def ask_the_oracle():
o = oracle.Oracle()
a = acolyte.Acolyte()
while True:
answer = 0
print("Ask the Oracle a [L]ikely, [N]eutral, or [U]likely Question")
print("Seek a [P]ortent or consult the [T]WENE")
print("or [D]epart in peace.")
print("\n")
inquiry = input('What knowledge do you seek: ')
if inquiry == 'L' or inquiry == 'l':
print("The Oracle responds to your LIKELY inquiry: ")
answer = o.ask("Advantage")
print(str(a.interpret(answer)) + '\n')
elif inquiry == 'N' or inquiry == 'n':
print("The Oracle responds to your NEUTRAL inquiry: ")
answer = o.ask('N')
print("\033[31m" + str(a.interpret(answer)) + "\033[37m" + "\n")
elif inquiry == 'U' or inquiry == 'u':
print("The Oracle responds to your UNLIKELY inquiry: ")
answer = o.ask("Disadvantage")
print(str(a.interpret(answer)) + '\n')
elif inquiry == 'P' or inquiry == 'p':
print("PORTENT")
elif inquiry == 'T' or inquiry == 't':
print("TWENE")
elif inquiry == 'D' or inquiry == 'd':
print("May Wisdom light your footsteps Adventurer!")
return False
else:
print(
"The Acolyte is uncertain how to explain your inquiry. Try again."
)
def main():
#assert(b64encode(encrypt(IV, pad(flag))) == b"XmmSv7+azqHCSPwBYfsVKVoqq+NpOaWrRHOYlLn3GlRAg4kdAVmEdc5L9koCHcxl5U0Ee28wMqTNdZYzd/BOaynUpmthknT0QdVGLXpx5Oko7QiK7+I0UVFhi8MP0+YFigbKhXMGzuv7ySqhnakeaRhaRGjRvVShMmjL0vitvuw=")
cipher = b64encode(encrypt(IV, pad(flag)))
out = out_message(IV, pad(flag))
o = oracle.Oracle(IV, cipher, 'YES', 'NO')
o.setGuessfunc(checkmsg)
print('plain = '+o.attack())
def oracle_learn():
state = HexapawnState(5)
red = oracle.Oracle()
blue = oracle.Oracle()
for i in range(100):
while not state.check_gameover():
move = red.consult(state)
try:
state.make_move(move)
state.alternate_turn()
except oracle.GameoverException:
state.winner = label.BLUE
red.loss(state, move)
break
if state.check_gameover():
break
move = blue.consult(state)
try:
state.make_move(move)
state.alternate_turn()
except oracle.GameoverException:
state.winner = label.RED
blue.loss(state, move)
break
if state.winner == label.RED:
print('Winner is RED player')
else:
print('Winner is BLUE player')
state.draw_board()
for state, moves in red.learned.items():
print(state, moves, os.linesep)
for state, moves in blue.learned.items():
print(state, moves, os.linesep)
def factory(**connection):
"""
фабрика для инстанса бд
:param connection: **dict
:return: instance :raise ValueError:
"""
conn_type = int(connection.get('conn_type', ''))
del connection['conn_type']
if conn_type == ConnectionChoices.POSTGRESQL:
return postgresql.Postgresql(connection)
elif conn_type == ConnectionChoices.MYSQL:
return mysql.Mysql(connection)
elif conn_type == ConnectionChoices.MS_SQL:
import mssql
return mssql.MsSql(connection)
elif conn_type == ConnectionChoices.ORACLE:
import oracle
return oracle.Oracle(connection)
else:
raise ValueError("Неизвестный тип подключения!")
def __init__(self, regions, ua, debug=False):
"""
Provides interactive Oracle functionality. This can be used to create bots and user interfaces.
:param regions: Path to NationStates regional data dump. Can be a string or file object.
