class DataClusterer(Clusterer):
def train(self, num_of_clusters=7, repeats=4):
self.vectors = [self.to_vector_space(line) for line in self.lines if line]
self.tree = Tree()
self.tree.words = self.vectors
for i in range(num_of_clusters-1):
biggest = self.tree.get_biggest()
biggest.clusterer = nltk_KMeansClusterer(2, self.distance, repeats)
biggest.clusterer.cluster(biggest.words)
l = [vector for vector in biggest.words if biggest.clusterer.classify(vector) == 0]
r = [vector for vector in biggest.words if biggest.clusterer.classify(vector) == 1]
biggest.words = None
biggest.left.words = l
biggest.right.words = r
def classify(self, sample):
ret = ''
tree = self.tree
while tree:
if tree.clusterer.classify(self.to_vector_space(sample)) == 0:
ret += 'l'
tree = tree.left
else:
ret += 'r'
tree = tree.right
if tree.words: # child
tree = None
return ret
