def build_sparse_matrix_target(limit=0):
targets_vector = DataDAO.get_targets()
targets_matrix = []
for ind, item in enumerate(targets_vector):
if limit != 0 and ind >= limit:
break
multiclass_vector = TrainingFactory.get_category_vector(item)
targets_matrix.append(multiclass_vector)
sparse = csr_matrix(targets_matrix)
return sparse
def build_target_vector_by_category(category, limit=0):
targets_vector = DataDAO.get_targets()
targets_cat = []
for ind, item in enumerate(targets_vector):
if limit != 0 and ind >= limit:
break
if item == category:
targets_cat.append(1)
else:
targets_cat.append(0)
return targets_cat