%% 神經(jīng)網(wǎng)絡(luò)二分類問題
clc,clear,close all
%% 對照組指標數(shù)據(jù) 0組
p1=[0.73 9.9 512.6 0.000488341 94.23
0.58 13.3 105.5 0.00028624 123.86
0.52 12.8 596.4 0.000898535 105.47
0.41 12.7 621.9 0.003519715 110.65
0.51 9 342 0.001070026 123.61
0.73 11.7 237.4 0.000592125 103.07
0.72 10.6 670.1 0.001646966 108.40
0.61 11.2 210.8 0.000714758 125.00
0.6 7.1 614.7 0.002473218 112.82
0.62 6 234.8 0.011008547 128.19
0.6 7.3 126.5 0.001716387 118.42
0.53 8 506.6 0.002412081 140.29
0.6 9.9 254.8 0.00047552 124.17
0.58 11.9 376.7 0.001550388 112.13
0.62 10.7 570.5 0.001077933 125.78
0.79 8.5 105.9 0.000389824 105.67
0.81 7.6 137.8 0.000960825 112.74
0.56 5.7 148.6 0.001310584 117.40
0.63 7.5 97 0.001244019 119.11
]; %歸為0
%% 實驗組指標數(shù)據(jù) 1組
p2=[0.88 14.1 168.9 0.00319027 104.30
0.67 7.2 141.2 0.000685297 90.61
0.9 9.4 298.5 0.004820393 90.46
0.68 10.8 70.3 0.000395073 98.36
1.25 9.2 1614.6 0.076472875 87.21
0.5 9.2 210.2 0.001139238 115.26
0.72 12.6 132.4 0.00102688 81.74
0.64 8.6 243.9 0.000489883 114.94
0.97 16.9 581.4 0.005763578 68.53
0.67 10.6 242.8 0.00101917 95.64
0.54 7 372.6 0.002005217 123.43
0.86 10.1 172.4 0.002088703 85.23
0.68 13.6 213.1 0.001856427 115.22
2.16 37.1 2206.6 0.027308957 33.12
1.02 21.5 75.3 0.010366863 70.40
0.73 11.8 100.8 0.000373529 109.38
3.14 22.1 1727.2 0.942625053 9.11
0.77 9.8 103.3 0.001319323 119.84
0.64 10.8 100.1 0.028400355 120.72
1.85 18.4 4761.6 0.232148915 31.60
0.84 7.7 93.3 0.001710846 96.96
1.3 9.8 410.1 0.570991575 74.78
]; %歸為1
%% 結(jié)果標簽
flag = [ones(size(p1,1),1); zeros(size(p2,1),1)];
%% 神經(jīng)網(wǎng)絡(luò)
p=[p1;p2]';
pr=minmax(p);
goal=[ones(1,size(p1,1)),zeros(1,size(p2,1));zeros(1,size(p1,1)),ones(1,size(p2,1))];
% plot(p1(:,1),p1(:,2),'h',p2(:,1),p2(:,2),'o')
net=newff(pr,[3,2],{'logsig','logsig'});? % 3個隱層，2種傳遞方式
net.trainParam.show = 10;
net.trainParam.lr = 0.05;
net.trainParam.goal = 1e-10;? ? % 容忍誤差
net.trainParam.epochs = 1000;? % 迭代次數(shù)
net = train(net,p,goal);
%% 訓練結(jié)果
outcome = sim(net,p) ;%給p1和p2分類作用，分為0和1
train_predict(outcome(1,:) >= 0.5,1) = 1;
train_predict(outcome(1,:) <? 0.5,1) = 0;
true = sum(1 - abs(flag - train_predict)) / size(flag,1)
%% 預測
%{
x = [55 0.89 2.89 1.16 1.03
461.7 0.97 1.13 1.37 0.47
148.3 0.49 1.56 0.71 0.66
]'; % 指標數(shù)據(jù)
y = sim(net,x) %給待鑒定樣本分類
test_predict(y(1,:) >= 0.5,1) = 1;
test_predict(y(1,:) <? 0.5,1) = 0;
%}
