Graph bfs dfs #9
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| not confident about graphs in general. that is to say, given that there are so | ||
| many applications of graphs, even implementing something 'basic' like dfs or bfs | ||
| is quite daunting because of all the potential applications, constraints, and so | ||
| on. for example, i've read through the word search ladder and the knights tour a |
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The word search ladder and knights tour are both quite tricky applications, and involve a lot of nuance that's kind of unrelated to graphs.
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| terminate when you find what you are looking for, or the queue is empty. | ||
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| an empty queue means that all nodes have been searched. |
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Specifically, all reachable nodes have been searched
| return info | ||
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| for v in graph[vertex]: | ||
| if v not in info["visited"]: |
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Given that info["visited"] is a list, what's the running time implication on (i) this step, (ii) the entire process? What would be a data structure that gives you an improved running time?
| info = { | ||
| "target": target, | ||
| "found": False, | ||
| "visited": [], |
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What are the consequences of generating a new visited array on each iteration? What happens if you run this on a graph with a cycle?
| info = { | ||
| "target": target, | ||
| "found": False, | ||
| "visited": [], |
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I've found it helpful to give a clear description of what "node is in visited" means. For example, does it mean:
- I've added the node to the queue (and don't need to add it again)
- I've dequeued the node from the queue and checked it (so if I dequeue the same node again, I can ignore it)
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| return info | ||
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| def minimum_depth_of_binary_tree(self, root): |
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Great!! This is a really nice solution.
finished the prework, and have a bunch of questions. those are in the .md file.
thanks in advance for your comments as usual, @robot-dreams!