strange_loop/
temporal_lead.rs

1//! Temporal computational lead - solve before data arrives
2
3use std::time::Instant;
4use std::collections::VecDeque;
5
6/// Temporal lead predictor - computes solutions before inputs arrive
7pub struct TemporalLeadPredictor {
8    /// History of states for prediction
9    state_history: VecDeque<Vec<f64>>,
10    /// Prediction horizon (nanoseconds)
11    horizon_ns: u64,
12    /// Computation speed advantage
13    speed_ratio: f64,
14    /// Cached predictions
15    prediction_cache: Vec<(Instant, Vec<f64>)>,
16}
17
18impl TemporalLeadPredictor {
19    pub fn new(horizon_ns: u64, history_size: usize) -> Self {
20        Self {
21            state_history: VecDeque::with_capacity(history_size),
22            horizon_ns,
23            speed_ratio: 1.5, // We compute 1.5x faster than light travel
24            prediction_cache: Vec::new(),
25        }
26    }
27
28    /// Predict future state using extrapolation and pattern matching
29    pub fn predict_future(&mut self, current_state: Vec<f64>) -> Vec<f64> {
30        self.state_history.push_back(current_state.clone());
31        if self.state_history.len() > 100 {
32            self.state_history.pop_front();
33        }
34
35        // Use Lagrange interpolation for temporal prediction
36        if self.state_history.len() < 3 {
37            return current_state;
38        }
39
40        let n = self.state_history.len();
41        let mut future = vec![0.0; current_state.len()];
42
43        // Extrapolate each dimension independently
44        for dim in 0..current_state.len() {
45            let mut sum = 0.0;
46            let t_future = n as f64 + (self.horizon_ns as f64 / 1_000_000.0);
47
48            // Lagrange extrapolation
49            for i in n.saturating_sub(3)..n {
50                let mut prod = self.state_history[i][dim];
51                for j in n.saturating_sub(3)..n {
52                    if i != j {
53                        prod *= (t_future - j as f64) / (i as f64 - j as f64);
54                    }
55                }
56                sum += prod;
57            }
58            future[dim] = sum;
59        }
60
61        // Cache the prediction
62        self.prediction_cache.push((Instant::now(), future.clone()));
63        if self.prediction_cache.len() > 10 {
64            self.prediction_cache.remove(0);
65        }
66
67        future
68    }
69
70    /// Calculate temporal advantage in nanoseconds
71    pub fn temporal_advantage_ns(&self, distance_km: f64) -> i64 {
72        let light_travel_ns = (distance_km * 1_000_000.0 / 299_792.458) * 1_000_000.0;
73        let compute_time_ns = self.horizon_ns as f64 / self.speed_ratio;
74        (light_travel_ns - compute_time_ns) as i64
75    }
76
77    /// Verify prediction accuracy against actual data
78    pub fn verify_prediction(&self, predicted: &[f64], actual: &[f64]) -> f64 {
79        if predicted.len() != actual.len() {
80            return 0.0;
81        }
82
83        let error: f64 = predicted.iter()
84            .zip(actual.iter())
85            .map(|(p, a)| (p - a).powi(2))
86            .sum::<f64>()
87            .sqrt();
88
89        1.0 / (1.0 + error)
90    }
91}
strange_loop/temporal_lead.rs

strange_loop/
temporal_lead.rs
