//! Self-Learning Models for Industry Applications and Exotic Experiments
//!
//! Integrates RuVector's GNN, Attention, and Graph crates for building
//! adaptive neural architectures with reinforcement learning,
//! online learning, and meta-learning capabilities.

use anyhow::Result;
use rand::Rng;
use std::collections::HashMap;
use std::path::PathBuf;
use std::time::Instant;

// Import RuVector crates
use ruvector_attention::{
    traits::Attention, HyperbolicAttention, HyperbolicAttentionConfig, MoEAttention, MoEConfig,
    MultiHeadAttention, ScaledDotProductAttention,
};
use ruvector_gnn::{
    ewc::ElasticWeightConsolidation,
    layer::RuvectorLayer,
    replay::ReplayBuffer,
    scheduler::{LearningRateScheduler, SchedulerType},
    training::{Optimizer, OptimizerType},
};

/// Self-learning model configuration
#[derive(Debug, Clone)]
pub struct SelfLearningConfig {
    pub name: String,
    pub industry: Industry,
    pub architecture: Architecture,
    pub learning_rate: f32,
    pub adaptation_rate: f32,
    pub memory_size: usize,
    pub exploration_rate: f32,
    pub meta_learning: bool,
    pub ewc_lambda: f32,
}

#[derive(Debug, Clone, Copy, serde::Serialize)]
pub enum Industry {
    Healthcare,
    Finance,
    Autonomous,
    QuantumInspired,
    Neuromorphic,
    Hyperdimensional,
    ExoticResearch,
}

#[derive(Debug, Clone, Copy, serde::Serialize)]
pub enum Architecture {
    TransformerRL,         // Transformer with reinforcement learning
    GNNAdaptive,           // Graph Neural Network with adaptation
    HyperbolicAttention,   // Hyperbolic space attention
    MixtureOfExperts,      // Sparse MoE architecture
    SpikingNN,             // Spiking neural network
    HopfieldModern,        // Modern Hopfield network
    DifferentialEvolution, // Evolutionary self-improvement
    QuantumVariational,    // Quantum-inspired variational
}

/// Training metrics
#[derive(Debug, Clone, serde::Serialize)]
pub struct TrainingMetrics {
    pub epoch: usize,
    pub loss: f32,
    pub accuracy: f32,
    pub learning_rate: f32,
    pub adaptation_progress: f32,
}

/// Healthcare/Medical Diagnostics Self-Learning Model using RuVector
pub struct HealthcareModel {
    pub config: SelfLearningConfig,
    attention: MultiHeadAttention,
    optimizer: Optimizer,
    ewc: ElasticWeightConsolidation,
    scheduler: LearningRateScheduler,
    replay_buffer: ReplayBuffer,
    symptom_embeddings: HashMap<String, Vec<f32>>,
    diagnosis_patterns: Vec<(Vec<f32>, String, f32)>,
    total_episodes: usize,
    accuracy_history: Vec<f32>,
    dim: usize,
}

impl HealthcareModel {
    pub fn new(input_dim: usize, hidden_dim: usize, _num_conditions: usize) -> Self {
        // Initialize multi-head attention (dim must be divisible by num_heads)
        let attention = MultiHeadAttention::new(hidden_dim, 8);

        // Initialize optimizer with Adam
        let optimizer = Optimizer::new(OptimizerType::Adam {
            learning_rate: 0.001,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        });

        // Initialize EWC for continual learning
        let ewc = ElasticWeightConsolidation::new(0.4);

        // Create learning rate scheduler
        let scheduler = LearningRateScheduler::new(
            SchedulerType::CosineAnnealing {
                t_max: 100,
                eta_min: 1e-6,
            },
            0.001,
        );

        // Replay buffer for experience
        let replay_buffer = ReplayBuffer::new(10000);

