/usr/share/octave/packages/3.2/nan-2.4.4/test_sc.m is in octave-nan 2.4.4-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 | 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: "signed" distance for each class.
% This represents the distances between sample D and the separating hyperplane
% The "signed distance" is possitive if it matches the target class, and
% and negative if it lays on the opposite side of the separating hyperplane.
% 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
% TYPE = 'LD3', 'LDA', 'FDA, 'FLDA' (Fisher's) linear discriminant analysis
% TYPE = 'LD4' linear discriminant analysis
% 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 8223 2011-04-20 09:16:06Z schloegl $
% Copyright (C) 2005,2006,2008,2009,2010 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., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
if nargin<3,
mode = [];
end;
[t1,t] = strtok(CC.datatype,':');
[t2,t] = strtok(t,':');
[t3] = 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'),strfind(CC.datatype,'/delet')];
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(d-log(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(d-log(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(d-log(mean(sum(CC.N,1)))-log(2*pi)/2);
elseif strcmp(CC.datatype,'classifier:statistical:rda')
% Friedman (1989) Regularized Discriminant analysis
if isfield(CC,'hyperparameter') && isfield(CC.hyperparameter,'lambda') && isfield(CC.hyperparameter,'gamma')
D = [ones(size(D,1),1),D]; % add 1-column
lambda = CC.hyperparameter.lambda;
gamma = CC.hyperparameter.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] = decovm(ECM0);
for k = 1:NC(3);
[M,sd,s,xc,N] = decovm(squeeze(ECM(:,:,k)));
s = ((1-lambda)*N*s+lambda*COV0)/((1-lambda)*N+lambda);
s = (1-gamma)*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 = filtfilt(CC.FiltB,CC.FiltA,(D*CC.csp_w).^2);
R = test_sc(CC.CSP,log(d)); % LDA classifier of
d = R.output;
elseif strcmp(CC.datatype,'classifier:svm:lib:1vs1') || strcmp(CC.datatype,'classifier:svm:lib:rbf');
cl = svmpredict_mex(ones(size(D,1),1), D, CC.model); %Use the classifier
%Create a pseudo tsd matrix for bci4eval
d = zeros(size(D,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;
elseif ~isempty(POS1) % GSVD, sparse & DELETION
CC.datatype = CC.datatype(1:POS1(1)-1);
r = test_sc(CC, D*sparse(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 1-column
W = repmat(NaN, size(D,2), size(CC.MD,3));
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] = decovm(ECM0);
for k = 1:NC(3);
ecm = squeeze(ECM(:,:,k));
[M1,sd,COV1] = decovm(ECM0-ecm);
[M2,sd,COV2] = decovm(ecm);
w = (COV1+COV2)\(M2'-M1')*2;
w0 = -M0*w;
W(:,k) = [w0; w];
end;
d = D*W;
elseif strcmpi(mode.TYPE,'LD3') || strcmpi(mode.TYPE,'FLDA');
%d = ldbc3(CC,D);
ECM = CC.MD./CC.NN;
NC = size(ECM);
ECM0 = squeeze(sum(ECM,3)); %decompose ECM
[M0,sd,COV0] = decovm(ECM0);
for k = 1:NC(3);
ecm = squeeze(ECM(:,:,k));
[M1] = decovm(ECM0-ecm);
[M2] = 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 = decovm(ECM0);
for k = 1:NC(3);
ecm = squeeze(ECM(:,:,k));
[M1,sd,COV1,xc,N1] = decovm(ECM0-ecm);
[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');
d = repmat(NaN,size(D,1),length(CC.IR));
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');
d = repmat(NaN,size(D,1),length(CC.IR));
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');
d = repmat(NaN,size(D,1),length(CC.IR));
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');
d = repmat(NaN,size(D,1),length(CC.IR));
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');
d = repmat(NaN,size(D,1),length(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.logSF5(k));
end;
elseif strcmpi(mode.TYPE,'QDA2');
d = repmat(NaN,size(D,1),length(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
d = repmat(NaN,size(D,1),length(CC.IR));
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
d = repmat(NaN,size(D,1),length(CC.IR));
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
d = repmat(NaN,size(D,1),length(CC.IR));
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
d = repmat(NaN,size(D,1),length(CC.IR));
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
d = repmat(NaN,size(D,1),length(CC.IR));
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);
else
error('Classifier %s not supported. see HELP TRAIN_SC for supported classifiers.',mode.TYPE);
end;
else
fprintf(2,'Error TEST_SC: unknown classifier\n');
return;
end;
if size(d,2)>1,
[tmp,cl] = max(d,[],2);
cl = CC.Labels(cl);
cl(isnan(tmp)) = NaN;
elseif size(d,2)==1,
cl = (d<0) + 2*(d>0);
cl(isnan(d)) = NaN;
end;
R.output = d;
R.classlabel = cl;
if nargin>3,
[R.kappa,R.sd,R.H,z,R.ACC] = kappa(classlabel(:),cl(:));
end;
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