DBSCAN cluster options.
One potential drawback of the DBSCAN algorithm for this application
is that it can produce clusters that are arbitrarily large as long
as they are densely connected. However, UI elements possess a high
degree of geometric regularity, and so with a correctly tuned
:epsilon
parameter it should be able to produce good results. A
naive grid clustering approach, while guaranteeing bounded clusters,
can potentially fail to cluster points that are adjacent, but on
opposite sides of a partition. There are hierarchical grid
clustering algorithms that might mitigate this issue, but with the
right tuning DBSCAN should be fine.
DBSCAN cluster options. One potential drawback of the DBSCAN algorithm for this application is that it can produce clusters that are arbitrarily large as long as they are densely connected. However, UI elements possess a high degree of geometric regularity, and so with a correctly tuned `:epsilon` parameter it should be able to produce good results. A naive grid clustering approach, while guaranteeing bounded clusters, can potentially fail to cluster points that are adjacent, but on opposite sides of a partition. There are hierarchical grid clustering algorithms that might mitigate this issue, but with the right tuning DBSCAN should be fine.
(px el k)
Gets the float value of the computed style.
Gets the float value of the computed style.
(rect el & [selectors])
Keep in mind that .getBoundingClientRect will return zeros during certain phases of the react lifecycle. Use with care.
Keep in mind that .getBoundingClientRect will return zeros during certain phases of the react lifecycle. Use with care.
Attempt to start panels on top all host UI elements. This still leaves over a billion panel interactions. Worst case the panels stop layering properly.
Attempt to start panels on top all host UI elements. This still leaves over a billion panel interactions. Worst case the panels stop layering properly.
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