To avoid this problem, you have to add your own fallback comparison by "decorating" each value. Note that for objects like your Vertex class, where the comparison is based on one value, but the objects have other state, a heap isn't stable two objects with the same total_cost could come out in either order, regardless of which was placed in the heap first. # 2 5 (only one child since heap lacks another element)Īs you can see, in each case, the P value is less than or equal to all of its children the heap-invariant is maintained, which is all that is necessary for heappop, heappush, etc. You find the children for a node at index i by looking at index 2*i+1 and 2*i+2, so in your example, putting P under each parent, and C under each of their children, we have: It means it satisfies the heap invariant, for a min-heap like Python's, this just means that the smallest value is at the top (if there's a tie, an arbitrary value wins), and that each node's children are always equal to or larger than the node. "Heap" doesn't mean "sorted" (if it did, you couldn't build it for arbitrary values in O(n) time). Return self.total_cost = other.total_cost Return self.total_cost < other.total_cost Self.total_cost = self.g_actual_cost + self.h_cost The max priority works in descending order.The heap is stored array value in a sorted form.The heap looks like the tree structure of the value.The priority queue is stored array value in a simple form.Self.h_cost = 0 # the cost given by the heuristic function The priority queue looks like the basic queue structure of the value.Super(Vertex, self)._init_(name, x_coordinate, y_coordinate, obstacle_flag) Self.neighbors = # list of neighbors of this nodeĭef _init_(self, name, x_coordinate, y_coordinate, obstacle_flag): Self.obstacle = obstacle_flag # if the name is '*' the obstacle is set to True ordinates = (x_coordinate, y_coordinate) # this will uniquely identify the node Whenever elements are pushed or popped, heap structure is maintained. The property of this data structure in Python is that each time the smallest heap element is popped (min-heap). In Python, it is available using the heapq module. Self.name = name # possible values should only be ' ', 'A-Z', '*' Heap data structure is mainly used to represent a priority queue. Here is the actual code I'm implementing for context: class Node(object):ĭef _init_(self, name, x_coordinate, y_coordinate, obstacle_flag=False): I would like to know how this works.Īn illustration of the behaviour is like so: > mylist = I'm thinking of implementing my own priority queue now. I'm trying to do so using heapq, and after some debugging I noticed if I would like to push a vertex that has some f score equal to some other pre-existing vertices in the heap, the order would be messed up. # to append a dummy value to avoid an illegal index.Coming from Java, I am trying to implement A* algorithm in python and I'm having trouble sorting vertices in my graph that have equal f scores. self.heap None if items is None: self.heapsize 0 else: self.heap + items self.heapsize len (items) self.build. def init (self, itemsNone): Post: A heap is created with specified items. In the resulting heap the smallest element gets pushed to the index position 0. heapify This function converts a regular list to a heap. This library has the relevant functions to carry out various operations on a heap data structure. # Since delete_max calls pop to remove an item, we need I have been stuck on this for awhile and I keep finding answers that include using the built in python functions such as queue and heapq. A heap queue is created by using python’s inbuilt library named heapq. Post: heap property is satisfied for entire heap'''įor i in range(self.heap_size // 2, 0, -1): # stops at 1 '''pre: self.heap has values in 1 to self.heap_size Post: item is inserted in proper location in heap''' Post: maximum element in heap is removed and returned''' Post: heap property is satisfied for the entire heap''' '''pre: items from 0 to position - 1 satisfy the heap property '''post: returns number of items in the heap''' '''post: a heap is created with specified items''' Max-heap code (provided by textbook): # Heap.py Output after I run the code and call main: 0 My priority queue code: from Heap import Heap If anyone could help me fix this, it would be greatly appreciated. I assume this means there is a problem with my enqueue and it is not inserting the items into the correct position in the heap based on their priority. The issue I am facing is that when I dequeue an item from the priority queue, it does not dequeue the item with highest priority. I'm using several names that have numbers associated with them as their priority (9 being the highest and 1 being the lowest). I am implementing a priority queue using a max-heap data structure in Python.
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