From dd70d575d42d9ac4f80e9b5ddb787102feb55e1f Mon Sep 17 00:00:00 2001
From: Yorick Barbanneau <ephase@xieme-art.org>
Date: Wed, 20 Dec 2023 21:37:59 +0100
Subject: [PATCH] Factorize code to remove dulicate parts

---
 src/classes/Engines.py | 75 ++++++++++++++++--------------------------
 1 file changed, 29 insertions(+), 46 deletions(-)

diff --git a/src/classes/Engines.py b/src/classes/Engines.py
index 534699f..d31c5fc 100644
--- a/src/classes/Engines.py
+++ b/src/classes/Engines.py
@@ -71,13 +71,17 @@ class MinmaxPlayerEngine(PlayerEngine):
 
     def get_move(self, board):
         super().get_move(board)
+        move, score = self._call(board, self.options['depth'])
+        return move
+
+    def _call(self, board, depth):
         value = -math.inf
         nodes = 1
         leafs = 0
         move = []
         for m in self.get_player_moves(board):
             board.push(m)
-            v, n, l = self.checkMinMax(board, False, self.options['depth'] - 1)
+            v, n, l = self.checkMinMax(board, False, depth - 1)
             if v > value:
                 value = v
                 move = m
@@ -94,7 +98,7 @@ class MinmaxPlayerEngine(PlayerEngine):
             leafs,
             value
         ))
-        return move
+        return move, value
 
     def checkMinMax(self, board, friend_move:bool, depth :int = 2):
         nodes = 1
@@ -131,6 +135,10 @@ class AlphabetaPlayerEngine(PlayerEngine):
 
     def get_move(self, board):
         super().get_move(board)
+        move, heuristic = self._call(board, self.options['depth'])
+        return move
+
+    def _call(self, board, depth):
         self.logger.debug("Enter AlphaBeta function")
         alpha = -math.inf
         beta = math.inf
@@ -139,7 +147,7 @@ class AlphabetaPlayerEngine(PlayerEngine):
         move = []
         for m in self.get_player_moves(board):
             board.push(m)
-            value, n, l = self.checkAlphaBeta(board, False, self.options['depth'] - 1, alpha, beta)
+            value, n, l = self.checkAlphaBeta(board, False, depth - 1, alpha, beta)
             board.pop()
             nodes += n
             leafs += l
@@ -156,7 +164,7 @@ class AlphabetaPlayerEngine(PlayerEngine):
             leafs,
             alpha
         ))
-        return move
+        return move, alpha
 
 
     def checkAlphaBeta(self, board, friend_move : bool, depth, alpha, beta):
@@ -209,9 +217,7 @@ class MinmaxDeepeningPlayerEngine(MinmaxPlayerEngine):
         signal.signal(signal.SIGALRM, self.alarm_handler)
         signal.alarm(self.options['time_limit'])
         depth = 1
-        value = -math.inf
-        nodes = 1
-        leafs = 0
+        heuristic = -math.inf
         move = None
 
         # We can go deeper than blank place in our board, then we must get
@@ -222,21 +228,14 @@ class MinmaxDeepeningPlayerEngine(MinmaxPlayerEngine):
         # Iterate depth while our alarm does not trigger and there is enougth
         # avaiable move to play
         while not self.interrupt_search and depth <= max_depth:
-            for m in board.legal_moves():
-                board.push(m)
-                v, n, l = self.checkMinMax(board, False, depth - 1)
-                if v > value:
-                    value = v
-                    move = m
-                    self.logger.debug("found a better move: {} (heuristic:{})".format(
-                        move,
-                        value
-                        ))
-                nodes += n
-                leafs += l
-                board.pop()
+            current_move, current_heuristic = self._call(board, depth)
+            # return the current move onli if heuristic is better than previous
+            # iteration
+            if current_heuristic > heuristic:
+                move = current_move
+
             depth = depth + 1
-            self.logger.debug("id_minmax - depth reached: {} | max depth : {}".format(
+        self.logger.debug("id_minmax - depth reached: {} | max depth : {}".format(
             depth - 1,
             max_depth
         ))
@@ -250,18 +249,15 @@ class MinmaxDeepeningPlayerEngine(MinmaxPlayerEngine):
 
 class AlphaBetaDeepeningPlayerEngine(AlphabetaPlayerEngine):
 
+
     def get_move(self, board):
-        #super().get_move(board)
         self.interrupt_search = False
         
         # Get an alarm signal to stop iterations
         signal.signal(signal.SIGALRM, self.alarm_handler)
         signal.alarm(self.options['time_limit'])
         depth = 1
-        alpha = -math.inf
-        beta = math.inf
-        nodes = 1
-        leafs = 0
+        heuristic = -math.inf
         move = None
 
         # We can go deeper than blank place in our board, then we must get
@@ -272,30 +268,17 @@ class AlphaBetaDeepeningPlayerEngine(AlphabetaPlayerEngine):
         # Iterate depth while our alarm does not trigger and there is enougth
         # avaiable move to play
         while not self.interrupt_search and depth <= max_depth:
-            for m in board.legal_moves():
-                board.push(m)
-                v, n, l = self.checkAlphaBeta(board, False, depth - 1, alpha, beta)
-                board.pop()
-                nodes += n
-                leafs += l
-                if  v >= alpha:
-                    alpha = v
-                    move = m
-                    self.logger.debug("found a better move: {} (heuristic:{})".format(
-                        move,
-                        alpha
-                    ))
+            current_move, current_heuristic = self._call(board, depth)
+            # return the current move only if heuristic is better than previous
+            # iteration can be possible id iteration is stopped by timer
+            if current_heuristic > heuristic:
+                move = current_move
             depth = depth + 1
-        self.logger.info("id_alphabeta - depth reached: {} | max depth : {}".format(
+
+        self.logger.debug("id_minmax - depth reached: {} | max depth : {}".format(
             depth - 1,
             max_depth
         ))
-        self.logger.info("Tree statistics:\n\tnodes:{}\n\tleafs:{}\n\theuristic:{}".format(
-            nodes,
-            leafs,
-            alpha
-        ))
-
         return move
 
     def alarm_handler(self, signal, frame):