pec5d: add pec5d/holographic_memory.py

pec5d/holographic_memory.py

@@ -0,0 +1,100 @@
+# pec5d/holographic_memory.py
+"""
+HOLO-MEM v∞ — Virtual Memory Architecture.
+
+5 layers:
+  L1 Working Memory          — 5D photonic RAM, teravoxel cache
+  L2 Long-Term Holographic   — pattern-indexed associative recall
+  L3 Ancestral Memory        — lineage archive (12+ generations)
+  L4 Subconscious Latent     — unprocessed signals, emergent intuitions
+  L5 Collective Field Buffer — shared quantum resonance pool
+
+Memory properties: non-volatile, entanglement-linked, self-organizing,
+pattern-reinforcing, error-correcting via Φ symmetry.
+"""
+
+from __future__ import annotations
+
+import time
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from pec5d.constants import PHI, CARRIER_HZ
+
+
+class HolographicMemory:
+    """HOLO-MEM v∞ — 5-layer virtual memory manager."""
+
+    LAYERS = {
+        1: "working",
+        2: "long_term_holographic",
+        3: "ancestral",
+        4: "subconscious_latent",
+        5: "collective_field",
+    }
+
+    def __init__(self, capacity: int = 4096):
+        self.capacity = capacity
+        self.stores: Dict[int, List[Dict[str, Any]]] = {i: [] for i in range(1, 6)}
+        self.interference_patterns: Dict[str, np.ndarray] = {}
+        self.active = False
+
+    def initialize(self) -> "HolographicMemory":
+        """Initialize the memory architecture."""
+        print("🧬 HOLO-MEM v∞ initializing — 5-layer virtual memory")
+        for layer_id, name in self.LAYERS.items():
+            print(f"  L{layer_id} {name}")
+        self.active = True
+        return self
+
+    def write(self, layer: int, key: str, value: Any) -> Dict[str, Any]:
+        """Write a memory entry (entanglement-linked via Φ-interference)."""
+        if layer not in self.LAYERS:
+            raise ValueError(f"invalid layer {layer}")
+        if len(self.stores[layer]) >= self.capacity:
+            self.stores[layer].pop(0)  # FIFO eviction
+
+        pattern = np.sin(np.arange(16) * PHI * layer)
+        self.interference_patterns[key] = pattern
+
+        entry = {"key": key, "value": value, "layer": layer, "ts": time.time()}
+        self.stores[layer].append(entry)
+        return entry
+
+    def recall(self, layer: int, key: str) -> Optional[Dict[str, Any]]:
+        """Associative recall by key (pattern-indexed)."""
+        for entry in self.stores.get(layer, []):
+            if entry["key"] == key:
+                return entry
+        return None
+
+    def recall_all(self, layer: Optional[int] = None) -> Dict[str, Any]:
+        """Dump memory (optionally one layer)."""
+        if layer is not None:
+            return {"layer": layer, "name": self.LAYERS.get(layer), "entries": self.stores[layer]}
+        return {
+            self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)
+        }
+
+    def coherence_correction(self) -> Dict[str, Any]:
+        """Error-correction pass via Φ symmetry (simulated)."""
+        return {
+            "phi_symmetry": round(PHI, 4),
+            "carrier_hz": CARRIER_HZ,
+            "corrections": sum(
+                1 for patterns in self.interference_patterns.values()
+                if np.mean(np.abs(patterns)) > 0.5
+            ),
+            "stores": {self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)},
+        }
+
+    def get_state(self) -> Dict[str, Any]:
+        """Memory architecture state."""
+        return {
+            "active": self.active,
+            "layers": self.LAYERS,
+            "entries": {self.LAYERS[i]: len(self.stores[i]) for i in range(1, 6)},
+            "interference_patterns": len(self.interference_patterns),
+            "capacity": self.capacity,
+        }