Mine

%% cv_demo.m
% From A First Course in Machine Learning, Chapter 1.
% Simon Rogers, 311011 [simon.rogers@glasgow.ac.uk]
% Revised by Sayyed Mohsen Vazirizade, Oct102017 [smvazirizade@email.arizona.edu]
% Demonstration of cross-validation for model selection
clc;clear all;close all;
rng(1);
%% Generate some data
% Generate x between -5 and 5
N = 100;
x = 10rand(N,1) - 5;
t = 5x.^3  - x.^2 + x + 150randn(size(x));
testx = [-50.015]'; % Large, independent test set
testt = 5testx.^3 - testx.^2 + testx + 150randn(size(testx));

%% Run a cross-validation over model orders
maxorder = 7;
X = [];
testX = [];
K = 9 %K-fold CV
sizes = repmat(floor(NK),1,K);
sizes(end) = sizes(end) + N - sum(sizes);
csizes = [0 cumsum(sizes)];

% Note that it is often sensible to permute the data objects before
% performing CV.  It is not necessary here as x was created randomly.  If
% it were necessary, the following code would work
% order = randperm(N);
% x = x(order); Or X = X(order,) if it is multi-dimensional.
% t = t(order);

for k = 0maxorder
    X = [X x.^k];
    testX = [testX testx.^k];
    for fold = 1K
        % Partition the data
        % foldX contains the data for just one fold
        % trainX contains all other data
     
        foldX = X(csizes(fold)+1csizes(fold+1),);
        trainX = X;
        trainX(csizes(fold)+1csizes(fold+1),) = [];
        foldt = t(csizes(fold)+1csizes(fold+1));
        traint = t;
        traint(csizes(fold)+1csizes(fold+1)) = [];     

        w = (trainX'trainX)trainX'traint;
        fold_pred = foldXw;
        cv_loss(fold,k+1) = mean((fold_pred-foldt).^2);
        ind_pred = testXw;
        ind_loss(fold,k+1) = mean((ind_pred - testt).^2);
        train_pred = trainXw;
        train_loss(fold,k+1) = mean((train_pred - traint).^2);
    end
end

%% Plot the results
figure(1);
subplot(131)
plot(0maxorder,mean(cv_loss,1),'linewidth',2)
xlabel('Model Order');
ylabel('Loss');
title('CV Loss');
ylim(1.2[0,max(max(max(mean(cv_loss,1),mean(train_loss,1)),mean(ind_loss,1)))])
subplot(132)
plot(0maxorder,mean(train_loss,1),'linewidth',2)
xlabel('Model Order');
ylabel('Loss');
title('Train Loss');
ylim(1.2[0,max(max(max(mean(cv_loss,1),mean(train_loss,1)),mean(ind_loss,1)))])
subplot(133)
plot(0maxorder,mean(ind_loss,1),'linewidth',2)
xlabel('Model Order');
ylabel('Loss');
title('Independent Test Loss')
ylim(1.2[0,max(max(max(mean(cv_loss,1),mean(train_loss,1)),mean(ind_loss,1)))])