Add vectorised DBSCAN outlier detection implementation

[sd2k]

It should be possible to use SIMD to vectorise the outlier detector calculation. I haven't used SIMD directly before but the std::simd ('portable SIMD') module should handle what we need (albeit behind a nightly only flag).

I made some very rough notes on the dbscan_1d algorithm which might help implementers:

Original data:

9 6 3 4 1 15 0 10 5

sort it and keep reverse index for sorted data:

0 1 3 4 5 6 9 10 15

6 4 2 3 8 1 0 7 5


dbscan_1d:

Given sorted data:

0 1 3 4 5 6 9 10 15 <...0 padding until lane length>

Shift it right:

0 0 1 3 4 5 6 9 10 15 <... 0 padding until lane length>

First calculate diffs:

 1 2 1 1 1 3 1  5

Then determine if <= eps, e.g. for 2:

 true true true true true false true false

Find start/end index of longest chain of trues:

   true  true true true true false true  false <...false padding until lane length>

   false true true true true true  false true false <...false padding until lane length>

  xor'd:
    1     0    0    0    0    1     1     1
    |                         -     |     -

 0 5 6 7

Odds are cluster starts, evens are cluster ends.

Everything in the largest range is in the cluster.

Everything not included in this index range is an outlier:

  indexes 6 7 8
  values 9 10 15