v0.6.2

Changes

• 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.

Build changes

• Build with oldest-supported-numpy
• Build linux wheels on manylinux2014 instead of manylinux2010, following numpy's example
• Build MacOS wheels on macOS-10.15 instead of macos-10.14 Azure VM
• Fix potential problem with clang-13, which actually does optimization with infinities using the -ffast-math flag