diff options
Diffstat (limited to 'Algorithm.py')
| -rw-r--r-- | Algorithm.py | 148 |
1 files changed, 0 insertions, 148 deletions
diff --git a/Algorithm.py b/Algorithm.py deleted file mode 100644 index 99488a8..0000000 --- a/Algorithm.py +++ /dev/null @@ -1,148 +0,0 @@ -# import MCTS
-import math
-from copy import deepcopy
-from time import clock
-from random import choice
-
-from GameState import GameState
-
-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
-
-# class MCTSNode(object):
-# """Monte Carlo Tree Node.
-# Each node encapsulates a particular game state, the moves that
-# are possible from that state and the strategic information accumulated
-# by the tree search as it progressively samples the game space.
-# """
-
-# def __init__(self, state, parent=None, move=None):
-# self.parent = parent
-# self.move = move
-# self.state = state
-
-# self.plays = 0
-# self.score = 0
-
-# self.pending_moves = state.get_moves()
-# self.children = []
-
-# def select_child_ucb(self):
-# # Note that each node's plays count is equal
-# # to the sum of its children's plays
-# def ucb(child):
-# win_ratio = child.score / child.plays \
-# + math.sqrt(2 * math.log(self.plays) / child.plays)
-# return win_ratio
-
-# return max(self.children, key=ucb)
-
-# def expand_move(self, move):
-# self.pending_moves.remove(move) # raises KeyError
-
-# child_state = deepcopy(self.state)
-# child_state.play_move(move)
-
-# child = MCTSNode(state=child_state, parent=self, move=move)
-# self.children.append(child)
-# return child
-
-# def get_score(self, result):
-# # return result
-# if result == 0.5:
-# return result
-
-# if self.state.player == 2:
-# if self.state.next_turn_player == result:
-# return 0.0
-# else:
-# return 1.0
-# else:
-# if self.state.next_turn_player == result:
-# return 1.0
-# else:
-# return 0.0
-
-# if self.state.next_turn_player == result:
-# return 0.0
-# else:
-# return 1.0
-
-# def __repr__(self):
-# s = 'ROOT\n' if self.parent is None else ''
-
-# children_moves = [c.move for c in self.children]
-
-# s += """Score ratio: {score} / {plays}
-# Pending moves: {pending_moves}
-# Children's moves: {children_moves}
-# State:
-# {state}\n""".format(children_moves=children_moves, **self.__dict__)
-
-# return s
\ No newline at end of file |