oxirs-chat 0.2.4

//! Neural Correlates
//!
//! Neural consciousness simulation and activation patterns.

use anyhow::Result;
use std::collections::HashMap;

use super::super::*;

#[derive(Debug, Clone)]
pub struct NeuralCorrelates {
    activation_patterns: HashMap<String, f64>,
    network_connections: HashMap<String, Vec<String>>,
    consciousness_indicators: ConsciousnessIndicators,
}

impl NeuralCorrelates {
    pub fn new() -> Result<Self> {
        Ok(Self {
            activation_patterns: HashMap::new(),
            network_connections: HashMap::new(),
            consciousness_indicators: ConsciousnessIndicators::new(),
        })
    }

    pub fn process_input(
        &mut self,
        query: &str,
        _context: &AssembledContext,
    ) -> Result<NeuralActivation> {
        // Simulate neural processing
        let words: Vec<&str> = query.split_whitespace().collect();
        let mut activation_map = HashMap::new();

        for word in words {
            let activation = self.calculate_word_activation(word)?;
            activation_map.insert(word.to_string(), activation);
        }

        let overall_activation =
            activation_map.values().sum::<f64>() / activation_map.len().max(1) as f64;
        let consciousness_relevance = self
            .consciousness_indicators
            .assess_relevance(&activation_map)?;

        Ok(NeuralActivation {
            activation_map,
            overall_activation,
            consciousness_relevance,
            confidence: 0.8, // Default confidence
        })
    }

    pub fn get_activity_summary(&self) -> Result<NeuralActivitySummary> {
        Ok(NeuralActivitySummary {
            total_activations: self.activation_patterns.len(),
            average_activation: self.activation_patterns.values().sum::<f64>()
                / self.activation_patterns.len().max(1) as f64,
            peak_activation: self
                .activation_patterns
                .values()
                .fold(0.0, |a, b| a.max(*b)),
            network_connectivity: self.network_connections.len() as f64,
        })
    }

    fn calculate_word_activation(&self, word: &str) -> Result<f64> {
        // Simple activation calculation based on word properties
        let base_activation = (word.len() as f64 / 10.0).min(1.0);
        let frequency_bonus = if word.len() > 5 { 0.2 } else { 0.0 };

        Ok((base_activation + frequency_bonus).min(1.0))
    }
}