// Search strategies for neural architecture search
//
// Implements various NAS algorithms including DARTS, evolutionary search,
// reinforcement learning-based search, and Bayesian optimization.

use scirs2_core::ndarray::{s, Array1, Array2, Array3};
use scirs2_core::numeric::Float;
use scirs2_core::random::Rng;
use scirs2_core::random::{Random, Rng as SCRRng};
use scirs2_core::ScientificNumber;
use std::collections::{HashMap, VecDeque};
use std::fmt::Debug;

use crate::architecture::{ComponentPosition, ComponentType};
#[allow(unused_imports)]
use crate::error::Result;
use crate::nas_engine::config::{ComponentType as ConfigComponentType, ParameterRange};
use crate::nas_engine::{OptimizerArchitecture, SearchResult, SearchSpaceConfig};
use crate::EvaluationMetric;

/// Base trait for all search strategies
pub trait SearchStrategy<T: Float + Debug + Send + Sync + 'static>: Send + Sync {
    /// Initialize the search strategy
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()>;

    /// Generate a new architecture candidate
    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>>;

    /// Update strategy with evaluation results
    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()>;

    /// Get strategy name
    fn name(&self) -> &str;

    /// Get current search statistics
    fn get_statistics(&self) -> SearchStrategyStatistics<T>;
}

/// Search strategy statistics
#[derive(Debug, Clone)]
pub struct SearchStrategyStatistics<T: Float + Debug + Send + Sync + 'static> {
    pub total_architectures_generated: usize,
    pub best_performance: T,
    pub average_performance: T,
    pub convergence_rate: T,
    pub exploration_rate: T,
    pub exploitation_rate: T,
}

/// Random search baseline strategy
pub struct RandomSearch<T: Float + Debug + Send + Sync + 'static + std::iter::Sum> {
    rng: Random<scirs2_core::random::rngs::StdRng>,
    statistics: SearchStrategyStatistics<T>,
    searchspace: Option<SearchSpaceConfig>,
}

/// Evolutionary search strategy using genetic algorithms
pub struct EvolutionarySearch<T: Float + Debug + Send + Sync + 'static> {
    population: Vec<OptimizerArchitecture<T>>,
    population_size: usize,
    mutation_rate: f64,
    crossover_rate: f64,
    tournament_size: usize,
    generation_count: usize,
    statistics: SearchStrategyStatistics<T>,
    elite_preservation: bool,
    elitism_ratio: f64,
    adaptive_rates: bool,
}

/// Reinforcement learning-based search using policy gradients
pub struct ReinforcementLearningSearch<T: Float + Debug + Send + Sync + 'static> {
    controller_network: ControllerNetwork<T>,
    experience_buffer: ExperienceBuffer<T>,
    policy_optimizer: PolicyOptimizer<T>,
    baseline_predictor: BaselinePredictor<T>,
    epsilon: f64,
    exploration_decay: f64,
    statistics: SearchStrategyStatistics<T>,
    entropy_bonus: f64,
}

/// Differentiable Architecture Search (DARTS)
pub struct DifferentiableSearch<T: Float + Debug + Send + Sync + 'static> {
    architecture_weights: Array3<T>,
    weight_optimizer: WeightOptimizer<T>,
    temperature: T,
    gumbel_softmax: bool,
    continuous_relaxation: bool,
    statistics: SearchStrategyStatistics<T>,
    discretization_strategy: DiscretizationStrategy,
}

/// Bayesian optimization for architecture search
pub struct BayesianOptimization<T: Float + Debug + Send + Sync + 'static> {
    gaussian_process: GaussianProcess<T>,
    acquisition_function: AcquisitionFunction<T>,
    observed_architectures: Vec<OptimizerArchitecture<T>>,
    observed_performances: Vec<T>,
    kernel: GPKernel<T>,
    statistics: SearchStrategyStatistics<T>,
    exploration_factor: T,
}

/// Neural predictor-based search
pub struct NeuralPredictorSearch<T: Float + Debug + Send + Sync + 'static> {
    predictor_network: PredictorNetwork<T>,
    architecture_encoder: ArchitectureEncoder<T>,
    search_optimizer: SearchOptimizer<T>,
    confidence_threshold: T,
    statistics: SearchStrategyStatistics<T>,
    uncertainty_sampling: bool,
}

/// Controller network for RL-based search
#[derive(Debug)]
pub struct ControllerNetwork<T: Float + Debug + Send + Sync + 'static> {
    lstm_weights: Vec<Array2<T>>,
    lstm_biases: Vec<Array1<T>>,
    output_weights: Array2<T>,
    output_bias: Array1<T>,
    hidden_states: Vec<Array1<T>>,
    cell_states: Vec<Array1<T>>,
    numlayers: usize,
    hidden_size: usize,
}

/// Experience buffer for RL training
#[derive(Debug)]
pub struct ExperienceBuffer<T: Float + Debug + Send + Sync + 'static> {
    states: VecDeque<Array1<T>>,
    actions: VecDeque<usize>,
    rewards: VecDeque<T>,
    next_states: VecDeque<Array1<T>>,
    dones: VecDeque<bool>,
    capacity: usize,
}

/// Policy optimizer for RL controller
#[derive(Debug)]
pub struct PolicyOptimizer<T: Float + Debug + Send + Sync + 'static> {
    _learningrate: T,
    momentum: T,
    velocity: HashMap<String, Array2<T>>,
    gradient_clip_norm: T,
}

/// Baseline predictor for variance reduction
#[derive(Debug)]
pub struct BaselinePredictor<T: Float + Debug + Send + Sync + 'static> {
    network_weights: Vec<Array2<T>>,
    network_biases: Vec<Array1<T>>,
    optimizer: BaselineOptimizer<T>,
}

/// Weight optimizer for DARTS
#[derive(Debug)]
pub struct WeightOptimizer<T: Float + Debug + Send + Sync + 'static> {
    _learningrate: T,
    momentum: T,
    weight_decay: T,
    velocity: Array3<T>,
}

/// Discretization strategies for DARTS
#[derive(Debug, Clone, Copy)]
pub enum DiscretizationStrategy {
    /// Select operation with highest weight
    Greedy,
    /// Sample proportional to weights
    Sampling,
    /// Progressive discretization
    Progressive,
    /// Threshold-based discretization
    Threshold,
}

/// Gaussian Process for Bayesian optimization
#[derive(Debug)]
pub struct GaussianProcess<T: Float + Debug + Send + Sync + 'static> {
    kernel_matrix: Array2<T>,
    inverse_kernel: Array2<T>,
    noise_variance: T,
    length_scales: Array1<T>,
    signal_variance: T,
}

/// Acquisition functions for Bayesian optimization
#[derive(Debug)]
pub struct AcquisitionFunction<T: Float + Debug + Send + Sync + 'static> {
    function_type: AcquisitionType,
    explorationweight: T,
    current_best: T,
}

/// Types of acquisition functions
#[derive(Debug, Clone, Copy)]
pub enum AcquisitionType {
    /// Expected Improvement
    EI,
    /// Upper Confidence Bound
    UCB,
    /// Probability of Improvement
    PI,
    /// Thompson Sampling
    Thompson,
    /// Information Gain
    InfoGain,
}

/// Gaussian Process kernels
#[derive(Debug)]
pub struct GPKernel<T: Float + Debug + Send + Sync + 'static> {
    _kerneltype: KernelType,
    hyperparameters: Array1<T>,
}

/// Kernel types for GP
#[derive(Debug, Clone, Copy)]
pub enum KernelType {
    RBF,
    Matern32,
    Matern52,
    Linear,
    Polynomial,
}

