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\postlssvm.m
function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing 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 = postssvm(model) % >> Xp = postlssvm(model, Xt) % >> [empty, Yp] = postlssvm(model, [], Yt) % >> [Xp, Yp] = postlssvm(model, Xt, Yt) % % Outputs % model : Preprocessed 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 % Inputs % model : 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 % % % See also: % prelssvm, trainlssvm % Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab % % test of postprocessing needed, and if model is properly coded before % decoding % if model.preprocess(1)~='p', if nargin>=2, % no postprocessing nor coding needed model=Xt; end return end % % postprocess the LS-SVM % if nargin==1, % % execute rescaling as defined % if (model.prestatus(1)=='o' & model.preprocess(1)=='p') | ... % 'preprocess' &'ok' (model.prestatus(1)=='c' & model.preprocess(1)=='o'), % 'original' &'changed' model=postmodel(model); model.preprocess = 'original'; end model.prestatus='ok'; % % rescaling of the to simulate inputs % else eval('Yt;','Yt=[];'); [model,Yt] = postmodel(model,Xt,Yt); end function [model,Yt] = postmodel(model,Xt,Yt) % % ' [Xt,Yt] = postmodel(model,Xt,Yt)' % ' [model] = postmodel(model)' % if nargin==1, for i=1:model.x_dim, % CONTINU VARIABLE: if model.pre_xscheme(i)=='c', model.xtrain(:,i) = post_zmuv(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); % CATHEGORICAL VARIBALE: elseif model.pre_xscheme(i)=='a', model.xtrain(:,i) = post_cat(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); % BINARY VARIBALE: elseif model.pre_xscheme(i)=='b', model.xtrain(:,i) = post_bin(model.xtrain(:,i),model.pre_xmean(i),model.pre_xstd(i)); end end for i=1:model.y_dim, % CONTINU VARIABLE: if model.pre_yscheme(i)=='c', model.ytrain(:,i) = post_zmuv(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); % CATHEGORICAL VARIBALE: elseif model.pre_yscheme(i)=='a', model.ytrain(:,i) = post_cat(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); % BINARY VARIBALE: elseif model.pre_yscheme(i)=='b', model.ytrain(:,i) = post_bin(model.ytrain(:,i),model.pre_ymean(i),model.pre_ystd(i)); end end else if nargin>1, % testdayta Xt, if ~isempty(Xt), if size(Xt,2)~=model.x_dim, warning('dimensions of Xt not compatible with dimensions of supprt vectors...');end for i=1:model.x_dim, % CONTINU VARIABLE: if model.pre_xscheme(i)=='c', Xt(:,i) = post_zmuv(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i)); % CATHEGORICAL VARIBALE: elseif model.pre_xscheme(i)=='a', Xt(:,i) = post_cat(Xt(:,i),model.pre_xmean(i),model.pre_xstd(i)); % BINARY VARIBALE: elseif model.pre_xscheme(i)=='b', Xt(:,i) = post_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 supprt vectors...');end for i=1:model.y_dim, % CONTINU VARIABLE: if model.pre_yscheme(i)=='c', Yt(:,i) = post_zmuv(Yt(:,i),model.pre_ymean(i), model.pre_ystd(i)); % CATHEGORICAL VARIBALE: elseif model.pre_yscheme(i)=='a', Yt(:,i) = post_cat(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i)); % BINARY VARIBALE: elseif model.pre_yscheme(i)=='b', Yt(:,i) = post_bin(Yt(:,i),model.pre_ymean(i),model.pre_ystd(i)); end end end model = Xt; end end function X = post_zmuv(X,mean,var) % % postprocessing a continu signal; rescaling to zero mean and unit % variance % 'c' % X = X.*var+mean; function X = post_cat(X,mean,range) % % postprocessing a cathegorical signal, rescaling to -1:1; % 'a' % X = X; function X = post_bin(X,min,max) % % postprocessing a binary signal, rescaling to -1:1; % 'a' % X = min.*(X<=0)+max.*(X>0);