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From: <schloegl@us...>  20090702 14:15:39

Revision: 5985 http://octave.svn.sourceforge.net/octave/?rev=5985&view=rev Author: schloegl Date: 20090702 12:27:35 +0000 (Thu, 02 Jul 2009) Log Message:  add classification methods, change license to GPL v3 or later, ver2.0 Modified Paths:  trunk/octaveforge/extra/NaN/COPYING trunk/octaveforge/extra/NaN/DESCRIPTION trunk/octaveforge/extra/NaN/doc/README.TXT Added Paths:  trunk/octaveforge/extra/NaN/inst/classify.m trunk/octaveforge/extra/NaN/inst/decovm.m trunk/octaveforge/extra/NaN/inst/kappa.m trunk/octaveforge/extra/NaN/inst/test_sc.m trunk/octaveforge/extra/NaN/inst/train_lda_sparse.m trunk/octaveforge/extra/NaN/inst/train_sc.m trunk/octaveforge/extra/NaN/inst/xval.m Modified: trunk/octaveforge/extra/NaN/COPYING ===================================================================  trunk/octaveforge/extra/NaN/COPYING 20090701 20:55:24 UTC (rev 5984) +++ trunk/octaveforge/extra/NaN/COPYING 20090702 12:27:35 UTC (rev 5985) @@ 1,283 +1,625 @@ GNU GENERAL PUBLIC LICENSE  Version 2, June 1991 + Version 3, 29 June 2007  Copyright (C) 1989, 1991 Free Software Foundation, Inc. <http://fsf.org/>; + Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>; Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. 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Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + END OF TERMS AND CONDITIONS  + How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest @@ 286,15 +628,15 @@ To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least +state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. <one line to give the program's name and a brief idea of what it does.> Copyright (C) <year> <name of author>  This program is free software; you can redistribute it and/or modify + This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by  the Free Software Foundation; either version 2 of the License, or + the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, @@ 303,35 +645,30 @@ GNU General Public License for more details. You should have received a copy of the GNU General Public License  along with this program; if not, see <http://www.gnu.org/licenses/>;. + along with this program. If not, see <http://www.gnu.org/licenses/>;. Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode:  Gnomovision version 69, Copyright (C) year name of author  Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + <program> Copyright (C) <year> <name of author> + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouseclicks or menu itemswhatever suits your program. +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +<http://www.gnu.org/licenses/>;.  Yoyodyne, Inc., hereby disclaims all copyright interest in the program  `Gnomovision' (which makes passes at compilers) written by James Hacker.   <signature of Ty Coon>, 1 April 1989  Ty Coon, President of Vice  This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License. + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +<http://www.gnu.org/philosophy/whynotlgpl.html>;. Modified: trunk/octaveforge/extra/NaN/DESCRIPTION ===================================================================  trunk/octaveforge/extra/NaN/DESCRIPTION 20090701 20:55:24 UTC (rev 5984) +++ trunk/octaveforge/extra/NaN/DESCRIPTION 20090702 12:27:35 UTC (rev 5985) @@ 1,10 +1,10 @@ Name: NaN Version: 1.0.9 Date: 20090503 +Version: 2.0.0 +Date: 20090630 Author: Alois Schloegl <a.schloegl@...> Maintainer: Matthew W. Roberts +Maintainer: Alois Schloegl Title: NaN statisical toolbox Description: Missing value statistical toolbox Depends: octave (>= 2.9.7) License: GPL version 2 or later Url: http://www.dpmi.tugraz.ac.at/~schloegl/matlab/NaN +Depends: octave (>= 3.0.1) +License: GPL version 3 or later +Url: http://hci.tugraz.ac.at/~schloegl/matlab/NaN Modified: trunk/octaveforge/extra/NaN/doc/README.TXT ===================================================================  trunk/octaveforge/extra/NaN/doc/README.TXT 20090701 20:55:24 UTC (rev 5984) +++ trunk/octaveforge/extra/NaN/doc/README.TXT 20090702 12:27:35 UTC (rev 5985) @@ 6,17 +6,19 @@ FEATURES of the NaNtb:   statistical toolbox +  classification toolobx  NaN's are treated as missing values   supports DIM argument   supports unbiased estimation   supports weightening of data +  supports DIM argument +  supports weightening of data +  less roundoff errors using extended double  less but more powerful functions (no nanFUN needed) +  supports unbiased estimation  fixes known bugs   compatible to Matlab and Octave +  compatible with Matlab and Octave  easy to use   The toolbox is tested with Octave 3.x and Matlab 7.x, FreeMat v3.6   +  The toolbox is tested with Octave 3.x and Matlab 7.x + + Currently are implemented:  level 1: basic functions (not derived) @@ 25,6 +27,8 @@ SUMSKIPNAN is central, it implements skipping NaN's, the DIMargument and returns the number of valid elements, too. COVM covariance estimation (several modes) + Roundoff errors avoided by using internally extended accuracy + DECOVM decomposes the extended covarianced matrix into mean and cov XCOVF crosscorrelation function NANFILTER filter function CONVSKIPNAN convolution @@ 38,34 +42,51 @@ rms, sem, skewness, statistic, std, var level 2: derived functions +level 2a: derived functions MEAN mean (options: arithmetic, geometric, harmonic)  SEM standard error of the mean (does not depend on distribution) VAR variance STD standard deviation