#! bin/bash # file which contains model names we want to use for simulation simulate_models="simulate_models.csv" start_index=0 size=24 # selection of four scenes (only maxwell) scenes="A, D, G, H" feature="lab" for label in {"0","1"}; do for highest in {"0","1"}; do for nb_zones in {4,6,8,10,12}; do for size in {5,10,15,20,25,30,35,40}; 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${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}" MODEL_NAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}" CUSTOM_MIN_MAX_FILENAME="${model}_N${size}_B${start_index}_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_corr_L${label}_H${highest}_min_max_values" echo ${MODEL_NAME} if grep -xq "${MODEL_NAME}" "${simulate_models}"; then echo "Run simulation for model ${MODEL_NAME}" python generate/generate/generate_data_model_corr_random.py --output ${FILENAME} --n ${size} --highest ${highest} --label ${label} --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom 1 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model} python prediction/prediction/predict_seuil_expe_maxwell_curve.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} --limit_detection '2' --custom ${CUSTOM_MIN_MAX_FILENAME} python others/save_model_result_in_md_maxwell.py --interval "${start_index},${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature} fi done done done done done done