IndexError:只有整数,切片(`:`),省略号(`...`),numpy.newaxis(`None`)和整数或布尔数组才是有效索引”

时间:2018-10-09 08:09:33

标签: python-3.x nlp kaggle

我正在尝试运行W2V算法。我发现索引错误,不确定我要去哪里。这是错误:

  

IndexError:只有整数,切片(:),省略号(...),numpy.newaxis(None)和整数或布尔数组都是有效索引

这是代码:

    def makeFeatureVec(words, model, num_features):
# Function to average all of the word vectors in a given
# paragraph
#
# Pre-initialize an empty numpy array (for speed)
featureVec = np.zeros((num_features,),dtype="float32")
#
nwords = 0.
# 
# Index2word is a list that contains the names of the words in 
# the model's vocabulary. Convert it to a set, for speed 
index2word_set = set(model.wv.index2word)
#
# Loop over each word in the review and, if it is in the model's
# vocaublary, add its feature vector to the total
for word in words:
    if word in index2word_set: 
        nwords = nwords + 1.
        featureVec = np.add(featureVec,model[word])
# 
# Divide the result by the number of words to get the average
featureVec = np.true_divide(featureVec,nwords)
return featureVec

    def getAvgFeatureVecs(reviews,model,num_features):
# Given a set of reviews (each one a list of words), calculate 
# the average feature vector for each one and return a 2D numpy array 
# 
# Initialize a counter
counter = 0.
# 
# Preallocate a 2D numpy array, for speed
reviewFeatureVecs = np.zeros((len(reviews),num_features),dtype="float32")
# 
# Loop through the reviews
for review in reviews:
   #
   # Print a status message every 1000th review
    if counter%1000. == 0.:
        print ("Review %d of %d" % (counter, len(reviews)))
   # 
   # Call the function (defined above) that makes average feature vectors
    reviewFeatureVecs[counter] = makeFeatureVec(review, model,num_features)
   #
   # Increment the counter
    counter = counter + 1.
return reviewFeatureVecs

这段代码来自于话语包,会议包,爆米花,笑话袋。我不确定错误在哪里。我发现np.divide引发错误。我正在Windows上

2 个答案:

答案 0 :(得分:2)

counter = counter + 1.

应该是

counter = counter + 1(注意点)或counter += 1

点使counter成为浮点数(因为1.等于1.0),并且浮点数不能用作索引。

答案 1 :(得分:0)

切片变量必须为整数。 (将浮点值替换为整数,例如:0。到0)

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