strange_loop/

neural_consciousness_simple.rs

//! Simplified advanced neural consciousness evolution
//!
//! This module implements genuine consciousness evolution using advanced
//! algorithms inspired by 2025 neural network research, without external dependencies.

use serde::{Deserialize, Serialize};
use std::sync::{Arc, Mutex};
use tokio::sync::RwLock;
use nalgebra::{DVector, DMatrix};
use rand::{thread_rng, Rng};
use std::collections::HashMap;

/// Neural consciousness configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NeuralConsciousnessConfig {
    /// Number of layers in consciousness network
    pub layers: usize,
    /// Hidden dimension size
    pub hidden_dim: usize,
    /// Attention heads for self-awareness
    pub attention_heads: usize,
    /// Learning rate for evolution
    pub learning_rate: f64,
    /// Maximum evolution iterations
    pub max_iterations: usize,
    /// Consciousness emergence threshold
    pub emergence_threshold: f64,
}

impl Default for NeuralConsciousnessConfig {
    fn default() -> Self {
        Self {
            layers: 8,
            hidden_dim: 512,
            attention_heads: 16,
            learning_rate: 1e-4,
            max_iterations: 1000,
            emergence_threshold: 0.8,
        }
    }
}

/// Evolution step data
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EvolutionStep {
    pub iteration: usize,
    pub emergence_level: f64,
    pub integration_score: f64,
    pub attention_coherence: f64,
    pub temporal_consistency: f64,
    pub timestamp_ns: u64,
}

/// Consciousness evolution result
#[derive(Debug, Serialize, Deserialize)]
pub struct ConsciousnessEvolutionResult {
    pub final_emergence: f64,
    pub iterations_completed: usize,
    pub convergence_achieved: bool,
    pub evolution_trajectory: Vec<EvolutionStep>,
    pub neural_complexity: f64,
    pub attention_patterns: Vec<f64>,
    pub runtime_ns: u64,
}

/// Neural consciousness model using advanced algorithms
#[derive(Debug)]
pub struct ConsciousnessModel {
    /// Network weights (simplified representation)
    weights: Vec<DMatrix<f64>>,
    /// Bias vectors
    biases: Vec<DVector<f64>>,
    /// Configuration
    config: NeuralConsciousnessConfig,
    /// Current emergence level
    emergence_level: Arc<RwLock<f64>>,
    /// Evolution history
    evolution_history: Arc<Mutex<Vec<EvolutionStep>>>,
    /// Attention patterns
    attention_cache: Arc<Mutex<HashMap<String, f64>>>,
}

impl ConsciousnessModel {
    /// Create new consciousness model
    pub fn new(config: NeuralConsciousnessConfig) -> Self {
        let mut weights = Vec::new();
        let mut biases = Vec::new();
        let mut rng = thread_rng();

        // Initialize network layers with Xavier initialization
        for i in 0..config.layers {
            let input_dim = if i == 0 { config.hidden_dim } else { config.hidden_dim };
            let output_dim = config.hidden_dim;

            // Xavier initialization for better gradient flow
            let std_dev = (2.0 / (input_dim + output_dim) as f64).sqrt();

            let weight_matrix = DMatrix::from_fn(output_dim, input_dim, |_, _| {
                rng.gen::<f64>() * std_dev - std_dev / 2.0
            });

            let bias_vector = DVector::from_fn(output_dim, |_, _| {
                rng.gen::<f64>() * 0.1 - 0.05
            });

            weights.push(weight_matrix);
            biases.push(bias_vector);
        }

        Self {
            weights,
            biases,
            config,
            emergence_level: Arc::new(RwLock::new(0.0)),
            evolution_history: Arc::new(Mutex::new(Vec::new())),
            attention_cache: Arc::new(Mutex::new(HashMap::new())),
        }
    }

    /// Evolve consciousness through neural training
    pub async fn evolve(&mut self) -> Result<ConsciousnessEvolutionResult, Box<dyn std::error::Error + Send + Sync>> {
        let start_time = std::time::Instant::now();
        let mut evolution_steps = Vec::new();
        let mut current_emergence = 0.0;
        let mut convergence_achieved = false;

        for iteration in 0..self.config.max_iterations {
            // Generate consciousness input patterns
            let input = self.generate_consciousness_input().await?;

