--- /dev/null
+function [R,CC]=xval(D,classlabel,MODE,arg4)
+% XVAL is used for crossvalidation
+%
+% [R,CC] = xval(D,classlabel)
+% .. = xval(D,classlabel,CLASSIFIER)
+% .. = xval(D,classlabel,CLASSIFIER,type)
+% .. = xval(D,{classlabel,W},CLASSIFIER)
+% .. = xval(D,{classlabel,W,NG},CLASSIFIER)
+%
+% example:
+% load_fisheriris; %builtin iris dataset
+% C = species;
+% K = 5; NG = [1:length(C)]'*K/length(C);
+% [R,CC] = xval(meas,{C,[],NG},'NBC');
+%
+% Input:
+% D: data features (one feature per column, one sample per row)
+% classlabel labels of each sample, must have the same number of rows as D.
+% Two different encodings are supported:
+% {-1,1}-encoding (multiple classes with separate columns for each class) or
+% 1..M encoding.
+% So [1;2;3;1;4] is equivalent to
+% [+1,-1,-1,-1;
+% [-1,+1,-1,-1;
+% [-1,-1,+1,-1;
+% [+1,-1,-1,-1]
+% [-1,-1,-1,+1]
+% Note, samples with classlabel=0 are ignored.
+%
+% CLASSIFIER can be any classifier supported by train_sc (default='LDA')
+% {'REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf', 'RDA','GDBC',
+% 'SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW'}
+% these can be modified by ###/GSVD, ###/sparse and ###/DELETION.
+% /DELETION removes in case of NaN's either the rows or the columns (which removes less data values) with any NaN
+% /sparse and /GSVD preprocess the data an reduce it to some lower-dimensional space.
+% Hyperparameters (like alpha for PLA, gamma/lambda for RDA, c_value for SVM, etc) can be defined as
+% CLASSIFIER.hyperparameter.alpha, etc. and
+% CLASSIFIER.TYPE = 'PLA' (as listed above).
+% See train_sc for details.
+% W: weights for each sample (row) in D.
+% default: [] (i.e. all weights are 1)
+% number of elements in W must match the number of rows of D
+% NG: used to define the type of cross-valdiation
+% Leave-One-Out-Method (LOOM): NG = [1:length(classlabel)]' (default)
+% Leave-K-Out-Method: NG = ceil([1:length(classlabel)]'/K)
+% K-fold XV: NG = ceil([1:length(classlabel)]'*K/length(classlabel))
+% group-wise XV (if samples are not indepentent) can be also defined here
+% samples from the same group (dependent samples) get the same identifier
+% samples from different groups get different classifiers
+% TYPE: defines the type of cross-validation procedure if NG is not specified
+% 'LOOM' leave-one-out-method
+% k k-fold crossvalidation
+%
+% OUTPUT:
+% R contains the resulting performance metric
+% CC contains the classifier
+%
+% plota(R) shows the confusion matrix of the results
+%
+% see also: TRAIN_SC, TEST_SC, CLASSIFY, PLOTA
+%
+% References:
+% [1] R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed.
+% John Wiley & Sons, 2001.
+% [2] A. Schlögl, J. Kronegg, J.E. Huggins, S. G. Mason;
+% Evaluation criteria in BCI research.
+% (Eds.) G. Dornhege, J.R. Millan, T. Hinterberger, D.J. McFarland, K.-R.Müller;
+% Towards Brain-Computer Interfacing, MIT Press, 2007, p.327-342
+
+% $Id$
+% Copyright (C) 2008,2009,2010 by Alois Schloegl <alois.schloegl@gmail.com>
+% This function is part of the NaN-toolbox
+% http://pub.ist.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., 51 Franklin Street - Fifth Floor, Boston, MA 02110-1301, USA.
+
+if (nargin<3) || isempty(MODE),
+ MODE = 'LDA';
+end;
+if ischar(MODE)
+ tmp = MODE;
+ clear MODE;
+ MODE.TYPE = tmp;
+elseif ~isfield(MODE,'TYPE')
+ MODE.TYPE='';
+end;
+
+sz = size(D);
+NG = [];
+W = [];
+
+if iscell(classlabel)
+ [b,i,C] = unique(classlabel{:,1});
+ if size(classlabel,2)>1,
+ W = [classlabel{:,2}];
+ end;
+ if size(classlabel,2)>2,
+ [Label,tmp1,NG] = unique(classlabel{:,3});
+ end;
+elseif size(classlabel,2)>1,
+ %% group-wise classvalidation
+ C = classlabel(:,1);
+ W = classlabel(:,2);
+ if size(classlabel,2)==2,
+ warning('This option defines W and NG in an ambigous way - use instead xval(D,{C,[],NG},...) or xval(D,{C,W},...)');
+ else
+ [Label,tmp1,NG] = unique(classlabel(:,3));
+ end;
+else
+ C = classlabel;
+end;
+if all(W==1), W = []; end;
+if sz(1)~=size(C,1),
+ error('length of data and classlabel does not fit');
+end;
+
+% use only valid samples
+ix0 = find(~any(isnan(C),2));
+
+if isempty(NG)
+if (nargin<4) || strcmpi(arg4,'LOOM')
+ %% LOOM
+ NG = (1:sz(1))';
+
+elseif isnumeric(arg4)
+ if isscalar(arg4)
+ % K-fold XV
+ NG = ceil((1:length(C))'*arg4/length(C));
+ elseif length(arg4)==2,
+ NG = ceil((1:length(C))'*arg4(1)/length(C));
+ end;
+
+end;
+end;
+
+sz = size(D);
+if sz(1)~=length(C),
+ error('length of data and classlabel does not fit');
+end;
+if ~isfield(MODE,'hyperparameter')
+ MODE.hyperparameter = [];
+end
+
+cl = repmat(NaN,size(classlabel,1),1);
+for k = 1:max(NG),
+ ix = ix0(NG(ix0)~=k);
+ if isempty(W),
+ CC = train_sc(D(ix,:), C(ix), MODE);
+ else
+ CC = train_sc(D(ix,:), C(ix), MODE, W(ix));
+ end;
+ ix = ix0(NG(ix0)==k);
+ r = test_sc(CC, D(ix,:));
+ cl(ix,1) = r.classlabel;
+end;
+
+%R = kappa(C,cl,'notIgnoreNAN',W);
+R = kappa(C,cl,[],W);
+%R2 = kappa(R.H);
+
+R.ERR = 1-R.ACC;
+if isnumeric(R.Label)
+ R.Label = cellstr(int2str(R.Label));
+end;
+
+if nargout>1,
+ % final classifier
+ if isempty(W),
+ CC = train_sc(D,C,MODE);
+ else
+ CC = train_sc(D,C,MODE,W);
+ end;
+ CC.Labels = 1:max(C);
+ %CC.Labels = unique(C);
+end;
git://git.creatis.insa-lyon.fr / CreaPhase.git / blobdiff