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@@ -5,6 +5,7 @@ simulate_models="simulate_models.csv"
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# selection of four scenes (only maxwell)
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scenes="A, D, G, H"
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+VECTOR_SIZE=200
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for size in {"4","8","16","26","32","40"}; do
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for metric in {"lab","mscn","mscn_revisited","low_bits_2","low_bits_3","low_bits_4"}; do
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@@ -27,17 +28,22 @@ for size in {"4","8","16","26","32","40"}; do
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for nb_zones in {4,6,8,10,12,14}; do
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- echo $start $end
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-
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for mode in {"svd","svdn","svdne"}; do
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for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
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+ FILENAME="data/data_maxwell_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}"
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+
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MODEL_NAME="${model}_N${size}_B${start}_E${end}_nb_zones_${nb_zones}_${metric}_${mode}"
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if grep -q "${MODEL_NAME}" "${simulate_models}"; then
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echo "Run simulation for model ${MODEL_NAME}"
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- python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2'
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+ # by default regenerate model
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+ python generate_data_model_random_maxwell.py --output ${FILENAME} --interval "${start},${end}" --kind ${mode} --metric ${metric} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --sep ';' --rowindex '0'
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+
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+ python models/${model}_train.py --data ${FILENAME} --output ${MODEL_NAME}
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+
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+ python predict_seuil_expe_maxwell.py --interval "${start},${end}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --metric ${metric} --limit_detection '2' &
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fi
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done
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done
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