396 lines
9.4 KiB
C
396 lines
9.4 KiB
C
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <ctype.h>
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#include <errno.h>
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#include "svm.h"
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#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
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void print_null(const char *s) {}
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void exit_with_help()
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{
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printf(
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"Usage: svm-train [options] training_set_file [model_file]\n"
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"options:\n"
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"-s svm_type : set type of SVM (default 0)\n"
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" 0 -- C-SVC (multi-class classification)\n"
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" 1 -- nu-SVC (multi-class classification)\n"
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" 2 -- one-class SVM\n"
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" 3 -- epsilon-SVR (regression)\n"
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" 4 -- nu-SVR (regression)\n"
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"-t kernel_type : set type of kernel function (default 2)\n"
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" 0 -- linear: u'*v\n"
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" 1 -- polynomial: (gamma*u'*v + coef0)^degree\n"
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" 2 -- radial basis function: exp(-gamma*|u-v|^2)\n"
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" 3 -- sigmoid: tanh(gamma*u'*v + coef0)\n"
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" 4 -- precomputed kernel (kernel values in training_set_file)\n"
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"-d degree : set degree in kernel function (default 3)\n"
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"-g gamma : set gamma in kernel function (default 1/num_features)\n"
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"-r coef0 : set coef0 in kernel function (default 0)\n"
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"-c cost : set the parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)\n"
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"-n nu : set the parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)\n"
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"-p epsilon : set the epsilon in loss function of epsilon-SVR (default 0.1)\n"
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"-m cachesize : set cache memory size in MB (default 100)\n"
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"-e epsilon : set tolerance of termination criterion (default 0.001)\n"
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"-h shrinking : whether to use the shrinking heuristics, 0 or 1 (default 1)\n"
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"-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0)\n"
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"-wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1)\n"
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"-v n: n-fold cross validation mode\n"
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"-q : quiet mode (no outputs)\n"
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"-W weight_file: set weight file\n"
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);
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exit(1);
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}
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void exit_input_error(int line_num)
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{
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fprintf(stderr,"Wrong input format at line %d\n", line_num);
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exit(1);
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}
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void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name);
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void read_problem(const char *filename);
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void do_cross_validation();
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struct svm_parameter param; // set by parse_command_line
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struct svm_problem prob; // set by read_problem
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struct svm_model *model;
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struct svm_node *x_space;
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char *weight_file;
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int cross_validation;
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int nr_fold;
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static char *line = NULL;
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static int max_line_len;
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static char* readline(FILE *input)
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{
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int len;
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if(fgets(line,max_line_len,input) == NULL)
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return NULL;
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while(strrchr(line,'\n') == NULL)
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{
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max_line_len *= 2;
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line = (char *) realloc(line,max_line_len);
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len = (int) strlen(line);
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if(fgets(line+len,max_line_len-len,input) == NULL)
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break;
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}
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return line;
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}
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int main(int argc, char **argv)
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{
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char input_file_name[1024];
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char model_file_name[1024];
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const char *error_msg;
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parse_command_line(argc, argv, input_file_name, model_file_name);
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read_problem(input_file_name);
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error_msg = svm_check_parameter(&prob,¶m);
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if(error_msg)
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{
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fprintf(stderr,"ERROR: %s\n",error_msg);
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exit(1);
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}
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if(cross_validation)
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{
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do_cross_validation();
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}
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else
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{
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model = svm_train(&prob,¶m);
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if(svm_save_model(model_file_name,model))
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{
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fprintf(stderr, "can't save model to file %s\n", model_file_name);
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exit(1);
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}
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svm_free_and_destroy_model(&model);
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}
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svm_destroy_param(¶m);
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free(prob.y);
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free(prob.x);
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free(x_space);
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free(line);
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return 0;
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}
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void do_cross_validation()
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{
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int i;
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int total_correct = 0;
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double total_error = 0;
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double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
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double *target = Malloc(double,prob.l);
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svm_cross_validation(&prob,¶m,nr_fold,target);
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if(param.svm_type == EPSILON_SVR ||
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param.svm_type == NU_SVR)
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{
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for(i=0;i<prob.l;i++)
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{
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double y = prob.y[i];
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double v = target[i];
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total_error += (v-y)*(v-y);
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sumv += v;
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sumy += y;
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sumvv += v*v;
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sumyy += y*y;
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sumvy += v*y;
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}
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printf("Cross Validation Mean squared error = %g\n",total_error/prob.l);
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printf("Cross Validation Squared correlation coefficient = %g\n",
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((prob.l*sumvy-sumv*sumy)*(prob.l*sumvy-sumv*sumy))/
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((prob.l*sumvv-sumv*sumv)*(prob.l*sumyy-sumy*sumy))
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);
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}
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else
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{
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for(i=0;i<prob.l;i++)
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if(target[i] == prob.y[i])
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++total_correct;
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printf("Cross Validation Accuracy = %g%%\n",100.0*total_correct/prob.l);
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}
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free(target);
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}
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void parse_command_line(int argc, char **argv, char *input_file_name, char *model_file_name)
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{
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int i;
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void (*print_func)(const char*) = NULL; // default printing to stdout
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// default values
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param.svm_type = C_SVC;
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param.kernel_type = RBF;
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param.degree = 3;
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param.gamma = 0; // 1/num_features
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param.coef0 = 0;
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param.nu = 0.5;
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param.cache_size = 100;
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param.C = 1;
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param.eps = 1e-3;
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param.p = 0.1;
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param.shrinking = 1;
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param.probability = 0;
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param.nr_weight = 0;
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param.weight_label = NULL;
