void my_read_input_parameters(int argc, char *argv[], char *trainfile, char* modelfile,
LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm, STRUCT_LEARN_PARM *struct_parm,
double *init_spl_weight, double *spl_factor) {
long i;
/* set default */
learn_parm->maxiter=20000;
learn_parm->svm_maxqpsize=100;
learn_parm->svm_c=100.0;
learn_parm->eps=0.001;
learn_parm->biased_hyperplane=12345; /* store random seed */
learn_parm->remove_inconsistent=10;
kernel_parm->kernel_type=0;
kernel_parm->rbf_gamma=0.05;
kernel_parm->coef_lin=1;
kernel_parm->coef_const=1;
kernel_parm->poly_degree=3;
/* default: no self-paced learning */
*init_spl_weight = 0.0;
*spl_factor = 1.3;
struct_parm->gram_regularization = 1E-7;
struct_parm->solve_dual = 1;
struct_parm->custom_argc=0;
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1]) {
case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
case 'e': i++; learn_parm->eps=atof(argv[i]); break;
case 's': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
case 'r': i++; learn_parm->biased_hyperplane=atol(argv[i]); break;
case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
case 'n': i++; learn_parm->maxiter=atol(argv[i]); break;
case 'p': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
case 'k': i++; *init_spl_weight = atof(argv[i]); break;
case 'm': i++; *spl_factor = atof(argv[i]); break;
case 'q': i++; struct_parm->solve_dual = atoi(argv[i]); break;
case '-': strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);i++; strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);break;
default: printf("\nUnrecognized option %s!\n\n",argv[i]);
exit(0);
}
}
*init_spl_weight = (*init_spl_weight)/learn_parm->svm_c;
if(i>=argc) {
printf("\nNot enough input parameters!\n\n");
my_wait_any_key();
exit(0);
}
strcpy (trainfile, argv[i]);
if((i+1)<argc) {
strcpy (modelfile, argv[i+1]);
}
else {
strcpy (modelfile, "lssvm.model");
}
/* self-paced learning weight should be non-negative */
if(*init_spl_weight < 0.0)
*init_spl_weight = 0.0;
/* self-paced learning factor should be greater than 1.0 */
if(*spl_factor < 1.0)
*spl_factor = 1.1;
parse_struct_parameters(struct_parm);
}
void my_read_input_parameters(int argc, char *argv[], char *trainfile, char* modelfile,
LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm, STRUCT_LEARN_PARM *struct_parm) {
long i;
/* set default */
learn_parm->maxiter=20000;
learn_parm->svm_maxqpsize=100;
learn_parm->svm_c=100.0;
//learn_parm->eps=0.001;
learn_parm->eps=0.1; //AJAY: Changing for faster convergence
learn_parm->biased_hyperplane=12345; /* store random seed */
learn_parm->remove_inconsistent=10;
kernel_parm->kernel_type=0;
kernel_parm->rbf_gamma=0.05;
kernel_parm->coef_lin=1;
kernel_parm->coef_const=1;
kernel_parm->poly_degree=3;
struct_parm->custom_argc=0;
// Ajay
learn_parm->totalEpochs = 1;
learn_parm->numChunks = 5;
for(i=1;(i<argc) && ((argv[i])[0] == '-');i++) {
switch ((argv[i])[1]) {
case 'c': i++; learn_parm->svm_c=atof(argv[i]); break;
case 'e': i++; learn_parm->eps=atof(argv[i]); break;
case 's': i++; learn_parm->svm_maxqpsize=atol(argv[i]); break;
case 'g': i++; kernel_parm->rbf_gamma=atof(argv[i]); break;
case 'd': i++; kernel_parm->poly_degree=atol(argv[i]); break;
case 'r': i++; learn_parm->biased_hyperplane=atol(argv[i]); break;
case 't': i++; kernel_parm->kernel_type=atol(argv[i]); break;
case 'n': i++; learn_parm->maxiter=atol(argv[i]); break;
case 'p': i++; learn_parm->remove_inconsistent=atol(argv[i]); break;
case '-': strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);i++; strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);break;
// Added by Ajay
case 'f': i++; strcpy(learn_parm->tmpdir,argv[i]); printf("\nTmp file is %s\n",learn_parm->tmpdir); break;
case 'y': i++; learn_parm->frac_sim=atof(argv[i]); printf("Frac Sim is %g\n", learn_parm->frac_sim); break;
case 'z': i++; strcpy(learn_parm->dataset_stats_file,argv[i]);printf("Dataset Stats file is %s\n",learn_parm->dataset_stats_file);break;
case 'w': i++; learn_parm->Fweight=atof(argv[i]); printf("Weigting param of F is %g\n",learn_parm->Fweight);break;
case 'o': i++; learn_parm->rho_admm=atof(argv[i]); printf("Rho is %g\n", learn_parm->rho_admm); break;
case 'a': i++; learn_parm->isExhaustive=atol(argv[i]);printf("isExhaustive is %ld",learn_parm->isExhaustive); break;
case 'b': i++; learn_parm->isLPrelaxation=atol(argv[i]);printf("isLPrelaxation is %ld",learn_parm->isLPrelaxation); break;
case 'K': i++; learn_parm->numChunks=atoi(argv[i]); break;
case 'E': i++; learn_parm->totalEpochs=atoi(argv[i]); break;
case 'C': i++; learn_parm->Cdash=atof(argv[i]); break;
////////////////////////
default: printf("\nUnrecognized option %s!\n\n",argv[i]);
exit(0);
}
}
if(i>=argc) {
printf("\nNot enough input parameters!\n\n");
my_wait_any_key();
exit(0);
}
strcpy (trainfile, argv[i]);
if((i+1)<argc) {
strcpy (modelfile, argv[i+1]);
}
parse_struct_parameters(struct_parm);
}
void my_read_input_parameters(int argc, char *argv[], char *trainfile, char* modelfile,
LEARN_PARM *learn_parm, KERNEL_PARM *kernel_parm, STRUCT_LEARN_PARM *struct_parm) {
long i;
/* set default */
learn_parm->maxiter=20000;
learn_parm->svm_maxqpsize=100;
learn_parm->svm_c=100.0;
learn_parm->eps=0.001;
learn_parm->biased_hyperplane=12345; /* store random seed */
learn_parm->remove_inconsistent=10;
kernel_parm->kernel_type=0;
kernel_parm->rbf_gamma=0.05;
kernel_parm->coef_lin=1;
kernel_parm->coef_const=1;
kernel_parm->poly_degree=3;
struct_parm->custom_argc=0;
for(i=1; (i<argc) && ((argv[i])[0] == '-'); i++) {
switch ((argv[i])[1]) {
case 'c':
i++;
learn_parm->svm_c=atof(argv[i]);
break;
case 'e':
i++;
learn_parm->eps=atof(argv[i]);
break;
case 's':
i++;
learn_parm->svm_maxqpsize=atol(argv[i]);
break;
case 'g':
i++;
kernel_parm->rbf_gamma=atof(argv[i]);
break;
case 'd':
i++;
kernel_parm->poly_degree=atol(argv[i]);
break;
case 'r':
i++;
learn_parm->biased_hyperplane=atol(argv[i]);
break;
case 't':
i++;
kernel_parm->kernel_type=atol(argv[i]);
break;
case 'n':
i++;
learn_parm->maxiter=atol(argv[i]);
break;
case 'p':
i++;
learn_parm->remove_inconsistent=atol(argv[i]);
break;
case '-':
strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);
i++;
strcpy(struct_parm->custom_argv[struct_parm->custom_argc++],argv[i]);
break;
default:
printf("\nUnrecognized option %s!\n\n",argv[i]);
exit(0);
}
}
if(i>=argc) {
printf("\nNot enough input parameters!\n\n");
my_wait_any_key();
exit(0);
}
strcpy (trainfile, argv[i]);
if((i+1)<argc) {
strcpy (modelfile, argv[i+1]);
}
parse_struct_parameters(struct_parm);
}
