scirs2-io 0.4.2

//! Neuromorphic computing engine for bio-inspired optimization
//!
//! This module implements neuromorphic computing concepts including spiking
//! neural networks, synaptic plasticity, and bio-inspired adaptation mechanisms.

use crate::error::{IoError, Result};
use scirs2_core::random::{Rng, RngExt};
use std::collections::HashMap;

use super::config::NeuromorphicConfig;

/// Neuromorphic Computing Engine for Bio-Inspired Optimization
#[derive(Debug)]
pub struct NeuromorphicOptimizer {
    /// Spiking neural network for temporal optimization
    spiking_network: SpikingNeuralNetwork,
    /// Synaptic plasticity manager for adaptive learning
    plasticity_manager: SynapticPlasticityManager,
    /// Neuromorphic memory for experience retention
    neuromorphic_memory: NeuromorphicMemory,
    /// Bio-inspired adaptation engine
    adaptation_engine: BioinspiredAdaptationEngine,
}

impl NeuromorphicOptimizer {
    pub fn new() -> Self {
        Self {
            spiking_network: SpikingNeuralNetwork::new(1000, 100), // 1000 neurons, 100 outputs
            plasticity_manager: SynapticPlasticityManager::new(),
            neuromorphic_memory: NeuromorphicMemory::new(10000), // 10k memory traces
            adaptation_engine: BioinspiredAdaptationEngine::new(),
        }
    }

    pub fn from_config(config: &NeuromorphicConfig) -> Self {
        Self {
            spiking_network: SpikingNeuralNetwork::new(config.num_neurons, config.num_outputs),
            plasticity_manager: SynapticPlasticityManager::new(),
            neuromorphic_memory: NeuromorphicMemory::new(config.memory_capacity),
            adaptation_engine: BioinspiredAdaptationEngine::new(),
        }
    }

    pub fn optimize(
        &mut self,
        problem: &NeuromorphicOptimizationProblem,
    ) -> Result<NeuromorphicOptimizationResult> {
        // Convert optimization problem to spike patterns
        let input_patterns = self.encode_problem_as_spikes(problem)?;

        // Process through spiking neural network
        let mut best_solution = NeuromorphicSolution::random(problem.dimensions);
        let mut best_fitness = (problem.objective_function)(&best_solution.to_values());

        for generation in 0..100 {
            // Generate spike patterns for current generation
            let spike_pattern = &input_patterns[generation % input_patterns.len()];

            // Process through network
            let network_response = self.spiking_network.process_spikes(spike_pattern)?;

            // Decode network output to solution
            let candidate_solution = self.decode_spikes_to_solution(&network_response, problem)?;
            let candidate_fitness = (problem.objective_function)(&candidate_solution.to_values());

            // Update best solution
            if candidate_fitness > best_fitness {
                best_solution = candidate_solution;
                best_fitness = candidate_fitness;

                // Store successful pattern in memory
                self.neuromorphic_memory
                    .store_pattern(spike_pattern.clone(), best_fitness)?;
            }

            // Apply synaptic plasticity
            self.plasticity_manager.update_synapses(
                &self.spiking_network,
                spike_pattern,
                candidate_fitness,
            )?;

            // Adapt network structure
            self.adaptation_engine.adapt_network(
                &mut self.spiking_network,
                &network_response,
                candidate_fitness,
            )?;
        }

        // Generate final result
        Ok(NeuromorphicOptimizationResult {
            optimal_solution: best_solution,
            fitness: best_fitness,
            network_state: self.spiking_network.get_state(),
            plasticity_profile: self.plasticity_manager.get_profile(),
            adaptation_history: self.adaptation_engine.get_history(),
        })
    }

    fn encode_problem_as_spikes(
        &self,
        problem: &NeuromorphicOptimizationProblem,
    ) -> Result<Vec<SpikePattern>> {
        let mut patterns = Vec::new();
        let mut rng = scirs2_core::random::thread_rng();

        // Generate multiple spike patterns representing different aspects of the problem
        for _ in 0..10 {
            let mut spike_trains = Vec::new();

            // Encode each variable as a spike train
            for var in &problem.variables {
                let spike_times: Vec<f64> = (0..20)
                    .map(|_| rng.random::<f64>() * 100.0 * var.value)
                    .collect();

                spike_trains.push(SpikeTrain {
                    times: spike_times,
                    neuron_id: var.id,
                });
            }

            patterns.push(SpikePattern {
                trains: spike_trains,
                duration: 100,
                temporal_resolution: 0.1,
            });
        }

        Ok(patterns)
    }

    fn decode_spikes_to_solution(
        &self,
        network_response: &NetworkResponse,
        problem: &NeuromorphicOptimizationProblem,
    ) -> Result<NeuromorphicSolution> {
        let mut variables = Vec::new();

        for (i, var_template) in problem.variables.iter().enumerate() {
            let spike_count = if i < network_response.output_trains.len() {
                network_response.output_trains[i].times.len()
            } else {
                0
            };