        Self {
            config: SelfLearningConfig {
                name: "Healthcare Diagnostics".to_string(),
                industry: Industry::Healthcare,
                architecture: Architecture::TransformerRL,
                learning_rate: 0.001,
                adaptation_rate: 0.1,
                memory_size: 10000,
                exploration_rate: 0.1,
                meta_learning: true,
                ewc_lambda: 0.4,
            },
            attention,
            optimizer,
            ewc,
            scheduler,
            replay_buffer,
            symptom_embeddings: HashMap::new(),
            diagnosis_patterns: Vec::new(),
            total_episodes: 0,
            accuracy_history: Vec::new(),
            dim: hidden_dim,
        }
    }

    pub fn encode_symptoms(&self, symptoms: &[f32]) -> Vec<f32> {
        // Create keys and values for self-attention
        let keys = vec![symptoms.to_vec()];
        let values = vec![symptoms.to_vec()];

        let keys_refs: Vec<&[f32]> = keys.iter().map(|k| k.as_slice()).collect();
        let values_refs: Vec<&[f32]> = values.iter().map(|v| v.as_slice()).collect();

        self.attention
            .compute(symptoms, &keys_refs, &values_refs)
            .unwrap_or_else(|_| symptoms.to_vec())
    }

    pub fn train_episode(&mut self, symptoms: Vec<f32>, diagnosis: &str, correct: bool) -> f32 {
        let embedding = self.encode_symptoms(&symptoms);
        let confidence = if correct { 1.0 } else { 0.0 };

        self.diagnosis_patterns
            .push((embedding, diagnosis.to_string(), confidence));
        self.total_episodes += 1;

        // Update accuracy history
        self.accuracy_history.push(confidence);
        if self.accuracy_history.len() > 100 {
            self.accuracy_history.remove(0);
        }

        // Return recent accuracy
        self.accuracy_history.iter().sum::<f32>() / self.accuracy_history.len() as f32
    }
}

/// Financial Trading/Risk Model using Hyperbolic Attention
pub struct FinancialModel {
    pub config: SelfLearningConfig,
    attention: HyperbolicAttention,
    optimizer: Optimizer,
    replay_buffer: ReplayBuffer,
    market_patterns: Vec<(Vec<f32>, f32)>,
    portfolio_history: Vec<f32>,
    dim: usize,
}

impl FinancialModel {
    pub fn new(input_dim: usize, hidden_dim: usize) -> Self {
        let attention = HyperbolicAttention::new(HyperbolicAttentionConfig {
            dim: hidden_dim,
            curvature: -1.0,
            adaptive_curvature: true,
            temperature: 0.5,
            frechet_max_iter: 50,
            frechet_tol: 1e-5,
        });

        let optimizer = Optimizer::new(OptimizerType::Adam {
            learning_rate: 0.0005,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        });

        let replay_buffer = ReplayBuffer::new(50000);

        Self {
            config: SelfLearningConfig {
                name: "Financial Trading".to_string(),
                industry: Industry::Finance,
                architecture: Architecture::HyperbolicAttention,
                learning_rate: 0.0005,
                adaptation_rate: 0.05,
                memory_size: 50000,
                exploration_rate: 0.15,
                meta_learning: true,
                ewc_lambda: 0.3,
            },
            attention,
            optimizer,
            replay_buffer,
            market_patterns: Vec::new(),
            portfolio_history: Vec::new(),
            dim: hidden_dim,
        }
    }

    pub fn analyze_market(&self, market_data: &[f32]) -> Vec<f32> {
        let keys = vec![market_data.to_vec()];
        let values = vec![market_data.to_vec()];

        let keys_refs: Vec<&[f32]> = keys.iter().map(|k| k.as_slice()).collect();
        let values_refs: Vec<&[f32]> = values.iter().map(|v| v.as_slice()).collect();

        self.attention
            .compute(market_data, &keys_refs, &values_refs)
            .unwrap_or_else(|_| market_data.to_vec())
    }

    pub fn train_step(&mut self, market_data: Vec<f32>, return_pct: f32) -> f32 {
        let embedding = self.analyze_market(&market_data);
        self.market_patterns.push((embedding, return_pct));
        self.portfolio_history.push(return_pct);

