1 %% Machine Learining----Linear Regression
2 close all
3 clear
4
5 %%data load
6 Year = linspace(0,13,14);
7 Price = [2,2.5,2.9,3.147,4.515,4.903,5.365,5.704,6.853,7.791,8.561,10,11.28,12.9];
8
9 %%train data
10 alpha = 0.01;
11 theta = [1,8];
12 obj_old = 1e10;
13 tor = 1e-2;
14
15 tic;
16 for time = 1:1000
17 delta = zeros(1,2);
18 objective = 0;
19 hypothesis = theta(1) + theta(2) * Year;
20 drawnow;
21 subplot(2,1,1);
22 plot(Year,Price,'rx',Year,hypothesis);
23 for i = 1:14
24 delta = (theta(1) + theta(2) * Year(i) - Price(i)) * [1,Year(i)] + delta;
25 objective = (theta(1) + theta(2) * Year(i) - Price(i)) * (theta(1) + theta(2) * Year(i) - Price(i))+ objective;
26 end
27 theta = theta - alpha * delta / 14;
28
29 fprintf('objective is %.4f\n', objective);
30 if abs(obj_old - objective) < tor
31 fprintf('torlerance is samller than %.4f\n', tor);
32 break;
33 end
34 obj_old = objective;
35 subplot(2,1,2);
36 plot(toc,obj_old);
37 axis([0 20 0 800]) %xmin是x最小，xmax是x最大，ymin，ymax类似
38 hold on;
39 end
