From 25da2cc3eb94d3d28c3e0c580e2bcb78f7fcba58 Mon Sep 17 00:00:00 2001
From: Nikolaj Danger <nikolajgade@live.dk>
Date: Wed, 12 Aug 2020 13:45:05 +0200
Subject: [PATCH] :broom:

---
 funcs/games/fourInARow.py | 34 +++++++++++++++-------------------
 funcs/games/gameLoops.py  |  2 +-
 2 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/funcs/games/fourInARow.py b/funcs/games/fourInARow.py
index 3947681..cae9a17 100644
--- a/funcs/games/fourInARow.py
+++ b/funcs/games/fourInARow.py
@@ -2,6 +2,7 @@ import json
 import random
 import copy
 import math
+import asyncio
 
 from . import fourInARowDraw
 from funcs import logThis, getName, getID
@@ -13,7 +14,7 @@ AIScores = {
     "enemy two in a row": -35,
     "enemy three in a row": -200,
     "enemy win": -10000,
-    "win": 1000,
+    "win": 10000,
     "avoid losing": 100
 }
 
@@ -232,7 +233,7 @@ def isWon(board):
     return won, winDirection, winCoordinates
 
 # Plays as the AI
-def fourInARowAI(channel):
+async def fourInARowAI(channel):
     logThis("Figuring out best move")
     with open("resources/games/games.json", "r") as f:
         data = json.load(f)
@@ -246,7 +247,7 @@ def fourInARowAI(channel):
         testBoard = copy.deepcopy(board)
         testBoard = placeOnBoard(testBoard,player,column)
         if testBoard != None:
-            scores[column] = minimax(testBoard,difficulty,player%2+1,player,-math.inf,math.inf,False)
+            scores[column] = await minimax(testBoard,difficulty,player%2+1,player,-math.inf,math.inf,False)
         logThis("Best score for column "+str(column)+" is "+str(scores[column]))
 
     possibleScores = scores.copy()
@@ -266,12 +267,10 @@ def AICalcPoints(board,player):
     otherPlayer = player%2+1
 
     # Adds points for middle placement
-    for row in range(len(board)):
-        if board[row][3] == player:
-            score += AIScores["middle"]
-
     # Checks horizontal
     for row in range(rowCount):
+        if board[row][3] == player:
+            score += AIScores["middle"]
         rowArray = [int(i) for i in list(board[row])]
         for place in range(columnCount-3):
             window = rowArray[place:place+4]
@@ -290,8 +289,6 @@ def AICalcPoints(board,player):
             window = [board[row][place],board[row+1][place+1],board[row+2][place+2],board[row+3][place+3]]
             score += evaluateWindow(window,player,otherPlayer)
 
-    # Checks left diagonal
-    for row in range(rowCount-3):
         for place in range(3,columnCount):
             window = [board[row][place],board[row+1][place-1],board[row+2][place-2],board[row+3][place-3]]
             score += evaluateWindow(window,player,otherPlayer)
@@ -319,11 +316,12 @@ def evaluateWindow(window,player,otherPlayer):
     else:
         return 0
 
-def minimax(board, depth, player , originalPlayer, alpha, beta, maximizingPlayer):
-    terminal = ((isWon(board)[0] != 0) or (0 not in board[0]))
+async def minimax(board, depth, player , originalPlayer, alpha, beta, maximizingPlayer):
+    #terminal = ((0 not in board[0]) or (isWon(board)[0] != 0))
+    terminal = 0 not in board[0]
+    points = AICalcPoints(board,originalPlayer)
     # The depth is how many moves ahead the computer checks. This value is the difficulty.
-    if depth == 0 or terminal:
-        points = AICalcPoints(board,originalPlayer)
+    if depth == 0 or terminal or (points > 5000 or points < -6000):
         return points
     if maximizingPlayer:
         value = -math.inf
@@ -331,12 +329,11 @@ def minimax(board, depth, player , originalPlayer, alpha, beta, maximizingPlayer
             testBoard = copy.deepcopy(board)
             testBoard = placeOnBoard(testBoard,player,column)
             if testBoard != None:
-                evaluation = minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,False)
+                evaluation = await minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,False)
                 if evaluation < -9000: evaluation += AIScores["avoid losing"]
                 value = max(value,evaluation)
                 alpha = max(alpha,evaluation)
-                if beta <= alpha:
-                    break
+                if beta <= alpha: break
         return value
     else:
         value = math.inf
@@ -344,10 +341,9 @@ def minimax(board, depth, player , originalPlayer, alpha, beta, maximizingPlayer
             testBoard = copy.deepcopy(board)
             testBoard = placeOnBoard(testBoard,player,column)
             if testBoard != None:
-                evaluation = minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,True)
+                evaluation = await minimax(testBoard,depth-1,player%2+1,originalPlayer,alpha,beta,True)
                 if evaluation < -9000: evaluation += AIScores["avoid losing"]
                 value = min(value,evaluation)
                 beta = min(beta,evaluation)
-                if beta <= alpha:
-                    break
+                if beta <= alpha: break
         return value
diff --git a/funcs/games/gameLoops.py b/funcs/games/gameLoops.py
index 1a9527c..4b3c143 100644
--- a/funcs/games/gameLoops.py
+++ b/funcs/games/gameLoops.py
@@ -94,7 +94,7 @@ async def fiar(channel,command,user):
         if gameDone == False:
             if gwendoTurn:
                 try:
-                    response, showImage, deleteImage, gameDone, gwendoTurn = fourInARowAI(str(channel.id))
+                    response, showImage, deleteImage, gameDone, gwendoTurn = await fourInARowAI(str(channel.id))
                 except:
                     logThis("AI error (error code 1420)")
                 await channel.send(response)