Initial commit

elorating.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+#----------------------------------------------
+# 参考代码:
+#       https://github.com/sublee/elo
+#       https://github.com/HankSheehan/EloPy
+# 参考资料：
+#       https://en.wikipedia.org/wiki/Elo_rating_system
+#       https://en.wikipedia.org/wiki/Go_ranks_and_ratings#Elo-like_rating_systems_as_used_in_Go
+#       https://gobase.org/studying/articles/elo/
+# 
+# CGOS用的bayesELO的介绍,没用这个，它公式不是很清楚 
+#     http://www.yss-aya.com/cgos/19x19/bayes.html  
+#     https://www.remi-coulom.fr/Bayesian-Elo/
+#
+#---------------------------------------------
+
+
+import numpy as np 
+import random
+
+def main():
+    models=[str(i) for i in range (0,50000,1000)]
+    play(models) #模拟5000盘游戏
+
+    elo=EloRatingSystem() #初始化
+    elo.addModels(models) #添加模型
+    elo.readRecords('gameRecord.txt') #从记录里恢复模型
+
+#测试函数与示例
+def play(models,total_game=5000):
+    elo=EloRatingSystem(recordSaveFile='gameRecord.txt') #初始化
+    elo.addModels(models) #添加模型
+    elo.summary()         #查看现有模型
+    
+    for i in range(total_game):  
+        players=elo.getPlayers(sortByRank=True)   #选择分数相近的作为比赛对手
+        black=random.choice(players).name
+        matchList=elo.getNearPlayer(black)
+        if matchList:        
+            white=random.choice(matchList)
+        else:
+            continue #找不到对手，谁都打不过，或者谁都打不过  
+        num=1 #比赛局数
+        winrate=pk(black,white,num) 
+        elo.playgame(black,white,winrate,numgames=num) #得到胜率后时用elo.playgame
+
+    elo.summary()
+
+#测试用pk函数
+def pk(model1,model2,num_games):
+    winrate=1-0.5**(abs(int(model1)-int(model2))/1000)  #假设新模型以一定概率战胜旧模型
+    res=random.choices([1,0.5,0],weights=[winrate,0,1-winrate],k=1) 
+    if int(model1)>int(model2):
+        return res[0]
+    else:
+        return 1-res[0]
+
+
+#以上是测试代码
+
+
+class Player:
+    def __init__(self,name,rating):
+        self.name=name
+        self.rating=rating
+        self.wins=0
+        self.losses=0
+        self.draw=0
+        self.num_game=0
+        
+
+class EloRatingSystem:
+    WIN=1
+    LOSS=0
+    DRAW=0.5
+    def __init__(self,base_rating=1500,recordSaveFile='gameRecord.txt'):
+        self.base_rating=base_rating
+        self.players={}
+        
+        self.saveFile=recordSaveFile
+        
+        self.readFlag=False
+        
+    def addModel(self,name):
+        p=Player(name,self.base_rating)
+        self.players[name]=p
+        return p
+    
+    def addModels(self,models):
+        for m in models:
+            self.addModel(m)
+        print('add model to system:{}'.format(models))
+    
+    def check(self,name,online=True):
+        if name not in self.players:
+            if online:
+                print('{} not in player list,please use <addmodel> to add it'.format(name))
+            return False
+        return True
+    
+
+    def playgame(self,p1,p2,winrate,numgames=1,online=True):
+        '''
+            result [int]:  
+                假设没有平局，赢了算1，输了算0，平局是0.5
+        '''
+        self.readFlag=True
+        if not self.check(p1,online) or not self.check(p2,online):
+            return
+        
+        if online:
+            #只是避免把文件里读出来的再写回去
+            with open(self.saveFile,'a') as f:
+                f.write('{},{},{},{}\n'.format(p1,p2,winrate,numgames))
+        
+        player1=self.players[p1]
+        player2=self.players[p2]
+        
+        p1_win=int(numgames*winrate)
+        p2_win=numgames-p1_win
+
+        player1.wins+=p1_win
+        player2.losses+=p1_win
+        player2.wins+=p2_win
+        player1.losses+=p2_win
+        player1.num_game+=numgames
+        player2.num_game+=numgames
+            
+        R1=player1.rating
+        R2=player2.rating    
+                
+        def expectScore(rating1,rating2):
+            #base on https://en.wikipedia.org/wiki/Elo_rating_system
+            #same function as E1=1/(1+10**((rating1-rating2)/400))
+            Q1=10**(rating1/400)
+            Q2=10**(rating2/400)
+            E1=Q1/(Q1+Q2)
+            E2=Q2/(Q1+Q2)
+            return E1,E2
+        
+        def choose_k(rating):
+            #参考FIDE:https://gobase.org/studying/articles/elo/
+            #另有EGD规则:http://europeangodatabase.eu/EGD/EGF_rating_system.php
+            if rating<2000:
+                return 30
+            elif rating>2400:
+                return 10
+            else:
+                return 130-rating/20
+            
+        
+        E1,E2=expectScore(R1,R2)
+        
+        k=choose_k(R1)
+        player1.rating=R1+k*(winrate-E1)
+        k=choose_k(R2)
+        player2.rating=R2+k*(1-winrate-E2)
+        
+#         if player1.rating<0:
+#             player1.rating=0
+#         elif player2.rating<0:
+#             player2.rating=0
+        
+        if online:
+            #print('{}\'s rating:{}->{} ; {}\'s rating:{}->{}'.format(p1,R1,player1.rating,p2,R2,player2.rating))
+            pass 
+        
+    def readRecords(self,filename):
+        '''
+           读取比赛记录
+        '''
+        if self.readFlag:
+            print('[warning] system is running! will not do anything')
+            return
+        self.readFlag=True
+        with open(filename,'r') as f:
+            lines=f.readlines()
+            for l in lines:
+                record=l.split(',')
+                self.playgame(record[0],record[1],float(record[2]),int(record[3]),False)
+        self.summary()
+     
+    def summary(self):
+        players=list(self.players.values())
+        def sortKey(p):
+            return p.rating
+        players.sort(key=sortKey,reverse=True)
+        
+        print('{:<10s}{:<10s}{:<10s}{:<10s}{:<10s}'.format('model','rating','games','wins','losses'))
+        for p in players:
+            print('{:<10s}{:<10.2f}{:<10d}{:<10d}{:<10d}'.format(str(p.name),p.rating,p.num_game,p.wins,p.losses))
+    
+    def getPlayers(self,sortByRank=False):
+        #默认按模型排序,加入选项可以按分数排序
+        players=list(self.players.values())
+        if sortByRank:           
+            def sortKey(p):
+                return p.rating
+            players.sort(key=sortKey,reverse=True)
+        return players     
+    
+    def getRating(self):
+        #获取分数,按模型输入顺序排,用来画图
+        return [float(p.rating) for p in list(self.players.values())]
+    
+    def getNearPlayer(self,name):
+        #获取同等级的对手，以供匹配，相差200分以内吧
+        res=[]
+        if not self.check(name):
+            return
+        R=self.players[name].rating
+        for p in self.players.values():
+            if abs(p.rating-R)<200 and p.name!=name:
+                res.append(p.name)
+        if len(res)==0:
+            return None
+        return res
+
+if __name__ == '__main__':
+    main()

