Comments (2)
The initialize function returns two n-dimensional numpy arrays containing the training and test indices which are randomly assigned. If you want to do this manually, then skip this function call and define your own lists/ndarrays with indices and assign them to tr_ind, te_ind and pass them to the deposit function as referred to on the active learning documentation page.
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If I understand correctly, instead of using tr_ind, te_ind = al.initialize()
, I simply have to do something like
tr_ind = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
te_ind = np.array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])
and then perform the following?
al.deposit(tr_ind, y[tr_ind])
al.deposit(te_ind, y[te_ind])
I tried the follwing but it is returning False
. Is there anything that I am missing here?
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