fugue-evo 0.3.1

//! Convergence detection for evolutionary algorithms
//!
//! This module provides various methods to detect when an evolutionary algorithm
//! has converged or should terminate.

use serde::{Deserialize, Serialize};

/// Result of a convergence check
#[derive(Clone, Debug, PartialEq, Eq)]
pub enum ConvergenceStatus {
    /// Algorithm has not converged
    NotConverged,
    /// Algorithm has converged with a reason
    Converged(ConvergenceReason),
}

impl ConvergenceStatus {
    /// Check if converged
    pub fn is_converged(&self) -> bool {
        matches!(self, Self::Converged(_))
    }
}

/// Reason for convergence
#[derive(Clone, Debug, PartialEq, Eq, Serialize, Deserialize)]
pub enum ConvergenceReason {
    /// Fitness has not improved for many generations
    FitnessStagnation { generations: usize },
    /// Population diversity is below threshold
    LowDiversity { diversity: u64 }, // Store as bits for Eq
    /// Target fitness reached
    TargetReached { target: u64 }, // Store as bits for Eq
    /// Maximum generations reached
    MaxGenerations { generations: usize },
    /// Maximum evaluations reached
    MaxEvaluations { evaluations: usize },
    /// R-hat statistic indicates convergence
    RhatConverged { rhat: u64 }, // Store as bits for Eq
    /// Multiple criteria satisfied
    MultipleReasons(Vec<ConvergenceReason>),
    /// Custom termination
    Custom(String),
}

impl ConvergenceReason {
    /// Create a fitness stagnation reason
    pub fn fitness_stagnation(generations: usize) -> Self {
        Self::FitnessStagnation { generations }
    }

    /// Create a low diversity reason
    pub fn low_diversity(diversity: f64) -> Self {
        Self::LowDiversity {
            diversity: diversity.to_bits(),
        }
    }

    /// Create a target reached reason
    pub fn target_reached(target: f64) -> Self {
        Self::TargetReached {
            target: target.to_bits(),
        }
    }

    /// Create an R-hat converged reason
    pub fn rhat_converged(rhat: f64) -> Self {
        Self::RhatConverged {
            rhat: rhat.to_bits(),
        }
    }
}

/// Configuration for convergence detection
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct ConvergenceConfig {
    /// Maximum generations before termination
    pub max_generations: Option<usize>,
    /// Maximum fitness evaluations before termination
    pub max_evaluations: Option<usize>,
    /// Target fitness to reach
    pub target_fitness: Option<f64>,
    /// Tolerance for target fitness comparison
    pub target_tolerance: f64,
    /// Number of generations without improvement before stagnation
    pub stagnation_generations: usize,
    /// Minimum improvement to not count as stagnation
    pub stagnation_threshold: f64,
    /// Diversity threshold below which convergence is detected
    pub diversity_threshold: f64,
    /// R-hat threshold for convergence (typically 1.1)
    pub rhat_threshold: f64,
    /// Whether to use R-hat based convergence
    pub use_rhat: bool,
}

impl Default for ConvergenceConfig {
    fn default() -> Self {
        Self {
            max_generations: None,
            max_evaluations: None,
            target_fitness: None,
            target_tolerance: 1e-6,
            stagnation_generations: 50,
            stagnation_threshold: 1e-9,
            diversity_threshold: 0.01,
            rhat_threshold: 1.1,
            use_rhat: false,
        }
    }
}

impl ConvergenceConfig {
    /// Create a new config with max generations
    pub fn with_max_generations(generations: usize) -> Self {
        Self {
            max_generations: Some(generations),
            ..Default::default()
        }
    }

    /// Set max generations
    pub fn max_generations(mut self, generations: usize) -> Self {
        self.max_generations = Some(generations);
        self
    }

    /// Set max evaluations
    pub fn max_evaluations(mut self, evaluations: usize) -> Self {
        self.max_evaluations = Some(evaluations);
        self
    }

    /// Set target fitness
    pub fn target_fitness(mut self, target: f64) -> Self {
        self.target_fitness = Some(target);
        self
    }

    /// Set target tolerance
    pub fn target_tolerance(mut self, tolerance: f64) -> Self {
        self.target_tolerance = tolerance;
        self
    }

    /// Set stagnation detection parameters
    pub fn stagnation(mut self, generations: usize, threshold: f64) -> Self {
        self.stagnation_generations = generations;
        self.stagnation_threshold = threshold;
        self
    }

    /// Set diversity threshold
    pub fn diversity_threshold(mut self, threshold: f64) -> Self {
        self.diversity_threshold = threshold;
        self
    }

    /// Enable R-hat based convergence
    pub fn with_rhat(mut self, threshold: f64) -> Self {
        self.use_rhat = true;
        self.rhat_threshold = threshold;
        self
    }
}

/// Convergence detector that tracks evolution state
#[derive(Clone, Debug)]
pub struct ConvergenceDetector {
    /// Configuration
    config: ConvergenceConfig,
    /// History of best fitness values
    best_fitness_history: Vec<f64>,
    /// History of mean fitness values (for R-hat). Retained in full so
    /// `compute_rhat` can take a numerically stable **two-pass** variance over
    /// each half-chain (see `compute_rhat`); an earlier revision kept running
    /// sum / sum-of-squares prefix arrays instead, but the one-pass variance
    /// they enabled was catastrophically unstable for large-offset fitness.
    mean_fitness_history: Vec<f64>,
    /// History of diversity values
    diversity_history: Vec<f64>,
    /// Current generation
    current_generation: usize,
    /// Current evaluations
    current_evaluations: usize,
    /// Best fitness seen so far, *thresholded* for stagnation tracking: only
    /// advanced when an update improves on it by more than `stagnation_threshold`
    /// (see `update`). Because of that throttle it can lag the true running max by
    /// up to `stagnation_threshold`, so it must NOT be used for target detection.
    best_fitness_overall: f64,
    /// Pure running maximum of every `best_fitness` ever passed to `update`, with
    /// no threshold throttle (REG-1). This is the authoritative "best seen so far"
    /// used by `best_fitness()` and the target-fitness check; keeping it separate
    /// from `best_fitness_overall` lets stagnation stay throttled while target
    /// detection sees the true best.
    running_best_fitness: f64,
    /// Generation when best fitness was last improved
    last_improvement_generation: usize,
}

