add a test for distance computation correctness

python_bindings/tests/bindings_test_spaces.py

@@ -0,0 +1,40 @@
+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))
+                    print(dim,space, diff)
+                    self.assertAlmostEqual(diff, 0, delta=1e-3)