def get_user():
#if no user file - create one using email - for user setup
tmp = fm.read_file("data/user.txt")
if tmp != None:
return tmp
else:
print("\nUser File Not Found: Please use aeneas to init a user file")
exit(0)
def get_training_data():
file_name = "data/scipio_training_data.txt"
data = fm.read_file(file_name)
data_clean = []
#vet data
for line in data:
data_clean.append(line.strip('\n').split(':'))
return data_clean
def check_progress():
tmp = fm.read_file("data/progress.txt")
if tmp != None:
return tmp
else:
print("Progress File Not Found: Creating One...")
fm.write_file("data/progress.txt", '', "w+")
print("Progress File Created!")
return []
def get_user():
tmp = fm.read_file("data/user.txt")
if tmp != None:
return tmp
else:
print("User File Not Found: Creating One...")
user_email = input('User Email: ') + "\n"
print("Retrieving User Token...")
token = json.dumps(get_token(user_email))
print("Token Collected!")
fm.write_file("data/user.txt", [user_email, token], 'w')
return [user_email, token]
def load_weights(file_name):
#default location for data files
data = fm.read_file('data/' + str(file_name))
if data == None:
#alt location
data = fm.read_file(str(file_name))
temp = []
for item in data:
temp.append([
float(x)
for x in str(item.split(':')[1]).replace('\n', '').replace(
'[', '').replace(']', '').split(', ')
])
i = 0
for perceptron in perceptrons:
perceptron.weight = temp[i]
i = i + 1
print("Weights loaded!")
return
def print_title():
lines = fm.read_file("titles/aeneas_title.txt")
for line in lines:
print(line.strip("\n"))
print("\t\t\t\t\t\t\t<3 valtyr")
def execute():
counter = 0
streak = 0
top_streak = 0
time_start = time.time()
while True:
(targets, blob) = request()
#set all outputs
for percep in perceptrons:
percep.input = compute_frequency(blob, percep.chunk)
percep.get_output()
count = {}
for percep in perceptrons:
if percep.output == 1 and targets.__contains__(percep.label):
if percep.label in count.keys():
count[percep.label] = count[percep.label] + 1
else:
count[percep.label] = 1
try:
guess = max(count, key=count.get)
#if confidence low ie low fire rate .. raise and pull
net_conf = int(count.get(guess))
if net_conf <= confidence:
print('low answer confidence...low fire[' +
str(int(count.get(guess))) + "]")
raise ('')
print("Guess:\t" + guess + '\tConfidence: ' + str(net_conf))
response = solve(guess)
correct_resp = response.json()['correct']
target = response.json()['target']
accuracy = response.json()['accuracy']
try:
hash_resp = response.json()['hash']
print("HASH COLLECTED!")
time_stop = time.time()
time_total = time_stop - time_start
save_hash("Streak: " +
str(fm.read_file('data/top_streak.txt')) +
"Correct: " + str(correct_resp) + "\tAccuracy: " +
str(accuracy) + "\tTotal Time: " + str(time_total) +
"\nHash: " + hash_resp + "\n")
# ask_hash(email)
except:
hash_resp = "None"
print('Answer: ' + target + '\tNumber: ' + str(correct_resp) +
"\tAccuracy: " + str(accuracy))
if (target == guess):
streak = streak + 1
# if streak > top_streak:
# top_streak=streak
print("streak: " + str(streak) + '\n')
else:
if int(fm.read_file('data/top_streak.txt')) < streak:
fm.write_file('data/top_streak.txt', top_streak, 'w+')
print('streak reset...\n')
streak = 0
for percep in perceptrons:
percep.adjust_weight(target)
percep.mod = random.choice([0, 1])
counter = counter + 1
save_training_data(target, blob)
#save weights every 10 cycles
if counter % 10 == 0:
print("Weights saved!")
save_weights()
except:
pass
return
def print_title():
lines = fm.read_file("titles/scipio_title.txt")
for line in lines:
print(line.strip('\n'))
print(" <3 valtyr")
return