452 lines
18 KiB
Python
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()
|
||
|
|
|