%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.
%
% REFERENCES
% 1. W.F. Schmidt, M.A. Kraaijveld, and R.P.W. Duin, Feed forward neural
% networks with random weights, Proc. ICPR11, Volume II, 1992, 1-4.
% 2. G.B. Huang, Q.Y. Zhu, C.K. Siew, Extreme learning machine: theory and
% applications, Neurocomputing, 70 (1), 2006, 489-501
%
% SEE ALSO (<a href="http://37steps.com/prtools">PRTools Guide</a>)
% 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)
checktoolbox('nnet');
mapname = 'RandNeuralNet';
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 = prmapping('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.
warning('off','NNET:Obsolete');
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';
if size(w_output,2) == 2
net.LW{2,1} = w_output.data.rot'; net.b{2,1} = w_output.data.offset';
else
wrot = []; woff = [];
for j=1:size(w_output,2)
wrot = [wrot w_output{j}.data.rot];
woff = [woff w_output{j}.data.offset];
end
net.LW{2,1} = wrot'; net.b{2,1} = woff';
end
w = prmapping('neurc','trained',{net},getlabels(w_output),k,c);
w = setname(w,mapname);
w = setcost(w,a);
return