%RNNC Random Neural Net classifier% % 	W = RNNC(A,N,S)% % INPUT%   A  Input dataset%   N  Number of neurons in the hidden layer%   S  Standard deviation of weights in an input layer (default: 1)%% OUTPUT%   W  Trained Random Neural Net classifier%% DESCRIPTION% W is a feed-forward neural net with one hidden layer of N sigmoid neurons.% The input layer rescales the input features to unit variance; the hidden% layer has normally distributed weights and biases with zero mean and% standard deviation S. The output layer is trained by the dataset A.% Default N is number of objects * 0.2, but not more than 100.% % If N and/or S is NaN they are optimised by REGOPTC.%% Uses the Mathworks' Neural Network toolbox.%% SEE ALSO% MAPPINGS, DATASETS, LMNC, BPXNC, NEURC, RBNC% Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Sciences, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: rnnc.m,v 1.4 2007/06/19 11:45:08 duin Exp $function w = rnnc(a,n,s)	prtrace(mfilename);		if exist('nnet') ~= 7		error('Neural network toolbox not found')	end		mapname = 'Random Neural Net';	if (nargin < 3)		prwarning(3,'standard deviation of hidden layer weights not given, assuming 1');		s = 1; 	end	if (nargin < 2)		n = []; 	end	% No input arguments: return an untrained mapping.	if (nargin < 1) | (isempty(a))		w = mapping('rnnc',{n,s});		w = setname(w,mapname);		return	end	islabtype(a,'crisp');	isvaldfile(a,1,2); % at least 1 object per class, 2 classes	a = testdatasize(a);	[m,k,c] = getsize(a);		if isempty(n)		n = min(ceil(m/5),100);    prwarning(3,['no number of hidden units specified, assuming ' num2str(n)]);	end	  if isnan(n) | isnan(s) % optimize complexity parameter: number of neurons, st. dev.		defs = {m/5,1};		parmin_max = [1,min(m,100);0.01,10];		w = regoptc(a,mfilename,{n,s},defs,[1,2],parmin_max,testc([],'soft'),[0,1]);		return 	end	% The hidden layer scales the input to unit variance, then applies a	% random rotation and offset.		w_hidden = scalem(a,'variance');	w_hidden = w_hidden * cmapm(randn(n,k)*s,'rot');	w_hidden = w_hidden * cmapm(randn(1,n)*s,'shift');	% The output layer applies a FISHERC to the nonlinearly transformed output	% of the hidden layer.	w_output = w_hidden * sigm;	w_output = fisherc(a*w_output);		% Construct the network and insert the weights.	transfer_fn = { 'logsig','logsig','logsig' };	net = newff(ones(k,1)*[0 1],[n size(w_output,2)],transfer_fn,'traingdx','learngdm','mse');	net.IW{1,1} = w_hidden.data.rot'; net.b{1,1} = w_hidden.data.offset';	net.LW{2,1} = w_output.data.rot'; net.b{2,1} = w_output.data.offset';	w = mapping('neurc','trained',{net},getlabels(w_output),k,c);	w = setname(w,mapname);	w = setcost(w,a);return