WIP: (feature) add anisotropic meshing to LearnerND #141
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taking into account the output dimension of the function
this way you can import them into other files
it prunes some circumcircles faster it results in: ~20% faster in 2d ~40% faster in 3d
…icity-to-learnerND
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T11:56:16.294Z on GitLab I notice that you aren't using transform when adding a point to the subtriangulations; could that be of relevance? |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T12:00:43.335Z on GitLab Shouldn't we rather get all neighbors from the triangulation? |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T12:05:07.599Z on GitLab In my trial runs the triangulation stays isotropic in the ring, regardless of the resolution. It doesn't look like this is working as expected. |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T12:11:59.837Z on GitLab I think the scale matrix should be applied first to the point coordinates instead; isn't that right? |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T12:18:22.404Z on GitLab Um, are you sure that you want to normalize the gradient? That sounds like it could be the explanation for the weird behavior with the circle. |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T12:36:18.536Z on GitLab ...But this line I don't understand. |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-07-25T13:11:11.040Z on GitLab Totally works with the ring:
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originally posted by Jorn Hoofwijk (@Jorn) at 2018-08-20T12:13:33.135Z on GitLab
This point is going to be a lot harder than I thought. Scipy does not support this and I couldn't find a lib that does. Also when I would just use |
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originally posted by Anton Akhmerov (@anton-akhmerov) at 2018-08-20T12:40:11.106Z on GitLab For plotting you could consider this approach:
Otherwise you'd need an rtree and searching over point's neighbors. |
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originally posted by Jorn Hoofwijk (@Jorn) at 2018-09-21T11:07:18.805Z on GitLab actually, @basnijholt pointed out yesterday that I was mistaken and that scipy does allow you to provide a triangulation to the linearNDinterpolator. However, I am not completely sure as to what methods must be implemented on the provided triangulation. Depending on what it is, we can either do it efficiently or it may be pretty inefficient. |
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(original merge request on GitLab)
opened by Jorn Hoofwijk (@Jorn) at 2018-07-25T11:37:55.550Z
Closes gitlab:#74
Depends on gitlab:!86 and gitlab:#80 and therefore it has the corresponding branches included as well
Still has a few to-do's:
LearnerND.ip()make use of our triangulation rather than building a new onelet the user configure the parameters (maximum stretch factor and number of points to take into account)Use one simplex and it's neighboursaddNo more RTree anymore :)rtreeas install requirementraise exception ifanistropic=Trueandrtreenot installed, pass ifanisotropic=Falselet's make it fast :)gitlab:#80As it seems it doesn't work that well with the ring, since this ring has a relative low average gradient and very high second derivative. So maybe this second derivative might be a more useful property to determine the
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