Ajout du travail de la séance.
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README.md
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README.md
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# BUT3Jeu
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# BUT3 Jeu
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Groupe : Dylan LANDRIN, Killian SCHIED
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##### Groupe : Dylan LANDRIN, Killian SCHIED
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Au cours de cette séance, nous avons implémenté 3 algorythmes en python:
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## Séance du 12/09/24
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Au cours de cette séance, nous avons implémenté 3 algorithmes en python:
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### exploreMax
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Le but d'explore max est de déterminer si le joueur 1 peux faire un coup qui mettrais en echec joueur 2.
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@ -11,4 +12,79 @@ Le but d'explore max est de déterminer si le joueur 1 peux faire un coup qui me
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Le but de Min est l'inverse, il voit s'il peut mettre joueur 1 en échec
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### miniMax
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miniMax appelle les explorations
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miniMax appelle les explorations
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## Séance du 17/09/24
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Au cours de cette séance, nous avons apportés des modifications dans les fonctions utilisées dans l'algorithme miniMax() ainsi qu'un affichage de l'objet Game.
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### exploreMax() et exploreMin()
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Des optimisations ont été apportés dans ces fonctions afin de stopper la recherche lorsque une solution a été trouvée.
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Exemple :
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#### Avant
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Test avec 5 allumettes :
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```
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$ python3 game.py
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-----------------------
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Game and algorithm data
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-----------------------
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== Game data ==
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Remaining matches : 5
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Game state : 0
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Actual player : 1
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== Algorithm data ==
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exploreMax calls = 17
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exploreMin calls = 19
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First player winning ? = False
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```
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Test avec 14 allumettes :
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```
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-----------------------
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Game and algorithm data
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-----------------------
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== Game data ==
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Remaining matches : 14
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Game state : 0
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Actual player : 1
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== Algorithm data ==
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exploreMax calls = 916
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exploreMin calls = 913
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First player winning ? = True
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```
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#### Après
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Test avec 5 allumettes :
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```
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$ python3 game_optimized.py
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-----------------------
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Game and algorithm data
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-----------------------
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== Game data ==
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Remaining matches : 5
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Game state : 0
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Actual player : 1
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== Algorithm data ==
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exploreMax calls = 3
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exploreMin calls = 3
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First player winning ? = False
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```
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Test avec 14 allumettes :
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```
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-----------------------
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Game and algorithm data
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-----------------------
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== Game data ==
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Remaining matches : 14
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Game state : 0
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Actual player : 1
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== Algorithm data ==
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exploreMax calls = 8
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exploreMin calls = 7
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First player winning ? = True
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```
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66
game.py
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game.py
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class Game(int):
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def __init__(self, start):
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#Game variables
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self.matches = start # number of matches
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self.state = 0 #-1 = lost, 0 = draw, +1 = won
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self.playerTurn = 1 # 1 = player 1 is playing, 2 = player 2 is playing
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#Algorithm variables
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self.exploreMaxCalls = 0 # Call counter of exploreMax
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self.exploreMinCalls = 0 # Call counter of exploreMax
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self.firstPlayerWinning = False # Result of MinMax
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def miniMax(self):
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'''
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`miniMax` function starts the MiniMax algorithm
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Params :
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self -- the current instance of the game
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Return :
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-1 = lost or 0 = draw or +1 = won -- depends on the possibility to win of the player
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'''
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if(self.playerTurn == 1):
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res = self.exploreMax(self.matches)
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if(self.playerTurn == 2):
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res = self.exploreMin(self.matches)
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print(res)
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if(res == -1):
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self.firstPlayerWinning = False
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else:
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self.firstPlayerWinning = True
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return res
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def exploreMax(self, n):
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'''
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`exploreMax` function tries to find if a player has possibilities to win the game
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Params :
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self -- the current instance of the game
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n -- a number of matches
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Return :
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-1 = lose or 0 = draw or +1 = win -- depends on the possibility to win of the player
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'''
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self.exploreMaxCalls += 1
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bestVal = -1
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if(n<=0):
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return 1
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return bestVal
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def exploreMin(self, n):
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'''
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`exploreMin` function tries to find if a player has possibilities to win the game
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Params :
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self -- the current instance of the game
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n -- a number of matches
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Return :
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-1 = lose or 0 = draw or +1 = win -- depends on the possibility to win of the player
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'''
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self.exploreMinCalls += 1
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worstVal = 1
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if(n<=0):