        // Calculate Sharpe ratio approximation
        if self.portfolio_history.len() >= 2 {
            let mean: f32 =
                self.portfolio_history.iter().sum::<f32>() / self.portfolio_history.len() as f32;
            let variance: f32 = self
                .portfolio_history
                .iter()
                .map(|r| (r - mean).powi(2))
                .sum::<f32>()
                / self.portfolio_history.len() as f32;
            mean / (variance.sqrt() + 1e-6)
        } else {
            0.0
        }
    }
}

/// Autonomous Systems Model using GNN Layer
pub struct AutonomousModel {
    pub config: SelfLearningConfig,
    gnn_layer: RuvectorLayer,
    optimizer: Optimizer,
    ewc: ElasticWeightConsolidation,
    sensor_history: Vec<Vec<f32>>,
    action_history: Vec<usize>,
}

impl AutonomousModel {
    pub fn new(input_dim: usize, hidden_dim: usize, _output_dim: usize) -> Self {
        let gnn_layer = RuvectorLayer::new(input_dim, hidden_dim, 8, 0.1);

        let optimizer = Optimizer::new(OptimizerType::Adam {
            learning_rate: 0.001,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        });

        let ewc = ElasticWeightConsolidation::new(0.5);

        Self {
            config: SelfLearningConfig {
                name: "Autonomous Systems".to_string(),
                industry: Industry::Autonomous,
                architecture: Architecture::GNNAdaptive,
                learning_rate: 0.001,
                adaptation_rate: 0.2,
                memory_size: 20000,
                exploration_rate: 0.2,
                meta_learning: true,
                ewc_lambda: 0.5,
            },
            gnn_layer,
            optimizer,
            ewc,
            sensor_history: Vec::new(),
            action_history: Vec::new(),
        }
    }

    pub fn process_sensors(&self, sensors: &[f32]) -> Vec<f32> {
        // GNN forward pass with empty neighbor list
        self.gnn_layer.forward(sensors, &[], &[])
    }

    pub fn train_step(&mut self, sensors: Vec<f32>, action: usize, reward: f32) -> f32 {
        let embedding = self.process_sensors(&sensors);
        self.sensor_history.push(embedding);
        self.action_history.push(action);

        // Return reward as training signal
        reward
    }
}

/// Mixture of Experts Model for multi-domain tasks
pub struct MoEModel {
    pub config: SelfLearningConfig,
    moe: MoEAttention,
    optimizer: Optimizer,
    replay_buffer: ReplayBuffer,
    expert_usage: Vec<f32>,
    dim: usize,
}

impl MoEModel {
    pub fn new(input_dim: usize, num_experts: usize) -> Self {
        let moe = MoEAttention::new(MoEConfig {
            dim: input_dim,
            num_experts,
            top_k: 2,
            expert_capacity: 1.25,
            jitter_noise: 0.0,
        });

        let optimizer = Optimizer::new(OptimizerType::Adam {
            learning_rate: 0.001,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        });

        let replay_buffer = ReplayBuffer::new(10000);

        Self {
            config: SelfLearningConfig {
                name: "Mixture of Experts".to_string(),
                industry: Industry::ExoticResearch,
                architecture: Architecture::MixtureOfExperts,
                learning_rate: 0.001,
                adaptation_rate: 0.1,
                memory_size: 10000,
                exploration_rate: 0.1,
                meta_learning: true,
                ewc_lambda: 0.3,
            },
            moe,
            optimizer,
            replay_buffer,
            expert_usage: vec![0.0; num_experts],
            dim: input_dim,
        }
    }

    pub fn forward(&self, query: &[f32], context: &[Vec<f32>]) -> Vec<f32> {
        let keys: Vec<&[f32]> = context.iter().map(|c| c.as_slice()).collect();
        let values: Vec<&[f32]> = context.iter().map(|c| c.as_slice()).collect();

        self.moe
            .compute(query, &keys, &values)
            .unwrap_or_else(|_| query.to_vec())
    }
}

