def get_hanabi_partners(setting, partner_type):
partners_dict = {
"": {
"ppo": {
"train": [
"output/hanabi_n=4_run=1240_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1241_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1242_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1243_netsz=500_mreg=0.00",
],
"test": [
"output/hanabi_n=4_run=1244_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1245_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1246_netsz=500_mreg=0.00",
"output/hanabi_n=4_run=1247_netsz=500_mreg=0.00",
]
}
}
}
partners = partners_dict[setting][partner_type]
if partner_type == "ppo":
train_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["train"]
]
test_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["test"]
]
return train_partners, test_partners
def setUp(self):
player1 = Player("bob")
player2 = Player("sue")
partner1 = Partner(player1,player2)
player3 = Player("ralph")
player4 = Player("greg")
partner2 = Partner(player3,player4)
self.table = Table("Awesome",partner1,partner2)
def getTable(self,table_name,name1,name2,name3,name4):
player1 = Player(name1)
player2 = Player(name2)
player3 = Player(name3)
player4 = Player(name4)
partner1 = Partner(player1,player2)
partner2 = Partner(player3,player4)
return Table(table_name,partner1,partner2)
def __init__(self, machine_id, database, ui):
Partner.__init__(self, ui)
self.machine_id = machine_id
self.database = database
self.text_format = XMLFormat()
self.server_info = {}
self.con = None
self.behind_proxy = None # Explicit variable for testability.
self.proxy = None
def __init__(self, machine_id, host, port, ui):
self.machine_id = machine_id
WSGIServer.__init__(self, (host, port), WSGIRequestHandler)
self.set_app(self.wsgi_app)
Partner.__init__(self, ui)
self.text_format = XMLFormat()
self.stopped = False
self.sessions = {} # {session_token: session}
self.session_token_for_user = {} # {user_name: session_token}
def __init__(self, machine_id, database, ui):
Partner.__init__(self, ui)
self.machine_id = machine_id
self.database = database
self.text_format = XMLFormat()
self.server_info = {}
self.con = None
self.behind_proxy = None # Explicit variable for testability.
self.proxy = None
def __init__(self, machine_id, port, ui):
self.machine_id = machine_id
# We only use 1 thread, such that subsequent requests don't run into
# SQLite access problems.
from cherrypy import wsgiserver
self.wsgi_server = wsgiserver.CherryPyWSGIServer\
(("0.0.0.0", port), self.wsgi_app, server_name="localhost",
numthreads=1, timeout=1000)
Partner.__init__(self, ui)
self.text_format = XMLFormat()
self.sessions = {} # {session_token: session}
self.session_token_for_user = {} # {user_name: session_token}
def create_Partner(iterate_dataset_name):
"""Instantiate partner object"""
part = Partner(partner_id=0)
dataset_name = iterate_dataset_name
if dataset_name == "cifar10":
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
part.y_train = data_cf.preprocess_dataset_labels(y_train)
if dataset_name == "mnist":
(x_train, y_train), (x_test, y_test) = mnist.load_data()
part.y_train = data_mn.preprocess_dataset_labels(y_train)
yield part
def create_partners_list(dataset_name, partners_count):
partners_list = []
for i in range(partners_count):
part = Partner(partner_id=i)
if dataset_name == "cifar10":
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
part.y_train = data_cf.preprocess_dataset_labels(y_train)
if dataset_name == "mnist":
(x_train, y_train), (x_test, y_test) = mnist.load_data()
part.y_train = data_mn.preprocess_dataset_labels(y_train)
partners_list.append(part)
return partners_list
def instantiate_scenario_partners(self):
"""Create the partners_list - self.partners_list should be []"""
if self.partners_list != []:
raise Exception("self.partners_list should be []")
self.partners_list = [Partner(i) for i in range(self.partners_count)]
def process_data(json_file):
"""
Processes the JSON file returned from the GET request, finds the minimum most consecutive dates for each country, and manipulates the data into a format ready for submission
"""
#dictionary with the keys set as countries and the values set as the "Results" class
results_dic = {}
#dictionary with keys set as countries and the values as partners from the respective country
country_dic = defaultdict(list)
for p in json_file['partners']:
p = Partner(p)
country_dic[p.country].append(p)
for country in country_dic:
available_dic = defaultdict(list)
for partner in country_dic[country]:
