def compute_alc(valid_repr, test_repr):
 n_valid = valid_repr.shape[0]
 n_test = test_repr.shape[0]
 _labvalid = numpy.hstack((numpy.ones((n_valid, 1)), numpy.zeros((n_valid, 1))))
 _labtest = numpy.hstack((numpy.zeros((n_test, 1)), numpy.ones((n_test, 1))))
 dataset = numpy.vstack((valid_repr, test_repr))
 label = numpy.vstack((_labvalid, _labtest))
 logger.info('... computing the ALC')
 raise NotImplementedError("This got broken by embed no longer being where it used to be (if it even still exists, I haven't looked for it)")

def compute_alc ( valid_repr , test_repr ) : n_valid = valid_repr . shape [ 0 ] n_test = test_repr . shape [ 0 ] _labvalid = numpy . hstack ( ( numpy . ones ( ( n_valid , 1 ) ) , numpy . zeros ( ( n_valid , 1 ) ) ) ) _labtest = numpy . hstack ( ( numpy . zeros ( ( n_test , 1 ) ) , numpy . ones ( ( n_test , 1 ) ) ) ) dataset = numpy . vstack ( ( valid_repr , test_repr ) ) label = numpy . vstack ( ( _labvalid , _labtest ) ) logger . info ( '... computing the ALC' ) raise NotImplementedError ( "This got broken by embed no longer being where it used to be (if it even still exists, I haven't looked for it)" )
returns the alc of the valid set vs test set note: this proxy wont work in the case of transductive learning but it seems to be a good proxy in the normal case parameters valid_repr : writeme test_repr : writeme returns writeme .