Function silhouette_samples

crates/openquant/src/onc.rs:404–467

(data: &DMatrix<f64>, labels: &[usize])

}

fn silhouette_samples(data: &DMatrix<f64>, labels: &[usize]) -> Vec<f64> {
    let n = data.nrows();
    let mut by_cluster: BTreeMap<usize, Vec<usize>> = BTreeMap::new();
    for (i, lbl) in labels.iter().copied().enumerate() {
        by_cluster.entry(lbl).or_default().push(i);
    }

    let mut pairwise = DMatrix::zeros(n, n);
    for i in 0..n {
        for j in i..n {
            let mut s = 0.0;
            for c in 0..data.ncols() {
                let diff = data[(i, c)] - data[(j, c)];
                s += diff * diff;
            }
            let d = s.sqrt();
            pairwise[(i, j)] = d;
            pairwise[(j, i)] = d;
        }
    }

    let mut scores = vec![0.0; n];
    for i in 0..n {
        let own = labels[i];
        let own_members = &by_cluster[&own];

        let a = if own_members.len() <= 1 {
            0.0
        } else {
            let mut s = 0.0;
            let mut cnt = 0usize;
            for &j in own_members {
                if j != i {
                    s += pairwise[(i, j)];
                    cnt += 1;
                }
            }
            if cnt == 0 {
                0.0
            } else {
                s / cnt as f64
            }
        };

        let mut b = f64::INFINITY;
        for (cluster, members) in &by_cluster {
            if *cluster == own || members.is_empty() {
                continue;
            }
            let mut s = 0.0;
            for &j in members {
                s += pairwise[(i, j)];
            }
            let mean = s / members.len() as f64;
            if mean < b {
                b = mean;
            }
        }