AI-chess-python/ChessAI.py at main · pard1s/AI-chess-python · GitHub

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import numpy as np
import ChessEngine
import random
from copy import deepcopy
from colorama import init
from colorama import Fore, Style

init()
class HeuristicAlphaBetaSearch():

	def alphaBetaSearch(self, state, d):
		value, move = self.maxValue(state, -10000000, 10000000, d)
		return move

	def maxValue(self, state, alpha, beta, d):
		if d == 0:
			return self.eval(state), None
		possibleMoves = state.getValidMoves()
		if possibleMoves == None:
			return self.eval(state), None
		value = -10000000
		for m in possibleMoves:
			state.makeMove(m)
			v2 = self.minValue(state, alpha, beta, d - 1)[0]
			m2 = deepcopy(self.minValue(state, alpha, beta, d - 1)[1])
			state.undoMove()
			if v2 > value:
				value = v2
				move = deepcopy(m)
				alpha = max(alpha, value)
			if value >= beta:
				return value, move
		return value, move

	def minValue(self, state, alpha, beta, d):
		if d == 0:
			return self.eval(state), None
		possibleMoves = state.getValidMoves()
		if possibleMoves == None:
			return self.eval(state), None
		value =  10000000
		for m in possibleMoves:
			state.makeMove(m)
			v2 = self.maxValue(state, alpha, beta, d - 1)[0]
			m2 = deepcopy(self.minValue(state, alpha, beta, d - 1)[1])
			state.undoMove()
			if v2 < value:
				value = v2
				move = deepcopy(m)
				beta = min(beta, value)
			if value <= alpha:
				return value, move
		return value, move

	def eval(self, state):
		piece = {"P": 100, "N": 280, "B": 320, "R": 479, "Q": 929, "K": 60000}
		pst = {
			"P": np.array([[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": np.array([[-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": np.array([[-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": np.array([[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": np.array([[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": np.array([[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]])
		}
		wvalue = 0
		bvalue = 0

		if state.checkmate:
			if state.whiteToMove:
				return 10000000    #black wins
			else:
				return  -10000000
		elif state.stalemate:
			return 0

		else:
			for r in range(8):
				for c in range(8):
					p = state.board[r][c][1]
					if state.board[r][c][0] == "w":
						wvalue += piece[p] + pst[p][r][c]
					elif state.board[r][c][0] == "b":
						bvalue += piece[p] + pst[p][7 - r][c]
		return bvalue - wvalue