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#!/usr/bin/env bash
set -euo pipefail
ROOT=/home/yurenh2/sdil
AUTHOR=/scratch/yurenh2/energy-based-learning
PYTHON=/scratch/yurenh2/venvs/burstccn/bin/python
PROFILE="$ROOT/results/physical_bias/p0_state_dependence.json"
OUT="${RAIN_EP_OUT:-$ROOT/results/ep_bias/centered_r5_upfront}"
mkdir -p "$OUT"
cd "$AUTHOR"
run_cell() {
local gpu=$1
local probes=$2
local mode=$3
local tag=$4
local predictor_kind=$5
CUBLAS_WORKSPACE_CONFIG=:4096:8 CUDA_VISIBLE_DEVICES="$gpu" "$PYTHON" \
"$ROOT/experiments/rain_ep_bias_train.py" \
--author-root "$AUTHOR" --device cuda \
--adapter dillavou --network-protocol comparative32 \
--beta-policy centered --beta-value 0.25 \
--mode "$mode" --bias-ratio 1 \
--dillavou-profile-json "$PROFILE" --predictor-rate 1 \
--predictor-kind "$predictor_kind" \
--dillavou-calibration-steps "$probes" --neutral-cadence 0 \
--epochs 1 --schedule-epochs 100 \
--train-limit 10000 --test-limit 2000 --batch-size 128 \
--training-iterations 15 --inference-iterations 60 \
--evaluation-split train_holdout --data-seed 6200 \
--seed 1988 --beta-seed 7100 --deterministic \
--output "$OUT/$tag.json" > "$OUT/$tag.log" 2>&1
}
probe_budgets=(2 4 8 16)
gpu=0
for probes in "${probe_budgets[@]}"; do
run_cell "$gpu" "$probes" constant "probes_${probes}_intercept" nlms &
gpu=$((gpu + 1))
run_cell "$gpu" "$probes" innovation "probes_${probes}_sdil" \
"${SDIL_PREDICTOR_KIND:-nlms}" &
gpu=$((gpu + 1))
done
wait
"$PYTHON" - "$OUT" <<'PY'
import json
from pathlib import Path
import sys
root = Path(sys.argv[1])
by_budget = {}
for path in sorted(root.glob("probes_*.json")):
_, probes, method = path.stem.split("_", 2)
report = json.loads(path.read_text())
by_budget.setdefault(probes, {})[method] = report["final"]["test_accuracy"]
for probes, values in by_budget.items():
values["sdil_minus_intercept"] = values["sdil"] - values["intercept"]
summary = {"by_upfront_probe_budget": by_budget}
(root / "summary.json").write_text(json.dumps(summary, indent=2) + "\n")
print(json.dumps(summary, indent=2))
PY
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