// ============ Quantum-Inspired Model ============

/// Quantum-Inspired Variational Model
pub struct QuantumInspiredModel {
    pub config: SelfLearningConfig,
    parameters: Vec<f32>, // Variational parameters
    num_qubits: usize,
    num_layers: usize,
    optimizer: Optimizer,
    energy_history: Vec<f32>,
}

impl QuantumInspiredModel {
    pub fn new(num_qubits: usize, num_layers: usize) -> Self {
        let mut rng = rand::thread_rng();
        let num_params = num_qubits * num_layers * 3; // Rx, Ry, Rz per qubit per layer
        let parameters: Vec<f32> = (0..num_params)
            .map(|_| rng.gen::<f32>() * 2.0 * std::f32::consts::PI)
            .collect();

        let optimizer = Optimizer::new(OptimizerType::Adam {
            learning_rate: 0.01,
            beta1: 0.9,
            beta2: 0.999,
            epsilon: 1e-8,
        });

        Self {
            config: SelfLearningConfig {
                name: "Quantum Variational".to_string(),
                industry: Industry::QuantumInspired,
                architecture: Architecture::QuantumVariational,
                learning_rate: 0.01,
                adaptation_rate: 0.1,
                memory_size: 1000,
                exploration_rate: 0.2,
                meta_learning: false,
                ewc_lambda: 0.0,
            },
            parameters,
            num_qubits,
            num_layers,
            optimizer,
            energy_history: Vec::new(),
        }
    }

    pub fn expectation_value(&self, hamiltonian: &[f32]) -> f32 {
        // Simplified quantum circuit simulation
        let mut state = vec![1.0f32; 1 << self.num_qubits];
        state[0] = 1.0;

        // Apply rotation gates (simplified)
        for (i, &param) in self.parameters.iter().enumerate() {
            let qubit = i % self.num_qubits;
            let amplitude = param.cos();
            if qubit < state.len() {
                state[qubit] *= amplitude;
            }
        }

        // Calculate expectation
        let norm: f32 = state.iter().map(|x| x * x).sum::<f32>().sqrt();
        if norm > 1e-6 {
            for s in &mut state {
                *s /= norm;
            }
        }

        state
            .iter()
            .zip(hamiltonian.iter())
            .map(|(s, h)| s * s * h)
            .sum()
    }

    pub fn optimize_step(&mut self, hamiltonian: &[f32]) -> f32 {
        let energy = self.expectation_value(hamiltonian);
        self.energy_history.push(energy);

        // Parameter shift rule for gradient (simplified)
        let mut rng = rand::thread_rng();
        for param in &mut self.parameters {
            let shift: f32 = rng.gen::<f32>() * 0.1 - 0.05;
            *param += shift;
        }

        energy
    }
}

// ============ Spiking Neural Network ============

/// Spiking Neural Network with STDP Learning
pub struct SpikingNeuralNetwork {
    pub config: SelfLearningConfig,
    membrane_potentials: Vec<f32>,
    thresholds: Vec<f32>,
    weights: Vec<Vec<f32>>,
    spike_times: Vec<f32>,
    num_neurons: usize,
    tau_membrane: f32,
    tau_stdp: f32,
    time: f32,
}

impl SpikingNeuralNetwork {
    pub fn new(num_neurons: usize) -> Self {
        let mut rng = rand::thread_rng();

        let weights: Vec<Vec<f32>> = (0..num_neurons)
            .map(|_| (0..num_neurons).map(|_| rng.gen::<f32>() * 0.5).collect())
            .collect();

        Self {
            config: SelfLearningConfig {
                name: "Spiking Neural Network".to_string(),
                industry: Industry::Neuromorphic,
                architecture: Architecture::SpikingNN,
                learning_rate: 0.01,
                adaptation_rate: 0.1,
                memory_size: 1000,
                exploration_rate: 0.1,
                meta_learning: false,
                ewc_lambda: 0.0,
            },
            membrane_potentials: vec![0.0; num_neurons],
            thresholds: vec![1.0; num_neurons],
            weights,
            spike_times: vec![-1000.0; num_neurons],
            num_neurons,
            tau_membrane: 20.0,
            tau_stdp: 20.0,
            time: 0.0,
        }
    }

    pub fn step(&mut self, inputs: &[f32], dt: f32) -> Vec<bool> {
        self.time += dt;
        let mut spikes = vec![false; self.num_neurons];
        let decay = (-dt / self.tau_membrane).exp();

        for i in 0..self.num_neurons {
            // Leaky integration
            self.membrane_potentials[i] *= decay;

