matlab的libsvm ovo工具包

#include <stdio.h> #include <math.h> #include <stdlib.h> #include <string.h> #include <ctype.h> #include <errno.h> #include "linear.h" #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) #define INF HUGE_VAL void print_null(const char *s) {} void exit_with_help() { printf( "Usage: train [options] training_set_file [model_file]\n" "options:\n" "-s type : set type of solver (default 1)\n" " for multi-class classification\n" " 0 -- L2-regularized logistic regression (primal)\n" " 1 -- L2-regularized L2-loss support vector classification (dual)\n" " 2 -- L2-regularized L2-loss support vector classification (primal)\n" " 3 -- L2-regularized L1-loss support vector classification (dual)\n" " 4 -- support vector classification by Crammer and Singer\n" " 5 -- L1-regularized L2-loss support vector classification\n" " 6 -- L1-regularized logistic regression\n" " 7 -- L2-regularized logistic regression (dual)\n" " for regression\n" " 11 -- L2-regularized L2-loss support vector regression (primal)\n" " 12 -- L2-regularized L2-loss support vector regression (dual)\n" " 13 -- L2-regularized L1-loss support vector regression (dual)\n" "-c cost : set the parameter C (default 1)\n" "-p epsilon : set the epsilon in loss function of SVR (default 0.1)\n" "-e epsilon : set tolerance of termination criterion\n" " -s 0 and 2\n" " |f'(w)|_2 <= eps*min(pos,neg)/l*|f'(w0)|_2,\n" " where f is the primal function and pos/neg are # of\n" " positive/negative data (default 0.01)\n" " -s 11\n" " |f'(w)|_2 <= eps*|f'(w0)|_2 (default 0.001)\n" " -s 1, 3, 4, and 7\n" " Dual maximal violation <= eps; similar to libsvm (default 0.1)\n" " -s 5 and 6\n" " |f'(w)|_1 <= eps*min(pos,neg)/l*|f'(w0)|_1,\n" " where f is the primal function (default 0.01)\n" " -s 12 and 13\n" " |f'(alpha)|_1 <= eps |f'(alpha0)|,\n" " where f is the dual function (default 0.1)\n" "-B bias : if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1)\n" "-wi weight: weights adjust the parameter C of different classes (see README for details)\n" "-M type: type of multiclass classification (default 0)\n" " -M 0: one-versus-all\n" " -M 1: one-versus-one\n" "-v n: n-fold cross validation mode\n" "-C : find parameter C (only for -s 0 and 2)\n" "-q : quiet mode (no outputs)\n" ); exit(1); } void exit_input_error(int line_num) { fprintf(stderr,"Wrong input format at line %d\n", line_num); exit(1); } static char *line = NULL; static int max_line_len; static char* readline(FILE *input) { int len; if(fgets(line,max_line_len,input) == NULL) return NULL; while(strrchr(line,'\n') == NULL) { max_line_len *= 2; line = (char *) realloc(line,max_line_len); len = (int) strlen(line); if(fgets(line+len,max_line_len-len,input) == NULL) break; } return line; } void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name); void read_problem(const char *filename); void do_cross_validation(); void do_find_parameter_C(); struct feature_node *x_space; struct parameter param; struct problem prob; struct model* model_; int flag_cross_validation; int flag_find_C; int flag_C_specified; int flag_solver_specified; int nr_fold; double bias; int main(int argc, char **argv) { char input_file_name[1024]; char model_file_name[1024]; const char *error_msg; parse_command_line(argc, argv, input_file_name, model_file_name); read_problem(input_file_name); error_msg = check_parameter(&prob,&param); if(error_msg) { fprintf(stderr,"ERROR: %s\n",error_msg); exit(1); } if (flag_find_C) { do_find_parameter_C(); } else if(flag_cross_validation) { do_cross_validation(); } else { model_=train(&prob, &param); if(save_model(model_file_name, model_)) { fprintf(stderr,"can't save model to file %s\n",model_file_name); exit(1); } free_and_destroy_model(&model_); } destroy_param(&param); free(prob.y); free(prob.x); free(x_space); free(line); return 0; } void do_find_parameter_C() { double start_C, best_C, best_rate; double max_C = 1024; if (flag_C_specified) start_C = param.C; else start_C = -1.0; printf("Doing parameter search with %d-fold cross validation.