sklears-simd 0.1.2

//! Neuromorphic computing acceleration support for SIMD operations
//!
//! This module provides interfaces for neuromorphic computing hardware
//! such as Intel Loihi, IBM TrueNorth, and SpiNNaker systems.

use crate::traits::SimdError;

#[cfg(feature = "no-std")]
use alloc::{
    boxed::Box,
    collections::BTreeMap as HashMap,
    string::{String, ToString},
    vec,
    vec::Vec,
};
#[cfg(feature = "no-std")]
use core::any;
#[cfg(not(feature = "no-std"))]
use std::{any, collections::HashMap, string::ToString};

/// Neuromorphic computing architectures
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NeuromorphicArch {
    Loihi,
    TrueNorth,
    SpiNNaker,
    BrainScaleS,
    Custom,
}

/// Neuromorphic device information
#[derive(Debug, Clone)]
pub struct NeuromorphicDevice {
    pub id: u32,
    pub name: String,
    pub architecture: NeuromorphicArch,
    pub neurons: u32,
    pub synapses: u64,
    pub cores: u32,
    pub memory_mb: u64,
    pub power_consumption_mw: f64,
    pub timestep_us: f64,
}

/// Spiking neuron model
#[derive(Debug, Clone)]
pub struct SpikingNeuron {
    pub id: u32,
    pub potential: f64,
    pub threshold: f64,
    pub reset_potential: f64,
    pub refractory_period: u32,
    pub time_constant: f64,
    pub bias: f64,
}

/// Synapse connection
#[derive(Debug, Clone)]
pub struct Synapse {
    pub pre_neuron: u32,
    pub post_neuron: u32,
    pub weight: f64,
    pub delay: u32,
    pub plasticity: SynapticPlasticity,
}

/// Synaptic plasticity models
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SynapticPlasticity {
    None,
    STDP,     // Spike-timing-dependent plasticity
    BCM,      // Bienenstock-Cooper-Munro
    Oja,      // Oja's rule
    Hebb,     // Hebbian learning
    AntiHebb, // Anti-Hebbian learning
}

/// Spike event
#[derive(Debug, Clone)]
pub struct SpikeEvent {
    pub neuron_id: u32,
    pub timestamp: u64,
    pub amplitude: f64,
}

/// Neural network configuration
#[derive(Debug, Clone)]
pub struct NeuralNetworkConfig {
    pub neurons: Vec<SpikingNeuron>,
    pub synapses: Vec<Synapse>,
    pub topology: NetworkTopology,
    pub learning_rate: f64,
    pub simulation_time_ms: f64,
    pub timestep_us: f64,
}

/// Network topology types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NetworkTopology {
    FullyConnected,
    Convolutional,
    Recurrent,
    Reservoir,
    Custom,
}

/// Neuromorphic memory buffer
#[derive(Debug)]
pub struct NeuromorphicBuffer<T> {
    pub ptr: *mut T,
    pub size: usize,
    pub device: NeuromorphicDevice,
    pub memory_type: NeuromorphicMemoryType,
    #[allow(dead_code)]
    // Reserved for native neuromorphic chip buffer handle (Intel Loihi / IBM TrueNorth)
    backend_handle: Option<Box<dyn any::Any + Send + Sync>>,
}

/// Neuromorphic memory types
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum NeuromorphicMemoryType {
    SynapticWeights,
    NeuronStates,
    SpikeBuffers,
    Configuration,
}

unsafe impl<T: Send> Send for NeuromorphicBuffer<T> {}
unsafe impl<T: Sync> Sync for NeuromorphicBuffer<T> {}

impl<T> Drop for NeuromorphicBuffer<T> {
    fn drop(&mut self) {
        // Free neuromorphic memory when buffer is dropped
    }
}

/// Neuromorphic context
pub struct NeuromorphicContext {
    pub device: NeuromorphicDevice,
    pub neural_networks: HashMap<String, NeuralNetworkConfig>,
    pub spike_trains: Vec<SpikeEvent>,
    #[allow(dead_code)]
    // Reserved for native neuromorphic chip context (Intel Loihi / IBM TrueNorth)
    backend_context: Option<Box<dyn any::Any + Send + Sync>>,
}

