libsvm-alphaseed-2.81.zip_K._alpha seeding_alphaseed

#include <stdio.h> #include <stdlib.h> #include <string.h> #include <ctype.h> #include "svm.h" #define Malloc(type,n) (type *)malloc((n)*sizeof(type)) void exit_with_help() { printf( "Usage: svm-train [options] training_set_file [model_file]\n" "options:\n" "-s svm_type : set type of SVM (default 0)\n" " 0 -- C-SVC\n" " 1 -- nu-SVC\n" " 2 -- one-class SVM\n" " 3 -- epsilon-SVR\n" " 4 -- nu-SVR\n" "-t kernel_type : set type of kernel function (default 2)\n" " 0 -- linear: u'*v\n" " 1 -- polynomial: (gamma*u'*v + coef0)^degree\n" " 2 -- radial basis function: exp(-gamma*|u-v|^2)\n" " 3 -- sigmoid: tanh(gamma*u'*v + coef0)\n" "-d degree : set degree in kernel function (default 3)\n" "-g gamma : set gamma in kernel function (default 1/k)\n" "-r coef0 : set coef0 in kernel function (default 0)\n" "-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n" "-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n" "-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n" "-m cachesize : set cache memory size in MB (default 100)\n" "-e epsilon : set tolerance of termination criterion (default 0.001)\n" "-h shrinking: whether to use the shrinking heuristics, 0 or 1 (default 1)\n" "-b probability_estimates: whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n" "-wi weight: set the parameter C of class i to weight*C, for C-SVC (default 1)\n" "-v n: n-fold cross validation mode\n" ); exit(1); } 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(); struct svm_parameter param; // set by parse_command_line struct svm_problem prob; // set by read_problem struct svm_model *model; struct svm_node *x_space; int cross_validation; int nr_fold; 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 = svm_check_parameter(&prob,&param); if(error_msg) { fprintf(stderr,"Error: %s\n",error_msg); exit(1); } if(cross_validation) { do_cross_validation(); } else { model = svm_train(&prob,&param); svm_save_model(model_file_name,model); svm_destroy_model(model); } svm_destroy_param(&param); free(prob.y); free(prob.x); free(x_space); return 0; } 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); svm_cross_validation(&prob,&param,nr_fold,target); if(param.svm_type == EPSILON_SVR || param.svm_type == NU_SVR) { 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; // default values param.svm_type = C_SVC; param.kernel_type = RBF; param.degree = 3; param.gamma = 0; // 1/k param.coef0 = 0; param.nu = 0.5; param.cache_size = 100; param.C = 1; param.eps = 1e-3; param.p = 0.1; param.shrinking = 1; param.probability = 0; param.nr_weight = 0; param.weight_label = NULL; param.weight = NULL; cross_validation = 0; // 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.svm_type = atoi(argv[i]); break; case 't': param.kernel_type = atoi(argv[i]); break; case 'd': param.degree = atof(argv[i]); break; case 'g': param.gamma = atof(argv[i]); break; case 'r': param.coef0 = atof(argv[i]); break; case 'n': param.nu = atof(argv[i]); break; case 'm': param.cache_size = atof(argv[i]); break; case 'c': param.C = atof(argv[i]); break; case 'e': param.eps = atof(argv[i]); break; case 'p': param.p = atof(argv[i]); break; case 'h': param.shrinking = atoi(argv[i]); break; case 'b': param.probability = atoi(argv[i]); break; case 'v': 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 '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; default: fprintf(stderr,"unknown option\n"); exit_with_help(); } } // 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); } } // read in a problem (in svmlight format) void read_problem(const char *filename) { int elements, max_index, i, j; FILE *fp = fopen(filename,"r"); if(fp == NULL) { fprintf(stderr,"can't open input file %s\n",filename); exit(1); } prob.l = 0; elements = 0; while(1) { int c = fgetc(fp); switch(c) { case '\n': ++prob.l; // fall through, // count the '-1' element case ':': ++elements; break; case EOF: goto out; default: ; } } out: rewind(fp); prob.y = Malloc(double,prob.l); prob.x = Malloc(struct svm_node *,prob.l); x_space = Malloc(struct svm_node,elements); max_index = 0; j=0; for(i=0;i<prob.l;i++) { double label; prob.x[i] = &x_space[j]; fscanf(fp,"%lf",&label); prob.y[i] = label; while(1) { int c; do { c = getc(fp); if(c=='\n') goto out2; } while(isspace(c)); ungetc(c,fp); fscanf(fp,"%d:%lf",&(x_space[j].index),&(x_space[j].value)); ++j; } out2: if(j>=1 && x_space[j-1].index > max_index) max_index = x_space[j-1].index; x_space[j++].index = -1; } if(param.gamma == 0) param.gamma = 1.0/max_index; fclose(fp); }