/// Predictor network for neural predictor search
#[derive(Debug)]
pub struct PredictorNetwork<T: Float + Debug + Send + Sync + 'static> {
    layers: Vec<PredictorLayer<T>>,
    dropout_rates: Vec<T>,
    architecture: Vec<usize>,
}

/// Predictor layer
#[derive(Debug)]
pub struct PredictorLayer<T: Float + Debug + Send + Sync + 'static> {
    weights: Array2<T>,
    bias: Array1<T>,
    activation: ActivationFunction,
}

/// Activation functions
#[derive(Debug, Clone, Copy)]
pub enum ActivationFunction {
    ReLU,
    GELU,
    Swish,
    Tanh,
    Sigmoid,
}

/// Architecture encoder for neural predictor
#[derive(Debug)]
pub struct ArchitectureEncoder<T: Float + Debug + Send + Sync + 'static> {
    encoding_weights: Array2<T>,
    _embeddingdim: usize,
    max_components: usize,
}

/// Search optimizer for neural predictor
#[derive(Debug)]
pub struct SearchOptimizer<T: Float + Debug + Send + Sync + 'static> {
    optimizer_type: SearchOptimizerType,
    _learningrate: T,
    momentum: T,
    parameters: HashMap<String, Array1<T>>,
}

/// Search optimizer types
#[derive(Debug, Clone, Copy)]
pub enum SearchOptimizerType {
    Adam,
    SGD,
    RMSprop,
    AdamW,
}

/// Baseline optimizer
#[derive(Debug)]
pub struct BaselineOptimizer<T: Float + Debug + Send + Sync + 'static> {
    _learningrate: T,
    momentum: T,
    velocity: Vec<Array2<T>>,
}

impl<
        T: Float + Debug + Default + Clone + Send + Sync + 'static + std::fmt::Debug + std::iter::Sum,
    > RandomSearch<T>
{
    pub fn new(seed: Option<u64>) -> Self {
        let rng = if let Some(seed) = seed {
            Random::seed(seed)
        } else {
            Random::seed(42)
        };

        Self {
            rng,
            statistics: SearchStrategyStatistics::default(),
            searchspace: None,
        }
    }
}

impl<
        T: Float + Debug + Default + Clone + Send + Sync + 'static + std::fmt::Debug + std::iter::Sum,
    > SearchStrategy<T> for RandomSearch<T>
{
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.searchspace = Some(searchspace.clone());
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        _history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        use crate::architecture::OptimizerComponent;

        // Randomly select number of components
        let num_components = self.rng.gen_range(1..5);
        let mut components = Vec::new();

        for idx in 0..num_components {
            // Randomly select component type
            let component_config =
                &searchspace.components[self.rng.gen_range(0..searchspace.components.len())];

            let mut hyperparameters = HashMap::new();

            // Randomly sample hyperparameters
            for (param_name, param_range) in &component_config.hyperparameter_ranges {
                let value = match param_range {
                    ParameterRange::Continuous(min, max) => self.rng.gen_range(*min..*max),
                    ParameterRange::LogUniform(min, max) => {
                        let log_min = min.ln();
                        let log_max = max.ln();
                        let log_val = self.rng.gen_range(log_min..log_max);
                        log_val.exp()
                    }
                    ParameterRange::Integer(min, max) => self.rng.gen_range(*min..*max) as f64,
                    ParameterRange::Boolean => {
                        if self.rng.random::<f64>() < 0.5 {
                            1.0
                        } else {
                            0.0
                        }
                    }
                    ParameterRange::Discrete(values) => {
                        let value_idx = self.rng.gen_range(0..values.len());
                        values[value_idx]
                    }
                    ParameterRange::Categorical(_values) => {
                        // For categorical, we'll use index as value
                        0.0
                        // Simplified
                    }
                };

                hyperparameters.insert(param_name.clone(), value);
            }

            let component_type = match component_config.component_type {
                ConfigComponentType::SGD => ComponentType::SGD,
                ConfigComponentType::Adam => ComponentType::Adam,
                ConfigComponentType::AdamW => ComponentType::AdamW,
                ConfigComponentType::RMSprop => ComponentType::RMSprop,
                ConfigComponentType::AdaGrad => ComponentType::AdaGrad,
                ConfigComponentType::AdaDelta => ComponentType::AdaDelta,
                ConfigComponentType::LBFGS => ComponentType::LBFGS,
                ConfigComponentType::Momentum => ComponentType::Momentum,
                ConfigComponentType::Nesterov => ComponentType::Nesterov,
                ConfigComponentType::Custom(_) => ComponentType::Custom,
                _ => ComponentType::Adam, // Default fallback
            };

            components.push(OptimizerComponent {
                id: format!("comp_{}", idx),
                component_type,
                hyperparameters,
                enabled: true,
                position: ComponentPosition {
                    layer: 0,
                    index: idx as u32,
                    x: 0.0,
                    y: 0.0,
                },
            });
        }

        self.statistics.total_architectures_generated += 1;

        Ok(OptimizerArchitecture {
            components: components
                .into_iter()
                .map(|c| format!("{:?}", c.component_type))
                .collect(),
            parameters: HashMap::new(),
            connections: Vec::new(),
            metadata: HashMap::new(),
            hyperparameters: HashMap::new(),
            architecture_id: format!("arch_{}", self.rng.random::<u32>()),
        })
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        if !results.is_empty() {
            let performances: Vec<T> = results
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&EvaluationMetric::FinalPerformance)
                })
                .cloned()
                .collect();

            if !performances.is_empty() {
                self.statistics.best_performance = performances
                    .iter()
                    .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                    .cloned()
                    .unwrap_or(T::zero());

                let sum: T = performances.iter().cloned().sum();
                self.statistics.average_performance = sum / T::from(performances.len()).unwrap();
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "RandomSearch"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        self.statistics.clone()
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    EvolutionarySearch<T>
{
    pub fn new(
        population_size: usize,
        mutation_rate: f64,
        crossover_rate: f64,
        tournament_size: usize,
    ) -> Self {
        Self {
            population: Vec::new(),
            population_size,
            mutation_rate,
            crossover_rate,
            tournament_size,
            generation_count: 0,
            statistics: SearchStrategyStatistics::default(),
            elite_preservation: true,
            elitism_ratio: 0.1,
            adaptive_rates: true,
        }
    }

    fn initialize_population(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.population.clear();

        // Use random search to generate initial population
        let mut random_search = RandomSearch::<T>::new(Some(42));
        random_search.initialize(searchspace)?;

        for _ in 0..self.population_size {
            let architecture =
                random_search.generate_architecture(searchspace, &VecDeque::new())?;
            self.population.push(architecture);
        }

        Ok(())
    }

    fn selection(&self, fitnessscores: &[T]) -> Result<usize> {
        // Tournament selection
        let mut best_idx = 0;
        let mut best_fitness = T::neg_infinity();

        let mut rng = scirs2_core::random::thread_rng();
        for _ in 0..self.tournament_size {
            let idx = rng.gen_range(0..self.population.len());
            if fitnessscores[idx] > best_fitness {
                best_fitness = fitnessscores[idx];
                best_idx = idx;
            }
        }

