import re
import math

MAX_SPLITLEN = 4#最大切分長度
corpus_lib = ''#corpus:語料


def init_corpus_lib(path): # 初始化語料庫
 global corpus_lib
 with open(path, 'r', encoding='utf-8', errors='ignore') as file:
  corpus_lib = str(file.readlines())


def get_candidate_words(sen):
 global MAX_SPLITLEN
 global corpus_lib
 candidate_words = []
 for sp in range(len(sen)):
  w = sen[sp]
  candidate_words.append([w, sp, sp]) # 有些字可能不在語料庫中，把它作為單個字加進去
  for mp in range(1, MAX_SPLITLEN): # 判斷1 ~ MAX_SPLITLEN-1這3種詞中是否有候選詞.
   if sp + mp < len(sen):
    w += sen[sp + mp]
    if w in corpus_lib:
     candidate_words.append([w, sp, sp + mp]) # 存儲詞，初始位置，結束位置
 print('候選詞有：%s' % candidate_words)
 return candidate_words


def segment_sentence(sen): # sen:sentence即要切分的句子
 global MAX_SPLITLEN
 global corpus_lib

 candidate_words = get_candidate_words(sen)
 count = 0
 for word in candidate_words:
  if count > 1000: # 為防止對長句子解析時間過長，放棄一部分精度追求效率
   break
  if word[1] == 0 and word[2] != len(sen) - 1: # 如果句子中開頭的部分，還沒有拼湊成整個詞序列的話
   no_whitespace_sen = ''.join(word[0].split())
   for word in candidate_words: # word比如：['今天', 1, 2]，1是今在句子中的位置，2是天的位置
    if word[1] == 0 and word[2] != len(sen) - 1:
     end = word[2]
     for later_word in candidate_words:
      if later_word[1] == end + 1: # 如果later_word是當前詞的后續詞，那么拼接到當前詞上
       word_seq = [word[0] + ' ' + later_word[0], word[1], later_word[2]] # 合并
       candidate_words.append(word_seq)
       # print('拼出了新詞：%s' % word_seq)
       count += 1
     candidate_words.remove(word) # 遍歷完后，這個開頭部分短語要移除掉，不然下次遍歷還會對它做無用功
 print('所有結果詞序列有：%s' % candidate_words)

 word_segment_res_list = [] # 存儲分詞結果序列
 for seque in candidate_words:
  if seque[1] == 0 and seque[2] == len(sen) - 1:
   word_segment_res_list.append(seque[0])
 print('獲得的所有分詞結果是：')
 print(word_segment_res_list)
 return word_segment_res_list


# P(w1,w2,...,wn) = P(w1/start)P(w2/w1)P(w3/w2).....P(Wn/Wn-1)
# 下標從0開始： = P(w0/start)P(w1/w0)...P(Wn-1/Wn-2)
def calculate_word_sequence_probability(sequence):
 global corpus_lib
 word_list = sequence.split(' ')
 total_word_num = len(corpus_lib)
 prob_total = 0.0
 word_start = word_list[0]
 # 計算第一個詞出現的概率P(w1/start)=Count(w1)/total
 count = len(re.findall(r'\s' + word_start + r'\s', corpus_lib)) + 1 # 加1平滑
 prob_total += math.log(count / total_word_num)
 # 計算P(w2/w1)P(w3/w2).....P(Wn/Wn-1)
 for i in range(len(word_list) - 1): # 0~ n-2
  prev_w = word_list[i]
  later_w = word_list[i + 1]
  count = len(re.findall(r'\s' + prev_w + r'\s' + later_w + r'\s', corpus_lib))
  count += 1 # 做一次加1平滑
  prob_total += math.log(count / total_word_num)
 print('%s的概率是：' % sequence)
 print(prob_total)
 return prob_total


def calculate_biggest_prob(word_segm_res):
 best_w_s = ''
 max_prob = 0.0
 for w_s in word_segm_res: # 改進：先只計算詞的數目<=0.6 句子字數的，如果不行再計算全部的概率
  no_whitespace_sen = ''.join(w_s.split())
  zi_shu = len(no_whitespace_sen)
  if len(w_s.split(' ')) <= zi_shu * 0.6:
   prob = calculate_word_sequence_probability(w_s)
   if max_prob == 0 or max_prob < prob:
    best_w_s = w_s
    max_prob = prob
  if best_w_s == '': # 如果上面的0.6不行的話，再計算全部的概率
   prob = calculate_word_sequence_probability(w_s)
   if max_prob == 0 or max_prob < prob:
    best_w_s = w_s
    max_prob = prob
 print('最好的分詞結果（概率為%s）是 ：%s' % (math.pow(math.e, max_prob), best_w_s))
 return best_w_s


def split_middle(sen_to_segment): # 從中間切分一下，返回中間切分的位置
 length = len(sen_to_segment)
 start = int(length / 2) - 2
 end = start + 5
 # 對中間的5個字進行切分，然后找第一個空格，按此把整個句子一分為二
 middle_part = sen_to_segment[start:end]
 best_segm_res = calculate_biggest_prob(segment_sentence(middle_part))
 return start + best_segm_res.index(' ') - 1


def split_mark_and_too_long_sent(sentences): # 按任意標點符號劃分句子，對每個短句進行分詞
 sen_list = sentences.splitlines()
 print(sen_list)

 out_text = ''
 for line in sen_list:
  sen_to_segment = '' #
  for single_char in line:
   if single_char.isalpha(): # isalpha()表示是否是單詞，如果是單詞的為True，標點符號等為False
    sen_to_segment += single_char
   elif not single_char.isalpha() and sen_to_segment == '': # 如果single_char是標點符號、數字,且前面沒有待分詞的句子
    out_text += single_char + ' '
    print(single_char)

   else: # 如果single_char是標點符號、數字,
    # 如果句子太長，先從中間切分一下
    if len(sen_to_segment) >= 20:
     middle = split_middle(sen_to_segment)
     left_half = sen_to_segment[0:middle + 1] # 左半部分
     best_segm_res = calculate_biggest_prob(segment_sentence(left_half))
     out_text += best_segm_res + ' '
     sen_to_segment = sen_to_segment[middle + 1:len(sen_to_segment)] # 右半部分交給后面幾行處理

    best_segm_res = calculate_biggest_prob(segment_sentence(sen_to_segment))
    print(single_char)
    sen_to_segment = ''
    out_text += best_segm_res + ' ' + single_char + ' ' # 標點兩側也用空格隔起來

  # 如果這行句子最后還有一些文字沒有切分的話
  if sen_to_segment != '':
   best_segm_res = calculate_biggest_prob(segment_sentence(sen_to_segment))
   out_text += best_segm_res + ' '
  out_text += '\n'

 with open('D:/1佩王的文件/計算語言學基礎/生成結果.txt','w') as file:
  file.write(out_text)
 print(out_text)


if __name__ == '__main__':
 path = 'D:/1佩王的文件/計算語言學基礎/北大(人民日報)語料庫199801.txt'
 init_corpus_lib(path)#初始化語料庫

 sentences = ''
 path = 'E:/study/1.研一的課/計算語言學基礎課件/testset.txt'#讀取要切分的文章
 with open(path, 'r', encoding='gbk', errors='ignore') as file:
  for line in file.readlines():
   sentences += line

 # 改進：先對句子按標點符號劃分成多個短句，然后對每個短句進行切分、計算概率
 split_mark_and_too_long_sent(sentences)