qualia_core_db/domains/mathematical/

geometric.rs

use crate::NQuin;

/// A point projected into the Lorentz Hyperboloid (Minkowski space).
/// The NQuin is unpacked into a standard Euclidean embedding and then projected.
#[derive(Debug, Clone, Copy)]
pub struct LorentzVector {
    pub x0: f32, // The time-like component (always positive)
    pub x1: f32,
    pub x2: f32,
    pub x3: f32,
}

impl LorentzVector {
    /// Maps a 48-byte NQuin into a 4D Lorentz vector for exact non-Euclidean representation.
    pub fn from_quin(quin: &NQuin) -> Self {
        // Unpack subject, predicate, object into floating-point coordinates.
        // In production, these map to standard LLM embedding weights.
        let v1 = (quin.subject % 1000) as f32 / 100.0;
        let v2 = (quin.predicate % 1000) as f32 / 100.0;
        let v3 = (quin.object % 1000) as f32 / 100.0;

        // Compute x0 to ensure it sits on the upper sheet of the hyperboloid (x0^2 - x1^2 - x2^2 - x3^2 = 1)
        let x0 = (1.0 + v1 * v1 + v2 * v2 + v3 * v3).sqrt();

        Self {
            x0,
            x1: v1,
            x2: v2,
            x3: v3,
        }
    }

    /// Computes the Minkowski inner product (Lorentz distance).
    /// Bypasses all trigonometric constraints, mapping perfectly to FMA instructions.
    #[inline(always)]
    pub fn lorentz_distance(&self, other: &LorentzVector) -> f32 {
        -(self.x0 * other.x0) + (self.x1 * other.x1) + (self.x2 * other.x2) + (self.x3 * other.x3)
    }
}

/// A Tropical Polynomial using the Min-Plus semiring (min, +).
pub struct MinPlusVoronoiCell {
    pub centroid: LorentzVector,
    pub cell_id: u32,
}

impl MinPlusVoronoiCell {
    /// Evaluates if a query belongs to this Voronoi cell using Min-Plus algebra.
    /// In Tropical geometry, standard Matrix Multiplication (x * y) becomes addition (x + y).
    /// Standard summation becomes finding the minimum.
    #[inline(always)]
    pub fn tropical_distance(&self, query: &LorentzVector) -> f32 {
        // x ⊗ y = x + y (Tropical Multiplication)
        let d0 = self.centroid.x0 + query.x0;
        let d1 = self.centroid.x1 + query.x1;
        let d2 = self.centroid.x2 + query.x2;
        let d3 = self.centroid.x3 + query.x3;

        // ⊕ = min (Tropical Addition)
        d0.min(d1).min(d2).min(d3)
    }
}

pub trait BoundingHull {}

pub struct VectorSectorMap {
    pub sector_id: u64,
    pub active: bool,
}

impl VectorSectorMap {
    pub fn contains(&self, projection: u64) -> bool {
        if !self.active {
            return false;
        }
        (projection % 10) == self.sector_id
    }
}

pub fn extract_spatial_projection(quin: &NQuin) -> u64 {
    quin.metadata
}

use core::sync::atomic::{AtomicBool, AtomicU32, Ordering};
use std::sync::atomic::AtomicU64;

/// Global halt flag for .q42 ingestion triggered by Topological Compression
pub static HALT_INGESTION: AtomicBool = AtomicBool::new(false);

/// Current active centroid packed into a 64-bit atomic (assuming lower fidelity or bit-packing for tracking)
/// Using AtomicU64 to store scaled f32 x0, x1 components for zero-allocation tracking.
pub static CURRENT_CENTROID_X0: AtomicU64 = AtomicU64::new(0);
pub static CURRENT_CENTROID_X1: AtomicU64 = AtomicU64::new(0);
pub static CURRENT_CENTROID_X2: AtomicU64 = AtomicU64::new(0);
pub static CURRENT_CENTROID_X3: AtomicU64 = AtomicU64::new(0);

pub static STABLE_TICK_COUNT: AtomicU32 = AtomicU32::new(0);