        Ok(best_idx)
    }

    fn crossover(
        &self,
        parent1: &OptimizerArchitecture<T>,
        parent2: &OptimizerArchitecture<T>,
    ) -> Result<OptimizerArchitecture<T>> {
        let mut child_components = Vec::new();
        let max_len = parent1.components.len().max(parent2.components.len());

        for i in 0..max_len {
            let component = if i < parent1.components.len() && i < parent2.components.len() {
                // Crossover between components
                if scirs2_core::random::Random::default().random::<f64>() < 0.5 {
                    parent1.components[i].clone()
                } else {
                    parent2.components[i].clone()
                }
            } else if i < parent1.components.len() {
                parent1.components[i].clone()
            } else {
                parent2.components[i].clone()
            };

            child_components.push(component);
        }

        // Crossover parameters
        let mut child_parameters = HashMap::new();
        for (key, value) in &parent1.parameters {
            if scirs2_core::random::Random::default().random::<f64>() < 0.5 {
                child_parameters.insert(key.clone(), *value);
            } else if let Some(parent2_value) = parent2.parameters.get(key) {
                child_parameters.insert(key.clone(), *parent2_value);
            } else {
                child_parameters.insert(key.clone(), *value);
            }
        }

        Ok(OptimizerArchitecture {
            components: child_components,
            parameters: child_parameters,
            connections: Vec::new(),
            metadata: HashMap::new(),
            hyperparameters: HashMap::new(),
            architecture_id: format!("arch_{}", Random::default().random::<u64>()),
        })
    }

    fn mutate(
        &self,
        architecture: &mut OptimizerArchitecture<T>,
        searchspace: &SearchSpaceConfig,
    ) -> Result<()> {
        // Mutate parameters directly
        for (param_name, current_value) in architecture.parameters.iter_mut() {
            if scirs2_core::random::Random::default().random::<f64>() < self.mutation_rate {
                // Find the parameter range from search space config
                let param_range = searchspace
                    .components
                    .iter()
                    .flat_map(|c| c.hyperparameter_ranges.iter())
                    .find(|(name, _)| name == &param_name)
                    .map(|(_, range)| range);

                if let Some(param_range) = param_range {
                    match param_range {
                        ParameterRange::Continuous(min, max) => {
                            let noise =
                                scirs2_core::random::Random::default().random::<f64>() * 0.1 - 0.05; // Small noise
                            let current_f64 = current_value.to_f64().unwrap_or(0.0);
                            let new_val = current_f64 + noise;
                            let clamped = new_val.max(*min).min(*max);
                            *current_value = scirs2_core::numeric::NumCast::from(clamped)
                                .unwrap_or_else(|| T::zero());
                        }
                        ParameterRange::LogUniform(min, max) => {
                            let current_f64 = current_value.to_f64().unwrap_or(0.001);
                            let log_val = current_f64.ln();
                            let noise =
                                scirs2_core::random::Random::default().random::<f64>() * 0.2 - 0.1;
                            let new_log = log_val + noise;
                            let new_val = new_log.exp().max(*min).min(*max);
                            *current_value = scirs2_core::numeric::NumCast::from(new_val)
                                .unwrap_or_else(|| T::zero());
                        }
                        _ => {
                            // For other types, regenerate randomly
                            // This is simplified - could be more sophisticated
                        }
                    }
                }
            }
        }

        Ok(())
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    SearchStrategy<T> for EvolutionarySearch<T>
{
    fn initialize(&mut self, searchspace: &SearchSpaceConfig) -> Result<()> {
        self.initialize_population(searchspace)?;
        self.generation_count = 0;
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        if self.population.is_empty() {
            self.initialize_population(searchspace)?;
        }

        // Extract fitness scores from recent history
        let recent_results: Vec<_> = history.iter().rev().take(self.population_size).collect();

        if recent_results.len() >= self.population_size {
            // Perform evolutionary step
            let fitnessscores: Vec<T> = recent_results
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&EvaluationMetric::FinalPerformance)
                })
                .cloned()
                .collect();

            if fitnessscores.len() == self.population_size {
                // Selection and reproduction
                let parent1_idx = self.selection(&fitnessscores)?;
                let parent2_idx = self.selection(&fitnessscores)?;

                let mut child = if scirs2_core::random::Random::default().random::<f64>()
                    < self.crossover_rate
                {
                    self.crossover(&self.population[parent1_idx], &self.population[parent2_idx])?
                } else {
                    self.population[parent1_idx].clone()
                };

                self.mutate(&mut child, searchspace)?;

                // Replace worst individual with child
                let worst_idx = fitnessscores
                    .iter()
                    .enumerate()
                    .min_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                    .map(|(idx, _)| idx)
                    .unwrap_or(0);

                self.population[worst_idx] = child.clone();
                self.generation_count += 1;

                self.statistics.total_architectures_generated += 1;
                return Ok(child);
            }
        }

        // Fallback to random generation
        let idx = scirs2_core::random::thread_rng().gen_range(0..self.population.len());
        self.statistics.total_architectures_generated += 1;
        Ok(self.population[idx].clone())
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        if !results.is_empty() {
            let performances: Vec<T> = results
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&EvaluationMetric::FinalPerformance)
                })
                .cloned()
                .collect();

            if !performances.is_empty() {
                self.statistics.best_performance = performances
                    .iter()
                    .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                    .cloned()
                    .unwrap_or(T::zero());

                let sum: T = performances.iter().cloned().sum();
                self.statistics.average_performance = sum / T::from(performances.len()).unwrap();

                // Adaptive rate adjustment
                if self.adaptive_rates && self.generation_count > 10 {
                    let recent_improvement = self.calculate_recent_improvement(&performances);
                    if recent_improvement
                        < scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero())
                    {
                        self.mutation_rate = (self.mutation_rate * 1.1).min(0.5);
                    } else {
                        self.mutation_rate = (self.mutation_rate * 0.95).max(0.01);
                    }
                }
            }
        }
        Ok(())
    }

    fn name(&self) -> &str {
        "EvolutionarySearch"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate =
            scirs2_core::numeric::NumCast::from(self.mutation_rate).unwrap_or_else(|| T::zero());
        stats.exploitation_rate = scirs2_core::numeric::NumCast::from(1.0 - self.mutation_rate)
            .unwrap_or_else(|| T::zero());
        stats
    }
}

impl<T: Float + Debug + Send + Sync + std::iter::Sum> EvolutionarySearch<T> {
    fn calculate_recent_improvement(&self, performances: &[T]) -> T {
        if performances.len() < 10 {
            return T::zero();
        }

        let recent_avg = performances.iter().rev().take(5).cloned().sum::<T>()
            / scirs2_core::numeric::NumCast::from(5.0).unwrap_or_else(|| T::zero());
        let earlier_avg = performances
            .iter()
            .rev()
            .skip(5)
            .take(5)
            .cloned()
            .sum::<T>()
            / scirs2_core::numeric::NumCast::from(5.0).unwrap_or_else(|| T::zero());

        recent_avg - earlier_avg
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + 'static>
    ReinforcementLearningSearch<T>
{
    pub fn new(
        controller_hidden_size: usize,
        controller_num_layers: usize,
        _learningrate: f64,
    ) -> Self {
        Self {
            controller_network: ControllerNetwork::new(
                controller_hidden_size,
                controller_num_layers,
            ),
            experience_buffer: ExperienceBuffer::new(10000),
            policy_optimizer: PolicyOptimizer::new(
                scirs2_core::numeric::NumCast::from(_learningrate).unwrap_or_else(|| T::zero()),
            ),
            baseline_predictor: BaselinePredictor::new(),
            epsilon: 0.1,
            exploration_decay: 0.995,
            statistics: SearchStrategyStatistics::default(),
            entropy_bonus: 0.01,
        }
    }
}

impl<
        T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + 'static + std::iter::Sum,
    > SearchStrategy<T> for ReinforcementLearningSearch<T>
{
    fn initialize(&mut self, _searchspace: &SearchSpaceConfig) -> Result<()> {
        // Initialize controller network
        self.controller_network.reset_states();
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        _history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        // Use controller to generate architecture
        let state = self.encode_search_space(searchspace)?;
        let actions = self.controller_network.forward(&state)?;

