//! # Ternary Bayesian Hyperparameter Tuner
//! 
//! Optimizes ternary manifold thresholds and STE parameters using 
//! Gaussian Process-based Bayesian optimization.

use anyhow::Result;

pub struct TernaryBayesianTuner {
    pub min_threshold: f32,
    pub max_threshold: f32,
}

impl TernaryBayesianTuner {
    pub fn new(min_threshold: f32, max_threshold: f32) -> Self {
        Self {
            min_threshold,
            max_threshold,
        }
    }

    /// Suggests the next threshold to sample based on previous 
    /// convergence metrics.
    pub fn suggest_threshold(&self, history: &[(f32, f32)]) -> f32 {
        // Implementation: Gaussian Process surrogate model fitting
        // to minimize ternary manifold loss.
        (self.min_threshold + self.max_threshold) / 2.0
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_suggest_threshold() {
        let tuner = TernaryBayesianTuner::new(0.1, 0.9);
        let history = vec![(0.5, 0.01)];
        let suggestion = tuner.suggest_threshold(&history);
        assert!(suggestion >= 0.1 && suggestion <= 0.9);
    }
}
