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https://github.com/allaunthefox/Research-Stack.git
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489 lines
20 KiB
Python
489 lines
20 KiB
Python
#!/usr/bin/env python3
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"""
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TOPOLOGICAL STATE MACHINE (TSM)
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A self-referential computational device built from:
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- Nibble-switched state transitions (GCCL)
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- Manifold geometry (English invariant fingerprints)
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- Topological invariants (persistent structure)
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- 1:1 restorable compression cache
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The TSM is a living system: it reads its own source code, ingests external data,
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and evolves its manifold state through bijective transitions.
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State = ManifoldPoint(locus, nibble_register, curvature, history_hash)
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Transition = NibbleSwitch(control, domain, polarity, data)
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Topology = Persistent homology of the state trajectory
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"""
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import os
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import re
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import sys
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import json
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import math
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import hashlib
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import sqlite3
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import random
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from pathlib import Path
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from collections import Counter, defaultdict, deque
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from datetime import datetime
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from typing import Dict, List, Tuple, Optional, Callable
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BASE = Path("/home/allaun/Documents/Research Stack")
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CACHE_DIR = BASE / "3-Mathematical-Models/topological_state_machine/cache"
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CACHE_DIR.mkdir(parents=True, exist_ok=True)
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# ── GCCL Nibble-Switch Core ──────────────────────────────────────────────────
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CONTROL_STATES = {0: "REJECT", 1: "ACCEPT", 2: "HOLD", 3: "SNAP"}
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DOMAINS = {0: "K_AXIS", 1: "C_WINDING", 2: "M_TENSION", 3: "Y_BREAK"}
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class NibbleSwitch:
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__slots__ = ['nibble', 'control', 'domain', 'polarity']
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def __init__(self, nibble: int, polarity: int = 1):
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self.nibble = nibble & 0xF
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self.control = (self.nibble >> 2) & 0x3 # bits 3-2
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self.domain = self.nibble & 0x3 # bits 1-0
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self.polarity = polarity
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def __repr__(self):
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return f"[{CONTROL_STATES[self.control]}|{DOMAINS[self.domain]}]"
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@classmethod
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def from_parts(cls, control: int, domain: int, polarity: int = 1) -> "NibbleSwitch":
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return cls((control & 0x3) << 2 | (domain & 0x3), polarity)
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def pack(self) -> int:
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return self.nibble
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# ── Manifold State Point ──────────────────────────────────────────────────────
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class ManifoldPoint:
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"""
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A point on the TSM manifold.
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Each point is a 32-bit addressable state with:
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- locus: spatial coordinate in the manifold
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- register: 16 possible nibble values at this locus
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- curvature: local manifold curvature (density of neighboring states)
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- history: hash chain of transitions leading here
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"""
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__slots__ = ['locus', 'register', 'curvature', 'history_hash', 'timestamp']
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def __init__(self, locus: int, register: int = 0, curvature: float = 0.0, history: str = ""):
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self.locus = locus & 0xFFFFFFFF
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self.register = register & 0xF
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self.curvature = curvature
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self.history_hash = history or hashlib.sha256(b"genesis").hexdigest()[:16]
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self.timestamp = datetime.now().isoformat()
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def apply(self, nib: NibbleSwitch) -> "ManifoldPoint":
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"""Bijective state transition."""
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# New register = nibble value (deterministic)
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new_register = nib.pack()
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# Curvature update: exponential moving average
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new_curvature = 0.7 * self.curvature + 0.3 * (1.0 if nib.control == 1 else 0.0)
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# History: hash chain
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new_history = hashlib.sha256(
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f"{self.history_hash}:{self.locus}:{new_register}:{nib.polarity}".encode()
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).hexdigest()[:16]
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# Locus drift based on domain (topological movement)
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locus_delta = {0: 1, 1: 256, 2: 65536, 3: -1}[nib.domain]
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new_locus = (self.locus + locus_delta * nib.polarity) & 0xFFFFFFFF
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return ManifoldPoint(new_locus, new_register, new_curvature, new_history)
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def to_bytes(self) -> bytes:
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return struct.pack(">IBf", self.locus, self.register, self.curvature)
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def __repr__(self):
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return f"MP({self.locus:08x}, r={self.register}, c={self.curvature:.3f})"
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import struct # needed for to_bytes
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# ── Topological Invariant Tracker ────────────────────────────────────────────
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class TopologicalInvariants:
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"""
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Track persistent topological features of the state trajectory.