#finds consecutive dates of each individual partner and uses those dates as keys for a dictionary
available_date = partner.get_consecutive()
for dates in available_date:
available_dic[dates].append(partner.email)
#first sort maintains the minimum order of dates
res = sorted(available_dic.items(), key=lambda item: item[0][0])
#second sort finds the maximum amount of attendees respective to minimum date
res.sort(key=lambda item: len(item[1]), reverse=True)
#adds result to "Results" class
results_dic[country] = Results(res, country)
return results_dic
def get_arms_human_partners(setting, partner_type):
partners_dict = {
"": {
"fixed": {
"train": [
[0, 2, 1],
[0, 2, 1],
[0, 2, 1],
[0, 2, 1],
[0, 3, 3],
[0, 2, 1],
[0, 2, 1],
[0, 2, 1],
[0, 2, 1],
[0, 3, 3],
[0, 2, 1],
[0, 2, 1],
],
"test": [
[0, 2, 1],
[0, 3, 3],
[0, 2, 1],
[0, 3, 3],
[0, 3, 1],
[0, 2, 1],
[0, 3, 3],
[0, 2, 1],
[0, 2, 1],
[0, 3, 1],
],
},
},
}
partners = partners_dict[setting][partner_type]
if partner_type == "fixed":
train_partners = [
Partner(ArmsPartnerPolicy(perm=perm)) for perm in partners["train"]
]
test_partners = [
Partner(ArmsPartnerPolicy(perm=perm)) for perm in partners["test"]
]
if partner_type == "ppo":
exit(1)
return train_partners, test_partners
class TestPartner(unittest.TestCase):
def setUp(self):
player1 = Player("bob")
player2 = Player("sue")
self.partner = Partner(player1, player2)
def testGetPlayers(self):
players = self.partner.getPlayers()
self.assertEqual(players[0].getName(), "bob")
self.assertEqual(players[1].getName(), "sue")
def testToString(self):
strung = str(self.partner)
self.assertEqual(strung, "Partners (bob,sue)")
def to_partner(partner):
id = partner['id']
tradingName = partner['tradingName']
ownerName = partner['ownerName']
document = partner['document']
address = json.dumps(partner['address'])
coverageArea = json.dumps(partner['coverageArea'])
return Partner(
id=id,
tradingName=tradingName,
ownerName=ownerName,
document=document,
address=address,
coverageArea=coverageArea
)
from partner import Partner
from create_partner import CreatePartner
p = CreatePartner()
partner = Partner.from_dict({
'owner_name': 'MnR',
'tranding_name': 'MnR',
'document': 0,
'address': [16, 16]
})
print(' '.join([str(point) for point in partner.address]))
result = p.set_data(partner)
print(result)
def process_data(data):
country_list = []
# this nested dictionary maintains a relationship of
# countries -> dictionary of dates -> set of people available at those dates
country_dates_attendees = dict()
# put all the relationship from the data we got into our dictionary
for p in data['partners']:
person = Partner(p)
if person.country not in country_dates_attendees:
country_dates_attendees[person.country] = dict()
for date in person.dates:
if date not in country_dates_attendees[person.country]:
country_dates_attendees[person.country][date] = set()
country_dates_attendees[person.country][date].add(person)
for country_name, dates_attendees in country_dates_attendees.items():
# the dates need to be in order to check for consecutive dates
sorted_dates = sorted(dates_attendees.keys())
# variables to keep track of the dates where attendees are maximized
most_attendees_count = float('-inf')
most_attendees_day = None
most_attendees = set()
for index in xrange(len(sorted_dates[:-1])):
# raw string dates
raw_curr_date = sorted_dates[index]
raw_next_date = sorted_dates[index + 1]
# parsed into python datetime objects for easier comparison
curr_date = parse(raw_curr_date)
next_date = parse(raw_next_date)
curr_attendees = dates_attendees[raw_curr_date]
next_attendees = dates_attendees[raw_next_date]
# check if next date is consecutive if not skip to next
if next_date - curr_date != datetime.timedelta(1):
continue
# check the number of attendees that are in both dates using set intersection
attendees = curr_attendees & next_attendees
attendees_count = len(attendees)
# update the most attendees count if the count is greater
# OR if the count is the same and the current date is earlier
if attendees_count > most_attendees_count or \
(attendees_count == most_attendees_count and raw_curr_date < most_attendees_day):
most_attendees_count = attendees_count
most_attendees_day = raw_curr_date
most_attendees = attendees
# create a new country result object and add it to the country list
country = Country()
country.name = country_name
if most_attendees_count > 0:
country.start_date = most_attendees_day
for person in most_attendees:
country.add_attendee(person)
country_list.append(country)
return country_list