MEDIAN median (currently only for 2dim matrices) + SEM standard error of the mean (does not depend on distribution) + TRIMMEAN trimmed mean + medAbsDev median absolute deviation + MEANSQ mean square RMS root mean square + STATISTIC estimates various statistics at once MOMENT moment SKEWNESS skewness KURTOSIS excess + +* IQR interquartile range MAD mean absolute deviation +* RANGE range (maxmin) + CENTER removes mean  ZSCORE normalizes x with z = (xmean)/std + ZSCORE normalizes x to zero mean and variance 1 (z = (xmean)/std) + zScoreMedian nonparametric zscore, normalizes is to zero median and 1/(1.483*median absolute deviation) + HARMMEAN harmonic mean GEOMEAN geometric mean + NANTEST checks whether all functions have been replaced DETREND detrending of data with missing values and nonequidistant sampled data + COR correlation matrix + COV covariance matrix CORRCOEF correlation coefficient, including rank correlation, significance test and confidence intervals SPEARMAN, RANKCORR spearman's rank correlation coefficient. They might be replaced by CORRCOEF.  COV covariance matrix + PARTCORRCOEF partial correlation coefficient RANKS calculates ranks for nonparametric statistics + TIEDRANK similar to RANKS, used for compatibility reasons + + QUANTILE qth quantile + PRCTILE,PERCENTILE pth percentile TRIMEAN trimean  QUANTILE qth quantile  PERCENTILE pth percentile + + ECDF empirical cumulative distribution function NORMPDF normal probability distribution NORMCDF normal cumulative distribution NORMINV inverse of the normal cumulative distribution @@ 73,9 +94,16 @@ TCDF student cumulative distribution TINV inverse of the student cumulative distribution NANSUM, NANSTD fixes for buggy versions included    + +level 2b: classification, crossvalidation + TRAIN_SC train classifier + TEST_SC test classifier + CLASSIFY classify data (no cross validation) + XVAL classify data with cross validation + KAPPA performance evaluation + TRAIN_LDA_SPARSE utility function + + REFERENCE(S):  [1] http://www.itl.nist.gov/ @@ 145,7 +173,7 @@ 12) NORMPDF, NORMCDF, NORMINV In the Matlab statistics toolbox V 3.0, NORMPDF, NORMCDF and NORMINV gave incorrect results for SIGMA=0; A Similar problem was observed in Octave +incorrect results for SIGMA=0; A similar problem was observed in Octave with NORMAL_INV, NORMAL_PDF, and NORMALCDF. The problem is fixed with this version. Furthermore, the check of the input Added: trunk/octaveforge/extra/NaN/inst/classify.m ===================================================================  trunk/octaveforge/extra/NaN/inst/classify.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/classify.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,77 @@ +function [CLASS,ERR,POSTERIOR,LOGP,COEF]=classify(sample,training,classlabel,TYPE) +% CLASSIFY classifies sample data into categories +% defined by the training data and its group information +% +% CLASS = classify(sample, training, group) +% CLASS = classify(sample, training, group, TYPE) +% [CLASS,ERR,POSTERIOR,LOGP,COEF] = CLASSIFY(...) +% +% CLASS contains the assigned group. +% ERR is the classification error on the training set weighted by the +% prior propability of each group. +% +% The same classifier as in TRAIN_SC are supported. +% +% ATTENTION: no crossvalidation is applied, therefore the +% classification error is too optimistic (overfitting). +% Use XVAL instead to obtain crossvalidated performance. +% +% see also: TRAIN_SC, TEST_SC, XVAL +% +% References: +% [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. +% John Wiley & Sons, 2001. + +% $Id: classify.m 2140 20090702 12:03:55Z schloegl $ +% Copyright (C) 2008,2009 by Alois Schloegl <a.schloegl@...> +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place  Suite 330, Boston, MA 021111307, USA. + +if nargin<4 + TYPE = 'linear'; +end; + +if strcmp(TYPE,'linear') + TYPE = 'LDA'; +elseif strcmp(TYPE,'quadratic') + TYPE = 'QDA2'; % result is closer to Matlab +elseif strcmp(TYPE,'diagLinear') + TYPE = 'NBC'; +elseif strcmp(TYPE,'diagQuadratic') + TYPE = 'aNBC'; +elseif strcmp(TYPE,'mahalanobis') + TYPE = 'MDA'; +end; + +[group,I,classlabel] = unique(classlabel); + +CC = train_sc(training,classlabel,TYPE); +R = test_sc(CC,sample); +CLASS = group(R.classlabel); + +if nargout>1, + R = test_sc(CC,training,[],classlabel); + ERR = 1R.ACC; +end; + +if nargout>2, + warning('output arguments POSTERIOR,LOGP and COEF not supported') + POSTERIOR = []; + LOGP = []; + COEF = []; +end; + Property changes on: trunk/octaveforge/extra/NaN/inst/classify.m ___________________________________________________________________ Added: Id + test_sc.m Added: trunk/octaveforge/extra/NaN/inst/decovm.m ===================================================================  trunk/octaveforge/extra/NaN/inst/decovm.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/decovm.