Merge master and fix conflicts

2 files changed, 86 insertions, 8 deletions

diff --git a/src/python/test/test_diff.py b/src/python/test/test_diff.py
new file mode 100644
index 00000000..dca001a9
--- /dev/null
+++ b/src/python/test/test_diff.py
@@ -0,0 +1,78 @@
+from gudhi.tensorflow import *
+import numpy as np
+import tensorflow as tf
+import gudhi as gd
+
+def test_rips_diff():
+
+    Xinit = np.array([[1.,1.],[2.,2.]], dtype=np.float32)
+    X = tf.Variable(initial_value=Xinit, trainable=True)
+    rl = RipsLayer(maximum_edge_length=2., homology_dimensions=[0])
+
+    with tf.GradientTape() as tape:
+        dgm = rl.call(X)[0][0]
+        loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
+    grads = tape.gradient(loss, [X])
+    assert tf.norm(grads[0]-tf.constant([[-.5,-.5],[.5,.5]]),1) <= 1e-6
+
+def test_cubical_diff():
+
+    Xinit = np.array([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=np.float32)
+    X = tf.Variable(initial_value=Xinit, trainable=True)
+    cl = CubicalLayer(homology_dimensions=[0])
+
+    with tf.GradientTape() as tape:
+        dgm = cl.call(X)[0][0]
+        loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
+    grads = tape.gradient(loss, [X])
+    assert tf.norm(grads[0]-tf.constant([[0.,0.,0.],[0.,.5,0.],[0.,0.,-.5]]),1) <= 1e-6
+
+def test_nonsquare_cubical_diff():
+
+    Xinit = np.array([[-1.,1.,0.],[1.,1.,1.]], dtype=np.float32)
+    X = tf.Variable(initial_value=Xinit, trainable=True)
+    cl = CubicalLayer(homology_dimensions=[0])
+
+    with tf.GradientTape() as tape:
+        dgm = cl.call(X)[0][0]
+        loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
+    grads = tape.gradient(loss, [X])
+    assert tf.norm(grads[0]-tf.constant([[0.,0.5,-0.5],[0.,0.,0.]]),1) <= 1e-6
+
+def test_st_diff():
+
+    st = gd.SimplexTree()
+    st.insert([0])
+    st.insert([1]) 
+    st.insert([2]) 
+    st.insert([3]) 
+    st.insert([4]) 
+    st.insert([5]) 
+    st.insert([6]) 
+    st.insert([7]) 
+    st.insert([8]) 
+    st.insert([9]) 
+    st.insert([10]) 
+    st.insert([0, 1]) 
+    st.insert([1, 2]) 
+    st.insert([2, 3]) 
+    st.insert([3, 4]) 
+    st.insert([4, 5]) 
+    st.insert([5, 6]) 
+    st.insert([6, 7]) 
+    st.insert([7, 8]) 
+    st.insert([8, 9]) 
+    st.insert([9, 10]) 
+
+    Finit = np.array([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=np.float32)
+    F = tf.Variable(initial_value=Finit, trainable=True)
+    sl = LowerStarSimplexTreeLayer(simplextree=st, homology_dimensions=[0])
+
+    with tf.GradientTape() as tape:
+        dgm = sl.call(F)[0][0]
+        loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
+    grads = tape.gradient(loss, [F])
+
+    assert tf.math.reduce_all(tf.math.equal(grads[0].indices, tf.constant([2,4])))
+    assert tf.math.reduce_all(tf.math.equal(grads[0].values, tf.constant([-1.,1.])))
+
diff --git a/src/python/test/test_dtm.py b/src/python/test/test_dtm.py
index e46d616c..b276f041 100755
--- a/src/python/test/test_dtm.py
+++ b/src/python/test/test_dtm.py
@@ -91,11 +91,11 @@ def test_density():
 
 def test_dtm_overflow_warnings():
     pts = numpy.array([[10., 100000000000000000000000000000.], [1000., 100000000000000000000000000.]])
-
-    with warnings.catch_warnings(record=True) as w:
-        # TODO Test "keops" implementation as well when next version of pykeops (current is 1.5) is released (should fix the problem (cf. issue #543))
-        dtm = DistanceToMeasure(2, implementation="hnsw")
-        r = dtm.fit_transform(pts)
-        assert len(w) == 1
-        assert issubclass(w[0].category, RuntimeWarning)
-        assert "Overflow" in str(w[0].message)
+    impl_warn = ["keops", "hnsw"]
+    for impl in impl_warn:
+        with warnings.catch_warnings(record=True) as w:
+            dtm = DistanceToMeasure(2, implementation=impl)
+            r = dtm.fit_transform(pts)
+            assert len(w) == 1
+            assert issubclass(w[0].category, RuntimeWarning)
+            assert "Overflow" in str(w[0].message)