#!/usr/bin/env pypy3
from __future__ import print_function
import time, math
from itertools import count
from collections import namedtuple, defaultdict
# If we could rely on the env -S argument, we could just use "pypy3 -u"
# as the shebang to unbuffer stdout. But alas we have to do this instead:
#from functools import partial
#print = partial(print, flush=True)
version = "sunfish 2023"
###############################################################################
# Piece-Square tables. Tune these to change sunfish's behaviour
###############################################################################
# With xz compression this whole section takes 652 bytes.
# That's pretty good given we have 64*6 = 384 values.
# Though probably we could do better...
# For one thing, they could easily all fit into int8.
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"]
# Constants for tuning search
QS = 40
QS_A = 140
EVAL_ROUGHNESS = 15
# minifier-hide start
opt_ranges = dict(
QS = (0, 300),
QS_A = (0, 300),
EVAL_ROUGHNESS = (0, 50),
)
# minifier-hide end
###############################################################################
# Chess logic
###############################################################################
Move = namedtuple("Move", "i j prom")
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":
if d in (N, N + N) and q != ".": break
if d == N + N and (i < A1 + N or self.board[i + N] != "."): break
if (
d in (N + W, N + E)
and q == "."
and j not in (self.ep, self.kp, self.kp - 1, self.kp + 1)
#and j != self.ep and abs(j - self.kp) >= 2
):
break
# If we move to the last row, we can be anything
if A8 <= j <= H8:
for prom in "NBRQ":
yield Move(i, j, prom)
break
# Move it
yield Move(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 Move(j + E, j + W, "")
if i == H1 and self.board[j + W] == "K" and self.wc[1]:
yield Move(j + W, j + E, "")
def rotate(self, nullmove=False):
"""Rotates the board, preserving enpassant, unless nullmove"""
return Position(
self.board[::-1].swapcase(), -self.score, self.bc, self.wc,
119 - self.ep if self.ep and not nullmove else 0,
119 - self.kp if self.kp and not nullmove else 0,
)
def move(self, move):
i, j, prom = 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, prom)
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, prom = 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[prom][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, can_null=True):
""" Let s* be the "true" score of the sub-tree we are searching.
The method returns r, where
if gamma > s* then s* <= r < gamma (A better upper bound)
if gamma <= s* then gamma <= r <= s* (A better lower bound) """
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
# 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, can_null), Entry(-MATE_UPPER, MATE_UPPER))
if entry.lower >= gamma: return entry.lower
if entry.upper < gamma: return entry.upper
# Let's not repeat positions. We don't chat
# - at the root (can_null=False) since it is in history, but not a draw.
# - at depth=0, since it would be expensive and break "futility pruning".
if can_null and depth > 0 and pos in self.history:
return 0
# 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.
# FIXME: We also can't null move if we can capture the opponent king.
# Since if we do, we won't spot illegal moves that could lead to stalemate.
# For now we just solve this by not using null-move in very unbalanced positions.
# TODO: We could actually use null-move in QS as well. Not sure it would be very useful.
# But still.... We just have to move stand-pat to be before null-move.
#if depth > 2 and can_null and any(c in pos.board for c in "RBNQ"):
#if depth > 2 and can_null and any(c in pos.board for c in "RBNQ") and abs(pos.score) < 500:
if depth > 2 and can_null and abs(pos.score) < 500:
yield None, -self.bound(pos.rotate(nullmove=True), 1 - gamma, depth - 3)
# 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
# Look for the strongest ove from last time, the hash-move.
killer = self.tp_move.get(pos)
# If there isn't one, try to find one with a more shallow search.
# This is known as Internal Iterative Deepening (IID). We set
# can_null=True, since we want to make sure we actually find a move.
if not killer and depth > 2:
self.bound(pos, gamma, depth - 3, can_null=False)
killer = self.tp_move.get(pos)
