www.gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\LS_SVMlab\prelssvm.m
function [model,Yt] = prelssvm(model,Xt,Yt) % Preprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each in- % and output component (c for continuous, a for categorical or b % for binary variables). According to this label each dimension is rescaled: % % * continuous: zero mean and unit variance % * categorical: no preprocessing % * binary: labels -1 and +1 % % Full syntax (only using the object oriented interface): % % >> model = prelssvm(model) % >> Xp = prelssvm(model, Xt) % >> [empty, Yp] = prelssvm(model, [], Yt) % >> [Xp, Yp] = prelssvm(model, Xt, Yt) % % Outputs % model : Preprocessed object oriented representation of the LS-SVM model % Xp : Nt x d matrix with the preprocessed inputs of the test data % Yp : Nt x d matrix with the preprocessed outputs of the test data % Inputs % model : Object oriented representation of the LS-SVM model % Xt : Nt x d matrix with the inputs of the test data to preprocess % Yt : Nt x d matrix with the outputs of the test data to preprocess % % % See also: % postlssvm, trainlssvm % Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab if model.preprocess(1)~='p', % no 'preprocessing if nargin>=2, model = Xt; end return end % % what to do % if model.preprocess(1)=='p', eval('if model.prestatus(1)==''c'',model.prestatus=''unschemed'';end','model.prestatus=''unschemed'';'); end if nargin==1, % only model rescaling % % if UNSCHEMED, redefine a rescaling % if model.prestatus(1)=='u',% 'unschemed' ffx =[]; for i=1:model.x_dim, eval('ffx = [ffx model.pre_xscheme(i)];',... 'ffx = [ffx signal_type(model.xtrain(:,i))];'); end model.pre_xscheme = ffx; ff = []; for i=1:model.y_dim, eval('ff = [ff model.pre_yscheme(i)];',... 'ff = [ff signal_type(model.ytrain(:,i))];'); end model.pre_yscheme = ff; model.prestatus='schemed'; end % % execute rescaling as defined if not yet CODED % if model.prestatus(1)=='s',% 'schemed' model=premodel(model); model.prestatus = 'ok'; end % % rescaling of the to simulate inputs % elseif model.preprocess(1)=='p' if model.prestatus(1)=='o',%'ok' eval('Yt;','Yt=[];'); [model,Yt] = premodel(model,Xt,Yt); else warning('model rescaling inconsistent..redo ''model=prelssvm(model);''..'); end end function [type,ss] = signal_type(signal) % % determine the type of the signal, % binary classifier ('b'), categorical classifier ('a'), or continuous % signal ('c') % % ss = sort(signal); dif = sum(ss(2:end)~=ss(1:end-1))+1; % binary if dif==2, type = 'b'; % categorical elseif dif<sqrt(length(signal)), type='a'; % continu else type ='c'; end % % effective rescaling % function [model,Yt] = premodel(model,Xt,Yt) % % % if nargin==1, for i=1:model.x_dim, % CONTINUOUS VARIABLE: if model.pre_xscheme(i)=='c', model.pre_xmean(i)=mean(model.xtrain(:,i)); model.pre_xstd(i) = std(model.xtrain(:,i)); model.xtrain(:,i) = pre_zmuv(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); % CATEGORICAL VARIBALE: elseif model.pre_xscheme(i)=='a', model.pre_xmean(i)= 0; model.pre_xstd(i) = 0; model.xtrain(:,i) = pre_cat(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); % BINARY VARIBALE: elseif model.pre_xscheme(i)=='b', model.pre_xmean(i) = min(model.xtrain(:,i)); model.pre_xstd(i) = max(model.xtrain(:,i)); model.xtrain(:,i) = pre_bin(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); end end for i=1:model.y_dim, % CONTINUOUS VARIABLE: if model.pre_yscheme(i)=='c', model.pre_ymean(i)=mean(model.ytrain(:,i),1); model.pre_ystd(i) = std(model.ytrain(:,i),1); model.ytrain(:,i) = pre_zmuv(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); % CATEGORICAL VARIBALE: elseif model.pre_yscheme(i)=='a', model.pre_ymean(i)=0; model.pre_ystd(i) =0; model.ytrain(:,i) = pre_cat(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); % BINARY VARIBALE: elseif model.pre_yscheme(i)=='b', model.pre_ymean(i) = min(model.ytrain(:,i)); model.pre_ystd(i) = max(model.ytrain(:,i)); model.ytrain(:,i) = pre_bin(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); end end else %if nargin>1, % testdata Xt, if ~isempty(Xt), if size(Xt,2)~=model.x_dim, warning('dimensions of Xt not compatible with dimensions of support vectors...');end for i=1:model.x_dim, % CONTINUOUS VARIABLE: if model.pre_xscheme(i)=='c', Xt(:,i) = pre_zmuv(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i)); % CATEGORICAL VARIBALE: elseif model.pre_xscheme(i)=='a', Xt(:,i) = pre_cat(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i)); % BINARY VARIBALE: elseif model.pre_xscheme(i)=='b', Xt(:,i) = pre_bin(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i)); end end end if nargin>2 & ~isempty(Yt), if size(Yt,2)~=model.y_dim, warning('dimensions of Yt not compatible with dimensions of training output...');end for i=1:model.y_dim, % CONTINUOUS VARIABLE: if model.pre_yscheme(i)=='c', Yt(:,i) = pre_zmuv(Yt(:,i),model.pre_ymean(i), model.pre_ystd(i)); % CATEGORICAL VARIBALE: elseif model.pre_yscheme(i)=='a', Yt(:,i) = pre_cat(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i)); % BINARY VARIBALE: elseif model.pre_yscheme(i)=='b', Yt(:,i) = pre_bin(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i)); end end end % assign output model=Xt; end function X = pre_zmuv(X,mean,var) % % preprocessing a continuous signal; rescaling to zero mean and unit % variance % 'c' % X = (X-mean)./var; function X = pre_cat(X,mean,range) % % preprocessing a categorical signal; % 'a' % X=X; function X = pre_bin(X,min,max) % % preprocessing a binary signal; % 'b' % n = (X==min); p = not(n); X=-1.*(n)+p;