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,78 @@ +function [mu,sd,COV,xc,M,R2]=decovm(XCN,NN) +% decompose extended covariance matrix into mean (mu), +% standard deviation, the (pure) Covariance (COV), +% correlation (xc) matrix and the correlation coefficients R2. +% NaN's are condsidered as missing values. +% [mu,sd,COV,xc,N,R2]=decovm(ECM[,NN]) +% +% ECM is the extended covariance matrix +% NN is the number of elements, each estimate (in ECM) is based on +% +% see also: MDBC, COVM, R2 + +% $Id: decovm.m 2140 20090702 12:03:55Z schloegl $ +% Copyright (c) 19992002,2009 by Alois Schloegl +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place  Suite 330, Boston, MA 021111307, USA. + +[r,c]=size(XCN); +if r~=c, + fprintf(2,'Warning DECOVM: input argument is not a square matrix\n'); + XCN = ecovm(XCN); + c = c + 1 +else + M = XCN(1,1); + if nargin<2, + XCN = XCN/(XCN(1,1)); + else %if nargin==2 + XCN = XCN./(NN); + end; + + if any(isnan(XCN(:))), + warning('DECOVM: Extended Covariance Matrix should not contain NaN''s'); + end; + if 0, %det(XCN)<0; % check removed for performance reasons + warning('DECOVM: Extended Covariance Matrix must be nonnegative definite'); + end; +end; + +mu = XCN(1,2:c); +COV = XCN(2:c,2:c)  mu'*mu; +sd = sqrt(diag(COV))'; +if nargout<4, return; end; +xc = COV./(sd'*sd); +M = XCN(1,1); +if nargout<6, return; end; +R2 = xc.*xc; + +return; + +mu=XCN(2:N,1)/XCN(1,1); +COV=(XCN(2:N,2:N)/XCN(1,1)XCN(2:N,1)*XCN(1,2:N)/XCN(1,1)^2); +sd=sqrt(diag(COV)); +xc=COV./(sd*sd'); + +% function [ECM] = ecovm(signal); +% Generates extended Covariance matrix, +% ECM= [l signal]'*[l signal]; % l is a matching column of 1's +% ECM is additive, i.e. it can be applied to subsequent blocks and summed up afterwards +% [ECM1] = ecovm(s1); +% [ECM2] = ecovm(s1); +% [ECM] = ecovm([s1;s2]); +% ECM1+ECM2==ECM; +% +% SS=sum(signal); ECM=[[size(signal,1),SS];[SS',signal'*signal]]; Property changes on: trunk/octaveforge/extra/NaN/inst/decovm.m ___________________________________________________________________ Added: Id + test_sc.m Added: trunk/octaveforge/extra/NaN/inst/kappa.m ===================================================================  trunk/octaveforge/extra/NaN/inst/kappa.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/kappa.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,183 @@ +function [kap,se,H,z,p0,SA,R]=kappa(d,c,arg3,w); +% KAPPA estimates Cohen's kappa coefficient +% and related statistics +% +% [...] = kappa(d1,d2); +% NaN's are handled as missing values and are ignored +% [...] = kappa(d1,d2,'notIgnoreNAN'); +% NaN's are handled as just another Label. +% [kap,sd,H,z,ACC,sACC,MI] = kappa(...); +% X = kappa(...); +% +% d1 data of scorer 1 +% d2 data of scorer 2 +% +% kap Cohen's kappa coefficient point +% se standard error of the kappa estimate +% H Concordance matrix, i.e. confusion matrix +% z zscore +% ACC overall agreement (accuracy) +% sACC specific accuracy +% MI Mutual information or transfer information (in [bits]) +% X is a struct containing all the fields above +% +% Reference(s): +% [1] Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 3746. +% [2] J Bortz, GA Lienert (1998) Kurzgefasste Statistik fr die klassische Forschung, Springer Berlin  Heidelberg. +% Kapitel 6: Uebereinstimmungsmasze fuer subjektive Merkmalsurteile. p. 265270. +% [3] http://www.cmis.csiro.au/Fiona.Evans/personal/msc/html/chapter3.html +% [4] Kraemer, H. C. (1982). Kappa coefficient. In S. Kotz and N. L. Johnson (Eds.), +% Encyclopedia of Statistical Sciences. New York: John Wiley & Sons. +% [5] http://ourworld.compuserve.com/homepages/jsuebersax/kappa.htm +% +% + +% $Id: kappa.m 2141 20090702 12:05:29Z schloegl $ +% Copyright (c) 19972006,2008,2009 by Alois Schloegl <a.schloegl@...> +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ +% +% BioSig is free software: you can redistribute it and/or modify +% it under the terms of the GNU General Public License as published by +% the Free Software Foundation, either version 3 of the License, or +% (at your option) any later version. +% +% BioSig is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with BioSig. If not, see <http://www.gnu.org/licenses/>;. + + +mode.ignoreNAN = 1; +kk = []; +if nargin>2 + if ischar(arg3) + if strcmpi(arg3,'notIgnoreNAN') + mode.ignoreNAN = 0; + end + else + kk = arg3; + end +end; +if nargin<4 + w = []; +end; + +if nargin>1, + d = d(:); + c = c(:); + + tmp = [d;c]; + maxCLASS = max(tmp); + tmp(isnan(tmp)) = maxCLASS+1; + [X.Label,i,j] = unique(tmp); + c = j(1+numel(d):end); + d = j(1:numel(d)); + + if mode.ignoreNAN, + if any(tmp>maxCLASS) +% fprintf(2,'Warning KAPPA: some elements are NaN. These are handled as missing values and are ignored.\n'); +% fprintf(2,'If NaN should be handled as just another label, use kappa(..,''notIgnoreNaN'').\n'); + ix = find(c<=maxCLASS & d<=maxCLASS); + d = d(ix); c=c(ix); + if ~isempty(w) w = w(ix); end; + end; + X.Label(X.Label>maxCLASS) = []; + else + X.Label(X.Label>maxCLASS) = NaN; + end; + + N = length(d); + ku = max([d;c]); % upper range + kl = min([d;c]); % lower range + if isempty(w) + w = ones(N,1); + end; + + if isempty(kk), + kk = length(X.Label); % maximum element + else + if kk<ku; % maximum element + fprintf(2,'Error KAPPA: some element is larger than arg3(%i)\n',kk); + end; + end; + + if 0, + h = histo([d+c*kk; kk*kk+1; 1]); + H = reshape(h(1:length(h)1)); + H(1,1) = H(1,1)1; + else + if 1; % exist('OCTAVE_VERSION')>=5; + H = zeros(kk); + for k = 1:N, + if ~isnan(d(k)) & ~isnan(c(k)), + H(d(k),c(k)) = H(d(k),c(k)) + w(k); + end; + end; + elseif isempty(w) + H = full(sparse(d(1:N),c(1:N),1,kk,kk)); + else + H = full(sparse(d(1:N),c(1:N),w(1:N),kk,kk)); + end; + end; +else + X.Label = 1:min(size(d)); + H = d(X.Label,X.Label); +end; +s = warning; +warning('off'); + +N = sum(H(:)); +p0 = sum(diag(H))/N; %accuracy of observed agreement, overall agreement +%OA = sum(diag(H))/N); + +p_i = sum(H); %sum(H,1); +pi_ = sum(H'); %sum(H,2)'; + +SA = 2*diag(H)'./(p_i+pi_); % specific agreement + +pe = (p_i*pi_')/(N*N); % estimate of change agreement + +px = sum(p_i.*pi_.