def update(self, name, n=500, method='FastICA'):
settings = self._setstorage.load(encode_name(name))
clusterer = Clusterer(settings)
# load the models
dictionary = Dictionary.load(os.path.join(DICTIONARY_PATH, settings[DICTIONARY]))
ngram_size = len(dictionary[0])
transformer = NgramTransformer(ngram_size)
ldamodel = LdaModel.load(os.path.join(LDA_PATH, settings[LDA_MODEL]))
# get the input
segments = self._segstorage.load(name=settings[SEGMENT_NAME], limit=int(n))
documents = [s.value for s in segments]
# prepare args
kwargs = {'dictionary': dictionary,
'ngramtransformer': transformer,
'ldamodel': ldamodel,
'method': method}
Xt = clusterer.fit_transform(documents, **kwargs)
labels = clusterer.assign_labels(documents)
data = self._make_data(Xt, labels, documents)
return json.dumps({'result': 'OK',
'data': data})
