本文整理匯總了Python中lru.LRU.has_key方法的典型用法代碼示例。如果您正苦於以下問題：Python LRU.has_key方法的具體用法？Python LRU.has_key怎麽用？Python LRU.has_key使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類lru.LRU的用法示例。
在下文中一共展示了LRU.has_key方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_has_key
# 需要導入模塊: from lru import LRU [as 別名]
# 或者: from lru.LRU import has_key [as 別名]
def test_has_key(self):
for size in SIZES:
l = LRU(size)
for i in xrange(2*size):
l[i] = str(i)
self.assertTrue(l.has_key(i))
for i in xrange(size, 2*size):
self.assertTrue(l.has_key(i))
for i in xrange(size):
self.assertFalse(l.has_key(i))示例2: __init__
# 需要導入模塊: from lru import LRU [as 別名]
# 或者: from lru.LRU import has_key [as 別名]
class topic4:
def __init__(self, c_hash, c_user, c_words):
self.topic_count =1
self.l1 = LRU(c_hash)
self.l2 = LRU(c_user)
def set_hashLRU(self,l):
self.set(self.l1, l)
def set_userLRU(self,l):
self.set(self.l2, 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
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()
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)
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])) +c
return similarity示例3: __init__
# 需要導入模塊: from lru import LRU [as 別名]
# 或者: from lru.LRU import has_key [as 別名]
class topic4:
def __init__(self, c_hash, c_user, c_words):
self.topic_count =1
# self.time = (self.first,self.last)
self.l1 = LRU(c_hash)
self.first =""
self.last=""
self.lats=[]
self.longs=[]
self.l2 = LRU(c_user)
self.l3 = LRU(c_words)
self.l4 = LRU(400)
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,links, cords):
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
for k in links:
self.l4[k]=self.l4.get(k,0)+1
if(cords is not None):
self.lats.append(cords["coordinates"][1])
self.longs.append(cords["coordinates"][0])
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
return similarity
def flush1(self, cache, size):
if(len(cache.keys())>5):
tokens = reversed(cache.keys()[5])
cache.clear()
for i in tokens:
cache[i]=1
def flush(self):
self.flush1(self.l1,500)
self.flush1(self.l2, 500)
self.flush1(self.l3,3500)
self.topic_count=1