%?支持向量機(jī)Matlab工具箱C-SVC?孿生線性核函數(shù)二類分類算法
%?使用平臺(tái)?-?Matlab7.12


close?all
clear
clc

load?N_p???%?正類點(diǎn)數(shù)據(jù)，特征信息存放在列里面
load?N_n???%?負(fù)類點(diǎn)數(shù)據(jù)，特征信息存放在列里面

?
%%?第一步：數(shù)據(jù)預(yù)處理
%?由原來(lái)的數(shù)據(jù)產(chǎn)生80%的訓(xùn)練數(shù)據(jù)和20%的預(yù)測(cè)數(shù)據(jù)
n_p=size（N_p2）;
n_n=size（N_n2）;
n1=randperm（n_p）;??????%?對(duì)1到n的n個(gè)正整數(shù)進(jìn)行隨機(jī)不重復(fù)的排列，形成一個(gè)1行n列的矩陣
n1=n1‘;
n2=randperm（n_n）;
n2=n2‘;
f_p=floor（4*n_p/5）;????%?取80%的數(shù)據(jù)作為訓(xùn)練集，其余20%作為預(yù)測(cè)集
f_n=floor（4*n_n/5）;?
data_train_p=N_p（:n1（1:f_p））;??????????????????%?正類訓(xùn)練數(shù)據(jù)
data_train_n=N_n（:n2（1:f_n））;??????????????????%?負(fù)類訓(xùn)練數(shù)據(jù)
data_train=[data_train_pdata_train_n];?????????%?訓(xùn)練數(shù)據(jù)集（含正負(fù)類）
Y_train=[ones（1f_p）-ones（1f_n）];?????????????%?訓(xùn)練數(shù)據(jù)標(biāo)簽集?

data_predict_p=N_p（:n1（f_p+1:end））;????????????%?正類預(yù)測(cè)數(shù)據(jù)
data_predict_n=N_n（:n2（f_n+1:end））;????????????%?負(fù)類預(yù)測(cè)數(shù)據(jù)
data_predict=[data_predict_pdata_predict_n];???%?預(yù)測(cè)數(shù)據(jù)集（含正負(fù)類）
Y_predict=[ones（1n_p-f_p）-ones（1n_n-f_n）];???%?預(yù)測(cè)數(shù)據(jù)標(biāo)簽集?

%?相關(guān)參數(shù)初始化
bestAccuracy=0;
k=0;
c1min=-2;
c1max=2;
c2min=-2;
c2max=2;
aac=rand（101）;
best_c1=0;best_c2=0;
data_train=data_train‘;
Y_train=Y_train‘;


%%?第二步：數(shù)據(jù)訓(xùn)練尋找最優(yōu)參數(shù)c1c2
%??將c1和c2劃分網(wǎng)格進(jìn)行搜索
indices?=?crossvalind（‘Kfold‘Y_train10）;????
tic;
for?c1=2^（c1min）:1:2^（c1max）
????for?c2=2^（c2min）:1:2^（c2max）
????????%采用K-CV方法將data大致平均分為K組
????????for?run=?1:10
????????????test=（indices?==?run）;?train=~test;?
????????????
????????????train_data=data_train（train:）;????????????%?訓(xùn)練數(shù)據(jù)集（含正類負(fù)類點(diǎn)集）?數(shù)據(jù)信息全在行里面?????????
????????????train_data_label=Y_train（train:）;?????????%?訓(xùn)練集標(biāo)簽（含正類負(fù)類點(diǎn)集）?數(shù)據(jù)信息全在行里面
????????????
????????????test_data=data_train（test:）;??????????????%?預(yù)測(cè)數(shù)據(jù)集（含正類負(fù)類點(diǎn)集）?數(shù)據(jù)信息全在行里面
????????????test_data_label=Y_train（test:）;???????????%?預(yù)測(cè)集標(biāo)簽（含正類負(fù)類點(diǎn)集）?數(shù)據(jù)信息全在行里面
????????????
????????????groupA=ismember（train_data_label1）;???????%?分出正類點(diǎn)的位置????????????數(shù)據(jù)信息全在行里面
????????????groupB=ismember（train_data_label-1）;??????%?分出正類點(diǎn)的位置????????????數(shù)據(jù)信息全在行里面
???????%?關(guān)于ismember函數(shù)的具體用法，使用help?ismember可以進(jìn)一步了解
???????
????????????A=train_data（groupA:）;????????????????????%?正類訓(xùn)練數(shù)據(jù)????????????????數(shù)據(jù)信息全在行里面
????????????B=train_data（groupB:）;???
????????????%用模糊支持向量機(jī)訓(xùn)練數(shù)據(jù)
????????????[v1v2]=twinsvm_lin_train（ABc1c2）;??????
?????????????%下面用驗(yàn)證集進(jìn)行驗(yàn)證并記錄此時(shí)的準(zhǔn)確率
????????????row=size（test_data1）;
????????????result=rand（row1）;
????????????test_data1=[test_dataones（row1）];
????????????distance1=abs（test_data1*v1）;
????????????distance2=abs（test_data1*v2）;
????????????test_distance=min（distance1distance2）;
????????????for?run1=1:row
????????????????if?test_distance（run1）==distance1（run1）
????????????????????result（run1）=1;
????????????????else
????????????????????result（run1）=-1;
????????????????end
????????????end
??????????for?run1=1:row
??????????????if?test_data_label（run1）==result（run1）
??????????????????k=k+1;
??????????????end
??????????end
??????????aac（run）=k/row;
??????????k=0;
????????end
????????cv=sum（aac）/10;
????????if?（cv>bestAccuracy）
????????????bestAccuracy=cv;
????????????best_c1=c1;
????????????best_c2=c2;
????????e

?屬性????????????大小?????日期????時(shí)間???名稱
-----------?---------??----------?-----??----

?????文件???????1780??2012-05-10?20:37??N_p.mat

?????文件???????1290??2012-07-23?14:28??twinsvm_lin_train.m

?????文件???????5550??2012-09-03?17:56??lin_main.m

?????文件???????1782??2012-05-10?20:36??N_n.mat

-----------?---------??----------?-----??----

????????????????10402????????????????????4