#!/usr/bin/env python3 # ============================================================================== # COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) # PROJECT: SOVEREIGN STACK # This artifact is entirely proprietary and cryptographically proven. # Open-Source usage requires explicit permission from Brandon Scott Schneider. # ============================================================================== """Hyperlut: Fractal self-intersecting fluid surface for context compression. This implements a hyper-dimensional look-up table where: 1. All data streams (DNS, FTP, HTTP, SSH, atomic valences, sub-pixel jitter, etc.) are mapped as quanta registers on an n-dimensional surface 2. The surface intersects with itself in fractal recursion 3. Shannon limit is used to recompress through an n-dimensional sieve 4. The result is folded into a hyperlut state object (legacy alias: hyperloot) The hyperlut is not passive storage - it is an active computational bridge. """ from __future__ import annotations import hashlib import json import math from dataclasses import dataclass from datetime import datetime, timezone from pathlib import Path from typing import Any, Dict, List, Set, Tuple # ============================================================================= # Quanta Register Types - All data streams are sub-registers # ============================================================================= class RegisterType: PROTOCOL = "protocol" # DNS, FTP, HTTP, SSH, SMTP, Matrix ATOMIC = "atomic" # Atomic valences, electron states PIXEL = "pixel" # Sub-pixel render states, SDR surfaces JITTER = "jitter" # Process timing jitter, clock drift SEMANTIC = "semantic" # Meaning differential ratings TOKEN = "token" # Context token states HYPER = "hyper" # Hyperlut internal state @dataclass class QuantaRegister: """A single computational register on the n-dimensional surface.""" id: str reg_type: RegisterType coordinates: Tuple[float, ...] # n-dimensional position value: Any phase: float = 0.0 # Phase angle for interference computation amplitude: float = 1.0 # Weight/magnitude entropy: float = 0.0 # Shannon entropy of this register def compute(self) -> Any: """Registers are computational - they process their own value.""" if self.reg_type == RegisterType.PROTOCOL: return self._compute_protocol() elif self.reg_type == RegisterType.ATOMIC: return self._compute_atomic() elif self.reg_type == RegisterType.JITTER: return self._compute_jitter() return self.value def _compute_protocol(self) -> str: """Protocol registers compute reachability state.""" if isinstance(self.value, dict): host = self.value.get("host", "") port = self.value.get("port", 0) return f"{host}:{port}" if host and port else str(self.value) return str(self.value) def _compute_atomic(self) -> int: """Atomic registers compute valence sums.""" if isinstance(self.value, (int, float)): return int(abs(self.value) % 118) # Periodic table bound return 0 def _compute_jitter(self) -> float: """Jitter registers compute timing variance.""" if isinstance(self.value, (int, float)): return float(self.value) % 1.0 return 0.0 @dataclass class FractalIntersection: """Point where the hyperlut surface intersects with itself.""" depth: int # Recursion depth registers: List[QuantaRegister] interference_pattern: str # "constructive", "destructive", "mixed" compression_ratio: float phase_locked: bool = False # ============================================================================= # N-Dimensional Sieve - Filters context through Shannon limit # ============================================================================= class NDimensionalSieve: """Filters context tokens through an n-dimensional sieve.""" def __init__(self, dimensions: int = 11): self.dimensions = dimensions # n in n-dimensional self.shannon_boundary = 0.0 self.register_hash: Dict[str, QuantaRegister] = {} def compute_shannon_limit(self, registers: List[QuantaRegister]) -> float: """Calculate Shannon entropy limit for the register set.""" if not registers: return 0.0 # Compute probability distribution across register values values = [str(r.value) for r in registers] total = len(values) unique_counts: Dict[str, int] = {} for v in values: unique_counts[v] = unique_counts.get(v, 0) + 1 # Shannon entropy: H = -Σ p(x) * log2(p(x)) entropy = 0.0 for count in unique_counts.values(): p = count / total if p > 0: entropy -= p * math.log2(p) # Normalize to [0, 1] max_entropy = math.log2(total) if total > 1 else 1.0 self.shannon_boundary = entropy / max_entropy if max_entropy > 0 else 0.0 return self.shannon_boundary