##
##      *** REPLACE WITH YOUR CUSTOMIZED COMMENT BLOCK ***
##

## altNim implementation functions for Assigment #2.
##
##      *** ONLY ADD REQUESTED CODE ***
## 
##   DO NOT:
##      * MODIFY THE LONE IMPORT STATEMENT
##      * ADD OTHER IMPORT STATEMENTS (THIS INCLUDES IMPORT
##         STATEMENTS ADDED AUTOMATICALLY BY IDES SUCH AS PYCHARM);
##      * ADD, MODIFY, OR REMOVE FUNCTION DEFINITIONS OR MODIFY GIVEN 
##         COMMENTS ASSOCIATED WITH THOSE FUNCTIONS;
##      * ADD NEW CLASS DEFINITIONS OR GLOBAL VARIABLES (USE THE
##         PROVIDED CLASSES IN FILE MYSEARCHCLASSES.PY)
##      * ACCESS ANY PROVIDED CLASS ATRIBUTES DIRECTLY (USE
##         PROVIDED ATTRIBUTE ACCESS FUNCTIONS IN THE FILE 
##         MYSEARCHCLASSES.PY)
##         
##

from myAltNimClasses import *


## Function returns True if state STATE is a leaf in the altNim game tree
##  and False otherwise.

def isLeaf(state):
##   *** ADD REQUESTED CODE ***

      
## Function returns the value associated with state STATE in the altNim 
##  game tree. If state values indicate that the max player has won or 
##  lost, return 1000 or -1000 respectively (see hints in the assignment
##  description); otherwise, return ##  the nuumber of matchsticks 
##  remaining;

def eval(state, isMax):
##   *** ADD REQUESTED CODE ***


## Function returns a list of the game tree children of state STATE.
##  As such, it is analogous to function Expand called by the basic
#   search algorithms given in course lecture #4.

def getChildren(state):
##   *** ADD REQUESTED CODE ***


## Function performs a recursive MiniMax search of the altNim game
##  tree.  As such, it implements recursive algorithm MiniMax in 
#   course lecture #9 modified to store the action resulting in
##  the best MiniMax value for the max player in the root node of
##  the altNim game tree (see the final MiniMax + Alphabeta algorithm
##  given in the course lecture #9 notes for this modification).

def MiniMax(state, depth, isMax):
##   *** ADD REQUESTED CODE ***