cross_run.sh 1.8 KB

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  1. metric="min_diff_filter"
  2. scenes="A,B,D,G,H,I"
  3. all_scenes="A,B,C,D,E,F,G,H,I"
  4. # file which contains model names we want to use for simulation
  5. file_path="results/models_comparisons.csv"
  6. for window in {"3","5","7","9","11"}; do
  7. echo python generate/generate_reconstructed_data.py --features ${metric} --params ${window},${window} --size 100,100 --scenes ${all_scenes}
  8. done
  9. for scene in {"A","B","D","G","H","I"}; do
  10. # remove current scene test from dataset
  11. s="${scenes//,${scene}}"
  12. s="${s//${scene},}"
  13. for zone in {10,11,12}; do
  14. for balancing in {0,1}; do
  15. OUTPUT_DATA_FILE="${metric}_nb_zones_${zone}_W${width}_H${height}_balancing${balancing}_without_${scene}"
  16. OUTPUT_DATA_FILE_TEST="${metric}_nb_zones_${zone}_W${width}_H${height}_balancing${balancing}_scene_${scene}"
  17. if grep -q "${OUTPUT_DATA_FILE}" "${file_path}"; then
  18. echo "SVD model ${OUTPUT_DATA_FILE} already generated"
  19. else
  20. #echo "Run computation for SVD model ${OUTPUT_DATA_FILE}"
  21. echo python generate/generate_dataset.py --output data/${OUTPUT_DATA_FILE_TEST} --features ${metric} --scenes ${scene} --params ${width},${height} --nb_zones ${zone} --random 1 --size 100,100
  22. echo python generate/generate_dataset.py --output data/${OUTPUT_DATA_FILE} --features ${metric} --scenes ${s} --params ${width},${height} --nb_zones ${zone} --random 1 --size 100,100
  23. echo python train_model.py --data data/${OUTPUT_DATA_FILE} --output ${OUTPUT_DATA_FILE} --balancing ${balancing}
  24. echo python prediction_model.py --data data/${OUTPUT_DATA_FILE_TEST}.train --model saved_models/${OUTPUT_DATA_FILE}.json
  25. fi
  26. done
  27. done
  28. done