基于遗传算法的SVM方法

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,kernel_par,kernel,preprocess}) % >> model = trainlssvm(model) % % type can be 'classifier' or 'function estimation' (these strings % can be abbreviated into 'c' or 'f', respectively). X and Y are % matrices holding the training input and output data. The i-th % data point is represented by the i-th row X(i,:) and Y(i,:). gam % is the regularization parameter: for gam low minimizing of the % complexity of the model is emphasized, for gam high, good fitting % of the training data points is stressed. kernel_par is the % parameter of the kernel; in the common case of an RBF kernel, a % large sig2 indicates a stronger smoothing. The kernel_type % indicates the function that is called to compute the kernel value % (by default RBF_kernel). Other kernels can be used for example: % % >> [alpha, b] = trainlssvm({X,Y,type,gam,[d p],'poly_kernel'}) % >> [alpha, b] = trainlssvm({X,Y,type,gam,[] ,'lin_kernel'}) % % The kernel parameter(s) are passed as a row vector, in the case % no kernel parameter is needed, pass the empty vector! % % The training can either be proceeded by the preprocessing % function ('preprocess') (by default) or not ('original'). The % training calls the preprocessing (prelssvm, postlssvm) and the % encoder (codelssvm) if appropiate. % % In the remainder of the text, the content of the cell determining % the LS-SVM is given by {X,Y, type, gam, sig2}. However, the % additional arguments in this cell can always be added in the % calls. % % If one uses the object oriented interface (see also A.3.14), the training is done by % % >> model = trainlssvm(model) % >> model = trainlssvm(model, X, Y) % % The status of the model checks whether a retraining is % needed. The extra arguments X, Y allow to re-initialize the model % with this new training data as long as its dimensions are the % same as the old initiation. % % Three training implementations are included: % % * The C-implementation linked with CMEX: this implementation % is based on the iterative solver Conjugate Gradient algorithm % (CG) (lssvm.mex*). After this training call, a '-' is % displayed. This is recommended for use on larger data sets. % % * The C-implementation called via a buffer file: this is % based on CG; check if the executable 'lssvmFILE.x' is in the % current directory; (lssvmFILE.x). After this training call, a % '-' is displayed. % % * The Matlab implementation: a straightforward implementation % based on the matrix division '\' (lssvmMATLAB.m). After this % training call, a '~' is displayed. This is recommended for a % number of training data points smaller than 500 (depending on % the computer memory). % % By default, the cmex implementation is called. If this one fails, % the Matlab implementation is chosen instead. One can specify % explicitly which implementation to use using the object oriented % interface. % % This implementation allows to train a multidimensional output % problem. If each output uses the same kernel type, kernel % parameters and regularization parameter, this is % straightforward. If not so, one can specify the different types % and/or parameters as a row vector in the appropriate % argument. Each dimension will be trained with the corresponding % column in this vector. % % >> [alpha, b] = trainlssvm({X, [Y_1 ... Y_d],type,... % [gam_1 ... gam_d], ... % [sig2_1 ... sig2_d],... % {kernel_1,...,kernel_d}}) % % Full syntax % % 1. Using the functional interface: % % >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2}) % >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2,kernel}) % >> [alpha, b] = trainlssvm({X,Y,type,gam,sig2,kernel,preprocess}) % % Outputs % alpha : N x m matrix with support values of the LS-SVM % b : 1 x m vector with bias term(s) of the LS-SVM % Inputs % X : N x d matrix with the inputs of the training data % Y : N x 1 vector with the outputs of the training data % type : 'function estimation' ('f') or 'classifier' ('c') % gam : Regularization parameter % sig2 : Kernel parameter (bandwidth in the case of the 'RBF_kernel') % kernel(*) : Kernel type (by default 'RBF_kernel') % preprocess(*) : 'preprocess'(*) or 'original' % % % * Using the object oriented interface: % % >> model = trainlssvm(model) % >> model = trainlssvm({X,Y,type,gam,sig2}) % >> model = trainlssvm({X,Y,type,gam,sig2,kernel}) % >> model = trainlssvm({X,Y,type,gam,sig2,kernel,preprocess}) % % Outputs % model : Trained object oriented representation of the LS-SVM model % Inputs % model : Object oriented representation of the LS-SVM model % X(*) : N x d matrix with the inputs of the training data % Y(*) : N x 1 vector with the outputs of the training data % type(*) : 'function estimation' ('f') or 'classifier' ('c') % gam(*) : Regularization parameter % sig2(*) : Kernel parameter (bandwidth in the case of the 'RBF_kernel') % kernel(*) : Kernel type (by default 'RBF_kernel') % preprocess(*) : 'preprocess'(*) or 'original' % % See also: % simlssvm, initlssvm, changelssvm, plotlssvm, prelssvm, codelssvm % Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab % % initialise the model 'model' % if (iscell(model)), model = initlssvm(model{:}); end % % given X and Y? % %model = codelssvm(model); eval('model = changelssvm(model,''xtrain'',X);',';'); eval('model = changelssvm(model,''ytrain'',Y);',';'); eval('model = changelssvm(model,''selector'',1:size(X,1));',';'); % % no training needed if status = 'trained' % if model.status(1) == 't', if (nargout>1), % [alpha,b] X = model.xtrain; Y = model.ytrain; b = model.b; model = model.alpha; end return end % % control of the inputs % if ~((strcmp(model.kernel_type,'RBF_kernel') & length(model.kernel_pars)>=1) |... (strcmp(model.kernel_type,'lin_kernel') & length(model.kernel_pars)>=0) |... (strcmp(model.kernel_type,'MLP_kernel') & length(model.kernel_pars)>=2) |... (strcmp(model.kernel_type,'poly_kernel')& length(model.kernel_pars)>=1)), eval('feval(model.kernel_type,model.xtrain(1,:),model.xtrain(2,:),model.kernel_pars);model.implementation=''MATLAB'';',... 'error(''The kernel type is not valid or to few arguments'');'); elseif (model.steps<=0), error('steps must be larger then 0'); elseif (model.gam<=0), error('gamma must be larger then 0'); % elseif (model.kernel_pars<=0), % error('sig2 must be larger then 0'); elseif or(model.x_dim<=0, model.y_dim<=0), error('dimension of datapoints must be larger than 0'); end % % coding if needed % if model.code(1) == 'c', % changed model = codelssvm(model); end % % preprocess % eval('if model.prestatus(1)==''c'', changed=1; else changed=0;end;','changed=0;'); if model.preprocess(1) =='p' & changed, model = prelssvm(model); elseif model.preprocess(1) =='o' & changed model = postlssvm(model); end % clock tic; % % set & control input variables and dimensions % if (model.type(1) == 'f'), % function dyn_pars=[]; elseif (model.type(1) == 'c'), % class dyn_pars=[]; end % only MATLAB if size(model.gam,1)>1, model.implementation='MATLAB'; end % % output dimension > 1...recursive call on each dimension % if model.y_dim>1, if (length(model.kernel_pars)==model.y_dim | size(model.gam,2)==model.y_dim |prod(size(model.kernel_type,2))==model.y_dim) disp('multidime