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- #! 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 feature information"
- exit 1
- fi
- result_filename="results/models_comparisons.csv"
- VECTOR_SIZE=200
- size=$1
- feature=$2
- # selection of four scenes (only maxwell)
- scenes="A, D, G, H"
- start=0
- end=$size
- for nb_zones in {4,6,8,10,12}; 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_${nb_zones}_${feature}_${mode}"
- MODEL_NAME="${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}"
- CUSTOM_MIN_MAX_FILENAME="N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
- 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/generate_data_model_random_center.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 10 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
- python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
- #python prediction/predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${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},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
- fi
- done
- done
- done
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