def __init__(self, c_hash, c_user, c_words):

self.topic_count =1

self.l1 = LRU(c_hash)

self.l2 = LRU(c_user)

self.l3 = LRU(c_words)

def set_hashLRU(self,l):

self.set(self.l1, l)

def set_userLRU(self,l):

self.set(self.l2, l)

def set_wordLRU(self,l):

self.set(self.l3, l)

def set(self, lru, l):

for k in l:

v = lru.get(k,0)

lru[k]=v+1

def set_cluster(self, hashtags, users, words):

for k in hashtags:

self.l1[k]=self.l1.get(k,0)+1

for k in users:

self.l2[k]=self.l2.get(k,0)+1

for k in words:

self.l3[k]=self.l3.get(k,0)+1

self.topic_count+=1

def get_similarity(self,hashtags,users,words):

h_sum = 1

u_sum = 1

w_sum = 1

h_match =0

h_ind =0

u_ind =0

w_ind =0

c=0

h1 = self.l1.get_size()

u1 = self.l2.get_size()

w1 = self.l3.get_size()

for h in hashtags:

# l1_items=zip(*self.l1.items())

h_sum+= self.l1.get(h,0)

if(self.l1.has_key(h)):

ind = self.l1.keys().index(h)

h_ind+= h1 - ind

h_match+= 1 if ind<250 else 0

for u in users:

u_sum+= self.l2.get(u,0)

if(self.l2.has_key(u)):

u_ind+= u1 - self.l2.keys().index(u)

for w in words:

w_sum+= self.l3.get(w,0)

if(self.l3.has_key(w)):

w_ind+= w1 - self.l3.keys().index(w)

if(h_match !=0):

c = h_match -1

# print(h_ind,h1,u_ind,u1,w_ind,w1, h_sum,w_sum,)

similarity = (h_ind/(h1+1))*(h_sum/sum(self.l1.values() +[1])) + (u_ind/(u1+1))*(u_sum/sum(self.l2.values()+[1])) + (w_ind/(w1+1))*(w_sum/sum(self.l3.values()+[1])) +c