\n", nr_fold); find_parameter_C(&prob, &param, nr_fold, start_C, max_C, &best_C, &best_rate); printf("Best C = %g CV accuracy = %g%%\n", best_C, 100.0*best_rate); } void do_cross_validation() { int i; int total_correct = 0; double total_error = 0; double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0; double *target = Malloc(double, prob.l); cross_validation(&prob,&param,nr_fold,target); if(param.solver_type == L2R_L2LOSS_SVR || param.solver_type == L2R_L1LOSS_SVR_DUAL || param.solver_type == L2R_L2LOSS_SVR_DUAL) { for(i=0;i<prob.l;i++) { double y = prob.y[i]; double v = target[i]; total_error += (v-y)*(v-y); sumv += v; sumy += y; sumvv += v*v; sumyy += y*y; sumvy += v*y; } printf("Cross Validation Mean squared error = %g\n",total_error/prob.l); printf("Cross Validation Squared correlation coefficient = %g\n", ((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/ ((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy)) ); } else { for(i=0;i<prob.l;i++) if(target[i] == prob.y[i]) ++total_correct; printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l); } free(target); } void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name) { int i; void (*print_func)(const char*) = NULL; // default printing to stdout // default values param.solver_type = L2R_L2LOSS_SVC_DUAL; param.C = 1; param.eps = INF; // see setting below param.p = 0.1; param.nr_weight = 0; param.weight_label = NULL; param.weight = NULL; param.ovo = 0; param.find_C = 0; param.init_sol = NULL; flag_cross_validation = 0; flag_C_specified = 0; flag_solver_specified = 0; flag_find_C = 0; bias = -1; // parse options for(i=1;i<argc;i++) { if(argv[i][0] != '-') break; if(++i>=argc) exit_with_help(); switch(argv[i-1][1]) { case 's': param.solver_type = atoi(argv[i]); flag_solver_specified = 1; break; case 'c': param.C = atof(argv[i]); flag_C_specified = 1; break; case 'p': param.p = atof(argv[i]); break; case 'e': param.eps = atof(argv[i]); break; case 'B': bias = atof(argv[i]); break; case 'w': ++param.nr_weight; param.weight_label = (int *) realloc(param.weight_label,sizeof(int)*param.nr_weight); param.weight = (double *) realloc(param.weight,sizeof(double)*param.nr_weight); param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]); param.weight[param.nr_weight-1] = atof(argv[i]); break; case 'M': param.ovo = atoi(argv[i]); break; case 'v': flag_cross_validation = 1; nr_fold = atoi(argv[i]); if(nr_fold < 2) { fprintf(stderr,"n-fold cross validation: n must >= 2\n"); exit_with_help(); } break; case 'q': print_func = &print_null; i--; break; case 'C': flag_find_C = 1; param.find_C = 1; i--; break; default: fprintf(stderr,"unknown option: -%c\n", argv[i-1][1]); exit_with_help(); break; } } set_print_string_function(print_func); // determine filenames if(i>=argc) exit_with_help(); strcpy(input_file_name, argv[i]); if(i<argc-1) strcpy(model_file_name,argv[i+1]); else { char *p = strrchr(argv[i],'/'); if(p==NULL) p = argv[i]; else ++p; sprintf(model_file_name,"%s.model",p); } // default solver for parameter selection is L2R_L2LOSS_SVC if(flag_find_C) { if(!flag_cross_validation) nr_fold = 5; if(!flag_solver_specified) { fprintf(stderr, "Solver not specified. Using -s 2\n"); param.solver_type = L2R_L2LOSS_SVC; } else if(param.solver_type != L2R_LR && param.solver_type != L2R_L2LOSS_SVC) { fprintf(stderr, "Warm-start parameter search only availa