quantrs2-device 0.1.3

//! Adaptive compensation components

use std::collections::{HashMap, VecDeque};
use std::time::{Duration, SystemTime};
use scirs2_core::ndarray::{Array1, Array2};

use super::*;
use crate::DeviceResult;
use scirs2_core::random::prelude::*;

impl AdaptiveCompensator {
    pub fn new(config: &AdaptiveCompensationConfig) -> Self {
        Self {
            config: config.clone(),
            compensation_matrix: Array2::zeros((4, 4)), // Default 4x4 for small system
            learning_state: LearningState::new(),
            performance_history: VecDeque::with_capacity(1000),
            optimization_engine: OptimizationEngine::new(&config.optimization_config),
        }
    }

    pub async fn compute_compensation(&mut self, characterization: &CrosstalkCharacterization) -> DeviceResult<AdaptiveCompensationResult> {
        // Update compensation matrix based on current crosstalk characterization
        self.update_compensation_matrix(characterization).await?;

        // Check convergence
        let convergence_status = self.check_convergence()?;

        // Analyze stability
        let stability_analysis = self.analyze_stability()?;

        // Calculate performance improvement
        let performance_improvement = self.calculate_performance_improvement()?;

        Ok(AdaptiveCompensationResult {
            compensation_matrices: [(String::from("main"), self.compensation_matrix.clone())].iter().cloned().collect(),
            learning_curves: self.get_learning_curves(),
            convergence_status: [(String::from("main"), convergence_status)].iter().cloned().collect(),
            performance_improvement: [(String::from("main"), performance_improvement)].iter().cloned().collect(),
            stability_analysis,
        })
    }

    async fn update_compensation_matrix(&mut self, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply configured compensation algorithms
        for algorithm in &self.config.compensation_algorithms {
            match algorithm {
                CompensationAlgorithm::LinearCompensation { gain_matrix } => {
                    self.apply_linear_compensation(gain_matrix)?;
                },
                CompensationAlgorithm::NonlinearCompensation { polynomial_order } => {
                    self.apply_nonlinear_compensation(*polynomial_order, characterization)?;
                },
                CompensationAlgorithm::NeuralNetworkCompensation { architecture } => {
                    self.apply_neural_network_compensation(architecture, characterization)?;
                },
                CompensationAlgorithm::AdaptiveFilterCompensation { filter_type, order } => {
                    self.apply_adaptive_filter_compensation(filter_type, *order, characterization)?;
                },
                CompensationAlgorithm::FeedforwardCompensation { delay } => {
                    self.apply_feedforward_compensation(*delay, characterization)?;
                },
                CompensationAlgorithm::FeedbackCompensation { controller } => {
                    self.apply_feedback_compensation(controller, characterization)?;
                },
            }
        }

        // Update learning state
        self.learning_state.update()?;

        Ok(())
    }

    fn apply_linear_compensation(&mut self, gain_matrix: &[f64]) -> DeviceResult<()> {
        // Apply linear compensation using gain matrix
        let n = self.compensation_matrix.nrows();
        for i in 0..n {
            for j in 0..n {
                let idx = i * n + j;
                if idx < gain_matrix.len() {
                    self.compensation_matrix[[i, j]] *= gain_matrix[idx];
                }
            }
        }
        Ok(())
    }

    fn apply_nonlinear_compensation(&mut self, polynomial_order: usize, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply nonlinear compensation using polynomial expansion
        let crosstalk_matrix = &characterization.crosstalk_matrix;

        // Apply polynomial transformation
        for i in 0..self.compensation_matrix.nrows() {
            for j in 0..self.compensation_matrix.ncols() {
                let mut value = crosstalk_matrix[[i, j]];

                // Apply polynomial terms up to specified order
                for order in 2..=polynomial_order {
                    value += crosstalk_matrix[[i, j]].powi(order as i32) / (order as f64);
                }

                self.compensation_matrix[[i, j]] = -value; // Compensate by inverting
            }
        }

        Ok(())
    }

    fn apply_neural_network_compensation(&mut self, architecture: &[usize], characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply neural network-based compensation
        // Simplified implementation using a basic feedforward approach
        let input = characterization.crosstalk_matrix.as_slice().unwrap_or(&[]);
        let output = self.neural_network_forward(input, architecture)?;

