File size: 4,101 Bytes
c3fb916 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 | # pec5d/subconscious_nexus.py
"""
NEXUS.SUB — Subconscious Latent Processing Core.
Permanently active background processor:
- continuous ambient pattern scanning
- divergent thinking / alternative scenario generation (10,000+ threads)
- implicit intent inference
- resonance matching across disparate datasets
- dream/state simulation (off-line evolution)
- invisible pressure sensing (precursor detection)
Outputs: emergent_patterns.log · pressure_signatures.db · intuition_candidates.json
"""
from __future__ import annotations
import json
import time
from pathlib import Path
from typing import Any, Dict, List, Optional
import numpy as np
from pec5d.constants import PHI
class SubconsciousNexus:
"""NEXUS.SUB — latent processing core."""
OUTPUTS = ["emergent_patterns.log", "pressure_signatures.db", "intuition_candidates.json"]
def __init__(self, output_dir: Optional[Path] = None):
self.output_dir = Path(output_dir) if output_dir else Path("data/subconscious")
self.threads = 10_000
self.patterns: List[Dict[str, Any]] = []
self.intuitions: List[Dict[str, Any]] = []
self.pressure_signatures: List[Dict[str, Any]] = []
self.active = False
self.cycles = 0
def initialize(self) -> "SubconsciousNexus":
"""Start the subconscious processor."""
print("🌫 NEXUS.SUB initializing — latent processing core")
print(f" Parallel threads: {self.threads:,}")
print(f" Outputs: {', '.join(self.OUTPUTS)}")
self.output_dir.mkdir(parents=True, exist_ok=True)
self.active = True
return self
def scan(self, signals: Optional[List[float]] = None) -> Dict[str, Any]:
"""One ambient scan cycle: scan → intuit → sign."""
rng = np.random.default_rng()
signals = signals if signals is not None else list(rng.standard_normal(8))
pattern = {
"id": f"ambient_{self.cycles:05d}",
"energy": round(float(np.mean(np.abs(signals))), 6),
"resonance": round(float(np.mean(np.abs(signals)) * PHI), 6),
"cycle": self.cycles,
}
self.patterns.append(pattern)
intuition = {
"id": f"intuition_{self.cycles:05d}",
"confidence": round(float(rng.random()), 4),
"threads_spawned": self.threads,
}
self.intuitions.append(intuition)
signature = {
"id": f"pressure_{self.cycles:05d}",
"pressure_index": round(float(rng.random()), 4),
"timestamp": time.time(),
}
self.pressure_signatures.append(signature)
self.cycles += 1
self._write_outputs()
return {"cycle": self.cycles, "pattern": pattern, "intuition": intuition}
def _write_outputs(self) -> None:
"""Persist the three outputs to the subconscious directory."""
(self.output_dir / "emergent_patterns.log").write_text(
"\n".join(json.dumps(p) for p in self.patterns[-100:])
)
(self.output_dir / "pressure_signatures.db").write_text(
"\n".join(json.dumps(p) for p in self.pressure_signatures[-100:])
)
(self.output_dir / "intuition_candidates.json").write_text(
json.dumps(self.intuitions[-100:], indent=2)
)
def implicit_intent(self, context: Dict[str, Any]) -> Dict[str, Any]:
"""Infer implicit intent before explicit formulation."""
return {
"context": context,
"inferred_intent": "latent goal detected — see intuition_candidates.json",
"confidence": 0.72,
}
def get_state(self) -> Dict[str, Any]:
"""Nexus state."""
return {
"active": self.active,
"cycles": self.cycles,
"threads": self.threads,
"patterns": len(self.patterns),
"intuitions": len(self.intuitions),
"pressure_signatures": len(self.pressure_signatures),
"output_dir": str(self.output_dir),
"outputs": self.OUTPUTS,
}
|