/// The proxy for Vietoris-Rips topological features.
/// Evaluates the stability of the semantic context centroid.
pub struct HomologicalSieve;

impl HomologicalSieve {
    /// Evaluates if the semantic topology has stabilized (Topological Compression)
    pub fn evaluate_topology_tick(active_bitmask: *const u64, quins: &[NQuin], tier_mask: u8) {
        if quins.is_empty() {
            return;
        }

        // 1. SIMD Optimization: Bitwise Population Count (POPCNT)
        // Note: active_bitmask is a pointer to the GPU Sieve's bitmask output.
        // We simulate reading the bitmask for the active nodes.
        let active_nodes_count = unsafe { (*active_bitmask).count_ones() } as usize;

        if active_nodes_count == 0 {
            return;
        }

        // 2. Calculate new geometric centroid of active nodes
        let mut sum_x0 = 0.0;
        let mut sum_x1 = 0.0;
        let mut sum_x2 = 0.0;
        let mut sum_x3 = 0.0;

        // In real hardware, we'd use the bitmask to select exactly the active quins via PEXT.
        // Here we just average the first `active_nodes_count` for heuristic demonstration.
        let limit = active_nodes_count.min(quins.len());
        for i in 0..limit {
            let l_vec = LorentzVector::from_quin(&quins[i]);
            sum_x0 += l_vec.x0;
            sum_x1 += l_vec.x1;
            sum_x2 += l_vec.x2;
            sum_x3 += l_vec.x3;
        }

        let inv_count = 1.0 / limit as f32;
        let new_centroid = LorentzVector {
            x0: sum_x0 * inv_count,
            x1: sum_x1 * inv_count,
            x2: sum_x2 * inv_count,
            x3: sum_x3 * inv_count,
        };

        // Reconstruct CURRENT_CENTROID from atomics
        let scale = 1_000_000.0; // scale factor to store f32 in u64
        let curr_x0 = (CURRENT_CENTROID_X0.load(Ordering::Relaxed) as f32) / scale;
        let curr_x1 = (CURRENT_CENTROID_X1.load(Ordering::Relaxed) as f32) / scale;
        let curr_x2 = (CURRENT_CENTROID_X2.load(Ordering::Relaxed) as f32) / scale;
        let curr_x3 = (CURRENT_CENTROID_X3.load(Ordering::Relaxed) as f32) / scale;

        let curr_centroid = LorentzVector {
            x0: curr_x0,
            x1: curr_x1,
            x2: curr_x2,
            x3: curr_x3,
        };

        // 3. Calculate Centroid Drift (Lorentz Minkowski Distance)
        let drift = new_centroid.lorentz_distance(&curr_centroid).abs();

        let epsilon = 0.005; // Convergence tolerance

        // 4. Update the state and Halting Condition
        if drift < epsilon {
            STABLE_TICK_COUNT.fetch_add(1, Ordering::SeqCst);
        } else {
            STABLE_TICK_COUNT.store(0, Ordering::SeqCst);
        }

        // Store new centroid back to atomics
        CURRENT_CENTROID_X0.store((new_centroid.x0 * scale) as u64, Ordering::Relaxed);
        CURRENT_CENTROID_X1.store((new_centroid.x1 * scale) as u64, Ordering::Relaxed);
        CURRENT_CENTROID_X2.store((new_centroid.x2 * scale) as u64, Ordering::Relaxed);
        CURRENT_CENTROID_X3.store((new_centroid.x3 * scale) as u64, Ordering::Relaxed);

        // 5. Check Tier Configuration and Interrupt
        let threshold = Self::get_stability_threshold(tier_mask);
        if STABLE_TICK_COUNT.load(Ordering::SeqCst) >= threshold {
            HALT_INGESTION.store(true, Ordering::SeqCst);
        }
    }

    /// Determines the threshold of consecutive stable ticks required before throwing the interrupt.
    pub fn get_stability_threshold(tier_mask: u8) -> u32 {
        match tier_mask {
            0b01 => 3, // Permissive Commons: Aggressive early exit
            0b10 => 5, // Bilateral Micro-Commons: High certainty needed
            _ => 4,    // Standard/Default
        }
    }
}