/// Neuromorphic operations interface
pub trait NeuromorphicOperations {
    /// Allocate neuromorphic memory
    fn allocate<T>(
        &self,
        size: usize,
        memory_type: NeuromorphicMemoryType,
    ) -> Result<NeuromorphicBuffer<T>, SimdError>;

    /// Load neural network configuration
    fn load_network(&mut self, name: &str, config: &NeuralNetworkConfig) -> Result<(), SimdError>;

    /// Run network simulation
    fn simulate(
        &mut self,
        network_name: &str,
        input_spikes: &[SpikeEvent],
        simulation_time_ms: f64,
    ) -> Result<Vec<SpikeEvent>, SimdError>;

    /// Train network with STDP
    fn train_stdp(
        &mut self,
        network_name: &str,
        input_patterns: &[Vec<SpikeEvent>],
        target_patterns: &[Vec<SpikeEvent>],
        epochs: u32,
    ) -> Result<(), SimdError>;

    /// Get network state
    fn get_network_state(&self, network_name: &str) -> Result<NetworkState, SimdError>;

    /// Reset network
    fn reset_network(&mut self, network_name: &str) -> Result<(), SimdError>;
}

/// Network state information
#[derive(Debug, Clone)]
pub struct NetworkState {
    pub neuron_potentials: Vec<f64>,
    pub synaptic_weights: Vec<f64>,
    pub spike_counts: Vec<u32>,
    pub energy_consumption: f64,
    pub simulation_time: f64,
}

/// Neuromorphic runtime
pub struct NeuromorphicRuntime {
    devices: Vec<NeuromorphicDevice>,
    contexts: Vec<NeuromorphicContext>,
}

impl NeuromorphicRuntime {
    /// Create new neuromorphic runtime
    pub fn new() -> Result<Self, SimdError> {
        let devices = Self::discover_devices()?;
        let contexts = Vec::new();
        Ok(Self { devices, contexts })
    }

    /// Discover available neuromorphic devices
    fn discover_devices() -> Result<Vec<NeuromorphicDevice>, SimdError> {
        // In a real implementation, this would interface with neuromorphic drivers
        // For now, return empty list or simulated devices
        Ok(vec![])
    }

    /// Get available devices
    pub fn devices(&self) -> &[NeuromorphicDevice] {
        &self.devices
    }

    /// Create context for device
    pub fn create_context(
        &mut self,
        device_id: u32,
    ) -> Result<&mut NeuromorphicContext, SimdError> {
        let device = self.devices.get(device_id as usize).ok_or_else(|| {
            SimdError::InvalidArgument("Invalid neuromorphic device ID".to_string())
        })?;

        let context = NeuromorphicContext {
            device: device.clone(),
            neural_networks: HashMap::new(),
            spike_trains: Vec::new(),
            backend_context: None,
        };

        self.contexts.push(context);
        Ok(self.contexts.last_mut().expect("operation should succeed"))
    }

    /// Check if neuromorphic hardware is available
    pub fn is_available() -> bool {
        // In a real implementation, this would check for neuromorphic drivers
        false
    }
}

/// Neuromorphic algorithms
pub mod algorithms {
    use super::*;

    /// Leaky Integrate-and-Fire neuron model
    pub fn lif_neuron_step(neuron: &mut SpikingNeuron, input_current: f64, dt: f64) -> bool {
        // Update membrane potential
        let leak = neuron.potential / neuron.time_constant;
        neuron.potential += dt * (-leak + input_current + neuron.bias);

        // Check for spike
        if neuron.potential >= neuron.threshold {
            neuron.potential = neuron.reset_potential;
            true
        } else {
            false
        }
    }

    /// STDP learning rule
    pub fn stdp_update(
        synapse: &mut Synapse,
        pre_spike_time: u64,
        post_spike_time: u64,
        learning_rate: f64,
        tau_plus: f64,
        tau_minus: f64,
    ) {
        let dt = post_spike_time as f64 - pre_spike_time as f64;

        if dt > 0.0 {
            // Post-before-pre: potentiation
            let dw = learning_rate * (-dt / tau_plus).exp();
            synapse.weight += dw;
        } else if dt < 0.0 {
            // Pre-before-post: depression
            let dw = -learning_rate * (dt / tau_minus).exp();
            synapse.weight += dw;
        }

