scirs2-metrics 0.3.0

//! Learning controllers and adaptation strategies
//!
//! This module contains adaptive learning controllers, objectives,
//! and strategies for neuromorphic computing systems.

#![allow(clippy::too_many_arguments)]
#![allow(dead_code)]

use scirs2_core::numeric::Float;
use std::collections::VecDeque;
use std::time::Instant;

/// Adaptive learning controller
#[derive(Debug)]
pub struct AdaptiveLearningController<F: Float> {
    /// Learning objectives
    pub objectives: Vec<LearningObjective<F>>,
    /// Adaptation strategies
    pub strategies: Vec<AdaptationStrategy<F>>,
    /// Performance history
    pub performance_history: VecDeque<PerformanceSnapshot<F>>,
    /// Current adaptation state
    pub adaptation_state: AdaptationState<F>,
}

/// Learning objectives
#[derive(Debug)]
pub struct LearningObjective<F: Float> {
    /// Objective name
    pub name: String,
    /// Target value
    pub target: F,
    /// Current value
    pub current: F,
    /// Weight in multi-objective optimization
    pub weight: F,
    /// Tolerance
    pub tolerance: F,
}

/// Adaptation strategies
#[derive(Debug, Clone)]
pub enum AdaptationStrategy<F: Float> {
    /// Gradient-based adaptation
    GradientBased { learning_rate: F },
    /// Evolutionary strategies
    Evolutionary { population_size: usize },
    /// Bayesian optimization
    BayesianOptimization { acquisition_function: String },
    /// Reinforcement learning
    ReinforcementLearning { policy: String },
    /// Meta-learning
    MetaLearning { meta_parameters: Vec<F> },
}

/// Performance snapshot
#[derive(Debug, Clone)]
pub struct PerformanceSnapshot<F: Float> {
    pub timestamp: Instant,
    pub accuracy: F,
    pub processing_speed: F,
    pub energy_efficiency: F,
    pub stability: F,
    pub adaptability: F,
}

/// Adaptation state
#[derive(Debug)]
pub struct AdaptationState<F: Float> {
    /// Current strategy
    pub current_strategy: usize,
    /// Strategy effectiveness
    pub strategy_effectiveness: Vec<F>,
    /// Adaptation history
    pub adaptation_history: VecDeque<AdaptationEvent<F>>,
    /// Learning progress
    pub learning_progress: F,
}

/// Adaptation event
#[derive(Debug, Clone)]
pub struct AdaptationEvent<F: Float> {
    pub timestamp: Instant,
    pub strategy_used: String,
    pub performance_before: F,
    pub performance_after: F,
    pub adaptation_magnitude: F,
}

impl<F: Float + std::fmt::Debug> AdaptiveLearningController<F> {
    /// Create new adaptive learning controller
    pub fn new() -> Self {
        Self {
            objectives: Vec::new(),
            strategies: Vec::new(),
            performance_history: VecDeque::new(),
            adaptation_state: AdaptationState::new(),
        }
    }

    /// Add learning objective
    pub fn add_objective(&mut self, objective: LearningObjective<F>) {
        self.objectives.push(objective);
    }

    /// Add adaptation strategy
    pub fn add_strategy(&mut self, strategy: AdaptationStrategy<F>) {
        self.strategies.push(strategy);
    }

    /// Update controller with new performance data
    pub fn update(&mut self, performance: PerformanceSnapshot<F>) -> crate::error::Result<()> {
        self.performance_history.push_back(performance.clone());

        // Keep bounded
        if self.performance_history.len() > 1000 {
            self.performance_history.pop_front();
        }

        // Update objectives
        for objective in &mut self.objectives {
            match objective.name.as_str() {
                "accuracy" => objective.current = performance.accuracy,
                "speed" => objective.current = performance.processing_speed,
                "efficiency" => objective.current = performance.energy_efficiency,
                "stability" => objective.current = performance.stability,
                _ => {}
            }
        }

        // Evaluate adaptation need
        if self.should_adapt()? {
            self.perform_adaptation()?;
        }

        Ok(())
    }

    /// Determine if adaptation is needed
    fn should_adapt(&self) -> crate::error::Result<bool> {
        for objective in &self.objectives {
            let error = (objective.target - objective.current).abs();
            if error > objective.tolerance {
                return Ok(true);
            }
        }
        Ok(false)
    }

    /// Perform adaptation using current strategy
    fn perform_adaptation(&mut self) -> crate::error::Result<()> {
        if self.strategies.is_empty() {
            return Ok(());
        }

        let strategy_idx = self.adaptation_state.current_strategy;
        let strategy = self.strategies[strategy_idx].clone();

        let performance_before = self.get_current_performance();

        match strategy {
            AdaptationStrategy::GradientBased { learning_rate } => {
                self.apply_gradient_adaptation(learning_rate)?;
            }
            AdaptationStrategy::Evolutionary { population_size } => {
                self.apply_evolutionary_adaptation(population_size)?;
            }
            _ => {
                // Default adaptation
                self.apply_default_adaptation()?;
            }
        }

        let performance_after = self.get_current_performance();

