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import unittest
import numpy as np
import hnswlib
class RandomSelfTestCase(unittest.TestCase):
def testRandomSelf(self):
data1 = np.asarray([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[1, 0, 1],
[1, 1, 1],
])
for space, expected_distances in [
('l2', [[0., 1., 2., 2., 2.]]),
('ip', [[-2., -1., 0., 0., 0.]]),
('cosine', [[0, 1.835e-01, 4.23e-01, 4.23e-01, 4.23e-01]])]:
for rightdim in range(1, 128, 3):
for leftdim in range(1, 32, 5):
data2 = np.concatenate(
[np.zeros([data1.shape[0], leftdim]), data1, np.zeros([data1.shape[0], rightdim])], axis=1)
dim = data2.shape[1]
p = hnswlib.Index(space=space, dim=dim)
p.init_index(max_elements=5, ef_construction=100, M=16)
p.set_ef(10)
p.add_items(data2)
# Query the elements for themselves and measure recall:
labels, distances = p.knn_query(np.asarray(data2[-1:]), k=5)
diff=np.mean(np.abs(distances-expected_distances))
self.assertAlmostEqual(diff, 0, delta=1e-3)
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