Cola de prioridad de python
Ejemplos de código
python heapq
>>> import heapq
>>> heap = []
>>> heapq.heappush(heap, (5, 'write code'))
>>> heapq.heappush(heap, (7, 'release product'))
>>> heapq.heappush(heap, (1, 'write spec'))
>>> heapq.heappush(heap, (3, 'create tests'))
>>> heapq.heappop(heap)#pops smallest
(1, 'write spec')
>>> heapq.nlargest(2,heap)#displays n largest values without popping
[(7, 'release product'),(5, 'write code')]
>>> heapq.nsmallest(2,heap)#displays n smallest values without popping
[(3, 'create tests'),(5, 'write code')]
>>> heap = [1, 3, 5, 7, 9, 2, 4, 6, 8, 0]
>>> heapq.heapify(heap)#converts a list to heap
>>> heap
[0, 1, 2, 6, 3, 5, 4, 7, 8, 9]
>>> def heapsort(iterable):
... h = []
... for value in iterable:
... heappush(h, value)
... return [heappop(h) for i in range(len(h))]
...
>>> heapsort([1, 3, 5, 7, 9, 2, 4, 6, 8, 0])
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
cola de prioridad de python
from queue import PriorityQueue
class PqElement(object):
def __init__(self, value: int):
self.val = value
#Custom Compare Function (less than or equsal)
def __lt__(self, other):
"""self < obj."""
return self.val > other.val
#Print each element function
def __repr__(self):
return f'PQE:{self.val}'
#Usage-
pq = PriorityQueue()
pq.put(PqElement(v)) #Add Item - O(Log(n))
topValue = pq.get() #Pop top item - O(1)
topValue = pq.queue[0].val #Get top value - O(1)cola de prioridad de python
class PriorityQueueSet(object):
"""
Combined priority queue and set data structure.
Acts like a priority queue, except that its items are guaranteed to be
unique. Provides O(1) membership test, O(log N) insertion and O(log N)
removal of the smallest item.
Important: the items of this data structure must be both comparable and
hashable (i.e. must implement __cmp__ and __hash__). This is true of
Python's built-in objects, but you should implement those methods if you
want to use the data structure for custom objects.
"""
def __init__(self, items=[]):
"""
Create a new PriorityQueueSet.
Arguments:
items (list): An initial item list - it can be unsorted and
non-unique. The data structure will be created in O(N).
"""
self.set = dict((item, True) for item in items)
self.heap = self.set.keys()
heapq.heapify(self.heap)
def has_item(self, item):
"""Check if ``item`` exists in the queue."""
return item in self.set
def pop_smallest(self):
"""Remove and return the smallest item from the queue."""
smallest = heapq.heappop(self.heap)
del self.set[smallest]
return smallest
def add(self, item):
"""Add ``item`` to the queue if doesn't already exist."""
if item not in self.set:
self.set[item] = True
heapq.heappush(self.heap, item)
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