def add_rate(self, dict_nics):
"""Function to get RX_RATE and TX_RATE. Rate is not calculated for virtaual nics."""
for if_name, if_info in dict_nics.items():
if not self.first_poll:
if if_info[NIC_TYPE] == PHY:
rate = utils.get_rate(
RX_BYTES, if_info, self.prev_data[if_name])
if rate != NAN:
if_info[RX_RATE] = round(
((rate * BITFACTOR )/ (FACTOR * FACTOR)), FLOATING_FACTOR)
rate = utils.get_rate(
TX_BYTES, if_info, self.prev_data[if_name])
if rate != NAN:
if_info[TX_RATE] = round(
((rate * BITFACTOR ) / (FACTOR * FACTOR)), FLOATING_FACTOR)
elif if_info[NIC_TYPE] == AGGREGATE:
rate = utils.get_rate(
AGG + RX_BYTES, if_info, self.prev_data[if_name])
if rate != NAN:
if_info[AGG + RX_RATE] = round(
((rate * BITFACTOR ) / (FACTOR * FACTOR)), FLOATING_FACTOR)
rate = utils.get_rate(
AGG + TX_BYTES, if_info, self.prev_data[if_name])
if rate != NAN:
if_info[AGG + TX_RATE] = round(
((rate * BITFACTOR ) / (FACTOR * FACTOR)), FLOATING_FACTOR)
def add_nic_rate(self, dict_nics):
#Function to get RX_RATE and TX_RATE. Rate is not calculated for virtaual nics."""
#for if_name, if_info in dict_nics.items():
if not self.first_poll:
rate = utils.get_rate(AGG + RX_BYTES, dict_nics,
self.prev_nic_data)
if rate != NAN:
dict_nics[AGG + RX_RATE] = round((rate / (FACTOR * FACTOR)),
FLOATING_FACTOR)
rate = utils.get_rate(AGG + TX_BYTES, dict_nics,
self.prev_nic_data)
if rate != NAN:
dict_nics[AGG + TX_RATE] = round((rate / (FACTOR * FACTOR)),
FLOATING_FACTOR)
def add_disk_rate(self, dict_disks):
#Function to get READTHROUGHPUT, READIOPS, WRITETHROUGHPUT and WRITEIOPS."""
#for items in dict_disks():
#if self.prev_disk_data and disk_name in self.prev_disk_data:
if self.prev_disk_data:
#disk_name = AGGREGATE
collectd.info("add rate start ")
rate = utils.get_rate(READBYTE, dict_disks, self.prev_disk_data)
if rate != NAN:
dict_disks[AGG + READTHROUGHPUT] = round(rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITEBYTE, dict_disks, self.prev_disk_data)
if rate != NAN:
dict_disks[AGG + WRITETHROUGHPUT] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(READCOUNT, dict_disks, self.prev_disk_data)
if rate != NAN:
dict_disks[AGG + READIOPS] = round(rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITECOUNT, dict_disks, self.prev_disk_data)
if rate != NAN:
dict_disks[AGG + WRITEIOPS] = round(rate, FLOATING_FACTOR)
def add_rate(self, dict_disks):
"""Function to get READTHROUGHPUT, READIOPS, WRITETHROUGHPUT and WRITEIOPS."""
for disk_name, disk_info in dict_disks.items():
if self.prev_data and disk_name in self.prev_data:
if disk_name != "aggregate":
rate = utils.get_rate(READBYTE, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[READTHROUGHPUT] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITEBYTE, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[WRITETHROUGHPUT] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(READCOUNT, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[READIOPS] = round(rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITECOUNT, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[WRITEIOPS] = round(rate, FLOATING_FACTOR)
else:
rate = utils.get_rate(READBYTE, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[AGG + READTHROUGHPUT] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITEBYTE, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[AGG + WRITETHROUGHPUT] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(READCOUNT, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[AGG + READIOPS] = round(
rate, FLOATING_FACTOR)
rate = utils.get_rate(WRITECOUNT, disk_info,
self.prev_data[disk_name])
if rate != NAN:
disk_info[AGG + WRITEIOPS] = round(
rate, FLOATING_FACTOR)
def do_GET(self):
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.end_headers()
path_part_URI = self.path
print(path_part_URI)
if path_part_URI == "/favicon.ico":
print("Отработан запрос к серверу favicon")
print()
else:
try:
from_currency, to_currency, ante = parse_string(path_part_URI)
except: # любые ошибки вызова .parse_string
print()
print("Ошибка парсинга Query-компонента URI")
report = {"response code": "0", "error": "URI parsing error"}
else:
absolute_URL = construct_url(BASE_URL, APIKEY, from_currency,
(to_currency))
print()
print(f"Приложение запрашивает ресурс: \n{absolute_URL}")
rate = get_rate(absolute_URL, from_currency, to_currency)
if isinstance(rate, float):
total = ante * rate
report = {
"response code": "200",
"currency exchange":
f"{from_currency} to {to_currency}",
"requested value": f"{ante}",
"rate": f"{rate}",
"exchange result": f"{total:.2f}",
"error": ""
}
else: # возникла ошибка при запросе к API обмена валют
code = rate[0]
text = rate[1]
report = {"response code": f"{code}", "error": f"{text}"}
finally: # при любых ошибках отправляет ответ пользователю
print()
print("Результат запроса ресурса:")
response = json.dumps(report,
ensure_ascii=False).encode("utf-8")
print(response.decode())
print()
self.wfile.write(response)
def markup_inline_rate(currency):
price_per_one = float(get_rate(currency)) # price per one STX coin
list_ = ['BTC', 'ETH', 'LTC']
if currency in list_:
coinmarketcap = 'CoinMarketCap ({0:.6f} {1})'.format(
price_per_one, currency)
else:
coinmarketcap = 'CoinMarketCap ({0:.2f} {1})'.format(
price_per_one, currency)
markup_inline_rate = types.InlineKeyboardMarkup(row_width=1)
btn_coinmarketcap = types.InlineKeyboardButton(
coinmarketcap, callback_data='CoinMarketCap')
markup_inline_rate.add(btn_coinmarketcap, btn_back_to_set)
return markup_inline_rate
def index(header: str, **kwargs: Any) -> tuple[str, str]:
amount: str = kwargs.get('amount', '')
if not amount:
header = 'HTTP/1.1 400 Amount parameter not found\n\n' # If no amount parameter then return 400
return header, error_json('Amount parameter not found.')
rate: Optional[float] = get_rate()
if not rate:
# If no such CSS selector at all for source and selector in config)
header = 'HTTP/1.1 500 Bad currency rates service\n\n'
return header, error_json('Bad currency rates service.')
usd_amount: str = calculate_usd(amount, rate)
if not usd_amount:
header = 'HTTP/1.1 400 Amount should be float\n\n' # If amount parameter has bed format then return 500
return header, error_json('Amount should be float.')
api_logger.debug('Amount: ' + amount + ' Rate: ' + str(rate) +
' Result: ' + usd_amount)
return header, json.dumps({
'Currency': 'USD',
u'Amount': amount,
'Rate': str(rate),
'Result': usd_amount
})
# Use the adversarial examples from previous step
x_ae = adversarial_examples
y_ae = and_gen.get_y(x_ae)
pred_ae = pred_ae = model_svm.predict(x_ae)
score = accuracy_score(y_ae, pred_ae)
print(f'Accuracy on the given set = {score*100:.4f}%')
# Applicability Domain
# Stage 1 - Applicability
print('\n---------- Applicability ---------------')
x_passed_s1, ind_passed_s1 = ad.check_applicability(x_ae, x_train, y_train)
pred_passed_s1 = pred_ae[ind_passed_s1]
# Print
pass_rate = utils.get_rate(x_passed_s1, x_ae)
print(f'Pass rate = {pass_rate * 100:.4f}%')
# print('Blocked by Applicability Domain:')
# utils.print_blocked_samples(x_ae, ind_passed_s1)
# %%
# Stage 2 - Reliability
print('\n---------- Reliability -----------------')
# Parameters:
k = 9
# The test mean and training mean are close, thus using smaller zeta
zeta = 0.3
# Creating kNN models for each class
ind_train_c0 = np.where(y_train == 0)
model_knn_c0 = utils.unimodal_knn(x_train[ind_train_c0], k)
score = accuracy_score(y_new, pred_new)
print(f'Accuracy on the given set = {score*100:.4f}%')
print('Misclassified samples:')
print_misclassified_samples(x_new, y_new, np.array(pred_new))
# Applicability Domain
# Stage 1 - Applicability
print('\n---------- Applicability ---------------')
x_passed_s1, ind_passed_s1 = ad.check_applicability(x_new, x_train, y_train)
y_passed_s1 = y_new[ind_passed_s1]
# Print infomation
pass_rate_ad = utils.get_rate(x_passed_s1, x_new)
print(f'Pass rate = {pass_rate_ad * 100:.4f}%')
print('Blocked by Applicability Domain:')
print_blocked_samples(x_new, ind_passed_s1)
# Stage 2 - Reliability
print('\n---------- Reliability -----------------')
# Parameters:
k = 9
zeta = 1.959
# Creating kNN models for each class
ind_train_c1 = np.where(y_train == 1)
model_knn_c1 = utils.unimodal_knn(x_train[ind_train_c1], k)
ind_train_c0 = np.where(y_train == 0)
