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本人承接各种高校C语言、C++、Java语言、python等课程设计、数据采集、系统研发以及ppt等制造、科学上网等，以及常见的电脑故障有需要的私信我或者微信18476275715
这几年来，人工智能、大数据一直是一个大热门啊！但这些热门技术归根到底还是算法、数学的应用，本篇文章将讲解AI、大数据在一些具体应用的原理和解释！讲这些之前，先给大家普及一些井字棋、中国象棋的知识：井字棋是一个九格宫，棋子只有两种X、O，只要将其连成三个即可 获胜，规则简单，范围有限，所以我们可以将其赢得方式一一列举出来。
象棋相比而言就较为复杂的多，棋盘上有十条横线、九条竖线共分成90个交叉点；中国象棋的棋子共有32个，每种颜色16个棋子，分为7个兵种，摆放和活动在交叉点上。双方交替行棋，先把对方的将（帅）“将死”的一方获胜。如果模仿上面的井字棋一一人工列举出来，根本无法实现，只能通过机器学习去获得赢得方式，并保存下来。AlphaGo也应用到这一方面的知识，AlphaGo围棋会产生大量自我对弈棋局（最开始的阿尔法是以各种大型赛事等为基础数据的），为下一代的版本提供大量的基础数据，如此往复循环！所以AlphaGo可以说没有最强、只用更强！
总的来说，AI井字棋和AI中国象棋无非就是积累原始数据，然后进行训练，保存训练数据，为后面的更加智能做准备，循环往复。说起来简单，但如何对数据进行有效的处理呢？这一点便是AI的核心点。
井字棋：
#coding=utf-8
"""
[0,1,2]
[3,4,5]
[6,7,8]
"""
#胜利的走法
win_chess = [[0,4,8],[2,4,6],[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8]]
#最佳下棋顺序
best_way = [4,0,2,6,8,1,3,5,7]
#棋盘
chess = [0,0,0,0,0,0,0,0,0]
def is_win(now_chess,who):
"""
判断游戏方（who）是否赢局
"""
temp = now_chess[:]
for w_c in win_chess:
if temp[w_c[0]] == who and temp[w_c[1]] == who and temp[w_c[2]] == who :
return who
return 0
def count_zero(now_chess):
"""
统计剩余格子
返回个数
"""
temp = now_chess[:]
count = 0
for te in temp:
if te == 0:
count = count + 1
return count
def evaluation(now_chess):
"""
估价函数（以X为对象）
可以赢的行数 +1
可以赢的行数上有自己的棋子 +2
可导致自己赢 +2
可导致对手赢 -2
"""
temp = now_chess[:]
count = 0
for w_c in win_chess:
if temp[w_c[0]] >= 0 and temp[w_c[1]] >= 0 and temp[w_c[2]] >= 0 :
if temp[w_c[0]] == 1 or temp[w_c[1]] == 1 or temp[w_c[2]] == 1 :
count += 1
count += 1
if is_win(temp,1) == 1:
count = count + 2
if is_win(temp,-1) == -1:
count = count - 2
return count
def all_go(now_chess,who):
"""
遍历所有走法
"""
temp = now_chess[:]
tempp = []
for i in best_way:
if temp[i] == 0:
temppp = temp[:]
temppp[i]=who
tempp.append([temppp,i])
return tempp
def get_next_x(now_chess,who):
"""
x获取下一个位置
"""
temp = now_chess[:]
best_list = None
best_one = -1
if count_zero(temp) <= 3 :
for te in all_go(temp,who):
if best_one == -1:
best_list = te[0]
best_one = te[1]
else :
if evaluation(te[0]) > evaluation(best_list):
best_list = te[0]
best_one = te[1]
return best_one
for te in all_go(temp,who):
for tee in all_go(te[0],who*-1):
for teee in all_go(tee[0],who):
if best_list is None:
best_list = teee[0]
best_one = te[1]
else:
if evaluation(teee[0]) > evaluation(best_list) :
best_list = teee[0]
best_one = te[1]
return best_one
def get_next_o(now_chess,who):
"""
o获取下一个位置
"""
temp = now_chess[:]
best_list = None
best_one = -1
if count_zero(temp) <= 2 :
for te in all_go(temp,who):
if best_one == -1:
best_list = te[0]
best_one = te[1]
else :
if evaluation(te[0]) < evaluation(best_list):
