本文整理匯總了Python中nltk.classify.accuracy方法的典型用法代碼示例。如果您正苦於以下問題：Python classify.accuracy方法的具體用法？Python classify.accuracy怎麽用？Python classify.accuracy使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類nltk.classify的用法示例。
在下文中一共展示了classify.accuracy方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: demo
# 需要導入模塊: from nltk import classify [as 別名]
# 或者: from nltk.classify import accuracy [as 別名]
def demo():
def gender_features(word):
return {'last_letter': word[-1], 'penultimate_letter': word[-2]}
from nltk.classify import accuracy
from nltk.corpus import names
import random
names = ([(name, 'male') for name in names.words('male.txt')] +
[(name, 'female') for name in names.words('female.txt')])
import random
random.seed(60221023)
random.shuffle(names)
featuresets = [(gender_features(n), g) for (n,g) in names]
train_set, test_set = featuresets[500:], featuresets[:500]
print '--- nltk.classify.svm demo ---'
print 'Number of training examples:', len(train_set)
classifier = SvmClassifier.train(train_set)
print 'Total SVM dimensions:', len(classifier._svmfeatureindex)
print 'Label mapping:', classifier._labelmapping
print '--- Processing an example instance ---'
print 'Reference instance:', names[0]
print 'NLTK-format features:\n ' + str(test_set[0])
print 'SVMlight-format features:\n ' + str(map_instance_to_svm(test_set[0], classifier._labelmapping, classifier._svmfeatureindex))
distr = classifier.prob_classify(test_set[0][0])
print 'Instance classification and confidence:', distr.max(), distr.prob(distr.max())
print '--- Measuring classifier performance ---'
print 'Overall accuracy:', accuracy(classifier, test_set) 示例2: evaluate
# 需要導入模塊: from nltk import classify [as 別名]
# 或者: from nltk.classify import accuracy [as 別名]
def evaluate(training, tesing, classifier):
print ('Training Accuracy is ' + str(classify.accuracy(classifier,train_set)))
print ('Testing Accuracy i ' + str(classify.accuracy(classifier,test_set))) 開發者ID:PacktPublishing，項目名稱:Mastering-Machine-Learning-for-Penetration-Testing，代碼行數:5，代碼來源:SpamDetection_NLTK.py