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#!/usr/bin/env bash
# Three-way residualization control with per-neuron soma-dendrite coupling:
# raw = unmodified apical teaching signal
# matched = raw direction, per-sample norm matched to the innovation
# residual = soma-dendritic innovation
#
# Usage:
# bash experiments/nuisance_control_sweep.sh \
# <gpu> "<rhos>" "<seeds>" <epochs> <prefix> [pert_mode] [n_dirs]
#
# Example:
# bash experiments/nuisance_control_sweep.sh \
# 5 "0 0.05 0.2 0.5" "0 1 2" 15 nuis_ctrl simultaneous 16
set -eu
cd "$(dirname "$0")/.."
PY=/home/yurenh2/miniconda3/envs/ep_pascal/bin/python3
GPU="${1:?GPU index required}"
RHOS="${2:-0 0.05 0.2 0.5}"
SEEDS="${3:-0}"
EPOCHS="${4:-15}"
PREFIX="${5:-nuis_ctrl}"
PERT_MODE="${6:-simultaneous}"
N_DIRS="${7:-16}"
export CUDA_VISIBLE_DEVICES="$GPU"
export OMP_NUM_THREADS=2
mkdir -p results logs/nuisance
run_one() {
local rho="$1"
local arm="$2"
local seed="$3"
local residual=0
local scale_control=none
local rho_tag="${rho//./p}"
local tag="${PREFIX}_rho${rho_tag}_${arm}_s${seed}"
local result="results/${tag}.json"
local log="logs/nuisance/${tag}.log"
if [[ "$arm" == residual ]]; then
residual=1
elif [[ "$arm" == matched ]]; then
scale_control=match_innovation_norm
fi
if [[ -f "$result" ]]; then
echo "skip $tag (result exists)"
return
fi
echo ">>> $tag $(date --iso-8601=seconds) gpu=$GPU"
"$PY" experiments/run.py \
--mode sdil --dataset mnist --depth 3 --width 256 --epochs "$EPOCHS" \
--seed "$seed" --nuis_rho "$rho" --use_residual "$residual" \
--raw_scale_control "$scale_control" --predictor_mode diagonal \
--p_warmup_steps 200 --p_warmup_eta 0.05 \
--pert_mode "$PERT_MODE" --pert_ndirs "$N_DIRS" --log_every 100 \
--tag "$tag" --outdir results > "$log" 2>&1
grep -h DONE "$log"
}
for seed in $SEEDS; do
for rho in $RHOS; do
for arm in raw matched residual; do
run_one "$rho" "$arm" "$seed"
done
done
done
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