def data_mix_for_SVM_general(first, last): ''' training datasets for general SVM (near- and post-fixation) ''' train_mix = {} # set simulation range if(first is None): first = p.first_sim if(last is None): last = p.last_sim reg = regimes.get_regimes() for i, regime_dict in enumerate(reg): train_mix[i,"data"], train_mix[i,"states"] = data_and_states_for_dict(p.case_type, p.cont_type, regime_dict, first, last) return train_mix
def data_mix_for_SVM_general_exclude(s, t): ''' training datasets for general SVM (near- and post-fixation), excluding data from given parameters ''' train_mix = {} reg = regimes.get_regimes() for i, regime_dict in enumerate(reg): if(s in regime_dict.keys() and t in regime_dict[s]): regime_dict[s] = [x for x in regime_dict[s] if x != t] # to-exclude in current epoch, remove train_mix[i,"data"], train_mix[i,"states"] = data_and_states_for_dict(p.case_type, p.cont_type, regime_dict, p.first_sim, p.last_sim) return train_mix
def data_mix_for_SVM_general(first, last): ''' training datasets for general SVM (near- and post-fixation) ''' train_mix = {} # set simulation range if (first is None): first = p.first_sim if (last is None): last = p.last_sim reg = regimes.get_regimes() for i, regime_dict in enumerate(reg): train_mix[i, "data"], train_mix[i, "states"] = data_and_states_for_dict( p.case_type, p.cont_type, regime_dict, first, last) return train_mix
def data_mix_for_SVM_general_exclude(s, t): ''' training datasets for general SVM (near- and post-fixation), excluding data from given parameters ''' train_mix = {} reg = regimes.get_regimes() for i, regime_dict in enumerate(reg): if (s in regime_dict.keys() and t in regime_dict[s]): regime_dict[s] = [x for x in regime_dict[s] if x != t] # to-exclude in current epoch, remove train_mix[i, "data"], train_mix[i, "states"] = data_and_states_for_dict( p.case_type, p.cont_type, regime_dict, p.first_sim, p.last_sim) return train_mix