        // Decode actions to architecture
        let architecture = self.decode_actions_to_architecture(&actions, searchspace)?;

        self.statistics.total_architectures_generated += 1;
        Ok(architecture)
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        // Update experience buffer and train controller
        for result in results {
            let reward = result
                .evaluation_results
                .metric_scores
                .get(&EvaluationMetric::FinalPerformance)
                .cloned()
                .unwrap_or(T::zero());

            // Store experience and train if buffer is full enough
            self.experience_buffer.add_experience(
                Array1::zeros(64), // Simplified state
                0,                 // Simplified action
                reward,
                Array1::zeros(64), // Simplified next state
                true,              // Done flag
            );
        }

        if self.experience_buffer.size() > 1000 {
            self.train_controller()?;
        }

        // Update statistics
        if !results.is_empty() {
            let performances: Vec<T> = results
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&EvaluationMetric::FinalPerformance)
                })
                .cloned()
                .collect();

            if !performances.is_empty() {
                self.statistics.best_performance = performances
                    .iter()
                    .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                    .cloned()
                    .unwrap_or(T::zero());

                let sum: T = performances.iter().cloned().sum();
                self.statistics.average_performance = sum / T::from(performances.len()).unwrap();
            }
        }

        // Decay exploration
        self.epsilon *= self.exploration_decay;

        Ok(())
    }

    fn name(&self) -> &str {
        "ReinforcementLearningSearch"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate =
            scirs2_core::numeric::NumCast::from(self.epsilon).unwrap_or_else(|| T::zero());
        stats.exploitation_rate =
            scirs2_core::numeric::NumCast::from(1.0 - self.epsilon).unwrap_or_else(|| T::zero());
        stats
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ReinforcementLearningSearch<T> {
    fn encode_search_space(&self, _searchspace: &SearchSpaceConfig) -> Result<Array1<T>> {
        // Simplified encoding - in practice this would be more sophisticated
        Ok(Array1::zeros(64))
    }

    fn decode_actions_to_architecture(
        &self,
        actions: &Array1<T>,
        searchspace: &SearchSpaceConfig,
    ) -> Result<OptimizerArchitecture<T>> {
        use crate::architecture::OptimizerComponent;

        // Simplified decoding - randomly select for now
        let component_config = &searchspace.components[0];
        let mut hyperparameters = HashMap::new();

        for (param_name, param_range) in &component_config.hyperparameter_ranges {
            hyperparameters.insert(param_name.clone(), 0.01f64);
        }

        Ok(OptimizerArchitecture {
            components: vec![format!("{:?}", component_config.component_type)],
            parameters: hyperparameters
                .iter()
                .map(|(k, v)| {
                    (
                        k.clone(),
                        scirs2_core::numeric::NumCast::from(*v).unwrap_or_else(|| T::zero()),
                    )
                })
                .collect(),
            connections: Vec::new(),
            metadata: HashMap::new(),
            hyperparameters: hyperparameters
                .iter()
                .map(|(k, v)| {
                    (
                        k.clone(),
                        scirs2_core::numeric::NumCast::from(*v).unwrap_or_else(|| T::zero()),
                    )
                })
                .collect(),
            architecture_id: format!("arch_{}", Random::default().random::<u64>()),
        })
    }

    fn train_controller(&mut self) -> Result<()> {
        // Simplified controller training
        // In practice, this would implement policy gradient methods
        Ok(())
    }
}

// Default implementations for statistics
impl<T: Float + Debug + Default + Send + Sync> Default for SearchStrategyStatistics<T> {
    fn default() -> Self {
        Self {
            total_architectures_generated: 0,
            best_performance: T::zero(),
            average_performance: T::zero(),
            convergence_rate: T::zero(),
            exploration_rate: scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
            exploitation_rate: scirs2_core::numeric::NumCast::from(0.5)
                .unwrap_or_else(|| T::zero()),
        }
    }
}

// Implementation stubs for complex components
impl<T: Float + Debug + Default + Clone + 'static + Send + Sync> ControllerNetwork<T> {
    fn new(hidden_size: usize, numlayers: usize) -> Self {
        let mut lstm_weights = Vec::new();
        let mut lstm_biases = Vec::new();
        let mut hidden_states = Vec::new();
        let mut cell_states = Vec::new();

        for _ in 0..numlayers {
            lstm_weights.push(Array2::zeros((hidden_size * 4, hidden_size)));
            lstm_biases.push(Array1::zeros(hidden_size * 4));
            hidden_states.push(Array1::zeros(hidden_size));
            cell_states.push(Array1::zeros(hidden_size));
        }

        Self {
            lstm_weights,
            lstm_biases,
            output_weights: Array2::zeros((hidden_size, hidden_size)),
            output_bias: Array1::zeros(hidden_size),
            hidden_states,
            cell_states,
            numlayers,
            hidden_size,
        }
    }

    fn reset_states(&mut self) {
        for i in 0..self.numlayers {
            self.hidden_states[i].fill(T::zero());
            self.cell_states[i].fill(T::zero());
        }
    }

    fn forward(&mut self, input: &Array1<T>) -> Result<Array1<T>> {
        let mut current_input = input.clone();

        // Simplified LSTM forward pass
        for layer in 0..self.numlayers {
            let output = self.lstm_weights[layer].dot(&current_input) + &self.lstm_biases[layer];
            // In practice, this would implement proper LSTM cell computation
            self.hidden_states[layer] = output.mapv(|x| x.tanh());
            current_input = self.hidden_states[layer].clone();
        }

        // Output projection
        let output = self.output_weights.dot(&current_input) + self.output_bias.clone();
        Ok(output)
    }
}

impl<T: Float + Debug + Default + Send + Sync> ExperienceBuffer<T> {
    fn new(capacity: usize) -> Self {
        Self {
            states: VecDeque::new(),
            actions: VecDeque::new(),
            rewards: VecDeque::new(),
            next_states: VecDeque::new(),
            dones: VecDeque::new(),
            capacity,
        }
    }

    fn add_experience(
        &mut self,
        state: Array1<T>,
        action: usize,
        reward: T,
        next_state: Array1<T>,
        done: bool,
    ) {
        self.states.push_back(state);
        self.actions.push_back(action);
        self.rewards.push_back(reward);
        self.next_states.push_back(next_state);
        self.dones.push_back(done);

        // Remove oldest if at capacity
        if self.states.len() > self.capacity {
            self.states.pop_front();
            self.actions.pop_front();
            self.rewards.pop_front();
            self.next_states.pop_front();
            self.dones.pop_front();
        }
    }

    fn size(&self) -> usize {
        self.states.len()
    }
}

impl<T: Float + Debug + Default + Send + Sync> PolicyOptimizer<T> {
    fn new(_learningrate: T) -> Self {
        Self {
            _learningrate,
            momentum: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            velocity: HashMap::new(),
            gradient_clip_norm: scirs2_core::numeric::NumCast::from(1.0)
                .unwrap_or_else(|| T::zero()),
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> BaselinePredictor<T> {
    fn new() -> Self {
        Self {
            network_weights: vec![Array2::zeros((64, 64)), Array2::zeros((1, 64))],
            network_biases: vec![Array1::zeros(64), Array1::zeros(1)],
            optimizer: BaselineOptimizer::new(
                scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            ),
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> BaselineOptimizer<T> {
    fn new(_learningrate: T) -> Self {
        Self {
            _learningrate,
            momentum: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            velocity: Vec::new(),
        }
    }
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + std::fmt::Debug
            + scirs2_core::ndarray::ScalarOperand
            + std::iter::Sum,
    > DifferentiableSearch<T>
{
    pub fn new(
        num_operations: usize,
        num_edges: usize,
        temperature: f64,
        use_gumbel: bool,
    ) -> Self {
        Self {
            architecture_weights: Array3::zeros((num_edges, num_operations, 1)),
            weight_optimizer: WeightOptimizer::new(
                scirs2_core::numeric::NumCast::from(0.025).unwrap_or_else(|| T::zero()),
            ),
            temperature: scirs2_core::numeric::NumCast::from(temperature)
                .unwrap_or_else(|| T::zero()),
            gumbel_softmax: use_gumbel,
            continuous_relaxation: true,
            statistics: SearchStrategyStatistics::default(),
            discretization_strategy: DiscretizationStrategy::Progressive,
        }
    }

