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| # Natural Language Toolkit: Clusterer Interfaces | |
| # | |
| # Copyright (C) 2001-2023 NLTK Project | |
| # Author: Trevor Cohn <[email protected]> | |
| # Porting: Steven Bird <[email protected]> | |
| # URL: <https://www.nltk.org/> | |
| # For license information, see LICENSE.TXT | |
| from abc import ABCMeta, abstractmethod | |
| from nltk.probability import DictionaryProbDist | |
| class ClusterI(metaclass=ABCMeta): | |
| """ | |
| 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. | |
| """ | |
| def classify(self, token): | |
| """ | |
| Classifies the token into a cluster, setting the token's CLUSTER | |
| parameter to that cluster identifier. | |
| """ | |
| 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. | |
| """ | |
| def cluster_names(self): | |
| """ | |
| Returns the names of the clusters. | |
| :rtype: list | |
| """ | |
| return list(range(self.num_clusters())) | |
| def cluster_name(self, index): | |
| """ | |
| Returns the names of the cluster at index. | |
| """ | |
| return index | |