X-Git-Url: https://git.creatis.insa-lyon.fr/pubgit/?p=CreaPhase.git;a=blobdiff_plain;f=octave_packages%2Fnan-2.5.5%2Ftrain_sc.m;fp=octave_packages%2Fnan-2.5.5%2Ftrain_sc.m;h=6d727ea2b3f6d1b4b8684020dbfc767533e54209;hp=0000000000000000000000000000000000000000;hb=f5f7a74bd8a4900f0b797da6783be80e11a68d86;hpb=1705066eceaaea976f010f669ce8e972f3734b05 diff --git a/octave_packages/nan-2.5.5/train_sc.m b/octave_packages/nan-2.5.5/train_sc.m new file mode 100644 index 0000000..6d727ea --- /dev/null +++ b/octave_packages/nan-2.5.5/train_sc.m @@ -0,0 +1,942 @@ +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,MODE, W) +% weighting D(k,:) with weight W(k) (not all classifiers supported weighting) +% +% CC contains the model parameters of a classifier which can be applied +% to test data using test_sc. +% R = test_sc(CC,D,...) +% +% D training samples (each row is a sample, each column is a feature) +% 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. +% +% 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', 'FDA', 'LDA', 'FLDA' +% 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 [default] +% Hint: hyperparameter 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.hyperparameter before calling test_sc. +% 'GDBC' general distance based classifier [1] +% '' statistical classifier, requires Mode argument in TEST_SC +% '###/DELETION' if the data contains missing values (encoded as NaNs), +% a row-wise or column-wise deletion (depending on which method +% removes less data values) is applied; +% '###/GSVD' GSVD and statistical classifier [2,3], +% '###/sparse' sparse [5] +% '###' must be 'LDA' or any other classifier +% 'PLS' (linear) partial least squares regression +% 'REG' regression analysis; +% 'WienerHopf' Wiener-Hopf equation +% 'NBC' Naive Bayesian Classifier [6] +% 'aNBC' Augmented Naive Bayesian Classifier [6] +% 'NBPW' Naive Bayesian Parzen Window [9] +% +% 'PLA' Perceptron Learning Algorithm [11] +% MODE.hyperparameter.alpha = alpha [default: 1] +% w = w + alpha * e'*x +% 'LMS', 'AdaLine' Least mean squares, adaptive line element, Widrow-Hoff, delta rule +% MODE.hyperparameter.alpha = alpha [default: 1] +% 'Winnow2' Winnow2 algorithm [12] +% +% 'PSVM' Proximal SVM [8] +% MODE.hyperparameter.nu (default: 1.0) +% 'LPM' Linear Programming Machine +% uses and requires train_LPM of the iLog CPLEX optimizer +% MODE.hyperparameter.c_value = +% 'CSP' CommonSpatialPattern is very experimental and just a hack +% uses a smoothing window of 50 samples. +% 'SVM','SVM1r' support vector machines, one-vs-rest +% MODE.hyperparameter.c_value = +% 'SVM11' support vector machines, one-vs-one + voting +% MODE.hyperparameter.c_value = +% 'RBF' Support Vector Machines with RBF Kernel +% MODE.hyperparameter.c_value = +% MODE.hyperparameter.gamma = +% 'SVM:LIB' libSVM [default SVM algorithm) +% 'SVM:bioinfo' uses and requires svmtrain from the bioinfo toolbox +% 'SVM:OSU' uses and requires mexSVMTrain from the OSU-SVM toolbox +% 'SVM:LOO' uses and requires svcm_train from the LOO-SVM toolbox +% 'SVM:Gunn' uses and requires svc-functios from the Gunn-SVM toolbox +% 'SVM:KM' uses and requires svmclass-function from the KM-SVM toolbox +% 'SVM:LINz' LibLinear [10] (requires train.mex from LibLinear somewhere in the path) +% z=0 (default) LibLinear with -- L2-regularized logistic regression +% z=1 LibLinear with -- L2-loss support vector machines (dual) +% z=2 LibLinear with -- L2-loss support vector machines (primal) +% z=3 LibLinear with -- L1-loss support vector machines (dual) +% 'SVM:LIN4' LibLinear with -- multi-class support vector machines by Crammer and Singer +% 'DT' decision tree - not implemented yet. +% +% {'REG','MDA','MD2','QDA','QDA2','LD2','LD3','LD4','LD5','LD6','NBC','aNBC','WienerHopf','LDA/GSVD','MDA/GSVD', 'LDA/sparse','MDA/sparse', 'PLA', 'LMS','LDA/DELETION','MDA/DELETION','NBC/DELETION','RDA/DELETION','REG/DELETION','RDA','GDBC','SVM','RBF','PSVM','SVM11','SVM:LIN4','SVM:LIN0','SVM:LIN1','SVM:LIN2','SVM:LIN3','WINNOW', 'DT'}; +% +% 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 +% so-called "extended covariance matrices". Nowadays, also other +% classifiers are supported. +% +% see also: TEST_SC, COVM, ROW_COL_DELETION +% +% 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://www-static.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): 423-437, 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. KDD-2001: Knowledge Discovery and Data Mining, August 26-29, 2001, San Francisco, CA. +% p. 77-86. +% [9] Kai Keng Ang, Zhang Yang Chin, Haihong Zhang, Cuntai Guan. +% Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface. +% IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). +% 1-8 June 2008 Page(s):2390 - 2397 +% [10] R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. +% LIBLINEAR: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874. +% Software available at http://www.csie.ntu.edu.tw/~cjlin/liblinear +% [11] http://en.wikipedia.org/wiki/Perceptron#Learning_algorithm +% [12] Littlestone, N. (1988) +% "Learning Quickly When Irrelevant Attributes Abound: A New Linear-threshold Algorithm" +% Machine Learning 285-318(2) +% http://en.wikipedia.org/wiki/Winnow_(algorithm) + +% $Id: train_sc.m 9601 2012-02-09 14:14:36Z schloegl $ +% Copyright (C) 2005,2006,2007,2008,2009,2010 by Alois Schloegl +% 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<2, + error('insufficient input arguments\n\tusage: train_sc(D,C,...)\n'); + end + 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 + + if isfield(MODE,'hyperparameters') && ~isfield(MODE,'hyperparameter'), + %% for backwards compatibility, this might become obsolete + warning('MODE.hyperparameters are used, You should use MODE.hyperparameter instead!!!'); + MODE.hyperparameter = MODE.hyperparameters; + end + + sz = size(D); + if sz(1)~=size(classlabel,1), + error('length of data and classlabel does not fit'); + end + + % 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),'/delet')) + POS1 = find(MODE.TYPE=='/'); + [rix,cix] = row_col_deletion(D); + if ~isempty(W), W=W(rix); end + CC = train_sc(D(rix,cix),classlabel(rix,:),MODE.TYPE(1:POS1(1)-1),W); + CC.G = sparse(cix, 1:length(cix), 1, size(D,2), length(cix)); + if isfield(CC,'weights') + W = [CC.weights(1,:); CC.weights(2:end,:)]; + CC.weights = sparse(size(D,2)+1, size(W,2)); + CC.weights([1,cix+1],:) = W; + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + else + CC.datatype = [CC.datatype,'/delet']; + end + + elseif ~isempty(strfind(lower(MODE.TYPE),'nbpw')) + error('NBPW not implemented yet') + %%%% Naive Bayesian Parzen Window Classifier. + [classlabel,CC.Labels] = CL1M(classlabel); + 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 + [classlabel,CC.Labels] = CL1M(classlabel); + 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.N-1,0); + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'lpm')) + if ~isempty(W) + error('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 + [classlabel,CC.Labels] = CL1M(classlabel); + + M = length(CC.Labels); + if M==2, M=1; end % For a 2-class 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),'pla')), + % Perceptron Learning Algorithm + + [rix,cix] = row_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + weights = sparse(length(cix)+1,M); + + %ix = randperm(size(D,1)); %% randomize samples ??? + if ~isfield(MODE.hyperparameter,'alpha') + if isfield(MODE.hyperparameter,'alpha') + alpha = MODE.hyperparameter.alpha; + else + alpha = 1; + end + for k = rix(:)', + %e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); + weights = weights + alpha * [1,D(k,cix)]' * e ; + end + + else %if ~isempty(W) + if isfield(MODE.hyperparameter,'alpha') + W = W*MODE.hyperparameter.alpha; + end + for k = rix(:)', + %e = ((classlabel(k)==(1:M))-.5) - sign([1, D(k,cix)] * weights)/2; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); + weights = weights + W(k) * [1,D(k,cix)]' * e ; + end + end + CC.weights = sparse(size(D,2)+1,M); + CC.weights([1,cix+1],:) = weights; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'adaline')) || ~isempty(strfind(lower(MODE.TYPE),'lms')), + % adaptive linear elemente, least mean squares, delta rule, Widrow-Hoff, + + [rix,cix] = row_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + weights = sparse(length(cix)+1,M); + + %ix = randperm(size(D,1)); %% randomize samples ??? + if isempty(W) + if isfield(MODE.hyperparameter,'alpha') + alpha = MODE.hyperparameter.alpha; + else + alpha = 1; + end + for k = rix(:)', + %e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); + weights = weights + alpha * [1,D(k,cix)]' * e ; + end + + else %if ~isempty(W) + if isfield(MODE.hyperparameter,'alpha') + W = W*MODE.hyperparameter.alpha; + end + for k = rix(:)', + %e = (classlabel(k)==(1:M)) - [1, D(k,cix)] * weights; + e = CL101(k,:) - sign([1, D(k,cix)] * weights); + weights = weights + W(k) * [1,D(k,cix)]' * e ; + end + end + CC.weights = sparse(size(D,2)+1,M); + CC.weights([1,cix+1],:) = weights; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'winnow')) + % winnow algorithm + if ~isempty(W) + error('Classifier (%s) does not support weighted samples.',MODE.TYPE); + end + + [rix,cix] = row_col_deletion(D); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + weights = ones(length(cix),M); + theta = size(D,2)/2; + + for k = rix(:)', + e = CL101(k,:) - sign(D(k,cix) * weights - theta); + weights = weights.* 2.