def __init__(self, iterations=5):
results = []
situations = []
logging.basicConfig()
for i in range(0, iterations):
g = Game(print_board=False)
round_situations = []
while not g.game_over:
choices = g.available_cols()
choice = random.choice(choices)
round_situations.append(self.game_to_sit(g, choice))
g.place_piece(choice)
for situation in round_situations:
results.append(g.points)
situations.extend(round_situations)
#self.pipeline = Pipeline([
# ('min/max scaler', MinMaxScaler(feature_range=(0.0, 1.0))),
# ('neural network', Regressor(
self.nn = Regressor(layers=[
Layer("Rectifier", units=100),
Layer("Linear")],
learning_rate=0.00002,
n_iter=10)
#self.pipeline.fit(np.array(situations), np.array(results))
print np.array(situations).shape
self.nn.fit(np.array(situations), np.array(results))
def naive_scores(iterations):
scores = []
for i in range(iterations):
g = Game(print_board=False, classic_mode=False)
while not g.game_over:
choices = g.available_cols()
choice = random.choice(choices)
g.place_piece(choice)
scores.append(g.points)
return max(scores), sum(scores)/len(scores)