# Natural Language Toolkit: Clusterer Interfaces
#
# Copyright (C) 2001-2015 NLTK Project
# Author: Trevor Cohn <tacohn@cs.mu.oz.au>
# Porting: Steven Bird <stevenbird1@gmail.com>
# URL: <http://nltk.org/>
# 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 list(range(self.num_clusters()))
def cluster_name(self, index):
"""
Returns the names of the cluster at index.
"""
return index