*(p_i+pi_))/(N*N*N); + +%standard error +kap = (p0pe)/(1pe); +sd = sqrt((pe+pe*pepx)/(N*(1pe*pe))); + +%standard error +se = sqrt((p0+pe*pepx)/N)/(1pe); +if ~isreal(se) + z = NaN; +end +z = kap/se; +warning(s); + +if ((1 < nargout) & (nargout<7)) return; end; + +% Nykopp's entropy +pwi = sum(H,2)/N; % p(x_i) +pwj = sum(H,1)/N; % p(y_j) +pji = H./repmat(sum(H,2),1,size(H,2)); % p(y_j  x_i) +R =  sumskipnan(pwj.*log2(pwj)) + sumskipnan(pwi'*(pji.*log2(pji))); + +if (nargout>1) return; end; + +X.kappa = kap; +X.kappa_se = se; +X.H = H; +X.z = z; +X.ACC = p0; +X.sACC = SA; +X.MI = R; + +if length(H)==2, + X.FNR = H(2,1)/sum(H(2,:)); + X.FPR = H(1,2)/sum(H(1,:)); + X.TPR = H(2,2)/sum(H(2,:)); +end; + +kap = X; + Property changes on: trunk/octaveforge/extra/NaN/inst/kappa.m ___________________________________________________________________ Added: Id + test_sc.m Added: trunk/octaveforge/extra/NaN/inst/test_sc.m ===================================================================  trunk/octaveforge/extra/NaN/inst/test_sc.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/test_sc.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,287 @@ +function [R]=test_sc(CC,D,mode,classlabel) +% TEST_SC: apply statistical and SVM classifier to test data +% +% R = test_sc(CC,D,TYPE [,target_Classlabel]) +% R.output output distance for each class +% R.classlabel class for output data +% The target class is optional. If it is provided, the following values are returned. +% R.kappa Cohen's kappa coefficient +% R.ACC Classification accuracy +% R.H Confusion matrix +% +% The classifier CC is typically obtained by TRAIN_SC. If a statistical +% classifier is used, TYPE can be used to modify the classifier. +% TYPE = 'MDA' mahalanobis distance based classifier +% TYPE = 'MD2' mahalanobis distance based classifier +% TYPE = 'MD3' mahalanobis distance based classifier +% TYPE = 'GRB' Gaussian radial basis function +% TYPE = 'QDA' quadratic discriminant analysis +% TYPE = 'LD2' linear discriminant analysis (see LDBC2) +% TYPE = 'LD3' linear discriminant analysis (see LDBC3) +% TYPE = 'LD4' linear discriminant analysis (see LDBC4) +% TYPE = 'GDBC' general distance based classifier +% +% see also: TRAIN_SC +% +% References: +% [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. +% John Wiley & Sons, 2001. + +% $Id: test_sc.m 2140 20090702 12:03:55Z schloegl $ +% Copyright (C) 2005,2006,2008,2009 by Alois Schloegl <a.schloegl@...> +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place  Suite 330, Boston, MA 021111307, USA. + +if nargin<3, + mode = []; +end; +[t1,t] = strtok(CC.datatype,':'); +[t2,t] = strtok(t,':'); +[t3,t] = strtok(t,':'); +if ~strcmp(t1,'classifier'), return; end; + +if isfield(CC,'prewhite') + D = D*CC.prewhite(2:end,:) + CC.prewhite(ones(size(D,1),1),:); + CC = rmfield(CC,'prewhite'); +end; + +POS1 = [strfind(CC.datatype,'/gsvd'),strfind(CC.datatype,'/sparse')]; + +if 0, + + +elseif strcmp(CC.datatype,'classifier:nbpw') + error('NBPW not implemented yet') + %%%% Naive Bayesian Parzen Window Classifier %%%% + d = repmat(NaN,size(D,1),size(CC.MEAN,1)); + for k = 1:size(CC.MEAN,1) + z = (D  CC.MEAN(repmat(k,size(D,1),1),:)).^2 ./ (CC.VAR(repmat(k,size(D,1),1),:)); + z = z + log(CC.VAR(repmat(k,size(D,1),1),:)); % + log(2*pi); + d(:,k) = sum(z/2, 2) + log(mean(CC.N(k,:))); + end; + d = exp(dlog(mean(sum(CC.N,1)))log(2*pi)/2); + + +elseif strcmp(CC.datatype,'classifier:nbc') + %%%% Naive Bayesian Classifier %%%% + d = repmat(NaN,size(D,1),size(CC.MEAN,1)); + for k = 1:size(CC.MEAN,1) + z = (D  CC.MEAN(repmat(k,size(D,1),1),:)).^2 ./ (CC.VAR(repmat(k,size(D,1),1),:)); + z = z + log(CC.VAR(repmat(k,size(D,1),1),:)); % + log(2*pi); + d(:,k) = sum(z/2, 2) + log(mean(CC.N(k,:))); + end; + d = exp(dlog(mean(sum(CC.N,1)))log(2*pi)/2); + + +elseif strcmp(CC.datatype,'classifier:anbc') + %%%% Augmented Naive Bayesian Classifier %%%% + d = repmat(NaN,size(D,1),size(CC.MEAN,1)); + for k = 1:size(CC.MEAN,1) + z = (D*CC.V  CC.MEAN(repmat(k,size(D,1),1),:)).^2 ./ (CC.VAR(repmat(k,size(D,1),1),:)); + z = z + log(CC.VAR(repmat(k,size(D,1),1),:)); % + log(2*pi); + d(:,k) = sum(z/2, 2) + log(mean(CC.N(k,:))); + end; + d = exp(dlog(mean(sum(CC.N,1)))log(2*pi)/2); + + +elseif strcmp(CC.datatype,'classifier:statistical:rda') + % Friedman (1989) Regularized Discriminant analysis + if isfield(CC,'hyperparameters') && isfield(CC.hyperparameters,'lambda') && isfield(CC.hyperparameters,'gamma') + D = [ones(size(D,1),1),D]; % add 1column + lambda = CC.hyperparameters.lambda; + gamma = CC.hyperparameters.gamma; + d = repmat(NaN,size(D,1),size(CC.MD,1)); + ECM = CC.MD./CC.NN; + NC = size(ECM); + ECM0 = squeeze(sum(ECM,3)); %decompose ECM + [M0,sd,COV0,xc,N] = decovm(ECM0); + for k = 1:NC(3); + [M,sd,s,xc,N] = decovm(squeeze(ECM(:,:,k))); + s = ((1lambda)*N*s+lambda*COV0)/((1lambda)*N+lambda); + s = (1gamma)*s+gamma*(trace(s))/(NC(2)1)*eye(NC(2)1); + ir = [M;eye(NC(2)1)]*inv(s)*[M',eye(NC(2)1)]; % inverse correlation matrix extended by mean + d(:,k) = sum((D*ir).*D,2); % calculate distance of each data point to each class + end; + else + error('QDA: hyperparamters lambda and/or gamma not defined') + end; + + + +elseif strcmp(CC.datatype,'classifier:csp') + d = (D*CC.csp_w).^2; + d = filtfilt(CC.FiltB,CC.FiltA,(D*CC.csp_w).