def on_bt_time_add_clicked(self, widget):
if self.time_mark == "A":
astr = self.label_A.get_text()
a = utils.string_to_time(astr)
a = a+1
self.label_A.set_text(utils.time_to_string(a))
elif self.time_mark == "B":
astr = self.label_B.get_text()
b = utils.string_to_time(astr)
b = b+1
self.label_B.set_text(utils.time_to_string(b))
def on_bt_time_sub_clicked(self, widget):
if self.time_mark == "A":
astr = self.label_A.get_text()
a = utils.string_to_time(astr)
a = a-1
if a< 0 :
a = 0
self.label_A.set_text(utils.time_to_string(a))
elif self.time_mark == "B":
astr = self.label_B.get_text()
b = utils.string_to_time(astr)
b = b-1
if b < 0:
b=0
self.label_B.set_text(utils.time_to_string(b))
def list(self, tid=None):
'''
list all records whose parent is tid
'''
R = Query()
Records = self._DB.search(R['status'] != "DONE" and R['parent'] == tid)
Records.sort(key=lambda R: utils.string_to_time(R['eta']))
return Records
def predict_time_instance(time, date, model):
"""
Predict the state of the light at a given time, based on previous collected data
Input:
time: time the prediction needs to be made. Given in string format "HH:MM"
date: date of the prediction. Given in format "%yyyy-%mm-%dd"
model: the trained model
Returns:
prediction: prediction of the state of the light (1 or 0).
"""
year, month, day = (int(x) for x in date.split('-'))
day = datetime(year, month, day).strftime("%A")
time = u.string_to_time(time) / TOTAL_TIME
predict_vector = pd.DataFrame(np.array([time]), columns=[date])
predict_vector = u.day_of_week_one_hot(predict_vector)
prediction = model.predict(predict_vector.values)
prediction_class = tf.nn.sigmoid(prediction).numpy()[0, 0]
return int(round(prediction_class))