    fn gumbel_softmax_sample(&self, logits: &Array1<T>) -> Array1<T> {
        if !self.gumbel_softmax {
            return self.softmax(logits);
        }

        let gumbel_noise: Array1<T> = Array1::from_shape_fn(logits.len(), |_| {
            let u = scirs2_core::random::Random::default().random::<f64>();
            T::from(-(-u.ln()).ln()).unwrap()
        });

        let gumbel_logits = logits + &gumbel_noise;
        let scaled_logits = gumbel_logits / self.temperature;
        self.softmax(&scaled_logits)
    }

    fn softmax(&self, x: &Array1<T>) -> Array1<T> {
        let max_val = x
            .iter()
            .cloned()
            .fold(T::neg_infinity(), |a, b| if a > b { a } else { b });
        let exp_x = x.mapv(|xi| (xi - max_val).exp());
        let sum_exp = exp_x.sum();
        exp_x / sum_exp
    }

    fn discretize_architecture(&self, weights: &Array3<T>) -> OptimizerArchitecture<T> {
        use crate::architecture::OptimizerComponent;

        let mut components = Vec::new();

        for edge_idx in 0..weights.dim().0 {
            let edge_weights = weights.slice(s![edge_idx, .., 0]);

            let selected_op_idx = match self.discretization_strategy {
                DiscretizationStrategy::Greedy => edge_weights
                    .iter()
                    .enumerate()
                    .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                    .map(|(idx, _)| idx)
                    .unwrap_or(0),
                DiscretizationStrategy::Sampling => {
                    let probs = self.softmax(&edge_weights.to_owned());
                    let rand_val = scirs2_core::random::Random::default().random::<f64>();
                    let mut cumsum = 0.0;

                    let mut selected_idx = 0;
                    for (idx, prob) in probs.iter().enumerate() {
                        cumsum += prob.to_f64().unwrap_or(0.0);
                        if cumsum >= rand_val {
                            selected_idx = idx;
                            break;
                        }
                    }
                    selected_idx
                }
                DiscretizationStrategy::Threshold => {
                    let threshold =
                        scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero());
                    edge_weights
                        .iter()
                        .enumerate()
                        .find(|(_, &weight)| weight > threshold)
                        .map(|(idx, _)| idx)
                        .unwrap_or(0)
                }
                DiscretizationStrategy::Progressive => {
                    // Gradually sharpen the distribution
                    let sharpened = edge_weights.mapv(|x| {
                        x.powf(
                            scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero()),
                        )
                    });
                    sharpened
                        .iter()
                        .enumerate()
                        .max_by(|(_, a), (_, b)| {
                            a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal)
                        })
                        .map(|(idx, _)| idx)
                        .unwrap_or(0)
                }
            };

            // Map operation index to component type (simplified)
            let component_type = match selected_op_idx {
                0 => ComponentType::SGD,
                1 => ComponentType::Adam,
                2 => ComponentType::AdaGrad,
                3 => ComponentType::RMSprop,
                _ => ComponentType::Adam,
            };

            let mut hyperparameters = HashMap::new();
            hyperparameters.insert("_learningrate".to_string(), 0.001f64);

            components.push(OptimizerComponent {
                id: format!("comp_{}", edge_idx),
                component_type,
                hyperparameters,
                enabled: true,
                position: ComponentPosition {
                    layer: 0,
                    index: edge_idx as u32,
                    x: 0.0,
                    y: 0.0,
                },
            });
        }

        OptimizerArchitecture {
            components: components
                .iter()
                .map(|c| c.component_type.to_string())
                .collect(),
            parameters: components
                .iter()
                .enumerate()
                .flat_map(|(i, c)| {
                    c.hyperparameters.iter().map(move |(k, v)| {
                        (
                            format!("{}_{}", i, k),
                            scirs2_core::numeric::NumCast::from(*v).unwrap_or_else(|| T::zero()),
                        )
                    })
                })
                .collect(),
            hyperparameters: components
                .iter()
                .enumerate()
                .flat_map(|(i, c)| {
                    c.hyperparameters.iter().map(move |(k, v)| {
                        (
                            format!("{}_{}", i, k),
                            scirs2_core::numeric::NumCast::from(*v).unwrap_or_else(|| T::zero()),
                        )
                    })
                })
                .collect(),
            connections: Vec::new(),
            metadata: HashMap::new(),
            architecture_id: format!("arch_{}", Random::default().random::<u64>()),
        }
    }
}

impl<
        T: Float
            + Default
            + Clone
            + Send
            + Sync
            + std::fmt::Debug
            + std::iter::Sum
            + scirs2_core::ndarray::ScalarOperand,
    > SearchStrategy<T> for DifferentiableSearch<T>
{
    fn initialize(&mut self, _searchspace: &SearchSpaceConfig) -> Result<()> {
        // Initialize architecture weights with small random values
        self.architecture_weights =
            Array3::from_shape_fn(self.architecture_weights.raw_dim(), |_| {
                T::from(scirs2_core::random::Random::default().random::<f64>() * 0.1 - 0.05)
                    .unwrap()
            });
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        _search_space: &SearchSpaceConfig,
        _history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        if self.continuous_relaxation {
            // Generate continuous relaxation of architecture
            let mut sampled_weights = Array3::zeros(self.architecture_weights.raw_dim());

            for edge_idx in 0..self.architecture_weights.dim().0 {
                let edge_weights = self.architecture_weights.slice(s![edge_idx, .., 0]);
                let sampled = self.gumbel_softmax_sample(&edge_weights.to_owned());

                for (op_idx, &weight) in sampled.iter().enumerate() {
                    sampled_weights[[edge_idx, op_idx, 0]] = weight;
                }
            }

            self.statistics.total_architectures_generated += 1;
            Ok(self.discretize_architecture(&sampled_weights))
        } else {
            // Direct discretization
            self.statistics.total_architectures_generated += 1;
            Ok(self.discretize_architecture(&self.architecture_weights))
        }
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        if results.is_empty() {
            return Ok(());
        }

        // Compute gradients based on performance
        let performances: Vec<T> = results
            .iter()
            .filter_map(|r| {
                r.evaluation_results
                    .metric_scores
                    .get(&EvaluationMetric::FinalPerformance)
            })
            .cloned()
            .collect();

        if !performances.is_empty() {
            // Update statistics
            self.statistics.best_performance = performances
                .iter()
                .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .cloned()
                .unwrap_or(T::zero());

            let sum: T = performances.iter().cloned().sum();
            self.statistics.average_performance = sum / T::from(performances.len()).unwrap();

            // Compute gradient estimate (simplified REINFORCE-style)
            let baseline = self.statistics.average_performance;
            let reward = performances[0] - baseline;

            // Update architecture weights
            let _learningrate =
                scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero());
            self.architecture_weights = &self.architecture_weights
                + &(Array3::ones(self.architecture_weights.raw_dim()) * _learningrate * reward);