readme.md

+#### 基本类
+
+``` python
+class EloRatingSystem:
+    def __init__(self,base_rating=1500,recordSaveFile='gameRecord.txt')
+    def addModel(self,name)
+    def addModels(self,models):
+        #添加一个/多个模型
+    def check(self,name,online=True):
+        #检查模型是否在列表中
+    def playgame(self,p1,p2,winrate,numgames=1,online=True):
+        #进行比赛，此过程将更新模型的rating
+    def readRecords(self,filename):
+        #读取比赛记录
+    def summary(self):
+        #总结整体情况
+    def getPlayers(self,sortByRank=False):
+        #返回player类型的列表，默认按模型排序,加入选项可以按分数排序
+    def getRating(self):
+        #获取分数,按模型输入顺序排,用来画图
+    def getNearPlayer(self,name):  
+        #获取同等级的对手，以供匹配，相差200分以内吧
+```
+
+
+
+#### 公式
+
+详细的见 https://en.wikipedia.org/wiki/Elo_rating_system 
+
+A对B的胜率估计如下，Ra,Rb为elo rating
+$$
+E_A=\frac{1}{1+10^{(R_B-R_A)/400}}
+$$
+
+$$
+E_B=1-E_A
+$$
+
+
+
+更新公式，Sa为实际胜率，K为随rating变化的常数，一般在rating较高时较小
+$$
+R_A'=R_A+K(S_A-E_A)
+$$
+
+
+
+
+
+
+#### 参考代码:
+
+​    https://github.com/sublee/elo
+
+​    https://github.com/HankSheehan/EloPy
+
+#### 参考资料：
+
+​    https://en.wikipedia.org/wiki/Elo_rating_system
+
+​    https://en.wikipedia.org/wiki/Go_ranks_and_ratings#Elo-like_rating_systems_as_used_in_Go
+
+​    https://gobase.org/studying/articles/elo/
+
+
+
+   CGOS用的bayesELO的介绍,没用这个，它公式不是很清楚 
+
+   http://www.yss-aya.com/cgos/19x19/bayes.html 
+
+   https://www.remi-coulom.fr/Bayesian-Elo/
+