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 random

from 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 -> female

Number of end letters: 2

Accuracy = 78.79%

Rajesh -> male

Gaurav -> male

Swati -> female

Shubha -> female

Number of end letters: 3

Accuracy = 77.22%

Rajesh -> male

Gaurav -> female

Swati -> female

Shubha -> female

Number of end letters: 4

Accuracy = 69.98%

Rajesh -> female

Gaurav -> female

Swati -> female

Shubha -> female

Number of end letters: 5

Accuracy = 64.63%

Rajesh -> female

Gaurav -> female

Swati -> female

Shubha -> female

完整代码

import random

from 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__':

    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)))