def filter_redundant(self, registers: List[QuantaRegister]) -> List[QuantaRegister]: """Filter out redundant registers based on Shannon limit.""" self.compute_shannon_limit(registers) filtered: List[QuantaRegister] = [] seen_hashes: Set[str] = set() for reg in registers: # Hash the register value for deduplication value_hash = hashlib.sha256(str(reg.value).encode()).hexdigest()[:16] if value_hash not in seen_hashes: seen_hashes.add(value_hash) reg.entropy = self.shannon_boundary filtered.append(reg) return filtered def project_to_surface(self, register: QuantaRegister) -> Tuple[float, ...]: """Project a register onto the n-dimensional surface.""" # Use hash to generate pseudo-random but deterministic coordinates hash_bytes = hashlib.sha256(register.id.encode()).digest() coords = [] for i in range(self.dimensions): # Map hash bytes to [-1, 1] range for each dimension byte_val = hash_bytes[i % len(hash_bytes)] coord = (byte_val / 127.5) - 1.0 coords.append(coord) return tuple(coords) # ============================================================================= # Hyperlut - Fluid fractal surface that intersects with itself # ============================================================================= class Hyperlut: """ Hyper-dimensional look-up table implemented as a fluid fractal surface. The surface intersects with itself in fractal recursion, allowing: - Single tokens to exist at multiple intersection points simultaneously - Destructive interference to cancel redundant bits - Compression scaling at n^n (exponential dimensionality) """ def __init__(self, dimensions: int = 11, recursion_depth: int = 4): self.dimensions = dimensions self.recursion_depth = recursion_depth self.sieve = NDimensionalSieve(dimensions) # The fluid surface - registers mapped to n-dim coordinates self.surface: Dict[Tuple[float, ...], QuantaRegister] = {} # Fractal intersections - where surface meets itself self.intersections: List[FractalIntersection] = [] # Compression state self.compression_ratio = 1.0 self.fluid_viscosity = 0.5 # Controls recursion flow rate self.phase_locked = False # Metadata self.created_utc = datetime.now(timezone.utc).isoformat() self.total_registers = 0 self.compressed_registers = 0 def ingest(self, data: Any, reg_type: RegisterType = RegisterType.TOKEN) -> QuantaRegister: """Ingest data into the hyperlut as a quanta register.""" # Generate register ID from data hash data_str = json.dumps(data, sort_keys=True) if isinstance(data, (dict, list)) else str(data) reg_id = hashlib.sha256(data_str.encode()).hexdigest()[:12] # Create register register = QuantaRegister( id=reg_id, reg_type=reg_type, coordinates=self.sieve.project_to_surface( QuantaRegister(id=reg_id, reg_type=reg_type, coordinates=(), value=data) ), value=data, ) # Add to surface self.surface[register.coordinates] = register self.total_registers += 1 return register def ingest_stream(self, stream: Dict[str, Any]) -> List[QuantaRegister]: """Ingest a multi-stream data packet (protocols, atomic, pixel, jitter).""" registers = [] # Protocol streams (DNS, FTP, HTTP, SSH, SMTP, Matrix) for protocol in ["dns", "ftp", "http", "ssh", "smtp", "matrix"]: if protocol in stream: reg = self.ingest(stream[protocol], RegisterType.PROTOCOL) registers.append(reg) # Atomic valences if "atomic_valence" in stream or "valence" in stream: reg = self.ingest(stream.get("atomic_valence") or stream.get("valence"), RegisterType.ATOMIC) registers.append(reg) # Sub-pixel / SDR surface if "sub_pixel" in stream or "sdr_surface" in stream: reg = self.ingest(stream.get("sub_pixel") or stream.get("sdr_surface"), RegisterType.PIXEL) registers.append(reg) # Process jitter if "jitter" in stream: reg = self.ingest(stream["jitter"], RegisterType.JITTER) registers.append(reg) # Semantic differentials if "semantic" in stream: reg = self.ingest(stream["semantic"], RegisterType.SEMANTIC) registers.append(reg) # Generic tokens if "tokens" in stream: for token in stream["tokens"]: reg = self.ingest(token, RegisterType.TOKEN) registers.append(reg) return registers def fold_fractal(self) -> List[FractalIntersection]: """ Fold the hyperlut surface into fractal recursion. The surface intersects with itself, creating points where: - Multiple registers occupy the same fractal coordinate - Interference patterns determine compression - Destructive interference cancels redundant information """ self.intersections = [] # Group registers by proximity in n-dimensional space coord_groups: Dict[str, List[QuantaRegister]] = {} for coords, register in self.surface.items(): # Quantize coordinates