# Pour le moment, le run ne fait qu'executer
  # quelques petites commandes que l'on souhaite
  # tester.

  OUTDIR="exp/kmeans_teacher_1/pvector-1"
  DATADIR="data"

  NEW_LSTDIR="${OUTDIR}/lst"

  
  kmin=2
  kmax=100

  if [ ! -d "$OUTDIR" ];
  then
      mkdir -p $OUTDIR
  fi

  if [ ! -d "${NEW_LSTDIR}" ];
  then
      mkdir -p ${NEW_LSTDIR}
  fi
  
  for kfold in 4 #..4}

  do
      #echo "kfold = ${kfold}"
      pvector_file="${DATADIR}/pvectors_1rst/pvectors_teacher_${kfold}.txt"
      lst_dir="${DATADIR}/pvectors_1rst/lst"
      output_kfold="${OUTDIR}/${kfold}"
      
      if [ ! -d "${output_kfold}" ];
      then
          mkdir -p ${output_kfold}
      fi

          
      
      # Extract character information
      echo "Extracting character information"
      python3 "bin/replace_label.py" \
          "${DATADIR}/masseffect.lst" \
          "${DATADIR}/character_information.csv" \
          --field "type" --lst "data/pvectors_1rst/lst/train_${kfold}.lst" \
          --outfile "${NEW_LSTDIR}/train_${kfold}_type.lst"

      python3 "bin/replace_label.py" \
          "${DATADIR}/masseffect.lst" \
          "${DATADIR}/character_information.csv" \
          --field "type" --lst "data/pvectors_1rst/lst/val_${kfold}.lst" \
          --outfile "${NEW_LSTDIR}/val_${kfold}_type.lst"
      cat "${NEW_LSTDIR}/train_${kfold}_type.lst" "${NEW_LSTDIR}/val_${kfold}_type.lst" > "${NEW_LSTDIR}/metas_${kfold}_type.lst"
  
      # -- TRAIN KMEANS 

      echo "Clustering - ${kfold}"
      python3 bin/cluster_kmeans.py "${pvector_file}" \
          "${lst_dir}/train_${kfold}.lst" \
          "${output_kfold}" --kmin ${kmin} --kmax ${kmax}
  
      for k in $(seq ${kmin} 1 ${kmax})
      do

          # -- EXTRACT KMEANS VALUES

          echo "Kmeans Measuring and extraction - ${k}"

          python3 bin/extract_kmeans.py "${output_kfold}/${k}/clustering_${k}.pkl" \
              "${pvector_file}" \
              --outfile "${output_kfold}/${k}/clustered_${k}.txt"

          
          
          # -- MEASURES AND PLOT WITH RESPECT TO CHARACTER VAR
          # Measures

          python3 bin/measure_clustering.py "${output_kfold}/${k}/clustered_${k}.txt" "${pvector_file}" "${lst_dir}/train_${kfold}.lst" "${lst_dir}/val_${kfold}.lst" --outfile "${output_kfold}/${k}/measures.json"

          
          # Plot count matrix for train
          python3 bin/plot-count-matrix.py ${output_kfold}/${k}/clustered_${k}.txt \
          ${pvector_file} ${lst_dir}/train_${kfold}.lst \
          --outfile ${output_kfold}/${k}/train_count_matrix.pdf
          
          # Plot count matrix for val
          python3 bin/plot-count-matrix.py ${output_kfold}/${k}/clustered_${k}.txt \
              ${pvector_file} ${lst_dir}/val_${kfold}.lst \
              --outfile ${output_kfold}/${k}/val_count_matrix.pdf
  
          # Regroup measures with respect to character var
          python3 bin/regroup-measures.py exp/kmeans_teacher_1/pvector-1/
  
          # -- MEASURES AND PLOT WITH RESPECT TO TYPE VAR
          # Measures
          python3 bin/measure_clustering.py "${output_kfold}/${k}/clustered_${k}.txt" "${NEW_LSTDIR}/metas_${kfold}_type.lst" "${lst_dir}/train_${kfold}.lst" "${lst_dir}/val_${kfold}.lst" --outfile "${output_kfold}/${k}/measures_type.json"
          
          # This script plot the count matrix of the train set
          python3 bin/plot-count-matrix.py ${output_kfold}/${k}/clustered_${k}.txt \
          ${NEW_LSTDIR}/metas_${kfold}_type.lst ${lst_dir}/train_${kfold}.lst \
          --outfile ${output_kfold}/${k}/train_count_matrix_type.pdf
          
          # This script plot the count matrix of the validation set
          python3 bin/plot-count-matrix.py ${output_kfold}/${k}/clustered_${k}.txt \
          ${NEW_LSTDIR}/metas_${kfold}_type.lst ${lst_dir}/val_${kfold}.lst \
          --outfile ${output_kfold}/${k}/val_count_matrix_type.pdf
  
          # Regroup measures with respect to type var 
          python3 bin/regroup-measures.py exp/kmeans_teacher_1/pvector-1/ --suffix "_type" --measurefile "measures_type.j

      done
  done