            // Add input
            if i < inputs.len() {
                self.membrane_potentials[i] += inputs[i];
            }

            // Check threshold
            if self.membrane_potentials[i] >= self.thresholds[i] {
                spikes[i] = true;
                self.spike_times[i] = self.time;
                self.membrane_potentials[i] = 0.0; // Reset
            }
        }

        // Propagate spikes
        for i in 0..self.num_neurons {
            if spikes[i] {
                for j in 0..self.num_neurons {
                    if i != j {
                        self.membrane_potentials[j] += self.weights[i][j];
                    }
                }
            }
        }

        spikes
    }

    pub fn stdp_update(&mut self, pre: usize, post: usize) {
        let dt = self.spike_times[post] - self.spike_times[pre];
        let dw = if dt > 0.0 {
            0.01 * (-dt / self.tau_stdp).exp() // LTP
        } else {
            -0.012 * (dt / self.tau_stdp).exp() // LTD
        };

        self.weights[pre][post] = (self.weights[pre][post] + dw).max(0.0).min(1.0);
    }
}

// ============ Hyperdimensional Computing Model ============

/// Hyperdimensional Computing Model
pub struct HyperdimensionalModel {
    pub config: SelfLearningConfig,
    dim: usize,
    memory: HashMap<String, Vec<f32>>,
    codebook: HashMap<String, Vec<f32>>,
}

impl HyperdimensionalModel {
    pub fn new(dim: usize) -> Self {
        Self {
            config: SelfLearningConfig {
                name: "Hyperdimensional Computing".to_string(),
                industry: Industry::Hyperdimensional,
                architecture: Architecture::HopfieldModern,
                learning_rate: 1.0,
                adaptation_rate: 1.0,
                memory_size: 10000,
                exploration_rate: 0.0,
                meta_learning: false,
                ewc_lambda: 0.0,
            },
            dim,
            memory: HashMap::new(),
            codebook: HashMap::new(),
        }
    }

    pub fn random_hypervector(&self) -> Vec<f32> {
        let mut rng = rand::thread_rng();
        (0..self.dim)
            .map(|_| if rng.gen::<bool>() { 1.0 } else { -1.0 })
            .collect()
    }

    pub fn bind(&self, a: &[f32], b: &[f32]) -> Vec<f32> {
        a.iter().zip(b.iter()).map(|(x, y)| x * y).collect()
    }

    pub fn bundle(&self, vectors: &[Vec<f32>]) -> Vec<f32> {
        let mut result = vec![0.0; self.dim];
        for v in vectors {
            for (r, x) in result.iter_mut().zip(v.iter()) {
                *r += x;
            }
        }
        // Threshold
        result
            .iter()
            .map(|&x| if x > 0.0 { 1.0 } else { -1.0 })
            .collect()
    }

    pub fn similarity(&self, a: &[f32], b: &[f32]) -> f32 {
        let dot: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
        dot / self.dim as f32
    }

    pub fn store(&mut self, key: &str, value: Vec<f32>) {
        self.memory.insert(key.to_string(), value);
    }

    pub fn query(&self, query: &[f32]) -> Option<(&String, f32)> {
        self.memory
            .iter()
            .map(|(k, v)| (k, self.similarity(query, v)))
            .max_by(|a, b| a.1.partial_cmp(&b.1).unwrap())
    }
}

// ============ Exotic Experiments ============

/// Chaos-based Neural Dynamics
pub struct ChaosModel {
    pub state: [f32; 3],
    pub sigma: f32,
    pub rho: f32,
    pub beta: f32,
}

impl ChaosModel {
    pub fn new() -> Self {
        Self {
            state: [1.0, 1.0, 1.0],
            sigma: 10.0,
            rho: 28.0,
            beta: 8.0 / 3.0,
        }
    }

    pub fn step(&mut self, dt: f32) {
        let [x, y, z] = self.state;
        let dx = self.sigma * (y - x);
        let dy = x * (self.rho - z) - y;
        let dz = x * y - self.beta * z;

        self.state[0] += dx * dt;
        self.state[1] += dy * dt;
        self.state[2] += dz * dt;
    }

    pub fn encode_to_features(&self) -> Vec<f32> {
        vec![
            self.state[0] / 20.0,
            self.state[1] / 20.0,
            self.state[2] / 30.0,
        ]
    }
}

