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';
