error-index exceeds matrix dimension

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);

please help

Answer 1

[Walter Roberson]

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]

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

Answer 2

[Andreas Goser]

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]

I get error as

Error using trainlm (line 109)

Inputs and targets have different numbers of samples.

Error in network/train (line 106)

[net,tr] = feval(net.trainFcn,net,X,T,Xi,Ai,EW,net.trainParam);

Error in cfour (line 60)

[net,tr]=train(net,P1(:,trains),Tar(trains));

[Andreas Goser]

You just asked why you got this error. Now you know ;-)

I may know more about MATLAB, but hope fully you know more about neural networks... The message "Inputs and targets have different numbers of samples." That sounds like an actionable error message, isn't it?

[FIR]

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"

Answer 3

[Greg Heath]

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

[Greg Heath]

[ O N ] = size(Tar) % [ 1 4 ]

Should be on a new line.

Greg

[Greg Heath]

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