embedding_dim=self.embedding_dim,
total_updates=
self.total_updates,
total_hits=int(total),
coverage_fraction=coverage_fraction,
coverage_gini=gini,
mean_hits_per_cell=float(self.hit_counts.mean()),
max_hits_cell=(int(max_idx[0]), int(max_idx[1])),
max_hits_value=int(self.hit_counts.max()),
current_learning_rate=float(self.learning_rate),
current_neighborhood_sigma=float(self.neighborhood_sigma),
gap_field_max=float(gap.max()),
gap_field_argmax=(int(gap_argmax[0]), int(gap_argmax[1])),
gap_field_mean=float(gap.mean()),
gap_field_std=float(gap.std()),
weight_norm_mean=float(weight_norms.mean()),
weight_norm_std=float(weight_norms.std()),
)
from dataclasses import dataclass, field
from typing import Optional, Dict, Any
@dataclass
class BiofieldLatticeSnapshot(LatticeSnapshot):
"""Extended snapshot for biofield protection monitoring.
Adds qualia integration, coherence, and elevation metrics."""
qualia_updates: int = 0
biofield_coherence: float = 0.0 # 1 - normalized gap mean (higher = more integrated)
gap_closure_rate: float = 0.0 # recent reduction in gap energy per qualia update
fragmentation_risk: float = 0.0 # composite: high gini high gap_mean
dominant_attractor_stability: float = 0.0 # inverse of weight_norm_std low local gap variance
elevation_score: float = 0.0 # delta in coherence - weighted fragmentation
last_gap_max_locus: Tuple[int, int] = (0, 0)
protection_triggered: bool = False
class BiofieldProtection:
"""
Qualia-Driven Gap Closure (QDGC) Protection Algorithm.
Models the physical body's multi-scale state as a toroidal embedding lattice.
Qualia vectors (subjective phenomenal states correlated with physiological shifts)
are used as directed learning signals to increase coverage, reduce fragmentation,
and stabilize resilient attractors.
"Protection" = active detection and closure of high-gap (under-integrated) regions.
"Elevation" = measurable improvement in snapshot metrics toward coherent regime.
This is a hypothetical computational model using only verified classical methods
(Kohonen 1982 toroidal SOM exact FFT). It is simulation/hypothesis grade.
Not a medical claim. Any "biofield" here is the emergent lattice field of state
embeddings visitation gap energy.
"""
def __init__(
self,
lattice: ToroidalLattice,
coherence_threshold: float = 0.55,
qvm_risk_threshold: float = 0.75,
qualia_boost: float = 1.5,
protection_window: int = 50,
):
self.lattice = lattice
self.coherence_threshold = coherence_threshold
self.qvm_risk_threshold = qvm_risk_threshold
self.qualia_boost = qualia_boost
self.protection_window = protection_window
self.qualia_update_count = 0
self.last_coherence = 0.0
self.history: list[BiofieldLatticeSnapshot] = []
def _compute_extended_snapshot(self, base: LatticeSnapshot) -> BiofieldLatticeSnapshot:
gap =
self.lattice.gap_field()
gap_mean = float(gap.mean())
coherence = 1.0 - gap_mean
frag_risk = 0.6 * base.coverage_gini 0.4 * gap_mean
stab = 1.0 / (1.0 base.weight_norm_std) * (1.0 - gap.std() / (gap.max() 1e-9))
elev = coherence - frag_risk
return BiofieldLatticeSnapshot(
**base.__dict__,