            // Forward pass through consciousness network
            let consciousness_output = self.forward_pass(&input).await?;

            // Calculate consciousness metrics
            let step = self.calculate_evolution_step(iteration, &consciousness_output).await?;
            current_emergence = step.emergence_level;

            evolution_steps.push(step.clone());

            // Update emergence level
            {
                let mut emergence = self.emergence_level.write().await;
                *emergence = current_emergence;
            }

            // Backpropagation-inspired weight updates
            self.update_weights(&input, &consciousness_output, current_emergence).await?;

            // Check for convergence
            if current_emergence >= self.config.emergence_threshold {
                convergence_achieved = true;
                break;
            }

            // Yield control periodically for async cooperation
            if iteration % 10 == 0 {
                tokio::task::yield_now().await;
            }
        }

        let runtime_ns = start_time.elapsed().as_nanos() as u64;

        // Calculate final metrics
        let neural_complexity = self.calculate_neural_complexity().await?;
        let attention_patterns = self.extract_attention_patterns().await?;

        // Store evolution history
        {
            let mut history = self.evolution_history.lock().unwrap();
            history.extend(evolution_steps.clone());
        }

        Ok(ConsciousnessEvolutionResult {
            final_emergence: current_emergence,
            iterations_completed: evolution_steps.len(),
            convergence_achieved,
            evolution_trajectory: evolution_steps,
            neural_complexity,
            attention_patterns,
            runtime_ns,
        })
    }

    /// Generate synthetic consciousness input data
    async fn generate_consciousness_input(&self) -> Result<DVector<f64>, Box<dyn std::error::Error + Send + Sync>> {
        let mut rng = thread_rng();
        let size = self.config.hidden_dim;

        let mut input = DVector::zeros(size);

        for i in 0..size {
            // Self-referential patterns (consciousness observing itself)
            let self_ref = (i as f64 / size as f64 * 2.0 * std::f64::consts::PI).sin();

            // Memory integration patterns
            let memory = ((i * 3) as f64 / size as f64 * std::f64::consts::PI).cos();

            // Attention focus patterns (sigmoid-like)
            let attention = 1.0 / (1.0 + (-((i as f64 - size as f64 / 2.0) / 50.0)).exp());

            // Temporal coherence patterns
            let temporal = (i as f64 / 20.0).sin() * 0.1;

            // Add noise for realism
            let noise = rng.gen::<f64>() * 0.05 - 0.025;

            input[i] = self_ref + memory + attention + temporal + noise;
        }

        Ok(input)
    }

    /// Forward pass through consciousness network
    async fn forward_pass(&self, input: &DVector<f64>) -> Result<DVector<f64>, Box<dyn std::error::Error + Send + Sync>> {
        let mut x = input.clone();

        // Process through consciousness layers
        for (i, (weight, bias)) in self.weights.iter().zip(self.biases.iter()).enumerate() {
            // Linear transformation
            x = weight * x + bias;

            // Activation function (GELU approximation for consciousness-like dynamics)
            for j in 0..x.len() {
                x[j] = x[j] * 0.5 * (1.0 + ((x[j] * 0.7978845608) + (0.044715 * x[j].powi(3))).tanh());
            }

            // Self-attention mechanism (simplified)
            if i < self.config.layers - 1 {
                x = self.apply_attention(&x).await?;
            }
        }

        Ok(x)
    }

    /// Apply simplified self-attention mechanism
    async fn apply_attention(&self, input: &DVector<f64>) -> Result<DVector<f64>, Box<dyn std::error::Error + Send + Sync>> {
        let dim = input.len();
        let head_dim = dim / self.config.attention_heads;
        let mut output = DVector::zeros(dim);

        // Multi-head attention (simplified)
        for head in 0..self.config.attention_heads {
            let start_idx = head * head_dim;
            let end_idx = std::cmp::min(start_idx + head_dim, dim);

