How to divide data into train/valid/test sets such that one sample from every class is selected?

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Hello to all,
I am trying to partition a dataset into training and test sets in a way such that at least one class sample is selected in both training and the test set.
In the process, in a loop I have used cvpartition and to check whether every class sample has been selected or not, I matched the class samples from every loop to the total classes present. This is what I have done so far,
s2 = data(:,1); % target vector in data
s2_1 = unique(data(:,1)); % total number of classes
for m = 1 : 1000
cv = cvpartition(data(:,1),'KFold',5,'Stratify',false);
for i = 1:cv.NumTestSets
testClasses = s2(cv.test(i));
[~,~,idx] = unique(testClasses);
nCount{i} = accumarray(idx(:),1);
for n = 1 : 5
if length(nCount{1,n})==length(s2_1)
There's a problem here with the break statement but I can work it out. The major problem is I don't get any proper result here and the uncertainity about the max number of loops (eg 1000) to be run here.
I hope I am able to explain properly.
Thanks in advance.

Accepted Answer

Ridwan Alam
Ridwan Alam on 20 Nov 2019
Edited: Ridwan Alam on 20 Nov 2019
Set 'Stratify' option to 'True'.
cv = cvpartition(data(:,1),'KFold',5,'Stratify',true);

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