File size: 3,034 Bytes
d09dbc2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | # pec5d/lineage_oracle.py
"""
LINEAGE.ORACLE — Ancestral & Temporal Core.
12+ generation ancestral archive · bloodline signature matching ·
timeline divergence tracking. The archive is a vector-space model:
each generation is a resonance signature vector.
"""
from __future__ import annotations
import time
from typing import Any, Dict, List, Optional
import numpy as np
from pec5d.constants import LINEAGE_GENERATIONS, PHI
class LineageOracle:
"""LINEAGE.ORACLE — ancestral archive + signature matching engine."""
def __init__(self, generations: int = LINEAGE_GENERATIONS):
self.generations = generations
self.archive: Dict[int, Dict[str, Any]] = {}
self.signatures: List[np.ndarray] = []
self.active = False
def initialize(self) -> "LineageOracle":
"""Initialize the lineage archive."""
print(f"📜 LINEAGE.ORACLE initializing — {self.generations}+ generations")
rng = np.random.default_rng(seed=432)
for gen in range(1, self.generations + 1):
sig = rng.standard_normal(16)
sig = sig / (np.linalg.norm(sig) or 1.0)
self.signatures.append(sig)
self.archive[gen] = {
"generation": gen,
"weight": round(float(np.mean(sig)), 4),
"resonance": round(PHI ** (-gen / 12), 4),
"signature_id": f"lineage_{gen:03d}",
}
self.active = True
print(f"✅ LINEAGE.ORACLE archive loaded ({len(self.archive)} generations)")
return self
def match_signature(self, query: np.ndarray, top_k: int = 3) -> Dict[str, Any]:
"""Match a bloodline signature against the archive."""
if not self.active:
self.initialize()
q = query / (np.linalg.norm(query) or 1.0)
scores = [
(gen, float(np.abs(np.vdot(q, sig))))
for gen, sig in enumerate(self.signatures, start=1)
]
scores.sort(key=lambda x: x[1], reverse=True)
top = [{"generation": g, "similarity": round(s, 4)} for g, s in scores[:top_k]]
return {"matches": top, "archive_size": len(self.archive)}
def timeline_divergence(self, anchor_year: int, horizon: int = 20) -> Dict[str, Any]:
"""Track timeline divergence over a horizon (simulated)."""
divergences = []
for step in range(horizon):
divergences.append({
"step": step + 1,
"divergence": round(float(np.random.random() * PHI / 10), 4),
})
return {
"anchor_year": anchor_year,
"horizon": horizon,
"divergences": divergences,
"generations_anchored": self.generations,
}
def get_state(self) -> Dict[str, Any]:
"""Oracle state."""
return {
"active": self.active,
"generations": self.generations,
"archive_entries": len(self.archive),
"phi": PHI,
"timestamp": time.time(),
}
|