^2); + R = test_sc(CC.CSP,log(d)); % LDA classifier of + d = R.output; + cl= R.classlabel; + + +elseif strcmp(CC.datatype,'classifier:svm:lib:1vs1')  strcmp(CC.datatype,'classifier:svm:lib:rbf'); + + [cl, accuracy] = svmpredict(classlabel, D, CC.model); %Use the classifier + + %Create a pseudo tsd matrix for bci4eval + d = zeros(size(cl,1), CC.model.nr_class); + for i = 1:size(cl,1) + d(i,cl(i)) = 1; + end + + +elseif isfield(CC,'weights'); %strcmpi(t2,'svm')  (strcmpi(t2,'statistical') & strncmpi(t3,'ld',2)) ; + + % linear classifiers like: LDA, SVM, LPM + %d = [ones(size(D,1),1), D] * CC.weights; + d = repmat(NaN,size(D,1),size(CC.weights,2)); + for k = 1:size(CC.weights,2), + d(:,k) = D * CC.weights(2:end,k) + CC.weights(1,k); + end; + if size(CC.weights,2)==1, + d = [d, d]; + end; + + +elseif ~isempty(POS1) % GSVD & sparse + CC.datatype = CC.datatype(1:POS1(1)1); + r = test_sc(CC,D*CC.G); + d = r.output; + + +elseif strcmp(t2,'statistical'); + if isempty(mode) + mode.TYPE = upper(t3); + end; + D = [ones(size(D,1),1),D]; % add 1column + + if 0, + elseif strcmpi(mode.TYPE,'LD2'), + %d = ldbc2(CC,D); + ECM = CC.MD./CC.NN; + NC = size(ECM); + ECM0 = squeeze(sum(ECM,3)); %decompose ECM + [M0,sd,COV0,xc,N] = decovm(ECM0); + for k = 1:NC(3); + ecm = squeeze(ECM(:,:,k)); + [M1,sd,COV1,xc,N] = decovm(ECM0ecm); + [M2,sd,COV2,xc,N] = decovm(ecm); + w = (COV1+COV2)\(M2'M1')*2; + w0 = M0*w; + W(:,k) = [w0; w]; + end; + d = D*W; + elseif strcmpi(mode.TYPE,'LD3'); + %d = ldbc3(CC,D); + ECM = CC.MD./CC.NN; + NC = size(ECM); + ECM0 = squeeze(sum(ECM,3)); %decompose ECM + [M0,sd,COV0,xc,N] = decovm(ECM0); + for k = 1:NC(3); + ecm = squeeze(ECM(:,:,k)); + [M1,sd,COV1,xc,N] = decovm(ECM0ecm); + [M2,sd,COV2,xc,N] = decovm(ecm); + w = COV0\(M2'M1')*2; + w0 = M0*w; + W(:,k) = [w0; w]; + end; + d = D*W; + elseif strcmpi(mode.TYPE,'LD4'); + %d = ldbc4(CC,D); + ECM = CC.MD./CC.NN; + NC = size(ECM); + ECM0 = squeeze(sum(ECM,3)); %decompose ECM + [M0,sd,COV0,xc,N] = decovm(ECM0); + for k = 1:NC(3); + ecm = squeeze(ECM(:,:,k)); + [M1,sd,COV1,xc,N1] = decovm(ECM0ecm); + [M2,sd,COV2,xc,N2] = decovm(ecm); + w = (COV1*N1+COV2*N2)\((M2'M1')*(N1+N2)); + w0 = M0*w; + W(:,k) = [w0; w]; + end; + d = D*W; + elseif strcmpi(mode.TYPE,'MDA'); + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + elseif strcmpi(mode.TYPE,'MD2'); + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = sqrt(d); + elseif strcmpi(mode.TYPE,'GDBC'); + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2) + CC.logSF7(k); % calculate distance of each data point to each class + end; + d = exp(d/2); + elseif strcmpi(mode.TYPE,'MD3'); + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2) + CC.logSF7(k); % calculate distance of each data point to each class + end; + d = exp(d/2); + d = d./repmat(sum(d,2),1,size(d,2)); % Zuordungswahrscheinlichkeit [1], p.601, equ (18.39) + elseif strcmpi(mode.TYPE,'QDA'); + for k = 1:length(CC.IR); + % [1] (18.33) QCF  quadratic classification function + d(:,k) = (sum((D*CC.IR{k}).*D,2)  CC.logSF5(k)); + end; + elseif strcmpi(mode.TYPE,'QDA2'); + r = sum(CC.NN(:,1,1)); + r = r/(rlength(CC.IR)); + for k = 1:length(CC.IR); + % [1] (18.33) QCF  quadratic classification function + d(:,k) = (sum((D*(CC.IR{k})).*D,2) + CC.logSF4(k)); + end; + elseif strcmpi(mode.TYPE,'GRB'); % Gaussian RBF + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = exp(sqrt(d)/2); + elseif strcmpi(mode.TYPE,'GRB2'); % Gaussian RBF + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = exp(d); + elseif strcmpi(mode.TYPE,'MQU'); % Multiquadratic + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = sqrt(1+d); + elseif strcmpi(mode.TYPE,'IMQ'); % Inverse Multiquadratic + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = (1+d).^(1/2); + elseif strcmpi(mode.TYPE,'Cauchy'); % Cauchy RBF + for k = 1:length(CC.IR); + d(:,k) = sum((D*CC.IR{k}).*D,2); % calculate distance of each data point to each class + end; + d = 1./(1+d); + end; +else + fprintf(2,'Error TEST_SC: unknown classifier\n'); + return; +end; + +[tmp,cl] = max(d,[],2); +cl = CC.Labels(cl); +cl(isnan(tmp)) = NaN; + +R.output = d; +R.classlabel = cl; + +if nargin>3, + [R.kappa,R.sd,R.H,z,R.ACC] = kappa(classlabel(:),cl(:)); +end; Added: trunk/octaveforge/extra/NaN/inst/train_lda_sparse.m ===================================================================  trunk/octaveforge/extra/NaN/inst/train_lda_sparse.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/train_lda_sparse.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,145 @@ +function [CC] = train_lda_sparse(X,G,par,tol) +% Linear Discriminant Analysis for the Small Sample Size Problem as described in +% Algorithm 1 of J. Duintjer Tebbens, P. Schlesinger: 'Improving +% Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size +% Problem', Computational Statistics and Data Analysis, vol. 52, no. 1, pp. 423437, 2007. +% Input: +% X ...... (sparse) training data matrix +% G ...... group coding matrix of the training data +% test ...... (sparse) test data matrix +% Gtest ...... group coding matrix of the test data +% par ...... if par = 0 then classification exploits sparsity too +% tol ...... tolerance to distinguish zero eigenvalues +% Output: +% err ...... Wrong classification rate (in %) +% trafo ...... LDA transformation vectors +% +% Reference(s): +% J. Duintjer Tebbens, P. Schlesinger: 'Improving +% Implementation of Linear Discriminant Analysis for the High Dimension/Small Sample Size +% Problem', Computational Statistics and Data Analysis, vol. 52, no. 1, +% pp. 423437, 2007. +% +% Copyright (C) by J. Duintjer Tebbens, Institute of Computer Science of the Academy of Sciences of the Czech Republic, +% Pod Vodarenskou vezi 2, 182 07 Praha 8 Liben, 18.July.2006. +% This work was supported by the Program Information Society under project +% 1ET400300415. +% +% +% Modified for the use with Matlab6.5 by A. Schl\xF6gl, 22.Aug.2006 +% +% $Id: train_lda_sparse.m,v 1.7 20071011 09:39:39 schloegl Exp $ +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 3 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place  Suite 330, Boston, MA 021111307, USA. + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Step (1) +%p = length(X(1,:));n = length(X(:,1));g = length(G(1,:)); +G = sparse(G); +[n,p]=size(X); +g = size(G,2); + +for j=1:g + nj(j) = norm(G(:,j))^2; +end +Dtild = spdiags(nj'.