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#include <time.h>
#include <math.h>
#include "libMSVMMaj.h"
#include "msvmmaj_train.h"
#include "util.h"
#include "MSVMMaj.h"
#define MINARGS 2
extern FILE *MSVMMAJ_OUTPUT_FILE;
void print_null(const char *s) {}
void exit_with_help();
void parse_command_line(int argc, char **argv, struct MajModel *model,
char *input_filename, char *output_filename, char *model_filename);
void exit_with_help()
{
printf("This is MSVMMaj, version %1.1f\n\n", VERSION);
printf("Usage: trainMSVMMaj [options] training_data_file\n");
printf("Options:\n");
printf("-e epsilon : set the value of the stopping criterion\n");
printf("-h | -help : print this help.\n");
printf("-k kappa : set the value of kappa used in the Huber hinge\n");
printf("-l lambda : set the value of lambda (lambda > 0)\n");
printf("-m model_file : use previous model as seed for W and t\n");
printf("-o output_file : write output to file\n");
printf("-p p-value : set the value of p in the lp norm (1.0 <= p <= 2.0)\n");
printf("-q : quiet mode (no output)\n");
printf("-r rho : choose the weigth specification (1 = unit, 2 = group)\n");
exit(0);
}
/*
Main
*/
int main(int argc, char **argv)
{
char input_filename[MAX_LINE_LENGTH];
char model_filename[MAX_LINE_LENGTH];
char output_filename[MAX_LINE_LENGTH];
struct MajModel *model = Malloc(struct MajModel, 1);
struct MajData *data = Malloc(struct MajData, 1);
if (argc < MINARGS || msvmmaj_check_argv(argc, argv, "-help")
|| msvmmaj_check_argv_eq(argc, argv, "-h") )
exit_with_help();
parse_command_line(argc, argv, model, input_filename, output_filename, model_filename);
// read data file
msvmmaj_read_data(data, input_filename);
// copy dataset parameters to model
model->n = data->n;
model->m = data->m;
model->K = data->K;
model->data_file = input_filename;
// allocate model and initialize weights
msvmmaj_allocate_model(model);
msvmmaj_initialize_weights(data, model);
srand(time(NULL));
if (msvmmaj_check_argv_eq(argc, argv, "-m")) {
struct MajModel *seed_model = Malloc(struct MajModel, 1);
msvmmaj_read_model(seed_model, model_filename);
msvmmaj_seed_model_V(seed_model, model);
msvmmaj_free_model(seed_model);
} else {
msvmmaj_seed_model_V(NULL, model);
}
// start training
msvmmaj_optimize(model, data);
// write_model to file
if (msvmmaj_check_argv_eq(argc, argv, "-o")) {
msvmmaj_write_model(model, output_filename);
note("Output written to %s\n", output_filename);
}
// free model and data
msvmmaj_free_model(model);
msvmmaj_free_data(data);
return 0;
}
void parse_command_line(int argc, char **argv, struct MajModel *model,
char *input_filename, char *output_filename, char *model_filename)
{
int i;
// default values
model->p = 1.0;
model->lambda = pow(2, -8.0);
model->epsilon = 1e-6;
model->kappa = 0.0;
model->weight_idx = 1;
MSVMMAJ_OUTPUT_FILE = stdout;
// 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 'e':
model->epsilon = atof(argv[i]);
break;
case 'k':
model->kappa = atof(argv[i]);
break;
case 'l':
model->lambda = atof(argv[i]);
break;
case 'm':
strcpy(model_filename, argv[i]);
break;
case 'o':
strcpy(output_filename, argv[i]);
break;
case 'p':
model->p = atof(argv[i]);
break;
case 'r':
model->weight_idx = atoi(argv[i]);
break;
case 'q':
MSVMMAJ_OUTPUT_FILE = NULL;
i--;
break;
default:
fprintf(stderr, "Unknown option: -%c\n", argv[i-1][1]);
exit_with_help();
}
}
// read input filename
if (i >= argc)
exit_with_help();
strcpy(input_filename, argv[i]);
}
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