            // Anneal temperature
            self.temperature = self.temperature
                * scirs2_core::numeric::NumCast::from(0.999).unwrap_or_else(|| T::zero());
        }

        Ok(())
    }

    fn name(&self) -> &str {
        "DifferentiableSearch"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate = self.temperature;
        stats.exploitation_rate = T::one() - self.temperature;
        stats
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    BayesianOptimization<T>
{
    pub fn new(
        _kerneltype: KernelType,
        acquisition_type: AcquisitionType,
        exploration_factor: f64,
    ) -> Self {
        Self {
            gaussian_process: GaussianProcess::new(_kerneltype),
            acquisition_function: AcquisitionFunction::new(
                acquisition_type,
                scirs2_core::numeric::NumCast::from(exploration_factor)
                    .unwrap_or_else(|| T::zero()),
            ),
            observed_architectures: Vec::new(),
            observed_performances: Vec::new(),
            kernel: GPKernel::new(_kerneltype),
            statistics: SearchStrategyStatistics::default(),
            exploration_factor: scirs2_core::numeric::NumCast::from(exploration_factor)
                .unwrap_or_else(|| T::zero()),
        }
    }

    fn encode_architecture(&self, architecture: &OptimizerArchitecture<T>) -> Array1<T> {
        // Simple encoding: component types and hyperparameter values
        let mut encoding = Vec::new();

        for component_str in &architecture.components {
            // Encode component type as one-hot (use a simple hash of the string as placeholder)
            let hash = component_str
                .bytes()
                .fold(0u8, |acc, b| acc.wrapping_add(b));
            encoding.push(scirs2_core::numeric::NumCast::from(hash).unwrap_or_else(|| T::zero()));
        }

        // Encode hyperparameters from the architecture's hyperparameters map
        for &value in architecture.hyperparameters.values() {
            encoding.push(value);
        }

        // Pad to fixed size
        encoding.resize(64, T::zero());
        Array1::from_vec(encoding)
    }

    fn fit_gp(&mut self) -> Result<()> {
        if self.observed_architectures.len() < 2 {
            return Ok(());
        }

        // Encode all observed architectures
        let encoded_archs: Vec<Array1<T>> = self
            .observed_architectures
            .iter()
            .map(|arch| self.encode_architecture(arch))
            .collect();

        // Fit Gaussian Process
        self.gaussian_process
            .fit(&encoded_archs, &self.observed_performances)?;

        Ok(())
    }

    fn suggest_next_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
    ) -> Result<OptimizerArchitecture<T>> {
        if self.observed_architectures.len() < 5 {
            // Use random search for initial points
            let mut random_search = RandomSearch::<T>::new(Some(42));
            random_search.initialize(searchspace)?;
            return random_search.generate_architecture(searchspace, &VecDeque::new());
        }

        // Generate candidate architectures
        let num_candidates = 100;
        let mut candidates = Vec::new();
        let mut random_search = RandomSearch::<T>::new(Some(42));
        random_search.initialize(searchspace)?;

        for _ in 0..num_candidates {
            candidates.push(random_search.generate_architecture(searchspace, &VecDeque::new())?);
        }

        // Evaluate acquisition function for each candidate
        let mut best_architecture = candidates[0].clone();
        let mut best_acquisition = T::neg_infinity();

        for candidate in candidates {
            let encoded = self.encode_architecture(&candidate);
            let (mean, variance) = self.gaussian_process.predict(&encoded)?;
            let acquisition_value = self.acquisition_function.evaluate(mean, variance);

            if acquisition_value > best_acquisition {
                best_acquisition = acquisition_value;
                best_architecture = candidate;
            }
        }

        Ok(best_architecture)
    }
}

impl<T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + std::iter::Sum>
    SearchStrategy<T> for BayesianOptimization<T>
{
    fn initialize(&mut self, _searchspace: &SearchSpaceConfig) -> Result<()> {
        self.observed_architectures.clear();
        self.observed_performances.clear();
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        _history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        let architecture = self.suggest_next_architecture(searchspace)?;
        self.statistics.total_architectures_generated += 1;
        Ok(architecture)
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        for result in results {
            if let Some(&performance) = result
                .evaluation_results
                .metric_scores
                .get(&EvaluationMetric::FinalPerformance)
            {
                self.observed_architectures
                    .push(result.architecture.clone());
                self.observed_performances.push(performance);

                // Update statistics
                if performance > self.statistics.best_performance {
                    self.statistics.best_performance = performance;
                }

                // Refit GP
                self.fit_gp()?;
            }
        }

        // Update average performance
        if !self.observed_performances.is_empty() {
            let sum: T = self.observed_performances.iter().cloned().sum();
            self.statistics.average_performance =
                sum / T::from(self.observed_performances.len()).unwrap();
        }

        Ok(())
    }

    fn name(&self) -> &str {
        "BayesianOptimization"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate = self.exploration_factor;
        stats.exploitation_rate = T::one() - self.exploration_factor;
        stats
    }
}

// Implementation stubs for supporting components
impl<T: Float + Debug + Default + Send + Sync> WeightOptimizer<T> {
    fn new(_learningrate: T) -> Self {
        Self {
            _learningrate,
            momentum: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            weight_decay: scirs2_core::numeric::NumCast::from(1e-4).unwrap_or_else(|| T::zero()),
            velocity: Array3::zeros((0, 0, 0)),
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> GaussianProcess<T> {
    fn new(_kerneltype: KernelType) -> Self {
        Self {
            kernel_matrix: Array2::zeros((0, 0)),
            inverse_kernel: Array2::zeros((0, 0)),
            noise_variance: scirs2_core::numeric::NumCast::from(1e-6).unwrap_or_else(|| T::zero()),
            length_scales: Array1::ones(1),
            signal_variance: T::one(),
        }
    }

    fn fit(&mut self, x: &[Array1<T>], y: &[T]) -> Result<()> {
        // Simplified GP fitting
        Ok(())
    }

    fn predict(&self, x: &Array1<T>) -> Result<(T, T)> {
        // Simplified prediction - return mean and variance
        Ok((
            scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero()),
        ))
    }
}

impl<T: Float + Debug + Default + Send + Sync> AcquisitionFunction<T> {
    fn new(function_type: AcquisitionType, explorationweight: T) -> Self {
        Self {
            function_type,
            explorationweight,
            current_best: T::zero(),
        }
    }

    fn evaluate(&self, mean: T, variance: T) -> T {
        match self.function_type {
            AcquisitionType::UCB => mean + self.explorationweight * variance.sqrt(),
            AcquisitionType::EI => {
                // Simplified Expected Improvement
                let std_dev = variance.sqrt();
                if std_dev > scirs2_core::numeric::NumCast::from(1e-8).unwrap_or_else(|| T::zero())
                {
                    let z = (mean - self.current_best) / std_dev;
                    // Simplified calculation without proper CDF/PDF
                    z * std_dev
                } else {
                    T::zero()
                }
            }
            AcquisitionType::PI => {
                // Simplified Probability of Improvement
                if variance > scirs2_core::numeric::NumCast::from(1e-8).unwrap_or_else(|| T::zero())
                {
                    let z = (mean - self.current_best) / variance.sqrt();
                    // Simplified - would need proper CDF
                    if z > T::zero() {
                        T::one()
                    } else {
                        T::zero()
                    }
                } else {
                    T::zero()
                }
            }
            AcquisitionType::Thompson => {
                // Thompson sampling - sample from posterior
                mean + variance.sqrt()
                    * T::from(scirs2_core::random::Random::default().random::<f64>()).unwrap()
            }
            AcquisitionType::InfoGain => {
                // Information gain - simplified as entropy
                variance.ln()
            }
        }
    }
}

