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414
chess_ai.py
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414
chess_ai.py
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from chess_tools import get_possible_moves, move_piece, init_board, get_time
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import numpy as np
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import time
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import random
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inf = float("inf")
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score_table = {"Q" : 9, "R" : 5, "B" : 3.3, "N" : 3.2, "P" : 1, "K" : 10**5 ,"q" : -9, "r" : -5, "b" : -3, "n" : -3, "p" : -1, "k" : -10**5, '':0, '.':0}
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count = 0
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class Heuristics:
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# The tables denote the points scored for the position of the chess pieces on the board.
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# Source : https://www.chessprogramming.org/Simplified_Evaluation_Function
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PAWN_TABLE = [
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[ 0, 0, 0, 0, 0, 0, 0, 0],
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[ 5, 10, 10,-20,-20, 10, 10, 5],
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[ 5, -5,-10, 0, 0,-10, -5, 5],
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[ 0, 0, 0, 20, 20, 0, 0, 0],
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[ 5, 5, 10, 25, 25, 10, 5, 5],
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[10, 10, 20, 30, 30, 20, 10, 10],
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[50, 50, 50, 50, 50, 50, 50, 50],
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[ 0, 0, 0, 0, 0, 0, 0, 0]
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]
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KNIGHT_TABLE = [
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[-50, -40, -30, -30, -30, -30, -40, -50],
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[-40, -20, 0, 5, 5, 0, -20, -40],
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[-30, 5, 10, 15, 15, 10, 5, -30],
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[-30, 0, 15, 20, 20, 15, 0, -30],
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[-30, 5, 15, 20, 20, 15, 0, -30],
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[-30, 0, 10, 15, 15, 10, 0, -30],
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[-40, -20, 0, 0, 0, 0, -20, -40],
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[-50, -40, -30, -30, -30, -30, -40, -50]
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]
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BISHOP_TABLE = [
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[-20, -10, -10, -10, -10, -10, -10, -20],
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[-10, 5, 0, 0, 0, 0, 5, -10],
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[-10, 10, 10, 10, 10, 10, 10, -10],
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[-10, 0, 10, 10, 10, 10, 0, -10],
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[-10, 5, 5, 10, 10, 5, 5, -10],
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[-10, 0, 5, 10, 10, 5, 0, -10],
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[-10, 0, 0, 0, 0, 0, 0, -10],
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[-20, -10, -10, -10, -10, -10, -10, -20]
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]
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ROOK_TABLE = [
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[ 0, 0, 0, 5, 5, 0, 0, 0],
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[-5, 0, 0, 0, 0, 0, 0, -5],
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[-5, 0, 0, 0, 0, 0, 0, -5],
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[-5, 0, 0, 0, 0, 0, 0, -5],
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[-5, 0, 0, 0, 0, 0, 0, -5],
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[-5, 0, 0, 0, 0, 0, 0, -5],
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[ 5, 10, 10, 10, 10, 10, 10, 5],
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[ 0, 0, 0, 0, 0, 0, 0, 0]
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]
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QUEEN_TABLE = [
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[-20, -10, -10, -5, -5, -10, -10, -20],
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[-10, 0, 0, 0, 0, 0, 0, -10],
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[-10, 0, 0, 0, 0, 0, 0, -10],
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[ 0, 0, 0, 0, 0, 0, 0, -5],
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[ -5, 0, 0, 0, 0, 0, 0, -5],
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[-10, 0, 0, 0, 0, 0, 0, -10],
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[-10, 0, 0, 0, 0, 0, 0, -10],
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[-20, -10, -10, -5, -5, -10, -10, -20]
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]
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KING_TABLE_MG = [
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[ 20, 50, 10, 0, 0, 10, 10, 20],
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[ 20, 20, 0, 0, 0, 0, 20, 20],
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[-10, -20, -20, -20, -20, -20, -20, -10],
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[-20, -30, -30, -40, -40, -30, -30, -20],
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[-30, -40, -40, -50, -50, -40, -40, -30],
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[-30, -40, -40, -50, -50, -40, -40, -30],
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[-30, -40, -40, -50, -50, -40, -40, -30],
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[-30, -40, -40, -50, -50, -40, -40, -30]
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]
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KING_TABLE_EG = [
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[-50, -30, -30, -30, -30, -30, -30, -50],
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[-30, -30, 0, 0, 0, 0, -30, -30],
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[-30, -10, 20, 30, 30, 20, -10, -30],
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[-30, -10, 30, 40, 40, 30, -10, -30],
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[-30, -10, 30, 40, 40, 30, -10, -30],
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[-30, -10, 20, 30, 30, 20, -10, -30],
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[-30, -20, -10, 0, 0, -10, -20, -30],
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[-50, -40, -30, -20, -20, -30, -40, -50]
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]
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def get_position_score(start, board):
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"""Evaluate the position of a piece on the board."""
