# Don't forget to install volia before or to run it in volia root directory.
  # You can uncomment "debug" flag parameter to see print dedicated to the debug.
  
  # Example k-means with (default) euclidian distance
  python -m volia.clustering \
    --features "example/feats_example.txt" \
    --lst "example/example.lst" \
    -k 4 \
    --output "tests/kmeans_on_example.pkl" \
    #--debug
  
  python -m volia.clustering measure \
    --measure "entropy" "v-measure" "homogeneity" "completeness" "purity" \
    --features "example/feats_example.txt" \
    --lst "example/example.lst" \
    --truelabels "example/utt2grp_example" \
    --model "tests/kmeans_on_example.pkl" \
    --modeltype "k-means"
  
  
  # Example k-means with mahalanobis distance
  python -m volia.clustering kmeans \
    --features "example/feats_example.txt" \
    --lst "example/example.lst" \
    -k 4 \
    --output "tests/kmeans_mahalanobis_on_example.pkl" \
    --mahalanobis
    #-- debug
  
  python -m volia.clustering measure \
    --measure "entropy" "v-measure" "homogeneity" "completeness" "purity" \
    --features "example/feats_example.txt" \
    --lst "example/example.lst" \
    --truelabels "example/utt2grp_example" \
    --model "kmeans_mahalanobis_on_example.pkl" \
    --modeltype "k-means-mahalanobis"