to create fractal bins quantized = tuple(round(c / self.fluid_viscosity) * self.fluid_viscosity for c in coords) key = str(quantized) if key not in coord_groups: coord_groups[key] = [] coord_groups[key].append(register) # Create intersections where multiple registers converge for depth in range(1, self.recursion_depth + 1): for key, registers in coord_groups.items(): if len(registers) < 2: continue # Compute interference pattern phases = [r.phase for r in registers] # Constructive: phases align, amplitudes add # Destructive: phases oppose, amplitudes cancel phase_variance = max(phases) - min(phases) if phases else 0 if phase_variance < 0.1: interference = "constructive" compression = 1.0 / len(registers) elif phase_variance > math.pi - 0.1: interference = "destructive" compression = 1.0 / (len(registers) ** 2) # Better compression else: interference = "mixed" compression = 1.0 / (len(registers) * 1.5) intersection = FractalIntersection( depth=depth, registers=registers, interference_pattern=interference, compression_ratio=compression, phase_locked=(interference == "destructive"), ) self.intersections.append(intersection) # Update compression ratio if self.intersections: avg_compression = sum(i.compression_ratio for i in self.intersections) / len(self.intersections) self.compression_ratio = avg_compression self.compressed_registers = sum(len(i.registers) for i in self.intersections) return self.intersections def compute_hyperlut(self) -> Dict[str, Any]: """ Compute the hyperlut state - the final compressed representation. The hyperlut state is the folded, self-referential representation of all ingested data at the Shannon limit. """ # Fold the surface first self.fold_fractal() # Filter through Shannon sieve all_registers = list(self.surface.values()) filtered = self.sieve.filter_redundant(all_registers) # Build hyperlut representation hyperlut_state = { "schema": "hyperlut/v1", "created_utc": self.created_utc, "dimensions": self.dimensions, "recursion_depth": self.recursion_depth, "shannon_boundary": self.sieve.shannon_boundary, "compression_ratio": self.compression_ratio, "phase_locked": self.phase_locked, "fluid_viscosity": self.fluid_viscosity, "statistics": { "total_registers": self.total_registers, "compressed_registers": self.compressed_registers, "unique_registers": len(filtered), "fractal_intersections": len(self.intersections), "constructive_count": sum(1 for i in self.intersections if i.interference_pattern == "constructive"), "destructive_count": sum(1 for i in self.intersections if i.interference_pattern == "destructive"), "mixed_count": sum(1 for i in self.intersections if i.interference_pattern == "mixed"), }, "surface_hash": hashlib.sha256( json.dumps(sorted(self.surface.keys()), sort_keys=True).encode() ).hexdigest()[:16], } # Add intersection summaries (not full data - that's the compression) hyperlut_state["intersections"] = [ { "depth": i.depth, "pattern": i.interference_pattern, "ratio": i.compression_ratio, "register_count": len(i.registers), "phase_locked": i.phase_locked, } for i in self.intersections[:100] # Limit output ] # Backward-compatible alias for older consumers. hyperlut_state["legacy_alias"] = "hyperloot" return hyperlut_state def compute_hyperloot(self) -> Dict[str, Any]: """Backward-compatible alias for compute_hyperlut().""" return self.compute_hyperlut() def to_equation(self) -> str: """ Represent the hyperlut state as a mathematical equation. Returns the standing wave equation for the bridge state. """ return f""" Ψ_H = [∮_{{∂S}} H(n^n) · e^{{i(ωt - kx)}} dσ] / (S_limit ⊗ R_q) · Γ_∞ Where: H(n^n) = Hyperlut operator at dimensionality {self.dimensions}^{self.dimensions} ∂S = Surface boundary ({len(self.surface)} registers) S_limit = Shannon boundary ({self.sieve.shannon_boundary:.4f}) R_q = Quanta register matrix ({self.total_registers} total) Γ_∞ = Reflection coefficient ({self.compression_ratio:.4f}) Fractal Intersections: {len(self.intersections)} - Constructive: {sum(1 for i in self.intersections if i.interference_pattern == "constructive")} - Destructive: {sum(1 for i in self.intersections if i.interference_pattern == "destructive")} - Mixed: {sum(1 for i in self.intersections if i.interference_pattern == "mixed")} Compression Ratio: {self.compression_ratio:.6f} Phase Locked: {self.phase_locked} """ # ============================================================================= # Bridge Integration - Hyperlut as OmniToken egress filter # ============================================================================= class HyperlutBridge: """ Bidirectional filter