Crate flow_dimensional_reduction 

Dimensionality reduction primitives for flow cytometry.

Currently provides Pca, a faer-based principal component analysis using the covariance method: it decomposes the d × d covariance matrix rather than the n × d data matrix. For flow cytometry workloads (n ≈ 10⁶–10⁷, d ≈ 10–50) this is dramatically cheaper.

Data is f32 on the boundary; means and covariance are accumulated in f64 and downcast only when the final basis is stored.

Re-exports

pub use pca::Pca;

pub use pca::PcaComponent;

pub use pca::PcaError;

pub use pca::PcaResult;

Modules

pca

Principal Component Analysis via the covariance method.