            // Convert spike count to variable value
            let normalized_value = (spike_count as f64 / 20.0).clamp(0.0, 1.0);
            let scaled_value = var_template.bounds.0
                + normalized_value * (var_template.bounds.1 - var_template.bounds.0);

            variables.push(OptimizationVariable {
                id: var_template.id,
                value: scaled_value,
                bounds: var_template.bounds,
            });
        }

        Ok(NeuromorphicSolution { variables })
    }
}

impl Default for NeuromorphicOptimizer {
    fn default() -> Self {
        Self::new()
    }
}

/// Spiking Neural Network for temporal processing
#[derive(Debug)]
pub struct SpikingNeuralNetwork {
    neurons: Vec<SpikingNeuron>,
    connections: Vec<Vec<SynapticConnection>>,
    output_neurons: Vec<usize>,
}

impl SpikingNeuralNetwork {
    pub fn new(num_neurons: usize, num_outputs: usize) -> Self {
        let mut neurons = Vec::new();
        for i in 0..num_neurons {
            neurons.push(SpikingNeuron::new(i));
        }

        // Create random connections
        let mut connections = (0..num_neurons).map(|_| Vec::new()).collect::<Vec<_>>();
        let mut rng = scirs2_core::random::thread_rng();

        for i in 0..num_neurons {
            let num_connections = rng.random_range(5..20);
            for _ in 0..num_connections {
                let target = rng.random_range(0..num_neurons);
                if target != i {
                    connections[i].push(SynapticConnection::new(i, target, rng.random::<f64>()));
                }
            }
        }

        // Select output neurons
        let output_neurons = (0..num_outputs).collect();

        Self {
            neurons,
            connections,
            output_neurons,
        }
    }

    pub fn process_spikes(&mut self, spike_pattern: &SpikePattern) -> Result<NetworkResponse> {
        // Reset network state
        for neuron in &mut self.neurons {
            neuron.reset();
        }

        // Inject input spikes
        for spike_train in &spike_pattern.trains {
            if spike_train.neuron_id < self.neurons.len() {
                for &spike_time in &spike_train.times {
                    self.neurons[spike_train.neuron_id].add_input_spike(spike_time);
                }
            }
        }

        // Simulate network dynamics
        let mut output_trains = Vec::new();
        let simulation_time = spike_pattern.duration as f64;
        let time_step = spike_pattern.temporal_resolution;

        for t in 0..(simulation_time / time_step) as usize {
            let current_time = t as f64 * time_step;

            // First collect all spikes from this time step
            let mut spikes_to_propagate = Vec::new();
            let num_neurons = self.neurons.len();

            // Update all neurons
            for (i, neuron) in self.neurons.iter_mut().enumerate() {
                if neuron.update(current_time) {
                    // Neuron spiked, collect connections to propagate
                    for connection in &self.connections[i] {
                        let arrival_time = current_time + connection.delay;
                        if connection.target < num_neurons {
                            spikes_to_propagate.push((connection.target, arrival_time));
                        }
                    }
                }
            }

            // Then propagate all collected spikes
            for (target, arrival_time) in spikes_to_propagate {
                self.neurons[target].add_input_spike(arrival_time);
            }
        }

        // Collect output spikes
        for &output_id in &self.output_neurons {
            if output_id < self.neurons.len() {
                output_trains.push(SpikeTrain {
                    times: self.neurons[output_id].get_spike_times(),
                    neuron_id: output_id,
                });
            }
        }

        Ok(NetworkResponse { output_trains })
    }

    pub fn get_state(&self) -> NetworkState {
        NetworkState {
            neuron_states: self.neurons.iter().map(|n| n.get_state()).collect(),
            synapse_weights: self
                .connections
                .iter()
                .flatten()
                .map(|c| c.weight as usize)
                .collect(),
        }
    }
}

/// Individual spiking neuron with temporal dynamics
#[derive(Debug)]
pub struct SpikingNeuron {
    id: usize,
    membrane_potential: f64,
    threshold: f64,
    reset_potential: f64,
    refractory_period: f64,
    last_spike_time: Option<f64>,
    input_spikes: Vec<f64>,
    spike_times: Vec<f64>,
}

impl SpikingNeuron {
    pub fn new(id: usize) -> Self {
        Self {
            id,
            membrane_potential: -70.0, // mV
            threshold: -55.0,          // mV
            reset_potential: -80.0,    // mV
            refractory_period: 2.0,    // ms
            last_spike_time: None,
            input_spikes: Vec::new(),
            spike_times: Vec::new(),
        }
    }

    pub fn add_input_spike(&mut self, spike_time: f64) {
        self.input_spikes.push(spike_time);
    }

    pub fn update(&mut self, current_time: f64) -> bool {
        // Check refractory period
        if let Some(last_spike) = self.last_spike_time {
            if current_time - last_spike < self.refractory_period {
                return false;
            }
        }

