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zeropod/plugin/chess/sunfish.py

452 lines
18 KiB
Python

#!/usr/bin/env pypy
# -*- coding: utf-8 -*-
from __future__ import print_function
import re, sys, time
from itertools import count
from collections import namedtuple
###############################################################################
# Piece-Square tables. Tune these to change sunfish's behaviour
###############################################################################
piece = { 'P': 100, 'N': 280, 'B': 320, 'R': 479, 'Q': 929, 'K': 60000 }
pst = {
'P': ( 0, 0, 0, 0, 0, 0, 0, 0,
78, 83, 86, 73, 102, 82, 85, 90,
7, 29, 21, 44, 40, 31, 44, 7,
-17, 16, -2, 15, 14, 0, 15, -13,
-26, 3, 10, 9, 6, 1, 0, -23,
-22, 9, 5, -11, -10, -2, 3, -19,
-31, 8, -7, -37, -36, -14, 3, -31,
0, 0, 0, 0, 0, 0, 0, 0),
'N': ( -66, -53, -75, -75, -10, -55, -58, -70,
-3, -6, 100, -36, 4, 62, -4, -14,
10, 67, 1, 74, 73, 27, 62, -2,
24, 24, 45, 37, 33, 41, 25, 17,
-1, 5, 31, 21, 22, 35, 2, 0,
-18, 10, 13, 22, 18, 15, 11, -14,
-23, -15, 2, 0, 2, 0, -23, -20,
-74, -23, -26, -24, -19, -35, -22, -69),
'B': ( -59, -78, -82, -76, -23,-107, -37, -50,
-11, 20, 35, -42, -39, 31, 2, -22,
-9, 39, -32, 41, 52, -10, 28, -14,
25, 17, 20, 34, 26, 25, 15, 10,
13, 10, 17, 23, 17, 16, 0, 7,
14, 25, 24, 15, 8, 25, 20, 15,
19, 20, 11, 6, 7, 6, 20, 16,
-7, 2, -15, -12, -14, -15, -10, -10),
'R': ( 35, 29, 33, 4, 37, 33, 56, 50,
55, 29, 56, 67, 55, 62, 34, 60,
19, 35, 28, 33, 45, 27, 25, 15,
0, 5, 16, 13, 18, -4, -9, -6,
-28, -35, -16, -21, -13, -29, -46, -30,
-42, -28, -42, -25, -25, -35, -26, -46,
-53, -38, -31, -26, -29, -43, -44, -53,
-30, -24, -18, 5, -2, -18, -31, -32),
'Q': ( 6, 1, -8,-104, 69, 24, 88, 26,
14, 32, 60, -10, 20, 76, 57, 24,
-2, 43, 32, 60, 72, 63, 43, 2,
1, -16, 22, 17, 25, 20, -13, -6,
-14, -15, -2, -5, -1, -10, -20, -22,
-30, -6, -13, -11, -16, -11, -16, -27,
-36, -18, 0, -19, -15, -15, -21, -38,
-39, -30, -31, -13, -31, -36, -34, -42),
'K': ( 4, 54, 47, -99, -99, 60, 83, -62,
-32, 10, 55, 56, 56, 55, 10, 3,
-62, 12, -57, 44, -67, 28, 37, -31,
-55, 50, 11, -4, -19, 13, 0, -49,
-55, -43, -52, -28, -51, -47, -8, -50,
-47, -42, -43, -79, -64, -32, -29, -32,
-4, 3, -14, -50, -57, -18, 13, 4,
17, 30, -3, -14, 6, -1, 40, 18),
}
# Pad tables and join piece and pst dictionaries
for k, table in pst.items():
padrow = lambda row: (0,) + tuple(x+piece[k] for x in row) + (0,)
pst[k] = sum((padrow(table[i*8:i*8+8]) for i in range(8)), ())
pst[k] = (0,)*20 + pst[k] + (0,)*20
###############################################################################
# Global constants
###############################################################################
# Our board is represented as a 120 character string. The padding allows for
# fast detection of moves that don't stay within the board.
A1, H1, A8, H8 = 91, 98, 21, 28
initial = (
' \n' # 0 - 9
' \n' # 10 - 19
' rnbqkbnr\n' # 20 - 29
' pppppppp\n' # 30 - 39
' ........\n' # 40 - 49
' ........\n' # 50 - 59
' ........\n' # 60 - 69
' ........\n' # 70 - 79
' PPPPPPPP\n' # 80 - 89
' RNBQKBNR\n' # 90 - 99
' \n' # 100 -109
' \n' # 110 -119
)