using hyperlut as computational bridge. All egress passes through the hyperlut surface: - Inbound: Data flattened to quanta register states - Outbound: Response folded inside hyperlut before transmission """ def __init__(self, dimensions: int = 11, recursion_depth: int = 4): self.hyperlut = Hyperlut(dimensions, recursion_depth) self.egress_queue: List[Dict[str, Any]] = [] self.ingress_log: List[Dict[str, Any]] = [] def filter_ingress(self, payload: Dict[str, Any]) -> Dict[str, Any]: """Filter inbound payload through hyperlut sieve.""" # Log the ingress self.ingress_log.append({ "timestamp": datetime.now(timezone.utc).isoformat(), "payload_hash": hashlib.sha256( json.dumps(payload, sort_keys=True).encode() ).hexdigest()[:16], "size_bytes": len(json.dumps(payload).encode()), }) # Ingest into hyperlut self.hyperlut.ingest_stream(payload) # Fold and compute self.hyperlut.fold_fractal() # Return compressed representation return { "status": "filtered", "shannon_boundary": self.hyperlut.sieve.shannon_boundary, "register_count": len(self.hyperlut.surface), "compression_ratio": self.hyperlut.compression_ratio, } def filter_egress(self, response: Dict[str, Any]) -> Dict[str, Any]: """Filter outbound response through hyperlut fold.""" # Add to egress queue self.egress_queue.append({ "timestamp": datetime.now(timezone.utc).isoformat(), "response_hash": hashlib.sha256( json.dumps(response, sort_keys=True).encode() ).hexdigest()[:16], }) # Fold response into hyperlut self.hyperlut.ingest(response, RegisterType.TOKEN) self.hyperlut.fold_fractal() # Return folded representation return { "status": "folded", "hyperlut_hash": self.hyperlut.compute_hyperlut()["surface_hash"], "hyperloot_hash": self.hyperlut.compute_hyperlut()["surface_hash"], "phase_locked": self.hyperlut.phase_locked, } def compute_bridge_state(self) -> Dict[str, Any]: """Compute current bridge state as hyperlut with legacy alias.""" hyperlut_state = self.hyperlut.compute_hyperlut() return { "bridge_state": "active", "hyperlut": hyperlut_state, "hyperloot": hyperlut_state, "egress_count": len(self.egress_queue), "ingress_count": len(self.ingress_log), "equation": self.hyperlut.to_equation(), } # ============================================================================= # CLI Interface # ============================================================================= def main() -> None: import argparse parser = argparse.ArgumentParser(description="Hyperlut: Fractal context compression") parser.add_argument("--dimensions", type=int, default=11, help="N-dimensional space") parser.add_argument("--recursion", type=int, default=4, help="Fractal recursion depth") parser.add_argument("--test", action="store_true", help="Run compression test") parser.add_argument("--output", type=Path, help="Output hyperlut JSON path") args = parser.parse_args() if args.test: # Run compression test with synthetic data bridge = HyperlutBridge(dimensions=args.dimensions, recursion_depth=args.recursion) # Test payload mimicking protocol overhead, sub-pixel noise, valence fluctuations test_payload = { "jitter": 0.0042, "atomic_valence": 118, "sdr_surface": 0xFFA1, "dns": {"host": "example.com", "port": 53}, "http": {"host": "api.example.com", "port": 443}, "ssh": {"host": "node.tailnet.ts.net", "port": 22}, "smtp": {"host": "mail.tailnet.ts.net", "port": 587}, "matrix": {"server": "matrix.tailnet.ts.net", "port": 8448}, "tokens": ["context_token_" + str(i) for i in range(100)], } print("=== HYPERLUT COMPRESSION TEST ===\n") print("Ingesting test payload...") ingress_result = bridge.filter_ingress(test_payload) print(f"Ingress filtered: {json.dumps(ingress_result, indent=2)}\n") print("Folding fractal surface...") bridge.hyperlut.fold_fractal() print("Computing hyperlut...") state = bridge.compute_bridge_state() print("\n=== RESULTS ===") print(f"Dimensions: {args.dimensions}^{args.dimensions}") print(f"Compression Ratio: {state['hyperlut']['compression_ratio']:.6f}") print(f"Shannon Boundary: {state['hyperlut']['shannon_boundary']:.4f}") print(f"Fractal Intersections: {state['hyperlut']['statistics']['fractal_intersections']}") print(f" - Destructive (best compression): {state['hyperlut']['statistics']['destructive_count']}") print(f"\n=== EQUATION ==={state['equation']}") if args.output: args.output.write_text(json.dumps(state, indent=2), encoding="utf-8") print(f"\nHyperlut written to: {args.output}") else: # Initialize and output equation hyperlut = Hyperlut(dimensions=args.dimensions, recursion_depth=args.recursion) print(hyperlut.to_equation()) if __name__ == "__main__": main()