def test_shuffle_labels_type(self):
"""shuffle_labels should be a numpy.ndarray"""
with pytest.raises(TypeError):
part = Partner(partner_id=0)
part.shuffle_labels(part)
from flask import Flask, url_for, request
from partner import Partner
app = Flask(__name__)
manager = Partner()
@app.route('/')
def index():
return 'Welcome, Starbucks! :) '
@app.route('/get_order', methods=['POST'])
def get_order():
order_info = request.get_json()
ordered_menu = manager.take_order(order_info)
# return str(ordered_menu)
return 'total_price: {}, total_time: {}'.format(ordered_menu['price'],
ordered_menu['time'])
def setUp(self):
player1 = Player("bob")
player2 = Player("sue")
self.partner = Partner(player1, player2)
def test_corrupt_labels_type(self):
"""partner.y_train should be a numpy.ndarray"""
with pytest.raises(TypeError):
part = Partner(partner_id=0)
part.corrupt_labels()
def get_blocks_partners(setting, partner_type):
partners_dict = {
"": {
"fixed": {
"train": [
[1, 3, 0, 2], # clockwise
[2, 0, 3, 1], # counter-clockwise
[3, 2, 1, 0], # diagonal
],
"test": [
[1, 0, 3, 2],
[2, 3, 1, 0],
[3, 2, 0, 1],
[1, 2, 3, 0],
[2, 3, 0, 1],
[3, 0, 1, 2],
],
"inverttrain": [
[1, 3, 0, 2], # clockwise
[2, 0, 3, 1], # counter-clockwise
[3, 2, 1, 0], # diagonal
],
"inverttest": [
[1, 0, 3, 2],
[2, 3, 1, 0],
[3, 2, 0, 1],
],
},
"ppo": {
"train": [
"output/blocks_n=2_run=1230_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1231_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1232_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1233_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1234_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1235_vis1=1_vis2=3_onesided=0_mreg=0.00",
],
"test": [
"output/blocks_n=2_run=1240_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1241_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1242_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1243_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1244_vis1=1_vis2=3_onesided=0_mreg=0.00",
"output/blocks_n=2_run=1245_vis1=1_vis2=3_onesided=0_mreg=0.00",
]
}
}
}
partners = partners_dict[setting][partner_type]
if partner_type == "fixed":
train_partners = [
Partner(BlocksPermutationPartnerPolicy(perm=perm))
for perm in partners["train"]
]
test_partners = [
Partner(BlocksPermutationPartnerPolicy(perm=perm))
for perm in partners["test"]
]
inverttrain_partners = [
Partner(BlocksPermutationPartnerPolicy(perm=perm))
for perm in partners["inverttrain"]
]
inverttest_partners = [
Partner(BlocksPermutationPartnerPolicy(perm=perm))
for perm in partners["inverttest"]
]
if partner_type == "ppo":
train_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["train"]
]
test_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["test"]
]
inverttrain_partners = None
inverttest_partners = None
return train_partners, test_partners, inverttrain_partners, inverttest_partners
beta_IR = 0.01 beta_SR = 0.01 beta_SV = 0.01 beta_PI = 0.01 beta_IV = 0.01 beta_RV = 0.01 beta_condom = 0.000001 beta_SI2 = beta beta_II2 = 0.0 beta_RI2 = beta_IR beta_VI2 = beta_IV phi_V = phi phi_T = 0.95 population = Person.make_population(N) partner_nwk = Partner(population) filename = '' # Default file name to export (.csv). Change when use prompt 'export' cmd. mode_master_list = [] # All objects should add into mode_master_list mode01 = mode.Mode01(population, partner_nwk) mode02 = mode.Mode02(population) mode04 = mode.Mode04(population, partner_nwk) mode05 = mode.Mode05(population, partner_nwk) mode06 = mode.Mode06(population, partner_nwk) mode21 = mode.Mode21(population, partner_nwk) mode31 = mode.Mode31(population) mode51 = mode.Mode51(population, partner_nwk) mode52 = mode.Mode52(population, partner_nwk) mode_master_list = [mode01, mode02, mode04, mode05, mode06, mode21, mode31]
import time
from counter import Counter
from partner import Partner
if __name__ == "__main__":
partner = Partner("partner@localhost", "password")
partner_fut = partner.start()
partner_fut.result()
counter = Counter("counter@localhost", "password")
counter_fut = counter.start()
counter_fut.result()
while not (counter.behaviour.is_killed()
and partner.behaviour.is_killed()):
try:
time.sleep(1)
except KeyboardInterrupt:
break
counter.stop()
partner.stop()
def set_PPO_partners(self, partner_model_paths: List[str]):
self.set_partners(partners=[
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partner_model_paths
])
def get_arms_partners(setting, partner_type):
partners_dict = {
"n4m0": {
"fixed": {
"train": [
[0, 1, 2, 3],
