1 files changed, 29 insertions, 11 deletions
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index 93b13e1a..bff4c267 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -8,43 +8,61 @@
       - YYYY/MM Author: Description of the modification
 """
 
-from gudhi.point_cloud.dtm import DTM
+from gudhi.point_cloud.dtm import DistanceToMeasure
 import numpy
 import pytest
+import torch
 
 
 def test_dtm_compare_euclidean():
     pts = numpy.random.rand(1000, 4)
-    k = 3
-    dtm = DTM(k, implementation="ckdtree")
+    k = 6
+    dtm = DistanceToMeasure(k, implementation="ckdtree")
     r0 = dtm.fit_transform(pts)
-    dtm = DTM(k, implementation="sklearn")
+    dtm = DistanceToMeasure(k, implementation="sklearn")
     r1 = dtm.fit_transform(pts)
     assert r1 == pytest.approx(r0)
-    dtm = DTM(k, implementation="sklearn", algorithm="brute")
+    dtm = DistanceToMeasure(k, implementation="sklearn", algorithm="brute")
     r2 = dtm.fit_transform(pts)
     assert r2 == pytest.approx(r0)
-    dtm = DTM(k, implementation="hnsw")
+    dtm = DistanceToMeasure(k, implementation="hnsw")
     r3 = dtm.fit_transform(pts)
-    assert r3 == pytest.approx(r0)
+    assert r3 == pytest.approx(r0, rel=0.1)
     from scipy.spatial.distance import cdist
 
     d = cdist(pts, pts)
-    dtm = DTM(k, metric="precomputed")
+    dtm = DistanceToMeasure(k, metric="precomputed")
     r4 = dtm.fit_transform(d)
     assert r4 == pytest.approx(r0)
-    dtm = DTM(k, implementation="keops")
+    dtm = DistanceToMeasure(k, metric="precomputed", n_jobs=2)
+    r4b = dtm.fit_transform(d)
+    assert r4b == pytest.approx(r0)
+    dtm = DistanceToMeasure(k, implementation="keops")
     r5 = dtm.fit_transform(pts)
     assert r5 == pytest.approx(r0)
+    pts2 = torch.tensor(pts, requires_grad=True)
+    assert pts2.grad is None
+    dtm = DistanceToMeasure(k, implementation="keops", enable_autodiff=True)
+    r6 = dtm.fit_transform(pts2)
+    assert r6.detach().numpy() == pytest.approx(r0)
+    r6.sum().backward()
+    assert not torch.isnan(pts2.grad).any()
+    pts2 = torch.tensor(pts, requires_grad=True)
+    assert pts2.grad is None
+    dtm = DistanceToMeasure(k, implementation="ckdtree", enable_autodiff=True)
+    r7 = dtm.fit_transform(pts2)
+    assert r7.detach().numpy() == pytest.approx(r0)
+    r7.sum().backward()
+    assert not torch.isnan(pts2.grad).any()
 
 
 def test_dtm_precomputed():
     dist = numpy.array([[1.0, 3, 8], [1, 5, 5], [0, 2, 3]])
-    dtm = DTM(2, q=1, metric="neighbors")
+    dtm = DistanceToMeasure(2, q=1, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 3, 1])
 
     dist = numpy.array([[2.0, 2], [0, 1], [3, 4]])
-    dtm = DTM(2, q=2, metric="neighbors")
+    dtm = DistanceToMeasure(2, q=2, metric="neighbors")
     r = dtm.fit_transform(dist)
     assert r == pytest.approx([2.0, 0.707, 3.5355], rel=0.01)