def main(args):
states = read_states(args.input)
stats = Stats(sys.argv)
cipher = Spritz()
settings = Settings(args)
prompt_step = max(1, len(states) // 20)
i = 0
for initial_state, revealed_state, prefix_length in states:
if args.verbosity > 1 and i % prompt_step == 0:
print('test #:', i)
i += 1
KNOWN_KEYSTREAM_SIZE = 3 * initial_state.size
cipher.initialize_state(initial_state.state)
known_keystream = cipher.keystream(prefix_length + KNOWN_KEYSTREAM_SIZE)
settings.prefix_length = prefix_length
# in case we want to skip less keystream than revealed_state is
# generate in, we cut off beginning of keystream and move
# initial_state apropriatelly
if args.input and args.force_prefix_length and args.force_prefix_length < prefix_length:
new_prefix_length = args.force_prefix_length
new_start_offset = prefix_length - new_prefix_length
settings.prefix_length = new_prefix_length
known_keystream = known_keystream[new_start_offset:]
cipher.initialize_state(initial_state.state)
cipher.keystream(new_start_offset)
initial_state = SpritzState(cipher.state)
cipher.initialize_state(initial_state.state)
cipher.keystream(prefix_length)
found_state, round_stats = backtrack.kpa(
known_keystream,
revealed_state,
settings,
)
if found_state and initial_state != found_state:
print('incorrect result, this should not happen')
assert False
stats.add(round_stats)
stats.print_stats(args.verbosity)
# dump pickled stats object
if not args.no_stats_log:
timestamp = datetime.datetime.today().strftime('%y%m%d_%H%M%S_%f')
os.makedirs('stats/', exist_ok=True)
with open('stats/' + timestamp, 'wb') as f:
pickle.dump(stats, f)
class Casino:
NO_ITERATIONS = 1000
NO_GENERATIONS = 50
TEST_ITERATIONS = 100
INITIAL_ITERATIONS = 1000
DATA_LEN = 1000
pop = None
rng = 0
KEYS = ["-training", "-pop_size", "-initializion_size"]
PAYOUTS = {5: 24.6250, 6: 19.7, 7: 16.4166, 8: 14.0714, 9: 12.3125, 10: 10.9444, 11: 9.8500, 12: 8.9545, 13: 8.2083, 14: 7.5769, 15: 7.0357, 16: 6.5666, 17: 6.1562, 18: 5.7941, 19: 5.4722, 20: 5.1842, 21: 4.9250, 22: 4.6904, 23: 4.4772, 24: 4.2826, 25: 4.1041, 26: 3.9400, 27: 3.7884, 28: 3.6481, 29: 3.5178, 30: 3.3965, 31: 3.2833, 32: 3.1774, 33: 3.0781, 34: 2.9848, 35: 2.8970, 36: 2.8142, 37: 2.7361, 38: 2.6621, 39: 2.5921, 40: 2.5256, 41: 2.4625, 42: 2.4024, 43: 2.3452, 44: 2.2906, 45: 2.2386, 46: 2.1888, 47: 2.1413, 48: 2.0957, 49: 2.0520, 50: 2.0102,
51: 1.9700, 52: 1.9213, 53: 1.8942, 54: 1.8584, 55: 1.8240, 56: 1.7909, 57: 1.7589, 58: 1.7280, 59: 1.6982, 60: 1.6694, 61: 1.6416, 62: 1.6147, 63: 1.5887, 64: 1.5634, 65: 1.5390, 66: 1.5153, 67: 1.4924, 68: 1.4701, 69: 1.4485, 70: 1.4275, 71: 1.4071, 72: 1.3873, 73: 1.3680, 74: 1.3493, 75: 1.3310, 76: 1.3133, 77: 1.2960, 78: 1.2792, 79: 1.2628, 80: 1.2468, 81: 1.2312, 82: 1.2160, 83: 1.2012, 84: 1.1867, 85: 1.1726, 86: 1.1588, 87: 1.1453, 88: 1.1321, 89: 1.1193, 90: 1.1067, 91: 1.0944, 92: 1.0824, 93: 1.0706, 94: 1.0591, 95: 1.0478, 96: 1.0368}
def __init__(self, args):
definitions = self.definitions(args)
self.execute(definitions)
def definitions(self, args):
definitions = []
if args.training == None:
training = ""
while training not in ["t", "e"]:
training = input("enter t for training or e for examination/test")
training = training == "t"
else:
training = args.training
if args.initialized == None:
num_ais = -1
while num_ais < 0 or num_ais > 10:
num_ais = int(input("how many ais you want to already be initalized"))
else:
initialized = args.initialized
if args.pop_size == None:
pop_size = -1
while pop_size < 0 or pop_size > 100:
pop_size = int(input("How many ais the population should be made of"))
else:
pop_size = args.pop_size
return [training, initialized, pop_size]
def execute(self, definitions):
training, initialized, pop_size = definitions
self.pop = Population(save_size = initialized, population_size=pop_size)
self.setupInitialState()
if training:
self.runTraining()
else:
self.runTest()
self.results()
def setupInitialState(self):
self.data_results = Stats(self.DATA_LEN)
print('Setting initial state')
for _ in range(self.INITIAL_ITERATIONS):
self.getRandom()
self.data_results.add(self.rng)
print('Initial state set')
def runTraining(self):
for i in range(self.NO_GENERATIONS):
print('Generation', i+1 )
self.runGeneration()
self.pop.savePop()
def runTest(self):
for _ in range(self.TEST_ITERATIONS):
self.runAis()
self.pop.fitness()
def runGeneration(self):
for _ in range(self.NO_ITERATIONS):
self.runAis()
self.pop.updateAis()
def runAis(self):
self.getRandom()
self.updateStats()
self.game_state = self.getGameState()
'''
#Multiprocessing
pool = mp.Pool(mp.cpu_count())