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global game_phase
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piece = board[start]
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if piece == 'P':
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return Heuristics.PAWN_TABLE[start[0]][start[1]]
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if piece == 'p':
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return -Heuristics.PAWN_TABLE[7 - start[0]][start[1]]
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if piece == 'N':
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return Heuristics.KNIGHT_TABLE[start[0]][start[1]]
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if piece == 'n':
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return -Heuristics.KNIGHT_TABLE[7 - start[0]][start[1]]
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if piece == 'B':
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return Heuristics.BISHOP_TABLE[start[0]][start[1]]
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if piece == 'b':
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return -Heuristics.BISHOP_TABLE[7 - start[0]][start[1]]
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if piece == 'R':
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return Heuristics.ROOK_TABLE[start[0]][start[1]]
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if piece == 'r':
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return -Heuristics.ROOK_TABLE[7 - start[0]][start[1]]
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if piece == 'Q':
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return Heuristics.QUEEN_TABLE[start[0]][start[1]]
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if piece == 'q':
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return -Heuristics.QUEEN_TABLE[7 - start[0]][start[1]]
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if piece == 'K':
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if game_phase < 45:
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return Heuristics.KING_TABLE_EG[start[0]][start[1]]
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else:
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return Heuristics.KING_TABLE_MG[start[0]][start[1]]
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if piece == 'k':
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if game_phase < 45:
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return -Heuristics.KING_TABLE_EG[7 - start[0]][start[1]]
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else:
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return -Heuristics.KING_TABLE_MG[7 - start[0]][start[1]]
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return 0
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def get_king_safety(start, board):
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"""King is safe if protected by a wall of pawns"""
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x, y = start
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piece = board[x, y]
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if piece == "K":
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king_safety = (board[max(x-1, 0), y+1] == "P") + (board[x, y+1] == "P") + (board[min(x+1, 7), y+1] == "P")
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elif piece == "k":
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king_safety = (board[max(x-1, 0), y-1] == "p") + (board[x, y-1] == "p") + (board[min(x+1, 7), y-1] == "p")
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king_safety = -king_safety
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else:
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king_safety = 0
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return king_safety
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def get_pawn_defend(start, board):
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"""A good pawn is a pawn that defend another piece"""
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x, y = start
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piece = board[x, y]
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if piece == "P" and y+1<=7:
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if x-1 >= 0:
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if x+1<=7:
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pawn_defend = board[x-1, y+1].isupper() + board[x+1, y+1].isupper()
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else:
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pawn_defend = board[x-1, y+1].isupper()
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else:
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pawn_defend = board[x+1, y+1].isupper()
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elif piece == "p" and y-1>=0:
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if x-1 >= 0:
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if x+1<=7:
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pawn_defend = board[x-1, y-1].islower() + board[x+1, y-1].islower()
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else:
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pawn_defend = board[x-1, y-1].islower()
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else:
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pawn_defend = board[x+1, y-1].islower()
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else:
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pawn_defend = 0
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return pawn_defend
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def get_rook_score(start, board):
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"""A good rook is a rook that can move in a straight line"""
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x, y = start
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piece = board[x, y]
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rook_score = 0
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if piece == "R" or piece == "r":
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for i in range(1, 8):
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if x-i >= 0:
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if board[x-i, y] == ".":
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rook_score += 1
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else:
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break
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else:
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break
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for i in range(1, 8):
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if x+i <= 7:
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if board[x+i, y] == ".":
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rook_score += 1
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else:
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break
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else:
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break
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for i in range(1, 8):
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if y-i >= 0:
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if board[x, y-i] == ".":
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rook_score += 2
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else:
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break
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else:
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break
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for i in range(1, 8):
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if y+i <= 7:
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if board[x, y+i] == ".":
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rook_score += 2
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else:
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break
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else:
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break
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return rook_score
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def get_other_eval(start, board):
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"""Evaluation not based on the conventional chess pieces values and on the position"""
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king_safety = get_king_safety(start, board)
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pawn_defend = get_pawn_defend(start, board)
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rook_score = get_rook_score(start, board)
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return king_safety*10 + pawn_defend*10 + rook_score*2
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def leaf_eval(board):
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"""Evaluate a certain board position"""
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global count
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count += 1
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#evalute using score_table, position score and other eval
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evaluate = lambda x,y: score_table[board[x, y]] * 100 + get_position_score((x, y), board) + get_other_eval((x, y), board)
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return sum(evaluate(x, y) for y in range(8) for x in range(8)) + random.random()*10
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game_phase = 0
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def get_game_phase(board):
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global game_phase
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game_phase = sum(abs(score_table[board[x, y]]) for y in range(8) for x in range(8)) - 2 * score_table['K']
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def minmax(node, depth, alpha=-inf, beta=inf):
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is_maximizing = node.color == 'white'
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if depth == 0 or node.is_leaf:
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return node.score
