def test_large_k(self):
x = random_vectors()
q = random_vectors(1)
k = x.shape[0] + 1 # make k larger than # of vectors in x
index = Index(x)
self.assertTrue(index.is_using_pyarray)
index.add_points(x.shape[0])
with self.assertRaises(ValueError):
index.knn_search(0, k)
with self.assertRaises(ValueError):
index.knn_search_points(q, k)
def test_knn_search():
"""
KNN_SEARCH
----------
GIVEN INDEX, RETURN INDEXES & DISTANCES in ASCENDING ORDER (including itself)
Parameters
----------
pid: index of target point
k: number of points to find (WE MUST SET K LESS THAN OR EQUAL TO THE # OF POINTS)
cores: number of cores to use
checks:
eps:
sorted:
Returns
-------
ids: ids of points found (numpy 2D array)
dists: distances from target point (numpy 2D array)
"""
x = random_vectors()
index = Index(x)
index.add_points(x.shape[0])
# pick random integer
pt = np.random.randint(x.shape[0]) # id. e.g.) 94
print(x[[pt]]) # data. e.g.) [[0.64, ...]]
idx, dist = index.knn_search(pt, 5, cores=1)
print(idx) # if pt=10, array([[10, 80, 87, 5, 95]])
print(dist) # array([[0, 0.76741797, 0.86952025, 0.90387696, 0.9157505 ]])
def test_return_shape_64(self):
x = random_vectors(dtype=np.float64)
index = Index(x)
self.assertIs(x, index.array)
self.assertTrue(index.is_using_pyarray)
index.add_points(x.shape[0])
for i in range(x.shape[0]):
ids, dists = index.knn_search(i, 5)
self.assertEqual(ids.shape, (1, 5))
self.assertEqual(dists.shape, (1, 5))
