pec5d: add pec5d/lineage_oracle.py

pec5d/lineage_oracle.py

@@ -0,0 +1,83 @@
+# 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(),
+        }