/// Numerically stable **two-pass** sample mean and (Bessel-corrected) variance
/// of `xs`, returned as `(mean, variance)`.
///
/// The first pass computes the mean; the second sums squared deviations from
/// that mean. This avoids the catastrophic cancellation of the one-pass
/// `(Σx² − (Σx)²/n)/(n−1)` form, which loses precision when the values share a
/// large offset relative to their spread (the exact failure that motivated
/// this helper — see `compute_rhat`). `xs` must be non-empty; the variance is
/// `0.0` for a single element. The operations mirror those in
/// [`evolutionary_rhat`], so the two agree to within rounding.
fn two_pass_mean_var(xs: &[f64]) -> (f64, f64) {
    let n = xs.len() as f64;
    let mean = xs.iter().sum::<f64>() / n;
    if xs.len() < 2 {
        return (mean, 0.0);
    }
    let ss: f64 = xs.iter().map(|x| (x - mean).powi(2)).sum();
    (mean, ss / (n - 1.0))
}

impl ConvergenceDetector {
    /// Create a new convergence detector
    pub fn new(config: ConvergenceConfig) -> Self {
        Self {
            config,
            best_fitness_history: Vec::new(),
            mean_fitness_history: Vec::new(),
            diversity_history: Vec::new(),
            current_generation: 0,
            current_evaluations: 0,
            best_fitness_overall: f64::NEG_INFINITY,
            running_best_fitness: f64::NEG_INFINITY,
            last_improvement_generation: 0,
        }
    }

    /// Create with default config
    pub fn with_defaults() -> Self {
        Self::new(ConvergenceConfig::default())
    }

    /// Update with generation statistics
    pub fn update(
        &mut self,
        generation: usize,
        evaluations: usize,
        best_fitness: f64,
        mean_fitness: f64,
        diversity: f64,
    ) {
        self.current_generation = generation;
        self.current_evaluations = evaluations;
        self.best_fitness_history.push(best_fitness);
        self.mean_fitness_history.push(mean_fitness);
        self.diversity_history.push(diversity);

        // Pure running max (REG-1): tracks the true best regardless of the
        // stagnation throttle below, so target detection never lags.
        if best_fitness > self.running_best_fitness {
            self.running_best_fitness = best_fitness;
        }

        // Track improvement (stagnation): intentionally throttled — only counts as
        // an improvement when it beats the previous best by more than
        // `stagnation_threshold`, so tiny gains don't reset the stagnation clock.
        if best_fitness > self.best_fitness_overall + self.config.stagnation_threshold {
            self.best_fitness_overall = best_fitness;
            self.last_improvement_generation = generation;
        }
    }

    /// Check if algorithm has converged
    pub fn check(&self) -> ConvergenceStatus {
        let mut reasons = Vec::new();

        // Check max generations
        if let Some(max_gen) = self.config.max_generations {
            if self.current_generation >= max_gen {
                reasons.push(ConvergenceReason::MaxGenerations {
                    generations: self.current_generation,
                });
            }
        }

        // Check max evaluations
        if let Some(max_eval) = self.config.max_evaluations {
            if self.current_evaluations >= max_eval {
                reasons.push(ConvergenceReason::MaxEvaluations {
                    evaluations: self.current_evaluations,
                });
            }
        }

        // Check target fitness.
        // EV-49 / REG-1: read the true running best (`running_best_fitness`, the
        // same value returned by `best_fitness()`), not the last per-generation
        // value and NOT the stagnation-throttled `best_fitness_overall`. A caller
        // may legitimately pass a non-monotonic per-generation best, so
        // `best_fitness_history.last()` can dip below a target already reached in
        // an earlier generation; and `best_fitness_overall` lags the true best by
        // up to `stagnation_threshold`, which would miss a reached target whenever
        // `stagnation_threshold > target_tolerance`. The pure running max keeps
        // target detection consistent with the struct's own `best_fitness()`.
        if let Some(target) = self.config.target_fitness {
            if !self.best_fitness_history.is_empty() {
                let best = self.running_best_fitness;
                if (best - target).abs() <= self.config.target_tolerance || best >= target {
                    reasons.push(ConvergenceReason::target_reached(best));
                }
            }
        }

        // Check stagnation
        let generations_since_improvement =
            self.current_generation - self.last_improvement_generation;
        if generations_since_improvement >= self.config.stagnation_generations {
            reasons.push(ConvergenceReason::fitness_stagnation(
                generations_since_improvement,
            ));
        }

        // Check diversity
        if let Some(&diversity) = self.diversity_history.last() {
            if diversity < self.config.diversity_threshold {
                reasons.push(ConvergenceReason::low_diversity(diversity));
            }
        }

        // Check R-hat if enabled
        if self.config.use_rhat && self.mean_fitness_history.len() >= 10 {
            // Split history into "chains" for R-hat calculation
            let rhat = self.compute_rhat();
            if rhat < self.config.rhat_threshold {
                reasons.push(ConvergenceReason::rhat_converged(rhat));
            }
        }

