写一个程序，用crf实现中文分词

好的，下面是一个简单的中文分词程序，使用CRF模型实现：

import pycrfsuite
import jieba

# 读取训练数据
def read_data(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        data = f.readlines()
    return [line.strip().split() for line in data]

# 特征提取函数
def extract_features(sentence):
    features = []
    for i in range(len(sentence)):
        features.append([
            'BOS' if i == 0 else 'NOT_BOS',
            'EOS' if i == len(sentence) - 1 else 'NOT_EOS',
            'SINGLE' if len(sentence[i]) == 1 else 'NOT_SINGLE',
            'FIRST_' + sentence[i][0],
            'LAST_' + sentence[i][-1],
            'PREFIX_' + sentence[i][:2],
            'SUFFIX_' + sentence[i][-2:],
            'IN_DICT' if sentence[i] in word_dict else 'NOT_IN_DICT'
        ])
    return features

# 训练模型
def train_model(train_data, model_path):
    trainer = pycrfsuite.Trainer(verbose=False)
    for sentence, labels in train_data:
        features = extract_features(sentence)
        trainer.append(features, labels)
    trainer.set_params({
        'c1': 1.0,
        'c2': 1e-3,
        'max_iterations': 100,
        'feature.possible_transitions': True
    })
    trainer.train(model_path)

# 加载字典
def load_dict(dict_path):
    with open(dict_path, 'r', encoding='utf-8') as f:
        word_dict = set([line.strip() for line in f.readlines()])
    return word_dict

# 分词函数
def segment(sentence, model_path, word_dict):
    model = pycrfsuite.Tagger()
    model.open(model_path)
    features = extract_features(sentence)
    labels = model.tag(features)
    segment_list = []
    for i in range(len(labels)):
        if labels[i] == 'B' or labels[i] == 'M':
            segment_list[-1] += sentence[i]
        else:
            segment_list.append(sentence[i])
    # 使用jieba对未分出的词进行分词
    result = []
    for segment in segment_list:
        if segment in word_dict:
            result.append(segment)
        else:
            result += jieba.lcut(segment)
    return result

if __name__ == '__main__':
    train_data = read_data('train.txt')
    word_dict = load_dict('dict.txt')
    train_model(train_data, 'model.crfsuite')
    sentence = '今天天气真好，适合出去玩。'
    print(segment(sentence, 'model.crfsuite', word_dict))

其中，`train.txt`是训练数据，每行是一个句子和对应的分词结果，用空格分隔；`dict.txt`是自定义的字典，用于判断词语是否在字典中出现过。在训练模型和分词时，我们使用了一些手工设计的特征，包括词首字、词尾字、前缀、后缀等。对于未能分出的词语，我们使用jieba库进行分词。