# If depth == 0 we only try moves with high intrinsic score (captures and
# promotions). Otherwise we do all moves. This is called quiescent search.
val_lower = QS - depth * QS_A
# Only play the move if it would be included at the current val-limit,
# since otherwise we'd get search instability.
# We will search it again in the main loop below, but the tp will fix
# things for us.
if killer and pos.value(killer) >= val_lower:
yield killer, -self.bound(pos.move(killer), 1 - gamma, depth - 1)
# Then all the other moves
for val, move in sorted(((pos.value(m), m) for m in pos.gen_moves()), reverse=True):
# Quiescent search
if val < val_lower:
break
# If the new score is less than gamma, the opponent will for sure just
# stand pat, since ""pos.score + val < gamma === -(pos.score + val) >= 1-gamma""
# This is known as futility pruning.
if depth <= 1 and pos.score + val < gamma:
# Need special case for MATE, since it would normally be caught
# before standing pat.
yield move, pos.score + val if val < MATE_LOWER else MATE_UPPER
# We can also break, since we have ordered the moves by value,
# so it can't get any better than this.
break
yield move, -self.bound(pos.move(move), 1 - gamma, depth - 1)
# Run through the moves, shortcutting when possible
best = -MATE_UPPER
for move, score in moves():
best = max(best, score)
if best >= gamma:
# Save the move for pv construction and killer heuristic
if move is not None:
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.
# We will fix this problem another way: We add the requirement to bound, that
# it always returns MATE_UPPER if the king is capturable. Even if another move
# was also sufficient to go above gamma. If we see this value we know we are either
# mate, or stalemate. It then suffices to check whether we're in check.
# Note that at low depths, this may not actually be true, since maybe we just pruned
# all the legal moves. So sunfish may report "mate", but then after more search
# realize it's not a mate after all. That's fair.
# This is too expensive to test at depth == 0
if depth > 2 and best == -MATE_UPPER:
flipped = pos.rotate(nullmove=True)
# Hopefully this is already in the TT because of null-move
in_check = self.bound(flipped, MATE_UPPER, 0) == MATE_UPPER
best = -MATE_LOWER if in_check else 0
# Table part 2
if best >= gamma:
self.tp_score[pos, depth, can_null] = Entry(best, entry.upper)
if best < gamma:
self.tp_score[pos, depth, can_null] = Entry(entry.lower, best)
return best
def search(self, history):
"""Iterative deepening MTD-bi search"""
self.nodes = 0
self.history = set(history)
self.tp_score.clear()
gamma = 0
# In finished games, we could potentially go far enough to cause a recursion
# limit exception. Hence we bound the ply. We also can't start at 0, since
# that's quiscent search, and we don't always play legal moves there.
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 it's too much effort to spend
# on what's probably not going to change the move played.
lower, upper = -MATE_LOWER, MATE_LOWER
while lower < upper - EVAL_ROUGHNESS:
score = self.bound(history[-1], gamma, depth, can_null=False)
if score >= gamma:
lower = score
if score < gamma:
upper = score
yield depth, gamma, score, self.tp_move.get(history[-1])
gamma = (lower + upper + 1) // 2
###############################################################################
# UCI User interface
###############################################################################
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)
hist = [Position(initial, 0, (True, True), (True, True), 0, 0)]
#input = raw_input
# minifier-hide start
import sys, tools.uci
tools.uci.run(sys.modules[__name__], hist[-1])
sys.exit()
# minifier-hide end
searcher = Searcher()
while True:
args = input().split()
if args[0] == "uci":
print("id name", version)
print("uciok")
elif args[0] == "isready":
print("readyok")
elif args[0] == "quit":
break
elif args[:2] == ["position", "startpos"]:
del hist[1:]
for ply, move in enumerate(args[3:]):
i, j, prom = parse(move[:2]), parse(move[2:4]), move[4:].upper()
if ply % 2 == 1:
i, j = 119 - i, 119 - j
hist.append(hist[-1].move(Move(i, j, prom)))
elif args[0] == "go":
wtime, btime, winc, binc = [int(a) / 1000 for a in args[2::2]]
if len(hist) % 2 == 0:
wtime, winc = btime, binc
think = min(wtime / 40 + winc, wtime / 2 - 1)
start = time.time()
move_str = None
for depth, gamma, score, move in Searcher().search(hist):
# The only way we can be sure to have the real move in tp_move,
# is if we have just failed high.
if score >= gamma:
i, j = move.i, move.j
if len(hist) % 2 == 0:
i, j = 119 - i, 119 - j
move_str = render(i) + render(j) + move.prom.lower()
print("info depth", depth, "score cp", score, "pv", move_str)
if move_str and time.time() - start > think * 0.8:
break
print("bestmove", move_str or '(none)')
这段代码需要数据训练模型吗
本文探讨了哈希表与二叉树在数据查找效率、内存使用及应用场景上的区别，包括哈希表的O(1)查找时间和可能增加的内存使用，以及二叉树的O(logN)查找时间和较低的内存使用。讨论了哈希表和二叉树在处理特定类型搜索（如完全或部分匹配）时的优缺点。
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