[SciPy-User] Efficient Dijkstra on a large grid
Wed Apr 10 00:24:16 CDT 2013
In scikit-learn, we have a Dijkstra implemented in cython, using
On Tue, Apr 09, 2013 at 04:07:46PM -0700, Chris Weisiger wrote:
> I'm working on a roguelike videogame (basically a top-down dungeon crawler),
> and more specifically, right now I'm working on monster pathfinding. The
> monsters all need to be able to home in on the player -- a classic many-to-one
> pathfinding situation. I implemented A* first, but it's one-to-one and thus
> doesn't scale well to large numbers of monsters. So I figured calculating the
> shortest path length from the player to each cell via Dijkstra's method would
> be a good substitute. But I'm having trouble implementing an efficient
> Dijkstra's method for this use case (thousands of nodes) in Python.
> Here's what I have thus far: http://pastebin.com/Pts19hQp
> My test case is, I grant, a bit excessive -- a 360x120 grid that is almost
> entirely open space. It takes about .4s to calculate on my laptop. Angband, the
> game I am basing this on, handles this situation mostly by "deactivating"
> monsters that are far away from the player, by not having large open spaces,
> and by having fairly dumb pathfinding. I'm hoping that there's a more elegant
> solution; at the very least, I'd like this particular portion of the algorithm
> to be as efficient as possible before I move on to heuristic improvements.
> Any suggestions? I looked but did not find a builtin Dijkstra calculation
> algorithm in numpy, presumably because the situation in which your map can be
> represented as a 2D array is fairly rare. Am I simply butting into the limits
> of what Python can do efficiently, here?
> SciPy-User mailing list
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