Matlab Iris Classification input size mistmach - matlab

Question

I am very new to Matlab. What i am trying to do is classify the iris dataset using Cross-Validation (that means that i have to split the dataset in 3: trainingSet, validationSet, and test set) . In my mind everything i write here is ok (beeing a beginner is hard sometimes). So i could use a little help...
This is the function that splits the data (first 35(70% of the data) are the training set, the rest is the validation set(15%) and 15% i will use later for the test set)
close all; clear ;
load fisheriris;
for i = 1:35
for j = 1:4
trainSeto(i,j) = meas(i,j);
end
end
for i = 51:85
for j = 1:4
trainVers(i-50,j) = meas(i,j);
end
end
for i = 101:135
for j = 1:4
trainVirg(i-100,j) = meas(i,j);
end
end
for i = 36:43
for j = 1:4
valSeto(i-35,j) = meas(i,j);
end
end
for i = 86:93
for j = 1:4
valVers(i-85,j) = meas(i,j);
end
end
for i = 136:143
for j = 1:4
valVirg(i-135,j) = meas(i,j);
end
end
for i = 44:50
for j = 1:4
testSeto(i-43,j) = meas(i,j);
end
end
for i = 94:100
for j = 1:4
testVers(i-93,j) = meas(i,j);
end
end
for i = 144:150
for j = 1:4
testVirg(i-143,j) = meas(i,j);
end
end
And this is the main script:
close all; clear;
%%the 3 tipes of iris
run divinp
% the representation of the 3 classes(their coding)
a = [-1 -1 +1]';
b = [-1 +1 -1]';
c = [+1 -1 -1]';
%training set
trainInp = [trainSeto trainVers trainVirg];
%the targets
T = [repmat(a,1,length(trainSeto)) repmat(b,1,length(trainVers)) repmat(c,1,length(trainVirg))];
%%the training
trainCor = zeros(10,10);
valCor = zeros(10,10);
Xn = zeros(1,10);
Yn = zeros(1,10);
for k = 1:10,
Yn(1,k) = k;
for n = 1:10,
Xn(1,n) = n;
net = newff(trainInp,T,[k n],{},'trainbfg');
net = init(net);
net.divideParam.trainRatio = 1;
net.divideParam.valRatio = 0;
net.divideParam.testRatio = 0;
net.trainParam.max_fail = 2;
valInp = [valSeto valVers valVirg];
valT = [repmat(a,1,length(valSeto)) repmat(b,1,length(valVers)) repmat(c,1,length(valVirg))];
[net,tr] = train(net,trainInp,T);
Y = sim(net,trainInp);
[Yval,Pfval,Afval,Eval,perfval] = sim(net,valInp,[],[],valT);
% calculate [%] of correct classifications
trainCor(k,n) = 100 * length(find(T.*Y > 0)) / length(T);
valCor(k,n) = 100 * length(find(valT.*Yval > 0)) / length(valT);
end
end
figure
surf(Xn,Yn,trainCor/3);
view(2)
figure
surf(Xn,Yn,valCor/3);
view(2)
I get this error
Error using trainbfg (line 120) 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 ClassIris (line 38)
[net,tr] = train(net,trainInp,T);
close all; clear ;
load fisheriris;
trainSetoIndx = 1:35;
trainVersIndx = 51:85; % or: trainVersIndx = trainSetoIndx + 50;
trainVirgIndx = 101:135;
colIndx = 1:4;
trainSeto = meas(trainSetoIndx, colIndx);
trainVers = meas(trainVersIndx, colIndx);
trainVirg = meas(trainVirgIndx, colIndx);
valSetoIndx = 36:43;
valVersIndx = 86:93;
valVirgIndx = 136:143
valSeto = meas(valSetoIndx, colIndx);
valVers = meas(valVersIndx, colIndx);
valVirg = meas(valVirgIndx, colIndx);
testSetoIndx = 44:50;
testVersIndx = 94:100;
testVirgIndx = 144:150
testSeto = meas(testSetoIndx, colIndx);
testVers = meas(testVersIndx, colIndx);
testVirg = meas(testVirgIndx, colIndx);
i have writen it with ":" also still the same problem it's something with repmat.. i don't know how to use it properly or newff :D

Answer 1

Just to get you started, you can rewrite your code loops as follows:
trainSetoIndx = 1:35;
trainVersIndx = 51:85; % or: trainVersIndx = trainSetoIndx + 50;
trainVirgIndx = 101:135; % or: trainVirgIndx = trainSetoIndx + 100;
colIndx = 1:4; % can't tell if this is all the columns in meas
trainSeto = meas(trainIndx, colIndx);
trainVers = meas(trainVersIndx, colIndx);
trainVirg = meas(trainVirgIndx, colIndx);
The do the same thing for all the others:
valSetoIndx = 36:43;
etc.
Next, simply type whos at the command prompt and you will see the sizes of all the arrays you have created. See whether the ones that need to be the same size have, in fact, the same dimensions.