From 136458d96e5f0ae683d42d49ec4c85124d255b22 Mon Sep 17 00:00:00 2001 From: Matt Strapp Date: Mon, 26 Apr 2021 13:07:08 -0500 Subject: Test commit --- python/alphaBeta.py | 253 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 148 insertions(+), 105 deletions(-) (limited to 'python') diff --git a/python/alphaBeta.py b/python/alphaBeta.py index 8e041fe..7279368 100644 --- a/python/alphaBeta.py +++ b/python/alphaBeta.py @@ -1,105 +1,148 @@ -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 +# 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 -- cgit v1.2.3