www.gusucode.com > 《MATLAB智能算法30个案例》分析源代码 > 《MATLAB智能算法30个案例》分析源代码/code/chapter30/elmpredict.m
function Y = elmpredict(P,IW,B,LW,TF,TYPE) % ELMPREDICT Simulate a Extreme Learning Machine % Syntax % Y = elmtrain(P,IW,B,LW,TF,TYPE) % Description % Input % P - Input Matrix of Training Set (R*Q) % IW - Input Weight Matrix (N*R) % B - Bias Matrix (N*1) % LW - Layer Weight Matrix (N*S) % TF - Transfer Function: % 'sig' for Sigmoidal function (default) % 'sin' for Sine function % 'hardlim' for Hardlim function % TYPE - Regression (0,default) or Classification (1) % Output % Y - Simulate Output Matrix (S*Q) % Example % Regression: % [IW,B,LW,TF,TYPE] = elmtrain(P,T,20,'sig',0) % Y = elmtrain(P,IW,B,LW,TF,TYPE) % Classification % [IW,B,LW,TF,TYPE] = elmtrain(P,T,20,'sig',1) % Y = elmtrain(P,IW,B,LW,TF,TYPE) % See also ELMTRAIN % Yu Lei,11-7-2010 % Copyright www.matlabsky.com % $Revision:1.0 $ if nargin < 6 error('ELM:Arguments','Not enough input arguments.'); end % Calculate the Layer Output Matrix H Q = size(P,2); BiasMatrix = repmat(B,1,Q); tempH = IW * P + BiasMatrix; switch TF case 'sig' H = 1 ./ (1 + exp(-tempH)); case 'sin' H = sin(tempH); case 'hardlim' H = hardlim(tempH); end % Calculate the Simulate Output Y = (H' * LW)'; if TYPE == 1 temp_Y = zeros(size(Y)); for i = 1:size(Y,2) [max_Y,index] = max(Y(:,i)); temp_Y(index,i) = 1; end Y = vec2ind(temp_Y); end