        // Record adaptation event
        let event = AdaptationEvent {
            timestamp: Instant::now(),
            strategy_used: format!("{:?}", strategy),
            performance_before,
            performance_after,
            adaptation_magnitude: (performance_after - performance_before).abs(),
        };

        self.adaptation_state.adaptation_history.push_back(event);

        // Keep bounded
        if self.adaptation_state.adaptation_history.len() > 100 {
            self.adaptation_state.adaptation_history.pop_front();
        }

        Ok(())
    }

    /// Apply gradient-based adaptation
    fn apply_gradient_adaptation(&mut self, learning_rate: F) -> crate::error::Result<()> {
        // Simplified gradient-based adaptation
        for objective in &mut self.objectives {
            let error = objective.target - objective.current;
            let adjustment = error * learning_rate * objective.weight;
            objective.current = objective.current + adjustment;
        }
        Ok(())
    }

    /// Apply evolutionary adaptation
    fn apply_evolutionary_adaptation(
        &mut self,
        _population_size: usize,
    ) -> crate::error::Result<()> {
        // Simplified evolutionary adaptation
        // In practice, this would involve population-based optimization
        for objective in &mut self.objectives {
            let noise = F::from(0.01).expect("Failed to convert constant to float"); // Small random perturbation
            objective.current = objective.current + noise;
        }
        Ok(())
    }

    /// Apply default adaptation
    fn apply_default_adaptation(&mut self) -> crate::error::Result<()> {
        for objective in &mut self.objectives {
            let error = objective.target - objective.current;
            let adjustment = error
                * F::from(0.1).expect("Failed to convert constant to float")
                * objective.weight;
            objective.current = objective.current + adjustment;
        }
        Ok(())
    }

    /// Get current overall performance
    fn get_current_performance(&self) -> F {
        let mut total_performance = F::zero();
        let mut total_weight = F::zero();

        for objective in &self.objectives {
            let performance = F::one() - (objective.target - objective.current).abs();
            total_performance = total_performance + performance * objective.weight;
            total_weight = total_weight + objective.weight;
        }

        if total_weight > F::zero() {
            total_performance / total_weight
        } else {
            F::zero()
        }
    }

    /// Get adaptation statistics
    pub fn get_adaptation_stats(&self) -> AdaptationStats<F> {
        let total_adaptations = self.adaptation_state.adaptation_history.len();
        let recent_performance = if !self.adaptation_state.adaptation_history.is_empty() {
            self.adaptation_state
                .adaptation_history
                .back()
                .expect("Operation failed")
                .performance_after
        } else {
            F::zero()
        };

        AdaptationStats {
            total_adaptations,
            recent_performance,
            current_strategy: self.adaptation_state.current_strategy,
            learning_progress: self.adaptation_state.learning_progress,
        }
    }
}

impl<F: Float> LearningObjective<F> {
    /// Create new learning objective
    pub fn new(name: String, target: F, weight: F, tolerance: F) -> Self {
        Self {
            name,
            target,
            current: F::zero(),
            weight,
            tolerance,
        }
    }

    /// Check if objective is satisfied
    pub fn is_satisfied(&self) -> bool {
        (self.target - self.current).abs() <= self.tolerance
    }

    /// Get objective error
    pub fn get_error(&self) -> F {
        (self.target - self.current).abs()
    }
}

impl<F: Float> AdaptationState<F> {
    /// Create new adaptation state
    pub fn new() -> Self {
        Self {
            current_strategy: 0,
            strategy_effectiveness: Vec::new(),
            adaptation_history: VecDeque::new(),
            learning_progress: F::zero(),
        }
    }

    /// Update strategy effectiveness
    pub fn update_strategy_effectiveness(&mut self, strategy_idx: usize, effectiveness: F) {
        if strategy_idx >= self.strategy_effectiveness.len() {
            self.strategy_effectiveness
                .resize(strategy_idx + 1, F::zero());
        }
        self.strategy_effectiveness[strategy_idx] = effectiveness;
    }

    /// Select best strategy based on effectiveness
    pub fn select_best_strategy(&mut self) -> usize {
        if self.strategy_effectiveness.is_empty() {
            return 0;
        }

        let mut best_idx = 0;
        let mut best_effectiveness = self.strategy_effectiveness[0];

        for (idx, &effectiveness) in self.strategy_effectiveness.iter().enumerate().skip(1) {
            if effectiveness > best_effectiveness {
                best_effectiveness = effectiveness;
                best_idx = idx;
            }
        }

        self.current_strategy = best_idx;
        best_idx
    }
}

/// Adaptation statistics
#[derive(Debug, Clone)]
pub struct AdaptationStats<F: Float> {
    pub total_adaptations: usize,
    pub recent_performance: F,
    pub current_strategy: usize,
    pub learning_progress: F,
}