        // Return result
        match reasons.len() {
            0 => ConvergenceStatus::NotConverged,
            1 => ConvergenceStatus::Converged(reasons.pop().unwrap()),
            _ => ConvergenceStatus::Converged(ConvergenceReason::MultipleReasons(reasons)),
        }
    }

    /// Compute the split-R-hat statistic (Gelman & Rubin) from the mean-fitness
    /// history.
    ///
    /// The history is split into two equal-length half-chains — indices
    /// `[0, l)` and `[l, 2l)` where `l = len / 2` — matching the common-length
    /// truncation performed by [`evolutionary_rhat`]; the returned value equals
    /// `evolutionary_rhat(&[history[0..l], history[l..2l]])`.
    ///
    /// Each half-chain's mean and (Bessel-corrected) within-chain variance are
    /// computed with a numerically stable **two-pass** formula (subtract the
    /// chain mean, then sum squared deviations) directly over the raw history.
    /// This replaces an earlier one-pass `(Σx² − (Σx)²/l)/(l−1)` form evaluated
    /// over running sum / sum-of-squares prefix arrays. That form suffered
    /// catastrophic cancellation for large-offset fitness (e.g. mean-fitness
    /// ~1e6 with a true within-chain variance ~1 lost ~12 significant digits):
    /// it could drive `w <= 0` and report a spurious R-hat of exactly `1.0`
    /// ("converged"), and the running Σx² could overflow to `+inf` over very
    /// long runs, yielding `NaN`. The two-pass form has no such cancellation.
    ///
    /// Returns `f64::INFINITY` when there are fewer than 5 draws per half-chain.
    fn compute_rhat(&self) -> f64 {
        let n = self.mean_fitness_history.len();
        let l = n / 2;

        if l < 5 {
            return f64::INFINITY; // Not enough data
        }

        let l_f = l as f64;

        // chain1 = indices [0, l), chain2 = indices [l, 2l) — matching the
        // common-length truncation performed by `evolutionary_rhat`.
        let (mean1, var1) = two_pass_mean_var(&self.mean_fitness_history[0..l]);
        let (mean2, var2) = two_pass_mean_var(&self.mean_fitness_history[l..2 * l]);

        let m = 2.0;
        let grand_mean = (mean1 + mean2) / m;
        let b = l_f / (m - 1.0) * ((mean1 - grand_mean).powi(2) + (mean2 - grand_mean).powi(2));
        let w = (var1 + var2) / m;

        if w <= 0.0 {
            return 1.0; // Perfect convergence (identical chains)
        }

        let var_plus = ((l_f - 1.0) / l_f) * w + b / l_f;
        (var_plus / w).sqrt()
    }

    /// Get the best fitness seen (true running maximum, not the
    /// stagnation-throttled bookkeeping value).
    pub fn best_fitness(&self) -> f64 {
        self.running_best_fitness
    }

    /// Get generations since last improvement
    pub fn generations_without_improvement(&self) -> usize {
        self.current_generation - self.last_improvement_generation
    }

    /// Get the latest diversity value
    pub fn current_diversity(&self) -> Option<f64> {
        self.diversity_history.last().copied()
    }

    /// Get the fitness history
    pub fn fitness_history(&self) -> &[f64] {
        &self.best_fitness_history
    }

    /// Get the diversity history
    pub fn diversity_history(&self) -> &[f64] {
        &self.diversity_history
    }

    /// Reset the detector
    pub fn reset(&mut self) {
        self.best_fitness_history.clear();
        self.mean_fitness_history.clear();
        self.diversity_history.clear();
        self.current_generation = 0;
        self.current_evaluations = 0;
        self.best_fitness_overall = f64::NEG_INFINITY;
        self.running_best_fitness = f64::NEG_INFINITY;
        self.last_improvement_generation = 0;
    }
}

/// R-hat analog for evolutionary convergence
///
/// Compares fitness distributions across multiple runs/chains.
/// Values close to 1.0 indicate convergence.
pub fn evolutionary_rhat(runs: &[Vec<f64>]) -> f64 {
    if runs.is_empty() || runs[0].is_empty() {
        return f64::INFINITY;
    }

    let m = runs.len() as f64;
    // EV-14: the split-R-hat statistic (Gelman & Rubin, 1992) is defined for
    // equal-length chains. When chains differ in length we truncate every chain
    // to the common minimum `n` (standard practice) and use ONLY the first `n`
    // draws of each chain for both the mean and the sum-of-squares. The previous
    // code summed over the full (possibly longer) chain while dividing by the
    // shorter `n`, corrupting R-hat whenever chain lengths differed.
    let n_len = runs.iter().map(|r| r.len()).min().unwrap_or(0);
    let n = n_len as f64;

    if n < 2.0 || m < 2.0 {
        return f64::INFINITY;
    }

    // Between-chain variance (each chain truncated to its first `n` draws)
    let chain_means: Vec<f64> = runs
        .iter()
        .map(|r| r[..n_len].iter().sum::<f64>() / n)
        .collect();
    let grand_mean = chain_means.iter().sum::<f64>() / m;
    let b = n / (m - 1.0)
        * chain_means
            .iter()
            .map(|cm| (cm - grand_mean).powi(2))
            .sum::<f64>();