# Lists of possible moves for each piece type.
N, E, S, W = -10, 1, 10, -1
directions = {
'P': (N, N+N, N+W, N+E),
'N': (N+N+E, E+N+E, E+S+E, S+S+E, S+S+W, W+S+W, W+N+W, N+N+W),
'B': (N+E, S+E, S+W, N+W),
'R': (N, E, S, W),
'Q': (N, E, S, W, N+E, S+E, S+W, N+W),
'K': (N, E, S, W, N+E, S+E, S+W, N+W)
}
# Mate value must be greater than 8*queen + 2*(rook+knight+bishop)
# King value is set to twice this value such that if the opponent is
# 8 queens up, but we got the king, we still exceed MATE_VALUE.
# When a MATE is detected, we'll set the score to MATE_UPPER - plies to get there
# E.g. Mate in 3 will be MATE_UPPER - 6
MATE_LOWER = piece['K'] - 10*piece['Q']
MATE_UPPER = piece['K'] + 10*piece['Q']
# The table size is the maximum number of elements in the transposition table.
TABLE_SIZE = 1e7
# Constants for tuning search
QS_LIMIT = 219
EVAL_ROUGHNESS = 13
DRAW_TEST = True
###############################################################################
# Chess logic
###############################################################################
class Position(namedtuple('Position', 'board score wc bc ep kp')):
""" A state of a chess game
board -- a 120 char representation of the board
score -- the board evaluation
wc -- the castling rights, [west/queen side, east/king side]
bc -- the opponent castling rights, [west/king side, east/queen side]
ep - the en passant square
kp - the king passant square
"""
def gen_moves(self):
# For each of our pieces, iterate through each possible 'ray' of moves,
# as defined in the 'directions' map. The rays are broken e.g. by
# captures or immediately in case of pieces such as knights.
for i, p in enumerate(self.board):
if not p.isupper(): continue
for d in directions[p]:
for j in count(i+d, d):
q = self.board[j]
# Stay inside the board, and off friendly pieces
if q.isspace() or q.isupper(): break
# Pawn move, double move and capture
if p == 'P' and d in (N, N+N) and q != '.': break
if p == 'P' and d == N+N and (i < A1+N or self.board[i+N] != '.'): break
if p == 'P' and d in (N+W, N+E) and q == '.' \
and j not in (self.ep, self.kp, self.kp-1, self.kp+1): break
# Move it
yield (i, j)
# Stop crawlers from sliding, and sliding after captures
if p in 'PNK' or q.islower(): break
# Castling, by sliding the rook next to the king
if i == A1 and self.board[j+E] == 'K' and self.wc[0]: yield (j+E, j+W)
if i == H1 and self.board[j+W] == 'K' and self.wc[1]: yield (j+W, j+E)
def rotate(self):
''' Rotates the board, preserving enpassant '''
return Position(
self.board[::-1].swapcase(), -self.score, self.bc, self.wc,
119-self.ep if self.ep else 0,
119-self.kp if self.kp else 0)
def nullmove(self):
''' Like rotate, but clears ep and kp '''
return Position(
self.board[::-1].swapcase(), -self.score,
self.bc, self.wc, 0, 0)
def move(self, move):
i, j = move
p, q = self.board[i], self.board[j]
put = lambda board, i, p: board[:i] + p + board[i+1:]
# Copy variables and reset ep and kp
board = self.board
wc, bc, ep, kp = self.wc, self.bc, 0, 0
score = self.score + self.value(move)
# Actual move
board = put(board, j, board[i])
board = put(board, i, '.')
# Castling rights, we move the rook or capture the opponent's
if i == A1: wc = (False, wc[1])
if i == H1: wc = (wc[0], False)
if j == A8: bc = (bc[0], False)
if j == H8: bc = (False, bc[1])
# Castling
if p == 'K':
wc = (False, False)
if abs(j-i) == 2:
kp = (i+j)//2
board = put(board, A1 if j < i else H1, '.')
board = put(board, kp, 'R')
# Pawn promotion, double move and en passant capture
if p == 'P':
if A8 <= j <= H8:
board = put(board, j, 'Q')
if j - i == 2*N:
ep = i + N
if j == self.ep:
board = put(board, j+S, '.')
