NLP：TypeError：减少预期的至少2个参数，得到1

Question

import math, functools

def splitPairs(word):
   return [(word[:i+1], word[i+1:]) for i in range(len(word))]

def segment(word):
   if not word: return []
   allSegmentations = [[first] + segment(rest)
                       for (first, rest) in splitPairs(word)]
   return max(allSegmentations, key = wordSegFitness)

class OneGramDist(dict):
    def __init__(self):
        self.gramCount = 0
        for line in open('Norvig Word Library.txt'):
            (word, count) = line[:-1].split('\t')
            self[word] = int(count)
            self.gramCount += self[word]

    def __call__(self, word):
        if word in self:
            return float(self[word]) / self.gramCount
        else:
            return 1.0/ self.gramCount

singleWordProb = OneGramDist()

def wordSegFitness(words):
    return functools.reduce(lambda x,y: x+y),
    (math.log10(singleWordProblem(w)) for w in words)

我正在尝试改进一些文本文件的分词。这些文件中的某些单词已加入（例如'howmuchdoesthecarcost或'helloworld'），我正在运行朴素贝叶斯过程来将单词分开。

但是，当我运行类似segment("helloworld")的内容时，我收到以下错误：TypeError: reduce expected at least 2 arguments, got 1。如何在不失去wordSegFitness()的效力的情况下更改reduce中的参数？