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032a961 | 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 | #!/usr/bin/env python3
"""Probe reservoir scaling (E/I balance) + resulting in-vocab accuracy."""
import sys
import time
import numpy as np
sys.path.insert(0, "/home/z/my-project/download/spira")
from spira import SPIRA, SPIRAConfig
from spira.spiking import SpikingCore
data = open("/home/z/my-project/data/train.bin", "rb").read()
cfg = SPIRAConfig(snn_tokens=40_000, am_tokens=1000, epochs=1)
model = SPIRA.build(data, cfg, log=lambda m: None)
ids = model._ids_train
N = 40_000
neurons = SPIRA.input_neurons_stream(ids[:N], cfg.d_in, cfg.k_in)
cols = model.cols_of(ids[: N + 1])
cov = (cols[1:] != cfg.vocab_size)[: N - 1].mean()
print(f"coverage: {cov:.3f}")
variants = [
# (w_in_scale, w_rec_scale, inh_frac, lam)
(1.5, 0.35, 0.50, 0.90),
(1.2, 0.30, 0.50, 0.90),
(1.5, 0.25, 0.50, 0.90),
(1.0, 0.35, 0.50, 0.90),
(1.5, 0.35, 0.50, 0.70),
(2.0, 0.40, 0.50, 0.90),
]
for w_in, w_rec, inh, lam in variants:
core = SpikingCore(n_out=cfg.vocab_size + 1, seed=cfg.seed,
w_in_scale=w_in, w_rec_scale=w_rec, inh_frac=inh, lam=lam,
eta_r=0.002)
# firing rate over first 2000 ticks
core.reset()
sp = 0
for t in range(2000):
core.step(neurons[t])
sp += int(core.s.sum())
rate = sp / 2000 / core.n_res
res = core.train_pass(neurons[: N - 1], cols[1:N], log_every=20_000, log_fn=lambda d: None)
print(f"w_in={w_in:.1f} w_rec={w_rec:.2f} inh={inh:.2f} lam={lam:.2f} | "
f"rate={rate:.3f} overall={res['accuracy']:.4f} in-vocab~={res['accuracy']/cov:.4f}")
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