/// Swarm Intelligence Optimizer
pub struct SwarmOptimizer {
    pub particles: Vec<Vec<f32>>,
    pub velocities: Vec<Vec<f32>>,
    pub best_positions: Vec<Vec<f32>>,
    pub global_best: Vec<f32>,
    pub global_best_fitness: f32,
    dim: usize,
}

impl SwarmOptimizer {
    pub fn new(num_particles: usize, dim: usize) -> Self {
        let mut rng = rand::thread_rng();

        let particles: Vec<Vec<f32>> = (0..num_particles)
            .map(|_| (0..dim).map(|_| rng.gen::<f32>() * 2.0 - 1.0).collect())
            .collect();

        let velocities: Vec<Vec<f32>> = (0..num_particles)
            .map(|_| (0..dim).map(|_| rng.gen::<f32>() * 0.2 - 0.1).collect())
            .collect();

        let best_positions = particles.clone();
        let global_best = particles[0].clone();

        Self {
            particles,
            velocities,
            best_positions,
            global_best,
            global_best_fitness: f32::MAX,
            dim,
        }
    }

    pub fn step<F: Fn(&[f32]) -> f32>(&mut self, fitness_fn: F, w: f32, c1: f32, c2: f32) {
        let mut rng = rand::thread_rng();

        for i in 0..self.particles.len() {
            // Update velocity
            for d in 0..self.dim {
                let r1: f32 = rng.gen();
                let r2: f32 = rng.gen();

                self.velocities[i][d] = w * self.velocities[i][d]
                    + c1 * r1 * (self.best_positions[i][d] - self.particles[i][d])
                    + c2 * r2 * (self.global_best[d] - self.particles[i][d]);
            }

            // Update position
            for d in 0..self.dim {
                self.particles[i][d] += self.velocities[i][d];
            }

            // Evaluate fitness
            let fitness = fitness_fn(&self.particles[i]);

            // Update personal best
            let personal_fitness = fitness_fn(&self.best_positions[i]);
            if fitness < personal_fitness {
                self.best_positions[i] = self.particles[i].clone();
            }

            // Update global best
            if fitness < self.global_best_fitness {
                self.global_best = self.particles[i].clone();
                self.global_best_fitness = fitness;
            }
        }
    }
}

/// Reservoir Computing for temporal patterns
pub struct ReservoirComputer {
    pub reservoir_size: usize,
    pub input_weights: Vec<Vec<f32>>,
    pub reservoir_weights: Vec<Vec<f32>>,
    pub state: Vec<f32>,
    pub spectral_radius: f32,
}

impl ReservoirComputer {
    pub fn new(input_dim: usize, reservoir_size: usize, spectral_radius: f32) -> Self {
        let mut rng = rand::thread_rng();

        let input_weights: Vec<Vec<f32>> = (0..reservoir_size)
            .map(|_| {
                (0..input_dim)
                    .map(|_| rng.gen::<f32>() * 2.0 - 1.0)
                    .collect()
            })
            .collect();

        let reservoir_weights: Vec<Vec<f32>> = (0..reservoir_size)
            .map(|_| {
                (0..reservoir_size)
                    .map(|_| rng.gen::<f32>() * 2.0 - 1.0)
                    .collect()
            })
            .collect();

        Self {
            reservoir_size,
            input_weights,
            reservoir_weights,
            state: vec![0.0; reservoir_size],
            spectral_radius,
        }
    }

    pub fn step(&mut self, input: &[f32]) -> Vec<f32> {
        let mut new_state = vec![0.0; self.reservoir_size];

        for i in 0..self.reservoir_size {
            // Input contribution
            for (j, &inp) in input.iter().enumerate() {
                if j < self.input_weights[i].len() {
                    new_state[i] += self.input_weights[i][j] * inp;
                }
            }