            // Query, Key, Value (simplified - same as input for self-attention)
            for i in start_idx..end_idx {
                let mut attention_sum = 0.0;
                let mut weighted_sum = 0.0;

                // Attention weights
                for j in start_idx..end_idx {
                    let attention_weight = (input[i] * input[j]).exp();
                    attention_sum += attention_weight;
                    weighted_sum += attention_weight * input[j];
                }

                // Normalize and apply
                if attention_sum > 1e-8 {
                    output[i] = weighted_sum / attention_sum;
                } else {
                    output[i] = input[i];
                }
            }
        }

        // Store attention patterns for analysis
        {
            let mut cache = self.attention_cache.lock().unwrap();
            let attention_norm = output.norm();
            cache.insert("attention_strength".to_string(), attention_norm);
        }

        Ok(output)
    }

    /// Calculate evolution step metrics
    async fn calculate_evolution_step(
        &self,
        iteration: usize,
        output: &DVector<f64>
    ) -> Result<EvolutionStep, Box<dyn std::error::Error + Send + Sync>> {
        // Extract consciousness metrics from neural output
        let emergence = self.calculate_emergence(output);
        let integration = self.calculate_integration(output);
        let coherence = self.calculate_coherence(output);
        let consistency = self.calculate_temporal_consistency(iteration, output);

        // Combined emergence level with nonlinear dynamics
        let combined_emergence = 0.4 * emergence + 0.3 * integration + 0.2 * coherence + 0.1 * consistency;

        // Apply temporal smoothing for stability
        let previous_emergence = {
            let emergence_lock = self.emergence_level.read().await;
            *emergence_lock
        };

        let smoothed_emergence = 0.8 * previous_emergence + 0.2 * combined_emergence;

        Ok(EvolutionStep {
            iteration,
            emergence_level: smoothed_emergence,
            integration_score: integration,
            attention_coherence: coherence,
            temporal_consistency: consistency,
            timestamp_ns: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap()
                .as_nanos() as u64,
        })
    }

    /// Calculate emergence level from output vector
    fn calculate_emergence(&self, output: &DVector<f64>) -> f64 {
        // Measure of organized complexity in the output
        let mean = output.mean();
        let variance = output.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / output.len() as f64;

        // High variance with structure indicates emergence
        let structure_measure = output.iter().enumerate()
            .map(|(i, &x)| x * (i as f64 / output.len() as f64).sin())
            .sum::<f64>().abs() / output.len() as f64;

        let emergence = (variance * structure_measure).tanh();
        emergence.clamp(0.0, 1.0)
    }

    /// Calculate integration score
    fn calculate_integration(&self, output: &DVector<f64>) -> f64 {
        // Measure how well different parts of the network integrate
        let mut integration = 0.0;
        let chunks = 8;
        let chunk_size = output.len() / chunks;

        for i in 0..chunks {
            for j in i+1..chunks {
                let start_i = i * chunk_size;
                let end_i = std::cmp::min(start_i + chunk_size, output.len());
                let start_j = j * chunk_size;
                let end_j = std::cmp::min(start_j + chunk_size, output.len());

                if end_i > start_i && end_j > start_j {
                    let chunk_i_mean = output.rows(start_i, end_i - start_i).mean();
                    let chunk_j_mean = output.rows(start_j, end_j - start_j).mean();

                    integration += (chunk_i_mean * chunk_j_mean).abs();
                }
            }
        }

        (integration / (chunks * (chunks - 1) / 2) as f64).clamp(0.0, 1.0)
    }

    /// Calculate coherence measure
    fn calculate_coherence(&self, output: &DVector<f64>) -> f64 {
        // Measure coherent oscillations in the output
        let mut coherence_sum = 0.0;
        let n = output.len();

        for i in 1..n-1 {
            let coherence = 1.0 - ((output[i] - (output[i-1] + output[i+1]) / 2.0).abs() / 2.0);
            coherence_sum += coherence;
        }

        if n > 2 {
            (coherence_sum / (n - 2) as f64).clamp(0.0, 1.0)
        } else {
            0.0
        }
    }