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param.weight = NULL;
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cross_validation = 0;
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// parse options
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for(i=1;i<argc;i++)
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{
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if(argv[i][0] != '-') break;
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if(++i>=argc)
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exit_with_help();
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switch(argv[i-1][1])
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{
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case 's':
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param.svm_type = atoi(argv[i]);
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break;
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case 't':
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param.kernel_type = atoi(argv[i]);
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break;
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case 'd':
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param.degree = atoi(argv[i]);
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break;
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case 'g':
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param.gamma = atof(argv[i]);
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break;
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case 'r':
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param.coef0 = atof(argv[i]);
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break;
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case 'n':
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param.nu = atof(argv[i]);
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break;
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case 'm':
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param.cache_size = atof(argv[i]);
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break;
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case 'c':
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param.C = atof(argv[i]);
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break;
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case 'e':
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param.eps = atof(argv[i]);
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break;
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case 'p':
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param.p = atof(argv[i]);
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break;
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case 'h':
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param.shrinking = atoi(argv[i]);
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break;
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case 'b':
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param.probability = atoi(argv[i]);
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break;
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case 'q':
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print_func = &print_null;
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i--;
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break;
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case 'v':
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cross_validation = 1;
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nr_fold = atoi(argv[i]);
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if(nr_fold < 2)
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{
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fprintf(stderr,"n-fold cross validation: n must >= 2\n");
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exit_with_help();
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}
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break;
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case 'w':
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++param.nr_weight;
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param.weight_label = (int *)realloc(param.weight_label,sizeof(int)*param.nr_weight);
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param.weight = (double *)realloc(param.weight,sizeof(double)*param.nr_weight);
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param.weight_label[param.nr_weight-1] = atoi(&argv[i-1][2]);
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param.weight[param.nr_weight-1] = atof(argv[i]);
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break;
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case 'W':
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weight_file = argv[i];
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break;
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default:
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fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
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exit_with_help();
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}
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}
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svm_set_print_string_function(print_func);
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// determine filenames
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if(i>=argc)
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exit_with_help();
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strcpy(input_file_name, argv[i]);
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if(i<argc-1)
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strcpy(model_file_name,argv[i+1]);
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else
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{
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char *p = strrchr(argv[i],'/');
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if(p==NULL)
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p = argv[i];
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else
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++p;
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sprintf(model_file_name,"%s.model",p);
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}
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}
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// read in a problem (in svmlight format)
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void read_problem(const char *filename)
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{
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int max_index, inst_max_index, i;
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size_t elements, j;
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FILE *fp = fopen(filename,"r");
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char *endptr;
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char *idx, *val, *label;
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if(fp == NULL)
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{
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fprintf(stderr,"can't open input file %s\n",filename);
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exit(1);
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}
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prob.l = 0;
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elements = 0;
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max_line_len = 1024;
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line = Malloc(char,max_line_len);
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while(readline(fp)!=NULL)
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{
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char *p = strtok(line," \t"); // label
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// features
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while(1)
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{
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p = strtok(NULL," \t");
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if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
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break;
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++elements;
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}
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++elements;
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++prob.l;
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}
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rewind(fp);
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prob.y = Malloc(double,prob.l);
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prob.x = Malloc(struct svm_node *,prob.l);
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prob.W = Malloc(double,prob.l);
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x_space = Malloc(struct svm_node,elements);
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max_index = 0;
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j=0;
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for(i=0;i<prob.l;i++)
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{
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inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
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readline(fp);
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prob.x[i] = &x_space[j];
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label = strtok(line," \t\n");
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if(label == NULL) // empty line
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exit_input_error(i+1);
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prob.y[i] = strtod(label,&endptr);
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if(endptr == label || *endptr != '\0')
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exit_input_error(i+1);
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prob.W[i] = 1;
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while(1)
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{
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idx = strtok(NULL,":");
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val = strtok(NULL," \t");
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if(val == NULL)
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break;
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errno = 0;
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x_space[j].index = (int) strtol(idx,&endptr,10);
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if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
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exit_input_error(i+1);
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else
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inst_max_index = x_space[j].index;
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errno = 0;
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x_space[j].value = strtod(val,&endptr);
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if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
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exit_input_error(i+1);
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++j;
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}
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if(inst_max_index > max_index)
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max_index = inst_max_index;
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x_space[j++].index = -1;
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}
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if(param.gamma == 0 && max_index > 0)
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param.gamma = 1.0/max_index;
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if(param.kernel_type == PRECOMPUTED)
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for(i=0;i<prob.l;i++)
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{
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if (prob.x[i][0].index != 0)
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{
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fprintf(stderr,"Wrong input format: first column must be 0:sample_serial_number\n");
|
||
|
|
exit(1);
|
||
|
|
}
|
||
|
|
if ((int)prob.x[i][0].value <= 0 || (int)prob.x[i][0].value > max_index)
|
||
|
|
{
|
||
|
|
fprintf(stderr,"Wrong input format: sample_serial_number out of range\n");
|
||
|
|
exit(1);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
fclose(fp);
|
||
|
|
|
||
|
|
if(weight_file)
|
||
|
|
{
|
||
|
|
fp = fopen(weight_file,"r");
|
||
|
|
for(i=0;i<prob.l;i++)
|
||
|
|
fscanf(fp,"%lf",&prob.W[i]);
|
||
|
|
fclose(fp);
|
||
|
|
}
|
||
|
|
}
|