Python bindings

We should add Python bindings to the public API of linfa crates. This will allow us to fairly benchmark linfa against scikit-learn, which also uses a Python API, as well as making linfa easier to use, allowing for wider adoption. This process can be done piece by piece. I suggest we start with linfa-clustering, since that's the most-used linfa crate and also has prior art behind it.

Questions

• How close do we make the API to scikit-learn? Do we want exact parity?
• How do we support numpy in our API without lots of data copying?
• linfa makes heavy use of generics, but for Python bindings we need to pick one monomorphization to build. For type params like F we can just pick f64, but for other it's less clear cut. We may also need to choose between different params at runtime instead of compile time. Do we use an enum? A trait object?
• Similarly, which features do we want to build linfa with?

We'll likely put all the bindings into one Python package, so that we don't build multiple copies of linfa across multiple packages.

Prior Art
When @LukeMathWalker first released linfa, he also released Python bindings here, for benchmarking against scikit-learn. AFAIK these bindings only support KMeans, and they are also 3 years old, but they should provide a good starting point.

Start by reviewing the public API of the linfa-clustering crate and the prior art in the linked linfa-python project, which supports KMeans. The work is not yet defined: first establish the Python package structure, scikit-learn compatibility, NumPy data handling, generic type choices, and enabled features before implementing bindings and benchmarks.