#! bin/bash # file which contains model names we want to use for simulation simulate_models="simulate_models.csv" # selection of four scenes (only maxwell) scenes="A, D, G, H" VECTOR_SIZE=200 for size in {"4","8","16","26","32","40"}; do for metric in {"lab","mscn","mscn_revisited","low_bits_2","low_bits_3","low_bits_4","low_bits_5","low_bits_6","low_bits_4_shifted_2"}; do half=$(($size/2)) start=-$half for counter in {0..4}; do end=$(($start+$size)) if [ "$end" -gt "$VECTOR_SIZE" ]; then start=$(($VECTOR_SIZE-$size)) end=$(($VECTOR_SIZE)) fi if [ "$start" -lt "0" ]; then start=$((0)) end=$(($size)) fi for nb_zones in {4,6,8,10,12,14}; do for mode in {"svd","svdn","svdne"}; do for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do FILENAME="data/${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}" MODEL_NAME="${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}" if grep -xq "${MODEL_NAME}" "${simulate_models}"; then echo "Run simulation for model ${MODEL_NAME}" # by default regenerate model python generate/generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model} python predict_seuil_expe_maxwell_curve.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' python others/save_model_result_in_md_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} fi done done done if [ "$counter" -eq "0" ]; then start=$(($start+50-$half)) else start=$(($start+50)) fi done done done