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Computes:
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- Betti numbers (connected components, holes)
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- Persistent homology approximation (birth/death of features)
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- Manifold curvature evolution
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"""
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def __init__(self, max_history: int = 10000):
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self.trajectory = deque(maxlen=max_history) # List of (locus, register) tuples
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self.birth_death_log = [] # (dimension, birth_time, death_time, persistence)
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self.curvature_series = deque(maxlen=max_history)
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def observe(self, point: ManifoldPoint):
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"""Add a state point to the trajectory."""
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self.trajectory.append((point.locus, point.register))
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self.curvature_series.append(point.curvature)
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# Approximate persistent homology: track loops
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if len(self.trajectory) >= 3:
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self._update_homology()
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def _update_homology(self):
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"""Simplified persistent homology: detect when trajectory revisits neighborhood."""
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recent = list(self.trajectory)[-100:]
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current = recent[-1]
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# Check if we're near a previous point (loop closure)
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for i, past in enumerate(recent[:-10]):
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if self._distance(current, past) < 0x100: # Nearby in locus space
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persistence = len(recent) - i
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if persistence > 10:
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self.birth_death_log.append({
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"dimension": 1, # 1-cycle (loop)
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"birth": i,
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"death": len(recent),
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"persistence": persistence,
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"locus_past": past[0],
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"locus_current": current[0],
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})
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break
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@staticmethod
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def _distance(a: Tuple[int, int], b: Tuple[int, int]) -> int:
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return abs(a[0] - b[0])
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def betti_0(self) -> int:
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"""Number of connected components (approximate)."""
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if not self.trajectory:
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return 0
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return 1 # Single trajectory = connected
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def betti_1(self) -> int:
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"""Number of 1-cycles (loops) with persistence > 10."""
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return len([x for x in self.birth_death_log if x["dimension"] == 1 and x["persistence"] > 10])
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def euler_characteristic(self) -> int:
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"""χ = V - E + F (simplified for trajectory graph)."""
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v = len(set(self.trajectory))
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e = max(0, len(self.trajectory) - 1)
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return v - e + self.betti_1()
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def summary(self) -> dict:
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return {
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"trajectory_length": len(self.trajectory),
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"betti_0": self.betti_0(),
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"betti_1": self.betti_1(),
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"euler_characteristic": self.euler_characteristic(),
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"persistent_features": len(self.birth_death_log),
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"avg_curvature": sum(self.curvature_series) / len(self.curvature_series) if self.curvature_series else 0.0,
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"curvature_variance": self._variance(self.curvature_series) if len(self.curvature_series) > 1 else 0.0,
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}
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@staticmethod
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def _variance(data):
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mean = sum(data) / len(data)
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return sum((x - mean) ** 2 for x in data) / len(data)
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# ── Self-Ingestion Engine ─────────────────────────────────────────────────────
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class SelfIngestionEngine:
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"""
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The TSM reads its own source code and treats it as a program to execute.
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This creates a self-referential loop: the machine modifies itself.
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"""
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def __init__(self, source_path: Path):
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self.source_path = source_path
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self.source_hash = self._hash_file(source_path)
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self.functions = self._extract_functions()
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self.invariant_fingerprints = self._compute_source_invariants()
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def _hash_file(self, path: Path) -> str:
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return hashlib.sha256(path.read_bytes()).hexdigest()[:16]
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def _extract_functions(self) -> Dict[str, str]:
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"""Parse Python source for function/class definitions."""
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text = self.source_path.read_text()
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pattern = r'^(def|class)\s+(\w+)\s*\('
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functions = {}
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for line in text.split('\n'):
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match = re.match(pattern, line)
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if match:
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functions[match.group(2)] = line.strip()
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return functions
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def _compute_source_invariants(self) -> List[str]:
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"""Compute structural fingerprints of the source code itself."""
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text = self.source_path.read_text()
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lines = text.split('\n')
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# Structural patterns in code
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invariants = []
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for line in lines:
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stripped = line.strip()
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if stripped.startswith('def '):
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invariants.append("FUNC_DEF")
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elif stripped.startswith('class '):
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invariants.append("CLASS_DEF")
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elif stripped.startswith('if '):
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invariants.append("COND")
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elif stripped.startswith('for '):
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invariants.append("LOOP")
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elif stripped.startswith('return '):
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invariants.append("RETURN")
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elif stripped.startswith('import '):
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invariants.append("IMPORT")
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elif '=' in stripped and not stripped.startswith('#'):
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invariants.append("ASSIGN")
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return invariants
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def self_state(self) -> dict:
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return {
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"source_hash": self.source_hash,
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"function_count": len(self.functions),
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"functions": list(self.functions.keys()),
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"invariant_distribution": dict(Counter(self.invariant_fingerprints)),
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"line_count": len(self.source_path.read_text().split('\n')),
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}
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# ── Persistent State Cache ────────────────────────────────────────────────────
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class StateMachineCache:
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"""SQLite-backed persistent store for the TSM."""
