masouduut94

import unittest
import sys
from datetime import datetime
from os import listdir
from time import sleep
from tempfile import mkdtemp
from unittest.case import TestCase

# from json_parser import JsonParser

masouduut94

import json
from os import makedirs
from os.path import exists, join
from bunch import bunchify
from datetime import datetime

class JsonMeta(object):
    HOURS = 4
    MINUTES = 59
    SECONDS = 59

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class UnionFind:
    """
    Notes:
        unionfind data structure specialized for finding hex connections.
        Implementation inspired by UAlberta CMPUT 275 2015 class notes.

    Attributes:
        parent (dict): Each group parent
        rank (dict): Each group rank
        groups (dict): Stores the groups and chain of cells

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from numpy import zeros, int_
from unionfind import UnionFind
from meta import GameMeta


class GameState:
    """
    Stores information representing the current state of a game of hex, namely
    the board and the current turn. Also provides functions for playing game.
    """

masouduut94

class GameMeta:
    PLAYERS = {'none': 0, 'white': 1, 'black': 2}
    INF = float('inf')
    GAME_OVER = -1
    EDGE1 = 1
    EDGE2 = 2
    NEIGHBOR_PATTERNS = ((-1, 0), (0, -1), (-1, 1), (0, 1), (1, 0), (1, -1))

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class Node:
    """
    Node for the MCTS. Stores the move applied to reach this node from its parent,
    stats for the associated game position, children, parent and outcome
    (outcome==none unless the position ends the game).
    Args:
        move:
        parent:
        N (int): times this position was visited.
        Q (int): average reward (wins-losses) from this position.

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class UctMctsAgent:
    """
    Basic no frills implementation of an agent that preforms MCTS for hex.
    Attributes:
        root_state (GameState): Game simulator that helps us to 
                     understand the game situation.
        root (Node): Root of the tree search.
        run_time (int): time per each run.
        node_count (int): the whole nodes in tree.
        num_rollouts (int): The number of rollouts for each search.

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def select_node(self) -> tuple:
    """
    Select a node in the tree to preform a single simulation from.

    """
    node = self.root
    state = deepcopy(self.root_state)

    # stop if we find reach a leaf node
    while len(node.children) != 0:

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@staticmethod
def roll_out(state: GameState) -> int:
    """
    Simulate an entirely random game from the passed state and return the winning
    player.

    Args:
        state: game state

    Returns:

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@staticmethod
def backup(node: Node, turn: int, outcome: int) -> None:
    """
    Update the node statistics on the path from the passed node to root to reflect
    the outcome of a randomly simulated playout.

    Args:
        node:
        turn: winner turn
        outcome: outcome of the rollout