TypeError: ufunc 'add' did not contain a loop with signature matching types 类型错误：ufunc 'add' 不包含与类型匹配的循环签名。

我正在创建句子的词袋表示。然后，我将句子中存在的单词与文件“vectors.txt”进行比较，以获取它们的嵌入向量。在获取句子中每个存在的单词的向量后，我将这些向量的平均值取出。这是我的代码：

import nltk
import numpy as np
from nltk import FreqDist
from nltk.corpus import brown
news = brown.words(categories='news') 
news_sents = brown.sents(categories='news') 
fdist = FreqDist(w.lower() for w in news) 
vocabulary = [word for word, _ in fdist.most_common(10)] 
num_sents = len(news_sents) 
def averageEmbeddings(sentenceTokens, embeddingLookupTable):
    listOfEmb=[]
    for token in sentenceTokens:
        embedding = embeddingLookupTable[token] 
        listOfEmb.append(embedding)
    return sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
embeddingVectors = {}
with open("D:\\Embedding\\vectors.txt") as file: 
    for line in file:
        (key, *val) = line.split()
        embeddingVectors[key] = val
for i in range(num_sents): 
    features = {}
    for word in vocabulary: 
        features[word] = int(word in news_sents[i])        
    print(features) 
    print(list(features.values()))  
    
    sentenceTokens = [] 
    for key, value in features.items(): 
        if value == 1:
            sentenceTokens.append(key)
    sentenceTokens.remove(".")    
    print(sentenceTokens)        
    print(averageEmbeddings(sentenceTokens, embeddingVectors))
    print(features.keys()) 

不确定为什么，但我遇到了这个错误：

TypeError                                 Traceback (most recent call last)
 in ()
     39     sentenceTokens.remove(".")
     40     print(sentenceTokens)
---> 41     print(averageEmbeddings(sentenceTokens, embeddingVectors))
     42 
     43 print(features.keys()) 
 in averageEmbeddings(sentenceTokens, embeddingLookupTable)
     18         listOfEmb.append(embedding)
     19 
---> 20     return sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
     21 
     22 embeddingVectors = {}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('

附注：嵌入向量的示例如下：
the 0.011384 0.010512 -0.008450 -0.007628 0.000360 -0.010121 0.004674 -0.000076 

of 0.002954 0.004546 0.005513 -0.004026 0.002296 -0.016979 -0.011469 -0.009159 
and 0.004691 -0.012989 -0.003122 0.004786 -0.002907 0.000526 -0.006146 -0.003058 
one 0.014722 -0.000810 0.003737 -0.001110 -0.011229 0.001577 -0.007403 -0.005355 
in -0.001046 -0.008302 0.010973 0.009608 0.009494 -0.008253 0.001744 0.003263 

使用np.sum后，我得到了这个错误：

TypeError                                 Traceback (most recent call last)
 in ()
     40     sentenceTokens.remove(".")
     41     print(sentenceTokens)
---> 42     print(averageEmbeddings(sentenceTokens, embeddingVectors))
     43 
     44 print(features.keys()) 
 in averageEmbeddings(sentenceTokens, embeddingLookupTable)
     18         listOfEmb.append(embedding)
     19 
---> 20     return np.sum(np.asarray(listOfEmb)) / float(len(listOfEmb))
     21 
     22 embeddingVectors = {}
C:\Anaconda3\lib\site-packages\numpy\core\fromnumeric.py in sum(a, axis, dtype, out, keepdims)
   1829     else:
   1830         return _methods._sum(a, axis=axis, dtype=dtype,
-> 1831                              out=out, keepdims=keepdims)
   1832 
   1833 
C:\Anaconda3\lib\site-packages\numpy\core\_methods.py in _sum(a, axis, dtype, out, keepdims)
     30 
     31 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
---> 32     return umr_sum(a, axis, dtype, out, keepdims)
     33 
     34 def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
TypeError: cannot perform reduce with flexible type