import time start_time = time.time() # 三重循环 for a in range(1001): for b in range(1001): for c in range(1001): if a + b + c == 1000 and a**2 + b**2 == c**2: print("a:{}, b:{}, c:{}".format(a, b, c)) end_time = time.time() print("used time: %...
This feature allows users to generate a report based on the outputs received from calling thebig-ofunction. The report defines the best time complexity along with the the others estimates and returns them as a string. >>> best, others=big_o.big_o(heapify, data_generator_heapify,max_n=10...
Time complexity The running time complexity of the building heap is O(n log(n)) where each call for heapify costs O(log(n)) and the cost of building heap is O(n). Therefore, the overall time complexity will be O(n log(n)). Applications of Heap A heap is used for a variety of...
The algorithm has a time complexity of O(n log n), making it efficient for large datasets. Heap Sort ExampleThe following example demonstrates heap sort in Python for numeric data. heap_sort.py #!/usr/bin/python def heapify(arr, n, i): largest = i left = 2 * i + 1 right = 2 ...
def heapify(self,array, count): start = count-1 while start >= 0: self.siftDown(array, start, count-1) start -= 1 def siftDown(self, array, start, end): root = start iLeftChild = lambda x:x*2+1 iRightChild = lambda x:x*2+2 while iLeftChild(root) <= end : child = iLe...
During the build_max_heap stage, we do that for n/2 elements so the worst case complexity of the build_heap step is n/2*log n ~ nlog n. During the sorting step, we exchange the root element with the last element and heapify the root element. For each element, this again takes log...
实现这一点的有效方法是使用以下代码: def Top6TestScoresOutof10(): name = input("Enter name: ") data = input("Enter 10 test scores separated by commas: ") scores = [int(score) for score in data.split()] heapq.heapify(scores) return name, heapq.nlargest(6, scores) name, scores = ...
#Python code to demonstrate working of#heapify(), heappush() and heappop()#importing "heapq" to implement heap queueimportheapq#initializing listli = [5, 7, 9, 1, 3]#using heapify to convert list into heapheapq.heapify(li)#printing created heapprint("The created heap is :",end="")...
heapify(self.nums) def displayHeap(self): for item in self.nums: print(item) def isEmpty(self): return len(self.nums) == 0 def addToHeap(self, value): heapq.heappush(self.nums, value) def getSmallesValue(self): return heapq.heappop(self.nums) def popAllElements(self): while not...
len(heap) // 2, -1, -1): cls.maxHeapify(...