Function fit_impl

crates/openquant/src/fingerprint.rs:127–164

(
    predictor: F,
    x: &[Vec<f64>],
    num_values: usize,
    pairwise_combinations: Option<&[(usize, usize)]>,
)

        ];
        if let Some(p) = pair {
            lines.push(format!("pairwise:{} pairs", p.raw.len()));
        }
        Ok(lines)
    }
}

fn fit_impl<F>(
    predictor: F,
    x: &[Vec<f64>],
    num_values: usize,
    pairwise_combinations: Option<&[(usize, usize)]>,
) -> Result<(Effect, Effect, Option<PairwiseEffect>), FingerprintError>
where
    F: Fn(&[Vec<f64>]) -> Vec<f64>,
{
    if x.is_empty() {
        return Err(FingerprintError::Empty("x"));
    }
    if num_values < 2 {
        return Err(FingerprintError::Invalid { name: "num_values", requirement: ">= 2" });
    }
    let n_features = x[0].len();
    if n_features == 0 {
        return Err(FingerprintError::NoFeatures);
    }
    if x.iter().any(|r| r.len() != n_features) {
        return Err(FingerprintError::RaggedX);
    }

    let feature_values = get_feature_values(x, num_values);
    let partial_dep = get_individual_partial_dependence(&predictor, x, &feature_values);
    let linear_raw = get_linear_effect(&feature_values, &partial_dep);
    let nonlin_raw = get_non_linear_effect(&feature_values, &partial_dep);
    let linear = Effect { norm: normalize_usize_map(&linear_raw), raw: linear_raw };
    let non_linear = Effect { norm: normalize_usize_map(&nonlin_raw), raw: nonlin_raw };

    let pair = pairwise_combinations.map(|pairs| {
        let raw =
            get_pairwise_effect(pairs, &predictor, x, num_values, &feature_values, &partial_dep);
        PairwiseEffect { norm: normalize_string_map(&raw), raw }
    });