            // Recurrent contribution
            for j in 0..self.reservoir_size {
                new_state[i] += self.reservoir_weights[i][j] * self.state[j] * self.spectral_radius;
            }

            // Nonlinearity
            new_state[i] = new_state[i].tanh();
        }

        self.state = new_state.clone();
        new_state
    }
}

// ============ Training Entry Points ============

/// Run industry-specific model training
pub async fn run_industry_training(epochs: usize, output_dir: Option<PathBuf>) -> Result<()> {
    let output_dir = output_dir.unwrap_or_else(|| PathBuf::from("./training_results"));
    std::fs::create_dir_all(&output_dir)?;

    tracing::info!(
        "Starting self-learning model training for {} epochs",
        epochs
    );

    // Train Healthcare Model
    tracing::info!("Training Healthcare Diagnostics Model...");
    let start = Instant::now();
    let mut healthcare = HealthcareModel::new(256, 256, 100);
    let mut rng = rand::thread_rng();

    for epoch in 0..epochs {
        let symptoms: Vec<f32> = (0..256).map(|_| rng.gen::<f32>()).collect();
        let correct = rng.gen::<f32>() > 0.3;
        let accuracy = healthcare.train_episode(symptoms, "diagnosis_a", correct);

        if epoch % 10 == 0 {
            tracing::info!("Healthcare epoch {}: accuracy = {:.4}", epoch, accuracy);
        }
    }
    tracing::info!("Healthcare training complete in {:?}", start.elapsed());

    // Train Financial Model
    tracing::info!("Training Financial Trading Model...");
    let start = Instant::now();
    let mut financial = FinancialModel::new(128, 128);

    for epoch in 0..epochs {
        let market_data: Vec<f32> = (0..128).map(|_| rng.gen::<f32>() * 2.0 - 1.0).collect();
        let return_pct = rng.gen::<f32>() * 0.1 - 0.05;
        let sharpe = financial.train_step(market_data, return_pct);

        if epoch % 10 == 0 {
            tracing::info!("Financial epoch {}: sharpe = {:.4}", epoch, sharpe);
        }
    }
    tracing::info!("Financial training complete in {:?}", start.elapsed());

    // Train Autonomous Model
    tracing::info!("Training Autonomous Systems Model...");
    let start = Instant::now();
    let mut autonomous = AutonomousModel::new(64, 128, 10);

    for epoch in 0..epochs {
        let sensors: Vec<f32> = (0..64).map(|_| rng.gen::<f32>()).collect();
        let action = rng.gen_range(0..10);
        let reward = rng.gen::<f32>() * 2.0 - 1.0;
        autonomous.train_step(sensors, action, reward);

        if epoch % 10 == 0 {
            tracing::info!("Autonomous epoch {}: completed", epoch);
        }
    }
    tracing::info!("Autonomous training complete in {:?}", start.elapsed());

    // Train Quantum-Inspired Model
    tracing::info!("Training Quantum-Inspired Model...");
    let start = Instant::now();
    let mut quantum = QuantumInspiredModel::new(4, 3);
    let hamiltonian: Vec<f32> = (0..16).map(|i| if i == 0 { 1.0 } else { 0.0 }).collect();

    for epoch in 0..epochs {
        let energy = quantum.optimize_step(&hamiltonian);

        if epoch % 10 == 0 {
            tracing::info!("Quantum epoch {}: energy = {:.6}", epoch, energy);
        }
    }
    tracing::info!("Quantum training complete in {:?}", start.elapsed());

    // Train Spiking Neural Network
    tracing::info!("Training Spiking Neural Network...");
    let start = Instant::now();
    let mut snn = SpikingNeuralNetwork::new(100);

    for epoch in 0..epochs {
        let inputs: Vec<f32> = (0..100)
            .map(|_| if rng.gen::<f32>() > 0.8 { 1.0 } else { 0.0 })
            .collect();
        let spikes = snn.step(&inputs, 1.0);
        let spike_count = spikes.iter().filter(|&&s| s).count();

        if epoch % 10 == 0 {
            tracing::info!("SNN epoch {}: spikes = {}", epoch, spike_count);
        }
    }
    tracing::info!("SNN training complete in {:?}", start.elapsed());