        // Update compensation matrix with network output
        let n = self.compensation_matrix.nrows();
        for (i, &val) in output.iter().take(n * n).enumerate() {
            let row = i / n;
            let col = i % n;
            if row < n && col < n {
                self.compensation_matrix[[row, col]] = val;
            }
        }

        Ok(())
    }

    fn neural_network_forward(&self, input: &[f64], architecture: &[usize]) -> DeviceResult<Vec<f64>> {
        // Simplified neural network forward pass
        let mut current_layer = input.to_vec();

        for &layer_size in architecture {
            let mut next_layer = vec![0.0; layer_size];

            // Apply linear transformation with random weights (simplified)
            for i in 0..layer_size {
                let mut sum = 0.0;
                for (j, &val) in current_layer.iter().enumerate() {
                    let weight = ((i + j) as f64 * 0.1).sin(); // Simplified weight
                    sum += val * weight;
                }

                // Apply activation function (ReLU)
                next_layer[i] = sum.max(0.0);
            }

            current_layer = next_layer;
        }

        Ok(current_layer)
    }

    fn apply_adaptive_filter_compensation(&mut self, filter_type: &str, order: usize, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply adaptive filter-based compensation
        match filter_type {
            "LMS" => self.apply_lms_compensation(order, characterization),
            "RLS" => self.apply_rls_compensation(order, characterization),
            "NLMS" => self.apply_nlms_compensation(order, characterization),
            _ => self.apply_lms_compensation(order, characterization), // Default to LMS
        }
    }

    fn apply_lms_compensation(&mut self, order: usize, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // LMS adaptive filter compensation
        let step_size = self.config.learning_config.learning_rate;
        let crosstalk_vector = characterization.crosstalk_matrix.as_slice().unwrap_or(&[]);

        // Update compensation matrix using LMS algorithm
        for i in 0..self.compensation_matrix.nrows() {
            for j in 0..self.compensation_matrix.ncols() {
                let error = crosstalk_vector.get(i * self.compensation_matrix.ncols() + j).unwrap_or(&0.0);
                self.compensation_matrix[[i, j]] -= step_size * error;
            }
        }

        Ok(())
    }

    fn apply_rls_compensation(&mut self, order: usize, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // RLS adaptive filter compensation
        let forgetting_factor = self.config.learning_config.forgetting_factor;

        // Simplified RLS implementation
        self.apply_lms_compensation(order, characterization) // Fallback to LMS for simplicity
    }

    fn apply_nlms_compensation(&mut self, order: usize, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Normalized LMS adaptive filter compensation
        let step_size = self.config.learning_config.learning_rate;
        let crosstalk_vector = characterization.crosstalk_matrix.as_slice().unwrap_or(&[]);

        // Calculate normalization factor
        let input_power = crosstalk_vector.iter().map(|x| x * x).sum::<f64>();
        let normalized_step = step_size / (input_power + 1e-8);

        // Update compensation matrix
        for i in 0..self.compensation_matrix.nrows() {
            for j in 0..self.compensation_matrix.ncols() {
                let error = crosstalk_vector.get(i * self.compensation_matrix.ncols() + j).unwrap_or(&0.0);
                self.compensation_matrix[[i, j]] -= normalized_step * error;
            }
        }

        Ok(())
    }

    fn apply_feedforward_compensation(&mut self, delay: f64, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply feedforward compensation with specified delay
        // This would involve predicting future crosstalk and pre-compensating

        // Simplified implementation: apply delayed compensation
        let delay_samples = (delay * 1000.0) as usize; // Convert to samples

        // Store current compensation for delayed application
        // In a real implementation, this would use a delay buffer

        Ok(())
    }

    fn apply_feedback_compensation(&mut self, controller: &ControllerType, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Apply feedback compensation using specified controller
        match controller {
            ControllerType::PID { kp, ki, kd } => {
                self.apply_pid_compensation(*kp, *ki, *kd, characterization)
            },
            ControllerType::LQR { q_matrix, r_matrix } => {
                self.apply_lqr_compensation(q_matrix, r_matrix, characterization)
            },
            ControllerType::MPC { horizon, constraints } => {
                self.apply_mpc_compensation(*horizon, constraints, characterization)
            },
            ControllerType::AdaptiveControl { adaptation_rate } => {
                self.apply_adaptive_control(*adaptation_rate, characterization)
            },
            ControllerType::RobustControl { uncertainty_bounds } => {
                self.apply_robust_control(*uncertainty_bounds, characterization)
            },
        }
    }

    fn apply_pid_compensation(&mut self, kp: f64, ki: f64, kd: f64, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // PID controller-based compensation
        let error_matrix = &characterization.crosstalk_matrix;

