![]() ![]() In the next article, we’ll explore linked lists. In cases where several tasks have the same priority, they are returned in the order of insertion. Our priority queue returns the task with the highest priority first. In this article, we used a heapq to implement a priority queue. The full implementationīelow is the full implementation and some test cases: Conclusion This method has a constant time complexity O(1). The size method returns the length of the priority queue. As with pull, we need to return None if the priority queue is empty to avoid an Inde圎rror. The peek method returns the task with the highest priority. This method has a logarithmic time complexity O(log n). To avoid getting an Inde圎rror when pulling from an empty priority queue, we first check if it is empty and return None. The pull method returns and removes the task with the highest priority using the heappop method from heapq. As a result, only child.getH+count is be considered as the 'item' that get will retrieve. We use the insertion count to decide which task to return when several tasks have the same priority. 1 Answer Sorted by: 0 priorityQueue.put (child.getH+count, count, child) The above line calls put with the arguments: itemchild.getH+count, blockcount, and timeoutchild. We increase the insertion count after every insert. The item added is a tuple that contains the priority, the insertion count, and the task. The insert method adds an item to the priority queue, using the heappush method from heapq. The is_empty method returns True if the priority queue is empty. At the top of the module, we must import heapq: pq.is_empty(): We use this attribute to keep track of the insertion order. The class has an insertion_count attribute. We define a PriorityQueue class with a constructor that instantiates an empty list and a _str_ method that returns a readable representation of the priority queue object. Let’s go over our priority queue implementation. The reason for using heapq instead of other data types is that you can push and pop in O(log n) time while keeping the underlying data in order of priority. We’ll use the heapq available from the Lib/heapq.py module in Python for our priority queue implementation.
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