# We rotate the returned position, so it's ready for the next player
return Position(board, score, wc, bc, ep, kp).rotate()
def value(self, move):
i, j = move
p, q = self.board[i], self.board[j]
# Actual move
score = pst[p][j] - pst[p][i]
# Capture
if q.islower():
score += pst[q.upper()][119-j]
# Castling check detection
if abs(j-self.kp) < 2:
score += pst['K'][119-j]
# Castling
if p == 'K' and abs(i-j) == 2:
score += pst['R'][(i+j)//2]
score -= pst['R'][A1 if j < i else H1]
# Special pawn stuff
if p == 'P':
if A8 <= j <= H8:
score += pst['Q'][j] - pst['P'][j]
if j == self.ep:
score += pst['P'][119-(j+S)]
return score
###############################################################################
# Search logic
###############################################################################
# lower <= s(pos) <= upper
Entry = namedtuple('Entry', 'lower upper')
class Searcher:
def __init__(self):
self.tp_score = {}
self.tp_move = {}
self.history = set()
self.nodes = 0
def bound(self, pos, gamma, depth, root=True):
""" returns r where
s(pos) <= r < gamma if gamma > s(pos)
gamma <= r <= s(pos) if gamma <= s(pos)"""
self.nodes += 1
# Depth <= 0 is QSearch. Here any position is searched as deeply as is needed for
# calmness, and from this point on there is no difference in behaviour depending on
# depth, so so there is no reason to keep different depths in the transposition table.
depth = max(depth, 0)
# Sunfish is a king-capture engine, so we should always check if we
# still have a king. Notice since this is the only termination check,
# the remaining code has to be comfortable with being mated, stalemated
# or able to capture the opponent king.
if pos.score <= -MATE_LOWER:
return -MATE_UPPER
# We detect 3-fold captures by comparing against previously
# _actually played_ positions.
# Note that we need to do this before we look in the table, as the
# position may have been previously reached with a different score.
# This is what prevents a search instability.
# FIXME: This is not true, since other positions will be affected by
# the new values for all the drawn positions.
if DRAW_TEST:
if not root and pos in self.history:
return 0
# Look in the table if we have already searched this position before.
# We also need to be sure, that the stored search was over the same
# nodes as the current search.
entry = self.tp_score.get((pos, depth, root), Entry(-MATE_UPPER, MATE_UPPER))
if entry.lower >= gamma and (not root or self.tp_move.get(pos) is not None):
return entry.lower
if entry.upper < gamma:
return entry.upper
# Here extensions may be added
# Such as 'if in_check: depth += 1'
# Generator of moves to search in order.
# This allows us to define the moves, but only calculate them if needed.
def moves():
# First try not moving at all. We only do this if there is at least one major
# piece left on the board, since otherwise zugzwangs are too dangerous.
if depth > 0 and not root and any(c in pos.board for c in 'RBNQ'):
yield None, -self.bound(pos.nullmove(), 1-gamma, depth-3, root=False)
# For QSearch we have a different kind of null-move, namely we can just stop
# and not capture anything else.
if depth == 0:
yield None, pos.score
# Then killer move. We search it twice, but the tp will fix things for us.
# Note, we don't have to check for legality, since we've already done it
# before. Also note that in QS the killer must be a capture, otherwise we
# will be non deterministic.
killer = self.tp_move.get(pos)
if killer and (depth > 0 or pos.value(killer) >= QS_LIMIT):
yield killer, -self.bound(pos.move(killer), 1-gamma, depth-1, root=False)
# Then all the other moves
for move in sorted(pos.gen_moves(), key=pos.value, reverse=True):
#for val, move in sorted(((pos.value(move), move) for move in pos.gen_moves()), reverse=True):
# If depth == 0 we only try moves with high intrinsic score (captures and
# promotions). Otherwise we do all moves.
if depth > 0 or pos.value(move) >= QS_LIMIT:
yield move, -self.bound(pos.move(move), 1-gamma, depth-1, root=False)
# Run through the moves, shortcutting when possible
best = -MATE_UPPER
for move, score in moves():
best = max(best, score)
if best >= gamma:
# Clear before setting, so we always have a value
if len(self.tp_move) > TABLE_SIZE: self.tp_move.clear()
