class PriorityQueue(object):
def __init__(self):
self._heap = BinaryHeap()
def peek(self):
return self._heap.peek_min()
def is_empty(self):
return self._heap.is_empty()
def enqueue(self, value):
self._heap.insert(value)
def dequeue(self):
self._heap.extract_min()
def __iter__(self):
yield from iter(self._heap)
def __len__(self):
return len(self._heap)
def heap_sort(arr):
'''
This consists of 2 steps:
1. build a min heap, which is O(nlogn)
2. extract all n elements of the heap, which is O(nlogn)
Overall, this takes O(nlogn)
'''
heap = BinaryHeap(arr)
result = []
while not heap.is_empty():
result.append(heap.extract_min())
return result
class PriorityQueue(object): def __init__(self): self._heap = BinaryHeap() def peek(self): return self._heap.peek_min() def is_empty(self): return self._heap.is_empty() def enqueue(self, value): self._heap.insert(value) def dequeue(self): self._heap.extract_min() def __iter__(self): yield from iter(self._heap) def __len__(self): return len(self._heap)
def heap_sort(arr):
'''
This consists of 2 steps:
1. build a min heap, which is O(nlogn)
2. extract all n elements of the heap, which is O(nlogn)
Overall, this takes O(nlogn)
'''
heap = BinaryHeap(arr)
result = []
while not heap.is_empty():
result.append(heap.extract_min())
return result
class PriorityQueue:
def __init__(self):
self.queue = BinaryHeap()
# log(n) worst case (both time and space)
def enqueue(self, elem):
self.queue.insert_element(elem)
# log(n) worst case (both time and space)
def dequeue(self):
return self.queue.extract_min()
def peek_min(self):
return self.queue[0]
def isEmpty(self):
return self.queue.isEmpty()
def __str__(self):
return self.queue.__str__()
