5 ms·
This would almost certainly be more difficult, but I'd love if the diagram's relative sizes (optionally) accounted for the size of the sets' data – e.g. if set
by connorlanewhite 2y ago
This would almost certainly be more difficult, but I'd love if the diagram's relative sizes (optionally) accounted for the size of the sets' data – e.g. if set A and B have 2 items each with zero overlap, it would show two circles of equal size, completely apart from one another
- aargh_aargh 2y agoI've just found this for the exact same reason, for 2-10 sets. The gradient descent is done client-side (Tensorflow.js): https://www.deepvenn.com/ https://www.deepvenn.com/ I've stress-tested it with ~5 sets around ~50k items, comes back in seconds, well under a minute.
- pimlottc 2y agoI've used this before, it's a cool idea but it only sort of works. For most non-trivial examples, there will be still be a big discrepancy between the visual area of overlaps and the and the actual element count. Claiming that the graph is area-proportional might actually be more misleading if it's not actually accurate. To improve it, you'd need more ways to mutate the shapes to try to find a solution with less error. E.g. if the circles could be squashed into ellipses, that would probably help. Of course, some higher-order graphs can't be shown with convex shapes at all, so you'd ideally want to be able to pull and stretch areas as needed. It's a hard problem.