libsvm工具箱C++下编程实践1

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step1 :  理论学习，资料随便找

step2:  源码分析，资料随便找 

step3; 简单实践

   

一、生成数据

 为了将问题简化，特意生成训练集  tain_data.txt

类1 ： y<=x-1 ，

类2 ： y>= x+1 .

个100组数据，特征为2维。

 测试集100组数据test_data.txt

int main(){    freopen("tain_data.txt" ,  "w" , stdout) ;    srand( (unsigned int) time(NULL) ) ;    int n = 0 , x , y ;    while(n < 100){        x = rand() % 10 + 1 ;        y = rand() % 10 + 1 ;        if(y <= x - 1)  printf("-1 %d %d\n" , x, y) ;        else continue ;        n++ ;    }    n = 0 ;    while(n < 100){        x = rand() % 10 + 1 ;        y = rand() % 10 + 1 ;        if(y >= x + 1)  printf("1 %d %d\n" , x, y) ;        else continue ;        n++ ;    }    freopen("test_data.txt" ,  "w" , stdout) ;    n = 0 ;    while(n < 100){        x = rand() % 10 + 1 ;        y = rand() % 10 + 1 ;        if(y <= x - 1)       printf("-1 %d %d\n" , x, y) ;        else if(y >= x + 1)  printf("1 %d %d\n" , x, y) ;        else continue ;        n++ ;    }    return 0;}

二： 实验

引用《交大源码分析》

引用《交大源码分析》

代码部分： 此代码难度系数很小，没有细讲必要。

#include "svm.h"using namespace std ;const int feature_size = 2 ;const int train_size = 200 ;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)] ;     ifstream  in ;     in.open("tain_data.txt") ;     double value , lb ;     for(int i = 0 ; i < train_size ; i++){         in>>lb ;         //prob.y[i] = lb ;         if(i < train_size/2) prob.y[i] = 1 ;         else       prob.y[i] = -1 ;         for(int j = 0 ; j < feature_size ; j++){             in>>value ;             if(value != 0.0){                x_space[i*(feature_size+1) + j].index = j + 1 ;                x_space[i*(feature_size+1) + j].value = value ;             }         }         x_space[i*(feature_size+1) + feature_size].index = -1 ;         prob.x[i] = &x_space[i*(feature_size+1)] ;     }     in.close() ;}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 = 11;      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 = 100 ;double predict_lable[test_size] ;double test_lable[test_size] ;int  main(){     init_svm_problem() ;     init_svm_parameter() ;     if(param.gamma == 0) param.gamma = 0.5 ;     svm_model* model = svm_train(&prob , &param) ;     ifstream in ;     in.open("test_data.txt") ;     svm_node *test = new svm_node[3] ;     for(int i = 0 ; i < test_size ; i++){          double value ;          in>>test_lable[i] ;          for(int j = 0 ; j < feature_size ; j++){               in>>value ;               if(value != 0.0){                   test[j].index = j + 1 ;                   test[j].value = value ;               }          }          test[feature_size].index = -1 ;          predict_lable[i] = svm_predict(model , test) ;     }     int yes = 0 ;     for(int i = 0 ; i < test_size ; i++){         // cout<<test_lable[i] <<" , "<<predict_lable[i]<<endl ;         if(test_lable[i] == predict_lable[i])  yes++ ;     }     cout<<yes<<endl ;     printf("%.2lf%%\n" , (0.0+yes)/test_size) ;     in.close() ;     return 0 ;}

结果

效果不好，没有寻找参数。 

待续文。

-----------------------------

问题解决，由于文件单词拼写错误。 

--------------------------------------测试数据-----------------------------------------------------------

tain_data.txt

-1 6 2-1 8 1-1 9 2-1 2 1-1 8 3-1 5 2-1 10 2-1 8 5-1 2 1-1 7 6-1 7 2-1 8 3-1 9 8-1 4 2-1 4 2-1 9 8-1 10 1-1 10 7-1 10 2-1 8 1-1 9 4-1 10 1-1 6 2-1 8 2-1 7 5-1 8 1-1 5 1-1 5 2-1 10 1-1 6 3-1 7 6-1 10 3-1 8 4-1 10 5-1 10 8-1 7 1-1 10 2-1 5 3-1 9 8-1 10 1-1 10 9-1 7 5-1 9 7-1 6 1-1 8 6-1 5 1-1 7 1-1 9 3-1 9 1-1 7 5-1 5 1-1 10 9-1 4 1-1 7 5-1 4 2-1 9 4-1 7 2-1 9 5-1 10 6-1 10 4-1 5 1-1 8 2-1 7 4-1 5 3-1 5 3-1 7 2-1 10 6-1 10 7-1 8 2-1 10 3-1 10 4-1 10 4-1 8 7-1 6 1-1 3 2-1 9 3-1 5 1-1 10 9-1 9 2-1 5 4-1 10 4-1 9 7-1 4 3-1 4 1-1 10 7-1 5 1-1 9 3-1 6 1-1 6 1-1 3 1-1 6 1-1 8 2-1 8 7-1 10 6-1 8 5-1 7 1-1 7 6-1 9 2-1 8 5-1 9 21 5 101 5 71 4 101 4 91 9 101 2 41 2 81 3 71 7 81 9 101 1 51 5 91 4 51 4 81 6 71 2 81 2 101 4 51 6 81 1 91 4 61 4 51 1 91 2 41 1 91 5 81 3 71 6 71 4 101 3 71 4 61 4 91 3 61 1 41 5 91 1 61 2 51 4 61 1 91 4 81 9 101 7 91 2 31 4 91 1 41 3 81 4 91 4 81 8 101 9 101 6 81 1 101 2 31 1 91 1 81 8 101 3 71 2 71 1 101 5 71 1 101 4 71 3 51 6 91 2 71 2 51 1 51 1 41 5 61 1 61 3 71 5 91 6 101 1 91 3 101 6 101 2 71 4 71 2 71 6 71 2 81 7 91 1 71 4 81 1 51 4 71 3 61 2 41 4 91 5 91 3 101 7 91 3 71 1 101 4 101 6 81 3 41 6 91 3 71 3 5

test_data.txt

1 2 101 1 7-1 7 31 1 6-1 8 11 4 8-1 9 61 3 10-1 4 21 1 9-1 8 5-1 9 4-1 9 3-1 8 3-1 8 61 1 51 1 31 1 5-1 6 3-1 9 5-1 8 21 2 101 3 6-1 9 81 1 41 5 6-1 9 8-1 8 21 3 61 5 6-1 8 41 6 7-1 9 31 9 101 3 51 1 2-1 10 7-1 8 61 4 6-1 9 3-1 8 21 2 8-1 2 1-1 9 7-1 8 3-1 9 31 1 2-1 8 5-1 7 41 3 101 3 8-1 10 71 3 61 5 6-1 6 21 1 61 1 71 5 6-1 9 31 3 101 4 7-1 8 2-1 6 2-1 7 6-1 9 1-1 10 51 3 7-1 7 2-1 7 2-1 10 41 2 8-1 10 21 3 8-1 7 11 5 10-1 5 4-1 3 1-1 10 11 6 101 8 10-1 7 31 1 81 1 101 3 7-1 2 11 2 7-1 7 4-1 6 51 4 81 2 71 5 71 7 10-1 10 9-1 5 1-1 10 1-1 7 41 3 4-1 9 2-1 8 71 2 6