knn.rar_K._KNN K_knn_knn matlab

data=load(''); plot(data(1:50,3),data(1:50,4),'o',data(51:100,3),data(51:100,4),'x',data(101:150,3),data(101:150,4),'*'); class1 = data(1:50,1:4); class2 = data(51:100,1:4); class3 = data(101:150,1:4); %%randperm(50) (1~50)亂數排列 p1= randperm(50); p2= randperm(50); p3= randperm(50); %%建立train ， 並將陣列垂直排列 train = [class1(p1(1:25),:);class2(p2(1:25),:);class3(p3(1:25),:)]; test = [class1(p1(26:50),:);class2(p2(26:50),:);class3(p3(26:50),:)]; %%建立一個25*1的全一矩陣 label = [ones(25,1);2*ones(25,1);3*ones(25,1)]; %%NN 進?分? d = []; result = []; %%此時 [h_tr,w_tr] = [75 4] [h_tr,w_tr]= size(train); [h_ts,w_ts]= size(test); for I = 1: h_ts for j = 1:h_tr d(j,1) = norm(train(j,:)-test(I,:)); end %%取出的最小值和其索引值 [value, ind ] = min(d); result(i,1) = label(ind,1); end %%計算錯誤? error_num = 0; for i = 1:25 if result(i,1) ~= 1 error_num = error_num +1; end end for i = 26:50 if result (i , 1 ) ~= 2 error_num = error_num +1; end end for i = 51:75 if result (i , 1)~= 3 error_num = error_num +1; end end error_rate = error+num/h_ts;