在這個問題陳述中，將通過提供名字來訓練分類器以找到性別(男性或女性)。 我們需要使用啟發式構造特征向量并訓練分類器。這里使用scikit-learn軟件包中的標簽數據。 以下是構建性別查找器的Python代碼 -

導入必要的軟件包 -

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

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現在需要從輸入字中提取最后的N個字母。 這些字母將作為功能 - def extract_features(word, N = 2): last_n_letters = word[-N:] return {'feature': last_n_letters.lower()} if __name__=='__main__':

使用NLTK中提供的標簽名稱(男性和女性)創建培訓數據 -

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)

現在，測試數據將被創建如下 -

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

使用以下代碼定義用于列車和測試的樣本數 -

train_sample = int(0.8 * len(data))

現在，需要迭代不同的長度，以便可以比較精度 -

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)

分類器的準確度可以計算如下 -

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

現在，可以預測輸出結果 -

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

上述程序將生成以下輸出 -

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 在上面的輸出中可以看到，結束字母的最大數量的準確性是兩個，并且隨著結束字母數量的增加而減少。

完整代碼

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)))

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轉載于:https://www.cnblogs.com/duoba/p/9535701.html

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