^(D(k,cix)' * e); + end + + CC.weights = sparse(size(D,2)+1,M); + CC.weights(cix+1,:) = weights; + 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. + + if nargin<4, + W = []; + end + [rix, cix] = row_col_deletion(D); + wD = [ones(length(rix),1),D(rix,cix)]; + + if ~isempty(W) + %% wD = diag(W)*wD + W = W(:); + for k=1:size(wD,2) + wD(:,k) = W(rix).*wD(:,k); + end + end + [CL101, CC.Labels] = cl101(classlabel(rix,:)); + M = size(CL101,2); + CC.weights = sparse(sz(2)+1,M); + + %[rix, cix] = row_col_deletion(wD); + [q,r] = qr(wD,0); + + if isempty(W) + CC.weights([1,cix+1],:) = r\(q'*CL101); + else + CC.weights([1,cix+1],:) = r\(q'*(W(rix,ones(1,M)).*CL101)); + end + %for k = 1:M, + % CC.weights(cix,k) = r\(q'*(W.*CL101(rix,k))); + %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 + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + CC.weights = sparse(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.weights = cc\covm([ones(size(D,1),1),D],CL101,'M',W); + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'/gsvd')) + if ~isempty(W) + error('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://www-static.cc.gatech.edu/~kihwan23/face_recog_gsvd.htm + + [classlabel,CC.Labels] = CL1M(classlabel); + [rix,cix] = row_col_deletion(D); + + Hw = zeros(length(rix)+length(CC.Labels), length(cix)); + Hb = []; + m0 = mean(D(rix,cix)); + K = length(CC.Labels); + N = zeros(1,K); + for k = 1:K, + ix = find(classlabel(rix)==CC.Labels(k)); + N(k) = length(ix); + [Hw(ix,:), mu] = center(D(rix(ix),cix)); + %Hb(k,:) = sqrt(N(k))*(mu(k,:)-m0); + Hw(length(rix)+k,:) = sqrt(N(k))*(mu-m0); % 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:length(rix),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(:,cix)*G,classlabel,MODE.TYPE(1:find(MODE.TYPE=='/')-1)); + CC.G = sparse(size(D,2),size(G,2)); + CC.G(cix,:) = G; + if isfield(CC,'weights') + CC.weights = sparse([CC.weights(1,:); CC.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('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 + + [classlabel,CC.Labels] = CL1M(classlabel); + [rix,cix] = row_col_deletion(D); + + warning('sparse LDA is sensitive to linear transformations') + M = length(CC.Labels); + G = sparse([],[],[],length(rix),M,length(rix)); + for k = 1:M, + G(classlabel(rix)==CC.Labels(k),k) = 1; + end + tol = 1e-10; + + G = train_lda_sparse(D(rix,cix),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); + + CC = train_sc(D(rix,cix)*G.trafo,classlabel(rix),MODE.TYPE(1:POS1(1)-1)); + CC.G = sparse(size(D,2),size(G.trafo,2)); + CC.G(cix,:) = G.trafo; + if isfield(CC,'weights') + CC.weights = sparse([CC.weights(1,:); CC.G*CC.weights(2:end,:)]); + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + else + CC.datatype = [CC.datatype,'/sparse']; + end + + elseif ~isempty(strfind(lower(MODE.TYPE),'rbf')) + if ~isempty(W) + error('Classifier (%s) does not support weighted samples.',MODE.TYPE); + end + + % Martin Hieden's RBF-SVM + if exist('svmpredict_mex','file')==3, + MODE.TYPE = 'SVM:LIB:RBF'; + else + error('No SVM training algorithm available. Install LibSVM for Matlab.