class HMM(object): def __init__(self): import os# 主要是用于存取算法中间结果，不用每次都训练模型 self.model_file = 'model/hmm_model.pkl'# 状态值集合 self.state_list = ['B', 'M', 'E', 'S'] # 参数加载,用于判断是否需要重新加载model_file self.load_para = False# 用于加载已计算的中间结果，当需要重新训练时，需初始化清空结果 def try_load_model(self, trained): if trained: import pickle with open(self.model_file, 'rb') as f: self.A_dic = pickle.load(f) self.B_dic = pickle.load(f) self.Pi_dic = pickle.load(f) self.load_para = Trueelse: # 状态转移概率（状态->状态的条件概率） self.A_dic = {} # 发射概率（状态->词语的条件概率） self.B_dic = {} # 状态的初始概率 self.Pi_dic = {} self.load_para = False# 计算转移概率、发射概率以及初始概率 def train(self, path):# 重置几个概率矩阵 self.try_load_model(False)# 统计状态出现次数，求p(o) Count_dic = {}# 初始化参数 def init_parameters(): for state in self.state_list: self.A_dic[state] = {s: 0.0 for s in self.state_list} self.Pi_dic[state] = 0.0 self.B_dic[state] = {}Count_dic[state] = 0def makeLabel(text): out_text = [] if len(text) == 1: out_text.append('S') else: out_text += ['B'] + ['M'] * (len(text) - 2) + ['E']return out_textinit_parameters() line_num = -1 # 观察者集合，主要是字以及标点等 words = set() with open(path, encoding='utf8') as f: for line in f: line_num += 1line = line.strip() if not line: continueword_list = [i for i in line if i != ' '] words |= set(word_list)# 更新字的集合linelist = line.split()line_state = [] for w in linelist: line_state.extend(makeLabel(w))assert len(word_list) == len(line_state)for k, v in enumerate(line_state): Count_dic[v] += 1 if k == 0: self.Pi_dic[v] += 1# 每个句子的第一个字的状态，用于计算初始状态概率 else: self.A_dic[line_state[k - 1]][v] += 1# 计算转移概率 self.B_dic[line_state[k]][word_list[k]] = \ self.B_dic[line_state[k]].get(word_list[k], 0) + 1.0# 计算发射概率self.Pi_dic = {k: v * 1.0 / line_num for k, v in self.Pi_dic.items()} self.A_dic = {k: {k1: v1 / Count_dic[k] for k1, v1 in v.items()} for k, v in self.A_dic.items()} # 加1平滑 self.B_dic = {k: {k1: (v1 + 1) / Count_dic[k] for k1, v1 in v.items()} for k, v in self.B_dic.items()} # 序列化 import pickle with open(self.model_file, 'wb') as f: pickle.dump(self.A_dic, f) pickle.dump(self.B_dic, f) pickle.dump(self.Pi_dic, f)return selfdef viterbi(self, text, states, start_p, trans_p, emit_p): V = [{}] path = {} # 初始状态 for y in states: V[0][y] = start_p[y] * emit_p[y].get(text[0], 0) path[y] = [y]# 后面状态 for t in range(1, len(text)): V.append({}) newpath = {}# 检验训练的发射概率矩阵中是否有该字 neverSeen = text[t] not in emit_p['S'].keys() and \ text[t] not in emit_p['M'].keys() and \ text[t] not in emit_p['E'].keys() and \ text[t] not in emit_p['B'].keys() for y in states: emitP = emit_p[y].get(text[t], 0) if not neverSeen else 1.0# 设置未知字单独成词 (prob, state) = max( [(V[t - 1][y0] * trans_p[y0].get(y, 0) * emitP, y0) for y0 in states if V[t - 1][y0] > 0]) V[t][y] = probnewpath[y] = path[state] + [y] path = newpathif emit_p['M'].get(text[-1], 0) > emit_p['S'].get(text[-1], 0): (prob, state) = max([(V[len(text) - 1][y], y) for y in ('E', 'M')]) else: (prob, state) = max([(V[len(text) - 1][y], y) for y in states])return (prob, path[state])def cut(self, text): import os if not self.load_para: self.try_load_model(os.path.exists(self.model_file)) prob, pos_list = self.viterbi(text, self.state_list, self.Pi_dic, self.A_dic, self.B_dic) begin, next = 0, 0 for i, char in enumerate(text): pos = pos_list[i] if pos == 'B': begin = i elif pos == 'E': yield text[begin: i + 1] next = i + 1 elif pos == 'S': yield char next = i + 1 if next < len(text): yield text[next:]
语料库地址： 链接：https://pan.baidu.com/s/1dozkaKD8yQdt51Z-R4ExHQ提取码：f705
【HMM分词实例】
转载于:https://www.cnblogs.com/callyblog/p/10131001.html
