From e58a60ed18bde5db28ba96910df518a61b3999b2 Mon Sep 17 00:00:00 2001
From: Matt Strapp <strap012@umn.edu>
Date: Mon, 26 Apr 2021 17:06:13 -0500
Subject: Refactor jsut about everything

---
 dotsandboxes/agents/algorithms/alphaBeta.py | 105 ++++++++++++++++++++++++++++
 1 file changed, 105 insertions(+)
 create mode 100644 dotsandboxes/agents/algorithms/alphaBeta.py

(limited to 'dotsandboxes/agents/algorithms/alphaBeta.py')

diff --git a/dotsandboxes/agents/algorithms/alphaBeta.py b/dotsandboxes/agents/algorithms/alphaBeta.py
new file mode 100644
index 0000000..8e041fe
--- /dev/null
+++ b/dotsandboxes/agents/algorithms/alphaBeta.py
@@ -0,0 +1,105 @@
+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
-- 
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