在这个问题陈述中，将通过提供名字来训练分类器以找到性别(男性或女性)。 我们需要使用启发式构造特征向量并训练分类器。这里使用scikit-learn软件包中的标签数据。 以下是构建性别查找器的Python代码 -

导入必要的软件包 -

import randomfrom nltk import NaiveBayesClassifier
from nltk.classify import accuracy as nltk_accuracy
from nltk.corpus import names

def extract_features(word, N = 2): last_n_letters = word[-N:] return {'feature': last_n_letters.lower()} if __name__=='__main__': 

使用NLTK中提供的标签名称(男性和女性)创建培训数据 -

male_list = [(name, 'male') for name in names.words('male.txt')] female_list = [(name, 'female') for name in names.words('female.txt')] data = (male_list + female_list) random.seed(5) random.shuffle(data) 

现在，测试数据将被创建如下 -

namesInput = ['Rajesh', 'Gaurav', 'Swati', 'Shubha'] 

使用以下代码定义用于列车和测试的样本数 -

train_sample = int(0.8 * len(data)) 

现在，需要迭代不同的长度，以便可以比较精度 -

for i in range(1, 6): print('\nNumber of end letters:', i) features = [(extract_features(n, i), gender) for (n, gender) in data] train_data, test_data = features[:train_sample], features[train_sample:] classifier = NaiveBayesClassifier.train(train_data) 

分类器的准确度可以计算如下 -

accuracy_classifier = round(100 * nltk_accuracy(classifier, test_data), 2) print('Accuracy = ' + str(accuracy_classifier) + '%') 

现在，可以预测输出结果 -

for name in namesInput:print(name, '==>', classifier.classify(extract_features(name, i)) 

上述程序将生成以下输出 -

Number of end letters: 1
Accuracy = 74.7%
Rajesh -> female
Gaurav -> male
Swati -> female
Shubha -> femaleNumber of end letters: 2
Accuracy = 78.79%
Rajesh -> male
Gaurav -> male
Swati -> female
Shubha -> femaleNumber of end letters: 3
Accuracy = 77.22%
Rajesh -> male
Gaurav -> female
Swati -> female
Shubha -> femaleNumber of end letters: 4
Accuracy = 69.98%
Rajesh -> female
Gaurav -> female
Swati -> female
Shubha -> femaleNumber of end letters: 5
Accuracy = 64.63%
Rajesh -> female
Gaurav -> female
Swati -> female
Shubha -> female

完整代码

import randomfrom nltk import NaiveBayesClassifier
from nltk.classify import accuracy as nltk_accuracy
from nltk.corpus import namesdef extract_features(word, N=2):last_n_letters = word[-N:]return {'feature': last_n_letters.lower()}if __name__ == '__main__':male_list = [(name, 'male') for name in names.words('male.txt')]female_list = [(name, 'female') for name in names.words('female.txt')]data = (male_list + female_list)random.seed(5)random.shuffle(data)namesInput = ['Rajesh', 'Gaurav', 'Swati', 'Shubha']train_sample = int(0.8 * len(data))for i in range(1, 6):print('\nNumber of end letters:', i)features = [(extract_features(n, i), gender) for (n, gender) in data]train_data, test_data = features[:train_sample], features[train_sample:]classifier = NaiveBayesClassifier.train(train_data)accuracy_classifier = round(100 * nltk_accuracy(classifier, test_data), 2)print('Accuracy = ' + str(accuracy_classifier) + '%')for name in namesInput:print(name, '==>', classifier.classify(extract_features(name, i)))