Prashant Kumar answered . 2024-04-19 03:59:07

 [P, T ] = simplenarx_dataset;
 whos
 p= cell2mat(P);
 t = cell2mat(T);

 ID = 1:1
 FD = 1:1
 H = 5

 NID= length(ID)
 NFD=length(FD)
 Nw = (NID*I+NFD*O+1)*H+(H+1)*O

 rng(0)
 net = narxnet(ID,FD,H);
 [inputs,inputStates,layerStates,targets] = preparets(net,P,{},T);
 whos P T inputs inputStates layerStates targets

 [ I N ] = size(inputs)
 [ O N ] = size(targets)

 net.divideFcn='divideblock';
 [trainInd,valInd,testInd] = divideblock(N,0.7,0.15,0.15); 
 ttrn = targets(trainInd);
 tval = targets(valInd);
 ttest = targets(testInd);
 Ntst = length(ttrn) 
 Nval = length(valInd)
 Ntst = length(testInd)
 Ntrneq = prod(size(ttrn))     % Ntrn*O
 Ndof = Ntrneq-Nw

 ytrn00= mean(ttrn,2);     
 Nw00 = size(ytrn00,2)
 Ndof00 = Ntrneq-Nw00
 MSEtrn00 = sse(ttrn-ytn000)/Ntrneq
 MSEtrn00=mean(var(ttrn,1))
 MSEtrn00a = sse(ttrn-ytrn00)/Ndof00
 MSEtrn00a=mean(var(ttrn,0))

 MSEval00 = mse(tval-ytrn00)
 MSEtst00 = mse(tttst-ytrn00)

 net.trainParam.goal = 0.01*Ndof*MSEtrn00a/Ntrneq;  % R2trna >= 0.99
 rng(0)
 [net,tr,Ys,Es,Xf,Af] = train(net,inputs,targets,inputStates,layerStates);

 outputs = net(inputs,inputStates,layerStates);

 errors = gsubtract(targets,outputs);

 MSE = perform(net,targets,outputs);

 MSEa=Neq*MSE/(Neq-Nw)

 R2=1-MSE/MSE00

 R2a=1-MSEa/MSE00a

% 10. The DOF "a"djustment is only applied to the training data

% 11. Can delete the last 6 equations that refer to all of the data instead of the trn/val/tst division.

 MSEtrn=tr.perf(end)
 MSEtrna = Ntrneq*MSEtrn/Ndof
 MSEval=tr.vperf(end)
 MSEtst=tr.tperf(end)

 R2trn=1-MSEtrn/MSEtrn00
 R2trna=1-MSEtrna/MSEtrn00a

 R2val=1-MSEval/MSEval00
 R2tst=1-MSEtst/MSEtst00

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