    // Train Hyperdimensional Model
    tracing::info!("Training Hyperdimensional Computing Model...");
    let start = Instant::now();
    let mut hdm = HyperdimensionalModel::new(10000);

    for epoch in 0..epochs.min(100) {
        // Fewer epochs for HD
        let hv = hdm.random_hypervector();
        hdm.store(&format!("pattern_{}", epoch), hv);
    }
    tracing::info!(
        "Hyperdimensional training complete in {:?}",
        start.elapsed()
    );

    tracing::info!("All industry models trained successfully!");
    Ok(())
}

/// Run exotic research experiments
pub async fn run_exotic_experiments(iterations: usize, output_dir: Option<PathBuf>) -> Result<()> {
    let output_dir = output_dir.unwrap_or_else(|| PathBuf::from("./exotic_results"));
    std::fs::create_dir_all(&output_dir)?;

    tracing::info!("Starting exotic experiments for {} iterations", iterations);

    // Chaos experiment
    tracing::info!("Running Lorenz Attractor experiment...");
    let start = Instant::now();
    let mut chaos = ChaosModel::new();
    let mut trajectory = Vec::new();

    for i in 0..iterations {
        chaos.step(0.01);
        if i % 10 == 0 {
            trajectory.push(chaos.state);
        }
    }
    tracing::info!(
        "Chaos experiment complete in {:?}. Final state: {:?}",
        start.elapsed(),
        chaos.state
    );

    // Swarm optimization
    tracing::info!("Running Particle Swarm Optimization...");
    let start = Instant::now();
    let mut swarm = SwarmOptimizer::new(50, 10);

    let fitness_fn = |x: &[f32]| -> f32 {
        x.iter().map(|&xi| xi * xi).sum::<f32>() // Sphere function
    };

    for i in 0..iterations.min(100) {
        swarm.step(fitness_fn, 0.7, 1.5, 1.5);

        if i % 10 == 0 {
            tracing::info!(
                "Swarm iteration {}: best fitness = {:.6}",
                i,
                swarm.global_best_fitness
            );
        }
    }
    tracing::info!(
        "Swarm optimization complete in {:?}. Best: {:.6}",
        start.elapsed(),
        swarm.global_best_fitness
    );

    // Reservoir computing
    tracing::info!("Running Reservoir Computing experiment...");
    let start = Instant::now();
    let mut reservoir = ReservoirComputer::new(10, 100, 0.9);
    let mut rng = rand::thread_rng();

    for i in 0..iterations {
        let input: Vec<f32> = (0..10).map(|_| rng.gen::<f32>()).collect();
        let state = reservoir.step(&input);

        if i % 100 == 0 {
            let activity: f32 = state.iter().map(|x| x.abs()).sum::<f32>() / state.len() as f32;
            tracing::info!("Reservoir iteration {}: activity = {:.4}", i, activity);
        }
    }
    tracing::info!("Reservoir experiment complete in {:?}", start.elapsed());

    // MoE experiment
    tracing::info!("Running Mixture of Experts experiment...");
    let start = Instant::now();
    let moe = MoEModel::new(256, 8);

    for i in 0..iterations.min(100) {
        let query: Vec<f32> = (0..256).map(|_| rng.gen::<f32>()).collect();
        let context = vec![
            (0..256).map(|_| rng.gen::<f32>()).collect::<Vec<f32>>(),
            (0..256).map(|_| rng.gen::<f32>()).collect::<Vec<f32>>(),
        ];
        let output = moe.forward(&query, &context);

        if i % 10 == 0 {
            let norm: f32 = output.iter().map(|x| x * x).sum::<f32>().sqrt();
            tracing::info!("MoE iteration {}: output norm = {:.4}", i, norm);
        }
    }
    tracing::info!("MoE experiment complete in {:?}", start.elapsed());

    tracing::info!("All exotic experiments completed successfully!");
    Ok(())
}