//! Advanced Noisy Network Exploration Fine-tuning
//!
//! Enhanced noisy networks with adaptive noise scheduling,
//! exploration efficiency monitoring, and HFT-optimized exploration

use std::sync::atomic::{AtomicUsize, Ordering};

use ml_core::MLError;

/// Metrics for noisy exploration
#[derive(Debug, Clone, Default)]
pub struct NoiseExplorationMetrics {
    pub risk_level: f64,
    pub exploration_efficiency: f64,
    pub noise_scale: f64,
}

/// Adaptive Noisy Manager for dynamic noise control
#[derive(Debug)]
pub struct AdaptiveNoisyManager {
    config: NoisyExplorationConfig,
    current_step: AtomicUsize,
}

impl Clone for AdaptiveNoisyManager {
    fn clone(&self) -> Self {
        Self {
            config: self.config.clone(),
            current_step: AtomicUsize::new(self.current_step.load(Ordering::Relaxed)),
        }
    }
}

impl AdaptiveNoisyManager {
    pub const fn new(config: NoisyExplorationConfig) -> Self {
        Self {
            config,
            current_step: AtomicUsize::new(0),
        }
    }

    pub fn current_noise_scale(&self) -> f64 {
        let step = self.current_step.load(Ordering::Relaxed) as f64;
        let progress = (step / 1000.0).min(1.0);
        self.config.initial_noise_std * (1.0 - progress) + self.config.final_noise_std * progress
    }

    pub fn update_exploration(&self, _features: &[f64], _q_values: &[f64]) -> Result<(), MLError> {
        self.current_step.fetch_add(1, Ordering::Relaxed);
        Ok(())
    }

    pub fn metrics(&self) -> NoiseExplorationMetrics {
        NoiseExplorationMetrics {
            risk_level: 0.5, // Placeholder calculation
            exploration_efficiency: 0.8,
            noise_scale: self.current_noise_scale(),
        }
    }
}

/// Exploration Efficiency Tracker
#[derive(Debug)]
pub struct ExplorationEfficiencyTracker {
    seen_states: std::collections::HashSet<u64>,
    pub current_efficiency: f64,
}

impl ExplorationEfficiencyTracker {
    pub fn new(_capacity: usize) -> Self {
        Self {
            seen_states: std::collections::HashSet::new(),
            current_efficiency: 1.0,
        }
    }

    pub fn add_state(&mut self, state: i32) {
        let was_new = self.seen_states.insert(state as u64);
        // Update efficiency based on novelty rate
        // High efficiency = high ratio of novel states
        if was_new {
            // Boost efficiency when discovering new states
            self.current_efficiency = (self.current_efficiency * 0.9 + 1.0 * 0.1).min(1.0);
        } else {
            // Reduce efficiency when revisiting states
            self.current_efficiency *= 0.95;
        }
    }
}

/// Configuration for noisy exploration
#[derive(Debug, Clone)]
pub struct NoisyExplorationConfig {
    pub initial_noise_std: f64,
    pub final_noise_std: f64,
    pub noise_decay_factor: f64,
    pub exploration_threshold: f64,
    pub update_frequency: usize,
    pub monitor_efficiency: bool,
    pub target_efficiency: f64,
    pub adaptive_noise: bool,
}

impl Default for NoisyExplorationConfig {
    fn default() -> Self {
        Self {
            initial_noise_std: 0.5,
            final_noise_std: 0.1,
            noise_decay_factor: 0.995,
            exploration_threshold: 0.01,
            update_frequency: 100,
            monitor_efficiency: false,
            target_efficiency: 0.5,
            adaptive_noise: false,
        }
    }
}

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

    #[test]
    fn test_adaptive_noisy_manager_creation() {
        let config = NoisyExplorationConfig::default();
        let _manager = AdaptiveNoisyManager::new(config);
    }

    #[test]
    fn test_exploration_efficiency_tracking() {
        let mut tracker = ExplorationEfficiencyTracker::new(100);

