# error-index exceeds matrix dimension

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FIR on 17 Apr 2012
In the following code i get error a s
P1 = [-1 -1 2 2; 0 5 0 5];
Tar = [0 ;1 ]
indices=crossvalind('kfold',Tar,10);
for i=1:10
test=(indices==i);trains= ~test
tst = (indices==i);
val = (indices== mod(i+1,10));
trn = ~[tst,val];
net=newff(P1(:,trains),Tar(:,trains),2);
net=init(net);
[net,tr]=train(net,P1(:,trains),Tar(:,trains));
out = round(sim(net,P(:,test)));
end
Index exceeds matrix dimensions.
Error in cfour (line 58)
net=newff(P1(:,trains),Tar(:,trains),2);

Walter Roberson on 17 Apr 2012
That code is going to generate an error unless "indices" is of length 1 exactly. If it is longer than 1, then "test" and "train" will be longer than 1, and would then be too long to use as logical vectors against the columns of the single-column Tar array.
FIR on 17 Apr 2012
Walter can u please tell ho wto process newff with cross validation for fisheriris data set plz,i am getting many errors while processing as stated below in my comments

Andreas Goser on 17 Apr 2012
net=newff(P1(:,trains),Tar(:,trains),2);
throws an error in the first run, as Tar has no second dimension. Probably you mean:
net=newff(P1(:,trains),Tar(trains),2);
FIR on 17 Apr 2012
yes Andreas,hopefully i dont know more about neural networks ,can u please telll how to correct this error please ,and also please answer for my post "edit-error in classsifier"

Greg Heath on 22 Apr 2012
1. The input and target matrices must have the same number of columns:
Tar = [ 0 0 1 1 ]
[ I N ] = size( P1) % [ 2 4 ] [ O N ] = size(Tar) % [ 1 4 ]
k = 10
indices=crossvalind('kfold',Tar,k)
2. a. It doesn't make sense to use k > N
b.Instead of using CROSSVALIND from the Bioinformatics TBX, the algorithm
might be more portable if you use CROSSVAL from the Statistics TBX.
3. trains= ~test
Rename. TRAINS is a MATLAB function.
Hope this helps.
Greg
##### 2 CommentsShow 1 older commentHide 1 older comment
Greg Heath on 22 Apr 2012
Typical nontrivial classification examples should have classes with
many more I/O training pairs than input dimensions.
For the FisherIris example/demo (c = 3, I = 4, N = 150).
Although that ratio is
N/(3*4) = 12.5,
the scatter plot in the PetalLength/PetalWidth plane indicates
that the 3 classes are linearly separable with two hidden nodes.
Hope this helps.
Greg

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