human
What is a code to 5 given the following:
5.3 Automatic document clustering (Unsupervised machine learning)

Usually based on LDA (Latent Dirichet Allocation)
You give a number of topic to find (very tricky topic), the computer does the rest"
Please write code following the instructions in jupyter notebook style.
assistant
#Here is code to 5.

import lda
import pickle
from nltk.corpus import stopwords

import bisect

class LDA():
    def __init__(self,lang="english"):
        self.lang = lang
        self.vocab = self.create_dictionary()
        
        
    def create_dictionary(self):
        """
        top 5000 words, using labMTsimple
        """
        from labMTsimple.storyLab import emotionFileReader, emotion, stopper, emotionV
        labMT,labMTvector,labMTwordList = emotionFileReader(stopval=0.0,lang=self.lang,returnVector=True)
        return  sorted(list(set(labMTwordList) - set(stopwords.words("english")) ))
        
        

    def bi_contains(self,lst, item):
        """ efficient `item in lst` for sorted lists """
        pos = bisect.bisect_left(lst, item)
        return [((item <= lst[-1]) and (lst[pos] == item)),pos]

    def train(self,messages,topicNum,n_iter=1000):
        len_vocab = len(self.vocab)
        print("Number of articles: {0}".format(len(messages)))
        allMessages = np.zeros((len(messages),len_vocab))
        j = -1
        for mess in messages:
            j += 1
            # Kind-of efficient way to count words (better to use a dictionary)
            c = Counter(mess)
            del c['']
            values = np.zeros(len_vocab)
            for word in c:
                pos = self.bi_contains(self.vocab, word)
                if pos[0]:
                    values[pos[1]] = c[word]
            allMessages[j,:] += values

        allMessages = np.array(allMessages,dtype=int)
        self.corpus = allMessages
    
        print("Corpus created, running lda")
        
        #run lda with allMessages and print topics
        model = lda.LDA(n_topics=topicNum, n_iter=n_iter, random_state=1)

        model.fit(self.corpus )
        topic_word = model.topic_word_
        #print(model.loglikelihood())
        
        for i, topic_dist in enumerate(topic_word):
            topic_words = np.array(self.vocab)[np.argsort(topic_dist)][:]#-[:n_top_words:-1]
            freq_words =...