:param ua: User agent string that identifies the operator, as required by NS TOS
:return:
"""
self.debug = debug
self.regions = regions
self.ua = ua
self.oracle = oracle.Oracle(regions, ua)
self.time_now = datetime.datetime.utcnow().replace(tzinfo=UTC())
self.time_base = datetime.datetime.utcnow().replace(tzinfo=UTC(),
hour=0,
minute=0,
second=0,
microsecond=0)
# determine start time of closest update
# 16h = minor
# 4h = major
# easy: anything less than 16h is major (we don't really care about weird exception cases anyhow)
if self.time_now < self.time_base + datetime.timedelta(hours=16):
self.mode = "major"
else:
self.mode = "minor"
self.tracking = False
self.target = ""
self.runner = threading.Thread(target=self._runner, daemon=True)
self.runner.start()
self.log = []
def main():
np.seterr(divide='ignore', invalid='ignore') #ignore divide by 0 warnings
orcl = oracle.Oracle("data4.npz")
print("loaded:", orcl.get_filename(), "gamma = ", orcl.get_gamma())
num_iterations = 20
lower_limit_eta = 1
step = 5
upper_limit_eta = min(
int(orcl.get_num_samples() / orcl.get_num_clusters()), 50)
xs = []
ys = []
for eta in range(lower_limit_eta, upper_limit_eta, step):
success = 0
total_queries = 0
total_cost = 0
for i in range(num_iterations):
predicted_clustering, queries = clusterise(orcl, eta)
total_queries += queries
if orcl.check_predicted_clustering(predicted_clustering):
success += 1
total_cost += find_cost(orcl, predicted_clustering)
average_queries = total_queries / num_iterations
average_cost = total_cost / num_iterations
xs.append(average_queries)
ys.append(average_cost / orcl.get_cost())
print("eta = ", eta, ", success rate:", success, "/", num_iterations,
"av. cost = ", average_cost, ", av. queries = ", average_queries,
", av.cost/oracle_cost = ", average_cost / orcl.get_cost())
#Plot the graph of cost relative to underlying clustering vs number of queries taken
plt.xlabel('num_queries')
plt.ylabel('predicted_cluster_cost/oracle_cluster_cost')
plt.ylim([0.99, 1.01])
plt.plot(xs, ys)
plt.savefig('graphs/dataset4.png')
try:
configfilename = sys.argv[1]
except IndexError:
sys.stderr.write("usage: trainer_slave.py config-file [options...]\n")
sys.exit(1)
if log.level >= 1:
log.write("Reading configuration from %s\n" % configfilename)
execfile(configfilename)
opts, args = optparser.parse_args(args=sys.argv[2:])
maxmargin.watch_features = watch_features
theoracle = oracle.Oracle(order=4,
variant=opts.bleuvariant,
oracledoc_size=10)
thedecoder = make_decoder()
thelearner = Learner()
weight_stack = []
if log.level >= 1:
gc.collect()
log.write("all structures loaded, memory=%s\n" % (monitor.memory()))
comm = MPI.Comm.Get_parent()
log.prefix = '[%s] ' % (comm.Get_rank(), )
instances = []
while True:
msg = comm.recv()
import os
import oracle
from ucca import diffutil, ioutil, textutil, layer1, evaluation
from pdb import set_trace
files = [
'../ucca_corpus_pickle/' + f for f in os.listdir('../ucca_corpus_pickle')
]
passages = list(ioutil.read_files_and_dirs(files))
passage = passages[0]
ora = oracle.Oracle(passage)
set_trace()
def make_oracle():
return oracle.Oracle(order=4, variant=opts.bleuvariant, oracledoc_size=10)
def qk_means(mixture, numOracles, numClusters, qubitStringLen, qGenerations,
dim, timeDB=False, earlyStop=0):
# matrix for fitness evolution;
# +1 for the column that indicates the best score in each gen
fitnessEvolution = np.zeros((qGenerations,numOracles+1))
if earlyStop != 0:
useEarlyStop=True
earlyStopCounter=0
else:
useEarlyStop=False
if timeDB:
db_timings = list() #timing list for Davies-Bouldin index computation
qk_timings_cg = list()
start = datetime.now()
best = 0 #index of best oracle (starts at 0)
oras = list()
qk_centroids = [0]*numOracles
qk_estimator = [0]*numOracles
qk_assignment = [0]*numOracles
for i in range(0,numOracles):
oras.append(oracle.Oracle())
oras[i].initialization(numClusters*dim,qubitStringLen)
