static int KernelParm_init(svms_KernelParmObject *self, PyObject *args,
PyObject *kwds) {
const char *buff;
int bufflen;
static char *kwlist[] = {"kernel_type","poly_degree","rbf_gamma",
"coef_lin","coef_const","custom",NULL};
LEARN_PARM lp;
KERNEL_PARM*k=self->kparm;
set_learning_defaults(&lp, k);
if (!PyArg_ParseTupleAndKeywords
(args, kwds, "|iiddds#", kwlist, &k->kernel_type, &k->poly_degree,
&k->rbf_gamma, &k->coef_lin, &k->coef_const, &buff, &bufflen))
return -1;
strncpy(k->custom, buff, sizeof(k->custom)-1);
k->custom[sizeof(k->custom)-1]='\0';
return 0;
}
int read_input_parameters(int argc,char *argv[],char *docfile,char *modelfile,
char *restartfile,long *verbosity,
LEARN_PARM *learn_parm,KERNEL_PARM *kernel_parm)
{
long i;
char type[100];
/* set default */
set_learning_defaults(learn_parm, kernel_parm);
strcpy (modelfile, "svm_model");
strcpy (restartfile, "");
(*verbosity)=1;
strcpy(type,"c");
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1])
{
case '?': print_help(); return(-1);
case 'z': i++; strcpy(type,argv[i]); break;
case 'v': i++; (*verbosity)=atol(argv[i]); break;
case 'b': i++; learn_parm->biased_hyperplane=atol(argv[i]); break;
case 'i': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
case 'f': i++; learn_parm->skip_final_opt_check=!atol(argv[i]); break;
case 'q': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
case 'n': i++; learn_parm->svm_newvarsinqp=atol(argv[i]); break;
case '#': i++; learn_parm->maxiter=atol(argv[i]); break;
case 'h': i++; learn_parm->svm_iter_to_shrink=atol(argv[i]); break;
case 'm': i++; learn_parm->kernel_cache_size=atol(argv[i]); break;
case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
case 'w': i++; learn_parm->eps=atof(argv[i]); break;
case 'p': i++; learn_parm->transduction_posratio=atof(argv[i]); break;
case 'j': i++; learn_parm->svm_costratio=atof(argv[i]); break;
case 'e': i++; learn_parm->epsilon_crit=atof(argv[i]); break;
case 'o': i++; learn_parm->rho=atof(argv[i]); break;
case 'k': i++; learn_parm->xa_depth=atol(argv[i]); break;
case 'x': i++; learn_parm->compute_loo=atol(argv[i]); break;
case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
case 's': i++; kernel_parm->coef_lin=atof(argv[i]); break;
case 'r': i++; kernel_parm->coef_const=atof(argv[i]); break;
case 'u': i++; strcpy(kernel_parm->custom,argv[i]); break;
case 'l': i++; strcpy(learn_parm->predfile,argv[i]); break;
case 'a': i++; strcpy(learn_parm->alphafile,argv[i]); break;
case 'y': i++; strcpy(restartfile,argv[i]); break;
default: //printf("\nUnrecognized option %s!\n\n",argv[i]);
//print_help();
return(-1);
}
}
//if(i>=argc) {
// printf("\nNot enough input parameters!\n\n");
// wait_any_key();
// print_help();
// exit(0);
//}
//strcpy (docfile, argv[i]);
//if((i+1)<argc) {
// strcpy (modelfile, argv[i+1]);
//}
if(learn_parm->svm_iter_to_shrink == -9999) {
if(kernel_parm->kernel_type == LINEAR)
learn_parm->svm_iter_to_shrink=2;
else
learn_parm->svm_iter_to_shrink=100;
}
if(strcmp(type,"c")==0) {
learn_parm->type=CLASSIFICATION;
}
else if(strcmp(type,"r")==0) {
learn_parm->type=REGRESSION;
}
else if(strcmp(type,"p")==0) {
learn_parm->type=RANKING;
}
else if(strcmp(type,"o")==0) {
learn_parm->type=OPTIMIZATION;
}
else if(strcmp(type,"s")==0) {
learn_parm->type=OPTIMIZATION;
learn_parm->sharedslack=1;
}
else {
//printf("\nUnknown type '%s': Valid types are 'c' (classification), 'r' regession, and 'p' preference ranking.\n",type);
//wait_any_key();
//print_help();
return(-1);
}
if (!check_learning_parms(learn_parm, kernel_parm)) {
//wait_any_key();
//print_help();
return(-1);
}
return 0;
}
