#!/bin/bash
# Night: neural-rule pipeline with K=1600 (online WTA features → exact spiking → dopamine readout), validation.
cd /home/int/run/cats-dogs
~/anvenv/bin/python - <<'PY'
import sys; sys.path.insert(0, "/mnt/c/knp/objects")
import numpy as np, kmeans_features as kf
kf.CONTRAST_DIVIDE = False
tr = np.load("data-v3/train.npz"); base = dict(np.load("v3_k800_lin.npz"))
m = kf.learn_online(tr["rgb"], k=1600, n_patches=600000, base={"mean": base["mean"], "zca": base["zca"]})
np.savez("v3_k1600_online.npz", **m)
PY
~/anvenv/bin/python /mnt/c/knp/objects/spiking_pipeline.py --model v3_k1600_online.npz --thr1 108.0791244506836 --out spiking_v3_k1600_online.npz > spiking_v3_k1600_online.log 2>&1
~/anvenv/bin/python /mnt/c/knp/objects/dopamine_readout.py spiking_v3_k1600_online.npz 2e-4 >> spiking_v3_k1600_online.log 2>&1