在赋值之前引用的另一个局部变量 - Python

Question

我会开始说： 我知道这是一个被问到很多问题。我已阅读其他答案，并已排除：

我没有使用+ =作为分配;

我试过明确地分配函数中的每个变量，以确保它们不是空的，以防其他工作函数失败;

他们是而不是全局变量，我不希望他们是 - 他们只是我用来确定我最终返回的内部变量。

## Gets the data from external website - refreshes whenever the programme is called. 
## Urllib2 required module 
## csv to make life easier handling the data 

import urllib2 
import csv 
import sys 
import math 
# import sqlite3 #don't need this just now, will probably run Django with MySQL when it comes to it 
# import MySQLdb Likewise, don't need this just now. 
#python3 
import atexit 
from time import time 
from datetime import timedelta 

def secondsToStr(t): 
    return str(timedelta(seconds=t)) 

line = "="*40 
def log(s, elapsed=None): 
    print(line) 
    print(secondsToStr(time()), '-', s) 
    if elapsed: 
     print("Elapsed time:", elapsed) 
    print(line) 
    print() 

def endlog(): 
    end = time() 
    elapsed = end-start 
    log("End Program", secondsToStr(elapsed)) 

def now(): 
    return secondsToStr(time()) 

start = time() 
atexit.register(endlog) 
log("Start Program") 
def open_external_source(): 
    # Checks if the core file's been modified since the last time we used it - if it hasn't, then we skip all of the file reading stuff. 
    #need to change this to just pull the headers the first time. 
    master_data_file = urllib2.urlopen("http://www.football-data.co.uk/mmz4281/1213/E0.csv", "GET") 
    print master_data_file 
    headers = master_data_file.info() 
    last_mod = headers["last-modified"] 
    settings = open ("settings.csv","r+") 
    historic_last_mod = settings.readline() #this only works when the setting is a 1 line file 
    print "Local file version: " + historic_last_mod 
    print "Server file version: " +last_mod 
    if last_mod == historic_last_mod : 
     print "It's the same, file not loaded" 
     return true 
    else : 
     return false 
    settings.close() 

#the if statement's commented out because it was messing up the variables into the function 
#if open_external_source == False: 
master_data_file = urllib2.urlopen("http://www.football-data.co.uk/mmz4281/1213/E0.csv", "GET") 
data = list(tuple(rec) for rec in csv.reader(master_data_file, delimiter=',')) 
print len(data) 
print "printing full file" 
print data 
league_list = ["Arsenal", "Chelsea", "Liverpool", "Man City", "Man United", "Newcastle", "Newcastle", "Norwich","Reading","Southampton", "Stoke", "Sunderland", "Swansea", "Tottenham", "West Brom", "West Ham", "Wigan"] 

league_stats = league_list 

#for teams in league_list: - come back to this, will do this as a split and append. 


#call the next set of functions to skip the data reading stuff 
#This is the data reading section, that puts the data into our system 
#If we do proceed, then we redo all of the calculations, and read the data file in again, in case of any corrections, etc. 

#Column references: 
#Home Goals 4 
#Away Goals 5 
#Full Time Result 6 
#Home Shots 10 
#Away Shots 11 
#Home Shots on Target 12 
#Away Shots on Target 13 


#Calculates the average for a given team at home, columns are 4 Home Goals, 5 Away Goa 
def CalcAverageHome(team, column, data): 
    total = 0 
    count = 0 
    n=0 
    for row in data: 
     if data[count][2] == team: 
      total += int(data[count][column]) 
      n+=1 
     count += 1  
    try: 
     average = float(total)/n 
    except ZeroDivisionError: 
     average = 'Not played' 
    return average 

def CalcAverageAway(team, column, data): 
    total = 0 
    count = 0 
    n=0 
    for row in data: 
     if data[count][3] == team: 
      total += int(data[count][column]) 
      n+=1 
     count += 1  
    try: 
     average = float(total)/n 
    except ZeroDivisionError: 
     average = 'Not played' 
    return average 


home_team = "Chelsea" 
away_team = "Newcastle" 
print "Here's the Average number of goals scored Home" 
home_goals = CalcAverageHome(home_team, 4, data) 
away_goals = CalcAverageAway(home_team, 5, data) 
home_conceded = CalcAverageHome(home_team, 5, data) 
away_conceded = CalcAverageAway(away_team, 4, data) 
adjusted_home = home_goals * away_conceded 
adjusted_away = away_goals * home_conceded 

print home_team, home_goals, home_conceded, adjusted_home 
print away_team, away_goals, away_conceded, adjusted_away 

print "starting to try and work the league averages out here." 

def poisson_probability(actual, mean): 
    # naive: math.exp(-mean) * mean**actual/factorial(actual) 

    # iterative, to keep the components from getting too large or small: 
    p = math.exp(-mean) 
    for i in xrange(actual): 
     p *= mean 
     p /= i+1 
    return p 

for i in range (10): 
    print str((100*poisson_probability(i,adjusted_home)))+"%" 


league_list = ["Arsenal", "Chelsea", "Liverpool", "Man City", "Man United", "Newcastle", "Newcastle", "Norwich","Reading","Southampton", "Stoke", "Sunderland", "Swansea", "Tottenham", "West Brom", "West Ham", "Wigan"] 


