From afa6504b05147fc9168d619507910bf047c5d1cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Christoffer=20M=C3=BCller=20Madsen?=
 <christoffer@guava.space>
Date: Thu, 26 Apr 2018 16:49:49 +0200
Subject: [PATCH] ply again again

---
 network.py | 90 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 90 insertions(+)

diff --git a/network.py b/network.py
index 82cd095..ebe7d8f 100644
--- a/network.py
+++ b/network.py
@@ -211,6 +211,96 @@ class Network:
 
         return [best_board, max(all_rolls_scores)]
 
+
+    def n_ply(self, n_init, sess, boards_init, player_init):
+        def ply(n, boards, player):
+            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) ]
+
+                return [ Board.calculate_legal_states(board, player, roll)
+                         for roll
+                         in  possible_rolls ]
+
+            def find_best_state_score(boards):
+                score_pairs = [ (board, self.eval_state(sess, self.board_trans_func(board, player)))
+                                for board
+                                in  boards ]
+                scores = [ pair[1]
+                           for pair
+                           in score_pairs ]
+                best_score_pair = score_pairs[np.array(scores).argmax()]
+
+                return best_score_pair
+
+            def average_score(boards):
+                return sum(boards)/len(boards)
+
+            def average_ply_score(board):
+                states_for_rolls = calculate_possible_states(board)
+
+                best_state_score_for_each_roll = [
+                    find_best_state_score(states)
+                    for states
+                    in  states_for_rolls ]
+                best_score_for_each_roll = [ x[1]
+                                             for x
+                                             in best_state_score_for_each_roll ]
+
+                average_score_var = average_score(best_score_for_each_roll)
+                return average_score_var
+
+
+            if n == 1:
+                print("blalhlalha")
+                average_score_pairs = [ (board, average_ply_score(board))
+                                        for board
+                                        in  boards ]
+                return average_score_pairs
+            elif n > 1: # n != 1
+                def average_for_score_pairs(score_pairs):
+                    scores = [ pair[1]
+                               for pair
+                               in score_pairs ]
+                    return sum(scores)/len(scores)
+
+                def average_plain(scores):
+                    return sum(scores)/len(scores)
+
+                print("+"*20)
+                print(n)
+                print(type(boards))
+                print(boards)
+                possible_states_for_boards = [
+                    (board, calculate_possible_states(board))
+                    for board
+                    in  boards ]
+
+                average_score_pairs = [
+                    (inner_boards[0], average_plain([ average_for_score_pairs(ply(n - 1, inner_board, player * -1))
+                                                      for inner_board
+                                                      in  inner_boards[1] ]))
+                    for inner_boards
+                    in  possible_states_for_boards ]
+
+                return average_score_pairs
+
+            else:
+                assert False
+
+        if n_init < 1: print("Unexpected argument n = {}".format(n_init)); exit()
+
+        boards_with_scores = ply(n_init, boards_init, player_init)
+        print(boards_with_scores)
+        scores = [ pair[1]
+                   for pair
+                   in boards_with_scores ]
+        best_score_pair = boards_with_scores[np.array(scores).argmax()]
+        return best_score_pair[0]
+
     def do_ply(self, sess, boards, player):
         """
         Calculates a single extra ply, resulting in a larger search space for our best move.