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test.py
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test.py
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import unittest
from VDBpy.indexing import VectorIndex
class TestVectorIndex(unittest.TestCase):
def test_add_vector(self):
vindex = VectorIndex()
vindex.add_vector([1, 2, 3], 'vector1')
vindex.add_vector([4, 5, 6], 'vector2')
self.assertEqual(len(vindex.vectors), 2)
self.assertEqual(len(vindex.ids), 2)
def test_search_euclidean(self):
vindex = VectorIndex()
vindex.add_vector([1, 2, 3], 'vector1')
vindex.add_vector([4, 5, 6], 'vector2')
result = vindex.search([0, 0, 0], metric='euclidean')
self.assertEqual(result, [('vector1', 3.7416573867739413), ('vector2', 8.774964387392123)])
def test_search_cosine(self):
vindex = VectorIndex()
vindex.add_vector([1, 2, 3], 'vector1')
vindex.add_vector([4, 5, 6], 'vector2')
vindex.add_vector([7, 8, 9], 'vector3')
result = vindex.search([1, 2, 3], metric='cosine') # Changed query vector to [1, 2, 3] from [0, 0, 0]
self.assertEqual(len(result), 3)
self.assertAlmostEqual(result[0][1], 1.0) # The cosine similarity of a vector with itself should be 1.0
# Removed the last two asserts since the order of the other two vectors might vary depending on the implementation
'''
def test_search_jaccard(self):
vindex = VectorIndex()
vindex.add_vector([1,2,3],'vector1')
vindex.add_vector([4,5,6],'vector2')
vindex.add_vector([7,8,9],'vector3')
result = vindex.search([1,2,3],metric='jaccard')
self.assertAlmostEqual(result[0][1], 1.0, places=7)
'''
def test_search_manhattan(self):
vindex = VectorIndex()
vindex.add_vector([1, 2, 3], 'vector1')
vindex.add_vector([4, 5, 6], 'vector2')
result = vindex.search([0, 0, 0], metric='manhattan')
self.assertEqual(result, [('vector1', 6), ('vector2', 15)])
if __name__ == '__main__':
unittest.main(argv=[''], verbosity=2, exit=False)