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 in matlab

main.m
function out = mse_vs_m(x_tr, t_tr, x_te, t_te)
%Function to compute empirical MSE test loss
%Inputs:
%x_tr: Training input data matrix
%t_tr: Training target data vector
%x_te: Test input data matrix
%t_te: Test target data vector
%Outputs:
%out: Empirical MSE test loss

n_tr = length(t_tr); %Number of training samples
n_te = length(t_te); %Number of test samples
out = zeros(1, n_te); %Initialize the output variable

%For each value of m, compute the corresponding MSE value on test data
for m = 1:n_tr
    x_m = x_tr(1:m, :); %Training input data matrix for m samples
    t_m = t_tr(1:m); %Training target data vector for m samples
    
    %Compute weight vector using training data
    w = (x_m'*x_m)\x_m'*t_m;
    
    %Compute predicted target values for test data
    y_te = x_te*w;
    
    %Compute the MSE value for this value of m
    out(m) = sum((t_te-y_te).^2)/n_te;
end
end
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