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import numpy as np
def random_sampling(n_pool, idxs_lb, acc_idxs, rej_idxs, NUM_QUERY):
curr_selected = np.concatenate((idxs_lb, acc_idxs), axis=0)
curr_selected = np.concatenate((curr_selected, rej_idxs), axis=0)
idxs_ulb = np.setdiff1d(np.arange(n_pool), curr_selected)
selected = np.random.choice(idxs_ulb, size=NUM_QUERY, replace=False)
return selected, np.ones(selected.shape[0])
def uncerainty_sampling(n_pool, idxs_lb, acc_idxs, rej_idxs, NUM_QUERY, uncertainty):
curr_selected = np.concatenate((idxs_lb, acc_idxs), axis=0)
curr_selected = np.concatenate((curr_selected, rej_idxs), axis=0)
idxs_ulb = np.setdiff1d(np.arange(n_pool), curr_selected)
uncertainty_ulb = uncertainty[idxs_ulb]
idxs = np.argsort(uncertainty_ulb)[-NUM_QUERY:]
scores = uncertainty_ulb[idxs]
selected = idxs_ulb[idxs]
return selected, scores
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