        // Bound weights
        synapse.weight = synapse.weight.clamp(0.0, 1.0);
    }

    /// Spike-based convolution
    pub fn spike_convolution(
        input_spikes: &[SpikeEvent],
        kernel_weights: &[f64],
        kernel_size: usize,
        stride: usize,
        output_size: usize,
    ) -> Result<Vec<SpikeEvent>, SimdError> {
        let mut output_spikes = Vec::new();

        for i in 0..output_size {
            let start_idx = i * stride;
            let mut potential = 0.0;

            for j in 0..kernel_size {
                if start_idx + j < input_spikes.len() {
                    potential += input_spikes[start_idx + j].amplitude * kernel_weights[j];
                }
            }

            // Simple threshold for spike generation
            if potential > 0.5 {
                output_spikes.push(SpikeEvent {
                    neuron_id: i as u32,
                    timestamp: input_spikes.get(start_idx).map_or(0, |s| s.timestamp),
                    amplitude: potential,
                });
            }
        }

        Ok(output_spikes)
    }

    /// Reservoir computing
    pub fn reservoir_compute(
        input_spikes: &[SpikeEvent],
        reservoir_config: &ReservoirConfig,
    ) -> Result<Vec<f64>, SimdError> {
        let mut reservoir_states = vec![0.0; reservoir_config.size];
        let mut outputs = Vec::new();

        for spike in input_spikes {
            // Update reservoir state
            let input_idx = spike.neuron_id as usize % reservoir_config.size;
            reservoir_states[input_idx] += spike.amplitude;

            // Apply reservoir dynamics
            for i in 0..reservoir_config.size {
                reservoir_states[i] *= reservoir_config.decay_factor;

                // Add recurrent connections
                for j in 0..reservoir_config.size {
                    if i != j {
                        let connection_strength = reservoir_config.connectivity_matrix[i][j];
                        reservoir_states[i] += connection_strength * reservoir_states[j];
                    }
                }
            }

            // Compute output
            let output: f64 = reservoir_states
                .iter()
                .zip(reservoir_config.output_weights.iter())
                .map(|(state, weight)| state * weight)
                .sum();

            outputs.push(output);
        }

        Ok(outputs)
    }
}

/// Reservoir computing configuration
#[derive(Debug, Clone)]
pub struct ReservoirConfig {
    pub size: usize,
    pub decay_factor: f64,
    pub connectivity_matrix: Vec<Vec<f64>>,
    pub output_weights: Vec<f64>,
}

/// Spike encoding methods
pub mod encoding {
    use super::*;

    /// Rate coding: encode value as spike frequency
    pub fn rate_encoding(
        value: f64,
        max_rate: f64,
        duration_ms: f64,
        _timestep_us: f64,
    ) -> Vec<SpikeEvent> {
        let mut spikes = Vec::new();
        let rate = (value.abs() * max_rate).min(max_rate);
        let interval = 1000.0 / rate; // ms between spikes

        let mut time = 0.0;
        while time < duration_ms {
            spikes.push(SpikeEvent {
                neuron_id: 0,
                timestamp: (time * 1000.0) as u64, // Convert to microseconds
                amplitude: value.signum(),
            });
            time += interval;
        }

        spikes
    }

    /// Temporal coding: encode value as spike timing
    pub fn temporal_encoding(value: f64, max_delay_ms: f64, reference_time: u64) -> SpikeEvent {
        let delay = (1.0 - value.abs()) * max_delay_ms;
        SpikeEvent {
            neuron_id: 0,
            timestamp: reference_time + (delay * 1000.0) as u64,
            amplitude: value.signum(),
        }
    }

    /// Population coding: encode value across multiple neurons
    pub fn population_encoding(value: f64, num_neurons: usize, timestamp: u64) -> Vec<SpikeEvent> {
        let mut spikes = Vec::new();

        for i in 0..num_neurons {
            let neuron_preferred = (i as f64) / (num_neurons as f64 - 1.0);
            let response = (-0.5 * ((value - neuron_preferred) / 0.2).powi(2)).exp();

            if response > 0.5 {
                spikes.push(SpikeEvent {
                    neuron_id: i as u32,
                    timestamp,
                    amplitude: response,
                });
            }
        }

        spikes
    }
}

#[allow(non_snake_case)]
#[cfg(all(test, not(feature = "no-std")))]
mod tests {
    use super::*;