# Save the move for pv construction and killer heuristic
self.tp_move[pos] = move
break
# Stalemate checking is a bit tricky: Say we failed low, because
# we can't (legally) move and so the (real) score is -infty.
# At the next depth we are allowed to just return r, -infty <= r < gamma,
# which is normally fine.
# However, what if gamma = -10 and we don't have any legal moves?
# Then the score is actaully a draw and we should fail high!
# Thus, if best < gamma and best < 0 we need to double check what we are doing.
# This doesn't prevent sunfish from making a move that results in stalemate,
# but only if depth == 1, so that's probably fair enough.
# (Btw, at depth 1 we can also mate without realizing.)
if best < gamma and best < 0 and depth > 0:
is_dead = lambda pos: any(pos.value(m) >= MATE_LOWER for m in pos.gen_moves())
if all(is_dead(pos.move(m)) for m in pos.gen_moves()):
in_check = is_dead(pos.nullmove())
best = -MATE_UPPER if in_check else 0
# Clear before setting, so we always have a value
if len(self.tp_score) > TABLE_SIZE: self.tp_score.clear()
# Table part 2
if best >= gamma:
self.tp_score[pos, depth, root] = Entry(best, entry.upper)
if best < gamma:
self.tp_score[pos, depth, root] = Entry(entry.lower, best)
return best
def search(self, pos, history=()):
""" Iterative deepening MTD-bi search """
self.nodes = 0
if DRAW_TEST:
self.history = set(history)
# print('# Clearing table due to new history')
self.tp_score.clear()
# In finished games, we could potentially go far enough to cause a recursion
# limit exception. Hence we bound the ply.
for depth in range(1, 1000):
# The inner loop is a binary search on the score of the position.
# Inv: lower <= score <= upper
# 'while lower != upper' would work, but play tests show a margin of 20 plays
# better.
lower, upper = -MATE_UPPER, MATE_UPPER
while lower < upper - EVAL_ROUGHNESS:
gamma = (lower+upper+1)//2
score = self.bound(pos, gamma, depth)
if score >= gamma:
lower = score
if score < gamma:
upper = score
# We want to make sure the move to play hasn't been kicked out of the table,
# So we make another call that must always fail high and thus produce a move.
self.bound(pos, lower, depth)
# If the game hasn't finished we can retrieve our move from the
# transposition table.
yield depth, self.tp_move.get(pos), self.tp_score.get((pos, depth, True)).lower
###############################################################################
# User interface
###############################################################################
# Python 2 compatability
if sys.version_info[0] == 2:
input = raw_input
def parse(c):
fil, rank = ord(c[0]) - ord('a'), int(c[1]) - 1
return A1 + fil - 10*rank
def render(i):
rank, fil = divmod(i - A1, 10)
return chr(fil + ord('a')) + str(-rank + 1)
def print_pos(pos):
print()
uni_pieces = {'R':'', 'N':'', 'B':'', 'Q':'', 'K':'', 'P':'',
'r':'', 'n':'', 'b':'', 'q':'', 'k':'', 'p':'', '.':'·'}
for i, row in enumerate(pos.board.split()):
print(' ', 8-i, ' '.join(uni_pieces.get(p, p) for p in row))
print(' a b c d e f g h \n\n')
def main():
hist = [Position(initial, 0, (True,True), (True,True), 0, 0)]
searcher = Searcher()
while True:
print_pos(hist[-1])
if hist[-1].score <= -MATE_LOWER:
print("You lost")
break
# We query the user until she enters a (pseudo) legal move.
move = None
while move not in hist[-1].gen_moves():
match = re.match('([a-h][1-8])'*2, input('Your move: '))
if match:
move = parse(match.group(1)), parse(match.group(2))
else:
# Inform the user when invalid input (e.g. "help") is entered
print("Please enter a move like g8f6")
hist.append(hist[-1].move(move))
# After our move we rotate the board and print it again.
# This allows us to see the effect of our move.
print_pos(hist[-1].rotate())
if hist[-1].score <= -MATE_LOWER:
print("You won")
break
# Fire up the engine to look for a move.
start = time.time()
for _depth, move, score in searcher.search(hist[-1], hist):
if time.time() - start > 1:
break
if score == MATE_UPPER:
print("Checkmate!")
# The black player moves from a rotated position, so we have to
# 'back rotate' the move before printing it.
print("My move:", render(119-move[0]) + render(119-move[1]))
hist.append(hist[-1].move(move))
if __name__ == '__main__':
main()