commit ABPruning + MinMax

Author: AjayBadrinath

game_search_algo.py

@@ -0,0 +1,59 @@
+import math,numpy as np
+# Alpha - Beta Pruning
+# some upper and lower bounds
+MAX=np.inf
+MIN=-np.inf
+# define game-state array here
+arr=[8,7,3,9,9,8,2,4,1,8,8,9,9,9,3,4 ]
+# lim Finds the number of levels to recur.
+lim=math.log(len(arr),2)
+
+# AB Pruning is a wrapper to the min max algorithm that cuts the search tree by saving compute time..
+def Alpha_Beta_Pruning(state_arr,index,depth,ismaxnode,alpha,beta):
+    if(depth==lim):
+        return state_arr[index]
+    if(ismaxnode):
+        best=MIN
+        for i in range(2):
+            val=Alpha_Beta_Pruning(state_arr,index*2+i,depth+1,False,alpha,beta)
+            best=max(best,val)
+            alpha=max(alpha,best)
+            if(alpha>=beta):
+                print(f"Pruned at depth :{depth+1}")
+                break
+        return best
+    else:
+        best=MAX
+        for i in range(2):
+            val=Alpha_Beta_Pruning(state_arr,index*2+i,depth+1,True,alpha,beta)
+            best=min(best,val)
+            beta=min(beta,best)
+            if(alpha>=beta):
+                print(f"Pruned at depth :{depth+1}")
+                break
+        return best
+
+'''
+Min max
+'''    
+#Min max is the base algorithm for alpha beta pruning so the code base has less changes .
+
+def minmax(state_arr,index,depth,ismaxnode):
+    if(depth==lim):
+        return state_arr[index]
+    if(ismaxnode):
+        best=MIN
+        for i in range(2):
+            val=minmax(state_arr,index*2+i,depth+1,False)
+            best=max(best,val)
+        return best
+    else:
+        best=MAX
+        for i in range(2):
+            val=minmax(state_arr,index*2+i,depth+1,True)
+            best=min(best,val)
+        return best
+
+    
+print("Alpha Beta Pruning:",Alpha_Beta_Pruning(arr,0,0,True,MIN,MAX))
+print("Min-Max Algorithm :",minmax(arr,0,0,True))