best_list = te[0]
best_one = te[1]
return best_one
for te in all_go(temp,who):
for tee in all_go(te[0],who*-1):
if best_list is None:
best_list = tee[0]
best_one = te[1]
else:
if evaluation(tee[0]) < evaluation(best_list) :
best_list = tee[0]
best_one = te[1]
return best_one
def is_danger(now_chess,who=0):
"""
判断自己是否处于危险状态（即 对手可能已经差一子赢局）
"""
temp = now_chess[:]
for te in all_go(temp,who*-1):
if is_win(te[0],who*-1) == who*-1:
return te[1]
return -1
if __name__ == "__main__":
"""
测试用
"""
chess = [0,0,0,\
0,1,0,\
0,0,0]
#print(get_next_old(chess,-1,1))
#print(all_go(chess,1))
print(get_next_o(chess,-1))
#coding=utf-8
"""
"""
import tkinter as tk
import time
import threading
import random
import chess
init_chess = [0,0,0,0,0,0,0,0,0] #原始棋盘
the_chess = [0,0,0,0,0,0,0,0,0] #记录棋盘
show_chess = ''
flag = True
who = 1
count_x = 0
count_y = 0
count_z = 0
top = tk.Tk()
top.title('井字棋 -> Fighting')
top.geometry("300x300")
top.resizable()
show_str = tk.StringVar(top)
tips = tk.StringVar(top) #提示信息
#初始化棋盘信息
ch = []
for i in range(9):
ch.append(tk.StringVar(top))
#初始化提示信息
tips.set("")
frame_top = tk.Frame(top)
frame_cont = tk.Frame(top)
frame_bot = tk.Frame(top)
frame_cont1 = tk.Frame(frame_cont)
frame_cont2 = tk.Frame(frame_cont)
frame_cont3 = tk.Frame(frame_cont)
label1 = tk.Label(frame_cont,justify=tk.CENTER,textvariable=show_str,font=("幼圆",30))
# 棋盘显示label 0~9
l0 = tk.Label(frame_cont1,textvariable=ch[0],font=("幼圆",30),padx=0)
l1 = tk.Label(frame_cont1,textvariable=ch[1],font=("幼圆",30),padx=0)
l2 = tk.Label(frame_cont1,textvariable=ch[2],font=("幼圆",30),padx=0)
l3 = tk.Label(frame_cont2,textvariable=ch[3],font=("幼圆",30),padx=0)
l4 = tk.Label(frame_cont2,textvariable=ch[4],font=("幼圆",30),padx=0)
l5 = tk.Label(frame_cont2,textvariable=ch[5],font=("幼圆",30),padx=0)
l6 = tk.Label(frame_cont3,textvariable=ch[6],font=("幼圆",30),padx=0)
l7 = tk.Label(frame_cont3,textvariable=ch[7],font=("幼圆",30),padx=0)
l8 = tk.Label(frame_cont3,textvariable=ch[8],font=("幼圆",30),padx=0)
label_bottom = tk.Label(frame_bot,justify=tk.CENTER,textvariable=tips,font=("幼圆",20),padx=0)
def update_chess():
for i in range(9):
if the_chess[i] == 1 :
ch[i].set('|X|')
elif the_chess[i] == -1 :
ch[i].set('|O|')
else :
ch[i].set('| |')
#print(i)
def init_ch():
"""
初始化棋盘
"""
for i in range(9):
the_chess[i] = init_chess[i]
update_chess()
return the_chess
def ai_go_first():
if chess.count_zero(the_chess) == 9:
the_chess[random.randint(0,8)] = -1
update_chess()
forget()
ai_go_fir_b = tk.Button(frame_cont,text='机器先下',command=ai_go_first)
def forget():
ai_go_fir_b.pack_forget()
def but1():
"""
