"ffnet" neural network crossvalidation

Indices = crossvalind('Kfold',inputs , 10);
for i=1:10
     test = (Indices == i); 
     train = ~test;

net = newff(inputs(:,train),targets(:,train),20,{},'trainscg');
[net,TR] = traingd(net,inputs,targets);
 a = sim(net,inputs(:,train));
     b=sim(net,inputs(:,test));
end

this is the code to apply crossvalidation feel free to use it:)

but there is a problem in crossvalind when your input set has a higher dimension than 1

in case your input set consists of row vectors then the crossvalind command should be modified as following:

[M, N] = size (inputs)
indices=crossvalind('Kfold',inputs(1:M,N),10);
*
*so now my question is: in case my input set consists of column vectors how should i modify crossvalind to assign indices NOT to each element of every column vector *BUT to each column vector itself***

: in case someone wants to pass indices to the elements of column vectors of the "inputs matrix" he can do:

    C = num2cell(inputs,1);%this will "break" the matrix(inputs) to column vectors

      for i=1:length(inputs)
             indices=crossvalind('Kfold',C{i},10)
       end

[Greg Heath]

I don't have crossvalind. However, I recall a post recommending cvpartition as a superior alternative. That reference also recommended crossval.

Hope this helps.