def interp_target_rank_recall(cmc, target_rank):
if(target_rank < cmc[0][0] or target_rank > cmc[0][-1]):
print 'target_fpr out of bound, will return -1'
return -1.0
# This interpolation might be the one that MegaFace officially uses
# for i, fpr in enumerate(cmc[0]):
# if fpr > target_rank:
# return cmc[1][i]
# linear interpolation
for i, rank in enumerate(cmc[0]):
if rank > target_rank:
break
if cmc[0][i - 1] == target_rank:
target_recall = cmc[1][i - 1]
else:
target_recall = linear_interp(target_rank,
cmc[0][i - 1], cmc[0][i],
cmc[1][i - 1], cmc[1][i]
)
# NN interpolation
# target_recall = nearest_neighbor_interp(target_rank, cmc[0], cmc[1])
return target_recall
def test_linear_interp(self):
x = np.arange(0,10,1)
y = 2*x
self.assertEqual(interp.linear_interp(x,y,2), 4)
self.assertEqual(interp.linear_interp(x,y,2.5), 5)
self.assertRaises(Exception,interp.linear_interp,x,2,2) #check that passing an int raises an error