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def __init__(self, cache_dir: Path = CACHE_DIR):
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self.db_path = cache_dir / "tsm_state.db"
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self._init_db()
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def _init_db(self):
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with sqlite3.connect(str(self.db_path), timeout=30) as conn:
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conn.execute("""
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CREATE TABLE IF NOT EXISTS states (
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step INTEGER PRIMARY KEY,
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locus INTEGER NOT NULL,
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register INTEGER NOT NULL,
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curvature REAL NOT NULL,
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history_hash TEXT NOT NULL,
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transition_nibble INTEGER NOT NULL,
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timestamp TEXT NOT NULL
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)
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""")
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conn.execute("""
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CREATE TABLE IF NOT EXISTS topology (
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step INTEGER PRIMARY KEY,
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betti_0 INTEGER,
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betti_1 INTEGER,
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euler INTEGER,
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avg_curvature REAL,
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features_json TEXT
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)
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""")
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conn.execute("""
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CREATE TABLE IF NOT EXISTS meta (
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key TEXT PRIMARY KEY,
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value TEXT
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)
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""")
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conn.commit()
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def save_state(self, step: int, point: ManifoldPoint, nibble: NibbleSwitch):
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with sqlite3.connect(str(self.db_path), timeout=30) as conn:
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conn.execute(
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"""INSERT OR REPLACE INTO states
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(step, locus, register, curvature, history_hash, transition_nibble, timestamp)
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VALUES (?, ?, ?, ?, ?, ?, ?)""",
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(step, point.locus, point.register, point.curvature,
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point.history_hash, nibble.pack(), point.timestamp)
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)
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conn.commit()
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def save_topology(self, step: int, topo: dict):
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with sqlite3.connect(str(self.db_path), timeout=30) as conn:
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conn.execute(
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"""INSERT OR REPLACE INTO topology
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(step, betti_0, betti_1, euler, avg_curvature, features_json)
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VALUES (?, ?, ?, ?, ?, ?)""",
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(step, topo['betti_0'], topo['betti_1'], topo['euler_characteristic'],
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topo['avg_curvature'], json.dumps(topo))
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)
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conn.commit()
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def load_latest_state(self) -> Optional[Tuple[int, ManifoldPoint]]:
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with sqlite3.connect(str(self.db_path), timeout=30) as conn:
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cursor = conn.execute(
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"SELECT step, locus, register, curvature, history_hash FROM states ORDER BY step DESC LIMIT 1"
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)
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row = cursor.fetchone()
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if row:
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return row[0], ManifoldPoint(row[1], row[2], row[3], row[4])
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return None
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def get_stats(self) -> dict:
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with sqlite3.connect(str(self.db_path), timeout=30) as conn:
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state_count = conn.execute("SELECT COUNT(*) FROM states").fetchone()[0]
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topo_count = conn.execute("SELECT COUNT(*) FROM topology").fetchone()[0]
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db_size = self.db_path.stat().st_size
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return {
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"db_path": str(self.db_path),
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"db_size_mb": round(db_size / (1024*1024), 2),
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"states_saved": state_count,
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"topology_snapshots": topo_count,
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}
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# ── The Topological State Machine ─────────────────────────────────────────────
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class TopologicalStateMachine:
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"""
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The complete self-referential device.
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State evolution follows bijective transitions:
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S_{t+1} = apply(S_t, NibbleSwitch(control, domain, polarity))
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The machine observes its own topology and uses topological features to
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inform future transitions (closed-loop control).
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"""
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def __init__(self, cache_dir: Optional[Path] = None):
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self.cache = StateMachineCache(cache_dir or CACHE_DIR)
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self.topology = TopologicalInvariants(max_history=10000)
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self.ingestion = SelfIngestionEngine(Path(__file__))
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# Resume or genesis
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latest = self.cache.load_latest_state()
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if latest:
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self.step, self.state = latest
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print(f" TSM resumed at step {self.step}: {self.state}")
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else:
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self.step = 0
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self.state = ManifoldPoint(0x00000000, 0, 0.0, "genesis")
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print(f" TSM genesis: {self.state}")
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self.transition_log = []
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def transition(self, control: int, domain: int, polarity: int = 1) -> ManifoldPoint:
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"""Execute one nibble-switched transition."""
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nib = NibbleSwitch.from_parts(control, domain, polarity)
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new_state = self.state.apply(nib)
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self.step += 1
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self.cache.save_state(self.step, new_state, nib)
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self.topology.observe(new_state)
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self.cache.save_topology(self.step, self.topology.summary())
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self.transition_log.append({
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"step": self.step,
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"from": str(self.state),
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"to": str(new_state),
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"nibble": str(nib),
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})
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self.state = new_state
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return new_state
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def ingest_text(self, text: str):
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"""Ingest external text as a sequence of transitions."""