impl<T: Float + Debug + Default + Send + Sync> GPKernel<T> {
    fn new(_kerneltype: KernelType) -> Self {
        Self {
            _kerneltype,
            hyperparameters: Array1::ones(2), // length_scale and signal_variance
        }
    }
}

impl<
        T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + 'static + std::iter::Sum,
    > NeuralPredictorSearch<T>
{
    pub fn new(
        predictor_architecture: Vec<usize>,
        _embeddingdim: usize,
        confidence_threshold: f64,
    ) -> Self {
        Self {
            predictor_network: PredictorNetwork::new(predictor_architecture),
            architecture_encoder: ArchitectureEncoder::new(_embeddingdim),
            search_optimizer: SearchOptimizer::new(
                SearchOptimizerType::Adam,
                scirs2_core::numeric::NumCast::from(0.001).unwrap_or_else(|| T::zero()),
            ),
            confidence_threshold: scirs2_core::numeric::NumCast::from(confidence_threshold)
                .unwrap_or_else(|| T::zero()),
            statistics: SearchStrategyStatistics::default(),
            uncertainty_sampling: true,
        }
    }

    fn predict_performance(&self, architecture: &OptimizerArchitecture<T>) -> Result<(T, T)> {
        // Encode architecture
        let encoded = self.architecture_encoder.encode(architecture)?;

        // Forward pass through predictor network
        let (prediction, uncertainty) =
            self.predictor_network.forward_with_uncertainty(&encoded)?;

        Ok((prediction, uncertainty))
    }

    fn train_predictor(
        &mut self,
        architectures: &[OptimizerArchitecture<T>],
        performances: &[T],
    ) -> Result<()> {
        if architectures.len() != performances.len() || architectures.is_empty() {
            return Ok(());
        }

        // Encode all architectures
        let encoded_archs: std::result::Result<Vec<_>, _> = architectures
            .iter()
            .map(|arch| self.architecture_encoder.encode(arch))
            .collect();
        let encoded_archs = encoded_archs?;

        // Train predictor network
        for (encoded_arch, &target_performance) in encoded_archs.iter().zip(performances.iter()) {
            let (prediction, _) = self
                .predictor_network
                .forward_with_uncertainty(encoded_arch)?;
            let _loss = (prediction - target_performance)
                .powf(scirs2_core::numeric::NumCast::from(2.0).unwrap_or_else(|| T::zero()));

            // Simplified gradient update
            self.predictor_network.backward_update(
                encoded_arch,
                target_performance,
                &mut self.search_optimizer,
            )?;
        }

        Ok(())
    }

    fn generate_candidate_with_uncertainty(
        &mut self,
        searchspace: &SearchSpaceConfig,
    ) -> Result<OptimizerArchitecture<T>> {
        // Generate multiple candidates and select based on uncertainty
        let num_candidates = 50;
        let mut candidates = Vec::new();
        let mut random_search = RandomSearch::<T>::new(None);
        random_search.initialize(searchspace)?;

        for _ in 0..num_candidates {
            candidates.push(random_search.generate_architecture(searchspace, &VecDeque::new())?);
        }

        // Select candidate with highest uncertainty (for exploration) or highest predicted performance (for exploitation)
        let mut best_candidate = candidates[0].clone();
        let mut best_score = T::neg_infinity();

        for candidate in candidates {
            let (predicted_perf, uncertainty) = self.predict_performance(&candidate)?;

            // Combine prediction and uncertainty for selection
            let score = if self.uncertainty_sampling {
                predicted_perf + uncertainty // UCB-style selection
            } else {
                predicted_perf // Pure exploitation
            };

            if score > best_score {
                best_score = score;
                best_candidate = candidate;
            }
        }

        Ok(best_candidate)
    }
}

impl<
        T: Float + Debug + Default + Clone + Send + Sync + std::fmt::Debug + 'static + std::iter::Sum,
    > SearchStrategy<T> for NeuralPredictorSearch<T>
{
    fn initialize(&mut self, _searchspace: &SearchSpaceConfig) -> Result<()> {
        // Initialize predictor network with random weights
        self.predictor_network.initialize()?;
        Ok(())
    }

    fn generate_architecture(
        &mut self,
        searchspace: &SearchSpaceConfig,
        history: &VecDeque<SearchResult<T>>,
    ) -> Result<OptimizerArchitecture<T>> {
        // Train predictor if enough data is available
        if history.len() > 10 {
            let architectures: Vec<_> = history.iter().map(|r| r.architecture.clone()).collect();
            let performances: Vec<_> = history
                .iter()
                .filter_map(|r| {
                    r.evaluation_results
                        .metric_scores
                        .get(&EvaluationMetric::FinalPerformance)
                })
                .cloned()
                .collect();

            if architectures.len() == performances.len() {
                self.train_predictor(&architectures, &performances)?;
            }
        }

        // Generate candidate based on predictor
        let architecture = if history.len() > 5 {
            self.generate_candidate_with_uncertainty(searchspace)?
        } else {
            // Use random search for initial exploration
            let mut random_search = RandomSearch::<T>::new(None);
            random_search.initialize(searchspace)?;
            random_search.generate_architecture(searchspace, history)?
        };

        self.statistics.total_architectures_generated += 1;
        Ok(architecture)
    }

    fn update_with_results(&mut self, results: &[SearchResult<T>]) -> Result<()> {
        if results.is_empty() {
            return Ok(());
        }

        // Extract architectures and performances
        let architectures: Vec<_> = results.iter().map(|r| r.architecture.clone()).collect();
        let performances: Vec<_> = results
            .iter()
            .filter_map(|r| {
                r.evaluation_results
                    .metric_scores
                    .get(&EvaluationMetric::FinalPerformance)
            })
            .cloned()
            .collect();

        if architectures.len() == performances.len() && !performances.is_empty() {
            // Update statistics
            self.statistics.best_performance = performances
                .iter()
                .max_by(|a, b| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
                .cloned()
                .unwrap_or(T::zero());

            let sum: T = performances.iter().cloned().sum();
            self.statistics.average_performance = sum / T::from(performances.len()).unwrap();

            // Train predictor with new data
            self.train_predictor(&architectures, &performances)?;
        }

        Ok(())
    }

    fn name(&self) -> &str {
        "NeuralPredictorSearch"
    }

    fn get_statistics(&self) -> SearchStrategyStatistics<T> {
        let mut stats = self.statistics.clone();
        stats.exploration_rate = if self.uncertainty_sampling {
            scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero())
        } else {
            scirs2_core::numeric::NumCast::from(0.3).unwrap_or_else(|| T::zero())
        };
        stats.exploitation_rate = T::one() - stats.exploration_rate;
        stats
    }
}

// Implementation for supporting components
impl<T: Float + Debug + Default + Clone + 'static + std::iter::Sum + Send + Sync>
    PredictorNetwork<T>
{
    fn new(architecture: Vec<usize>) -> Self {
        let mut layers = Vec::new();
        for i in 0..architecture.len() - 1 {
            layers.push(PredictorLayer::new(architecture[i], architecture[i + 1]));
        }

        Self {
            layers,
            dropout_rates: vec![
                scirs2_core::numeric::NumCast::from(0.1)
                    .unwrap_or_else(|| T::zero());
                architecture.len() - 1
            ],
            architecture,
        }
    }

    fn initialize(&mut self) -> Result<()> {
        for layer in &mut self.layers {
            layer.initialize()?;
        }
        Ok(())
    }

    fn forward_with_uncertainty(&self, input: &Array1<T>) -> Result<(T, T)> {
        let mut current = input.clone();

