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receipts/clustering_example.sh
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# 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" |