unlabeled_uncertainty = np.concatenate((unlabeled, np.expand_dims(uncertainty_scores, axis=1)), axis=1)
unlabeled_uncertainty_copy = np.copy(unlabeled_uncertainty)
instance_index_ranking = deque()
ceiling = np.minimum(unlabeled.shape[0], n_instances)
// TODO (dataframing) there must be a better way...maybe?
for _ in range(ceiling):
After Change
unlabeled_uncertainty_copy = np.copy(unlabeled_uncertainty)
// Define our record container and the maximum number of records to sample.
instance_index_ranking = []
ceiling = np.minimum(unlabeled.shape[0], n_instances)
// TODO (dataframing) is there a better way to do this? Inherently sequential.
for _ in range(ceiling):
