generateAndTrain_maxwell_custom_filters.sh 1.8 KB

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  1. #! bin/bash
  2. if [ -z "$1" ]
  3. then
  4. echo "No argument supplied"
  5. echo "Need of vector size"
  6. exit 1
  7. fi
  8. if [ -z "$2" ]
  9. then
  10. echo "No argument supplied"
  11. echo "Need of feature information"
  12. exit 1
  13. fi
  14. if [ -z "$3" ]
  15. then
  16. echo "No argument supplied"
  17. echo "Need of kind of data to use"
  18. exit 1
  19. fi
  20. result_filename="results/models_comparisons.csv"
  21. VECTOR_SIZE=200
  22. size=$1
  23. feature=$2
  24. data=$3
  25. # selection of four scenes (only maxwell)
  26. scenes="A, D, G, H"
  27. for nb_zones in {4,6,8,10,12}; do
  28. for mode in {"svd","svdn","svdne"}; do
  29. for model in {"svm_model","ensemble_model","ensemble_model_v2"}; do
  30. FILENAME="data/${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
  31. MODEL_NAME="${model}_N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}"
  32. CUSTOM_MIN_MAX_FILENAME="N${size}_B0_E${size}_nb_zones_${nb_zones}_${feature}_${mode}_min_max"
  33. echo $FILENAME
  34. # only compute if necessary (perhaps server will fall.. Just in case)
  35. if grep -q "${MODEL_NAME}" "${result_filename}"; then
  36. echo "${MODEL_NAME} results already generated..."
  37. else
  38. python generate/generate_data_model_random_${data}.py --output ${FILENAME} --interval "0,${size}" --kind ${mode} --feature ${feature} --scenes "${scenes}" --nb_zones "${nb_zones}" --percent 1 --renderer "maxwell" --step 40 --random 1 --custom ${CUSTOM_MIN_MAX_FILENAME}
  39. python train_model.py --data ${FILENAME} --output ${MODEL_NAME} --choice ${model}
  40. python others/save_model_result_in_md_maxwell.py --interval "0,${size}" --model "saved_models/${MODEL_NAME}.joblib" --mode "${mode}" --feature ${feature}
  41. fi
  42. done
  43. done
  44. done