| svm_model* svm_train | ( | const svm_problem * | prob, | |
| const svm_parameter * | param | |||
| ) |
Definition at line 1850 of file svm.cpp.
References decision_function::alpha, EPSILON_SVR, svm_model::free_sv, svm_model::l, svm_problem::l, svm_model::label, Malloc, svm_model::nr_class, svm_model::nSV, NU_SVR, ONE_CLASS, svm_model::param, svm_model::probA, svm_parameter::probability, svm_model::probB, decision_function::rho, svm_model::rho, svm_model::SV, svm_model::sv_coef, svm_svr_probability(), svm_train_one(), svm_parameter::svm_type, and svm_problem::x.
Referenced by svm_binary_svc_probability(), svm_cross_validation(), and Impala::Core::Training::Svm::Train().
01851 { 01852 svm_model *model = Malloc(svm_model,1); 01853 model->param = *param; 01854 model->free_sv = 0; // XXX 01855 01856 if(param->svm_type == ONE_CLASS || 01857 param->svm_type == EPSILON_SVR || 01858 param->svm_type == NU_SVR) 01859 { 01860 // regression or one-class-svm 01861 model->nr_class = 2; 01862 model->label = NULL; 01863 model->nSV = NULL; 01864 model->probA = NULL; model->probB = NULL; 01865 model->sv_coef = Malloc(double *,1); 01866 01867 if(param->probability && 01868 (param->svm_type == EPSILON_SVR || 01869 param->svm_type == NU_SVR)) 01870 { 01871 model->probA = Malloc(double,1); 01872 model->probA[0] = svm_svr_probability(prob,param); 01873 } 01874 01875 decision_function f = svm_train_one(prob,param,0,0); 01876 model->rho = Malloc(double,1); 01877 model->rho[0] = f.rho; 01878 01879 int nSV = 0; 01880 int i; 01881 for(i=0;i<prob->l;i++) 01882 if(fabs(f.alpha[i]) > 0) ++nSV; 01883 model->l = nSV; 01884 model->SV = Malloc(svm_node *,nSV); 01885 model->sv_coef[0] = Malloc(double,nSV); 01886 int j = 0; 01887 for(i=0;i<prob->l;i++) 01888 if(fabs(f.alpha[i]) > 0) 01889 { 01890 model->SV[j] = prob->x[i]; 01891 model->sv_coef[0][j] = f.alpha[i]; 01892 ++j; 01893 } 01894 01895 free(f.alpha); 01896 } 01897 else 01898 { 01899 // classification 01900 // find out the number of classes 01901 int l = prob->l; 01902 int max_nr_class = 16; 01903 int nr_class = 0; 01904 int *label = Malloc(int,max_nr_class); 01905 int *count = Malloc(int,max_nr_class); 01906 int *index = Malloc(int,l); 01907 01908 int i; 01909 for(i=0;i<l;i++) 01910 { 01911 int this_label = (int)prob->y[i]; 01912 int j; 01913 for(j=0;j<nr_class;j++) 01914 if(this_label == label[j]) 01915 { 01916 ++count[j]; 01917 break; 01918 } 01919 index[i] = j; 01920 if(j == nr_class) 01921 { 01922 if(nr_class == max_nr_class) 01923 { 01924 max_nr_class *= 2; 01925 label = (int *)realloc(label,max_nr_class*sizeof(int)); 01926 count = (int *)realloc(count,max_nr_class*sizeof(int)); 01927 } 01928 label[nr_class] = this_label; 01929 count[nr_class] = 1; 01930 ++nr_class; 01931 } 01932 } 01933 01934 // group training data of the same class 01935 01936 int *start = Malloc(int,nr_class); 01937 start[0] = 0; 01938 for(i=1;i<nr_class;i++) 01939 start[i] = start[i-1]+count[i-1]; 01940 01941 svm_node **x = Malloc(svm_node *,l); 01942 01943 for(i=0;i<l;i++) 01944 { 01945 x[start[index[i]]] = prob->x[i]; 01946 ++start[index[i]]; 01947 } 01948 01949 start[0] = 0; 01950 for(i=1;i<nr_class;i++) 01951 start[i] = start[i-1]+count[i-1]; 01952 01953 // calculate weighted C 01954 01955 double *weighted_C = Malloc(double, nr_class); 01956 for(i=0;i<nr_class;i++) 01957 weighted_C[i] = param->C; 01958 for(i=0;i<param->nr_weight;i++) 01959 { 01960 int j; 01961 for(j=0;j<nr_class;j++) 01962 if(param->weight_label[i] == label[j]) 01963 break; 01964 if(j == nr_class) 01965 fprintf(stderr,"warning: class label %d specified in weight is not found\n", param->weight_label[i]); 01966 else 01967 weighted_C[j] *= param->weight[i]; 01968 } 01969 01970 // train k*(k-1)/2 models 01971 01972 bool *nonzero = Malloc(bool,l); 01973 for(i=0;i<l;i++) 01974 nonzero[i] = false; 01975 decision_function *f = Malloc(decision_function,nr_class*(nr_class-1)/2); 01976 01977 double *probA=NULL,*probB=NULL; 01978 if (param->probability) 01979 { 01980 probA=Malloc(double,nr_class*(nr_class-1)/2); 01981 probB=Malloc(double,nr_class*(nr_class-1)/2); 01982 } 01983 01984 int p = 0; 01985 for(i=0;i<nr_class;i++) 01986 for(int j=i+1;j<nr_class;j++) 01987 { 01988 svm_problem sub_prob; 01989 int si = start[i], sj = start[j]; 01990 int ci = count[i], cj = count[j]; 01991 sub_prob.l = ci+cj; 01992 sub_prob.x = Malloc(svm_node *,sub_prob.l); 01993 sub_prob.y = Malloc(double,sub_prob.l); 01994 int k; 01995 for(k=0;k<ci;k++) 01996 { 01997 sub_prob.x[k] = x[si+k]; 01998 sub_prob.y[k] = +1; 01999 } 02000 for(k=0;k<cj;k++) 02001 { 02002 sub_prob.x[ci+k] = x[sj+k]; 02003 sub_prob.y[ci+k] = -1; 02004 } 02005 02006 if(param->probability) 02007 svm_binary_svc_probability(&sub_prob,param,weighted_C[i],weighted_C[j],probA[p],probB[p]); 02008 02009 f[p] = svm_train_one(&sub_prob,param,weighted_C[i],weighted_C[j]); 02010 for(k=0;k<ci;k++) 02011 if(!nonzero[si+k] && fabs(f[p].alpha[k]) > 0) 02012 nonzero[si+k] = true; 02013 for(k=0;k<cj;k++) 02014 if(!nonzero[sj+k] && fabs(f[p].alpha[ci+k]) > 0) 02015 nonzero[sj+k] = true; 02016 free(sub_prob.x); 02017 free(sub_prob.y); 02018 ++p; 02019 } 02020 02021 // build output 02022 02023 model->nr_class = nr_class; 02024 02025 model->label = Malloc(int,nr_class); 02026 for(i=0;i<nr_class;i++) 02027 model->label[i] = label[i]; 02028 02029 model->rho = Malloc(double,nr_class*(nr_class-1)/2); 02030 for(i=0;i<nr_class*(nr_class-1)/2;i++) 02031 model->rho[i] = f[i].rho; 02032 02033 if(param->probability) 02034 { 02035 model->probA = Malloc(double,nr_class*(nr_class-1)/2); 02036 model->probB = Malloc(double,nr_class*(nr_class-1)/2); 02037 for(i=0;i<nr_class*(nr_class-1)/2;i++) 02038 { 02039 model->probA[i] = probA[i]; 02040 model->probB[i] = probB[i]; 02041 } 02042 } 02043 else 02044 { 02045 model->probA=NULL; 02046 model->probB=NULL; 02047 } 02048 02049 int total_sv = 0; 02050 int *nz_count = Malloc(int,nr_class); 02051 model->nSV = Malloc(int,nr_class); 02052 for(i=0;i<nr_class;i++) 02053 { 02054 int nSV = 0; 02055 for(int j=0;j<count[i];j++) 02056 if(nonzero[start[i]+j]) 02057 { 02058 ++nSV; 02059 ++total_sv; 02060 } 02061 model->nSV[i] = nSV; 02062 nz_count[i] = nSV; 02063 } 02064 02065 info("Total nSV = %d\n",total_sv); 02066 02067 model->l = total_sv; 02068 model->SV = Malloc(svm_node *,total_sv); 02069 p = 0; 02070 for(i=0;i<l;i++) 02071 if(nonzero[i]) model->SV[p++] = x[i]; 02072 02073 int *nz_start = Malloc(int,nr_class); 02074 nz_start[0] = 0; 02075 for(i=1;i<nr_class;i++) 02076 nz_start[i] = nz_start[i-1]+nz_count[i-1]; 02077 02078 model->sv_coef = Malloc(double *,nr_class-1); 02079 for(i=0;i<nr_class-1;i++) 02080 model->sv_coef[i] = Malloc(double,total_sv); 02081 02082 p = 0; 02083 for(i=0;i<nr_class;i++) 02084 for(int j=i+1;j<nr_class;j++) 02085 { 02086 // classifier (i,j): coefficients with 02087 // i are in sv_coef[j-1][nz_start[i]...], 02088 // j are in sv_coef[i][nz_start[j]...] 02089 02090 int si = start[i]; 02091 int sj = start[j]; 02092 int ci = count[i]; 02093 int cj = count[j]; 02094 02095 int q = nz_start[i]; 02096 int k; 02097 for(k=0;k<ci;k++) 02098 if(nonzero[si+k]) 02099 model->sv_coef[j-1][q++] = f[p].alpha[k]; 02100 q = nz_start[j]; 02101 for(k=0;k<cj;k++) 02102 if(nonzero[sj+k]) 02103 model->sv_coef[i][q++] = f[p].alpha[ci+k]; 02104 ++p; 02105 } 02106 02107 free(label); 02108 free(probA); 02109 free(probB); 02110 free(count); 02111 free(index); 02112 free(start); 02113 free(x); 02114 free(weighted_C); 02115 free(nonzero); 02116 for(i=0;i<nr_class*(nr_class-1)/2;i++) 02117 free(f[i].alpha); 02118 free(f); 02119 free(nz_count); 02120 free(nz_start); 02121 } 02122 return model; 02123 }
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