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If the element with the smallest value has the highest priority, then that priority queue is called the min priority queue.

Return self._f(self.value) >= self._f(obj. There are two types of priority queues based on the priority of elements. This is my implementation: def ucs (G, v): visited set () set of visited nodes visited.add (v) mark the starting vertex as visited q queue.PriorityQueue () we store vertices in the (priority) queue as tuples with cumulative cost q.put ( (0, v)) add the starting node, this has zero cumulative cost goalnode None this will be. Step 2: Check if HEAD has lower priority. If a join () is currently blocking, it will resume when all items have been processed (meaning that a taskdone () call was received for every item that had been put () into the queue). Algorithm : PUSH (HEAD, DATA, PRIORITY): Step 1: Create new node with DATA and PRIORITY. Return self._f(self.value) > self._f(obj.value) For each get () used to fetch a task, a subsequent call to taskdone () tells the queue that the processing on the task is complete. If you have a wrapper class for the elements, then you can use operator overloading.įor example, lets say you have a CustomNumber class (equivalent to your elements) where the order is determined by the modulo 16 value (the private function _f()), the you can override the comparison operators like: class CustomNumber: