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The important part of speeding up the app is you must understand very well about the topic of your app, then do some research to understand it even more. From the numbers of candidates and from the numbers of dividing factor. In above case we eliminate many iterations from two types of approach. We can speeding up our high-consumed memory computation by eleminating unnecessary computation. I'll make sure I'll be more detailed in the future and not skipped even a tiny testing-variable. I didn't include first two test-variable in fastGenerator because I guess it will be too small. # Outgoing edges from the node: (adjacent_node, cost) in graph 2.I stop testing the function if it passed 50s. Print( "Cost of the minimum spanning tree in graph 1 : " + str(cost) + " \n ") # Outgoing edges from the node: (adjacent_node, cost) in graph 1. _id) + ", cost now : " +str(min_span_tree_cost)) # Remove the node from the priority queue del priority_queue Node = min (priority_queue, key =priority_queue. # Choose the adjacent node with the least edge cost Min_span_tree_cost = 0 while priority_queue : Add source node as the first node # in the priority queue # The distance of source node from itself is 0. # Since the priority queue can have multiple entries for the # same adjacent node but a different cost, we have to use objects as # keys so that they can be stored in a dictionary. # Priority queue is implemented as a dictionary with # key as an object of 'Node' class and value as the cost of # reaching the node from the source. Python3 : Prim’s minimum spanning tree algorithm.įrom typing import List, Dict # For annotations class Node :ĭef _init_(self, source : int, adj_list : Dict]) : Push pair of ( adjacent node, cost ) into the dictionary PQ.Įxample of finding the minimum spanning tree using Prim’s algorithm For all vertices adjacent to vertex V not added to spanning tree.ġ1. If the adjacent node V is not added to the spanning tree.ġ0. Delete the key-value pair ( V, C ) from the dictionary PQ.ħ. Get the adjacent node V ( key ) with the smallest edge cost ( value ) from the dictionary PQ.Ħ. Push ( node, cost ) in the dictionary PQ i.e Cost of reaching vertex S from source node S is zero.Ĥ. Create a dictionary (to be used as a priority queue) PQ to hold pairs of ( node, cost ).Ģ. It is evident that the algorithm gets greedy by selecting the least expensive edge from the vertices already added to the spanning tree.Īlgorithm : Prims minimum spanning tree ( Graph G, Souce_Node S )ġ. The algorithm continues to add the least expensive edge from the vertices already added to the spanning tree to make it grow and terminates when all the vertices are added to the spanning tree. It begins by randomly selecting a vertex and adding the least expensive edge from this vertex to the spanning tree. Prim’s algorithm finds the cost of a minimum spanning tree from a weighted undirected graph.