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I added a scalar curvature function. I'm hoping this gives us some useful information. My hope is that when a metric space is already well clustered (say sampling from a single normal distribution) then the scalar curvature should be roughly a positive constant. If the metric space still needs clustering, then one would expect to find a few points with negative scalar curvature.
The test does seem to give some useful information, in some cases.
Just an observation, really. I tried a number of examples. Perhaps the process should be somewhat iterative? How to check if we're finished?
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