Struct Profiles 

pub struct Profiles {
    pub rows: Vec<Vec<(u32, f32)>>,
    pub size_factor: Vec<f32>,
    pub num_entities: usize,
    pub num_features: usize,
}

Sparse row-per-entity profile, owned by the refinement pass.

Materialized from either a sparse gene-sum format (from_gene_sums) or a projected centroid matrix (from_projection). Values are stored sorted by feature index within each row so downstream accumulators can rely on the invariant.

Fields

rows: Vec<Vec<(u32, f32)>>

size_factor: Vec<f32>

num_entities: usize

num_features: usize

Implementations

impl Profiles

pub fn from_gene_sums( gene_sums: &[Vec<(usize, f32)>], num_features: usize, ) -> Self

pub fn from_projection( basis: &DMatrix<f32>, entity_to_cells: &[Vec<usize>], ) -> Self

Build profiles from a dense projection.

basis is proj_dim × num_cells; each entity profile is the sum of basis columns over the cells constituting it. Gene weighting is not applied — projection dims aren’t gene-aligned.

The Poisson score needs nonnegative profiles, so the accumulated per-dimension sums must be >= 0 — a signed basis (e.g. a raw random projection) is rejected loudly rather than silently truncated.

pub fn weight_by_vec(&mut self, w: &[f32])

In-place reweighting by a caller-supplied per-feature weight vector. Used by Profiles::apply_feature_weighting for the NB Fisher-info path.

pub fn apply_feature_weighting(&mut self, method: FeatureWeighting)

Apply the chosen feature weighting in place.

FeatureWeighting::None is a no-op; FeatureWeighting::FisherInfoNb fits an NB dispersion trend from the current profiles and reweights each feature by 1 / (1 + π_f · s̄ · φ(μ_f)).

pub fn nb_fisher_weights(&self) -> Vec<f32>

Compute per-feature Fisher-info weights under an NB trend fit from the current profile contents. Returned vector has length num_features and is suitable for Profiles::weight_by_vec.