人机对战
"""
flag = True
init_ch()
tips.set("人机对战模式")
l0.bind("<Button-1>", touch_l0)
l1.bind("<Button-1>", touch_l1)
l2.bind("<Button-1>", touch_l2)
l3.bind("<Button-1>", touch_l3)
l4.bind("<Button-1>", touch_l4)
l5.bind("<Button-1>", touch_l5)
l6.bind("<Button-1>", touch_l6)
l7.bind("<Button-1>", touch_l7)
l8.bind("<Button-1>", touch_l8)
ai_go_fir_b.pack(side=tk.TOP)
def run(i,id):
new_chess = init_chess[:]
global count_x
global count_y
global count_z
if id == 1 :
new_chess[random.randint(0,8)] = -1
else :
new_chess[random.randint(0,8)] = 1
x = 0
for x in range(10):
if chess.count_zero(new_chess) > 0 :
#print(chess.count_zero(new_chess))
if id == 1:
#print('*****')
#print(chess.get_next_x(new_chess,id))
pos = chess.get_next_x(new_chess,id)
if pos != -1 :
new_chess[int(chess.get_next_x(new_chess,id))] = id
else :
for xx in range(9):
if new_chess[xx] == 0 :
new_chess[xx] = id
else :
pos = chess.get_next_o(new_chess,id)
if pos != -1 :
new_chess[int(chess.get_next_o(new_chess,id))] = id
else :
for xx in range(9):
if new_chess[xx] == 0 :
new_chess[xx] = id
id = id * -1
if chess.is_win(new_chess,id) == id :
name = ''
if id == 1 :
name = 'X'
update_chess()
print("第 {} 局 ： {} 赢了！".format(i+1,name) + ' ' + str(new_chess))
tips.set("第 {} 局 ： {} 赢了！".format(i+1,name))
threading.Lock()
count_x = count_x + 1
threading.RLock()
time.sleep(3)
break
else :
name = 'O'
update_chess()
print("第 {} 局 ： {} 赢了！".format(i+1,name) + ' ' + str(new_chess))
tips.set("第 {} 局 ： {} 赢了！".format(i+1,name))
threading.Lock()
count_y = count_y + 1
threading.RLock()
time.sleep(3)
break
elif chess.is_win(new_chess,id*-1) == id*-1 :
id = id * -1
name = ''
if id == 1 :
name = 'X'
print("第 {} 局 ： {} 赢了！".format(i+1,name) + ' ' + str(new_chess))
tips.set("第 {} 局 ： {} 赢了！".format(i+1,name))
threading.Lock()
count_x = count_x + 1
threading.RLock()
break
else :
name = 'O'
print("第 {} 局 ： {} 赢了！".format(i+1,name) + ' ' + str(new_chess))
tips.set("第 {} 局 ： {} 赢了！".format(i+1,name))
threading.Lock()
count_y = count_y + 1
threading.RLock()
break
elif chess.count_zero(new_chess) == 0:
print("第 {} 局 ： 平局".format(i+1) + ' ' + str(new_chess))
tips.set("第 {} 局 ： 平局".format(i+1))
threading.Lock()
count_z = count_z + 1
threading.RLock()
break
else :
pass
else :
print("第 {} 局 ： 平局".format(i+1) + ' ' + str(new_chess))
tips.set("第 {} 局 ： 平局".format(i+1))
threading.Lock()
count_z = count_z + 1
threading.RLock()
break
#print(str(i + 1) + ' ' + str(new_chess))
'''if i == 9:
print("第 {} 局 ： 平局".format(i+1) + ' ' + str(new_chess))
tips.set("第 {} 局 ： 平局".format(i+1))
threading.Lock()
count_z = count_z + 1
threading.RLock()'''
