def start(board, window, top, algorithm):
# tracker for when algorithm is finished
done = False
while True:
top.update_idletasks()
top.update()
# start time
start = time.time()
# reset iterations
game.iterations = 0
# call appropriate algorithm based on user input
if algorithm.lower() == 'reverse backtracking':
game.solve(board, window, top, start, reverse=True)
elif algorithm.lower() == 'best first search':
game.solve(board, window, top, start, optimized=True)
elif algorithm.lower() == 'algorithm x':
for solution in game.solve_Algorithm_X(board, window, top):
pass
else:
game.solve(board, window, top, start)
# once algorithm finishes, update the time taken and number of iterations
if not done:
window.redraw(board,
-1,
-1,
game.iterations,
round(time.time() - start, 10),
done=True)
done = True
def place(self, val):
row, col = self.selected
if self.cubes[row][col].value == 0:
self.cubes[row][col].set(val)
self.update_model()
if valid(self.model, val, (row, col)) and solve(self.model):
return True
else:
self.cubes[row][col].set(0)
self.cubes[row][col].set_temp(0)
self.update_model()
return False
def test_solve(n, expect):
assert expect == solve(n)
import ce
import game
import loss
import numpy
import routing
import sample
import sgd
N = 4 # number of players
T = 100000 # SGD iterations
M = 100 # samples of play
eps = 0.01 # multinomial smoothing
C = 0.01 # CE max welfare coefficient
c = C * numpy.ones(N)
w = numpy.array([0.0, 0.0, 0.0, 1.0]) # true utility function
g = routing.create(N)
truth = ce.solve(g, c, w, sgd.create(T, 1, 0, 0, sgd.Rpprox))
demon = sample.draw(M, truth)
inst = game.to_instance((g, demon))
w = game.solve(inst, sgd.create(T, 1, 0, 0))
pred = game.predict(inst, w)
print(('multinomial loss', loss.logloss(truth, demon, eps)))
print(('ice loss', loss.logloss(truth, pred)))
print(('truth entropy', loss.logloss(truth, truth)))
print 'Global dealine:', deadline
seed_deadline = None
fnum = 0
for fn in args.f:
if witht:
t = time.time()
file_deadline = t + ((deadline - t) / (len(args.f) - fnum))
if pdl:
print fn, 'deadline:', file_deadline
snum = 0
for seeds, game in konstruckt(fn, args.nodebug):
if witht:
t = time.time()
seed_deadline = t + ((file_deadline - t) / (seeds - snum))
if pdl:
print fn, 'seed', game.lcg.seed, 'deadline:', seed_deadline
s = game.solve(seed_deadline)
sols.append({'problemId': game.id, 'seed': game.lcg.seed, 'tag': curTag + '-' + str(game.id) + '-' + str(game.lcg.seed) + '-' + str(time.time()), 'solution': s})
snum += 1
fnum += 1
print json.dumps(sols)
if not args.nosave:
fn = 'solutions/solution_' + str(game.id) + '.json'
f = open(fn, 'w+')
f.write(json.dumps(sols))
height = 5
initial = L(B(G(1, 3), G(2, 3), G(1, 4), G(2, 4), cap='曹'),
B(G(0, 3), cap='董'), B(G(0, 2), G(0, 1), G(1, 2), cap='刘'),
B(G(3, 2), G(4, 2), cap='吕'), B(G(3, 3), cap='陶'),
B(G(4, 3), G(3, 4), G(4, 4), cap='袁'),
B(G(3, 0), G(4, 1), G(4, 0), cap='孙'),
B(G(1, 0), G(1, 1), cap='晋'), B(G(0, 4), cap='马'))
class Game99(Game):
title = '逗逼三国'
width = 5
height = 5
initial = L(
B(G(1, 3), G(2, 3), G(1, 4), G(2, 4), cap='曹'),
B(G(0, 2), G(0, 3), G(1, 2), G(1, 1), cap='刘'),
B(G(3, 1), G(3, 2), G(4, 1), G(2, 1), G(3, 0), cap='孙'),
# B(G(4, 3), cap='袁')
)
if __name__ == '__main__':
import sys
num = sys.argv[1]
gm_cls = locals()['Game' + str(num)]
solve(gm_cls())
def test_two(test_input, expected, capsys):
solve(test_input)
captured = capsys.readouterr()
assert captured.out.strip() == expected
import ce import game import loss import numpy import routing import sample import sgd N = 4 # number of players T = 100000 # SGD iterations M = 100 # samples of play eps = 0.01 # multinomial smoothing C = 0.01 # CE max welfare coefficient c = C*numpy.ones(N); w = numpy.array([0.0,0.0,0.0,1.0]) # true utility function g = routing.create(N) truth = ce.solve(g, c, w, sgd.create(T, 1, 0, 0, sgd.Rpprox)) demon = sample.draw(M, truth) inst = game.to_instance((g,demon)) w = game.solve(inst, sgd.create(T, 1, 0, 0)) pred = game.predict(inst, w) print 'multinomial loss', loss.logloss(truth, demon, eps) print 'ice loss', loss.logloss(truth, pred) print 'truth entropy', loss.logloss(truth, truth)
