def main():
# test_shuffle()
# test_shuffle_elimination(directions=[F, L, L, L, L, F])
# test_shuffle_elimination(directions=[F, L, L, L, L])
# test_shuffle_elimination(directions=[L, L, L, L])
# test_shuffle_elimination(directions=[L, L, L, L, F])
# test_shuffle_elimination(directions=[F, B, L, L, L, L, L, R])
# test_shuffle_elimination(directions=[F, B, L, L_, B_])
# for i in range(0, 100000):
# print i
# validate_shuffle_elimination()
cube = Cube()
game = Game(cube)
game.shuffle(min_step=3, max_step=3)
game.export_to_file(file_path='./sample_file.dat')
print 'Full step'
game.print_shuffle_full_steps()
print 'Step'
game.print_shuffle_steps()
with open('./sample_file.dat', 'r') as fp:
is_eof = False
while True:
step_sample = []
for i in range(0, CUBE_DATA_LEN):
data = fp.read(4)
if '' == data:
is_eof = True
else:
step_sample.append(data)
if is_eof:
break
print create_Sample_from_buffer(step_sample)
def generate_shuffle_bayesian():
for i in range(0, 10000):
c = Cube()
game = Game(c)
game.shuffle(min_step=10, max_step=1024)
for step in game.shuffle_steps:
bayesian.add_feature_and_decision(step.feature_string, DIRECTIONS[-step.direction + DIRECTION_OFFSET])
if i % 100 == 0:
print i
bayesian.export('bayesiantest.yaml')
def test_shuffle_elimination(directions):
cube = Cube()
game = Game(cube)
game.shuffle(directions=directions)
print '============= FULL STEPS =============='
game.print_shuffle_full_steps()
print '============= ELIMINATED STEPS =============='
game.print_shuffle_steps()
def test_shuffle():
cube = Cube()
game = Game(cube)
game.shuffle()
step = len(game.shuffle_full_steps)
print '=========== initialized with %s-step shuffle ============' % step
cube.show()
print ''
print 'Feature string is ', cube.calculate_feature_string()
print 'Is standard? ', cube.check()
command = raw_input("Command: ").upper()
while command != 'Q':
if command not in command_map:
print("Invalid input. Input again.")
continue
cube.transform(command_map[command])
print '=========== %s ============' % command
print ''
cube.show()
print ''
command = raw_input("Command: ").upper()
def generate_file(file_path, data_num, min_step, max_step):
with open(file_path, 'w') as fp:
for i in range(0, data_num):
c = Cube()
game = Game(c)
game.shuffle(min_step=min_step, max_step=max_step)
game.export_to_file_point(fp)
print '%s generate %i' % (file_path, i)
def validate_shuffle_elimination():
cube = Cube()
game = Game(cube)
game.shuffle()
# print '============= FULL STEPS =============='
# game.print_shuffle_full_steps()
# print '============= ELIMINATED STEPS =============='
# game.print_shuffle_steps()
print 'Full steps: ', len(game.shuffle_full_steps)
print 'Steps: ', len(game.shuffle_steps)
directions = [-step.direction for step in game.shuffle_steps]
directions.reverse()
# print [DIRECTIONS[direction + DIRECTION_OFFSET] for direction in directions]
game.play(directions)
assert game.cube.check()
print 'Validation PASS!'
return True
def test_train_and_solve():
cube = Cube()
game = Game(cube)
game.shuffle()
step = len(game.shuffle_full_steps)
print '=========== initialized with %s-step shuffle ============' % step
cube.show()
print 'Solving . . . . . . '
game.solve()
cube.show()
if game.is_solved:
print 'Congratulations!!!! Solved after %s steps!' % len(
game.solve_steps)
print(
'Steps: %s' % ''.join([
DIRECTIONS[step.direction + DIRECTION_OFFSET]
for step in game.solve_steps
]))
else:
print 'Stupid!!!! Cannot solve a cube in %s steps!' % len(
game.solve_steps)
def test_shuffle_bayesian():
cube = Cube()
game = Game(cube)
game.shuffle(min_step=3, max_step=100)
for step in game.shuffle_steps:
bayesian.add_feature_and_decision(step.feature_string, DIRECTIONS[-step.direction + DIRECTION_OFFSET])
cube = Cube()
game = Game(cube)
game.shuffle(min_step=3, max_step=100)
for step in game.shuffle_steps:
bayesian.add_feature_and_decision(step.feature_string, DIRECTIONS[-step.direction + DIRECTION_OFFSET])
cube = Cube()
game = Game(cube)
game.shuffle(min_step=3, max_step=100)
for step in game.shuffle_steps:
bayesian.add_feature_and_decision(step.feature_string, DIRECTIONS[-step.direction + DIRECTION_OFFSET])
print_possibility()
bayesian.export('bayesiantest.yaml')
bayesian.load('bayesiantest.yaml')
print_possibility()