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 collections
import sys
import jieba
import jieba.posseg as psg
from operator import itemgetter


class 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<min_rank:
                min_rank=w
            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 ws

class 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]