def process(self, mgrs):
train_data_ch, train_labels = prepare.get_train_set_from_mgr(
mgrs[0], self.num_per_avg, self.start_samples_to_norm,
self.downsample_level, self.start_sec_offset, self.duration)
l = Labels([str(i) for i in train_labels])
data = VectorDataSet(train_data_ch[0], L=l)
data.normalize(2)
return [data]
def run():
dr2 = '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/p300_10_12_2010/squares/'
f2_name = 'p300_128hz_laptop_training_6x6_square_CATDOGFISHWATERBOWL_longer_8trials2'
f2 = {
'info': os.path.join(dr2, f2_name+'_10HZ.obci.xml'),
'data': os.path.join(dr2, f2_name+'_10HZ.obci.bin'),
'tags':os.path.join(dr2, f2_name+'.obci.arts_free.svarog.tags')
}
"""dr2 = '/media/windows/wiedza/bci/EKSPERYMENTY_DANE/p300_10_12_2010/numbered_squares/'
f2_name = 'p300_128hz_laptop_training_6x6_squareNUMBERS_CATDOGFISHWATERBOWL_longer_8trials'
f2 = {
'info': os.path.join(dr2, f2_name+'.obci.filtered.xml'),
'data': os.path.join(dr2, f2_name+'_10HZ.obci.bin'),
'tags':os.path.join(dr2, f2_name+'.obci.arts_free.svarog.tags')
}"""
mgr = read_manager.ReadManager(f2['info'], f2['data'], f2['tags'])
train_data_ch, train_labels = prepare.get_train_set_from_mgr(mgr, num_per_avg=10, start_samples_to_norm=0, downsample_level=5)
class MY_SVM(object):
def __init__(self, C):
self.s = svm.SVM(C=C)
def process(self, data):
return self.s.stratifiedCV(data, 5).getBalancedSuccessRate()
def __repr__(self):
return "SVM: C - "+str(self.s.C)
class MY_STD(object):
def process(self, data):
#p = prepare.Standardizer()
#p.train(data)
data.normalize(2)
return data
ch = my_chain.Chain(
my_chain.ChainElement(MY_STD, {}),
my_chain.ChainElement(MY_SVM,
{'C':[0.01, 0.1, 0.5, 1, 10]})
)
l = Labels([str(i) for i in train_labels])
for i, train_data in enumerate(train_data_ch):
train_data = train_data_ch[11]
data = VectorDataSet(train_data, L=l)
cs, res = ch.process(data)
print("CSS: "+str(cs))
print("RES: "+str(res))
break
def process(self, mgrs):
train_data_ch, train_labels = prepare.get_train_set_from_mgr(mgrs[0], self.num_per_avg, self.start_samples_to_norm, self.downsample_level, self.start_sec_offset, self.duration)
l = Labels([str(i) for i in train_labels])
data = VectorDataSet(train_data_ch[0], L=l)
data.normalize(2)
return [data]
