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上周由于皮肤有点过敏，去医院来来回回一周。
前几天去上海比完赛，拿了个银牌靠前 ，遗憾总会有的。
于是更新放慢了 。
这篇博客没有什么含金量，只是拿heart_scale.txt这个文件的格式改了改部分代码，内容上没有什么。用到了一些C++的一些不太常用的知识点，也很水。
希望会对需要的人有点帮助。
我的看法，选择MATLAB做svm的分类和C++或者其他没有什么太大的区别。
可能MATLAB编码上会微快，但是运行速度明显满了点，当然对于数据预处理的部分都差不多。
#include "svm.h"
using namespace std ;
const int feature_size = 13 ;
const int train_size = 270 ;
svm_problem prob ;
void init_svm_problem(){
prob.l = train_size ;
prob.y = new double[train_size] ;
prob.x = new svm_node* [train_size] ;
svm_node *x_space = new svm_node[train_size*(1+feature_size)] ;
freopen("heart_scale.txt" , "r" , stdin) ;
double value ;
int indx ;
char str[200] ;
string s ;
int row = -1 , i = -1 , t ;
while(gets(str)){
istrstream in(str) ;
t = 0 ;
while(in>>s){
char *ch = (char *)s.c_str() ;
if(strcmp(ch , "+1") == 0){
row++ ;
prob.y[row] = 1 ;
}
else if(strcmp(ch , "-1") == 0){
row++ ;
prob.y[row] = -1 ;
}
else{
sscanf(ch , "%d:%lf" ,&indx , &value) ;
if(value != 0.0){
i++ ;
x_space[i].index = indx ;
x_space[i].value = value ;
}
if(t == 0) prob.x[row] = &x_space[i] ;
t++ ;
}
}
i++ ;
x_space[i].index = -1 ;
}
}
svm_parameter param ;
void init_svm_parameter(){
param.svm_type = C_SVC;
param.kernel_type = RBF;
param.degree = 3;
param.gamma = 0.0001;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 100;
param.C = 13;
param.eps = 1e-5;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;
}
const int test_size = 270 ;
double predict_lable[test_size] ;
double test_lable[test_size] ;
int main(){
int i , j , indx ;
double value ;
char str[200] ;
string s ;
init_svm_problem() ;
init_svm_parameter() ;
if(param.gamma == 0) param.gamma = 0.5 ;
svm_model* model = svm_train(&prob , ¶m) ;
freopen("heart_scale.txt" , "r" , stdin) ;
svm_node *test = new svm_node[13] ;
for(i = 0 ; i < test_size ; i++){
gets(str) ;
istrstream in(str) ;
j = -1 ;
while(in>>s){
char *ch = (char *)s.c_str() ;
if(strcmp(ch , "+1") == 0)
test_lable[i] = 1 ;
else if(strcmp(ch , "-1") == 0)
test_lable[i] = -1 ;
else{
sscanf(ch , "%d:%lf" ,&indx , &value) ;
if(value != 0.0){
j++ ;
test[j].index = indx ;
test[j].value = value ;
}
}
}
j++ ;
test[j].index = -1 ;
predict_lable[i] = svm_predict(model , test) ;
}
int yes = 0 ;
for(i = 0 ; i < test_size ; i++)
if(test_lable[i] == predict_lable[i]) yes++ ;
cout<<yes<<endl ;
printf("%.2lf%%\n" , (0.0+yes)/test_size) ;
return 0 ;
}
后文希望能研究出90% + 的数据处理算法。
heart_scal.txt 这个林教授官网上有，cadn上下载要积分，我做个善事吧。
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