 | 1835bef0dae8617c4fc4fd1338b48d85 | %SHOWFIGS Show all figures on the screen
%
% SHOWFIGS(K)
%
% Use K figures on a row
function showfigs(k)
h = sort(get(0,'children'));
n = length(h);
if nargin == 0
k = ceil(sqrt(n));
end
s = 0.95/k; % screen stitch
r = 0.93; % image size reduction
t = 0.055; % top gap
b = 0.005; % border gap
fig = 0;
fo... |
github | SASVDDwt/sa_svdd-master | parsc.m | .m | sa_svdd-master/matlab/prtools/parsc.m | 822 | utf_8 | c12237507f36a7b2589880ab583a1e25 | %PARSC Parse classifier
%
% PARSC(W)
%
% Displays the type and, for combining classifiers, the structure of the
% mapping W.
%
% See also MAPPINGS
% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl
% Faculty of Applied Physics, Delft University of Technology
% P.O. Box 5046, 2600 GA Delft, The Netherlands
% $Id: par... |
github | SASVDDwt/sa_svdd-master | nusvo.m | .m | sa_svdd-master/matlab/prtools/nusvo.m | 13,095 | utf_8 | 17235723e4b3aa42ce0e13980b9d7b09 | %NUSVO Support Vector Optimizer: NU algorithm
%
% [V,J,NU,C] = NUSVO(K,NLAB,NU,OPTIONS)
%
% INPUT
% K Similarity matrix
% NLAB Label list consisting of -1/+1
% NU Regularization parameter (0 < NU < 1): expected fraction of SV (optional; default: 0.01)
% OPTIONS
% .PD_CHECK force positi... |
github | SASVDDwt/sa_svdd-master | isdatafile.m | .m | sa_svdd-master/matlab/prtools/isdatafile.m | 432 | utf_8 | 3db749ec603b6905a19079febdbe4ad0 | %ISDATAFILE Test whether the argument is a datafile
%
% N = ISDATAFILE(A);
%
% INPUT
% A Input argument
%
% OUTPUT
% N 1/0 if A is/isn't a datafile
%
% DESCRIPTION
% The function ISDATAFILE test if A is a datafile object.
%
% SEE ALSO
% ISMAPPING, ISDATAIM, ISFEATIM
function n = isdatafile(a)
prtrace(mfilename)... |
github | SASVDDwt/sa_svdd-master | getlab.m | .m | sa_svdd-master/matlab/prtools/getlab.m | 1,053 | utf_8 | 41fc1a6cfc6fa8b2d2f6e2765dc39b01 |
%GETLAB Get labels of dataset or mapping
%
% LABELS = GETLAB(A)
% LABELS = GETLAB(W)
%
% INPUT
% A Dataset
% W Mapping
%
% OUTPUT
% LABELS Labels
%
% DESCRIPTION
% Returns the labels of the objects in the dataset A or the feature labels
% assigned by the mapping W.
%
% If A (or W) is neither a dataset n... |
github | SASVDDwt/sa_svdd-master | nlabeld.m | .m | sa_svdd-master/matlab/prtools/nlabeld.m | 1,491 | utf_8 | 68528c7a976a65375113b0826d3e77c2 | %NLABELD Return numeric labels of classified dataset
%
% NLABELS = NLABELD(Z)
% NLABELS = Z*NLABELD
% NLABELS = NLABELD(A,W)
% NLABELS = A*W*NLABELD
%
% INPUT
% Z Classified dataset, or
% A,W Dataset and classifier mapping
%
% OUTPUT
% NLABELS vector of numeric labels
%
% DESCRIPTION
% Returns the n... |
github | SASVDDwt/sa_svdd-master | meancov.m | .m | sa_svdd-master/matlab/prtools/meancov.m | 4,529 | utf_8 | f5cafd88337b0fc23db48ca814e60144 | %MEANCOV Estimation of the means and covariances from multiclass data
%
% [U,G] = MEANCOV(A,N)
%
% INPUT
% A Dataset
% N Normalization to use for calculating covariances: by M, the number
% of samples in A (N = 1) or by M-1 (default, unbiased, N = 0).
%
% OUTPUT
% U Mean vectors
% G Covariance matrices
... |
github | SASVDDwt/sa_svdd-master | lmnc.m | .m | sa_svdd-master/matlab/prtools/lmnc.m | 1,706 | utf_8 | bd990eaf2fdc0ae642a3575406ce3a1f | %LMNC Levenberg-Marquardt trained feed-forward neural net classifier
%
% [W,HIST] = LMNC (A,UNITS,ITER,W_INI,T)
%
% INPUT
% A Dataset
% UNITS Array indicating number of units in each hidden layer (default: [5])
% ITER Number of iterations to train (default: inf)
% W_INI Weight initialization netw... |
github | SASVDDwt/sa_svdd-master | nbayesc.m | .m | sa_svdd-master/matlab/prtools/nbayesc.m | 1,972 | utf_8 | 301d746c1412b3e9c6c39ed201315688 | %NBAYESC Bayes Classifier for given normal densities
%
% W = NBAYESC(U,G)
%
% INPUT
% U Dataset of means of classes
% G Covariance matrices (optional; default: identity matrices)
%
% OUTPUT
% W Bayes classifier
%
% DESCRIPTION
% Computation of the Bayes normal classifier between a set of classes.
