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import unittest
import numpy as np
import hnswlib
class RandomSelfTestCase(unittest.TestCase):
def testMetadata(self):
dim = 16
num_elements = 10000
# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))
# Declaring index
p = hnswlib.Index(space='l2', dim=dim) # possible options are l2, cosine or ip
# Initing index
# max_elements - the maximum number of elements, should be known beforehand
# (probably will be made optional in the future)
#
# ef_construction - controls index search speed/build speed tradeoff
# M - is tightly connected with internal dimensionality of the data
# stronlgy affects the memory consumption
p.init_index(max_elements=num_elements, ef_construction=100, M=16)
# Controlling the recall by setting ef:
# higher ef leads to better accuracy, but slower search
p.set_ef(100)
p.set_num_threads(4) # by default using all available cores
print("Adding all elements (%d)" % (len(data)))
p.add_items(data)
# test methods
self.assertEqual(p.get_max_elements(), num_elements)
self.assertEqual(p.get_current_count(), num_elements)
# test properties
self.assertEqual(p.space, 'l2')
self.assertEqual(p.dim, dim)
self.assertEqual(p.M, 16)
self.assertEqual(p.ef_construction, 100)
self.assertEqual(p.max_elements, num_elements)
self.assertEqual(p.element_count, num_elements)
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