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.
            #here we are adding the feature vectors of all the words that were in the review
            featureVec = np.add(featureVec,model[word])
    # 
    # Divide the result by the number of words to get the average
    featureVec = np.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

getAvgFeatureVecs( sentences, model, num_features )