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return -1
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if(worstVal == -1):
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break
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return worstVal
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def __str__(self):
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'''
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`__str__` function overrides the default function to get a custom display when printing the game object
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Params :
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self -- the current instance of the game
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Return :
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string -- contains all the game and algorithm values
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'''
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content = ( "-----------------------\n"
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"Game and algorithm data\n"
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"-----------------------\n"
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"== Game data ==\n"
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f"Remaining matches : {self.matches}\n"
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f"Game state : {self.state}\n"
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f"Actual player : {self.playerTurn}\n"
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"== Algorithm data ==\n"
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f"exploreMax calls = {self.exploreMaxCalls}\n"
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f"exploreMin calls = {self.exploreMinCalls}\n"
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f"First player winning ? = {self.firstPlayerWinning}\n"
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)
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return content
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game = Game(5) # Creating Nim game with 5 matches
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game.miniMax() # Checking if the player 1 is winning
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print(game) # Displays game and algorithm data
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119
game_optimized.py
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game_optimized.py
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class Game(int):
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def __init__(self, start):
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#Game variables
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self.matches = start # number of matches
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self.state = 0 #-1 = lost, 0 = draw, +1 = won
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self.playerTurn = 1 # 1 = player 1 is playing, 2 = player 2 is playing
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#Algorithm variables
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self.exploreMaxCalls = 0 # Call counter of exploreMax
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self.exploreMinCalls = 0 # Call counter of exploreMax
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self.firstPlayerWinning = False # Result of MinMax
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self.algorithmResFound = False # True if MiniMax algorithm found a result
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def miniMax(self):
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'''
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`miniMax` function starts the MiniMax algorithm
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Params :
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self -- the current instance of the game
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Return :
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-1 = lost or 0 = draw or +1 = won -- depends on the possibility to win of the player
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'''
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self.algorithmResFound = False
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if(self.playerTurn == 1):
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res = self.exploreMax(self.matches)
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if(self.playerTurn == 2):
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res = self.exploreMin(self.matches)
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if(res == -1):
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self.firstPlayerWinning = False
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else:
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self.firstPlayerWinning = True
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return res
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def exploreMax(self, n):
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'''
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`exploreMax` function tries to find if a player has possibilities to win the game
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Params :
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self -- the current instance of the game
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n -- a number of matches
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Return :
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-1 = lose or 0 = draw or +1 = win -- depends on the possibility to win of the player
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'''
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self.exploreMaxCalls += 1
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bestVal = -1
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if(self.algorithmResFound):
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return 1
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if(n<=0):
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self.algorithmResFound = True
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return 1
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for i in range (1,4):
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if(self.algorithmResFound):
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break
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res = self.exploreMin(n-i)
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if(res > bestVal):
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bestVal = res
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if(bestVal == 1):
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self.algorithmResFound = True
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break
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return bestVal
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def exploreMin(self, n):
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'''
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`exploreMin` function tries to find if a player has possibilities to win the game
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Params :
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self -- the current instance of the game
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n -- a number of matches
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Return :
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-1 = lose or 0 = draw or +1 = win -- depends on the possibility to win of the player
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'''
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self.exploreMinCalls += 1
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worstVal = 1
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if(self.algorithmResFound):
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return -1
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if(n<=0):
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self.algorithmResFound = True
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return -1
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for i in range (1,4):
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if(self.algorithmResFound):
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break
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res = self.exploreMax(n-i)
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if(res < worstVal):
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worstVal = res
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if(worstVal == -1):
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self.algorithmResFound = True
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break
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return worstVal
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def __str__(self):
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'''
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`__str__` function overrides the default function to get a custom display when printing the game object
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Params :
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self -- the current instance of the game
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Return :
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string -- contains all the game and algorithm values
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'''
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content = ( "-----------------------\n"
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"Game and algorithm data\n"
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"-----------------------\n"
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"== Game data ==\n"
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f"Remaining matches : {self.matches}\n"
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f"Game state : {self.state}\n"
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f"Actual player : {self.playerTurn}\n"
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"== Algorithm data ==\n"
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f"exploreMax calls = {self.exploreMaxCalls}\n"
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f"exploreMin calls = {self.exploreMinCalls}\n"
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f"First player winning ? = {self.firstPlayerWinning}\n"
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)
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return content
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game = Game(5) # Creating Nim game with 5 matches
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game.miniMax() # Checking if the player 1 is winning
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print(game) # Displays game and algorithm data
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