pool.map_async(self.handleAiBet, [ai for ai in self.pop.population])
pool.close()
pool.join()
'''
#No multiprocessing
self.handleAllBet()
def handleAllBet(self):
for ai in self.pop.population:
self.handleAiBet(ai)
def handleAiBet(self, ai):
bet = ai.askForBet(self.game_state[:])
profit = self.decideBet(bet)
ai.betResult(profit)
def decideBet(self, bet):
number = bet[0]
wage = bet[1]
lower = bet[2]
if lower > 0: # We win if obtain a lower number
return -wage if number >= self.rng else (self.PAYOUTS[number]-1) * wage
return -wage if number <= self.rng else (self.PAYOUTS[number] - 1) * wage
def getRandom(self):
self.rng = random.randint(1, 100)
def getGameState(self):
return [self.data_results.getAverage(), self.data_results.getStdev(), self.data_results.getSkew()]
def updateStats(self):
self.data_results.add(self.rng)
# Sets of stats that are kept about the progress of the chain of RNGs
def results(self):
print("Finished execution")
print([x.balance for x in self.pop.population])
#input()
#print([x.record_number for x in self.pop.population])
#input()
#print([x.record_amount for x in self.pop.population])
plt.plot([i for i in range(len(self.pop.population[0].record_balance))], np.transpose([x.record_balance for x in self.pop.population]))
plt.show()
input("Enter any string to continue")
class GameAi(object):
layers = [6, 10, 10, 3]
DATA_LEN = 20
def __init__(self, model=None):
if model is None:
self.nn = self.model()
else:
self.nn = model
self.getLayers()
self.fitness = 0
self.reset()
def reset(self):
#Specific to ai game state data
self.prediction = None
self.balance = 0
#To calculate fitness
self.game_state = None
#Stats (Used to prediction)
self.wins = []
self.bet_amount = Stats(20)
self.bet_number = Stats(20)
self.losses_in_a_row = 0
#Stats (Used just for debug)
self.record_number = []
self.record_amount = []
self.record_balance = []
def model(self):
"returns a neural network for training"
inputs = Input(shape=[self.layers[0]], name='input')
network = Dense(self.layers[1], activation='sigmoid')(inputs)
for i in range(2, len(self.layers) - 1):
network = Dense(self.layers[i], activation='sigmoid')(network)
#Output layer: Number, Wage
network = Dense(self.layers[len(self.layers) - 1],
activation='linear')(network)
model = Model(inputs=inputs, outputs=network)
opt = Adam(0.01)
model.compile(loss='mean_squared_error',
optimizer=opt,
metrics=['mse'])
return model
def betResult(self, profit):
self.balance += profit
self.record_balance.append(self.balance)
if profit < 0:
self.losses_in_a_row += 1
else:
self.losses_in_a_row = 0
pass
#deal with bet result
def askForBet(self, game_state):
bet_state = self.getBetState(game_state)
bet_state = np.array([bet_state])
self.prediction = self.nn.predict(bet_state)
#predition = [number, wage, if we are betting the number will be lower]
#self.prediction.append(1) #for now, the ai only decides number and wage
number = abs(self.prediction[0][0] * 100)
number = 5 if number < 5 else 96 if number > 96 else number
number = round(number)
wage = abs(self.prediction[0][1])
lower = self.prediction[0][2]
self.bet_amount.add(wage)
self.bet_number.add(number)
self.record_amount.append(wage)
self.record_number.append(number)
return [number, wage, lower]
def getBetState(self, game_state):
game_state.extend([
self.losses_in_a_row,
self.bet_amount.getAverage(),
self.bet_number.getStdev()
])
return game_state
def reproduce(self, ai):
new_weights = []
for x in range(len(self.weights)):
new_weights.append(
self.weights[x] if random.random() > 0.5 else ai.weights[x])
#Randomizes betweeen a weight from this ai or from the one it is beeing reproduced with
return new_weights
def decompressWeights(self):
weights = self.nn.get_weights()
new_weights = []
for x in range(len(weights)):
if (x % 2 == 0):
layer = weights[x]
for node in layer:
for conn in node:
new_weights.append(conn)
self.weights = new_weights
def compressWeights(self, weights):
counter = 0
new_weights = []
for i in range(len(self.layers) - 1):
layer_weights = []
for j in range(self.layers[i]):
node_weights = []
for k in range(self.layers[i + 1]):
node_weights.append(weights[counter])
counter += 1
layer_weights.append(node_weights)
new_weights.append(np.array(layer_weights))
new_weights.append(np.zeros(self.layers[i + 1]))
return new_weights
def setWeights(self, new_weights):
compressed_weights = self.compressWeights(new_weights)
self.nn.set_weights(compressed_weights)
def getLayers(self):
weights = self.nn.get_weights()
layers = []
layers.append(len(weights[0]))
for x in range(len(weights)):
if x % 2 == 0:
layers.append(np.size(weights[x][0]))
print(layers)
