Martijn Pieters :: zopatista.py (v9)

This bot has played 740 games (472 wins / 62 draws / 206 losses).

Play against this bot as X or as O.

These bots have done best aganist zopatista.py...

Bot P W D L Points Size From beginner One hit wonder Goldfish
Séverin Hatt : : romuald_no_last_check.py (v1) 10 10 0 0 10 1598 False False True
Daniel Patrick : : error_unable_to_connect4.py (v2) 10 5 0 5 0 757 False False True
Ian Ozsvald : : ian12.py 10 5 0 5 0 943 False False True
Robert Seaman : : not_minimax.py (v25) 10 5 0 5 0 3501 False False True
Jordan Banting : : trololololol.py (v1) 10 5 0 5 0 402 False False True

...and these bots have done worst

Bot P W D L Points Size From beginner One hit wonder Goldfish
Mac Chapman : : lines.py 10 0 0 10 -10 893 False False False
Ronan Murphy : : loser.py 10 0 0 10 -10 105 False False True
Luke Spademan : : lossingbot.py (v3) 10 0 0 10 -10 129 False False True
house : : opportunist.py 10 0 0 10 -10 None None None None
Peter Inglesby : : random.py 10 0 0 10 -10 26 False True True

All standings against zopatista.py

The code

      
import base64, gc, json, os, pickle, sys, types, zlib
import itertools
import operator

from array import array
from functools import partial
from typing import List, Optional, Iterable, NamedTuple

# Python version of https://github.com/PascalPons/connect4, simplified down to
# the 7*6 constrainsts of the botany game


def get_next_move(move_list: List[str], state: Optional["Position"]) -> int:
    if not move_list:
        # opening move
        return 3, Position().play_col(3)

    pos = (state or Position()).play_col(move_list[-1])
    if pos.key in cached_moves:
        move = cached_moves[pos.key]
        return move, pos.play_col(move)

    if pos.can_win_next:
        possible = pos.can_win_next
        winner = pos.play(best_mask(possible))
        return winner.last_move, winner

    min = -((7 * 6 - pos.moves) // 2)
    max = (7 * 6 + 1 - pos.moves) // 2

    best_score, best_move = min, None
    negamax.counter = 0
    negamax.tracer = sys.gettrace()
    try:
        while min < max:
            med = min + (max - min) // 2
            if med <= 0 and min // 2 < med:
                med = min // 2
            elif med >= 0 and max // 2 > med:  # med >= 0 and max//2 > med)
                med = max // 2
            r, move = negamax(pos, med, med + 1, 0)
            if r >= best_score:
                best_score, best_move = r, move
            if r <= med:
                max = r
            else:
                min = r
    except FastDecision as fast:
        best_move = fast.best_move

    if best_move is None:
        # pick *a* move
        possible = pos.possible_non_losing_moves or pos.possible
        next_pos = pos.play(best_mask(possible))
        best_move = next_pos.last_move
    else:
        next_pos = pos.play_col(best_move)

    if getattr(negamax.tracer, 'opcode_count', 0) > 58000 or negamax.counter > 50:
        cached_moves[pos.key] = best_move
        if "ZOPATISTA_DUMP_DATA" in os.environ:
            with open('/tmp/moves_cache.pickle', 'wb') as moves_cache:
                pickle.dump(cached_moves, moves_cache)

    return best_move, next_pos


def negamax(pos: "Position", alpha: int, beta: int, depth: int) -> (int, int):
    negamax.counter += 1
    if pos.key in cached_moves:
        raise FastDecision(cached_moves[pos.key])

    possible = pos.possible_non_losing_moves

    if not possible:  # we lose in one move
        return -((7 * 6 - pos.moves) // 2), None

    if pos.moves >= 7 * 6 - 2:  # game is drawn
        return 0, None

    min = -((7 * 6 - 2 - pos.moves) // 2)
    if alpha < min:
        alpha = min
        if alpha >= beta:
            return alpha, None

    max_ = (7 * 6 - 1 - pos.moves) // 2
    if beta > max_:
        beta = max_
        if alpha >= beta:
            return beta, None

