【发布时间】:2019-03-13 22:45:40
【问题描述】:
我正在尝试运行 W2V 算法。我发现索引错误,不知道哪里出错了。这是错误:
IndexError:只有整数、切片 (
:)、省略号 (...)、numpy.newaxis (None) 和整数或布尔数组是有效的索引
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 Bag-of-Words-Meets-Bags-of-Popcorn-Kaggle。我不确定错误在哪里。我的事情np.divide 正在引发错误。我在窗户上工作
【问题讨论】:
-
你确定这就是你看到的全部内容吗?没有提到发生错误的行号吗?另外,据我所知,您错误地定义了函数:缩进应该在函数的每一行之前,
def ...:行除外,反之亦然。
标签: python-3.x nlp kaggle