    #[cfg(feature = "no-std")]
    use alloc::{vec, vec::Vec};

    #[test]
    fn test_neuromorphic_runtime_creation() {
        let runtime = NeuromorphicRuntime::new();
        assert!(runtime.is_ok());
    }

    #[test]
    fn test_neuromorphic_availability() {
        assert!(!NeuromorphicRuntime::is_available());
    }

    #[test]
    fn test_lif_neuron() {
        let mut neuron = SpikingNeuron {
            id: 0,
            potential: 0.0,
            threshold: 1.0,
            reset_potential: 0.0,
            refractory_period: 0,
            time_constant: 10.0,
            bias: 0.0,
        };

        // No spike with low input
        let spiked = algorithms::lif_neuron_step(&mut neuron, 0.1, 1.0);
        assert!(!spiked);

        // Spike with high input
        neuron.potential = 0.9;
        let spiked = algorithms::lif_neuron_step(&mut neuron, 0.2, 1.0);
        assert!(spiked);
        assert_eq!(neuron.potential, 0.0);
    }

    #[test]
    fn test_stdp_learning() {
        let mut synapse = Synapse {
            pre_neuron: 0,
            post_neuron: 1,
            weight: 0.5,
            delay: 0,
            plasticity: SynapticPlasticity::STDP,
        };

        let initial_weight = synapse.weight;

        // Post-before-pre should increase weight
        algorithms::stdp_update(&mut synapse, 100, 105, 0.1, 20.0, 20.0);
        assert!(synapse.weight > initial_weight);

        // Pre-before-post should decrease weight
        let mid_weight = synapse.weight;
        algorithms::stdp_update(&mut synapse, 110, 105, 0.1, 20.0, 20.0);
        assert!(synapse.weight < mid_weight);
    }

    #[test]
    fn test_spike_convolution() {
        let input_spikes = vec![
            SpikeEvent {
                neuron_id: 0,
                timestamp: 0,
                amplitude: 1.0,
            },
            SpikeEvent {
                neuron_id: 1,
                timestamp: 1,
                amplitude: 0.8,
            },
            SpikeEvent {
                neuron_id: 2,
                timestamp: 2,
                amplitude: 0.6,
            },
        ];

        let kernel_weights = vec![0.5, 0.3, 0.2];

        let result = algorithms::spike_convolution(&input_spikes, &kernel_weights, 3, 1, 1);
        assert!(result.is_ok());
    }

    #[test]
    fn test_rate_encoding() {
        let spikes = encoding::rate_encoding(0.8, 100.0, 10.0, 1.0);
        assert!(!spikes.is_empty());

        // Higher value should produce more spikes
        let spikes_high = encoding::rate_encoding(0.9, 100.0, 10.0, 1.0);
        assert!(spikes_high.len() >= spikes.len());
    }

    #[test]
    fn test_temporal_encoding() {
        let spike = encoding::temporal_encoding(0.8, 10.0, 1000);
        assert_eq!(spike.neuron_id, 0);
        assert!(spike.timestamp >= 1000);
        assert_eq!(spike.amplitude, 1.0);
    }

    #[test]
    fn test_population_encoding() {
        let spikes = encoding::population_encoding(0.5, 10, 1000);
        assert!(!spikes.is_empty());

        // Check that spikes are distributed across neurons
        let neuron_ids: Vec<_> = spikes.iter().map(|s| s.neuron_id).collect();
        assert!(neuron_ids.len() > 1);
    }

    #[test]
    fn test_reservoir_config() {
        let config = ReservoirConfig {
            size: 3,
            decay_factor: 0.9,
            connectivity_matrix: vec![
                vec![0.0, 0.1, 0.2],
                vec![0.3, 0.0, 0.1],
                vec![0.2, 0.3, 0.0],
            ],
            output_weights: vec![0.5, 0.3, 0.2],
        };

        let input_spikes = vec![
            SpikeEvent {
                neuron_id: 0,
                timestamp: 0,
                amplitude: 1.0,
            },
            SpikeEvent {
                neuron_id: 1,
                timestamp: 1,
                amplitude: 0.8,
            },
        ];

        let result = algorithms::reservoir_compute(&input_spikes, &config);
        assert!(result.is_ok());

        let outputs = result.expect("operation should succeed");
        assert_eq!(outputs.len(), 2);
    }
}