# Natural Language Toolkit: Clusterer Interfaces # # Copyright (C) 2001-2012 NLTK Project # Author: Trevor Cohn # Porting: Steven Bird # URL: # For license information, see LICENSE.TXT from nltk.probability import DictionaryProbDist class ClusterI(object): """ Interface covering basic clustering functionality. """ def cluster(self, vectors, assign_clusters=False): """ Assigns the vectors to clusters, learning the clustering parameters from the data. Returns a cluster identifier for each vector. """ raise NotImplementedError() def classify(self, token): """ Classifies the token into a cluster, setting the token's CLUSTER parameter to that cluster identifier. """ raise NotImplementedError() def likelihood(self, vector, label): """ Returns the likelihood (a float) of the token having the corresponding cluster. """ if self.classify(vector) == label: return 1.0 else: return 0.0 def classification_probdist(self, vector): """ Classifies the token into a cluster, returning a probability distribution over the cluster identifiers. """ likelihoods = {} sum = 0.0 for cluster in self.cluster_names(): likelihoods[cluster] = self.likelihood(vector, cluster) sum += likelihoods[cluster] for cluster in self.cluster_names(): likelihoods[cluster] /= sum return DictionaryProbDist(likelihoods) def num_clusters(self): """ Returns the number of clusters. """ raise NotImplementedError() def cluster_names(self): """ Returns the names of the clusters. """ return range(self.num_clusters()) def cluster_name(self, index): """ Returns the names of the cluster at index. """ return index