中文分词算法

示例代码：

"""
中文分词技术
编辑者：馒头
博客：https://www.cnblogs.com/mantou0/
"""

# encoding=utf-8
class HMM(object):
    def __init__(self):
        """
            方法：初始化参数
        """
        # 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 = './data/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

    def try_load_model(self, trained):
        """
            方法：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
            输入：trained ：是否已经训练好
        """
        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 = True

        else:
            # 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

    def train(self, path):
        """
            方法：计算转移概率、发射概率以及初始概率
            输入：path：训练材料路径
        """

        # 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] = 0

        def makeLabel(text):
            """
                方法：为训练材料每个词划BMES
                输入：text：一个词
                输出：out_text:划好的一个BMES列表
            """
            out_text = []
            if len(text) == 1:
                out_text.append('S')
            else:
                out_text += ['B'] + ['M'] * (len(text) - 2) + ['E']

            return out_text

        init_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 += 1

                line = line.strip()
                if not line:
                    continue

                word_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 self

    def viterbi(self, text, states, start_p, trans_p, emit_p):
        """
            方法：维特比算法，寻找最优路径，即最大可能的分词方案
            输入：text：文本
                 states：状态集
                 start_p：第一个字的各状态的可能
                 trans_p：转移概率
                 emit_p：发射概率
            输出：prob:概率
                 path:划分方案
        """
        V = [{}]  # 路径图
        path = {}

        for y in states:  # 初始化第一个字的各状态的可能性
            V[0][y] = start_p[y] * emit_p[y].get(text[0], 0)
            path[y] = [y]
        for t in range(1, len(text)):  # 每一个字
            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:  # 每个字的每个状态的可能
                emitP = emit_p[y].get(
                    text[t], 0) if not neverSeen else 1.0  # 设置未知字单独成词
                # y0上一个字可能的状态，然后算出当前字最可能的状态，prob则是最大可能，state是上一个字的状态
                (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] = prob
                newpath[y] = path[state] + [y]  # 更新路径
            path = newpath

        if emit_p['M'].get(text[-1], 0) > emit_p['S'].get(text[-1], 0):  # 最后一个字是词中的可能大于单独成词的可能
            (prob, state) = max([(V[len(text) - 1][y], y) for y in ('E', 'M')])
        else:  # 否则就直接选最大可能的那条路
            (prob, state) = max([(V[len(text) - 1][y], y) for y in states])

        return (prob, path[state])

    # 用维特比算法分词，并输出
    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:]


hmm = HMM()
hmm.train('./data/trainCorpus.txt_utf8')
text = '研究生命的起源'
res = hmm.cut(text)
print(text)
print(str(list(res)))

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