[WIP] Expose SOAR as a public cluster API - #2540
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SOAR (Spilling with Orthogonality-Amplified Residuals, https://arxiv.org/abs/2404.00774) gives each vector a second centroid chosen to complement its primary assignment rather than to be merely the next closest. Indexing a vector under both partitions improves recall for queries near a partition boundary. The implementation lived in `neighbors/scann/detail/scann_soar.cuh` and was reachable only by building a ScaNN index, even though the algorithm needs nothing beyond centroids and primary k-means labels. Promotes it to a cluster-level API. `cuvs::cluster::soar::predict` takes a dataset, centroids, and primary labels, and writes one secondary label per row. `soar::params` exposes the `lambda` weight controlling how strongly a candidate centroid is penalized for having a residual aligned with the primary one. Moves `scann_soar.cuh` to `cluster/detail/soar.cuh` and points the ScaNN builder at the relocated entry point so there is a single implementation. `compute_soar_labels` now takes its centroids as a const view. The detail header also gains `compute_residuals`, which the public API needs to derive residuals from labels; the ScaNN builder already holds residuals for quantization and keeps supplying its own, so it does not pay for a second pass over the dataset. Adds `cpp/tests/cluster/soar.cu` to `CLUSTER_TEST`, covering assignments against an exhaustive host search, the residual computation against a host reference, a hand-checked separated-cluster case, and the shape-validation errors. Adds a C++ API documentation page. Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
`SOAR_EXAMPLE` shows the call sequence a caller needs: balanced k-means for the centroids and primary labels, then `soar::predict` to fill one secondary label per row. Signed-off-by: Ranjit Rajan <ranjitr@nvidia.com>
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Testing
SOAR_EXAMPLE.SCANN_EXAMPLEindex build time on NVIDIA GB10, 10 runs after warm-up: 412.3 ± 4.0 ms before and 410.8 ± 6.0 ms after; no measurable regression.