def do_continue(self, line):
'''
Will continue the training of the currently selected model
'''
if not self.has_model_selected():
print 'please select or create a model'
return False
m = ModelManager(self.prompt[:-1])
state = m.load_current_state(add_hidden=True)
model = m.load_currently_selected_model()
train.train2(m, state, model)
#evaluation.evaluate_lshf(m, context_and_answer_relevance)
exit()
from ann import lsh_forest
from ann.candidate_selection import *
m = ModelManager('ubuntu_vhred_vanilla')
#from ann.lsh_forest import save_linked_utterance_embeddings
#save_linked_utterance_embeddings(m)
#lsh_forest.train_lsh_forest(m, corpus_percentage=0.05)
ann = lsh_forest.load_lshf(m)
utt_embs = lsh_forest.load_utterance_embeddings(m)
encoder = m.load_currently_selected_model()
embs = encode('how do i update all packages ? __eou__', encoder)
d_emb = embs[0][0][0]
distances, labels, embeddings = ann.kneighbors(d_emb, 10)
translator = get_label_translator(m, as_text=True)
labels = [(label[0], label[1] + 1) for label in labels]
search_context = {
'distances': distances,
'labels': labels,
'candidate_dialogue_embeddings': embeddings,