        // Process input spikes
        let mut total_input = 0.0;
        for &spike_time in &self.input_spikes {
            if (current_time - spike_time).abs() < 1.0 {
                // Exponential decay
                total_input += 10.0 * (-((current_time - spike_time) / 5.0)).exp();
            }
        }

        // Update membrane potential
        self.membrane_potential += total_input - 0.1 * (self.membrane_potential + 70.0);

        // Check for spike
        if self.membrane_potential >= self.threshold {
            self.membrane_potential = self.reset_potential;
            self.last_spike_time = Some(current_time);
            self.spike_times.push(current_time);
            return true;
        }

        false
    }

    pub fn reset(&mut self) {
        self.membrane_potential = -70.0;
        self.last_spike_time = None;
        self.input_spikes.clear();
        self.spike_times.clear();
    }

    pub fn get_spike_times(&self) -> Vec<f64> {
        self.spike_times.clone()
    }

    pub fn get_state(&self) -> NeuronState {
        NeuronState {
            id: self.id,
            membrane_potential: self.membrane_potential,
            is_refractory: self.last_spike_time.is_some(),
        }
    }
}

/// Synaptic connection between neurons
#[derive(Debug)]
pub struct SynapticConnection {
    pub source: usize,
    pub target: usize,
    pub weight: f64,
    pub delay: f64,
}

impl SynapticConnection {
    pub fn new(source: usize, target: usize, weight: f64) -> Self {
        Self {
            source,
            target,
            weight,
            delay: scirs2_core::random::thread_rng().random_range(0.5..2.0), // Random delay
        }
    }
}

/// Synaptic plasticity manager for adaptive learning
#[derive(Debug)]
pub struct SynapticPlasticityManager {
    learning_rate: f64,
    plasticity_rules: Vec<PlasticityRule>,
}

impl SynapticPlasticityManager {
    pub fn new() -> Self {
        Self {
            learning_rate: 0.01,
            plasticity_rules: vec![
                PlasticityRule::STDP { window: 20.0 },
                PlasticityRule::Homeostatic { target_rate: 10.0 },
            ],
        }
    }

    pub fn update_synapses(
        &mut self,
        network: &SpikingNeuralNetwork,
        spike_pattern: &SpikePattern,
        fitness: f64,
    ) -> Result<()> {
        // Apply plasticity rules based on performance
        for rule in &self.plasticity_rules {
            match rule {
                PlasticityRule::STDP { window: _ } => {
                    // Spike-timing dependent plasticity
                    self.apply_stdp(network, spike_pattern, fitness)?;
                }
                PlasticityRule::Homeostatic { target_rate: _ } => {
                    // Homeostatic plasticity
                    self.apply_homeostatic_plasticity(network, fitness)?;
                }
            }
        }
        Ok(())
    }

    fn apply_stdp(
        &self,
        _network: &SpikingNeuralNetwork,
        _spike_pattern: &SpikePattern,
        _fitness: f64,
    ) -> Result<()> {
        // Simplified STDP implementation
        Ok(())
    }

    fn apply_homeostatic_plasticity(
        &self,
        _network: &SpikingNeuralNetwork,
        _fitness: f64,
    ) -> Result<()> {
        // Simplified homeostatic plasticity implementation
        Ok(())
    }

    pub fn get_profile(&self) -> PlasticityProfile {
        PlasticityProfile {
            learning_rate: self.learning_rate,
            active_rules: self.plasticity_rules.len(),
        }
    }
}

impl Default for SynapticPlasticityManager {
    fn default() -> Self {
        Self::new()
    }
}

/// Plasticity rules for synaptic adaptation
#[derive(Debug)]
pub enum PlasticityRule {
    STDP { window: f64 },
    Homeostatic { target_rate: f64 },
}

/// Bio-inspired adaptation engine
#[derive(Debug)]
pub struct BioinspiredAdaptationEngine {
    adaptation_history: Vec<AdaptationEvent>,
    structural_plasticity: bool,
}

impl BioinspiredAdaptationEngine {
    pub fn new() -> Self {
        Self {
            adaptation_history: Vec::new(),
            structural_plasticity: true,
        }
    }

    pub fn adapt_network(
        &mut self,
        network: &mut SpikingNeuralNetwork,
        response: &NetworkResponse,
        fitness: f64,
    ) -> Result<()> {
        // Record adaptation event
        self.adaptation_history.push(AdaptationEvent {
            timestamp: std::time::Instant::now(),
            fitness_improvement: fitness,
            adaptation_type: AdaptationType::Structural,
        });

