elm_train_predict_main.rar_effort1mb_machine learning_persone89_

function [IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE) % ELMTRAIN Create and Train a Extreme Learning Machine % Syntax % [IW,B,LW,TF,TYPE] = elmtrain(P,T,N,TF,TYPE) % Description % Input % P - Input Matrix of Training Set (R*Q) 训练集的输入矩阵（R * Q） % T - Output Matrix of Training Set (S*Q) 训练集的输出矩阵（S * Q） % N - Number of Hidden Neurons (default = Q) 隐藏神经元数量（默认= Q） % TF - Transfer Function: 传递函数 % 'sig' for Sigmoidal function (default) 用于Sigmoidal函数的％'sig'（默认） % 'sin' for Sine function 正弦函数％'sin' % 'hardlim' for Hardlim function Hardlim函数的％'hardlim' % TYPE - Regression (0,default) or Classification (1) TYPE - 回归（0，默认）或分类（1） % Output % IW - Input Weight Matrix (N*R) IW - 输入权重矩阵（N * R） % B - Bias Matrix (N*1) B - 偏差矩阵（N * 1） % LW - Layer Weight Matrix (N*S) LW - 层权矩阵（N * S） % 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 ELMPREDICT % Yu Lei,11-7-2010 % Copyright www.matlabsky.com % $Revision:1.0 $ if nargin < 2 error('ELM:Arguments','Not enough input arguments.'); end if nargin < 3 N = size(P,2); end if nargin < 4 TF = 'sig'; end if nargin < 5 TYPE = 0; end if size(P,2) ~= size(T,2) error('ELM:Arguments','The columns of P and T must be same.'); end [R,Q] = size(P); if TYPE == 1 T = ind2vec(T); end [S,Q] = size(T); % Randomly Generate the Input Weight Matrix IW = rand(N,R) * 2 - 1; % Randomly Generate the Bias Matrix B = rand(N,1); BiasMatrix = repmat(B,1,Q); % Calculate the Layer Output Matrix H 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 Output Weight Matrix LW = pinv(H') * T';