class KthLargest(object):
def __init__(self, k):
self.k = k
self.min_pq = PriorityQueue(k, MIN_PQ)
def insert(self, element):
if self.min_pq.get_size() < self.k:
self.min_pq.insert(element)
return
if element <= self.min_pq.peek():
return
self.min_pq.delete_top()
self.min_pq.insert(element)
def get_kth_largest(self):
return self.min_pq.peek()
# k = KthLargest(3)
# k.insert(2)
# k.insert(3)
# k.insert(1)
# print k.get_kth_largest()
# k.insert(0)
# print k.get_kth_largest()
# k.insert(4)
# k.insert(7)
# print k.get_kth_largest()
class KMostFrequentWords:
def __init__(self, words, k):
self.words = words
self.trie = TrieDS()
self.min_heap = PriorityQueue(k, MIN_PQ)
self.k = k
self.pre_process()
def pre_process(self):
# inserting first k words
li = []
for i in range(self.k):
w = self.words[i]
if self.trie.get(w) is None:
obj = WordFreq(w, 0)
self.trie.put(w, obj)
li.append(obj)
obj = self.trie.get(w)
obj.freq += 1
for ob in li:
self.min_heap.insert(ob)
# adding remaining items
for i in range(self.k, len(self.words)):
w = self.words[i]
obj = self.trie.get(w)
if obj is None:
obj = WordFreq(w, 1)
self.trie.put(obj)
if obj.freq <= self.min_heap.peek().freq:
continue
else:
self.min_heap.delete_top()
self.min_heap.insert(obj)
def get_k_most_freq(self):
return self.min_heap.array