2 files changed, 83 insertions, 0 deletions
diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt
index ec0ab1ca..d7a6a4db 100644
--- a/src/python/CMakeLists.txt
+++ b/src/python/CMakeLists.txt
@@ -411,6 +411,7 @@ if(PYTHONINTERP_FOUND)
 
     # DTM
     if(SCIPY_FOUND AND SKLEARN_FOUND AND TORCH_FOUND AND HNSWLIB_FOUND AND PYKEOPS_FOUND)
+      add_gudhi_py_test(test_knn)
       add_gudhi_py_test(test_dtm)
     endif()
 
diff --git a/src/python/test/test_knn.py b/src/python/test/test_knn.py
new file mode 100755
index 00000000..e455fb48
--- /dev/null
+++ b/src/python/test/test_knn.py
@@ -0,0 +1,82 @@
+""" This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+    See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+    Author(s):       Marc Glisse
+
+    Copyright (C) 2020 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+from gudhi.point_cloud.knn import KNN
+import numpy as np
+import pytest
+
+
+def test_knn_explicit():
+    base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
+    query = np.array([[1.0, 1], [2, 2], [4, 4]])
+    knn = KNN(2, metric="manhattan", return_distance=True, return_index=True)
+    knn.fit(base)
+    r = knn.transform(query)
+    assert r[0] == pytest.approx(np.array([[0, 1], [1, 0], [3, 2]]))
+    assert r[1] == pytest.approx(np.array([[0.0, 1], [1, 2], [1, 2]]))
+
+    knn = KNN(2, metric="chebyshev", return_distance=True, return_index=False)
+    knn.fit(base)
+    r = knn.transform(query)
+    assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
+    r = (
+        KNN(2, metric="chebyshev", return_distance=True, return_index=False, implementation="keops")
+        .fit(base)
+        .transform(query)
+    )
+    assert r == pytest.approx(np.array([[0.0, 1], [1, 1], [1, 2]]))
+
+    knn = KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True)
+    knn.fit(base)
+    r = knn.transform(query)
+    assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
+    r = (
+        KNN(2, metric="minkowski", p=3, return_distance=False, return_index=True, implementation="keops")
+        .fit(base)
+        .transform(query)
+    )
+    assert np.array_equal(r, [[0, 1], [1, 0], [3, 2]])
+
+    dist = np.array([[0.0, 3, 8], [1, 0, 5], [1, 2, 0]])
+    knn = KNN(2, metric="precomputed", return_index=True, return_distance=False)
+    r = knn.fit_transform(dist)
+    assert np.array_equal(r, [[0, 1], [1, 0], [2, 0]])
+    knn = KNN(2, metric="precomputed", return_index=True, return_distance=True)
+    r = knn.fit_transform(dist)
+    assert np.array_equal(r[0], [[0, 1], [1, 0], [2, 0]])
+    assert np.array_equal(r[1], [[0, 3], [0, 1], [0, 1]])
+
+
+def test_knn_compare():
+    base = np.array([[1.0, 1], [1, 2], [4, 2], [4, 3]])
+    query = np.array([[1.0, 1], [2, 2], [4, 4]])
+    r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=False).fit(base).transform(query)
+    r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=False).fit(base).transform(query)
+    r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=False).fit(base).transform(query)
+    r3 = KNN(2, implementation="keops", return_index=True, return_distance=False).fit(base).transform(query)
+    assert np.array_equal(r0, r1) and np.array_equal(r0, r2) and np.array_equal(r0, r3)
+
+    r0 = KNN(2, implementation="ckdtree", return_index=True, return_distance=True).fit(base).transform(query)
+    r1 = KNN(2, implementation="sklearn", return_index=True, return_distance=True).fit(base).transform(query)
+    r2 = KNN(2, implementation="hnsw", return_index=True, return_distance=True).fit(base).transform(query)
+    r3 = KNN(2, implementation="keops", return_index=True, return_distance=True).fit(base).transform(query)
+    assert np.array_equal(r0[0], r1[0]) and np.array_equal(r0[0], r2[0]) and np.array_equal(r0[0], r3[0])
+    d0 = pytest.approx(r0[1])
+    assert r1[1] == d0 and r2[1] == d0 and r3[1] == d0
+
+
+def test_knn_nop():
+    # This doesn't look super useful...
+    p = np.array([[0.0]])
+    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="sklearn").fit_transform(p)
+    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="ckdtree").fit_transform(p)
+    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="hnsw", ef=5).fit_transform(p)
+    assert None is KNN(k=1, return_index=False, return_distance=False, implementation="keops").fit_transform(p)
+    assert None is KNN(k=1, return_index=False, return_distance=False, metric="precomputed").fit_transform(p)