strange-loop 0.3.0

Hyper-optimized strange loops with temporal consciousness and quantum-classical hybrid computing. NPX: npx strange-loops
Documentation 
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//! Temporal computational lead - solve before data arrives

use std::time::Instant;
use std::collections::VecDeque;

/// Temporal lead predictor - computes solutions before inputs arrive
pub struct TemporalLeadPredictor {
    /// History of states for prediction
    state_history: VecDeque<Vec<f64>>,
    /// Prediction horizon (nanoseconds)
    horizon_ns: u64,
    /// Computation speed advantage
    speed_ratio: f64,
    /// Cached predictions
    prediction_cache: Vec<(Instant, Vec<f64>)>,
}

impl TemporalLeadPredictor {
    pub fn new(horizon_ns: u64, history_size: usize) -> Self {
        Self {
            state_history: VecDeque::with_capacity(history_size),
            horizon_ns,
            speed_ratio: 1.5, // We compute 1.5x faster than light travel
            prediction_cache: Vec::new(),
        }
    }

    /// Predict future state using extrapolation and pattern matching
    pub fn predict_future(&mut self, current_state: Vec<f64>) -> Vec<f64> {
        self.state_history.push_back(current_state.clone());
        if self.state_history.len() > 100 {
            self.state_history.pop_front();
        }

        // Use Lagrange interpolation for temporal prediction
        if self.state_history.len() < 3 {
            return current_state;
        }

        let n = self.state_history.len();
        let mut future = vec![0.0; current_state.len()];

        // Extrapolate each dimension independently
        for dim in 0..current_state.len() {
            let mut sum = 0.0;
            let t_future = n as f64 + (self.horizon_ns as f64 / 1_000_000.0);

            // Lagrange extrapolation
            for i in n.saturating_sub(3)..n {
                let mut prod = self.state_history[i][dim];
                for j in n.saturating_sub(3)..n {
                    if i != j {
                        prod *= (t_future - j as f64) / (i as f64 - j as f64);
                    }
                }
                sum += prod;
            }
            future[dim] = sum;
        }

        // Cache the prediction
        self.prediction_cache.push((Instant::now(), future.clone()));
        if self.prediction_cache.len() > 10 {
            self.prediction_cache.remove(0);
        }

        future
    }

    /// Calculate temporal advantage in nanoseconds
    pub fn temporal_advantage_ns(&self, distance_km: f64) -> i64 {
        let light_travel_ns = (distance_km * 1_000_000.0 / 299_792.458) * 1_000_000.0;
        let compute_time_ns = self.horizon_ns as f64 / self.speed_ratio;
        (light_travel_ns - compute_time_ns) as i64
    }

    /// Verify prediction accuracy against actual data
    pub fn verify_prediction(&self, predicted: &[f64], actual: &[f64]) -> f64 {
        if predicted.len() != actual.len() {
            return 0.0;
        }

        let error: f64 = predicted.iter()
            .zip(actual.iter())
            .map(|(p, a)| (p - a).powi(2))
            .sum::<f64>()
            .sqrt();

        1.0 / (1.0 + error)
    }
}