Sorting several functions in one function - matlab

Question

I have a function where I'm receiving input data and other data from four random sources. This function must be repeated for 12 times and 12 should be set so that. This function should also be repeated 10 times. Is there a more compact way to perform what I'm doing below?
for ii=1:10
Percent=0.7;
num_points1 = size(X_1,1);
split_point1 = round(num_points1*Percent);
sequence1 = randperm(num_points1);
X1_train{ii} = X_1(sequence1(1:split_point1),:);
Y1_train{ii} = Y_1(sequence1(1:split_point1));
X1_test{ii} = X_1(sequence1(split_point1+1:end),:);
Y1_test{ii}= Y_1(sequence1(split_point1+1:end));
num_points2 = size(X_2,1);
split_point2 = round(num_points2*Percent);
sequence2 = randperm(num_points2);
X2_train{ii} = X_2(sequence2(1:split_point2),:);
Y2_train{ii} = Y_2(sequence2(1:split_point2));
X2_test{ii} = X_2(sequence2(split_point2+1:end),:);
Y2_test{ii}= Y_2(sequence2(split_point2+1:end));
.
.
.
.
num_points12 = size(X_12,1);
split_point12 = round(num_points12*Percent);
sequence12 = randperm(num_points12);
X12_train{ii} = X_12(sequence12(1:split_point12),:);
Y12_train{ii} = Y_12(sequence12(1:split_point12));
X12_test{ii} = X_12(sequence12(split_point12+1:end),:);
Y12_test{ii}= Y_12(sequence12(split_point12+1:end));
end

Answer 1

The biggest problem you have currently is that you have 12 separate variables to do 12 related operations. Don't do that. Consolidate all of the variables into one container then iterate over the container.
I have the following suggestions for you:
Combine X_1, X_2, ... X_12 into one container. A cell array or structure may be prudent to use here. I'm going to use cell arrays in this case as your code currently employs them and it's probably the easiest thing for you to transition to.
Create four master cell arrays for the training and test set data and labels and within each cell array are nested cell arrays that contain each trial.
Loop over the cell array created in step #1 and assign the results to each of the four master cell arrays.
Therefore, something like this comes to mind:
X = {X_1, X_2, X_3, X_4, X_5, X_6, X_7, X_8, X_9, X_10, X_11, X_12};
Y = {Y_1, Y_2, Y_3, Y_4, Y_5, Y_6, Y_7, Y_8, Y_9, Y_10, Y_11, Y_12};
N = numel(X);
num_iterations = 10;
X_train = cell(1, num_iterations);
Y_train = cell(1, num_iterations);
X_test = cell(1, num_iterations);
Y_test = cell(1, num_iterations);
Percent = 0.7;
for ii = 1 : num_iterations
for jj = 1 : N
vals = X{jj};
labels = Y{jj};
num_points = size(vals,1);
split_point = round(num_points*Percent);
sequence = randperm(num_points);
X_train{ii}{jj} = vals(sequence(1:split_point),:);
Y_train{ii}{jj} = labels(sequence(1:split_point));
X_test{ii}{jj} = vals(sequence(split_point+1:end),:);
Y_test{ii}{jj} = labels(sequence(split_point+1:end));
end
end
As such, to access the training data for a particular iteration, you would do:
data = X_train{ii};
ii is the iteration you want to access. data would now be a cell array, so if you want to access the training data for a particular group, you would now do:
group = data{jj};
jj is the group you want to access. However, you can combine this into one step by:
group = X_train{ii}{jj};
You'll see this syntax in various parts of the code I wrote above. You'd do the same for the other data in your code (X_test, Y_train, Y_test).
I think you'll agree that this is more compact and to the point.