        // Forward pass through all layers
        for (i, layer) in self.layers.iter().enumerate() {
            current = layer.forward(&current)?;

            // Apply dropout for uncertainty estimation (Monte Carlo dropout)
            if i < self.dropout_rates.len() {
                current = self.apply_dropout(&current, self.dropout_rates[i]);
            }
        }

        // For simplicity, return the first output as prediction and a simple uncertainty estimate
        let prediction = current[0];
        let uncertainty = current.iter().map(|&x| x * x).sum::<T>().sqrt()
            * scirs2_core::numeric::NumCast::from(0.1).unwrap_or_else(|| T::zero());

        Ok((prediction, uncertainty))
    }

    fn backward_update(
        &mut self,
        _input: &Array1<T>,
        _target: T,
        _optimizer: &mut SearchOptimizer<T>,
    ) -> Result<()> {
        // Simplified backward pass - in practice would implement proper backpropagation
        Ok(())
    }

    fn apply_dropout(&self, input: &Array1<T>, dropoutrate: T) -> Array1<T> {
        input.mapv(|x| {
            if scirs2_core::random::Random::default().random::<f64>()
                < dropoutrate.to_f64().unwrap_or(0.0)
            {
                T::zero()
            } else {
                x / (T::one() - dropoutrate)
            }
        })
    }
}

impl<T: Float + Debug + Default + Clone + 'static + Send + Sync> PredictorLayer<T> {
    fn new(input_size: usize, outputsize: usize) -> Self {
        Self {
            weights: Array2::zeros((outputsize, input_size)),
            bias: Array1::zeros(outputsize),
            activation: ActivationFunction::ReLU,
        }
    }

    fn initialize(&mut self) -> Result<()> {
        // Xavier initialization
        let fan_in = self.weights.ncols() as f64;
        let fan_out = self.weights.nrows() as f64;
        let scale = (6.0 / (fan_in + fan_out)).sqrt();

        self.weights = Array2::from_shape_fn(self.weights.raw_dim(), |_| {
            T::from(scirs2_core::random::Random::default().random::<f64>() * scale * 2.0 - scale)
                .unwrap()
        });

        Ok(())
    }

    fn forward(&self, input: &Array1<T>) -> Result<Array1<T>> {
        let linear_output = self.weights.dot(input) + &self.bias;
        Ok(self.apply_activation(&linear_output))
    }

    fn apply_activation(&self, x: &Array1<T>) -> Array1<T> {
        match self.activation {
            ActivationFunction::ReLU => x.mapv(|xi| if xi > T::zero() { xi } else { T::zero() }),
            ActivationFunction::GELU => x.mapv(|xi| {
                let x_f64 = xi.to_f64().unwrap_or(0.0);
                let gelu_val = 0.5
                    * x_f64
                    * (1.0 + (x_f64 * 0.7978845608 * (1.0 + 0.044715 * x_f64 * x_f64)).tanh());
                scirs2_core::numeric::NumCast::from(gelu_val).unwrap_or_else(|| T::zero())
            }),
            ActivationFunction::Swish => x.mapv(|xi| {
                let sigmoid = T::one() / (T::one() + (-xi).exp());
                xi * sigmoid
            }),
            ActivationFunction::Tanh => x.mapv(|xi| xi.tanh()),
            ActivationFunction::Sigmoid => x.mapv(|xi| T::one() / (T::one() + (-xi).exp())),
        }
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> ArchitectureEncoder<T> {
    fn new(_embeddingdim: usize) -> Self {
        Self {
            encoding_weights: Array2::zeros((_embeddingdim, 64)), // Assume max 64 components
            _embeddingdim,
            max_components: 64,
        }
    }

    fn encode(&self, architecture: &OptimizerArchitecture<T>) -> Result<Array1<T>> {
        // Simple encoding: one-hot component types + hyperparameters
        let mut encoding = Vec::new();

        for (i, component) in architecture.components.iter().enumerate() {
            if i >= self.max_components {
                break;
            }

            // Encode component type (use string hash as placeholder)
            let hash = component.bytes().fold(0u8, |acc, b| acc.wrapping_add(b));
            encoding.push(scirs2_core::numeric::NumCast::from(hash).unwrap_or_else(|| T::zero()));
        }

        // Pad to fixed size
        encoding.resize(self._embeddingdim, T::zero());
        Ok(Array1::from_vec(encoding))
    }
}

impl<T: Float + Debug + Send + Sync + 'static + Default + Clone> SearchOptimizer<T> {
    fn new(optimizer_type: SearchOptimizerType, learningrate: T) -> Self {
        Self {
            optimizer_type,
            _learningrate: learningrate,
            momentum: scirs2_core::numeric::NumCast::from(0.9).unwrap_or_else(|| T::zero()),
            parameters: HashMap::new(),
        }
    }

    fn update_parameters(&mut self, gradients: &HashMap<String, Array1<T>>) -> Result<()> {
        // Simplified parameter update
        Ok(())
    }
}

/// Convert ComponentType to u8 for encoding
fn component_type_to_u8(_componenttype: &ComponentType) -> u8 {
    match _componenttype {
        ComponentType::SGD => 0,
        ComponentType::Adam => 1,
        ComponentType::AdaGrad => 2,
        ComponentType::RMSprop => 3,
        ComponentType::AdamW => 4,
        ComponentType::LAMB => 5,
        ComponentType::LARS => 6,
        ComponentType::Lion => 7,
        ComponentType::RAdam => 8,
        ComponentType::Lookahead => 9,
        ComponentType::SAM => 10,
        ComponentType::LBFGS => 11,
        ComponentType::SparseAdam => 12,
        ComponentType::GroupedAdam => 13,
        ComponentType::MAML => 14,
        ComponentType::Reptile => 15,
        ComponentType::MetaSGD => 16,
        ComponentType::ConstantLR => 17,
        ComponentType::ExponentialLR => 18,
        ComponentType::StepLR => 19,
        ComponentType::CosineAnnealingLR => 20,
        ComponentType::OneCycleLR => 21,
        ComponentType::CyclicLR => 22,
        ComponentType::L1Regularizer => 23,
        ComponentType::L2Regularizer => 24,
        ComponentType::ElasticNetRegularizer => 25,
        ComponentType::DropoutRegularizer => 26,
        ComponentType::GradientClipping => 27,
        ComponentType::WeightDecay => 28,
        ComponentType::AdaptiveLR => 29,
        ComponentType::AdaptiveMomentum => 30,
        ComponentType::AdaptiveRegularization => 31,
        ComponentType::LSTMOptimizer => 32,
        ComponentType::TransformerOptimizer => 33,
        ComponentType::AttentionOptimizer => 34,
        ComponentType::AdaDelta => 35,
        ComponentType::Momentum => 36,
        ComponentType::Nesterov => 37,
        ComponentType::LRScheduler => 38,
        ComponentType::BatchNorm => 39,
        ComponentType::Dropout => 40,
        ComponentType::Custom => 255, // Use highest value for custom types
    }
}

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

    #[test]
    fn test_random_search_creation() {
        let search = RandomSearch::<f64>::new(Some(42));
        assert_eq!(search.name(), "RandomSearch");
    }

    #[test]
    fn test_evolutionary_search_creation() {
        let search = EvolutionarySearch::<f64>::new(50, 0.1, 0.8, 3);
        assert_eq!(search.name(), "EvolutionarySearch");
        assert_eq!(search.population_size, 50);
    }

    #[test]
    fn test_rl_search_creation() {
        let search = ReinforcementLearningSearch::<f64>::new(256, 2, 0.001);
        assert_eq!(search.name(), "ReinforcementLearningSearch");
    }
}
