本文整理汇总了Python中sklearn.svm.LinearSVC.train方法的典型用法代码示例。如果您正苦于以下问题：Python LinearSVC.train方法的具体用法？Python LinearSVC.train怎么用？Python LinearSVC.train使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.svm.LinearSVC的用法示例。
在下文中一共展示了LinearSVC.train方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: print
# 需要导入模块: from sklearn.svm import LinearSVC [as 别名]
# 或者: from sklearn.svm.LinearSVC import train [as 别名]
LogisticRegression_classifier=SklearnClassifier(LogisticRegression())
LogisticRegression_classifier.train(training_set)
print("Logistic Regression Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(LogisticRegression_classifier,testing_set))*100)
SGDClassifier=SklearnClassifier(SGDClassifier())
SGDClassifier.train(training_set)
print("SGD Classifier Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(SGDClassifier,testing_set))*100)
SVC_Classifier=SklearnClassifier(SVC())
SVC_Classifier.train(training_set)
print("SVC classifier Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(SVC_Classifier,testing_set))*100)
LinearSVC=SklearnClassifier(LinearSVC())
LinearSVC.train(training_set)
print("Linear SVC Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(LinearSVC,testing_set))*100)
#NuSVC_Classifier=SklearnClassifier(NuSVC())
#NuSVC_Classifier.train(training_set)
#print("NuSVC Algorithm Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(NuSVC_Classifier,testing_set))*100)
voted_classifier=VoteClassifier(classifier,MNB_classifier,BernoulliNB_classifier,LogisticRegression_classifier,SGDClassifier,LinearSVC)
print("Voted Classifier Accuracy for Lambda Calculas Lexical Semantic Parsing: ",(nltk.classify.accuracy(voted_classifier,testing_set))*100)
print("Classification: ",voted_classifier.classify(testing_set[0][0]),"Confidence% :",voted_classifier.confidence(testing_set[0][0])*100)
print("Classification: ",voted_classifier.classify(testing_set[1][0]),"Confidence% :",voted_classifier.confidence(testing_set[1][0])*100)
#SAVE CLASSIFIER WITH PICKLE
#save_classifier=open("naivebayes.pickle","wb")
#pickle.dump(classifier,save_classifier)开发者ID:CopotronicRifat，项目名称:Text-Classification-using-Supervised-Learning-Algorithms，代码行数:32，代码来源:STRING+INPUT.py
示例2: pickle_all
# 需要导入模块: from sklearn.svm import LinearSVC [as 别名]
# 或者: from sklearn.svm.LinearSVC import train [as 别名]
## _all_discs_.append(Ellipse('B', allfish, 1e-4))
# _all_discs_.append(Ellipse('r', allfish, 1e-4))
def pickle_all(fname):
import cPickle
import sys
import time
fh = open(fname, 'w')
tt = {'discs': list(_all_discs_)}
tt['args'] = ' '.join(sys.argv)
tt['time'] = time.strftime('%b-%d-%Y-%H-%M-%S')
print 'Pickling %s' % ', '.join(dd.name() for dd in _all_discs_)
cPickle.dump(tt, fh, 2)
fh.close()
if __name__ == '__main__':
#just some tests cases
pass
import samples
aa = samples.get_dataset('pythia', 'w', 200, 0, 1.2, 0, start=0, end=100)
bb = samples.get_dataset('pythia', 'v', 200, 0, 1.2, 0, start=0, end=100)
cc = samples.get_dataset('pythia', 'v', 200, 0, 1.2, 0, start=1000, end=1010)
lt = LinearSVC()
lt.train(aa, bb)