libsvm的移植(CC++版本).doc

libsvm的移植（C/C+版本）现在先写一个测试libsvm的简单程序，程序参考了libsvm自带的svm-toy。现在要解决的问题是一个二类分类问题。样本总共有120个，每个样本只有两个特征。首先划分好训练集与测试集。训练集取用其中的80个（正、负样本各40），余下的则作为测试集。数据分别存入train.txt和test.txt中。1.2. #include 3. #include 4. #include 5. #include svm.h /包涵本目录下的SVM.H头文件6. #include 7. #include iostream.h8. 9. using std:ifstream;10. void main()11. 12. /*13. struct svm_problem14. 15. int l;16. double *y;17. struct svm_node *x;18. ;19. struct svm_node20. 21. int index;22. double value;23. ;24. */25. ifstream inFile;26. ifstream inFile1;27. svm_problem prob; /建立SVM数据28. prob.l=80; /80个样本，前40个为正样本，29. double d40;30. int probfeature=2; /样本特征维数31. prob.y=new doubleprob.l;32. svm_parameter param;33. 34. /svm参数设置35. param.svm_type = C_SVC;36. param.kernel_type = RBF;37. param.degree = 3;38. param.gamma = 0.0001;39. param.coef0 = 0;40. param.nu = 0.5;41. param.cache_size = 100;42. param.C = 10;43. param.eps = 1e-5;44. param.p = 0.1;45. param.shrinking = 1;46. bability = 0;47. param.nr_weight = 0;48. param.weight_label = NULL;49. param.weight = NULL;50.51. 52. /分类53. if(param.gamma = 0) param.gamma = 0.5; /gamma forpoly/rbf/sigmoid54. svm_node *x_space = new svm_node(probfeature+1) * prob.l; /样本特征存储空间55. prob.x = new svm_node *prob.l; /没一个X指向一个样本56. /数据填充开始57. inFile.open(train.txt); /打开训练数据文件58. 59. for(int i=0;iprob.l;i+)60. 61. /*/62. float fvalue;63. for (int j=0;jfvalue;66. if(fvalue!=0.0)67. 68. x_space(probfeature+1)*i+j.index=j+1;69. x_space(probfeature+1)*i+j.value=fvalue;70. 71. 72. x_space(probfeature+1)*i+probfeature.index=-1;73. prob.xi=&x_space(probfeature+1)*i;74. if (i40) /类别标签75. prob.yi=1;76. else77. prob.yi=-1;78. 79. inFile.close(); /关闭文件80.81. /数据填充结束82. / build model & classify83. svm_model *model = svm_train(&prob, ¶m); /开始训练84. int test_num=40;85. inFile1.open(test.txt);86. svm_node x3; /定义一个测试样本87. 88. for(int i1=0;i1test_num;i1+)89. 90. float fvalue;91. for (int j=0;jfvalue;94. if(fvalue!=0.0)95. 96. xj.index=j+1;97. xj.value=fvalue;98. 99. 100. x2.index=-1;101. di1=svm_predict(model,x);102. 103. inFile1.close();104. svm_destroy_model(model);105. delete x_space;106. delete prob.x;107. dele