main.py

import numpy as np
import time
import codecs
import jieba
import re

# 预处理(分词，去停用词，为每个word赋予一个编号，文档使用word编号的列表表示)
def preprocessing():
    # 读取停止词文件
    file = codecs.open('stopwords.dic','r','utf-8')
    stopwords = [line.strip() for line in file] 
    file.close()
    
    # 读数据集
    file = codecs.open('dataset.txt','r','utf-8')
    documents = [document.strip() for document in file] 
    file.close()
    
    word2id = {}
    id2word = {}
    docs = []
    currentDocument = []
    currentWordId = 0
    
    for document in documents:
        # 分词
        segList = jieba.cut(document)
        for word in segList: 
            word = word.lower().strip()
            # 单词长度大于1并且不包含数字并且不是停止词
            if len(word) > 1 and not re.search('[0-9]', word) and word not in stopwords:
                if word in word2id:
                    currentDocument.append(word2id[word])
                else:
                    currentDocument.append(currentWordId)
                    word2id[word] = currentWordId
                    id2word[currentWordId] = word
                    currentWordId += 1
        docs.append(currentDocument);
        currentDocument = []
    return docs, word2id, id2word
    
# 初始化，按照每个topic概率都相等的multinomial分布采样，等价于取随机数，并更新采样出的topic的相关计数
def randomInitialize():
	for d, doc in enumerate(docs):
		zCurrentDoc = []
		for w in doc:
			pz = np.divide(np.multiply(ndz[d, :], nzw[:, w]), nz)
			z = np.random.multinomial(1, pz / pz.sum()).argmax()
			zCurrentDoc.append(z)
			ndz[d, z] += 1
			nzw[z, w] += 1
			nz[z] += 1
		Z.append(zCurrentDoc)

# gibbs采样
def gibbsSampling():
	# 为每个文档中的每个单词重新采样topic
	for d, doc in enumerate(docs):
		for index, w in enumerate(doc):
			z = Z[d][index]
			# 将当前文档当前单词原topic相关计数减去1
			ndz[d, z] -= 1
			nzw[z, w] -= 1
			nz[z] -= 1
			# 重新计算当前文档当前单词属于每个topic的概率
			pz = np.divide(np.multiply(ndz[d, :], nzw[:, w]), nz)
			# 按照计算出的分布进行采样
			z = np.random.multinomial(1, pz / pz.sum()).argmax()
			Z[d][index] = z 
			# 将当前文档当前单词新采样的topic相关计数加上1
			ndz[d, z] += 1
			nzw[z, w] += 1
			nz[z] += 1

def perplexity():
	nd = np.sum(ndz, 1)
	n = 0
	ll = 0.0
	for d, doc in enumerate(docs):
		for w in doc:
			ll = ll + np.log(((nzw[:, w] / nz) * (ndz[d, :] / nd[d])).sum())
			n = n + 1
	return np.exp(ll/(-n))



alpha = 5
beta = 0.1	
iterationNum = 50
Z = []
K = 10
docs, word2id, id2word = preprocessing()
N = len(docs)
M = len(word2id)
ndz = np.zeros([N, K]) + alpha
nzw = np.zeros([K, M]) + beta
nz = np.zeros([K]) + M * beta
randomInitialize()
for i in range(0, iterationNum):
	gibbsSampling()
	print(time.strftime('%X'), "Iteration: ", i, " Completed", " Perplexity: ", perplexity())
 
topicwords = []
maxTopicWordsNum = 10
for z in range(0, K):
	ids = nzw[z, :].argsort()
	topicword = []
	for j in ids:
		topicword.insert(0, id2word[j])
	topicwords.append(topicword[0 : min(10, len(topicword))])