    // Within-chain variance (each chain truncated to its first `n` draws)
    let w: f64 = runs
        .iter()
        .map(|r| {
            let chain = &r[..n_len];
            let mean = chain.iter().sum::<f64>() / n;
            chain.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / (n - 1.0)
        })
        .sum::<f64>()
        / m;

    if w == 0.0 {
        return 1.0; // Perfect convergence
    }

    // Pooled variance estimate
    let var_plus = ((n - 1.0) / n) * w + b / n;

    (var_plus / w).sqrt()
}

/// Effective Sample Size (ESS) for evolutionary SMC
///
/// Measures the effective number of independent samples based on importance weights.
pub fn evolutionary_ess(weights: &[f64]) -> f64 {
    if weights.is_empty() {
        return 0.0;
    }

    // Normalize weights
    let sum: f64 = weights.iter().sum();
    if sum == 0.0 {
        return weights.len() as f64;
    }

    let normalized: Vec<f64> = weights.iter().map(|w| w / sum).collect();
    let sum_sq: f64 = normalized.iter().map(|w| w * w).sum();

    if sum_sq == 0.0 {
        weights.len() as f64
    } else {
        1.0 / sum_sq
    }
}

/// Effective Sample Size from log weights
pub fn evolutionary_ess_log(log_weights: &[f64]) -> f64 {
    if log_weights.is_empty() {
        return 0.0;
    }

    // Use log-sum-exp trick for numerical stability
    let max_log = log_weights
        .iter()
        .cloned()
        .fold(f64::NEG_INFINITY, f64::max);

    if max_log.is_infinite() {
        return log_weights.len() as f64;
    }

    let weights: Vec<f64> = log_weights.iter().map(|lw| (lw - max_log).exp()).collect();
    evolutionary_ess(&weights)
}

/// Detect fitness stagnation in a history of fitness values
///
/// Returns the number of generations the fitness has been stagnant.
pub fn detect_stagnation(fitness_history: &[f64], threshold: f64) -> usize {
    if fitness_history.len() < 2 {
        return 0;
    }

    let best = fitness_history
        .iter()
        .cloned()
        .fold(f64::NEG_INFINITY, f64::max);

    // Count generations since best was improved
    let mut stagnant_count: usize = 0;
    for &fitness in fitness_history.iter().rev() {
        if (fitness - best).abs() <= threshold {
            stagnant_count += 1;
        } else {
            break;
        }
    }

    stagnant_count.saturating_sub(1) // Don't count the best itself
}

/// Compute population convergence from fitness values
///
/// Returns a value between 0 (no convergence) and 1 (perfect convergence)
/// based on the coefficient of variation of fitness values.
pub fn fitness_convergence(fitness_values: &[f64]) -> f64 {
    if fitness_values.len() < 2 {
        return 1.0;
    }

    let mean = fitness_values.iter().sum::<f64>() / fitness_values.len() as f64;
    if mean.abs() < f64::EPSILON {
        return 1.0;
    }

    let variance = fitness_values
        .iter()
        .map(|f| (f - mean).powi(2))
        .sum::<f64>()
        / (fitness_values.len() - 1) as f64;
    let std = variance.sqrt();

    // Coefficient of variation (CV)
    let cv = std / mean.abs();

    // Convert to convergence metric (higher = more converged)
    // CV of 0 means perfect convergence
    // Use exponential decay so that small CV gives high convergence
    (-cv).exp()
}

/// Termination criteria for evolutionary algorithms
#[derive(Clone, Debug)]
pub struct TerminationCriteria {
    criteria: Vec<TerminationCriterion>,
    require_all: bool,
}

/// A single termination criterion
#[derive(Clone, Debug)]
pub enum TerminationCriterion {
    /// Maximum generations
    MaxGenerations(usize),
    /// Maximum evaluations
    MaxEvaluations(usize),
    /// Target fitness (maximize)
    TargetFitness(f64, f64), // (target, tolerance)
    /// Fitness stagnation
    Stagnation(usize, f64), // (generations, threshold)
    /// Diversity threshold
    DiversityThreshold(f64),
    /// Time limit in seconds
    TimeLimit(f64),
    /// Custom predicate
    Custom(String), // Description only, evaluation handled externally
}

impl TerminationCriteria {
    /// Create new empty criteria (any criterion triggers termination)
    pub fn new() -> Self {
        Self {
            criteria: Vec::new(),
            require_all: false,
        }
    }

    /// Create criteria where all must be satisfied
    pub fn require_all() -> Self {
        Self {
            criteria: Vec::new(),
            require_all: true,
        }
    }

    /// Add a criterion
    pub fn add(mut self, criterion: TerminationCriterion) -> Self {
        self.criteria.push(criterion);
        self
    }

    /// Add max generations criterion
    pub fn max_generations(self, generations: usize) -> Self {
        self.add(TerminationCriterion::MaxGenerations(generations))
    }

    /// Add max evaluations criterion
    pub fn max_evaluations(self, evaluations: usize) -> Self {
        self.add(TerminationCriterion::MaxEvaluations(evaluations))
    }

    /// Add target fitness criterion
    pub fn target_fitness(self, target: f64, tolerance: f64) -> Self {
        self.add(TerminationCriterion::TargetFitness(target, tolerance))
    }

    /// Add stagnation criterion
    pub fn stagnation(self, generations: usize, threshold: f64) -> Self {
        self.add(TerminationCriterion::Stagnation(generations, threshold))
    }

    /// Add diversity threshold criterion
    pub fn diversity_threshold(self, threshold: f64) -> Self {
        self.add(TerminationCriterion::DiversityThreshold(threshold))
    }

    /// Add time limit criterion
    pub fn time_limit(self, seconds: f64) -> Self {
        self.add(TerminationCriterion::TimeLimit(seconds))
    }

