NF: Find the closest vertex on a sphere for an input vector. #483
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| vertex (in angle). | ||
| """ | ||
| ang = np.arccos(np.dot(self.vertices, xyz)) | ||
| ang = np.min(np.vstack([ang, np.pi - ang]), 0) |
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I've been using something like the following for this:
cos_sim = abs(np.dot(self.vertices, xyz))
return np.argmax(cos_sim)
If this is being done repeatedly it might be worth building a KDtree for it.
arokem
changed the title
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Thanks for the comments and suggestions. How about the following solution? Have each of these classes have it's own (just very slightly different) implementation of this function, taking or not taking the absolute value, as needed. I have also added a test to demonstrate that scaling by l2norm is not necessary, as long as you are not interested in the angle, but rather just in finding the index of the minimal. |
Account for antipodal symmetry as appropriate for Sphere/Hemisphere.
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Just rebased on |
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Will have a look first thing tomorrow morning. Sorry for the delay. |
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Hi @arokem. The tests are a bit too tight because you only test finding an already existing point to itself. I would like to see in the future a test showing that if you add a new point that is equal to old point + tiny random shift you will still get the same point. But because this is a trivial problem and this PR is needed for other more complex PRs I believe is pointless to wait right now for extra testing on this and will go ahead and merge. Inf+!!! |
NF: Find the closest vertex on a sphere for an input vector.
This is useful for both #460 and #419 so needs to take precedence. It's not quite done yet, though. Will hopefully have a chance to finish this later today.