396 lines
9.4 KiB
C
396 lines
9.4 KiB
C
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <string.h>
|
|
#include <ctype.h>
|
|
#include <errno.h>
|
|
#include "svm.h"
|
|
#define Malloc(type,n) (type *)malloc((n)*sizeof(type))
|
|
|
|
void print_null(const char *s) {}
|
|
|
|
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 (multi-class classification)\n"
|
|
" 1 -- nu-SVC (multi-class classification)\n"
|
|
" 2 -- one-class SVM\n"
|
|
" 3 -- epsilon-SVR (regression)\n"
|
|
" 4 -- nu-SVR (regression)\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"
|
|
" 4 -- precomputed kernel (kernel values in training_set_file)\n"
|
|
"-d degree : set degree in kernel function (default 3)\n"
|
|
"-g gamma : set gamma in kernel function (default 1/num_features)\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"
|
|
"-q : quiet mode (no outputs)\n"
|
|
"-W weight_file: set weight file\n"
|
|
);
|
|
exit(1);
|
|
}
|
|
|
|
void exit_input_error(int line_num)
|
|
{
|
|
fprintf(stderr,"Wrong input format at line %d\n", line_num);
|
|
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;
|
|
char *weight_file;
|
|
int cross_validation;
|
|
int nr_fold;
|
|
|
|
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;
|
|
}
|
|
|
|
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,¶m);
|
|
|
|
if(error_msg)
|
|
{
|
|
fprintf(stderr,"ERROR: %s\n",error_msg);
|
|
exit(1);
|
|
}
|
|
|
|
if(cross_validation)
|
|
{
|
|
do_cross_validation();
|
|
}
|
|
else
|
|
{
|
|
model = svm_train(&prob,¶m);
|
|
if(svm_save_model(model_file_name,model))
|
|
{
|
|
fprintf(stderr, "can't save model to file %s\n", model_file_name);
|
|
exit(1);
|
|
}
|
|
svm_free_and_destroy_model(&model);
|
|
}
|
|
svm_destroy_param(¶m);
|
|
free(prob.y);
|
|
free(prob.x);
|
|
free(x_space);
|
|
free(line);
|
|
|
|
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,¶m,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;
|
|
void (*print_func)(const char*) = NULL; // default printing to stdout
|
|
|
|
// default values
|
|
param.svm_type = C_SVC;
|
|
param.kernel_type = RBF;
|
|
param.degree = 3;
|
|
param.gamma = 0; // 1/num_features
|
|
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 = atoi(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 'q':
|
|
print_func = &print_null;
|
|
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;
|
|
case 'W':
|
|
weight_file = argv[i];
|
|
break;
|
|
default:
|
|
fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
|
|
exit_with_help();
|
|
}
|
|
}
|
|
|
|
svm_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);
|
|
}
|
|
}
|
|
|
|
// read in a problem (in svmlight format)
|
|
|
|
void read_problem(const char *filename)
|
|
{
|
|
int max_index, inst_max_index, i;
|
|
size_t elements, j;
|
|
FILE *fp = fopen(filename,"r");
|
|
char *endptr;
|
|
char *idx, *val, *label;
|
|
|
|
if(fp == NULL)
|
|
{
|
|
fprintf(stderr,"can't open input file %s\n",filename);
|
|
exit(1);
|
|
}
|
|
|
|
prob.l = 0;
|
|
elements = 0;
|
|
|
|
max_line_len = 1024;
|
|
line = Malloc(char,max_line_len);
|
|
while(readline(fp)!=NULL)
|
|
{
|
|
char *p = strtok(line," \t"); // label
|
|
|
|
// features
|
|
while(1)
|
|
{
|
|
p = strtok(NULL," \t");
|
|
if(p == NULL || *p == '\n') // check '\n' as ' ' may be after the last feature
|
|
break;
|
|
++elements;
|
|
}
|
|
++elements;
|
|
++prob.l;
|
|
}
|
|
rewind(fp);
|
|
|
|
prob.y = Malloc(double,prob.l);
|
|
prob.x = Malloc(struct svm_node *,prob.l);
|
|
prob.W = Malloc(double,prob.l);
|
|
x_space = Malloc(struct svm_node,elements);
|
|
|
|
max_index = 0;
|
|
j=0;
|
|
for(i=0;i<prob.l;i++)
|
|
{
|
|
inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0
|
|
readline(fp);
|
|
prob.x[i] = &x_space[j];
|
|
label = strtok(line," \t\n");
|
|
if(label == NULL) // empty line
|
|
exit_input_error(i+1);
|
|
|
|
prob.y[i] = strtod(label,&endptr);
|
|
if(endptr == label || *endptr != '\0')
|
|
exit_input_error(i+1);
|
|
prob.W[i] = 1;
|
|
|
|
while(1)
|
|
{
|
|
idx = strtok(NULL,":");
|
|
val = strtok(NULL," \t");
|
|
|
|
if(val == NULL)
|
|
break;
|
|
|
|
errno = 0;
|
|
x_space[j].index = (int) strtol(idx,&endptr,10);
|
|
if(endptr == idx || errno != 0 || *endptr != '\0' || x_space[j].index <= inst_max_index)
|
|
exit_input_error(i+1);
|
|
else
|
|
inst_max_index = x_space[j].index;
|
|
|
|
errno = 0;
|
|
x_space[j].value = strtod(val,&endptr);
|
|
if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
|
|
exit_input_error(i+1);
|
|
|
|
++j;
|
|
}
|
|
|
|
if(inst_max_index > max_index)
|
|
max_index = inst_max_index;
|
|
x_space[j++].index = -1;
|
|
}
|
|
|
|
if(param.gamma == 0 && max_index > 0)
|
|
param.gamma = 1.0/max_index;
|
|
|
|
if(param.kernel_type == PRECOMPUTED)
|
|
for(i=0;i<prob.l;i++)
|
|
{
|
|
if (prob.x[i][0].index != 0)
|
|
{
|
|
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);
|
|
}
|
|
}
|