    /// Check if termination criteria are met.
    ///
    /// EV-50: the `Stagnation(generations, threshold)` criterion now computes its
    /// own stagnation count from `fitness_history` using its configured
    /// `threshold` (via [`detect_stagnation`]), instead of ignoring the threshold
    /// and trusting a pre-computed count. Pass the running best-fitness history so
    /// the threshold configured through the builder is actually honored.
    pub fn should_terminate(
        &self,
        generation: usize,
        evaluations: usize,
        best_fitness: f64,
        diversity: f64,
        fitness_history: &[f64],
        elapsed_seconds: f64,
    ) -> Option<ConvergenceReason> {
        let mut satisfied = Vec::new();

        for criterion in &self.criteria {
            let met = match criterion {
                TerminationCriterion::MaxGenerations(max) => generation >= *max,
                TerminationCriterion::MaxEvaluations(max) => evaluations >= *max,
                TerminationCriterion::TargetFitness(target, tolerance) => {
                    (best_fitness - target).abs() <= *tolerance || best_fitness >= *target
                }
                TerminationCriterion::Stagnation(gens, threshold) => {
                    detect_stagnation(fitness_history, *threshold) >= *gens
                }
                TerminationCriterion::DiversityThreshold(thresh) => diversity < *thresh,
                TerminationCriterion::TimeLimit(limit) => elapsed_seconds >= *limit,
                TerminationCriterion::Custom(_) => false, // Handled externally
            };

            if met {
                satisfied.push(criterion.to_reason(
                    generation,
                    evaluations,
                    best_fitness,
                    diversity,
                ));
            }
        }

        if satisfied.is_empty() {
            return None;
        }

        if self.require_all && satisfied.len() < self.criteria.len() {
            return None;
        }

        // Return the reason(s)
        if satisfied.len() == 1 {
            Some(satisfied.pop().unwrap())
        } else {
            Some(ConvergenceReason::MultipleReasons(satisfied))
        }
    }

    /// Get all criteria
    pub fn criteria(&self) -> &[TerminationCriterion] {
        &self.criteria
    }
}

impl Default for TerminationCriteria {
    fn default() -> Self {
        Self::new()
    }
}

impl TerminationCriterion {
    fn to_reason(
        &self,
        generation: usize,
        evaluations: usize,
        best_fitness: f64,
        diversity: f64,
    ) -> ConvergenceReason {
        match self {
            Self::MaxGenerations(_) => ConvergenceReason::MaxGenerations {
                generations: generation,
            },
            Self::MaxEvaluations(_) => ConvergenceReason::MaxEvaluations { evaluations },
            Self::TargetFitness(_, _) => ConvergenceReason::target_reached(best_fitness),
            Self::Stagnation(gens, _) => ConvergenceReason::fitness_stagnation(*gens),
            Self::DiversityThreshold(_) => ConvergenceReason::low_diversity(diversity),
            Self::TimeLimit(t) => ConvergenceReason::Custom(format!("Time limit of {t}s reached")),
            Self::Custom(desc) => ConvergenceReason::Custom(desc.clone()),
        }
    }
}

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

    #[test]
    fn test_convergence_detector_basic() {
        let config = ConvergenceConfig::with_max_generations(100);
        let mut detector = ConvergenceDetector::new(config);

        // Simulate improving fitness
        for i in 0..50 {
            detector.update(i, i * 10, i as f64, i as f64 * 0.5, 0.5);
        }

        let status = detector.check();
        assert!(!status.is_converged());
    }

    #[test]
    fn test_convergence_detector_max_generations() {
        let config = ConvergenceConfig::with_max_generations(50);
        let mut detector = ConvergenceDetector::new(config);

        for i in 0..60 {
            detector.update(i, i * 10, i as f64, i as f64 * 0.5, 0.5);
        }

        let status = detector.check();
        assert!(status.is_converged());
        if let ConvergenceStatus::Converged(reason) = status {
            assert!(matches!(reason, ConvergenceReason::MaxGenerations { .. }));
        }
    }

    #[test]
    fn test_convergence_detector_target_fitness() {
        let config = ConvergenceConfig::default()
            .target_fitness(100.0)
            .target_tolerance(1.0);
        let mut detector = ConvergenceDetector::new(config);

        detector.update(0, 10, 99.5, 50.0, 0.5);

        let status = detector.check();
        assert!(status.is_converged());
    }

    #[test]
    fn test_convergence_detector_stagnation() {
        let config = ConvergenceConfig::default().stagnation(10, 1e-9);
        let mut detector = ConvergenceDetector::new(config);

        // First improvement
        detector.update(0, 10, 50.0, 50.0, 0.5);

        // Then stagnation
        for i in 1..20 {
            detector.update(i, i * 10, 50.0, 50.0, 0.5);
        }

        let status = detector.check();
        assert!(status.is_converged());
        if let ConvergenceStatus::Converged(reason) = status {
            assert!(matches!(
                reason,
                ConvergenceReason::FitnessStagnation { .. }
            ));
        }
    }

    #[test]
    fn test_convergence_detector_low_diversity() {
        let config = ConvergenceConfig::default().diversity_threshold(0.1);
        let mut detector = ConvergenceDetector::new(config);

        detector.update(0, 10, 50.0, 50.0, 0.05);

        let status = detector.check();
        assert!(status.is_converged());
        if let ConvergenceStatus::Converged(reason) = status {
            assert!(matches!(reason, ConvergenceReason::LowDiversity { .. }));
        }
    }

