完整代码
时间: 2023-07-03 15:21:20 浏览: 36
```python
import random
import nltk
# 读取文件
with open('Chinese_Names_Corpus_Gender(120W).txt', 'r', encoding='utf-8') as f:
names = f.read().splitlines()
# 抽样比例
sample_rate = 0.01
# 随机抽样
random.shuffle(names)
names = names[:int(len(names) * sample_rate)]
# 移除性别为“未知”的姓名
names = [name for name in names if name.split()[1] != '未知']
# 分离男女姓名
male_names = [name.split()[0] for name in names if name.split()[1] == '男']
female_names = [name.split()[0] for name in names if name.split()[1] == '女']
# 划分训练集和测试集
train_size = 0.5
train_male_size = int(len(male_names) * train_size)
train_female_size = int(len(female_names) * train_size)
train_male_names = male_names[:train_male_size]
train_female_names = female_names[:train_female_size]
test_male_names = male_names[train_male_size:]
test_female_names = female_names[train_female_size:]
# 合并训练集和测试集
train_names = [(name, 'male') for name in train_male_names] + [(name, 'female') for name in train_female_names]
test_names = [(name, 'male') for name in test_male_names] + [(name, 'female') for name in test_female_names]
# 随机打乱
random.shuffle(train_names)
random.shuffle(test_names)
# 特征提取函数
def gender_features(name):
return {'feature': name[-1]}
# 特征集合
train_set = [(gender_features(name), gender) for (name, gender) in train_names]
# 训练模型
model = nltk.NaiveBayesClassifier.train(train_set)
# 测试模型
test_set = [(gender_features(name), gender) for (name, gender) in test_names]
accuracy = nltk.classify.accuracy(model, test_set)
print('Accuracy:', accuracy)
```
这段代码会按照您的要求,下载并处理 Chinese_Names_Corpus_Gender(120W).txt 数据集,将其随机抽样并分离男女姓名,然后将数据集划分为训练集和测试集,最后使用特征提取函数和 NaiveBayesClassifier 训练模型并计算准确率。
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