Refactor jsut about everything

1 files changed, 0 insertions, 151 deletions

diff --git a/python/MCTS.py b/python/MCTS.py
deleted file mode 100644
index 6c71ba9..0000000
--- a/python/MCTS.py
+++ /dev/null
@@ -1,151 +0,0 @@
-import math
-from copy import deepcopy
-from time import perf_counter
-from random import choice
-
-from GameState import GameState
-
-# Based on https://github.com/DieterBuys/mcts-player/
-
-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()
-
-
-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
-
-
-class MCTSGameController(GameController):
-    """Game controller that uses MCTS to determine the next move.
-    This is the class which implements the Monte Carlo Tree Search algorithm.
-    It builds a game tree of MCTSNodes and samples the game space until a set
-    time has elapsed.
-    """
-
-    def select(self):
-        node = self.root_node
-
-        # Descend until we find a node that has pending moves, or is terminal
-        while node.pending_moves == set() and node.children != []:
-            node = node.select_child_ucb()
-
-        return node
-
-    def expand(self, node):
-        assert node.pending_moves != set()
-
-        move = choice(tuple(node.pending_moves))
-        return node.expand_move(move)
-
-    def simulate(self, state, max_iterations=1000):
-        state = deepcopy(state)
-
-        move = state.get_random_move()
-        while move is not None:
-            state.play_move(move)
-            move = state.get_random_move()
-
-            max_iterations -= 1
-            if max_iterations <= 0:
-                return 0.5  # raise exception? (game too deep to simulate)
-
-        return state.game_result
-
-    def update(self, node, result):
-        while node is not None:
-            node.plays += 1
-            node.score += node.get_score(result)
-            node = node.parent
-
-    def get_next_move(self, state, time_allowed=1.0):
-        super(MCTSGameController, self).get_next_move(state)
-
-        # Create new tree (TODO: Preserve some state for better performance?)
-        self.root_node = MCTSNode(state)
-        iterations = 0
-
-        start_time = perf_counter()
-        while perf_counter() < start_time + time_allowed:
-            node = self.select()
-
-            if node.pending_moves != set():
-                node = self.expand(node)
-
-            result = self.simulate(node.state)
-            self.update(node, result)
-
-            iterations += 1
-
-        # Return most visited node's move
-        return max(self.root_node.children, key=lambda n: n.plays).move