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class Algo: # A class for defining algorithms used (minimax and alpha-beta pruning)
def miniMax(State, Ply_num): # Function for the minimax algorithm
for i in range(State.Current.dimY):
for j in range(State.Current.dimX):
if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
State.Make(j, i, True)
if Ply_num < 2:
return (i, j)
Minimum_Score = 1000
i = 0
j = 0
for k, z in State.children.items():
Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
if Minimum_Score > Result:
Minimum_Score = Result
i = k[0]
j = k[1]
return (i, j)
def Maximum(State, Ply_num, Alpha): # Alpha-beta pruning function for taking care of Alpha values
if Ply_num == 0:
return State.CurrentScore
for i in range(State.Current.dimY):
for j in range(State.Current.dimX):
if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
State.Make(j, i, False)
Maximum_Score = -1000
i = 0
j = 0
for k, z in State.children.items():
Result = Algo.Minimum(z, Ply_num - 1, Maximum_Score)
if Maximum_Score < Result:
Maximum_Score = Result
if Result > Alpha:
return Result
return Maximum_Score
def Minimum(State, Ply_num, Beta): # Alpha-beta pruning function for taking care of Beta values
if Ply_num == 0:
return State.CurrentScore
for i in range(State.Current.dimY):
for j in range(State.Current.dimX):
if State.Current.Mat[i][j] == ' ' and (j, i) not in State.children:
State.Make(j, i, True)
Minimum_Score = 1000
i = 0
j = 0
for k, z in State.children.items():
Result = Algo.Maximum(z, Ply_num - 1, Minimum_Score)
if Minimum_Score > Result:
Minimum_Score = Result
if Result < Beta:
return Result
return Minimum_Score
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