我正在创建句子的词袋表示。然后将句子中存在的单词与文件“vectors.txt”进行比较，以获得它们的嵌入向量。在获得句子中存在的每个单词的向量后，我将取句子中单词向量的平均值。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()) 

不知道为什么，但我得到了这个错误:

TypeError                                 Traceback (most recent call last)
<ipython-input-4-643ccd012438> in <module>()
 39     sentenceTokens.remove(".")
 40     print(sentenceTokens)
---> 41     print(averageEmbeddings(sentenceTokens, embeddingVectors))
 42 
 43 print(features.keys()) 

<ipython-input-4-643ccd012438> 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('<U9') dtype('<U9') dtype('<U9')

附:嵌入向量看起来像:

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 后出现此错误:

TypeError                                 Traceback (most recent call last)
<ipython-input-13-8a7edbb9d946> in <module>()
 40     sentenceTokens.remove(".")
 41     print(sentenceTokens)
---> 42     print(averageEmbeddings(sentenceTokens, embeddingVectors))
 43 
 44 print(features.keys()) 

<ipython-input-13-8a7edbb9d946> 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

最佳答案

你有一个 numpy 字符串数组，而不是 float 。这就是 dtype('<U9') 的意思。 -- 一个小端编码的 unicode 字符串，最多 9 个字符。

尝试:

return sum(np.asarray(listOfEmb, dtype=float)) / float(len(listOfEmb))

但是，这里根本不需要 numpy。你真的可以这样做:

return sum(float(embedding) for embedding in listOfEmb) / len(listOfEmb)

或者如果你真的打算使用 numpy。

return np.asarray(listOfEmb, dtype=float).mean()

关于python - 类型错误 : ufunc 'add' did not contain a loop with signature matching types，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/35013726/