% The m... |
github | SASVDDwt/sa_svdd-master | im2feat.m | .m | sa_svdd-master/matlab/prtools/im2feat.m | 2,247 | utf_8 | 457e621be73b5562724823c43534c4b3 | %IM2FEAT Convert Matlab images or datafile to dataset feature
%
% B = IM2FEAT(IM,A)
%
% INPUT
% IM X*Y image, X*Y*K array of K images, or cell-array of images
% The images may be given as a datafile.
% A Input dataset
%
% OUTPUT
% B Dataset with IM added
%
% DESCRIPTION
% Add standard Matlab ... |
github | SASVDDwt/sa_svdd-master | datasetconv.m | .m | sa_svdd-master/matlab/prtools/datasetconv.m | 335 | utf_8 | a08dd41ae2f8b6e956d8d7721f13f7a5 | %DATASETCONV Convert to dataset if needed
%
% A = DATASETCONV(A)
%
% If A is not a dataset it is converted to a dataset.
%
% SEE ALSO
% DATASETS, DATASET
function a = datasetconv(a)
% This is just programmed like this for speed, as
% a = dataset(a) will do the same but involves more checking
if ~isdataset(a)
a = d... |
github | SASVDDwt/sa_svdd-master | normm.m | .m | sa_svdd-master/matlab/prtools/normm.m | 2,861 | utf_8 | cb4e55cf8a9dfcb89939f885a33d9420 | %NORMM Apply Minkowski-P distance normalization map
%
% B = A*NORMM(P)
% B = NORMM(A,P)
%
% INPUT
% A Dataset or matrix
% P Order of the Minkowski distance (optional; default: 1)
%
% OUTPUT
% B Dataset or matrix of normalized Minkowski-P distances
%
% DESCRIPTION
% Normalizes the distances of all object... |
github | SASVDDwt/sa_svdd-master | isobjim.m | .m | sa_svdd-master/matlab/prtools/isobjim.m | 754 | utf_8 | 840e1b678d9d4c58c67602cc5b3cdd14 | %ISOBJIM test if the dataset contains objects that are images
%
% N = ISOBJIM(A)
% ISOBJIM(A)
%
% INPUT
% A input dataset
%
% OUTPUT
% N logical value
%
% DESCRIPTION
% True if dataset contains objects that are images. If no output is required,
% false outputs are turned into errors. This may be used for asse... |
github | SASVDDwt/sa_svdd-master | datgauss.m | .m | sa_svdd-master/matlab/prtools/datgauss.m | 2,405 | utf_8 | 3c48a1f9f64fa977d84f5b811837e027 | %DATGAUSS Apply Gaussian filter on images in a dataset
%
% B = DATGAUSS(A,SIGMA)
%
% INPUT
% A Dataset containing images
% SIGMA Standard deviation of Gaussian filter (default 1)
%
% OUTPUT
% B Dataset with filtered images
%
% DESCRIPTION
% All images stored as objects (rows) or as features (column... |
github | SASVDDwt/sa_svdd-master | qdc.m | .m | sa_svdd-master/matlab/prtools/qdc.m | 4,285 | utf_8 | 79854b39284dee2879b0f4726baca9e0 | %QDC Quadratic Bayes Normal Classifier (Bayes-Normal-2)
%
% [W,R,S,M] = QDC(A,R,S,M)
% W = A*QDC([],R,S)
%
% INPUT
% A Dataset
% R,S Regularization parameters, 0 <= R,S <= 1
% (optional; default: no regularization, i.e. R,S = 0)
% M Dimension of subspace structure in covariance matrix (default:... |
github | SASVDDwt/sa_svdd-master | roc.m | .m | sa_svdd-master/matlab/prtools/roc.m | 5,315 | utf_8 | 724a6b77ec38bc2b99fbb94383a7e377 | %ROC Receiver-Operator Curve
%
% E = ROC(A,W,C,N)
% E = ROC(B,C,N)
%
% INPUT
% A Dataset
% W Trained classifier, or
% B Classification result, B = A*W*CLASSC
% C Index of desired class (default: C = 1)
% N Number of points on the Receiver-Operator Curve (default: 100)
%
% OUTPUT
% E Structure con... |
github | SASVDDwt/sa_svdd-master | featselv.m | .m | sa_svdd-master/matlab/prtools/featselv.m | 994 | utf_8 | 737fa820876d949d67ac82a8dd5a1915 | %FEATSELV Varying feature selection
%
% W = FEATSELV(A)
% W = A*FEATSELV
%
% Selects all features with a non-zero variance.
