error-index exceeds matrix dimension

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FIR el 17 de Abr. de 2012
In the following code i get error a s
P1 = [-1 -1 2 2; 0 5 0 5];
Tar = [0 ;1 ]
for i=1:10
test=(indices==i);trains= ~test
tst = (indices==i);
val = (indices== mod(i+1,10));
trn = ~[tst,val];
out = round(sim(net,P(:,test)));
Index exceeds matrix dimensions.
Error in cfour (line 58)
please help

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Walter Roberson
Walter Roberson el 17 de Abr. de 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.
  1 comentario
FIR el 17 de Abr. de 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

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Más respuestas (2)

Andreas Goser
Andreas Goser el 17 de Abr. de 2012
throws an error in the first run, as Tar has no second dimension. Probably you mean:
  3 comentarios
FIR el 17 de Abr. de 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"

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Greg Heath
Greg Heath el 22 de Abr. de 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
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.
  2 comentarios
Greg Heath
Greg Heath el 22 de Abr. de 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.

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