[4, 1, 6, 7],
[0, 5, 2, 7],
[4, 5, 6, 3],
],
"test": [
[0, 1, 2, 7],
[4, 1, 6, 3],
[0, 5, 2, 3],
[4, 5, 6, 7],
],
"inverttrain": [
[0, 1, 2, 7],
[4, 1, 6, 3],
[0, 5, 2, 3],
[4, 5, 6, 7],
],
"inverttest": [
[0, 1, 2, 7],
[4, 1, 6, 3],
[0, 5, 2, 3],
[4, 5, 6, 7],
],
},
"ppo": {
"train": [
"output/arms_n=4_m=0_run=1240_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1241_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1242_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1243_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1244_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1245_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1246_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1247_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1248_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1249_netsz=030_mreg=0.00",
],
"test": [
"output/arms_n=4_m=0_run=1230_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1231_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1232_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1233_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1234_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1235_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1236_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1237_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1238_netsz=030_mreg=0.00",
"output/arms_n=4_m=0_run=1239_netsz=030_mreg=0.00",
]
}
},
"n4m1": {
"fixed": {
"train": [
[0, 1, 2, 3],
[0, 1, 6, 7],
[0, 5, 2, 7],
[0, 5, 6, 3],
],
"test": [
[0, 1, 2, 7],
[0, 1, 6, 3],
[0, 5, 2, 3],
[0, 5, 6, 7],
],
"inverttrain": [
[4, 1, 2, 3],
[4, 1, 6, 7],
[4, 5, 2, 7],
[4, 5, 6, 3],
],
"inverttest": [
[4, 1, 2, 7],
[4, 1, 6, 3],
[4, 5, 2, 3],
[4, 5, 6, 7],
],
},
"ppo": {
"train": [
"output/arms_n=4_m=1_run=1240_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1241_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1242_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1243_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1244_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1245_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1246_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1247_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1248_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1249_netsz=030_mreg=0.00",
],
"test": [
"output/arms_n=4_m=1_run=1230_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1231_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1232_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1233_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1234_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1235_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1236_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1237_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1238_netsz=030_mreg=0.00",
"output/arms_n=4_m=1_run=1239_netsz=030_mreg=0.00",
]
}
},
"n4m2": {
"fixed": {
"train": [
[0, 1, 2, 3],
[0, 1, 6, 7],
[0, 1, 2, 7],
[0, 1, 6, 3],
],
"test": [
[0, 1, 2, 7],
[0, 1, 6, 3],
[0, 1, 2, 3],
[0, 1, 6, 7],
],
"inverttrain": [
[4, 5, 2, 3],
[4, 5, 6, 7],
[4, 5, 2, 7],
[4, 5, 6, 3],
],
"inverttest": [
[4, 5, 2, 7],
[4, 5, 6, 3],
[4, 5, 2, 3],
[4, 5, 6, 7],
],
},
"ppo": {
"train": [
"output/arms_n=4_m=2_run=1240_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1241_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1242_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1243_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1244_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1245_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1246_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1247_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1248_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1249_netsz=030_mreg=0.00",
],
"test": [
"output/arms_n=4_m=2_run=1230_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1231_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1232_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1233_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1234_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1235_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1236_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1237_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1238_netsz=030_mreg=0.00",
"output/arms_n=4_m=2_run=1239_netsz=030_mreg=0.00",
]
}
},
"n4m3": {
"fixed": {
"train": [
[0, 1, 2, 3],
[0, 1, 2, 7],
[0, 1, 2, 7],