% Classifiers can be trained like A*(FEATSELV*LDC([],1E-3)) to make
% use of this feature selection
%
% SEE ALSO
% MAPPINGS, DATASETS, FEATEVAL, FEATSELO, FEATSELB, FEATSELF,
% FEATSEL, FEATSEL... |
github | SASVDDwt/sa_svdd-master | isvaldfile.m | .m | sa_svdd-master/matlab/prtools/isvaldfile.m | 1,753 | utf_8 | fd0e1330cd0a257cfe44353f125b71aa | %ISVALDFILE Test whether the argument is a valid datafile or dataset
%
% N = ISVALDFILE(A);
% N = ISVALDFILE(A,M);
% N = ISVALDFILE(A,M,C);
%
% INPUT
% A Input argument, to be tested on datafile or dataset
% M Minimum number of objects per class in A
% C Minimum number of classes in A
%
% OUTPUT
% ... |
github | SASVDDwt/sa_svdd-master | prprogress.m | .m | sa_svdd-master/matlab/prtools/prprogress.m | 2,061 | utf_8 | 8d1898123513fe029ae1a095e372cb67 | %PRPROGRESS Report progress of some PRTools iterative routines
%
% PRPROGRESS ON
%
% All progress of all routines will be written to the command window.
%
% PRPROGRESS(FID)
%
% Progress reports will be written to the file with file descriptor FID.
%
% PRPROGRESS(FID,FORMAT,...)
%
% Writes progress message to FID. If... |
github | SASVDDwt/sa_svdd-master | renumlab.m | .m | sa_svdd-master/matlab/prtools/renumlab.m | 4,771 | utf_8 | 9a4417affc8ed1e92e3a54062327db31 | %RENUMLAB Renumber labels
%
% [NLAB,LABLIST] = RENUMLAB(LABELS)
% [NLAB1,NLAB2,LABLIST] = RENUMLAB(LABELS1,LABELS2)
%
% INPUT
% LABELS,LABELS1,LABELS2 Array of labels
%
% OUTPUT
% NLAB,NLAB1,NLAB2 Vector of numeric labels
% LABLIST Unique labels
%
% DESCRIPTION
% If a single a... |
github | SASVDDwt/sa_svdd-master | rbnc.m | .m | sa_svdd-master/matlab/prtools/rbnc.m | 3,564 | utf_8 | bb869d5e943f4db455b170c030892516 | %RBNC Radial basis function neural network classifier
%
% W = RBNC(A,UNITS)
%
% INPUT
% A Dataset
% UNITS Number of RBF units in hidden layer
%
% OUTPUT
% W Radial basis neural network mapping
%
% DESCRIPTION
% A feed-forward neural network classifier with one hidden layer with
% UNITS radial b... |
github | SASVDDwt/sa_svdd-master | prdataset.m | .m | sa_svdd-master/matlab/prtools/prdataset.m | 2,123 | utf_8 | efee8cb944a2413292867e46aecbcb07 | %PRDATASET Load and convert dataset from disk
%
% A = PRDATASET(NAME,M,N)
%
% The dataset given in NAME is loaded from a .mat file and converted
% to the current PRTools definition. Objects and features requested
% by the index vectors M and N are returned.
%
% See PRDATA for loading arbitrary data into a PRTools data... |
github | SASVDDwt/sa_svdd-master | distmaha.m | .m | sa_svdd-master/matlab/prtools/distmaha.m | 2,988 | utf_8 | c76602b6f11acb2f6b951521040e660c | %DISTMAHA Mahalanobis distance
%
% D = DISTMAHA (A,U,G)
%
% INPUT
% A Dataset
% U Mean(s) (optional; default: estimate on classes in A)
% G Covariance(s) (optional; default: estimate on classes in A)
%
% OUTPUT
% D Mahalanobis distance matrix
%
% DESCRIPTION
% Computes the M*N Mahanalobis distance matrix of ... |
github | SASVDDwt/sa_svdd-master | selectim.m | .m | sa_svdd-master/matlab/prtools/selectim.m | 1,015 | utf_8 | 2dcf097a3481f61fd4c446a8648411e1 | %SELECTIM Select one or more images in multiband image or dataset
%
% B = SELECTIM(A,N)
% A = A*SELECTIM([],N)
%
% INPUT
% A Multiband image or dataset containing multiband images
% N Vector or scalar pointing to desired images
%
% OUTPUT
% B New, reduced, multiband image or dataset
fu... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.