\n'); + end + CC.options = '-t 2 -q'; %use RBF kernel, set C, set gamma + if isfield(MODE.hyperparameter,'gamma') + CC.options = sprintf('%s -c %g', CC.options, MODE.hyperparameter.c_value); % set C + end + if isfield(MODE.hyperparameter,'c_value') + CC.options = sprintf('%s -g %g', CC.options, MODE.hyperparameter.gamma); % set C + end + + % pre-whitening + [D,r,m]=zscore(D,1); + CC.prewhite = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); + CC.prewhite(1,:) = -m.*r; + + [classlabel,CC.Labels] = CL1M(classlabel); + CC.model = svmtrain_mex(classlabel, D, CC.options); % Call the training mex File + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'svm11')) + if ~isempty(W) + error('Classifier (%s) does not support weighted samples.',MODE.TYPE); + end + % 1-versus-1 scheme + if ~isfield(MODE.hyperparameter,'c_value') + MODE.hyperparameter.c_value = 1; + end + + CC.options=sprintf('-c %g -t 0 -q',MODE.hyperparameter.c_value); %use linear kernel, set C + CC.hyperparameter.c_value = MODE.hyperparameter.c_value; + + % pre-whitening + [D,r,m]=zscore(D,1); + CC.prewhite = sparse(2:sz(2)+1,1:sz(2),r,sz(2)+1,sz(2),2*sz(2)); + CC.prewhite(1,:) = -m.*r; + + [classlabel,CC.Labels] = CL1M(classlabel); + CC.model = svmtrain_mex(classlabel, D, CC.options); % Call the training mex File + + FUN = 'SVM:LIB:1vs1'; + CC.datatype = ['classifier:',lower(FUN)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'psvm')) + if ~isempty(W) + %%% error('Classifier (%s) does not support weighted samples.',MODE.TYPE); + warning('Classifier (%s) in combination with weighted samples is not tested.',MODE.TYPE); + end + if ~isfield(MODE,'hyperparameter') + nu = 1; + elseif isfield(MODE.hyperparameter,'nu') + nu = MODE.hyperparameter.nu; + else + nu = 1; + end + [m,n] = size(D); + [CL101,CC.Labels] = cl101(classlabel); + CC.weights = sparse(n+1,size(CL101,2)); + M = size(CL101,2); + for k = 1:M, + d = sparse(1:m,1:m,CL101(:,k)); + H = d * [ones(m,1),D]; + %%% r = sum(H,1)'; + r = sumskipnan(H,1,W)'; + %%% r = (speye(n+1)/nu + H' * H)\r; %solve (I/nu+H’*H)r=H’*e + [HTH, nn] = covm(H,H,'M',W); + r = (speye(n+1)/nu + HTH)\r; %solve (I/nu+H’*H)r=H’*e + u = nu*(1-(H*r)); + %%% CC.weights(:,k) = u'*H; + [c,nn] = covm(u,H,'M',W); + CC.weights(:,k) = c'; + end + CC.hyperparameter.nu = nu; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + elseif ~isempty(strfind(lower(MODE.TYPE),'svm:lin4')) + if ~isfield(MODE.hyperparameter,'c_value') + MODE.hyperparameter.c_value = 1; + end + + [classlabel,CC.Labels] = CL1M(classlabel); + M = length(CC.Labels); + CC.weights = sparse(size(D,2)+1,M); + + [rix,cix] = row_col_deletion(D); + + % pre-whitening + [D,r,m]=zscore(D(rix,cix),1); + sz2 = length(cix); + s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); + s(1,:) = -m.*r; + + CC.options = sprintf('-s 4 -B 1 -c %f -q', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + model = train(W, classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + weights = model.w([end,1:end-1],:)'; + + CC.weights([1,cix+1],:) = s * weights(2:end,:) + sparse(1,1:M,weights(1,:),sz2+1,M); % include pre-whitening transformation + CC.weights([1,cix+1],:) = s * CC.weights(cix+1,:) + sparse(1,1:M,CC.weights(1,:),sz2+1,M); % include pre-whitening transformation + CC.hyperparameter.c_value = MODE.hyperparameter.c_value; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'svm')) + + if ~isfield(MODE.hyperparameter,'c_value') + MODE.hyperparameter.c_value = 1; + end + if any(MODE.TYPE==':'), + % nothing to be done + elseif exist('train','file')==3, + MODE.TYPE = 'SVM:LIN'; %% liblinear + elseif exist('svmtrain_mex','file')==3, + MODE.TYPE = 'SVM:LIB'; + elseif (exist('svmtrain','file')==3), + MODE.TYPE = 'SVM:LIB'; + fprintf(1,'You need to rename %s to svmtrain_mex.mex !! \n Press any key to continue !!!