python 2中的语法无效，创建了一个简单的朴素贝叶斯分类器


Invalid syntax in python 2 creating a simple Naive Bayes classifie

创建简单的朴素贝叶斯分类器的最后一步是编写一个类Classifier，它将使用我们的类NBclass和Feature。

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class Classifier:

    def __init__(self, *nbclasses):
        self.nbclasses = nbclasses


    def prob(self, *d, best_only=True):

        nbclasses = self.nbclasses
        probability_list = []
        for nbclass in nbclasses:            
            ftrs = nbclass.features
            prob = 1
            for i in range(len(ftrs)):
                prob *= nbclass.probability_value_given_feature(d[i], ftrs[i])

            probability_list.append( (prob, nbclass.name) )
        prob_values = [f[0] for f in probability_list]
        prob_sum = sum(prob_values)
        if prob_sum==0:
            number_classes = len(self.nbclasses)
            pl = []
            for prob_element in probability_list:
                pl.append( ((1 / number_classes), prob_element[1]))
            probability_list = pl
        else:
            probability_list = [ (p[0] / prob_sum, p[1])  for p in probability_list]
        if best_only:
            return max(probability_list)
        else:
            return probability_list

// File"", line 7
def prob(self, *d, best_only=True):
^
SyntaxError: invalid syntax

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型

翻转*d和best_only=True就可以了。python3允许您仅指定关键字参数。python2没有，所以*d希望积累所有剩余的。在这之后指定best_only=True对python2语法没有意义。

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class Classifier:

    def __init__(self, *nbclasses):
        self.nbclasses = nbclasses


    def prob(self, best_only=True, *d):

        nbclasses = self.nbclasses
        probability_list = []
        for nbclass in nbclasses:            
            ftrs = nbclass.features
            prob = 1
            for i in range(len(ftrs)):
                prob *= nbclass.probability_value_given_feature(d[i], ftrs[i])

            probability_list.append( (prob, nbclass.name) )
        prob_values = [f[0] for f in probability_list]
        prob_sum = sum(prob_values)
        if prob_sum==0:
            number_classes = len(self.nbclasses)
            pl = []
            for prob_element in probability_list:
                pl.append( ((1 / number_classes), prob_element[1]))
            probability_list = pl
        else:
            probability_list = [ (p[0] / prob_sum, p[1])  for p in probability_list]
        if best_only:
            return max(probability_list)
        else:
            return probability_list
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