% Solve a Pattern Recognition Problem with a Neural Network
% Script generated by NPRTOOL
%
% This script assumes these variables are defined:
i=0;
d=50
%for s=70:d:550
 i=i+1;
 %s
inputs = rot90(isoel);
targets = rot90(yesno);

% Create a Pattern Recognition Network
hiddenLayerSize = 10;
net = patternnet(hiddenLayerSize);


% Set up Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;

for reps=1:20
% Train the Network
[net2,tr] = train(net,inputs,targets);

% Test the Network
outputs = net2(inputs);
errors = gsubtract(targets,outputs);
performance = perform(net2,targets,outputs);

% View the Network
%view(net)
%out=sim(net,inputs);
%0er(reps)=sum(abs(out-targets));
 tInd = tr.testInd;
 k=inputs(:,tInd);

 tstOutputs = net2(k);
%tstPerform = perform(net,targets(tInd),tstOutputs)
 [c,cm,ind,per] = confusion(targets(tInd),tstOutputs);
p_error_test(reps)=(1-(cm(1,1)+cm(2,2))/11)*100;
% Plots
% Uncomment these lines to enable various plots.
%figure, plotperform(tr)
%figure, plottrainstate(tr)
%figure, plotconfusion(inputs,outputs)
%figure, ploterrhist(errors)
end
avgerr(i)=mean(p_error_test);
%end