    key = pos.key
    val = table[key]
    if val:
        if val > 18 - -18:  # lower bound
            min = val + 2 * -18 - 18 - 2
            if alpha < min:
                alpha = min
                if alpha >= beta:
                    return alpha, None
        else:  # upper
            max_ = val + -18 - 1
            if beta > max_:
                beta = max_
                if alpha >= beta:
                    return beta, None

    best_move = None
    for i, pos2 in enumerate(pos.best_moves):
        if negamax.tracer.opcode_count + (((7 - i) * depth) * 500) > 50000:
            # lets not go overboard here, switch to a heuristic
            best = max(pos.best_moves or pos.possible, key=lambda p: p.heuristic)
            if negamax.tracer.opcode_count > 55000:
                # Cut our losses, and get out now!
                raise FastDecision(best.last_move)
            return best.heuristic, best.last_move

        score = negamax(pos2, -beta, -alpha, depth + 1)[0]
        if score >= beta:
            table[key] = score + 18 - 2 * -18 + 2
            return score, pos2.last_move
        if score > alpha:
            alpha = score
            best_move = pos2.last_move

    table[key] = alpha - -18 + 1
    return alpha, best_move


def setup():
    # load game stuff, only needed on import, avoids AST limits on top-level code
    global LOADED, FastDecision, Position, TranspositionTable, table, column_mask, cached_moves
    global best_order_mask, best_mask, filter_none

    kmask = 67108863

    class TranspositionTable:
        # basic limited hashmap
        def __init__(self):
            self.k = array("L")
            self.v = array("B")
            self.reset()

        def __setitem__(self, key: int, value: int) -> int:
            pos = key % 8388617
            self.k[pos] = key & kmask
            self.v[pos] = value

        def __getitem__(self, key: int) -> int:
            pos = key % 8388617
            return 0 if self.k[pos] != (key & kmask) else self.v[pos]

        def reset(self):
            with open("/dev/zero", "rb") as zf:
                self.k.fromfile(zf, 8388617)
                self.v.fromfile(zf, 8388617)

        def debug(self):
            size = len(self.k)
            entries = size - self.k.count(0)
            print(f"table stats: {entries} entries ({entries/size:.3%})")
            return entries

    table = TranspositionTable()

    cached_moves = cached_moves_data()

    bottom_mask = 4432676798593
    board_mask = 279258638311359
    column_mask = (
        63,
        8064,
        1032192,
        132120576,
        16911433728,
        2164663517184,
        277076930199552,
    )
    best_order = (3, 2, 4, 1, 5, 0, 6)
    best_order_mask = [column_mask[c] for c in best_order]
    bottom_mask_col = (1, 128, 16384, 2097152, 268435456, 34359738368, 4398046511104)

    filter_none = partial(filter, None)

    def best_mask(
        bitmap,
        a=operator.and_,
        n=next,
        m=map,
        f=filter_none,
        r=itertools.repeat,
        b=best_order_mask,
    ):
        return next(f(map(a, b, r(bitmap))))

    class FastDecision(Exception):
        def __init__(self, best_move):
            self.best_move = best_move

    class Position(NamedTuple):
        current_position: int = 0
        mask: int = 0
        last_move: int = -1
        moves: int = 0

        def __repr__(self):
            pos, mask, cell = self.current_position, self.mask, 1
            cols, col = [], []
            for i in range(49):
                if (i % 7) < 6:
                    if not (mask & cell):
                        # empty
                        col.append(".")
                    elif pos & cell:
                        # player
                        col.append("X")
                    else:
                        col.append("O")
                if i % 7 == 6:
                    cols.append(col[::-1])
                    col = []
                cell <<= 1
            return "\n".join([" ".join(r) for r in zip(*reversed(cols))])

        def play(self, move: int) -> "Position":
            return Position(
                self.current_position ^ self.mask,
                self.mask | move,
                next(c for c, mask in enumerate(column_mask) if move & mask),
                self.moves + 1,
            )

        def play_col(self, col: int) -> "Position":
            return self.play((self.mask + bottom_mask_col[col]) & column_mask[col])