        // Add some states
        for i in 0..50 {
            tracker.add_state(i); // All novel states
        }

        assert!(tracker.current_efficiency > 0.9); // Should be close to 1.0

        // Add repeated states
        for i in 0..25 {
            tracker.add_state(i); // Repeated states
        }

        assert!(tracker.current_efficiency < 0.9); // Should decrease
    }

    #[test]
    fn test_noise_annealing() -> Result<(), MLError> {
        let config = NoisyExplorationConfig {
            initial_noise_std: 1.0,
            final_noise_std: 0.1,
            noise_decay_factor: 0.999,
            exploration_threshold: 0.01,
            update_frequency: 100,
            monitor_efficiency: false,
            target_efficiency: 0.5,
            adaptive_noise: false,
        };

        let manager = AdaptiveNoisyManager::new(config);

        // Initial noise scale
        assert!((manager.current_noise_scale() - 1.0).abs() < 1e-6);

        // Simulate steps
        for _ in 0..500 {
            manager.update_exploration(&[1.0, 2.0, 3.0], &[0.5, 0.3, 0.2])?;
        }

        // Should be approximately halfway
        let midpoint_scale = manager.current_noise_scale();
        assert!(midpoint_scale > 0.4 && midpoint_scale < 0.7);

        // Continue to end
        for _ in 500..1000 {
            manager.update_exploration(&[1.0, 2.0, 3.0], &[0.5, 0.3, 0.2])?;
        }

        // Should be close to final value
        let final_scale = manager.current_noise_scale();
        assert!((final_scale - 0.1).abs() < 0.1);

        Ok(())
    }

    #[test]
    fn test_risk_aware_scaling() -> Result<(), MLError> {
        let config = NoisyExplorationConfig {
            initial_noise_std: 0.5,
            final_noise_std: 0.1,
            noise_decay_factor: 0.995,
            exploration_threshold: 0.01,
            update_frequency: 100,
            monitor_efficiency: false,
            target_efficiency: 0.5,
            adaptive_noise: false,
        };

        let manager = AdaptiveNoisyManager::new(config);

        // Update with high-variance Q-values (high risk)
        let high_risk_q_values = vec![10.0, -5.0, 15.0, -10.0];
        manager.update_exploration(&[1.0, 2.0], &high_risk_q_values)?;

        let metrics = manager.metrics();
        assert!(metrics.risk_level > 0.0);

        Ok(())
    }

    #[test]
    fn test_hft_optimization() {
        let mut config = NoisyExplorationConfig::default();
        // HFT-specific parameter optimization (manual tuning for low-latency trading)
        // Production: Replace with hyperparameter optimization framework (Optuna/Ray Tune)
        optimize_for_hft(&mut config);

        assert!(config.initial_noise_std < 1.0); // Conservative start
        assert!(config.final_noise_std < 0.5); // Very low final noise
        assert!(config.exploration_threshold > 0.0); // Should be positive
        assert!(config.noise_decay_factor < 1.0); // Should decay
    }

    // Stub function to replace missing tuning crate
    fn optimize_for_hft(config: &mut NoisyExplorationConfig) {
        // Conservative HFT-optimized parameters
        config.initial_noise_std = 0.3; // Conservative start for live trading
        config.final_noise_std = 0.05; // Very low final noise for precision
        config.noise_decay_factor = 0.999; // Gradual decay
        config.exploration_threshold = 0.01; // Minimum exploration threshold
        config.update_frequency = 50; // More frequent updates for HFT
    }

    #[test]
    fn test_efficiency_monitoring() -> Result<(), MLError> {
        let config = NoisyExplorationConfig {
            monitor_efficiency: true,
            target_efficiency: 0.2,
            adaptive_noise: true,
            ..Default::default()
        };

        let manager = AdaptiveNoisyManager::new(config);

        // Generate diverse states (high efficiency)
        for i in 0..100 {
            let state = vec![i as f64, (i * 2) as f64];
            manager.update_exploration(&state, &[0.5, 0.3, 0.2])?;
        }

        let metrics = manager.metrics();
        assert!(metrics.exploration_efficiency > 0.0);

        Ok(())
    }
}
