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style: apply rustfmt drift across the crate

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This commit is contained in:
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/bayesian_opt.rs
+47 -25
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@@ -85,8 +85,14 @@ where
self.config.initial_samples >= 2,
"BayesianOpt initial_samples must be >= 2",
);
assert!(self.config.signal_variance > 0.0, "BayesianOpt signal_variance must be > 0");
assert!(self.config.noise_variance > 0.0, "BayesianOpt noise_variance must be > 0");
assert!(
self.config.signal_variance > 0.0,
"BayesianOpt signal_variance must be > 0"
);
assert!(
self.config.noise_variance > 0.0,
"BayesianOpt noise_variance must be > 0"
);
assert!(
self.config.acquisition_samples >= 1,
"BayesianOpt acquisition_samples must be >= 1",
@@ -99,25 +105,24 @@ where
let direction = objectives.objectives[0].direction;
let dim = self.bounds.bounds.len();
if let Some(ls) = &self.config.length_scales {
assert_eq!(ls.len(), dim, "BayesianOpt length_scales.len() must equal dim");
assert_eq!(
ls.len(),
dim,
"BayesianOpt length_scales.len() must equal dim"
);
}
let length_scales: Vec<f64> = self
.config
.length_scales
.clone()
.unwrap_or_else(|| {
self.bounds
.bounds
.iter()
.map(|&(lo, hi)| 0.2 * (hi - lo).max(1e-9))
.collect()
});
let length_scales: Vec<f64> = self.config.length_scales.clone().unwrap_or_else(|| {
self.bounds
.bounds
.iter()
.map(|&(lo, hi)| 0.2 * (hi - lo).max(1e-9))
.collect()
});
let mut rng = rng_from_seed(self.config.seed);
// ---------------- Initial random design ----------------
let mut decisions: Vec<Vec<f64>> = Vec::with_capacity(
self.config.initial_samples + self.config.iterations,
);
let mut decisions: Vec<Vec<f64>> =
Vec::with_capacity(self.config.initial_samples + self.config.iterations);
let mut targets: Vec<f64> = Vec::with_capacity(decisions.capacity());
let mut evaluations = Vec::with_capacity(decisions.capacity());
for _ in 0..self.config.initial_samples {
@@ -153,8 +158,7 @@ where
}
};
let best_target =
targets.iter().cloned().fold(f64::INFINITY, f64::min);
let best_target = targets.iter().cloned().fold(f64::INFINITY, f64::min);
// Maximize EI by best-of-N random sampling.
let mut best_x = sample_uniform_in_bounds(&self.bounds, &mut rng);
@@ -183,7 +187,11 @@ where
.collect();
let mut best_idx = 0;
for i in 1..final_pop.len() {
if better(&final_pop[i].evaluation, &final_pop[best_idx].evaluation, direction) {
if better(
&final_pop[i].evaluation,
&final_pop[best_idx].evaluation,
direction,
) {
best_idx = i;
}
}
@@ -232,7 +240,13 @@ fn sample_uniform_in_bounds(bounds: &RealBounds, rng: &mut Rng) -> Vec<f64> {
bounds
.bounds
.iter()
.map(|&(lo, hi)| if lo == hi { lo } else { lo + (hi - lo) * rng.random::<f64>() })
.map(|&(lo, hi)| {
if lo == hi {
lo
} else {
lo + (hi - lo) * rng.random::<f64>()
}
})
.collect()
}
@@ -289,10 +303,19 @@ impl GpPosterior {
let n = self.decisions.len();
let mut k_star = vec![0.0_f64; n];
for (i, k_star_i) in k_star.iter_mut().enumerate() {
*k_star_i = rbf_kernel(x, &self.decisions[i], &self.length_scales, self.signal_variance);
*k_star_i = rbf_kernel(
x,
&self.decisions[i],
&self.length_scales,
self.signal_variance,
);
}
let _ = n;
let mu: f64 = k_star.iter().zip(self.alpha.iter()).map(|(a, b)| a * b).sum();
let mu: f64 = k_star
.iter()
.zip(self.alpha.iter())
.map(|(a, b)| a * b)
.sum();
// Var = k(x,x) - k_star^T · K^{-1} · k_star
// Compute K^{-1}·k_star = solve_upper_transpose(L, solve_lower(L, k_star))
let v_temp = crate::internal::cholesky::solve_lower(&self.chol_l, &k_star);
@@ -335,8 +358,7 @@ fn erf(x: f64) -> f64 {
let sign = if x < 0.0 { -1.0 } else { 1.0 };
let x = x.abs();
let t = 1.0 / (1.0 + p * x);
let y = 1.0
- (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * (-x * x).exp();
let y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * (-x * x).exp();
sign * y
}