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if is_maximizing:
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score = -inf
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for child in node.children:
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score = max(score, minmax(child, depth - 1, alpha, beta))
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alpha = max(alpha, score)
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if beta <= alpha:
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break
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return score
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else:
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score = inf
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for child in node.children:
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score = min(score, minmax(child, depth - 1, alpha, beta))
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beta = min(beta, score)
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if beta <= alpha:
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break
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return score
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def evaluate_move(start, end, board):
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"""Evaluate a move. Just used in "create_and_evaluate_tree" to sort the moves to optimize alpha-beta pruning"""
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position_score = get_position_score(start, board)
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take_score = score_table[board[end]]*100
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return position_score + take_score
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class Node:
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__slots__ = 'board', 'move', 'parent', 'children', 'score', 'depth', 'color', 'board_score'
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def __init__(self, board, move, parent, children, score, depth, color):
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self.board = board
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self.move = move
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self.parent = parent
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self.children = children
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self.score = score
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self.depth = depth
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self.color = color
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@property
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def is_leaf(self):
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return not self.children
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@property
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def is_root(self):
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return self.parent is None
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def evalute_tree(self):
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if self.is_leaf:
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self.score = leaf_eval(self.board)
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else:
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for child in self.children:
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child.evalute_tree()
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self.score = minmax(self, self.depth)
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def create_and_evaluate_tree(self, depth, alpha=-inf, beta=inf):
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if depth == 0:
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self.score = leaf_eval(self.board)
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else:
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movable_pieces = ((x, y) for x in range(8) for y in range(8) if
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self.board[x, y] != '.' and self.color == 'white' and self.board[x, y].isupper() or self.color == 'black' and self.board[x, y].islower())
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for piece in movable_pieces:
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possible_moves = get_possible_moves(piece, self.board)
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possible_moves.sort(key=lambda x: abs(evaluate_move(piece, x, self.board)), reverse=True)
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#possible_moves.sort(key=lambda x: evaluate_move(piece, x, self.board), reverse= self.color == "white")
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for move in possible_moves:
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new_board = move_piece(piece, move, self.board)
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new_move = (piece, move)
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new_node = Node(new_board, new_move, self, [], 0, self.depth - 1, 'white' if self.color == 'black' else 'black')
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self.children.append(new_node)
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if self.color == 'white':
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alpha = max(alpha, new_node.create_and_evaluate_tree(depth - 1, alpha, beta))
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if beta <= alpha:
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break
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#To stop exploring if one king is taken
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if abs(self.score) > 10**4:
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break
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else:
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beta = min(beta, new_node.create_and_evaluate_tree(depth - 1, alpha, beta))
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if beta <= alpha:
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break
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if abs(self.score) > 10**4:
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break
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if not self.children:
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self.score = leaf_eval(self.board)
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else:
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self.score = max(child.score for child in self.children if child.score is not None) if self.color == 'white' else min(child.score for child in self.children if child.score is not None)
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return self.score
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def get_best_move(self):
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self.evalute_tree()
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maximazing = self.color == 'white'
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if maximazing:
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maxi = max(self.children, key=lambda x: x.score)
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print(f"maxi: {maxi.score}")
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return maxi.move
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else:
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mini = min(self.children, key=lambda x: x.score)
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print(f"mini: {mini.score}")
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return mini.move
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def pretty(self):
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string = f"Node: {self.move} {self.score} {self.depth} {self.color} \n"
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for child in self.children:
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string += "| " + child.pretty() + "\n"
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return string
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def __repr__(self):
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return self.pretty()
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get_deeper = 0
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def get_best_move(board, color, depth):
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global count, get_deeper
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get_game_phase(board)
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print(f"End game: {game_phase}")
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depth += get_deeper
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print(f"curent depth: {depth}")
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a = time.time()
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root = Node(board, None, None, [], 0, 0, color)
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root.create_and_evaluate_tree(depth)
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best_move = root.get_best_move()
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exec_time = time.time() - a
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print(f"Tree evaluated in {time.time() - a} seconds")
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print(f"Number of leafs: {count}")
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if exec_time < 1.5:
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get_deeper += 1
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print("Getting deeper, current depth: ", depth)
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elif exec_time > 20:
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get_deeper += -1
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print("Getting shallower, current depth: ", depth)
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print(f"Mean number of children : {count**(1/depth)}")
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count = 0
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return best_move
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if __name__ == '__main__':
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board = init_board()
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b = get_best_move(board, 'white', 2)
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print(b)
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