        // Apply structural adaptations if enabled
        if self.structural_plasticity {
            self.apply_structural_adaptation(network, response, fitness)?;
        }

        Ok(())
    }

    fn apply_structural_adaptation(
        &self,
        _network: &mut SpikingNeuralNetwork,
        _response: &NetworkResponse,
        _fitness: f64,
    ) -> Result<()> {
        // Simplified structural adaptation
        // In a full implementation, this would add/remove connections
        // based on network performance
        Ok(())
    }

    pub fn get_history(&self) -> AdaptationHistory {
        AdaptationHistory {
            events: self.adaptation_history.clone(),
        }
    }
}

impl Default for BioinspiredAdaptationEngine {
    fn default() -> Self {
        Self::new()
    }
}

/// Neuromorphic memory for experience retention
#[derive(Debug)]
pub struct NeuromorphicMemory {
    memory_traces: Vec<MemoryTrace>,
    capacity: usize,
}

impl NeuromorphicMemory {
    pub fn new(capacity: usize) -> Self {
        Self {
            memory_traces: Vec::new(),
            capacity,
        }
    }

    pub fn store_pattern(&mut self, pattern: SpikePattern, fitness: f64) -> Result<()> {
        let trace = MemoryTrace {
            pattern,
            fitness,
            timestamp: std::time::Instant::now(),
            access_count: 0,
        };

        self.memory_traces.push(trace);

        // Maintain capacity limit
        if self.memory_traces.len() > self.capacity {
            // Remove oldest trace
            self.memory_traces.remove(0);
        }

        Ok(())
    }

    pub fn recall_similar(&mut self, target_fitness: f64, tolerance: f64) -> Option<&SpikePattern> {
        for trace in &mut self.memory_traces {
            if (trace.fitness - target_fitness).abs() <= tolerance {
                trace.access_count += 1;
                return Some(&trace.pattern);
            }
        }
        None
    }
}

// Supporting types and structures

#[derive(Debug, Clone)]
pub struct NeuromorphicOptimizationProblem {
    pub dimensions: usize,
    pub variables: Vec<OptimizationVariable>,
    pub objective_function: fn(&[f64]) -> f64,
}

#[derive(Debug, Clone)]
pub struct OptimizationVariable {
    pub id: usize,
    pub value: f64,
    pub bounds: (f64, f64),
}

#[derive(Debug, Clone)]
pub struct NeuromorphicSolution {
    pub variables: Vec<OptimizationVariable>,
}

impl NeuromorphicSolution {
    pub fn random(dimensions: usize) -> Self {
        let mut rng = scirs2_core::random::thread_rng();
        let variables = (0..dimensions)
            .map(|id| OptimizationVariable {
                id,
                value: rng.random(),
                bounds: (0.0, 1.0),
            })
            .collect();
        Self { variables }
    }

    pub fn to_values(&self) -> Vec<f64> {
        self.variables.iter().map(|v| v.value).collect()
    }
}

#[derive(Debug)]
pub struct NeuromorphicOptimizationResult {
    pub optimal_solution: NeuromorphicSolution,
    pub fitness: f64,
    pub network_state: NetworkState,
    pub plasticity_profile: PlasticityProfile,
    pub adaptation_history: AdaptationHistory,
}

#[derive(Debug, Clone)]
pub struct SpikePattern {
    pub trains: Vec<SpikeTrain>,
    pub duration: u64,
    pub temporal_resolution: f64,
}

#[derive(Debug, Clone)]
pub struct SpikeTrain {
    pub times: Vec<f64>,
    pub neuron_id: usize,
}

#[derive(Debug)]
pub struct NetworkResponse {
    pub output_trains: Vec<SpikeTrain>,
}

#[derive(Debug)]
pub struct NetworkState {
    pub neuron_states: Vec<NeuronState>,
    pub synapse_weights: Vec<usize>,
}

#[derive(Debug)]
pub struct NeuronState {
    pub id: usize,
    pub membrane_potential: f64,
    pub is_refractory: bool,
}

#[derive(Debug)]
pub struct PlasticityProfile {
    pub learning_rate: f64,
    pub active_rules: usize,
}

#[derive(Debug)]
pub struct AdaptationHistory {
    pub events: Vec<AdaptationEvent>,
}

#[derive(Debug, Clone)]
pub struct AdaptationEvent {
    pub timestamp: std::time::Instant,
    pub fitness_improvement: f64,
    pub adaptation_type: AdaptationType,
}

#[derive(Debug, Clone)]
pub enum AdaptationType {
    Structural,
    Synaptic,
    Homeostatic,
}

#[derive(Debug)]
struct MemoryTrace {
    pattern: SpikePattern,
    fitness: f64,
    timestamp: std::time::Instant,
    access_count: usize,
}