![]() ![]() ![]() # compare the distance to each node from the "start" node The last part of step 4 is to set the distance of the visited node to the shortest distance available. The second part of step 4 is to add the closest node (which we should now have selected) to the list of visited nodes. # all the nodes have been visited or we can't reach this node If not visited and (u = -1 or dist < dist): # than the distance we have to the "start" node # we haven't processed it or the distance we have for it is less # if the node 'i' hasn't been visited and # loop through all the nodes to check for visitation status # "start" our node as -1 (so we don't have a start/next node yet) If we’ve looped through all the available nodes and haven’t found a valid one, we break out of the inner loop. First let’s loop through the nodes and pick the next node to visit based on distance. Next, we create a list of visited nodes, all initialized to False. Step 2 is to initialize the distance of the root node to 0. # initialize distance list as all infinities We’ll create a function that takes two arguments, a graph argument, and a root argument.įirst, we create a list of distances initialized to Infinity. This implementation of Dijkstra’s algorithm has a runtime of O(N^2). Let’s go through each of these steps with a Naive implementation of Dijkstra’s algorithm. ![]() # each "node" in our list should be a node name and a distanceĪ visual representation of the adjacency list we’re going to run Dijkstra’s algorithm on will look like this.Įxample Graph for Dijkstra’s Algorithm Python Implement Naive Dijkstra’s Algorithm in Python # create our graph using an adjacency list representation We’ll create an adjacency list representation with 5 connected nodes. We use this because if you try to use a really large integer, it could overflow into a negative number. pip install numpyįor this example, we’ll be using the Inf object from numpy, this is a representation of infinity. You can install the numpy library with pip using the command below in the terminal. To do this example, we’ll have to install the numpy library. ![]()
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