if(start_C <= 0)
start_C = calc_start_C(prob, ¶m_tmp);
double max_C = 1024;
- start_C = min(start_C, max_C);
+ start_C = min(start_C, max_C);
double best_C_tmp, best_score_tmp;
-
+
find_parameter_C(prob, ¶m_tmp, start_C, max_C, &best_C_tmp, &best_score_tmp, fold_start, perm, subprob, nr_fold);
-
+
*best_C = best_C_tmp;
*best_score = best_score_tmp;
}
start_C_tmp = start_C;
start_C_tmp = min(start_C_tmp, max_C);
double best_C_tmp, best_score_tmp;
-
+
find_parameter_C(prob, ¶m_tmp, start_C_tmp, max_C, &best_C_tmp, &best_score_tmp, fold_start, perm, subprob, nr_fold);
-
+
if(best_score_tmp < *best_score)
{
*best_p = param_tmp.p;
// parameters for training only won't be assigned, but arrays are assigned as NULL for safety
param.nr_weight = 0;
param.weight_label = NULL;
- param.weight = NULL;
+ param.weight = NULL;
param.init_sol = NULL;
model_->label = NULL;
from itertools import izip as zip
_cstr = lambda s: s.encode("utf-8") if isinstance(s,unicode) else str(s)
else:
- _cstr = lambda s: bytes(s, "utf-8")
+ _cstr = lambda s: bytes(s, "utf-8")
__all__ = ['load_model', 'save_model', 'train', 'predict'] + liblinear_all + common_all