nnpred.m

人工神经网络：MATLAB源程序用于训练测试

%#										
%# function [ypred] = nnpred(topo,w1f,w2f,tablein,tableout,xnew)	
%#									
%# AIM:	Prediction of responses of new samples with a neural network model.
%#		The matrices of weights "w1f" and "w2f" can be obtained with the 
%#		function "nnmodel.m".				
%#										
%# PRINCIPLE:	The optimal topology of the network (topo) must have been determined on
%#		the calibration set. The training input and output data (xtrain and
%#		ytrain) that were used to build the model must also be used to scale 
%#		the new data.						 
%# 										
%# INPUT:	topo (2 x nh) : matrix containing topology of the NN (nh hidden nodes)
%#			        (H: hyperbolic tangent, L: linear, -: no function)
%#		w1f (nh x (p+1)) : matrix of weights between input and hidden layer
%#		w2f (1 x (nh+1)) : matrix of weights between hidden and output layer
%#		tablein (mxtr x 2):input range-scaling parameters.		
%#		tableout (1 x 2):  output range-scaling parameters.	
%#		xnew (nte x p) 	 : matrix of inputs for the new samples 								
%#									
%# OUTPUT:	ypred (nte x 1) : vector of predicted responses				
%# 										
%# SUBROUTINES: range.m : range-scaling of training, monitoring and test data	
%#		lmeval.m : estimation of responses with the final neural network model	
%#		invrange.mk : returns range-scaled data to original scale	
%#									
%# AUTHOR:	Frederic Despagne					
%#		Copyright(c) 1997 for ChemoAC				
%#		Dienst FABI, Vrije Universiteit Brussel			
%#		Laarbeeklaan 103, 1090 Jette			
%#									
%# VERSION: 1.1 (28/02/1998)					
%#									
%# TEST:	Krzysztof Szczubialka 				
%#									

function [ypred] = nnpred(topo,w1f,w2f,tablein,tableout,xnew)

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SCALING OF INPUT DATA %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[mxtr] = size(tablein,1);	% Size of the training input data
for i = 1:mxtr
	% Range-scaling of test input data between -1 and 1
	[xtes(:,i)] = rangenew(xnew(:,i),-1,1,tablein(i,:));
end

xte = xtes'; 	% Transposition of test input data


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% PREDICTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

[yte1,yte2] = lmeval(topo,w1f,w2f,xte);		% Estimation of test responses
ypred = invrange(yte2,0.2,0.8,tableout);	% Inverse-scaling of estimated test responses
ypred = ypred';


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%