static int chessScore[]={100, 80, 80, 20, 1500, 10, 10}; int redTotalScore = 0; int blackTotalScore = 0; //计算红棋总分 for(int i=0; i<16; i++) { //如果棋子已死则跳过累加 if(myChess[i].isDead) continue; redTotalScore += chessScore[myChess[i].chessType]; } //计算...
score =11returnscoreclassKnighs(Pieces):def__init__(self, player, x, y): self.player = playersuper().__init__(player, x, y)defgetImagekey(self):ifself.player == constants.player1Color:return"r_knigh"else:return"b_knigh"defcanmove(self, arr, moveto_x, moveto_y):ifself.x == ...
//qDebug()<<"chess "<<step.name<<" from "<<step.from<<" to "<<step.to; fakemove(mystep); int score = getmaxscore(level-1,minscore); unfakemove(mystep); if(curmaxscore>=score) return score; //qDebug()<<"THE SCORE IS--"<<score; if(score<minscore) minscore = score; ...
HEIGHT=(650,370)# 背景颜色BG_COLOR='#92877d'# 棋盘需要的数据MARGIN_SIZE=10# 间隔大小BLOCK_SIZE=80# 棋子位置大小defdraw_tips(screen):"""显示提示信息"""# 显示"分数:"tips_img=pygame.image.load("resources/images/tips.png")screen.blit(tips_img,(375,200))defget_score(chess_nums_...
turn)else:returnturndefcheckPut(self, pos):# check person putassertlen(pos)>=2,'move position disable'r =ord(pos[0]) -97c =ord(pos[1]) -97assert0<= r < self.nand0<= c < self.n,'move position disable'turnList = self.score(r, c)ifturnList:# turn chessforx,yinturnList+[...
本系统功能设计方面采用面向对象方式,将围棋对弈定义为chess类,并定义已提白棋数量、已提黑棋数量、需要减去妻子数量、窗口尺寸、棋格的边长等属性。函数方面有放置棋子图片函数、删除棋子图片函数、开始游戏函数、重新开始函数、放弃一手函数、悔棋函数、显示鼠标移动下棋子的移动函数、落子函数、保存游戏函数以及主函数控制...
c.move(QPoint(*Chesspos2Pixel(next_pos))) c.show() self.chessboard[next_pos[0]][next_pos[1]] = c# 落子声音响起self.drop_sound.play()# 最后落子位置标志对落子位置进行跟随self.chessman_sign.show() self.chessman_sign.move(c.pos()) ...
本文公开一个基于强化学习算法DQN的五子棋游戏自动下棋算法源码,并对思路进行讲解。 完整代码和预训练模型(Saver文件夹)地址: python_强化学习算法DQN_玩五子棋游戏 一个基于CNN构成的DQN算法的8*8的五子棋游戏 image.png 1、Q-Learning介绍 Q-Learning的思想并不是很复杂,很多文章都有详细的介绍,这里只是简单举个...
def get_score(chess_nums_temp): """ 计算当前棋盘的总分数 """ def sum_all(x, y): if isinstance(x, Iterable): return sum(x) + sum(y) return x + sum(y) return reduce(sum_all, chess_nums_temp) def draw_score(screen, score): ...
import classification_report # 模型评估from imblearn.over_sampling import RandomOverSampler # 用于过采样/欠采样import plotly.express as px # 数据可视化import plotly.graph_objects as go # 数据可视化我们将使用来自Kaggle的国际象棋游戏数据，你可以通过这个链接下载:https://www.kaggle.com/datasnaek/chess...