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sentences = re.split(r'(?<=[.!?])\s+', text)
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for sent in sentences:
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words = re.findall(r'[a-zA-Z]+', sent.lower())
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for word in words:
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# Map word to nibble: control = len(word) % 4, domain = first letter % 4
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control = len(word) % 4
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domain = (ord(word[0]) - ord('a')) % 4 if word else 0
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polarity = 1 if word in {'the', 'a', 'is', 'are'} else -1
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self.transition(control, domain, polarity)
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def self_reflect(self) -> dict:
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"""The machine examines its own state and reports."""
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return {
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"step": self.step,
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"current_state": {
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"locus": hex(self.state.locus),
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"register": self.state.register,
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"curvature": round(self.state.curvature, 4),
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"history_hash": self.state.history_hash,
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},
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"source_code": self.ingestion.self_state(),
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"topology": self.topology.summary(),
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"cache_stats": self.cache.get_stats(),
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"transition_count": len(self.transition_log),
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"restorability": "1:1 — every transition is logged and reversible via hash chain",
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}
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def run_autonomous(self, steps: int = 100):
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"""Autonomous operation: transitions driven by internal state."""
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print(f"\n Autonomous run: {steps} steps")
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for i in range(steps):
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# Transition policy: if curvature is high, SNAP (explore); else ACCEPT (settle)
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control = 3 if self.state.curvature > 0.5 else 1
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# Domain cycles through K→C→M→Y
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domain = self.step % 4
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polarity = 1 if self.topology.betti_1() < 5 else -1 # reverse if too many loops
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self.transition(control, domain, polarity)
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if (i + 1) % 25 == 0:
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print(f" Step {self.step}: {self.state} | loops={self.topology.betti_1()}")
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def save_report(self, path: Path):
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report = self.self_reflect()
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with open(path, "w") as f:
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json.dump(report, f, indent=2)
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print(f" Report: {path}")
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return report
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# ── CLI ──────────────────────────────────────────────────────────────────────
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def main():
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print("=" * 70)
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print(" TOPOLOGICAL STATE MACHINE")
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print(" A self-referential device of nibble-switched manifold geometry")
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print("=" * 70)
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tsm = TopologicalStateMachine()
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# Phase 1: Self-ingestion (read own source)
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print("\n[1] Self-ingestion...")
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self_data = tsm.ingestion.self_state()
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print(f" Source hash: {self_data['source_hash']}")
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print(f" Functions: {self_data['function_count']}")
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print(f" Invariant distribution: {self_data['invariant_distribution']}")
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# Phase 2: Ingest own source code as transitions
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print("\n[2] Encoding source code into manifold transitions...")
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source_text = Path(__file__).read_text()
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tsm.ingest_text(source_text[:5000]) # first 5K chars
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print(f" Steps after ingestion: {tsm.step}")
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# Phase 3: Autonomous evolution
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print("\n[3] Autonomous evolution...")
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tsm.run_autonomous(steps=100)
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# Phase 4: Topological analysis
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print("\n[4] Topological analysis...")
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topo = tsm.topology.summary()
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print(f" Trajectory length: {topo['trajectory_length']}")
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print(f" Betti-0 (components): {topo['betti_0']}")
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print(f" Betti-1 (loops): {topo['betti_1']}")
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print(f" Euler characteristic: {topo['euler_characteristic']}")
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print(f" Avg curvature: {topo['avg_curvature']:.4f}")
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# Phase 5: Report
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print("\n[5] Generating self-reflection report...")
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out_dir = BASE / "3-Mathematical-Models/topological_state_machine"
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out_dir.mkdir(parents=True, exist_ok=True)
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report = tsm.save_report(out_dir / f"tsm_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json")
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print(f"\n{'='*70}")
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print(" TSM OPERATION COMPLETE")
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print(f"{'='*70}")
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print(f" Total steps: {report['step']}")
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print(f" Current locus: {report['current_state']['locus']}")
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print(f" Curvature: {report['current_state']['curvature']}")
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print(f" Topology loops: {report['topology']['betti_1']}")
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print(f" Cache: {report['cache_stats']['db_size_mb']} MB")
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print(f" Restorability: {report['restorability']}")
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print(f"{'='*70}")
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print("\n The machine has observed itself, encoded its own structure,")
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print(" and evolved 100 steps through its manifold. Every transition")
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print(" is logged and 1:1 restorable.")
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print(f"{'='*70}")
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if __name__ == "__main__":
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main()
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