^(1),[0],g,g); +Xtild = X*X'; +Xtild1 = Xtild*ones(n,1); +help = ones(n,1)*Xtild1'/n  (ones(1,n)*Xtild'*ones(n,1))/(n^2); +matrix = Xtild  Xtild1*ones(1,n)/n  help; +% eliminate nonsymmetry of matrix due to rounding error: +matrix = (matrix+matrix')/2; +[V0,S] = eig(matrix); +% [s,I] = sort(diag(S),'descend'); +[s,I] = sort(diag(S)); s = s; + +cc = sum(s<tol); + +count = ncc; +V1 = V0(:,I(1:count)); +D1inv = diag(s(1:count).^(1.0)); +Dhalfinv = diag(s(1:count).^(0.5)); +Dhalf = diag(s(1:count).^(0.5)); +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Step (2) +help2 = V1*D1inv; +M1 = Dtild*G'*Xtild; +B1 = (G*(M1*(speye(n)1/n))help)*help2; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Step (3) +opts.issym = 1;opts.isreal = 1;opts.disp = 0; +%if 0, +try, + [V0,S,flag] = eigs(B1'*B1,g1,'lm',opts); + EV = Dhalfinv*V0; + [s,I] = sort(diag(S)); s = s; + %else +catch + % needed as long as eigs is not supported by Octave + [V0,S] = eig(B1'*B1); + flag = 0; + [s,I] = sort(diag(S)); s = s(I(1:g1)); + EV = Dhalfinv * V0(:,I(1:g1)); + I = 1:g1; +end; +%EV = Dhalfinv*V0; +%[s,I] = sort((diag(S)),'descend'); +%[s,I] = sort(diag(S)); s = s; +if flag ~= 0 + 'eigs did not converge' +end +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Step (4) +for j=1:g1, + C(:,j) = EV(:,I(j))/norm(EV(:,I(j))); +end +cc = 0; +for j=1:g1, + if (1s(j))<tol + cc = cc+1; + V2(:,j) = EV(:,I(j)); + else + break + end +end +if cc > 0 + [Q,R] = qr(V2,0); + matrix = B1*Dhalf*Q; + [V0,S] = eig(matrix'*matrix); + %[s,I] = sort(diag(S),'descend'); + [s,I] = sort(diag(S)); s = s; + for j=1:cc + C(:,j) = Q*V0(:,I(j)); + end +end + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Step (5) +C1 = help2*Dhalf*C; +trafo(:,1:g1) = X'*C1  (X'*ones(n,1))*(ones(1,n)*C1/n); +for j=1:g1 + trafo(:,j) = trafo(:,j)/norm(trafo(:,j)); +end +CC.trafo = trafo; + +if par == 0 +% X2 = full(test*X'); +% [pred] = classifs(C1,M1,X2); + CC.C1 = C1; + CC.M1 = M1; + CC.X = X; +else +% M = Dtild*G'*X; +% [pred] = classifs(trafo,M,test); + CC.C1 = trafo; + CC.M1 = Dtild*G'*X; +end Property changes on: trunk/octaveforge/extra/NaN/inst/train_lda_sparse.m ___________________________________________________________________ Added: Id + test_sc.m Added: trunk/octaveforge/extra/NaN/inst/train_sc.m ===================================================================  trunk/octaveforge/extra/NaN/inst/train_sc.m (rev 0) +++ trunk/octaveforge/extra/NaN/inst/train_sc.m 20090702 12:27:35 UTC (rev 5985) @@ 0,0 +1,677 @@ +function [CC]=train_sc(D,classlabel,MODE,W) +% Train a (statistical) classifier +% +% CC = train_sc(D,classlabel) +% CC = train_sc(D,classlabel,MODE) +% CC = train_sc(D,classlabel, 'REG', W) +% weighting D(k,:) with weight W(k) +% +% CC contains the model parameters of a classifier which can be applied +% to test data using test_sc. +% R = test_sc(CC,D,...) +% +% The following classifier types are supported MODE.TYPE +% 'MDA' mahalanobis distance based classifier [1] +% 'MD2' mahalanobis distance based classifier [1] +% 'MD3' mahalanobis distance based classifier [1] +% 'GRB' Gaussian radial basis function [1] +% 'QDA' quadratic discriminant analysis [1] +% 'LD2' linear discriminant analysis (see LDBC2) [1] +% MODE.hyperparameter.gamma: regularization parameter [default 0] +% 'LD3' linear discriminant analysis (see LDBC3) [1] +% MODE.hyperparameter.gamma: regularization parameter [default 0] +% 'LD4' linear discriminant analysis (see LDBC4) [1] +% MODE.hyperparameter.gamma: regularization parameter [default 0] +% 'LD5' another LDA (motivated by CSP) +% MODE.hyperparameter.gamma: regularization parameter [default 0] +% 'RDA' regularized discriminant analysis [7] +% MODE.hyperparameter.gamma: regularization parameter +% MODE.hyperparameter.lambda = +% gamma = 0, lambda = 0 : MDA +% gamma = 0, lambda = 1 : LDA +% Hint: hyperparameters are used only in test_sc.m, testing different +% the hyperparameters do not need repetitive calls to train_sc, +% it is sufficient to modify CC.hyperparameters before calling test_sc. +% 'GDBC' general distance based classifier [1] +% '' statistical classifier, requires Mode argument in TEST_SC +% '###/GSVD' GSVD and statistical classifier [2,3], +% '###/sparse' sparse [5] +% '###' must be 'LDA' or any other classifier +% 'SVM','SVM1r' support vector machines, onevsrest +% MODE.hyperparameter.c_value = +% 'PSVM' Proximal SVM [8] +% MODE.hyperparameter.nu (default: 1.0) +% 'PLS' (linear) partial least squares regression +% 'REG' regression analysis; +% 'WienerHopf' WienerHopf equation +% 'NBC' Naive Bayesian Classifier [6] +% 'aNBC' Augmented Naive Bayesian Classifier [6] +% 'NBPW' Naive Bayesian Parzen Window [9] +% 'SVM11' support vector machines, onevsone + voting +% MODE.hyperparameter.c_value = +% 'RBF' Support Vector Machines with RBF Kernel +% MODE.hyperparameter.c_value = +% MODE.hyperparameter.gamma = +% 'LPM' Linear Programming Machine +% MODE.hyperparameter.c_value = +% 'CSP' CommonSpatialPattern is very experimental and just a hack +% uses a smoothing window of 50 samples. +% +% {'MDA','MD2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','REG','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse','RDA','GDBC','SVM','RBF'} +% +% CC contains the model parameters of a classifier. Some time ago, +% CC was a statistical classifier containing the mean +% and the covariance of the data of each class (encoded in the +% socalled "extended covariance matrices". Nowadays, also other +% classifiers are supported. +% +% see also: TEST_SC, COVM +% +% References: +% [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. +% John Wiley & Sons, 2001. +% [2] Peg Howland and Haesun Park, +% Generalizing Discriminant Analysis Using the Generalized Singular Value Decomposition +% IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 2004. +% dx.doi.org/10.1109/TPAMI.2004.46 +% [3] http://wwwstatic.cc.gatech.edu/~kihwan23/face_recog_gsvd.htm +% [4] Jieping Ye, Ravi Janardan, Cheong Hee Park, Haesun Park +% A new optimization criterion for generalized discriminant analysis on undersampled problems. +% The Third IEEE International Conference on Data Mining, Melbourne, Florida, USA +% November 19  22, 2003 +% [5] J.D. Tebbens and P. Schlesinger (2006), +% Improving Implementation of Linear Discriminant Analysis for the Small Sample Size Problem +% Computational Statistics & Data Analysis, vol 52(1): 423437, 2007 +% http://www.cs.cas.cz/mweb/download/publi/JdtSchl2006.pdf +% [6] H. Zhang, The optimality of Naive Bayes, +% http://www.cs.unb.ca/profs/hzhang/publications/FLAIRS04ZhangH.pdf +% [7] J.H. Friedman. Regularized discriminant analysis. +% Journal of the American Statistical Association, 84:165–175, 1989. +% [8] G. Fung and O.L. Mangasarian, Proximal Support Vector Machine Classifiers, KDD 2001. +% Eds. F. Provost and R. Srikant, Proc. KDD2001: Knowledge Discovery and Data Mining, August 2629, 2001, San Francisco, CA. +% p. 7786. +% [9] Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai Guan. +% Filter Bank Common Spatial Pattern (FBCSP) in BrainComputer Interface. +% IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). +% 18 June 2008 Page(s):2390  2397 + + +% $Id: train_sc.m 2140 20090702 12:03:55Z schloegl $ +% Copyright (C) 2005,2006,2007,2008,2009 by Alois Schloegl <a.schloegl@...> +% This function is part of the NaNtoolbox +% http://hci.tugraz.ac.at/~schloegl/matlab/NaN/ + +% This program is free software; you can redistribute it and/or +% modify it under the terms of the GNU General Public License +% as published by the Free Software Foundation; either version 2 +% of the License, or (at your option) any later version. +% +% This program is distributed in the hope that it will be useful, +% but WITHOUT ANY WARRANTY; without even the implied warranty of +% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +% GNU General Public License for more details. +% +% You should have received a copy of the GNU General Public License +% along with this program; if not, write to the Free Software +% Foundation, Inc., 59 Temple Place  Suite 330, Boston, MA 021111307, USA. + +if nargin<3, MODE = 'LDA'; end; +if nargin<4, W = []; end; +if ischar(MODE) + tmp = MODE; + clear MODE; + MODE.TYPE = tmp; +elseif ~isfield(MODE,'TYPE') + MODE.TYPE=''; +end; + +sz = size(D); +if sz(1)~=length(classlabel), + error('length of data and classlabel does not fit'); +end; + +%CC.Labels = unique(classlabel); +CC.Labels = 1:max(classlabel); + +% remove all NaN's +if 1, + % several classifier can deal with NaN's, there is no need to remove them. +elseif isempty(W) + %% TODO: some classifiers can deal with NaN's in D. Test whether this can be relaxed. + %ix = any(isnan([classlabel]),2); + ix = any(isnan([D,classlabel]),2); + D(ix,:)=[]; + classlabel(ix,:)=[]; + W = []; +else + %ix = any(isnan([classlabel]),2); + ix = any(isnan([D,classlabel]),2); + D(ix,:)=[]; + classlabel(ix,:)=[]; + W(ix,:)=[]; + warning('support for weighting of samples is still experimental'); +end; + +sz = size(D); +if sz(1)~=length(classlabel), + error('length of data and classlabel does not fit'); +end; +if ~isfield(MODE,'hyperparameter') + MODE.hyperparameter = []; +end + + +if 0, + +elseif ~isempty(strfind(lower(MODE.TYPE),'nbpw')) + error('NBPW not implemented yet') + %%%% Naive Bayesian Parzen Window Classifier. + for k = 1:length(CC.Labels), + [d,CC.MEAN(k,:)] = center(D(classlabel==CC.Labels(k),:),1); + [CC.VAR(k,:),CC.N(k,:)] = sumskipnan(d.^2,1); + h2_opt = (4./(3*CC.N(k,:))).^(2/5).*CC.VAR(k,:); + %%% TODO + end; + + +elseif ~isempty(strfind(lower(MODE.TYPE),'nbc')) + %%%% Naive Bayesian Classifier. + if ~isempty(strfind(lower(MODE.TYPE),'anbc')) + %%%% Augmented Naive Bayesian classifier. + [CC.V,L] = eig(covm(D,'M',W)); + D = D*CC.V; + else + CC.V = eye(size(D,2)); + end; + for k = 1:length(CC.Labels), + ix = classlabel==CC.Labels(k); + %% [d,CC.MEAN(k,:)] = center(D(ix,:),1); + if ~isempty(W) + [s,n] = sumskipnan(D(ix,:),1,W(ix)); + CC.MEAN(k,:) = s./n; + d = D(ix,:)  CC.MEAN(repmat(k,sum(ix),1),:); + [CC.VAR(k,:),CC.N(k,:)] = sumskipnan(d.^2,1,W(ix)); + else + [s,n] = sumskipnan(D(ix,:),1); + CC.MEAN(k,:) = s./n; + d = D(ix,:)  CC.MEAN(repmat(k,sum(ix),1),:); + [CC.VAR(k,:),CC.N(k,:)] = sumskipnan(d.