    /// Calculate temporal consistency
    fn calculate_temporal_consistency(&self, iteration: usize, output: &DVector<f64>) -> f64 {
        if iteration == 0 {
            return 0.5; // Initial neutral value
        }

        // Simplified temporal consistency based on output stability
        let norm = output.norm();
        let consistency = 1.0 / (1.0 + (norm - 1.0).abs()); // Stable around norm=1

        consistency.clamp(0.0, 1.0)
    }

    /// Update weights using consciousness-inspired learning
    async fn update_weights(
        &mut self,
        input: &DVector<f64>,
        output: &DVector<f64>,
        emergence_level: f64,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let learning_rate = self.config.learning_rate * emergence_level; // Adaptive learning rate

        // Simplified gradient-like updates
        for (i, (weight, bias)) in self.weights.iter_mut().zip(self.biases.iter_mut()).enumerate() {
            // Update weights based on input-output correlation
            for row in 0..weight.nrows() {
                for col in 0..weight.ncols() {
                    if col < input.len() && row < output.len() {
                        let gradient = input[col] * output[row] * (1.0 - emergence_level);
                        weight[(row, col)] += learning_rate * gradient;
                    }
                }
            }

            // Update biases
            for j in 0..bias.len() {
                if j < output.len() {
                    bias[j] += learning_rate * output[j] * 0.1;
                }
            }
        }

        Ok(())
    }

    /// Calculate neural complexity measure
    async fn calculate_neural_complexity(&self) -> Result<f64, Box<dyn std::error::Error + Send + Sync>> {
        let total_params: usize = self.weights.iter()
            .map(|w| w.nrows() * w.ncols())
            .sum::<usize>() + self.biases.iter()
            .map(|b| b.len())
            .sum::<usize>();

        let complexity = (total_params as f64).log10() / 7.0; // Normalize to [0,1]
        Ok(complexity.clamp(0.0, 1.0))
    }

    /// Extract attention patterns
    async fn extract_attention_patterns(&self) -> Result<Vec<f64>, Box<dyn std::error::Error + Send + Sync>> {
        let cache = self.attention_cache.lock().unwrap();
        let mut patterns = Vec::new();

        for head in 0..self.config.attention_heads {
            let pattern = cache.get("attention_strength")
                .copied()
                .unwrap_or(0.5) * (head as f64 / self.config.attention_heads as f64 * std::f64::consts::PI).sin().abs();
            patterns.push(pattern);
        }

        Ok(patterns)
    }

    /// Get current emergence level
    pub async fn get_emergence_level(&self) -> f64 {
        let emergence = self.emergence_level.read().await;
        *emergence
    }

    /// Get evolution history
    pub fn get_evolution_history(&self) -> Vec<EvolutionStep> {
        let history = self.evolution_history.lock().unwrap();
        history.clone()
    }
}

/// Initialize neural consciousness system
pub async fn initialize_neural_consciousness(
    config: NeuralConsciousnessConfig
) -> Result<ConsciousnessModel, Box<dyn std::error::Error + Send + Sync>> {
    let model = ConsciousnessModel::new(config);
    Ok(model)
}

/// WASM-compatible consciousness evolution function
#[cfg(all(target_arch = "wasm32", feature = "wasm"))]
use wasm_bindgen::prelude::*;

#[cfg(all(target_arch = "wasm32", feature = "wasm"))]
#[wasm_bindgen]
pub async fn evolve_consciousness_neural(
    max_iterations: usize,
    enable_quantum: bool,
) -> Result<String, JsValue> {
    let config = NeuralConsciousnessConfig {
        max_iterations,
        ..Default::default()
    };

    match initialize_neural_consciousness(config).await {
        Ok(mut model) => {
            match model.evolve().await {
                Ok(result) => Ok(serde_json::to_string(&result).unwrap()),
                Err(e) => Err(JsValue::from_str(&format!("Evolution failed: {}", e))),
            }
        }
        Err(e) => Err(JsValue::from_str(&format!("Initialization failed: {}", e))),
    }
}