import time
import re
class Segment:

# 数据成员sentence = ""MaxLen = 0pos = 0len = 0result_MM = ""  # 寄存MM分词后果result_RMM = ""  # 寄存RMM分词后果final_res = ""dict = []# 构造函数def __init__(self, sentence, MaxLen):    self.sentence = sentence    self.MaxLen = MaxLen    self.pos = 0    self.len = self.MaxLen    self.result_MM = ""    self.readDict()# 读字典def readDict(self):    f = open("chineseDic.txt", "r", encoding="utf-8")    lines = f.readlines()    for line in lines:        # print(line)        words = line.split(",")        self.dict.append(words[0])# 正向最大匹配def MM(self, nLen, nPos):    length = len(self.sentence)    if (nPos > length):        return    substr = self.sentence[nPos:nPos + nLen]    if substr in self.dict:        self.result_MM = self.result_MM + substr + "/ "        nPos = nPos + nLen        nLen = self.MaxLen        self.MM(nLen, nPos)    elif nLen > 1:        nLen = nLen - 1        self.MM(nLen, nPos)    else:        self.result_MM = self.result_MM + substr + "/ "        nPos = nPos + 1        nLen = self.MaxLen        self.MM(nLen, nPos)# 逆向最大匹配def RMM(self, nLen, nPos):    if (nPos < 0):        return    substr = self.sentence[nPos - nLen:nPos]    if substr in self.dict:        self.result_RMM = self.result_RMM + "/" + substr        nPos = nPos - nLen        nLen = self.MaxLen        self.RMM(nLen, nPos)    elif nLen > 1:        nLen = nLen - 1        self.RMM(nLen, nPos)    else:        self.result_RMM = self.result_RMM + substr + "/"        nPos = nPos - 1        nLen = self.MaxLen        self.RMM(nLen, nPos)def getMMResult(self):    return self.result_MMdef getRMMResult(self):    return self.result_RMMdef getFinalResult(self):    return self.final_resdef printFinalResult(self):    print("正向最大匹配后果：")    seg_res_MM = self.result_MM.replace(" ", "")    print(seg_res_MM)    seg_list_MM = seg_res_MM.split('/')    del seg_list_MM[-1]  # 因为依照'/'宰割，所以最初会多出一个''，删去    print(seg_list_MM)    print("逆向最大匹配后果：")    seg_res_RMM = self.result_RMM.replace(" ", "")    print(seg_res_RMM)    seg_list_RMM = list(reversed(seg_res_RMM.split('/')))    del seg_list_RMM[0]    del seg_list_RMM[-1]    print(seg_list_RMM)    len_MM = len(seg_list_MM)    len_RMM = len(seg_list_RMM)    flag = 1    for i in range(0, min(len_MM, len_RMM)):        if seg_list_MM[i] != seg_list_RMM[i]:            print("两次分词后果不统一。")            flag = 0            break    if (flag):        print("两次分词后果统一。")        print("最终的分词后果为：")        self.final_res = self.result_MM        print(self.final_res)

def to_region(segmentation):

region = []start = 1for word in re.compile("\\s+").split(segmentation.strip()):  # 空格，回车，换行等空白符    end = start + len(word) - 2    region.append((start, end))    start = end + 1return region

def PRF(target, pred):

t_set, p_set = set(target), set(pred)target_num = len(t_set)pred_num = len(p_set)cap_num = len(t_set & p_set)p = cap_num / pred_numr = cap_num / target_numf = 2 * p * r / (p + r)print("P =", p)print("R =", r)print("F1 =", f)

if name == '__main__':

test_str = '在这一年中，中国的改革开放和现代化建设持续向前迈进。国民经济放弃了“高增长、[利率期货](https://www.gendan5.com/ff/if.html)低通胀”的良好倒退态势。农业生产再次取得好的收成，企业改革持续深入，人民生存进一步改善。对外经济技术单干与交换不断扩大。'seg = Segment(test_str, 3)time_start = time.time()seg.MM(3, 0)seg.RMM(3, len(test_str))time_end = time.time()seg.printFinalResult()print('分词工夫：', time_end - time_start, 's')target_str = "在/  这/  一/  年/  中/  ，/  中国/  的/  改革/  凋谢/  和/  现代化/  建设/  持续/  向前/  迈进/  。/  国民经济/  放弃/  了/  “/  高/  增长/  、/  低/  通胀/  ”/  的/  良好/  倒退/  态势/  。/  农业/  生产/  再次/  取得/  好/  的/  收成/  ，/  企业/  改革/  持续/  深入/  ，/  人民/  生存/  进一步/  改善/  。/  对外/  经济/  技术/  单干/  与/  交换/  一直/  扩充/  。/"re_pred = to_region(seg.getFinalResult())re_target = to_region(target_str)# 每个单词按它在文本中的起止地位可记作区间[i, j]print("分词后果：", re_pred)print("标准答案：", re_target)PRF(re_target, re_pred)