    #[test]
    fn test_evolutionary_rhat() {
        // Similar chains with some variation should give R-hat close to 1
        let chain1 = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
        let chain2 = vec![1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5];
        let rhat = evolutionary_rhat(&[chain1, chain2]);
        // R-hat should be close to 1 for similar chains (typically < 1.1 for convergence)
        assert!(rhat < 1.2, "R-hat was {}, expected < 1.2", rhat);
    }

    #[test]
    fn test_evolutionary_rhat_divergent() {
        // Very different chains with internal variation should give high R-hat
        let chain1 = vec![1.0, 2.0, 1.5, 2.5, 1.2, 2.8, 1.8, 2.2, 1.3, 2.7];
        let chain2 = vec![
            100.0, 101.0, 100.5, 101.5, 100.2, 101.8, 100.8, 101.2, 100.3, 101.7,
        ];
        let rhat = evolutionary_rhat(&[chain1, chain2]);
        // R-hat should be high for divergent chains
        assert!(rhat > 1.5, "R-hat was {}, expected > 1.5", rhat);
    }

    #[test]
    fn test_evolutionary_ess() {
        // Equal weights should give ESS = n
        let weights = vec![1.0, 1.0, 1.0, 1.0];
        let ess = evolutionary_ess(&weights);
        assert!((ess - 4.0).abs() < 0.01);
    }

    #[test]
    fn test_evolutionary_ess_unequal() {
        // One dominant weight should give low ESS
        let weights = vec![1.0, 0.0, 0.0, 0.0];
        let ess = evolutionary_ess(&weights);
        assert!((ess - 1.0).abs() < 0.01);
    }

    #[test]
    fn test_evolutionary_ess_log() {
        let log_weights = vec![0.0, 0.0, 0.0, 0.0];
        let ess = evolutionary_ess_log(&log_weights);
        assert!((ess - 4.0).abs() < 0.01);
    }

    #[test]
    fn test_detect_stagnation() {
        let history = vec![10.0, 20.0, 30.0, 30.0, 30.0, 30.0];
        let stagnant = detect_stagnation(&history, 1e-9);
        assert_eq!(stagnant, 3); // 3 generations at max
    }

    #[test]
    fn test_detect_stagnation_improving() {
        let history = vec![10.0, 20.0, 30.0, 40.0, 50.0];
        let stagnant = detect_stagnation(&history, 1e-9);
        assert_eq!(stagnant, 0);
    }

    #[test]
    fn test_fitness_convergence() {
        // All same fitness = perfect convergence
        let fitness = vec![50.0, 50.0, 50.0, 50.0];
        let conv = fitness_convergence(&fitness);
        assert!((conv - 1.0).abs() < 0.01);
    }

    #[test]
    fn test_fitness_convergence_diverse() {
        // Very diverse fitness = low convergence
        let fitness = vec![0.0, 100.0, 0.0, 100.0];
        let conv = fitness_convergence(&fitness);
        assert!(conv < 0.5);
    }

    #[test]
    fn test_termination_criteria_max_gen() {
        let criteria = TerminationCriteria::new().max_generations(100);

        let result = criteria.should_terminate(50, 500, 10.0, 0.5, &[], 10.0);
        assert!(result.is_none());

        let result = criteria.should_terminate(100, 1000, 10.0, 0.5, &[], 20.0);
        assert!(result.is_some());
    }

    #[test]
    fn test_termination_criteria_target() {
        let criteria = TerminationCriteria::new().target_fitness(100.0, 1.0);

        let result = criteria.should_terminate(10, 100, 50.0, 0.5, &[], 5.0);
        assert!(result.is_none());

        let result = criteria.should_terminate(10, 100, 99.5, 0.5, &[], 5.0);
        assert!(result.is_some());
    }

    #[test]
    fn test_termination_criteria_multiple() {
        let criteria = TerminationCriteria::new()
            .max_generations(100)
            .target_fitness(100.0, 1.0);

        // Neither met
        let result = criteria.should_terminate(10, 100, 50.0, 0.5, &[], 5.0);
        assert!(result.is_none());

        // Target met
        let result = criteria.should_terminate(10, 100, 100.0, 0.5, &[], 5.0);
        assert!(result.is_some());

        // Max gen met
        let result = criteria.should_terminate(100, 1000, 50.0, 0.5, &[], 50.0);
        assert!(result.is_some());
    }

    #[test]
    fn test_termination_criteria_require_all() {
        let criteria = TerminationCriteria::require_all()
            .max_generations(100)
            .stagnation(10, 1e-9);

        // Only max gen met: a 6-long flat history yields a stagnation count of 5
        // (< 10), so the stagnation criterion is not satisfied.
        let short_flat = [50.0; 6];
        let result = criteria.should_terminate(100, 1000, 50.0, 0.5, &short_flat, 50.0);
        assert!(result.is_none());

        // Both met: an 11-long flat history yields a stagnation count of 10 (>= 10).
        let long_flat = [50.0; 11];
        let result = criteria.should_terminate(100, 1000, 50.0, 0.5, &long_flat, 50.0);
        assert!(result.is_some());
    }

    #[test]
    fn test_convergence_config_builder() {
        let config = ConvergenceConfig::with_max_generations(500)
            .max_evaluations(10000)
            .target_fitness(1.0)
            .target_tolerance(0.01)
            .stagnation(100, 1e-6)
            .diversity_threshold(0.05)
            .with_rhat(1.05);

        assert_eq!(config.max_generations, Some(500));
        assert_eq!(config.max_evaluations, Some(10000));
        assert_eq!(config.target_fitness, Some(1.0));
        assert_eq!(config.target_tolerance, 0.01);
        assert_eq!(config.stagnation_generations, 100);
        assert_eq!(config.stagnation_threshold, 1e-6);
        assert_eq!(config.diversity_threshold, 0.05);
        assert!(config.use_rhat);
        assert_eq!(config.rhat_threshold, 1.05);
    }

