V6.2 improvements (vs V6.1):
- hyp_t.py: pgvector_lookup_before_punishment() method — HypT consults
SOM map BEFORE applying punishment (delta correction). If status ==
COMPLETE, punishment is SKIPPED (model trusts y_hat). If INCOMPLETE/
MISSING, punishment is applied normally.
(User requirement: "ative pgvector_lookup no nível de hipótese (hyp_t.py)
para que ele também consulte o mapa antes de aplicar punição")
- hyp_t.py: tracks n_complete_skip, n_incomplete_apply, n_missing_apply,
skip_rate (for monitoring)
- train_v6_2.py: BATCH_SIZE=16 (was 4) — user requirement
- train_v6_2.py: 5 datasets from streaming_datasets.py x 50 samples each
(250 total, 2 epochs) — user requirement (was 10 per dataset)
- train_v6_2.py: synthetic fallback expanded to 50 samples per dataset
with templates + random placeholders (ensures 250 samples even when
streaming fails)
- train_v6_2.py: datasets library installed (pip install datasets) for
real streaming
- xeon_runtime.py V6 always a…
V6.1: PGVector-like lookup + EWC math upgrade + 5 datasets x 10 samples
V6.1 improvements:
- orq_cell.py: pgvector_lookup() returns COMPLETE/INCOMPLETE/MISSING
based on neuron_filled_mask + cosine similarity thresholds
- orq_cell.py: neuron_filled_mask buffer (8 bool) tracks SOM-filled
neurons; fill_missing_neurons_random() fills unfilled with random
values (user requirement: "atribuir valores aleatórios aos
parâmetros faltantes")
- ewc.py: skip_som_filled_neurons=True (default) - EWC does NOT
compute Fisher for neurons already consolidated by SOM (user
requirement: "EWC mathematically improved to not use weights
already existing in SOM")
- ewc.py: classic Fisher for orq_cell.neuron_cache also masked
(avoids double-penalization of the same parameter)
- ewc.py: short-circuit when all SOM neurons filled (topology
already preserved by SOM Gaussian neighborhood)
- train_v6_1.py: 5 datasets from streaming_datasets.py x 10 samples
each (50 total, 2 epochs, BATCH_SIZE=4) with synthetic fallback
- train_v6_1.py: PGVector status captured per ste…