        @property
        def best_moves(
            self, f=filter_none, m=map, a=operator.and_, r=itertools.repeat
        ) -> Iterable["Position"]:
            possible = self.possible_non_losing_moves
            for move in sorted(
                f(m(a, r(possible), best_order_mask)), key=self.move_score, reverse=True
            ):
                yield self.play(move)

        @property
        def can_win_next(self) -> int:
            return self.winning_position & self.possible

        @property
        def key(self) -> int:
            return self.current_position + self.mask

        @property
        def possible_non_losing_moves(self) -> int:
            possible_mask = self.possible
            opponent_win = self.compute_winning_position(
                self.current_position ^ self.mask
            )
            forced_moves = possible_mask & opponent_win
            if forced_moves:
                if forced_moves & (forced_moves - 1):
                    # more than one forced move, we lost
                    return 0
                else:
                    # we need to counter this move
                    possible_mask = forced_moves

            # avoid to play below an opponent winning spot
            return possible_mask & (~(opponent_win >> 1) & 562949953421311)

        def move_score(self, move: int) -> int:
            return bin(
                self.compute_winning_position(self.current_position | move)
            ).count("1")

        @property
        def winning_position(self) -> int:
            return self.compute_winning_position(self.current_position)

        @property
        def possible(self) -> int:
            return (self.mask + bottom_mask) & board_mask

        def compute_winning_position(self, position):
            # vertical
            r = (position << 1) & (position << 2) & (position << 3)

            # horizontal
            p = (position << 7) & (position << 14)
            r |= p & (position << 21)
            r |= p & (position >> 7)
            p = (position >> 7) & (position >> 14)
            r |= p & (position << 7)
            r |= p & (position >> 21)

            # diagonal 1
            p = (position << 6) & (position << 12)
            r |= p & (position << 18)
            r |= p & (position >> 6)
            p = (position >> 6) & (position >> 12)
            r |= p & (position << 6)
            r |= p & (position >> 18)

            # diagonal 2
            p = (position << 8) & (position << 16)
            r |= p & (position << 24)
            r |= p & (position >> 8)
            p = (position >> 8) & (position >> 16)
            r |= p & (position << 8)
            r |= p & (position >> 24)

            return r & (board_mask ^ self.mask)

        def compute_better_position(self, position):
            # vertical
            r = (position << 1) & (position << 2)
            r |= (position << 2) & (position << 3)

            # horizontal
            r |= (position << 7) & (position << 14)
            r |= (position << 14) & (position << 21)
            r |= (position << 7) & (position >> 7)
            r |= (position << 14) & (position >> 7)
            r |= (position >> 7) & (position >> 14)
            r |= (position >> 14) & (position >> 21)
            r |= (position >> 7) & (position << 7)
            r |= (position >> 14) & (position << 7)

            # diagonal 1
            r |= (position << 6) & (position << 12)
            r |= (position << 12) & (position << 18)
            r |= (position << 6) & (position >> 6)
            r |= (position << 12) & (position >> 6)
            r |= (position >> 6) & (position >> 12)
            r |= (position >> 12) & (position >> 18)
            r |= (position >> 6) & (position << 6)
            r |= (position >> 12) & (position << 6)

            # diagonal 2
            r |= (position << 8) & (position << 16)
            r |= (position << 15) & (position << 24)
            r |= (position << 8) & (position >> 8)
            r |= (position << 16) & (position >> 8)
            r |= (position >> 8) & (position >> 16)
            r |= (position >> 16) & (position >> 24)
            r |= (position >> 8) & (position << 8)
            r |= (position >> 16) & (position << 8)

            return r & (board_mask ^ self.mask)

        @property
        def heuristic(self):
            w = bin(self.compute_winning_position(self.current_position)).count("1")
            b = bin(self.compute_better_position(self.current_position)).count("1")
            return w * 10 + b * 3


def cached_moves_data():
    return {}
    return pickle.loads(zlib.decompress(base64.b64decode("""\
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)))


def antigravity(cheating_bastard: None = setup()) -> None:
    # I'm totally weightless
    pass