    #[test]
    fn test_convergence_detector_reset() {
        let config = ConvergenceConfig::default();
        let mut detector = ConvergenceDetector::new(config);

        detector.update(0, 10, 50.0, 50.0, 0.5);
        detector.update(1, 20, 60.0, 55.0, 0.4);

        assert_eq!(detector.fitness_history().len(), 2);
        assert_eq!(detector.best_fitness(), 60.0);

        detector.reset();

        assert!(detector.fitness_history().is_empty());
        assert_eq!(detector.best_fitness(), f64::NEG_INFINITY);
    }

    #[test]
    fn test_convergence_status_is_converged() {
        let not_converged = ConvergenceStatus::NotConverged;
        assert!(!not_converged.is_converged());

        let converged =
            ConvergenceStatus::Converged(ConvergenceReason::MaxGenerations { generations: 100 });
        assert!(converged.is_converged());
    }

    // regression: EV-14 — unequal-length chains are truncated to the common
    // minimum before computing means/variances. chain1=[1,2,3,4] and a chain2
    // with an extra 5th draw truncate to identical [1,2,3,4], giving the exact
    // R-hat = sqrt(0.75). The pre-fix code summed the full chain2 while dividing
    // by the shorter n, yielding ~1.0066 instead.
    #[test]
    fn test_evolutionary_rhat_truncates_unequal_chains() {
        let chain1 = vec![1.0, 2.0, 3.0, 4.0];
        let chain2 = vec![1.0, 2.0, 3.0, 4.0, 5.0];
        let rhat = evolutionary_rhat(&[chain1, chain2]);
        let expected = 0.75_f64.sqrt();
        assert!(
            (rhat - expected).abs() < 1e-9,
            "R-hat was {rhat}, expected {expected}"
        );
        assert!(
            rhat < 0.9,
            "R-hat {rhat} still shows the unequal-length bug"
        );
    }

    // regression: EV-49 — once the running best reaches the target, a later
    // per-generation dip below the target must not un-converge the detector. The
    // pre-fix code read best_fitness_history.last() (the dipped value) and failed
    // to report TargetReached.
    #[test]
    fn test_target_fitness_uses_running_best() {
        let config = ConvergenceConfig::default()
            .target_fitness(100.0)
            .target_tolerance(1e-6)
            .stagnation(10_000, 1e-9); // keep stagnation from firing
        let mut detector = ConvergenceDetector::new(config);

        detector.update(0, 10, 100.0, 50.0, 1.0); // running best hits target
        detector.update(1, 20, 50.0, 50.0, 1.0); // per-generation best dips below

        assert_eq!(detector.best_fitness(), 100.0);
        let status = detector.check();
        assert!(
            status.is_converged(),
            "a target reached earlier must remain converged"
        );
        if let ConvergenceStatus::Converged(reason) = status {
            assert!(matches!(reason, ConvergenceReason::TargetReached { .. }));
        }
    }

    // regression: REG-1 — the EV-49 fix must read a *pure* running max, not the
    // stagnation-throttled `best_fitness_overall`, which lags the true best by up
    // to `stagnation_threshold`. With `stagnation_threshold > target_tolerance`,
    // a target that is reached-and-held is otherwise never reported.
    #[test]
    fn test_target_fitness_survives_stagnation_throttle() {
        let config = ConvergenceConfig::default()
            .target_fitness(100.0)
            .target_tolerance(1e-6)
            // threshold (0.01) deliberately larger than the tolerance (1e-6)
            .stagnation(50, 0.01);
        let mut detector = ConvergenceDetector::new(config);

        // gen0 seeds the throttled `best_fitness_overall` just below target.
        detector.update(0, 10, 99.995, 50.0, 1.0);
        // gen1 hits the target exactly, but 100.0 <= 99.995 + 0.01 = 100.005, so
        // the throttled value stays at 99.995. The pure running max must be 100.0.
        detector.update(1, 20, 100.0, 50.0, 1.0);

        assert_eq!(
            detector.best_fitness(),
            100.0,
            "the running best must reflect the true max, not the throttled value"
        );

        let status = detector.check();
        assert!(
            status.is_converged(),
            "target reached-and-held must be reported even when \
             stagnation_threshold > target_tolerance"
        );
        assert!(
            matches!(
                status,
                ConvergenceStatus::Converged(ConvergenceReason::TargetReached { .. })
                    | ConvergenceStatus::Converged(ConvergenceReason::MultipleReasons(_))
            ),
            "convergence reason must include TargetReached, got {status:?}"
        );

        // Hold at target for many generations: TargetReached must persist and the
        // pre-fix wrong-reason (stagnation ~49 gens later) must not be the sole
        // reason reported at the moment the target is first reached.
        for g in 2..10 {
            detector.update(g, 10 * (g + 1), 100.0, 50.0, 1.0);
            let s = detector.check();
            let has_target = match &s {
                ConvergenceStatus::Converged(ConvergenceReason::TargetReached { .. }) => true,
                ConvergenceStatus::Converged(ConvergenceReason::MultipleReasons(rs)) => rs
                    .iter()
                    .any(|r| matches!(r, ConvergenceReason::TargetReached { .. })),
                _ => false,
            };
            assert!(
                has_target,
                "target must stay reported while held, gen {g}: {s:?}"
            );
        }
    }