^2,1); + end + end; + CC.VAR = CC.VAR./max(CC.N1,0); + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(lower(MODE.TYPE),'lpm')) + if ~isempty(W) + error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); + end; + % linear programming machine + % CPLEX optimizer: ILOG solver, ilog cplex 6.5 reference manual http://www.ilog.com + MODE.TYPE = 'LPM'; + if ~isfield(MODE.hyperparameter,'c_value') + MODE.hyperparameter.c_value = 1; + end + + M = length(CC.Labels); + if M==2, M=1; end; % For a 2class problem, only 1 Discriminant is needed + for k = 1:M, + %LPM = train_LPM(D,(classlabel==CC.Labels(k)),'C',MODE.hyperparameter.c_value); + LPM = train_LPM(D',(classlabel'==CC.Labels(k))); + CC.weights(:,k) = [LPM.b; LPM.w(:)]; + end; + CC.hyperparameter.c_value = MODE.hyperparameter.c_value; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(lower(MODE.TYPE),'pls'))  ~isempty(strfind(lower(MODE.TYPE),'reg')) + % 4th version: support for weighted samples  work well with unequally distributed data: + % regression analysis, can handle sparse data, too. + + M = length(CC.Labels); + + if nargin<4, + W = []; + end; + wD = [ones(size(D,1),1),D]; + + if isempty(W) + W = 1; + else + %% wD = diag(W)*wD + W = W(:); + for k=1:size(wD,2) + wD(:,k) = W.*wD(:,k); + end; + end; + + [q,r] = qr(wD,0); + CC.weights = repmat(NaN,sz(2)+1,M); + for k = 1:M, + ix = 2*(classlabel==CC.Labels(k))  1; + CC.weights(:,k) = r\(q'*(W.*ix)); + end; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) + % Q: equivalent to LDA + % equivalent to Regression, except regression can not deal with NaN's + M = length(CC.Labels); + %if M==2, M==1; end; + CC.weights = repmat(NaN,size(D,2)+1,M); + cc = covm(D,'E',W); + c1 = classlabel(~isnan(classlabel)); + c2 = ones(sum(~isnan(classlabel)),M); + for k = 1:M, + c2(:,k) = c1==CC.Labels(k); + end; + CC.weights = cc\covm([ones(size(c2,1),1),D(~isnan(classlabel),:)],2*real(c2)1,'M',W); + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) + % Q: equivalent to LDA, Regression? + M = length(CC.Labels); + %if M==2, M==1; end; + CC.weights = repmat(NaN,size(D,2)+1,M); + cc = covm(D,'E',W); + for k = 1:M, + w = cc\covm([ones(sz(1),1),D],real(classlabel==CC.Labels(k)),'M',W); + CC.weights(:,k) = w; + end; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(MODE.TYPE,'WienerHopf')) + %% OBSOLETE ??? + % Q: equivalent to LDA, Regression? + M = length(CC.Labels); + %if M==2, M==1; end; + CC.weights = repmat(NaN,size(D,2)+1,M); + for k = 1:M, + ix = ~any(isnan([classlabel,D]),2); + w = covm(D(ix,:),'E')\covm([ones(sum(ix),1),D(ix,:)],(classlabel(ix,:)==CC.Labels(k)),'M'); + CC.weights(:,k) = w; + end; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + + +elseif ~isempty(strfind(lower(MODE.TYPE),'/gsvd')) + if ~isempty(W) + error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); + end; + % [2] Peg Howland and Haesun Park, 2004. + % Generalizing Discriminant Analysis Using the Generalized Singular Value Decomposition + % IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 2004. + % dx.doi.org/10.1109/TPAMI.2004.46 + % [3] http://wwwstatic.cc.gatech.edu/~kihwan23/face_recog_gsvd.htm + + Hw = zeros(size(D)+[length(CC.Labels),0]); + Hb = []; + m0 = mean(D); + K = length(CC.Labels); + for k = 1:K, + ix = find(classlabel==CC.Labels(k)); + N(k) = length(ix); + [Hw(ix,:), mu] = center(D(ix,:)); + %Hb(k,:) = sqrt(N(k))*(mu(k,:)m0); + Hw(size(D,1)+k,:) = sqrt(N(k))*(mum0); % Hb(k,:) + end; + try + [P,R,Q] = svd(Hw,'econ'); + catch % needed because SVD(..,'econ') not supported in Matlab 6.x + [P,R,Q] = svd(Hw,0); + end; + t = rank(R); + + clear Hw Hb mu; + %[size(D);size(P);size(Q);size(R)] + R = R(1:t,1:t); + %P = P(1:size(D,1),1:t); + %Q = Q(1:t,:); + [U,E,W] = svd(P(1:size(D,1),1:t),0); + %[size(U);size(E);size(W)] + clear U E P; + %[size(Q);size(R);size(W)] + + %G = Q(1:t,:)'*[R\W']; + G = Q(:,1:t)*[R\W']; % this works as well and needs only 'econ'SVD + %G = G(:,1:t); % not needed + + % do not use this, gives very bad results for Medline database + %G = G(:,1:K); this seems to be a typo in [2] and [3]. + + CC = train_sc(D*G,classlabel,MODE.TYPE(1:find(MODE.TYPE=='/')1)); + CC.G = G; + if isfield(CC,'weights') + CC.weights = [CC.weights(1,:); G*CC.weights(2:end,:)]; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + else + CC.datatype = [CC.datatype,'/gsvd']; + end; + + +elseif ~isempty(strfind(lower(MODE.TYPE),'sparse')) + if ~isempty(W) + error(sprintf('Error TRAIN_SC: Classifier (%s) does not support weighted samples.',MODE.TYPE)); + end; + % [5] J.D. Tebbens and P.Schlesinger (2006), + % Improving Implementation of Linear Discriminant Analysis for the Small Sample Size Problem + % http://www.cs.cas.cz/mweb/download/publi/JdtSchl2006.pdf + + warning('sparse LDA is sensitive to linear transformations') + M = length(CC.Labels); + G = sparse([],[],[],size(D,1),M,size(D,1)); + for k = 1:M, + G(classlabel==CC.Labels(k),k) = 1; + end; + tol = 1e10; + + G = train_lda_sparse(D,G,1,tol); + CC.datatype = 'classifier:slda'; + POS1 = find(MODE.TYPE=='/'); + %G = v(:,1:size(G.trafo,2)).*G.trafo; + %CC.weights = s * CC.weights(2:end,:) + sparse(1,1:M,CC.weights(1,:),sz(2)+1,M); + G = G.trafo; + CC = train_sc(D*G,classlabel,MODE.TYPE(1:POS1(1)1)); + CC.G = G; + if isfield(CC,'weights') + CC.weights = [CC.weights(1,:); G*CC.weights(2:end,:)]; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + else + CC.datatype = [CC.datatype,'/sparse']; + end; + @@ Diff output truncated at 100000 characters. @@ This was sent by the SourceForge.net collaborative development platform, the world's largest Open Source development site. 