3 dr@s2ddlmZmZddlmZGdddedZdS))ABCMetaabstractmethod)DictionaryProbDistc@sVeZdZdZedddZeddZddZd d Zed d Z d dZ ddZ dS)ClusterIz< Interface covering basic clustering functionality. FcCsdS)z Assigns the vectors to clusters, learning the clustering parameters from the data. Returns a cluster identifier for each vector. N)selfZvectorsZassign_clustersrr0/tmp/pip-build-v9q4h5k9/nltk/nltk/cluster/api.pyclusterszClusterI.clustercCsdS)z Classifies the token into a cluster, setting the token's CLUSTER parameter to that cluster identifier. Nr)rtokenrrrclassifyszClusterI.classifycCs|j||krdSdSdS)zi Returns the likelihood (a float) of the token having the corresponding cluster. g?gN)r )rvectorlabelrrr likelihood!szClusterI.likelihoodcCs`i}d}x,|jD] }|j||||<|||7}qWx |jD]}|||<q@Wt|S)z Classifies the token into a cluster, returning a probability distribution over the cluster identifiers. g) cluster_namesrr)rr Z likelihoodssumr rrrclassification_probdist+sz ClusterI.classification_probdistcCsdS)z1 Returns the number of clusters. Nr)rrrr num_clusters9szClusterI.num_clusterscCstt|jS)zI Returns the names of the clusters. :rtype: list )listranger)rrrrr?szClusterI.cluster_namescCs|S)z< Returns the names of the cluster at index. r)rindexrrr cluster_nameFszClusterI.cluster_nameN)F) __name__ __module__ __qualname____doc__rr r rrrrrrrrrrs    r) metaclassN)abcrrZnltk.probabilityrrrrrr s