hmm模型及手写python

文章参照

链接：https://www.cnblogs.com/pinard/p/6945257.html

# coding=utf-8
import re
import numpy as np


class Hmm(object):
    def __init__(self, train_path):
        self.train_path = train_path
        self.clean_data()

    def clean_data(self):
        with open(self.train_path, encoding='utf-8') as f:
            sents = f.read()
        self.sents = [[word.split(" ") for word in sent.split("\n")] for sent in sents.split("\n\n")]
        self.Q = sorted(list(set([word[1] for sent in self.sents for word in sent])))  # 隐含状态集合
        self.V = sorted(list(set([word[0] for sent in self.sents for word in sent])))  # 观测集合

    def train(self):
        # 1、求hmm的初试隐含状态概率pi
        first_label = [sent[0][1] for sent in self.sents]
        self.pi = np.array([round(first_label.count(q) / len(first_label), 4) for q in self.Q])
        # 2、求hmm的隐含状态转移概率矩阵A
        label = [[word[1] for word in sent] for sent in self.sents]
        two_label = [[tag[index:index + 2] for index in range(len(tag) - 1)] for tag in label]
        two_label = [''.join(word) for label in two_label for word in label]
        self.A = np.array(
            [[round(two_label.count(q1 + q2) / sum([1 for label in two_label if label[0] == q1]), 4) for q2 in self.Q]
             for q1 in self.Q])
        # 3、求hmm的发射概率矩阵B
        word_label = [[''.join(word) for word in sent] for sent in self.sents]
        word_label = [word for label in word_label for word in label]
        label = [t for tag in label for t in tag]
        self.B = np.array([[word_label.count(v + q) / label.count(q) for v in self.V] for q in self.Q])

    def predict(self, sent):
        O = np.array([self.V.index(word) for word in sent])
        δ = np.zeros((len(O), len(self.A)))  # 第一个局部
        Ψ = np.zeros((len(O), len(self.A)))  # 第二个局部
        # 1、初始化t=1时刻维特比的两个局部变量
        δ[0] = self.pi * self.B[:, O[0]]
        # 2、递归求序列每一步的两个局部变量
        for index in range(1, len(δ)):
            δ[index] = np.max(δ[index - 1] * self.A.T, 1) * self.B[:, O[index]]
            Ψ[index] = np.argmax(δ[index - 1] * self.A.T, 1)
            # 3、求最后一个概率最大对应的隐含标签
        label = [δ[-1].argmax()]
        # 4、回溯求整个序列的隐含标签
        for index, tag in enumerate(Ψ[::-1]):
            if index < len(Ψ) - 1:
                label.append(int(tag[int(label[-1])]))
        label = label[::-1]
        label = ''.join([self.Q[index] for index in label])
        return label


if __name__ == '__main__':
    text = '维特比算法是一个分词方法'
    train_path = 'test.txt'
    hmm = Hmm(train_path)
    hmm.train()
    label = hmm.predict(text)
    print([text[word.start():word.end()] for word in re.finditer(r'bi+|o', label)])

test.txt

维 b
特 i
比 i
算 b
法 i
也 o
是 o
寻 b
找 i
序 b
列 i
最 b
短 i
路 b
径 i
的 o
一 b
个 i
通 b
用 i
方 b
法 i

同 b
时 i
维 b
特 i
比 i
算 b
法 i
仅 b
仅 i
局 b
限 i
于 o
求 o
序 b
列 i
最 b
短 i
路 b
径 i

如 b
果 i
大 b
家 i
看 b
过 i
之 b
前 i
写 o
的 o
文 b
本 i
挖 b
掘 i
的 o
分 b
词 i
原 b
理 i
中 o
的 o
维 b
特 i
比 i
算 b
法 i