- Update to 0.6.2
* By default, we now use the MultiscaleMixture affinity model,
enabling us to pass in a list of perplexities instead of a
single perplexity value. This is fully backwards compatible.
* Previously, perplexity values would be changed according to the
dataset. E.g. we pass in perplexity=100 with N=150. Then
TSNE.perplexity would be equal to 50. Instead, keep this value
as is and add an effective_perplexity_ attribute (following the
convention from scikit-learn, which puts in the corrected
perplexity values.
* Fix bug where interpolation grid was being prepared even when
using BH optimization during transform.
* Enable calling .transform with precomputed distances. In this
case, the data matrix will be assumed to be a distance matrix.
* Fix potential problem with clang-13, which actually does
optimization with infinities using the -ffast-math flag
- Enable python310 build
- Skip a test in 32bit failing due to rounding errors