def evaluate(self):
eers = []
for subject in subjects:
genuine_user_data = data.loc[data.subject == "vaibhav", \
"H.period":"UD.l.Return"]
imposter_data = data.loc[data.subject != "vaibhav", :]
self.train = genuine_user_data[-1:]
self.test_genuine = genuine_user_data[:20]
self.test_imposter = imposter_data.groupby("subject"). \
head(20).loc[:, "H.period":"UD.l.Return"]
#total - selected genuine ,,,,, first five of every imposter displayed
#print(genuine_user_data[:200])
self.training()
self.testing()
eers.append(evaluateEER(self.u_scores, \
self.i_scores))
#print(evaluateEER(self.u_scores, \
# self.i_scores))
#print(np.mean(eers))
break
return np.mean(eers)
def evaluate(self):
eers = []
for subject in subjects:
genuine_user_data = data.loc[data.subject == subject, \
"H.period":"H.Return"]
imposter_data = data.loc[data.subject != subject, :]
self.train = genuine_user_data[:200]
self.test_genuine = genuine_user_data[200:]
self.test_imposter = imposter_data.groupby("subject"). \
head(5).loc[:, "H.period":"H.Return"]
self.training()
self.testing()
eers.append(evaluateEER(self.user_scores, \
self.imposter_scores))
return np.mean(eers)
