make_move now calls n_ply to search deeper and potentially give

better moves. It's hella fucking slow.

network.py

@@ -157,7 +157,7 @@ class Network:
             exit()
 
 
-    def make_move(self, sess, board, roll, player):
+    #def make_move(self, sess, board, roll, player):
         """
         Find the best move given a board, roll and a player, by finding all possible states one can go to
         and then picking the best, by using the network to evaluate each state. The highest score is picked
@@ -169,24 +169,28 @@ class Network:
         :param player: Current player
         :return: A pair of the best state to go to, together with the score of that state
         """
-        legal_moves = Board.calculate_legal_states(board, player, roll)
-        moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
-        scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
-        best_score_index = np.array(scores).argmax()
-        best_move_pair = moves_and_scores[best_score_index]
-        return best_move_pair
+     #   legal_moves = Board.calculate_legal_states(board, player, roll)
+     #   moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in legal_moves]
+     #   scores = [x[1] if np.sign(player) > 0 else 1-x[1] for x in moves_and_scores]
+     #   best_score_index = np.array(scores).argmax()
+     #   best_move_pair = moves_and_scores[best_score_index]
+     #   return best_move_pair
+
+    def make_move(self, sess, board, roll, player, n = 1):
+        best_pair = self.calc_n_ply(n, sess, board, player, roll)
+        return best_pair
 
 
-    def calculate_2_ply(self, sess, board, roll, player):
+    def calculate_1_ply(self, sess, board, roll, player):
         """
-        Find the best move based on a 2-ply look-ahead. First the best move is found for a single ply and then an
+        Find the best move based on a 1-ply look-ahead. First the best move is found for a single ply and then an
         exhaustive search is performed on the best 15 moves from the single ply.
 
         :param sess:
         :param board:
         :param roll: The original roll
         :param player: The current player
-        :return: Best possible move based on 2-ply look-ahead
+        :return: Best possible move based on 1-ply look-ahead
 
         """
 
@@ -205,7 +209,7 @@ class Network:
         if player == 1:
             best_fifteen.reverse()
 
-        best_fifteen_boards = [x[0] for x in best_fifteen[:15]]
+        best_fifteen_boards = [x[0] for x in best_fifteen[:10]]
 
         all_rolls_scores = self.do_ply(sess, best_fifteen_boards, player)
 
@@ -215,6 +219,29 @@ class Network:
 
         return [best_board, max(all_rolls_scores)]
 
+    def calc_n_ply(self, n_init, sess, board, player, roll):
+
+        # find all legal states from the given board and the given roll
+        init_legal_states = Board.calculate_legal_states(board, player, roll)
+
+        # find all values for the above boards
+        zero_ply_moves_and_scores = [(move, self.eval_state(sess, self.board_trans_func(move, player))) for move in init_legal_states]
+
+        # pythons reverse is in place and I can't call [:15] on it, without applying it to an object like so. Fuck.
+        sorted_moves_and_scores = sorted(zero_ply_moves_and_scores, key=itemgetter(1))
+
+
+        # They're sorted from smallest to largest, therefore we wan't to reverse if the current player is 1, since
+        # player 1 wishes to maximize. It's not needed for player -1, since that player seeks to minimize.
+        if player == 1:
+            sorted_moves_and_scores.reverse()
+
+        best_boards = [x[0] for x in sorted_moves_and_scores[:10]]
+
+        best_move_score_pair = self.n_ply(n_init, sess, best_boards, player)
+
+        return best_move_score_pair
+
 
     def n_ply(self, n_init, sess, boards_init, player_init):
         def ply(n, boards, player):
@@ -262,7 +289,6 @@ class Network:
 
 
             if n == 1:
-                print("blalhlalha")
                 average_score_pairs = [ (board, average_ply_score(board))
                                         for board
                                         in  boards ]
@@ -301,12 +327,13 @@ class Network:
         if n_init < 1: print("Unexpected argument n = {}".format(n_init)); exit()
 
         boards_with_scores = ply(n_init, boards_init, -1 * player_init)
-        print(boards_with_scores)
+        #print("Boards with scores:",boards_with_scores)
         scores = [ ( pair[1] if player_init == 1 else (1 - pair[1]) )
                    for pair
                    in boards_with_scores ]
+        #print("All the scores:",scores)
         best_score_pair = boards_with_scores[np.array(scores).argmax()]
-        return best_score_pair[0]
+        return best_score_pair
 
     def do_ply(self, sess, boards, player):
         """

network_test.py

@@ -80,13 +80,14 @@ def calculate_possible_states(board):
 #for board in boards:
 #    calculate_possible_states(board)
 
-print("-"*30)
-print(network.do_ply(session, boards, 1))
+#print("-"*30)
+#print(network.calculate_1_ply(session, Board.initial_state, [2,4], 1))
 
 #print(" "*10 + "network_test")
-#print(" "*20 + "Depth 1")
-scores = network.n_ply(1, session, boards, 1)
+print(" "*20 + "Depth 1")
+print(network.calc_n_ply(2, session, Board.initial_state, 1, [2, 4]))
 
+#print(scores)
 
 #print(" "*20 + "Depth 2")
 #print(network.n_ply(2, session, boards, 1))