machine learning - Matlab: How can I split my data matrix into two random subsets of column vectors while keeping the label information?

Question

I have a data matrix X (60x208) and a matrix of labels Y (1x208). I want to split my data matrix X into two random subsets of column vectors: training (which will be 70% of the data) and testing (which will be 30% of the data), but I need to still be able to identify which label from Y corresponds to each column vector. I couldn't find any function to do this, any ideas?

EDIT: Thought I should add, there are only two labels in Y: 1 and 2 (not sure if this makes a difference)

Answer 1

That's pretty easy to do. Use randperm to generate a random permutation of indices from 1 up to as many points as you have... which is 208 in your case.

Once you generate this sequence, simply use this and subset into your X and Y to extract the training and test data and labels. As such, do something like this:

num_points = size(X,2);
split_point = round(num_points*0.7);
seq = randperm(num_points);
X_train = X(:,seq(1:split_point));
Y_train = Y(seq(1:split_point));
X_test = X(:,seq(split_point+1:end));
Y_test = Y(seq(split_point+1:end));

The split_point determines how many points we need to place into our training set, and we will need to round it in case this calculation yields any decimal points. I also didn't hard code 208 in there because your data set might grow and so this will work with any size data set you choose. X_train and Y_train will contain your data and labels for your training set while X_test and Y_test will contain your data and labels for your test set.

As such, the first column of X_train is your data point for the first element of your training set, with the first element of Y_train serving as the label for that particular point... and so on and so forth!