Refactor jsut about everything

1 files changed, 0 insertions, 105 deletions

diff --git a/python/alphaBeta.py b/python/alphaBeta.py
deleted file mode 100644
index 8e041fe..0000000
--- a/python/alphaBeta.py
+++ /dev/null
@@ -1,105 +0,0 @@
-from GameState import GameState
-class GameController(object):
-    def get_next_move(self, state):
-        # when you get a new move, it is assumed that the game is not ended yet
-        assert state.get_moves()
-
-
-def alpha_beta(node, alpha, beta):
-
-    # Based on https://en.wikipedia.org/wiki/Alpha%E2%80%93beta_pruning#Pseudocode
-    # node needs to support three operations: isTerminal(), value(), getChildren(), maximizingPlayer()
-
-    if node.isTerminal():
-        return node.value()
-
-    if node.maximizingPlayer():
-
-        v = float("-inf")
-        for child in node.getChildren():
-
-            v = max(v, alpha_beta(child, alpha, beta))
-            alpha = max(alpha, v)
-            if beta <= alpha:
-                break
-
-    else:
-
-        v = float("inf")
-        for child in node.getChildren():
-
-            v = min(v, alpha_beta(child, alpha, beta))
-            beta = min(beta, v)
-            if beta <= alpha:
-                break
-
-    return v
-
-
-# A class for defining algorithms used (minimax and alpha-beta pruning)
-class AlphaBeta:
-
-    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)
-
-    # Alpha-beta pruning function for taking care of Alpha values
-    def Maximum(State, Ply_num, Alpha):
-        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