今天就为大家带来QIIME 2中两种常用的注释方法的介绍:classify-sklearn算法(Bokulich et al., 2018,https://github.com/QIIME2/q2-feature-classifier)和brocc算法(Nilsson et al.,2006,https://github.com/kylebittinger/q2-brocc#the-brocc-algorithm)。 QIIME 2分析流程的常用注释算法 和对应数据库 首先,大家...
self.classifier.train(feature_sets)""" Determines training error"""deftrainingError(self):feature_sets = self.getFeatures() p = nltk.classify.accuracy(self.classifier,feature_sets)returnp""" Make sure that the algorithm works on training data using a k fold cross validation scheme ""...
>> exactly the same as Python and R. So what is the real difference between >> calling the classify algorithm in Matlab and this ClassificationDiscriminant >> function? >> >> >> On Thu, Feb 14, 2013 at 4:14 PM, <amuel...@ais.uni-bonn.de> wrote: >> >>> matlab doc online says...
Multinomial Naive Bayes Algorithm using wrapper NLTK SklearnClassifier Memory problems can occur if very large dataset :param corpus: :param documents_training: :param documents_test: :param words_features: :param smoothing: :param kbest: :return: """printprint"--- Multinomial Bayes with wrapper ...
p = nltk.classify.accuracy(self.classifier,test_set)returnp""" Make sure that the algorithm works on training data using a leave one out cross validation scheme """defleave1OutCrossValidation(self):error =0feature_sets = self.getFeatures() ...