nltk.cluster.api.ClusterI

class documentation

class ClusterI: (source)

Interface covering basic clustering functionality.

Method	`classification_probdist`	Classifies the token into a cluster, returning a probability distribution over the cluster identifiers.
Method	`classify`	Classifies the token into a cluster, setting the token's CLUSTER parameter to that cluster identifier.
Method	`cluster`	Assigns the vectors to clusters, learning the clustering parameters from the data. Returns a cluster identifier for each vector.
Method	`cluster_name`	Returns the names of the cluster at index.
Method	`cluster_names`	Returns the names of the clusters. :rtype: list
Method	`likelihood`	Returns the likelihood (a float) of the token having the corresponding cluster.
Method	`num_clusters`	Returns the number of clusters.

def classification_probdist(self, vector): (source) ¶

Classifies the token into a cluster, returning a probability distribution over the cluster identifiers.

@abstractmethod
def classify(self, token): (source) ¶

overridden in nltk.cluster.util.VectorSpaceClusterer

Classifies the token into a cluster, setting the token's CLUSTER parameter to that cluster identifier.

@abstractmethod
def cluster(self, vectors, assign_clusters=False): (source) ¶

overridden in nltk.cluster.util.VectorSpaceClusterer

Assigns the vectors to clusters, learning the clustering parameters from the data. Returns a cluster identifier for each vector.

def cluster_name(self, index): (source) ¶

Returns the names of the cluster at index.

Returns the names of the clusters. :rtype: list

def likelihood(self, vector, label): (source) ¶

overridden in nltk.cluster.util.VectorSpaceClusterer

Returns the likelihood (a float) of the token having the corresponding cluster.

@abstractmethod
def num_clusters(self): (source) ¶

overridden in nltk.cluster.em.EMClusterer, nltk.cluster.gaac.GAAClusterer, nltk.cluster.kmeans.KMeansClusterer

Returns the number of clusters.