# just assign the league list to the stats for now - 
# eventually each team entry will become the first column of a new sublist 

def LeagueAverages(data,column): 
    total = 0 
    n = 0 
    for row in data : 
     string = row[column] 
     if string.isdigit() == True: 
      total = total + int(row[column]) 
      n += 1 
    league_average = float(total)/n 
    return league_average 


print "League home goals average is:", LeagueAverages(data, 4) 
print "League away goals average is:", LeagueAverages(data, 5) 

print "finished that loop..." 




league_stats = [] 
test_team = "Arsenal" 

# Function iterates through the league teams and calculates the averages 
# and places them in one long list. 
for team in league_list: 
    league_stats.append(team) 
    league_stats.append(CalcAverageHome(team, 4, data)) 
    print CalcAverageHome(team, 4, data) 
    league_stats.append(CalcAverageHome(team, 5, data)) 
    CalcAverageHome(team, 5, data) 
    league_stats.append(CalcAverageHome(team, 7, data)) 
    CalcAverageHome(team, 7, data) 
    league_stats.append(CalcAverageHome(team, 8, data)) 
    CalcAverageHome(team, 8, data) 
    league_stats.append(CalcAverageHome(team, 10, data)) 
    CalcAverageHome(team, 10, data) 
    league_stats.append(CalcAverageHome(team, 11, data)) 
    CalcAverageHome(team, 11, data) 
    league_stats.append(CalcAverageHome(team, 12, data)) 
    CalcAverageHome(team, 12, data) 
    league_stats.append(CalcAverageHome(team, 13, data)) 
    CalcAverageHome(team, 13, data) 

# This function should chunk the 'file', as when we run the above code, 
# we'll end up with one incredibly long list that contains every team on the same line 
def chunker(seq, size): 
    return (seq[pos:pos + size] for pos in xrange(0, len(seq), size)) 

chunker (league_stats, 9) 

final_stats = [] 
for group in chunker(league_stats, 9): 
    print repr(group) 
    final_stats.append(repr(group)) 

#retrieve a particular value from the final stats array 
""" 
    for row in final_stats: 
     if data[count][2] == team: 
      total += int(data[count][column]) 
      n+=1 
     count += 1 
""" 

def create_probability_table(hometeam, awayteam, final_stats): 
#reads in the home and away sides, calculates their performance adjusted 
#ratings and then calculates the likelihood of each team scoring a particular 
#number of goals (from 0-10) 
#those likelihoods are then combined to provide an 11x11 matrix of probabilities 
    poisson_array = [] 
    poisson_list_home = [] 
    poisson_list_away = [] 
    goals_home = 0 
    conceded_home = 0 
    goals_away = 0 
    conceded_away = 0 

    for team in final_stats: 
     if team == hometeam: 
      goals_home = team[1] 
      conceded_home = team [3] 
      print "home Goals, Home Conceded" 
      print goals_home, conceded_home 
     elif team == awayteam: 
      goals_away = team[2] 
      conceded_away = team[4] 
      print "Away Goals, Away Conceded" 
      print goals_away, conceded_away, 
     else:   
      pass 

    adjusted_goals_home = goals_home * conceded_away 
    adjusted_goals_away = goals_away * conceded_home 

    #this section creates the two probability lists for home and away num goals scored  
    for i in range (10): 

     poisson_list_home.append = (100*poisson_probability(i,adjusted_goals_home)) 
     poisson_list_away.append = (100*poisson_probability(i,adjusted_goals_away)) 

    print poisson_list_home 
    print poisson_list_away 

    for number in poisson_list_home: 
     for number in poisson_list_away: 
      probability_table.append(poisson_list_home[number] * poisson_list_away[number]) 
    return probability_table 

create_probability_table("Arsenal", "Chelsea", final_stats) 

#and this section cross multiplies them into a new list 

# for i in range (10): 

# print data_frame [0:100] prints to console to provide visual check 

master_data_file.close() 

当我运行它，它抛出一个

line 272, in create_probability_table 
adjusted_goals_home = goals_home * conceded_away UnboundLocalError: local variable 'conceded_away' referenced before assignment 

错误 - 我不明白为什么！它是在函数的开始处定义和分配的。这不是全球性的。

我已经看了这些问题，他们似乎并没有回答这个问题： Local (?) variable referenced before assignment Assigning to variable from parent function: "Local variable referenced before assignment" How is this "referenced before assignment"? UnboundLocalError: local variable 'Core_prices' referenced before assignment

Answer 1

你拼错 “承认”：

condeded_away = 0 
^

而且，您可能希望使用不同的数据结构，如字典：

teams = { 
    'team1': [...], 
    'team2': [...], 
    ... 
} 

你可以再看看一个团队的统计数据更迅速：

stats = teams['team2']