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style: apply rustfmt drift across the crate

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swaits
2026-05-05 11:40:14 -06:00
parent 84cee3f29e
commit 4a59041d1a
60 changed files with 1520 additions and 608 deletions
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src/algorithms/cma_es.rs
+17 -17
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@@ -122,9 +122,7 @@ where
// Standard CMA-ES strategy parameters (Hansen tutorial §7.1).
// ---------------------------------------------------------------
let c_sigma = (mu_eff + 2.0) / (n_f + mu_eff + 5.0);
let d_sigma = 1.0
+ 2.0 * ((mu_eff - 1.0) / (n_f + 1.0)).sqrt().max(0.0)
+ c_sigma;
let d_sigma = 1.0 + 2.0 * ((mu_eff - 1.0) / (n_f + 1.0)).sqrt().max(0.0) + c_sigma;
let c_c = (4.0 + mu_eff / n_f) / (n_f + 4.0 + 2.0 * mu_eff / n_f);
let c_1 = 2.0 / ((n_f + 1.3).powi(2) + mu_eff);
let c_mu = ((1.0 - c_1) * 2.0 * (mu_eff - 2.0 + 1.0 / mu_eff)
@@ -150,7 +148,11 @@ where
.map(|(v, &(lo, hi))| v.clamp(lo, hi))
.collect()
} else {
self.bounds.bounds.iter().map(|&(lo, hi)| 0.5 * (lo + hi)).collect()
self.bounds
.bounds
.iter()
.map(|&(lo, hi)| 0.5 * (lo + hi))
.collect()
};
let mut sigma = self.config.initial_sigma;
// Covariance C, eigenvectors B, eigenvalues d (square roots of eigenvalues of C).
@@ -181,10 +183,7 @@ where
let (eigenvalues, eigenvectors) = symmetric_eigen(&c_matrix, 1e-14, 100);
// eigenvectors is sorted descending; we don't depend on order
// for sampling correctness, but we do need positive eigenvalues.
d = eigenvalues
.iter()
.map(|&v| v.max(1e-20).sqrt())
.collect();
d = eigenvalues.iter().map(|&v| v.max(1e-20).sqrt()).collect();
// B is the matrix whose columns are the eigenvectors. The
// helper returns `eigenvectors[i]` as the i-th *eigenvector*,
// so b[r][c] should equal eigenvectors[c][r].
@@ -232,7 +231,11 @@ where
// Sort offspring by fitness ascending (best first).
let mut order: Vec<usize> = (0..lambda).collect();
order.sort_by(|&a, &b_| {
compare_so(&evaluated[a].evaluation, &evaluated[b_].evaluation, direction)
compare_so(
&evaluated[a].evaluation,
&evaluated[b_].evaluation,
direction,
)
});
// ----- Recompute mean from the μ best (weighted average of x) -----
@@ -284,13 +287,13 @@ where
// ----- Evolution path for C: p_c = (1 - c_c) p_c + h_σ · sqrt(c_c (2 - c_c) μ_eff) · (m_new - m_old)/σ -----
let factor_p_c = h_sigma * (c_c * (2.0 - c_c) * mu_eff).sqrt();
for i in 0..n {
p_c[i] = (1.0 - c_c) * p_c[i]
+ factor_p_c * (mean[i] - old_mean[i]) / sigma;
p_c[i] = (1.0 - c_c) * p_c[i] + factor_p_c * (mean[i] - old_mean[i]) / sigma;
}
// ----- Covariance matrix update (rank-1 + rank-μ) -----
let delta_h = (1.0 - h_sigma) * c_c * (2.0 - c_c);
#[allow(clippy::needless_range_loop)] // body uses both i and j to index c_matrix and offspring.
#[allow(clippy::needless_range_loop)]
// body uses both i and j to index c_matrix and offspring.
for i in 0..n {
for j in 0..n {
let mut update = (1.0 - c_1 - c_mu) * c_matrix[i][j]
@@ -443,7 +446,7 @@ mod tests {
generations: 30,
initial_sigma: 0.5,
eigen_decomposition_period: 1,
initial_mean: None,
initial_mean: None,
seed: 99,
};
let mut a = CmaEs::new(cfg.clone(), RealBounds::new(vec![(-5.0, 5.0)]));
@@ -459,10 +462,7 @@ mod tests {
#[test]
#[should_panic(expected = "single-objective")]
fn multi_objective_panics() {
let mut opt = CmaEs::new(
CmaEsConfig::default(),
RealBounds::new(vec![(-5.0, 5.0)]),
);
let mut opt = CmaEs::new(CmaEsConfig::default(), RealBounds::new(vec![(-5.0, 5.0)]));
let _ = opt.run(&SchafferN1);
}