75 lines
2.1 KiB
Python
75 lines
2.1 KiB
Python
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# Natural Language Toolkit: Clusterer Interfaces
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: Trevor Cohn <tacohn@cs.mu.oz.au>
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# Porting: Steven Bird <stevenbird1@gmail.com>
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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from abc import ABCMeta, abstractmethod
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from six import add_metaclass
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from nltk.probability import DictionaryProbDist
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@add_metaclass(ABCMeta)
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class ClusterI(object):
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"""
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Interface covering basic clustering functionality.
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"""
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@abstractmethod
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def cluster(self, vectors, assign_clusters=False):
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"""
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Assigns the vectors to clusters, learning the clustering parameters
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from the data. Returns a cluster identifier for each vector.
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"""
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@abstractmethod
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def classify(self, token):
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"""
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Classifies the token into a cluster, setting the token's CLUSTER
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parameter to that cluster identifier.
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"""
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def likelihood(self, vector, label):
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"""
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Returns the likelihood (a float) of the token having the
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corresponding cluster.
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"""
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if self.classify(vector) == label:
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return 1.0
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else:
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return 0.0
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def classification_probdist(self, vector):
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"""
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Classifies the token into a cluster, returning
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a probability distribution over the cluster identifiers.
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"""
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likelihoods = {}
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sum = 0.0
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for cluster in self.cluster_names():
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likelihoods[cluster] = self.likelihood(vector, cluster)
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sum += likelihoods[cluster]
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for cluster in self.cluster_names():
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likelihoods[cluster] /= sum
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return DictionaryProbDist(likelihoods)
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@abstractmethod
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def num_clusters(self):
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"""
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Returns the number of clusters.
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"""
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def cluster_names(self):
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"""
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Returns the names of the clusters.
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:rtype: list
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"""
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return list(range(self.num_clusters()))
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def cluster_name(self, index):
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"""
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Returns the names of the cluster at index.
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"""
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return index
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