\n',which('svmtrain.mex')); + elseif exist('svmtrain','file')==2, + MODE.TYPE = 'SVM:bioinfo'; + elseif exist('mexSVMTrain','file')==3, + MODE.TYPE = 'SVM:OSU'; + elseif exist('svcm_train','file')==2, + MODE.TYPE = 'SVM:LOO'; + elseif exist('svmclass','file')==2, + MODE.TYPE = 'SVM:KM'; + elseif exist('svc','file')==2, + MODE.TYPE = 'SVM:Gunn'; + else + error('No SVM training algorithm available. Install OSV-SVM, or LOO-SVM, or libSVM for Matlab.\n'); + end + + %%CC = train_svm(D,classlabel,MODE); + [CL101,CC.Labels] = cl101(classlabel); + M = size(CL101,2); + [rix,cix] = row_col_deletion(D); + CC.weights = sparse(sz(2)+1, M); + + % pre-whitening + [D,r,m]=zscore(D(rix,cix),1); + sz2 = length(cix); + s = sparse(2:sz2+1,1:sz2,r,sz2+1,sz2,2*sz2); + s(1,:) = -m.*r; + + for k = 1:M, + cl = CL101(rix,k); + if strncmp(MODE.TYPE, 'SVM:LIN',7); + if isfield(MODE,'options') + CC.options = MODE.options; + else + t = 0; + if length(MODE.TYPE)>7, t=MODE.TYPE(8)-'0'; end + if (t<0 || t>6) t=0; end + CC.options = sprintf('-s %i -B 1 -c %f -q',t, MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + end + model = train(W, cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, + w = -model.w'; + Bias = -model.bias; + w = -model.w(:,1:end-1)'; + Bias = -model.w(:,end)'; + + elseif strcmp(MODE.TYPE, 'SVM:LIB'); %% tested with libsvm-mat-2.9-1 + if isfield(MODE,'options') + CC.options = MODE.options; + else + CC.options = sprintf('-s 0 -c %f -t 0 -d 1 -q', MODE.hyperparameter.c_value); % C-SVC, C=1, linear kernel, degree = 1, + end + model = svmtrain_mex(cl, D, CC.options); % C-SVC, C=1, linear kernel, degree = 1, + w = cl(1) * model.SVs' * model.sv_coef; %Calculate decision hyperplane weight vector + % ensure correct sign of weight vector and Bias according to class label + Bias = model.rho * cl(1); + + elseif strcmp(MODE.TYPE, 'SVM:bioinfo'); + % SVM classifier from bioinformatics toolbox. + % Settings suggested by Ian Daly, 2011-06-06 + options = optimset('Display','iter','maxiter',20000, 'largescale','off'); + CC.SVMstruct = svmtrain(D, cl, 'AUTOSCALE', 0, 'quadprog_opts', options, 'Method', 'LS', 'kernel_function', 'polynomial'); + Bias = -CC.SVMstruct.Bias; + w = -CC.SVMstruct.Alpha'*CC.SVMstruct.SupportVectors; + + elseif strcmp(MODE.TYPE, 'SVM:OSU'); + [AlphaY, SVs, Bias] = mexSVMTrain(D', cl', [0 1 1 1 MODE.hyperparameter.c_value]); % Linear Kernel, C=1; degree=1, c-SVM + w = -SVs * AlphaY'*cl(1); %Calculate decision hyperplane weight vector + % ensure correct sign of weight vector and Bias according to class label + Bias = -Bias * cl(1); + + elseif strcmp(MODE.TYPE, 'SVM:LOO'); + [a, Bias, g, inds] = svcm_train(D, cl, MODE.hyperparameter.c_value); % C = 1; + w = D(inds,:)' * (a(inds).