[0, 1, 2, 3],
],
"test": [
[0, 1, 2, 7],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 7],
],
"inverttrain": [
[4, 5, 6, 3],
[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 3],
],
"inverttest": [
[4, 5, 6, 7],
[4, 5, 6, 3],
[4, 5, 6, 3],
[4, 5, 6, 7],
],
},
"ppo": {
"train": [
"output/arms_n=4_m=3_run=1240_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1241_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1242_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1243_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1244_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1245_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1246_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1247_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1248_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1249_netsz=030_mreg=0.00",
],
"test": [
"output/arms_n=4_m=3_run=1230_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1231_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1232_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1233_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1234_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1235_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1236_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1237_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1238_netsz=030_mreg=0.00",
"output/arms_n=4_m=3_run=1239_netsz=030_mreg=0.00",
]
}
},
"n4m4": {
"fixed": {
"train": [
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
],
"test": [
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
[0, 1, 2, 3],
],
"inverttrain": [
[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 7],
],
"inverttest": [
[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 7],
[4, 5, 6, 7],
],
},
"ppo": {
"train": [
"output/arms_n=4_m=4_run=1240_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1241_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1242_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1243_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1244_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1245_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1246_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1247_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1248_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1249_netsz=030_mreg=0.00",
],
"test": [
"output/arms_n=4_m=4_run=1230_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1231_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1232_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1233_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1234_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1235_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1236_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1237_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1238_netsz=030_mreg=0.00",
"output/arms_n=4_m=4_run=1239_netsz=030_mreg=0.00",
]
}
},
}
partners = partners_dict[setting][partner_type]
if partner_type == "fixed":
train_partners = [
Partner(ArmsPartnerPolicy(perm=perm)) for perm in partners["train"]
]
test_partners = [
Partner(ArmsPartnerPolicy(perm=perm)) for perm in partners["test"]
]
inverttrain_partners = [
Partner(ArmsPartnerPolicy(perm=perm))
for perm in partners["inverttrain"]
]
inverttest_partners = [
Partner(ArmsPartnerPolicy(perm=perm))
for perm in partners["inverttest"]
]
if partner_type == "ppo":
train_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["train"]
]
test_partners = [
Partner(PPOPartnerPolicy(pmpath)) for pmpath in partners["test"]
]
inverttrain_partners = None
inverttest_partners = None
return train_partners, test_partners, inverttrain_partners, inverttest_partners
if up:
flags += 'u'
if down:
flags += 'd'
print(flags)
pygame.init()
screen = pygame.display.set_mode([SCREEN_WIDTH, SCREEN_HEIGHT])
pygame.display.set_caption('Player Test')
all_sprites = pygame.sprite.Group()
player = Player(100, 50)
partner = Partner(50, 50, player)
#add line about player.walls later
all_sprites.add(player)
all_sprites.add(partner)
clock = pygame.time.Clock()
done = False
while not done:
for event in pygame.event.get():
if event.type == pygame.QUIT:
done = True
elif event.type == pygame.KEYDOWN:
if event.key == pygame.K_LEFT:
if not right and not up and not down:
def __init__(self, machine_id, database, ui):
Partner.__init__(self, ui)
self.machine_id = machine_id
self.database = database
self.text_format = XMLFormat()
self.server_info = {}
