All board reps should now work as input.

This commit is contained in:
Alexander Munch-Hansen 2018-05-10 10:49:25 +02:00
parent 9cfdd7e2b2
commit f2a67ca92e
3 changed files with 9 additions and 61 deletions

View File

@ -40,7 +40,7 @@ class Board:
def board_features_quack(board, player):
board = list(board)
board += ([1, 0] if np.sign(player) > 0 else [0, 1])
return np.array(board).reshape(1, -1)
return np.array(board).reshape(1,28)
# quack-fat
@staticmethod
@ -51,7 +51,7 @@ class Board:
board.append( 15 - sum(positives))
board.append(-15 - sum(negatives))
board += ([1, 0] if np.sign(player) > 0 else [0, 1])
return np.array(board).reshape(1,-1)
return np.array(board).reshape(1,30)
# quack-fatter
@ -68,7 +68,7 @@ class Board:
board.append(15 - sum(positives))
board.append(-15 - sum(negatives))
board += ([1, 0] if np.sign(player) > 0 else [0, 1])
return np.array(board).reshape(1, -1)
return np.array(board).reshape(1,30)
# tesauro
@staticmethod
@ -124,9 +124,9 @@ class Board:
# Calculate how many pieces there must be in the home state and divide it by 15
features.append((15 - sum) / 15)
features += ([1,0] if np.sign(cur_player) > 0 else [0,1])
test = np.array(features).reshape(1,-1)
test = np.array(features)
#print("TEST:",test)
return test
return test.reshape(1,198)

View File

@ -183,8 +183,7 @@ class Network:
legal_states = [list(tmp) for tmp in legal_moves]
legal_states = np.array([Board.board_features_quack_fat(tmp, player)[0] for tmp in legal_states])
legal_states = np.array([self.board_trans_func(tmp, player)[0] for tmp in legal_states])
scores = self.model.predict_on_batch(legal_states)
transformed_scores = [x if np.sign(player) > 0 else 1 - x for x in scores]

View File

@ -36,46 +36,12 @@ boards = {initial_state,
initial_state_2 }
def gen_21_rolls():
"""
Calculate all possible rolls, [[1,1], [1,2] ..]
:return: All possible rolls
"""
a = []
for x in range(1, 7):
for y in range(1, 7):
if not [x, y] in a and not [y, x] in a:
a.append([x, y])
return a
def calculate_possible_states(board):
possible_rolls = [(1, 1), (1, 2), (1, 3), (1, 4), (1, 5),
(1, 6), (2, 2), (2, 3), (2, 4), (2, 5),
(2, 6), (3, 3), (3, 4), (3, 5), (3, 6),
(4, 4), (4, 5), (4, 6), (5, 5), (5, 6),
(6, 6)]
for roll in possible_rolls:
meh = Board.calculate_legal_states(board, -1, roll)
print(len(meh))
return [Board.calculate_legal_states(board, -1, roll)
for roll
in possible_rolls]
#for board in boards:
# calculate_possible_states(board)
#print("-"*30)
#print(network.calculate_1_ply(session, Board.initial_state, [2,4], 1))
board = network.board_trans_func(Board.initial_state, 1)
#print(board)
pair = network.make_move(Board.initial_state, [3,2], 1)
@ -83,26 +49,9 @@ print(pair[1])
network.do_backprop(board, 0.9)
network.save_model(2, 342)
# all_input = np.array([input for _ in range(20)])
# print(network.calc_vals(all_input))
network.print_variables()
#print(" "*10 + "network_test")
#print(" "*20 + "Depth 1")
#print(network.calc_n_ply(1, session, Board.initial_state, 1, [2, 4]))
network.save_model(2)
#print(scores)
#print(" "*20 + "Depth 2")
#print(network.n_ply(2, session, boards, 1))
# #print(x.shape)
# with graph_lol.as_default():
# session_2 = tf.Session(graph = graph_lol)
# network_2 = Network(session_2)
# network_2.restore_model()
# print(network_2.eval_state(initial_state))
# print(network.eval_state(initial_state))