All board reps should now work as input.

board.py

@@ -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)
 
 
 

network.py

@@ -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]

network_test.py

@@ -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)])
+network.print_variables()
-# print(network.calc_vals(all_input))
 
 
-#print(" "*10 + "network_test")
+network.save_model(2)
-#print(" "*20 + "Depth 1")
-#print(network.calc_n_ply(1, session, Board.initial_state, 1, [2, 4]))
 
-#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))