def k_largest(arr, k):
h = MaxHeap()
for item in arr:
h.insert(item)
for i in range(k):
largest = h.extract()
print(largest)
return
class PriorityQueue(object):
def __init__(self, maxsize=None):
self.maxsize = maxsize
self._maxheap = MaxHeap(maxsize)
def push(self, priority, value):
entry = (priority, value)
self._maxheap.add(entry)
def pop(self, with_priority=False):
entry = self._maxheap.extract()
if with_priority:
return entry
else:
return entry[1]
def is_empty(self):
return len(self._maxheap) == 0
from heap import MinHeap, MaxHeap
from math import floor
import sys
#invariant: maintain min heap size = floor(n/2), median is the the max in max heap
with open('Median.txt') as f:
#with open('median10_test.txt') as f:
a = [int(l) for l in f]
minHeap = MinHeap([])
maxHeap = MaxHeap([])
medians = []
for i in range(len(a)):
if minHeap.size() == 0 and maxHeap.size() == 0:
maxHeap.insert(a[i])
else:
if a[i] < maxHeap.top():
maxHeap.insert(a[i])
else:
minHeap.insert(a[i])
if minHeap.size() > floor((i+1)/2):
maxHeap.insert(minHeap.extract())
elif minHeap.size() < floor((i+1)/2):
minHeap.insert(maxHeap.extract())
medians.append(maxHeap.top())
print(sum(medians)%10000)