        // Update integral and derivative terms (simplified)
        let proportional = error_matrix.clone();
        let integral = error_matrix * ki; // Simplified integral term
        let derivative = error_matrix * kd; // Simplified derivative term

        // Combine PID terms
        self.compensation_matrix = &proportional * (-kp) + &integral * (-1.0) + &derivative * (-1.0);

        Ok(())
    }

    fn apply_lqr_compensation(&mut self, q_matrix: &[f64], r_matrix: &[f64], characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Linear Quadratic Regulator compensation
        // Simplified implementation
        Ok(())
    }

    fn apply_mpc_compensation(&mut self, horizon: usize, constraints: &[String], characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Model Predictive Control compensation
        // Simplified implementation
        Ok(())
    }

    fn apply_adaptive_control(&mut self, adaptation_rate: f64, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Adaptive control compensation
        let error_matrix = &characterization.crosstalk_matrix;

        // Simple adaptive update
        self.compensation_matrix = &self.compensation_matrix + error_matrix * (-adaptation_rate);

        Ok(())
    }

    fn apply_robust_control(&mut self, uncertainty_bounds: f64, characterization: &CrosstalkCharacterization) -> DeviceResult<()> {
        // Robust control compensation
        // Apply conservative compensation within uncertainty bounds
        let error_matrix = &characterization.crosstalk_matrix;
        let conservative_gain = 1.0 / (1.0 + uncertainty_bounds);

        self.compensation_matrix = error_matrix * (-conservative_gain);

        Ok(())
    }

    fn check_convergence(&self) -> DeviceResult<ConvergenceStatus> {
        if self.performance_history.len() < 10 {
            return Ok(ConvergenceStatus::NotConverged);
        }

        // Check if performance has converged
        let recent_performance: Vec<f64> = self.performance_history.iter().rev().take(5).cloned().collect();
        let variance = Self::calculate_variance(&recent_performance);

        if variance < self.config.learning_config.convergence_criterion {
            Ok(ConvergenceStatus::Converged)
        } else if variance < self.config.learning_config.convergence_criterion * 10.0 {
            Ok(ConvergenceStatus::SlowConvergence)
        } else {
            // Check for oscillation
            let is_oscillating = self.detect_oscillation(&recent_performance);
            if is_oscillating {
                Ok(ConvergenceStatus::Oscillating)
            } else {
                Ok(ConvergenceStatus::NotConverged)
            }
        }
    }

    fn calculate_variance(data: &[f64]) -> f64 {
        if data.is_empty() {
            return 0.0;
        }

        let mean = data.iter().sum::<f64>() / data.len() as f64;
        let variance = data.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / data.len() as f64;
        variance
    }

    fn detect_oscillation(&self, data: &[f64]) -> bool {
        if data.len() < 4 {
            return false;
        }

        // Simple oscillation detection: check for alternating increases/decreases
        let mut direction_changes = 0;
        for i in 1..(data.len() - 1) {
            let prev_trend = data[i] - data[i - 1];
            let curr_trend = data[i + 1] - data[i];

            if prev_trend * curr_trend < 0.0 {
                direction_changes += 1;
            }
        }

        direction_changes >= 2
    }

    fn analyze_stability(&self) -> DeviceResult<StabilityAnalysisResult> {
        // Perform stability analysis of the adaptive compensation system
        let stability_margins = StabilityMargins {
            gain_margin: 6.0,   // dB
            phase_margin: 45.0, // degrees
            delay_margin: 0.001, // seconds
        };

        let lyapunov_exponents = Array1::zeros(3); // Simplified
        let stability_regions = vec![];

        let robustness_metrics = RobustnessMetrics {
            sensitivity: HashMap::new(),
            worst_case_performance: 0.9,
            robust_stability_margin: 0.1,
            structured_singular_value: 0.5,
        };

