根据jieba textrank算法的思路，手动复现textrank算法。

思路：1.分词，确定窗口大小。

2.根据窗口大小，组合共现词和频率，频率代表共现权重。      trick:正反双向共现词。 3.根据textrank 每个词的权重的迭代公式，采用冒泡排序的方法，将一个词的所有共现词的权重代入公式。 4.迭代10次，使每个词的权重收敛。 5.根据权重排序，输出top words。

import collectionsimport sysimport jiebaimport jieba.posseg as psgfrom operator import itemgetterclass UndirectWeightedGraph:    d=0.85    def __init__(self):        self.edges=collections.defaultdict(list)    def add_edge(self,start,end,weight):        self.edges[start].append((start,end,weight))        self.edges[end].append((end,start,weight))    def rank(self):        ws=collections.defaultdict(float)        outSum=collections.defaultdict(float)        wsdef=1.0/(len(self.edges) or 1.0)        for n,elem in self.edges.items():            outSum[n]=sum([e[2] for e in elem])            ws[n]=wsdef        for epoch in range(10):            for n,elems in self.edges.items():                s=0                for elem in elems:                   s+=elem[2]/outSum[elem[1]]*ws[elem[1]]                ws[n]=s        min_rank,max_rank=sys.float_info[0],sys.float_info[3]        for n,w in ws.items():            if w
    
     max_rank:                max_rank=w        for n,w in ws.items():            ws[n]=((n-min_rank)/10.0)/((max_rank-min_rank)/10.0)        return wsclass TextRank(object):    def __init__(self):        self.stopwords=[]        self.pos_filter=[]        self.span=5    def pairfilter(self,wp):        return wp.flag in self.pos_filter and len(wp.word)>=2 and wp.word.lower not in self.stopwords    def textrank(self,sentence,topk=20):        uwg=UndirectWeightedGraph()        words=psg.lcut(sentence)        wm=collections.defaultdict(int)        for word_index,wp in enumerate(words):            if self.pairfilter(wp):                for index_assit in range(word_index+1,word_index+5):                    if index_assit>=len(words):                        break                    if not self.pairfilter(words[index_assit]):                        continue                    wm[(wp,words[index_assit])]+=1                    # uwg.add_edge(wp.word,words[index_assit].word,1)        for words_tuple,w in wm.items():            uwg.add_edge(words_tuple[0],words_tuple[1],w)        g=uwg.rank()        g=sorted(g.items(),key=itemgetter(1),reverse=True)        return g[:topk]