Function rank_desc

crates/openquant/src/feature_importance.rs:437–445

(values: &[f64])

    let n = rows.len();
    let m = rows[0].len();
    let flat: Vec<f64> = rows.iter().flat_map(|r| r.iter().copied()).collect();
    DMatrix::<f64>::from_row_slice(n, m, &flat)
}

fn pearson_corr(x: &[f64], y: &[f64]) -> f64 {
    if x.len() != y.len() || x.is_empty() {
        return 0.0;
    }
    let mx = x.iter().sum::<f64>() / x.len() as f64;
    let my = y.iter().sum::<f64>() / y.len() as f64;
    let mut num = 0.0;
    let mut vx = 0.0;