RadialBasis Float64 faster the Float32

[agoscinski]

@abmazitov observed that float64 is faster than float32 for alchemical expansion. I observed the same with #21 The difference was more severe depending when there was no warmup of the benchmarks (using just --quick). I think it is because the NeighborlistTransformer takes the type of the given numpy arrays which is usually float64, then in the radial basis the one hot encoding is using torch.get_default_ytype() which is usally float32 https://github.com/frostedoyster/torch_spex/blob/08cfe0d296a1296b1b05596a868639df9a9ba6d1/torch_spex/radial_basis.py#L44
I assume the type conversion when the two types meet causes the #21 issue.

Temporary fix:
Use torch.set_default_dtype(torch.float64) before running the code

Real fix:
I want to take the chance and integrate asv benchmarks so we can actually track the change in performance when fixing tihs. Because the fix should be rather trivial (e.g. using dtype of r). I started this PR #21