#coding=utf-8

#!/usr/bin/python

import sys, re, collections

#读入文件

def read_file(filename):

    try:

        fp = open(filename)

        text = fp.read()

    except IOError:

        print ("Error opening or reading input file: ",filename)

        sys.exit()

    return text

#分割文件为单词，并将字母都转换为小写

def words(text):

    return re.findall('[a-z]+', text.lower())

# 该函数计算输入文本每个单词出现的次数

def train(features):

    # 生成了一个默认value=1的带key的数据字典

    model = collections.defaultdict(lambda: 1)

    for f in features:

        model[f] += 1

    return model

# big文本中每一个单词及其出现的次数

NWORDS = train(words(read_file('/home/aistudio/data/data12892/big.txt')))

alphabet = 'abcdefghijklmnopqrstxyz'

# 变换输入单词形式，得到那种是最可能的错误

def edist1(word):

    n = len(word)

    return set([word[0:i]+word[i+1: ] for i in range(n)] +                      #删除

               [word[0:i]+word[i+1]+word[i]+word[i+2: ] for i in range(n-1)] +  #错位

               [word[0:i]+c+word[i+1: ] for i in range(n) for c in alphabet] +  #变换

               [word[0:i]+c+word[i: ] for i in range(n+1) for c in alphabet])   #添加

# 在edist1的基础上进一步变换,要去是出现在字典内的词

def known_edist2(word):

    return set(e2 for e1 in edist1(word) for e2 in edist1(e1) if e2 in NWORDS)

# big.txt中已知的单词集合

def known(words):

    wordintxt = set([])

    for w in words:

        if w in NWORDS:

            wordintxt.add(w)

    return wordintxt

    # return set(w for w in words if w in NWORDS)

def correct(word):

    candidates = known([word]) or known(edist1(word)) or known_edist2(word) or [word]

    return max(candidates, key=lambda w:NWORDS[w])

print (correct("acacss"))