用MATLA实现机器学习中的批处理梯度下降法和随机梯度下降法

clc; close all; clear all; y = [19,26,19,20]; q = [0.01,2,5]; a = 0.01; x1 =[1;1;4]; x2 = [1;2;5]; x3 = [1;5;1]; x4 = [1;4;2]; sampe = [x1,x2,x3,x4]; k = 0; ht = 10; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %batch gradient descent %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % while(k < 10000 && ht > 0.00001) % %sum = 0; % for j = 1:3 % sum = 0; % for i = 1:4 % sum = sum + (y(i) - q * sampe(:,i)) * sampe(j,i); % ht = 1/2 * (y(i) - q * sampe(:,i)) * (y(i) - q * sampe(:,i)); % end % q(j) = q(j) + a * sum; % end % k = k + 1; % end % k %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %stochastic gradient descent %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % while(k < 10000 && ht > 0.00001) % % for j = 1:3 % % for i = 1:4 % q(j) =q(j) + a*(y(i) - q * sampe(:,i)) * sampe(j,i); % ht = 1/2 * (y(i) - q * sampe(:,i)) * (y(i) - q * sampe(:,i)); % end % %q(j) = q(j) + a * sum; % end % k = k + 1; % end % k %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %logistic regression %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% while(k < 10000 && ht > 0.00001) for j = 1:3 for i = 1:4 q(j) =q(j) + a*(y(i) - 1/(1 + exp(q * sampe(:,i)))) * sampe(j,i); ht = 1/2 * (y(i) - q * sampe(:,i)) * (y(i) - q * sampe(:,i)); end %q(j) = q(j) + a * sum; end k = k + 1; end k %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% b = [1,1,4]; q * b' b = [1,2,5]; q * b' b = [1,5,1]; q * b'