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