*cl(inds)) ; + + elseif strcmp(MODE.TYPE, 'SVM:Gunn'); + [nsv, alpha, Bias,svi] = svc(D, cl, 1, MODE.hyperparameter.c_value); % linear kernel, C = 1; + w = D(svi,:)' * alpha(svi) * cl(1); + Bias = mean(D*w); + + elseif strcmp(MODE.TYPE, 'SVM:KM'); + [xsup,w1,Bias,inds] = svmclass(D, cl, MODE.hyperparameter.c_value, 1, 'poly', 1); % C = 1; + w = -D(inds,:)' * w1; + + else + fprintf(2,'Error TRAIN_SVM: no SVM training algorithm available\n'); + return; + end + + CC.weights(1,k) = -Bias; + CC.weights(cix+1,k) = w; + end + CC.weights([1,cix+1],:) = s * CC.weights(cix+1,:) + sparse(1,1:M,CC.weights(1,:),sz2+1,M); % include pre-whitening transformation + CC.hyperparameter.c_value = MODE.hyperparameter.c_value; + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + + + elseif ~isempty(strfind(lower(MODE.TYPE),'csp')) + CC.datatype = ['classifier:',lower(MODE.TYPE)]; + [classlabel,CC.Labels] = CL1M(classlabel); + CC.MD = repmat(NaN,[sz(2)+[1,1],length(CC.Labels)]); + CC.NN = CC.MD; + for k = 1:length(CC.Labels), + %% [CC.MD(k,:,:),CC.NN(k,:,:)] = covm(D(classlabel==CC.Labels(k),:),'E'); + ix = classlabel==CC.Labels(k); + if isempty(W) + [CC.MD(:,:,k),CC.NN(:,:,k)] = covm(D(ix,:), 'E'); + else + [CC.MD(:,:,k),CC.NN(:,:,k)] = covm(D(ix,:), 'E', W(ix)); + end + end + ECM = CC.MD./CC.NN; + W = csp(ECM,'CSP3'); + %%% ### This is a hack ### + CC.FiltA = 50; + CC.FiltB = ones(CC.FiltA,1); + d = filtfilt(CC.FiltB,CC.FiltA,(D*W).^2); + CC.csp_w = W; + CC.CSP = train_sc(log(d),classlabel); + + + else % Linear and Quadratic statistical classifiers + CC.datatype = ['classifier:statistical:',lower(MODE.TYPE)]; + [classlabel,CC.Labels] = CL1M(classlabel); + CC.MD = repmat(NaN,[sz(2)+[1,1],length(CC.Labels)]); + CC.NN = CC.MD; + for k = 1:length(CC.Labels), + ix = classlabel==CC.Labels(k); + if isempty(W) + [CC.MD(:,:,k),CC.NN(:,:,k)] = covm(D(ix,:), 'E'); + else + [CC.MD(:,:,k),CC.NN(:,:,k)] = covm(D(ix,:), 'E', W(ix)); + end + end + + ECM = CC.MD./CC.NN; + NC = size(CC.MD); + if strncmpi(MODE.TYPE,'LD',2) || strncmpi(MODE.TYPE,'FDA',3) || strncmpi(MODE.TYPE,'FLDA',3), + + %if NC(1)==2, NC(1)=1; end % linear two class problem needs only one discriminant + CC.weights = repmat(NaN,NC(2),NC(3)); % memory allocation + type = MODE.TYPE(3)-'0'; + + ECM0 = squeeze(sum(ECM,3)); %decompose ECM + for k = 1:NC(3); + ix = [1:k-1,k+1:NC(3)]; + dM = CC.MD(:,1,k)./CC.NN(:,1,k) - sum(CC.MD(:,1,ix),3)./sum(CC.NN(:,1,ix),3); + switch (type) + case 2 % LD2 + ecm0 = (sum(ECM(:,:,ix),3)/(NC(3)-1) + ECM(:,:,k)); + case 4 % LD4 + ecm0 = 2*(sum(ECM(:,:,ix),3) + ECM(:,:,k))/NC(3); + % ecm0 = sum(CC.MD,3)./sum(CC.NN,3); + case 5 % LD5 + ecm0 = ECM(:,:,k); + case 6 % LD6 + ecm0 = sum(CC.MD(:,:,ix),3)./sum(CC.NN(:,:,ix),3); + otherwise % LD3, LDA, FDA + ecm0 = ECM0; + end + if isfield(MODE.hyperparameter,'gamma') + ecm0 = ecm0 + mean(diag(ecm0))*eye(size(ecm0))*MODE.hyperparameter.gamma; + end + + CC.weights(:,k) = ecm0\dM; + + end + %CC.weights = sparse(CC.weights); + + elseif strcmpi(MODE.TYPE,'RDA'); + if isfield(MODE,'hyperparameter') + CC.hyperparameter = MODE.hyperparameter; + end + % default values + if ~isfield(CC.hyperparameter,'gamma') + CC.hyperparameter.gamma = 0; + end + if ~isfield(CC.hyperparameter,'lambda') + CC.hyperparameter.lambda = 1; + end + else + ECM0 = sum(ECM,3); + nn = ECM0(1,1,1); % number of samples in training set for class k + XC = squeeze(ECM0(:,:,1))/nn; % normalize correlation matrix + M = XC(1,2:NC(2)); % mean + S = XC(2:NC(2),2:NC(2)) - M'*M;% covariance matrix + + try + [v,d]=eig(S); + U0 = v(diag(d)==0,:); + CC.iS2 = U0*U0'; + end + + %M = M/nn; S=S/(nn-1); + ICOV0 = inv(S); + CC.iS0 = ICOV0; + % ICOV1 = zeros(size(S)); + for k = 1:NC(3), + %[M,sd,S,xc,N] = decovm(ECM{k}); %decompose ECM + %c = size(ECM,2); + nn = ECM(1,1,k);% number of samples in training set for class k + XC = squeeze(ECM(:,:,k))/nn;% normalize correlation matrix + M = XC(1,2:NC(2));% mean + S = XC(2:NC(2),2:NC(2)) - M'*M;% covariance matrix + %M = M/nn; S=S/(nn-1); + + %ICOV(1) = ICOV(1) + (XC(2:NC(2),2:NC(2)) - )/nn + + CC.M{k} = M; + CC.IR{k} = [-M;eye(NC(2)-1)]*inv(S)*[-M',eye(NC(2)-1)]; % inverse correlation matrix extended by mean + CC.IR0{k} = [-M;eye(NC(2)-1)]*ICOV0*[-M',eye(NC(2)-1)]; % inverse correlation matrix extended by mean + d = NC(2)-1; + if exist('OCTAVE_VERSION','builtin') + S = full(S); + end + CC.logSF(k) = log(nn) - d/2*log(2*pi) - det(S)/2; + CC.logSF2(k) = -2*log(nn/sum(ECM(:,1,1))); + CC.logSF3(k) = d*log(2*pi) + log(det(S)); + CC.logSF4(k) = log(det(S)) + 2*log(nn); + CC.logSF5(k) = log(det(S)); + CC.logSF6(k) = log(det(S)) - 2*log(nn/sum(ECM(:,1,1))); + CC.logSF7(k) = log(det(S)) + d*log(2*pi) - 2*log(nn/sum(ECM(:,1,1))); + CC.logSF8(k) = sum(log(svd(S))) + log(nn) - log(sum(ECM(:,1,1))); + CC.SF(k) = nn/sqrt((2*pi)^d * det(S)); + %CC.datatype='LLBC'; + end + end + end +end + +function [CL101,Labels] = cl101(classlabel) + %% convert classlabels to {-1,1} encoding + + if (all(classlabel>=0) && all(classlabel==fix(classlabel)) && (size(classlabel,2)==1)) + M = max(classlabel); + if M==2, + CL101 = (classlabel==2)-(classlabel==1); + else + CL101 = zeros(size(classlabel,1),M); + for k=1:M, + %% One-versus-Rest scheme + CL101(:,k) = 2*real(classlabel==k) - 1; + end + end + CL101(isnan(classlabel),:) = NaN; %% or zero ??? + + elseif all((classlabel==1) | (classlabel==-1) | (classlabel==0) ) + CL101 = classlabel; + M = size(CL101,2); + else + classlabel, + error('format of classlabel unsupported'); + end + Labels = 1:M; + return; +end + + +function [cl1m, Labels] = CL1M(classlabel) + %% convert classlabels to 1..M encoding + if (all(classlabel>=0) && all(classlabel==fix(classlabel)) && (size(classlabel,2)==1)) + cl1m = classlabel; + + elseif all((classlabel==1) | (classlabel==-1) | (classlabel==0) ) + CL101 = classlabel; + M = size(classlabel,2); + if any(sum(classlabel==1,2)>1) + warning('invalid format of classlabel - at most one category may have +1'); + end + if (M==1), + cl1m = (classlabel==-1) + 2*(classlabel==+1); + else + [tmp, cl1m] = max(classlabel,[],2); + if any(tmp ~= 1) + warning('some class might not be properly represented - you might what to add another column to classlabel = [max(classlabel,[],2)<1,classlabel]'); + end + cl1m(tmp<1)= 0; %% or NaN ??? + end + else + classlabel + error('format of classlabel unsupported'); + end + Labels = 1:max(cl1m); + return; +end