def test_gp(constrained_search: bool, normalization: str):
logging.basicConfig(level=logging.INFO)
num_evaluations = 8
Xy_train, X_test, y_test = artificial_task1()
blackbox = Blackbox(
input_dim=2,
output_dim=1,
eval_fun=lambda x: x.sum(axis=-1, keepdims=True),
)
optimizer = GP(
input_dim=blackbox.input_dim,
output_dim=blackbox.output_dim,
normalization=normalization,
)
candidates = X_test
for i in range(num_evaluations):
x = optimizer.sample(
candidates) if constrained_search else optimizer.sample()
y = blackbox(x)
logging.info(f"criterion {y} for arguments {x}")
optimizer.observe(x=x, y=y)
def test_smoke_optimizers(optimizer_cls, constrained_search: bool):
logging.basicConfig(level=logging.INFO)
num_evaluations = 10
blackbox = Blackbox(
input_dim=2,
output_dim=1,
eval_fun=lambda x: x.sum(axis=-1, keepdims=True),
)
optimizer = optimizer_cls(
input_dim=blackbox.input_dim,
output_dim=blackbox.output_dim,
evaluations_other_tasks=Xy_train,
)
candidates = X_test
for i in range(num_evaluations):
if constrained_search:
x = optimizer.sample(candidates)
else:
x = optimizer.sample()
y = blackbox(x)
logging.info(f"criterion {y} for arguments {x}")
optimizer.observe(x=x, y=y)
def __init__(self,length=5,speed=0,location=0,
current_time=0,road=None,number=-1):
self.number = number
self.length = length
self.speed = speed
self.location = location
self.next_car = 0
self.max_speed = (100/3) #meters/second
self.distance_from_car_ahead = 0
self.blackbox = Blackbox(-1,self.location,self.speed)
self.road = road
self.road_length = len(self.road.road)
class BlackboxSmsBackend(BaseSmsBackend):
api_key = settings.BLACKBOX_API_KEY
api_signature = settings.BLACKBOX_API_SIGNATURE
shortcode = settings.BLACKBOX_SHORT_CODE
keyword = settings.BLACKBOX_KEYWORD
blackbox = Blackbox(api_key, api_signature)
def send_messages(self, messages):
for message in messages:
for to in message.to:
try:
self.blackbox.queue_sms(to, message.body, self.shortcode,
self.keyword)
self.blackbox.send_sms()
except:
if not self.fail_silently:
raise
import sys
import os
sys.path.append("ML/")
from blackbox import Blackbox
from annoy1 import save_to_annoy
from time import time
model = Blackbox(1)
def test_detection():
assert model.send_picture('tests/1_test.jpg')[0] == [7]
assert model.send_picture('tests/2_test.jpg') is None
def test_annoy_save():
save_to_annoy(10, ann_path='tests/stars_embeddings.ann')
assert time() - os.stat('tests/stars_embeddings.ann')[8] < 10
dur_time = round(time.time() - start, 4)
add_log(time.asctime(), event.user_id, event.message, resp, dur_time)
if __name__ == '__main__':
token = "01e71dc0db9523a795691bcdc5f346b834b2138deb7d6591b14af99e78ffe3119001c950623abb1edae46"
vk = vk_api.VkApi(token=token)
longpoll = VkLongPoll(vk)
keyboard = VkKeyboard(one_time=True)
keyboard.add_button(u'Ты ошибся...', color=VkKeyboardColor.NEGATIVE)
keyboard.add_button(u'Да это я! Похож!', color=VkKeyboardColor.POSITIVE)
box = Blackbox(1)
for event in longpoll.listen():
if event.type == VkEventType.MESSAGE_NEW and event.to_me:
start = time.time()
attach_data = event.attachments
if attach_data:
if len(attach_data) == 2:
if attach_data["attach1_type"] == "photo":
write_msg(event.user_id,
u"Фото принято, обрабатываю...")
upload_photo(event.message_id)
indxs = box.send_picture("test.jpg")
if indxs is None:
resp = u"На фото я не вижу лиц."
write_msg(event.user_id, resp)
return candidate[0]
else:
# (N,)
ei = acq(torch.Tensor(candidates).unsqueeze(dim=-2))
return torch.Tensor(candidates[ei.argmax()])
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
num_evaluations = 10
Xy_train, X_test, y_test = artificial_task1()
blackbox = Blackbox(
input_dim=2,
output_dim=1,
eval_fun=lambda x: x.sum(axis=-1, keepdims=True),
)
optimizer = G3P(
input_dim=blackbox.input_dim,
output_dim=blackbox.output_dim,
evaluations_other_tasks=Xy_train,
num_gradient_updates=2,
)
candidates = X_test
for i in range(num_evaluations):
x = optimizer.sample(candidates)
#x = optimizer.sample()
import psycopg2
import xmltodict
from blackbox import Blackbox
# Loop which will retrieve all subscribers which haven't
# acknowledged message receipt for the day, then send a
# message through the shortcode they're associated with.
# Setup API credentials
api_key = "af23046bff4abfc22ffb21fa57c7a9ee"
# Check under Settings->API Keys in Blackbox
api_signature = "QeWgu7HTnlE+3nOy4mSvZcyumvC1CFTp53OF7/a0o9jDAkbNwxEyyRQHJCUl3+xqsCF6Og/38fnR+cyugaCaBZoeKQZsSKXYfVJ3lQPNhNBcs7QNUWaHb+z7umdlA/OiwVzFWKtY7pwbOAwErkAXkq3Z+74ZWzFYOCfFxiyKKLk=" # Check under Settings->API Keys in Blackbox
# Make API request
blackbox = Blackbox(api_key, api_signature) # Instantiate API library
print('Establishing database connection...')
conn = psycopg2.connect(database="blackbox",
user="******",
password="******",
host="blackbox.c5oicakkxsxn.us-west-2.rds.amazonaws.com",
port="5432")
print("Database connection established.")
conn.set_session(autocommit=True)
cur = conn.cursor()
# Select subscriber number, shortcode, and keyword from blackbox.subscribers
sql = "SELECT DISTINCT subscriber, shortcode, keyword from subscribers where active_subscriber = 'true' and sent_today = 'false';"
cur.execute(sql)