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