design the function function out = mse_vs_m(x_tr, t_tr, x_te, t_te) that uses all given samples in the split data sets and then computes the empirical mse test loss column vector in matlab

main.m
function out = mse_vs_m(x_tr, t_tr, x_te, t_te)
% x_tr and t_tr are the training dataset
% x_te and t_te are the testing dataset
% out is the column vector of the empirical mse test loss for different values of m

% Find the size of x_te and t_te, as we need it for iteration
[n_te, d] = size(x_te);

% Create an empty vector to store the mse test loss for different values of m
out = zeros(d, 1);

% Iterate over different values of m
for i=1:d
    % Compute the weight matrix
    W = ((x_tr(:, 1:i)'*x_tr(:, 1:i))\x_tr(:, 1:i)')*t_tr;
    
    % Compute the predictions
    y_te = x_te(:, 1:i)*W;
    
    % Compute the mse loss
    out(i) = sum((t_te-y_te).^2)/n_te;
end
end
678 chars
24 lines