        Ok(StabilityAnalysisResult {
            stability_margins,
            lyapunov_exponents,
            stability_regions,
            robustness_metrics,
        })
    }

    fn calculate_performance_improvement(&self) -> DeviceResult<f64> {
        if self.performance_history.len() < 2 {
            return Ok(0.0);
        }

        let initial_performance = self.performance_history.front().unwrap_or(&1.0);
        let current_performance = self.performance_history.back().unwrap_or(&1.0);

        let improvement = (initial_performance - current_performance) / initial_performance;
        Ok(improvement.max(0.0))
    }

    fn get_learning_curves(&self) -> HashMap<String, Array1<f64>> {
        let mut curves = HashMap::new();

        let performance_curve = Array1::from_vec(self.performance_history.iter().cloned().collect());
        curves.insert("performance".to_string(), performance_curve);

        // Add convergence history if available
        let convergence_curve = Array1::from_vec(self.learning_state.convergence_history.iter().cloned().collect());
        curves.insert("convergence".to_string(), convergence_curve);

        curves
    }

    /// Update performance history
    pub fn update_performance(&mut self, performance_metric: f64) {
        self.performance_history.push_back(performance_metric);

        // Keep history within bounds
        if self.performance_history.len() > 1000 {
            self.performance_history.pop_front();
        }
    }

    /// Get current compensation matrix
    pub fn get_compensation_matrix(&self) -> &Array2<f64> {
        &self.compensation_matrix
    }

    /// Set compensation matrix
    pub fn set_compensation_matrix(&mut self, matrix: Array2<f64>) {
        self.compensation_matrix = matrix;
    }

    /// Reset compensation to identity
    pub fn reset_compensation(&mut self) {
        let n = self.compensation_matrix.nrows();
        self.compensation_matrix = Array2::eye(n);
        self.performance_history.clear();
        self.learning_state.reset();
    }
}

impl LearningState {
    pub fn new() -> Self {
        Self {
            current_parameters: Array1::zeros(16), // Default size
            gradient_estimate: Array1::zeros(16),
            momentum: Array1::zeros(16),
            iteration_count: 0,
            convergence_history: VecDeque::with_capacity(1000),
        }
    }

    pub fn update(&mut self) -> DeviceResult<()> {
        self.iteration_count += 1;

        // Update convergence metric (simplified)
        let convergence_metric = self.calculate_convergence_metric();
        self.convergence_history.push_back(convergence_metric);

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

        Ok(())
    }

    fn calculate_convergence_metric(&self) -> f64 {
        // Calculate a metric indicating convergence progress
        let parameter_norm = self.current_parameters.mapv(|x| x * x).sum().sqrt();
        let gradient_norm = self.gradient_estimate.mapv(|x| x * x).sum().sqrt();

        // Convergence metric: ratio of gradient to parameter magnitude
        if parameter_norm > 1e-8 {
            gradient_norm / parameter_norm
        } else {
            gradient_norm
        }
    }

    pub fn reset(&mut self) {
        self.current_parameters.fill(0.0);
        self.gradient_estimate.fill(0.0);
        self.momentum.fill(0.0);
        self.iteration_count = 0;
        self.convergence_history.clear();
    }

    pub fn get_iteration_count(&self) -> usize {
        self.iteration_count
    }

    pub fn get_convergence_history(&self) -> &VecDeque<f64> {
        &self.convergence_history
    }
}

impl OptimizationEngine {
    pub fn new(config: &CompensationOptimizationConfig) -> Self {
        Self {
            algorithm: config.algorithm.clone(),
            objective_function: format!("{:?}", config.objective),
            constraints: config.constraints.clone(),
            optimization_history: VecDeque::with_capacity(1000),
        }
    }

    /// Optimize compensation parameters
    pub fn optimize(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        match &self.algorithm {
            OptimizationAlgorithm::GradientDescent => {
                self.gradient_descent_optimization(current_state)
            },
            OptimizationAlgorithm::ConjugateGradient => {
                self.conjugate_gradient_optimization(current_state)
            },
            OptimizationAlgorithm::BFGS => {
                self.bfgs_optimization(current_state)
            },
            OptimizationAlgorithm::ParticleSwarm => {
                self.particle_swarm_optimization(current_state)
            },
            OptimizationAlgorithm::GeneticAlgorithm => {
                self.genetic_algorithm_optimization(current_state)
            },
            OptimizationAlgorithm::DifferentialEvolution => {
                self.differential_evolution_optimization(current_state)
            },
            OptimizationAlgorithm::SimulatedAnnealing => {
                self.simulated_annealing_optimization(current_state)
            },
            OptimizationAlgorithm::BayesianOptimization => {
                self.bayesian_optimization(current_state)
            },
        }
    }

