matlab SVM分类程序

>> clc;
>> load heart_scale.mat;%把数据粘过去
>> data=heart_scale_inst;
>> label=heart_scale_label;
>> ind=240;
>> data1=data(1:ind,:);
>> label1=label(1:ind,:);
>> data=data1;
>> label=label1;
>> train_data=[data(1:23,:);data(47:52,:);data(73:78,:);data(87:93,:);data(100:104,:);data(109:114,:);data(120:138,:);data(157:180,:);data(201:213,:);data(227:234,:)];
>> train_label=[label(1:23);label(47:52);label(73:78);label(87:93);label(100:104);label(109:114);label(120:138);label(157:180);label(201:213);label(227:234)];
>> test_data=[data(24:46,:);data(53:72,:);data(79:86,:);data(94:99,:);data(105:108,:);data(115:119,:);data(138:156,:);data(181:200,:);data(214:226,:);data(235:240,:)];
>> test_label=[label(24:46);label(53:72);label(79:86);label(94:99);label(105:108);label(115:119);label(138:156);label(181:200);label(214:226);label(235:240)];
>> [train_data,pstrain]=mapminmax(train_data');
>> pstrain.ymin=0;
>> pstrain.ymax=1;
>> [train_data,pstrain]=mapminmax(train_data,pstrain);
>> [test_data,pstest]=mapminmax(test_data');
>> pstest.ymin=0;
>> pstest.ymax=1;
>> [test_data,pstest]=mapminmax(test_data,pstest);
>> train_data=train_data';
>> test_data=test_data';
>> [bestacc,bestc,bestg] = SVMcg(train_label,train_data,-2,4,-4,4,3,0.5,0.5,0.9);
>> cmd = ['-c ',num2str(bestc),' -g ',num2str(bestg)];
>> model = svmtrain(train_label,train_data,cmd);
>> [predict,accuracy] = svmpredict(test_label,test_data,model);