time.sleep(3)
for i in range(9):
the_chess[i] = new_chess[i]
update_chess()
threading.Lock()
tips.set("50局已经结束！\nX 共赢 {}次\nO 共赢 {}次\n平局 {} 次".format(count_x,count_y,count_z))
threading.RLock()
def but2():
"""
机器对战
"""
print(" ")
ai_go_fir_b.pack_forget()
flag = False
global count_x
global count_y
global count_z
count_x = 0
count_y = 0
count_z = 0
init_ch()
tips.set("机器对战模式")
l0.unbind("<Button-1>")
l1.unbind("<Button-1>")
l2.unbind("<Button-1>")
l3.unbind("<Button-1>")
l4.unbind("<Button-1>")
l5.unbind("<Button-1>")
l6.unbind("<Button-1>")
l7.unbind("<Button-1>")
l8.unbind("<Button-1>")
id = 1
th = []
for i in range(50):
id = id * -1
try:
th.append(threading.Thread(target=run,args=(i,id)))
th[i].start()
except Exception as e:
print(e)
i = i - 1
#tips.set("50 局已经结束！ X 共赢 {}次， O 共赢 {}次， 平局 {} 次".format(count_x,count_y,count_z))
def ai_go(w):
"""
机器走棋 O
"""
if chess.count_zero(the_chess) < 9:
po = chess.is_danger(the_chess,1)
if po != -1 :
the_chess[po] = w
update_chess()
elif constraint(w) == False:
pass
else :
the_chess[chess.get_next_o(the_chess,-1)] = w
update_chess()
if chess.is_win(the_chess,-1) == -1:
tips.set("你输了！")
if chess.count_zero(the_chess) == 0:
tips.set("平局！")
def constraint(w):
"""
判断是否处于危险状态
"""
po = chess.is_danger(the_chess,-1)
if po != -1:
the_chess[po] = w
update_chess()
return False
return True
def peo_go(po):
"""
获取人们按键，并下棋
"""
if the_chess[po] == 0 :
the_chess[po] = who
update_chess()
if chess.is_win(the_chess,who) == who:
tips.set('你赢了！')
elif chess.count_zero(the_chess) == 0:
tips.set("平局！")
else :
ai_go(who*-1)
def touch_l0(e):
peo_go(0)
def touch_l1(e):
peo_go(1)
def touch_l2(e):
peo_go(2)
def touch_l3(e):
peo_go(3)
def touch_l4(e):
peo_go(4)
def touch_l5(e):
peo_go(5)
def touch_l6(e):
peo_go(6)
def touch_l7(e):
peo_go(7)
def touch_l8(e):
peo_go(8)
tk.Button(frame_top,text='人机对决',command=but1).pack(side=tk.LEFT)
tk.Button(frame_top,text='机器对决',command=but2).pack(side=tk.RIGHT)
update_chess()
l0.pack(side=tk.LEFT)
l1.pack(side=tk.LEFT)
l2.pack(side=tk.LEFT)
l3.pack(side=tk.LEFT)
l4.pack(side=tk.LEFT)
l5.pack(side=tk.LEFT)
l6.pack(side=tk.LEFT)
l7.pack(side=tk.LEFT)
l8.pack(side=tk.LEFT)
label_bottom.pack()
frame_cont1.pack()
frame_cont2.pack()
frame_cont3.pack()
frame_top.pack()
frame_cont.pack()
frame_bot.pack()
top.mainloop()
井字棋相对简单就没有太多的分析，代码已经给出如果还有小伙伴不明白可以加我联系！
AI中国象棋:
AI中国象棋就不展示了，有需要的小伙伴可以找我，这里主要展示象棋的核心部分。
一般来说AI在做出决策需要有三个条件，第一：AI会找出所有允许象棋规则的走法。第二：根据这些走法生成一个树来决定最佳的一步；树的大小随深度指数增长，但是树的深度可以是任意的。假设每次有20种走法，那深度为1对应20，深度为2对应400，以此类推。第三：遍历这个树，选择出某一种走法对应的结果最佳的走法。
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