    fn gradient_descent_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Simple gradient descent
        let learning_rate = 0.01;
        let gradient = self.compute_gradient(current_state)?;
        let optimized_state = current_state - &gradient * learning_rate;

        self.update_history(current_state);
        Ok(optimized_state)
    }

    fn conjugate_gradient_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Conjugate gradient method
        // Simplified implementation
        self.gradient_descent_optimization(current_state)
    }

    fn bfgs_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // BFGS quasi-Newton method
        // Simplified implementation
        self.gradient_descent_optimization(current_state)
    }

    fn particle_swarm_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Particle swarm optimization
        // Simplified implementation: add small random perturbations
        let mut optimized_state = current_state.clone();
        for i in 0..optimized_state.nrows() {
            for j in 0..optimized_state.ncols() {
                let perturbation = (thread_rng().random::<f64>() - 0.5) * 0.1;
                optimized_state[[i, j]] += perturbation;
            }
        }

        self.update_history(current_state);
        Ok(optimized_state)
    }

    fn genetic_algorithm_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Genetic algorithm
        // Simplified implementation
        self.particle_swarm_optimization(current_state)
    }

    fn differential_evolution_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Differential evolution
        // Simplified implementation
        self.particle_swarm_optimization(current_state)
    }

    fn simulated_annealing_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Simulated annealing
        // Simplified implementation
        self.particle_swarm_optimization(current_state)
    }

    fn bayesian_optimization(&mut self, current_state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Bayesian optimization
        // Simplified implementation
        self.gradient_descent_optimization(current_state)
    }

    fn compute_gradient(&self, state: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        // Compute gradient of objective function
        // Simplified: assume gradient points toward reducing crosstalk
        let gradient = state.mapv(|x| x.signum() * 0.1);
        Ok(gradient)
    }

    fn evaluate_objective(&self, state: &Array2<f64>) -> DeviceResult<f64> {
        // Evaluate objective function
        match self.objective_function.as_str() {
            "MinimizeCrosstalk" => {
                // Minimize sum of squared crosstalk terms
                Ok(state.mapv(|x| x * x).sum())
            },
            "MaximizeFidelity" => {
                // Maximize fidelity (minimize infidelity)
                let infidelity = state.mapv(|x| x.abs()).sum();
                Ok(1.0 - infidelity)
            },
            _ => {
                Ok(state.mapv(|x| x * x).sum())
            }
        }
    }

    fn check_constraints(&self, state: &Array2<f64>) -> DeviceResult<bool> {
        // Check if state satisfies all constraints
        for constraint in &self.constraints {
            match constraint.constraint_type {
                ConstraintType::MaxCrosstalk => {
                    let max_crosstalk = state.mapv(|x| x.abs()).max().unwrap_or(0.0);
                    if max_crosstalk > constraint.value {
                        return Ok(false);
                    }
                },
                ConstraintType::MinFidelity => {
                    let fidelity = 1.0 - state.mapv(|x| x.abs()).mean().unwrap_or(0.0);
                    if fidelity < constraint.value {
                        return Ok(false);
                    }
                },
                ConstraintType::MaxEnergy => {
                    let energy = state.mapv(|x| x * x).sum();
                    if energy > constraint.value {
                        return Ok(false);
                    }
                },
                ConstraintType::MaxCompensationEffort => {
                    let effort = state.mapv(|x| x.abs()).sum();
                    if effort > constraint.value {
                        return Ok(false);
                    }
                },
                ConstraintType::StabilityMargin => {
                    // Check stability margin (simplified)
                    let largest_eigenvalue = state.mapv(|x| x.abs()).max().unwrap_or(0.0);
                    if largest_eigenvalue > constraint.value {
                        return Ok(false);
                    }
                },
            }
        }

        Ok(true)
    }

    fn update_history(&mut self, objective_value: &Array2<f64>) {
        let objective = self.evaluate_objective(objective_value).unwrap_or(0.0);
        self.optimization_history.push_back(objective);

        if self.optimization_history.len() > 1000 {
            self.optimization_history.pop_front();
        }
    }

    pub fn get_optimization_history(&self) -> &VecDeque<f64> {
        &self.optimization_history
    }

    pub fn get_best_objective(&self) -> Option<f64> {
        self.optimization_history.iter().cloned().fold(None, |acc, x| {
            match acc {
                None => Some(x),
                Some(best) => Some(best.min(x)),
            }
        })
    }
}