#! bin/bash if [ -z "$1" ] then echo "No argument supplied" echo "Need of vector size" exit 1 fi if [ -z "$2" ] then echo "No argument supplied" echo "Need of metric information" exit 1 fi result_filename="models_info/models_comparisons.csv" VECTOR_SIZE=200 size=$1 metric=$2 # selection of four scenes (only maxwell) scenes="A, D, G, H" half=$(($size/2)) start=-$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}; do echo $start $end for mode in {"svd","svdn","svdne"}; do for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do FILENAME="data/${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}" echo $FILENAME # only compute if necessary (perhaps server will fall.. Just in case) if grep -q "${MODEL_NAME}" "${result_filename}"; then echo "${MODEL_NAME} results already generated..." else python generate_data_model_random.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --renderer "maxwell" --step 40 --random 1 --percent 1 python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model} #python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' python save_model_result_in_md_maxwell.py --interval "${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