oras[i].collapse()
qk_timings_cg.append((datetime.now() - start).total_seconds())
start = datetime.now()
for qGen_ in range(0,qGenerations):
## Clustering step
for i,ora in enumerate(oras):
if qGen_ != 0 and i == best: # current best shouldn't be modified
continue
qk_centroids[i] = np.vstack(np.hsplit(ora.getIntArrays(),numClusters))
qk_estimator[i] = KMeans(n_clusters=numClusters,init=qk_centroids[i],n_init=1)
qk_assignment[i] = qk_estimator[i].fit_predict(mixture)
qk_centroids[i] = qk_estimator[i].cluster_centers_
ora.setIntArrays(np.concatenate(qk_centroids[i]))
## Compute fitness
# start DB timing
if timeDB:
db_start = datetime.now()
# compute DB
score = DaviesBouldin.DaviesBouldin(mixture,qk_centroids[i],qk_assignment[i])
ora.score = score.eval()
# save timing
if timeDB:
db_timings.append((datetime.now() - db_start).total_seconds())
## Store best from this generation
for i in range(1,numOracles):
if oras[i].score < oras[best].score:
best = i
## Quantum Rotation Gate
for i in range(0,numOracles):
if i == best:
continue
oras[i].QuantumGateStep(oras[best])
## Collapse qubits
oras[i].collapse()
qk_timings_cg.append((datetime.now() - start).total_seconds())
for i in range(0,numOracles):
fitnessEvolution[qGen_,i]=oras[i].score
fitnessEvolution[qGen_,-1]=best
# check early stop
if useEarlyStop:
# increment counter if this generetion's fitness is the same as last
if oras[best].score == fitnessEvolution[qGen_,fitnessEvolution[qGen_-1,-1]]:
earlyStopCounter += 1
else:
earlyStopCounter == 0
if earlyStopCounter == earlyStop:
break
start = datetime.now()
# delete empty rows on fitness matrix
emptyRows=range(qGen_+1,qGenerations)
fitnessEvolution=np.delete(fitnessEvolution,emptyRows,axis=0)
if timeDB:
return qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg,db_timings
return qk_centroids,qk_assignment,fitnessEvolution,qk_timings_cg
import oracle as o
import vueworks as vw
v = vw.VUEWorks()
O = o.Oracle()
for x in O.incidents['items']:
invw = v.checkVUEWorks(x['id'])
if not invw['in']:
incident = O.getIncidentData(x['id'])
if incident["customFields"]["c"]["latitude"] is not None and incident[
"customFields"]["c"]["longitude"] is not None:
result = v.createSR(x['id'],
incident["customFields"]["c"]["latitude"],
incident["customFields"]["c"]["longitude"])
print "created Service Request: " + result
else:
print "In VUEWorks already"
import sym
import svector
import sgml
# usage: hope.py <forest> <source> <ref>+
optparser = optparse.OptionParser()
optparser.add_option("-w", "--weights", dest="weights", help="weights")
opts, args = optparser.parse_args()
# Feature weights
weights = svector.Vector("lm1=0.1 gt_prob=0.1")
if opts.weights:
weights = svector.Vector(opts.weights)
theoracle = oracle.Oracle(4, variant="ibm")
srcfilename = args[1]
forestfilename = args[0]
reffilenames = args[2:]
srcfile = open(srcfilename)
forestfile = open(forestfilename) if forestfilename != "-" else sys.stdin
reffiles = [open(reffilename) for reffilename in reffilenames]
def output(f):
deriv = f.viterbi_deriv()
hypv = deriv.vector()
hyp = deriv.english()
return "hyp={{{%s}}} derivation={{{%s}}} %s" % (" ".join(sym.tostring(e) for e in hyp), deriv, hypv)
import json, discord, os, string, re, random, hashlib, unicodedata, urllib.request
import fetcher, oracle, config
# Globals
legal_cards = []
client = discord.Client()
config = config.Config()
oracle = oracle.Oracle()
def init():
update_legality()
client.run(config.get("token"))
def update_legality():
global legal_cards
legal_cards = fetcher.Fetcher().legal_cards()
print("Legal cards: {0}".format(str(len(legal_cards))))
def normalize_filename(str_input):
# Remove spaces
str_input = '-'.join(str_input.split(' ')).lower()
# Remove Pipes
str_input = '-'.join(str_input.split('|')).lower()
# Remove nasty accented characters.
return ''.join((c for c in unicodedata.normalize('NFD', str_input)
if unicodedata.category(c) != 'Mn'))