Improved CAGRA build parameter heuristics

[achirkin]

Changes to the build parameter heuristics:

• Move the code from HNSW namespace to CAGRA namespace to avoid depending on HNSW target
• Add one more variant of the heuristics: allow generating smaller graph to better match the performance of the HNSW-generated graph
• Implement automatic switch between NN-Descent and IVF-PQ as the graph-build algorithms depending on the dataset size: NN-Descent tends to perform better on smaller-scale datasets

PR also include C and java bindings.
Resolves #1265

[KyleFromNVIDIA]

Approved trivial CMake changes

[tfeher]

The hard am variant sets graph_degree = 2 * M, it is surprising to see that soft variant can lead to similar search performance with graph_degree < M. The benchmarks for 768 and 1536 dimension looked good. Was it also tested for smaller dimensional datasets?

[achirkin]

Yes, I've tested it on DEEP-100M and glove datasets. The hard-M variant actually shows much higher recall and lower throughput for the same search 'ef' parameter (the QPS-recall curve is close to HNSW, but all points on it are 'shifted' towards higher recall and lower throughput).

[tfeher]

Thanks Artem for the PR, it looks good to me!

[mythrocks]

Looks good to my eyes. Minor nit regarding javadoc for the new function.

[achirkin]

/merge