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c102a10 | 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 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 | # 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,
}
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