    // regression: EV-50 — the configured stagnation threshold is actually honored.
    // The same history is stagnant under a loose threshold but not a tight one.
    // Pre-fix, the threshold was discarded (bound to `_threshold`) and an external
    // pre-computed count was used, so both thresholds behaved identically.
    #[test]
    fn test_stagnation_threshold_is_wired() {
        let history = [10.0, 9.5, 9.5, 9.5, 9.5];

        let loose = TerminationCriteria::new().stagnation(3, 1.0);
        let tight = TerminationCriteria::new().stagnation(3, 0.1);

        assert!(
            loose
                .should_terminate(0, 0, 9.5, 1.0, &history, 0.0)
                .is_some(),
            "loose threshold should read the flat tail as stagnant"
        );
        assert!(
            tight
                .should_terminate(0, 0, 9.5, 1.0, &history, 0.0)
                .is_none(),
            "tight threshold should not read a 0.5 drop as stagnant"
        );
    }

    // regression: EV-88 — `compute_rhat` must equal the from-scratch
    // `evolutionary_rhat` recompute over the full mean-fitness history at every
    // length, proving the half-chain split and two-pass statistics stay
    // behavior-identical to the reference implementation.
    #[test]
    fn test_compute_rhat_matches_naive_recompute() {
        let mut detector = ConvergenceDetector::with_defaults();
        let values: Vec<f64> = (0..40)
            .map(|i| {
                let x = i as f64;
                (x * 0.37).sin() * 3.0 + (x * 0.11).cos() * 1.5 + x * 0.05
            })
            .collect();

        for (i, &v) in values.iter().enumerate() {
            detector.update(i, i * 10, v, v, 0.5);
            if detector.mean_fitness_history.len() >= 10 {
                let n = detector.mean_fitness_history.len();
                let half = n / 2;
                let chain1 = detector.mean_fitness_history[..half].to_vec();
                let chain2 = detector.mean_fitness_history[half..].to_vec();
                let naive = evolutionary_rhat(&[chain1, chain2]);
                let incremental = detector.compute_rhat();
                assert!(
                    (naive - incremental).abs() < 1e-9,
                    "at n={n}: incremental R-hat {incremental} != naive {naive}"
                );
            }
        }
    }

    // regression (re-verification low): `compute_rhat` previously derived each
    // half-chain's within-chain variance from running sum / sum-of-squares
    // prefix arrays via `(sq - sum*sum/l)/(l-1)`. With mean-fitness values
    // offset by ~1e6 and a true within-chain variance of ~1, that one-pass form
    // subtracts two ~2e13 quantities to recover ~19, losing ~12 significant
    // digits: it can drive `w <= 0` and report a spurious R-hat of exactly 1.0
    // (false "converged"), and the running Σx² can overflow to +inf (→ NaN)
    // over long runs. The stable two-pass formulation must (a) match a directly
    // computed two-pass R-hat to 1e-9 and (b) NOT collapse to the spurious 1.0.
    #[test]
    fn test_compute_rhat_stable_under_large_offset() {
        // Two chains sharing a ~1e6 offset, each with small, distinct
        // deviations (true within-chain variance ~1) plus a real between-chain
        // mean shift of ~4 — so the correct R-hat is clearly > 1.
        let offset = 1e6;
        let chain1: Vec<f64> = (0..20)
            .map(|i| offset + (i as f64 * 0.7).sin() * 1.3)
            .collect();
        let chain2: Vec<f64> = (0..20)
            .map(|i| offset + 4.0 + (i as f64 * 0.9 + 0.5).cos() * 1.1)
            .collect();

        // History = chain1 then chain2, so with len = 40 the half-chain split
        // (`l = 20`) reproduces exactly [chain1, chain2].
        let mut detector = ConvergenceDetector::with_defaults();
        for (i, &v) in chain1.iter().chain(chain2.iter()).enumerate() {
            detector.update(i, i, v, v, 0.5);
        }
        let got = detector.compute_rhat();

        // Directly compute the reference two-pass R-hat, independently of the
        // module helper (subtract mean, then sum squared deviations).
        let tp = |xs: &[f64]| -> (f64, f64) {
            let n = xs.len() as f64;
            let mean = xs.iter().sum::<f64>() / n;
            let ss: f64 = xs.iter().map(|x| (x - mean).powi(2)).sum();
            (mean, ss / (n - 1.0))
        };
        let (mean1, var1) = tp(&chain1);
        let (mean2, var2) = tp(&chain2);
        let l = chain1.len() as f64;
        let m = 2.0;
        let grand = (mean1 + mean2) / m;
        let b = l / (m - 1.0) * ((mean1 - grand).powi(2) + (mean2 - grand).powi(2));
        let w = (var1 + var2) / m;
        assert!(w > 0.0, "true within-chain variance must be positive");
        let var_plus = ((l - 1.0) / l) * w + b / l;
        let reference = (var_plus / w).sqrt();

        // (a) stable formulation matches the directly computed two-pass R-hat.
        assert!(
            (got - reference).abs() < 1e-9,
            "compute_rhat {got} != directly computed two-pass R-hat {reference}"
        );
        // (b) must NOT report the spurious `w <= 0` convergence value of 1.0.
        assert!(
            (got - 1.0).abs() > 1e-6,
            "compute_rhat collapsed to the spurious 1.0 (got {got})"
        );
        assert!(got.is_finite(), "compute_rhat must be finite, got {got}");
        // And it agrees with the public reference over the same two chains.
        let via_public = evolutionary_rhat(&[chain1, chain2]);
        assert!(
            (got - via_public).abs() < 1e-9,
            "compute_rhat {got} != evolutionary_rhat {via_public}"
        );
    }
}