# 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"