removed padding

1 files changed, 39 insertions, 13 deletions

diff --git a/src/python/test/test_diff.py b/src/python/test/test_diff.py
index d42e25cd..129b9f03 100644
--- a/src/python/test/test_diff.py
+++ b/src/python/test/test_diff.py
@@ -1,41 +1,67 @@
 from gudhi.differentiation 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)
-    model = RipsModel(X=X, mel=2., dim=0, card=10)
+    rl = RipsLayer(maximum_edge_length=2., dimension=0)
 
     with tf.GradientTape() as tape:
-        dgm = model.call()
+        dgm = rl.call(X)
         loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
-        grads = tape.gradient(loss, [X])
-        assert np.abs(grads[0].numpy()-np.array([[-.5,-.5],[.5,.5]])).sum() <= 1e-6
+    grads = tape.gradient(loss, [X])
+    assert np.abs(grads[0].numpy()-np.array([[-.5,-.5],[.5,.5]])).sum() <= 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)
-    model = CubicalModel(X, dim=0, card=10)
+    cl = CubicalLayer(dimension=0)
 
     with tf.GradientTape() as tape:
-        dgm = model.call()
+        dgm = cl.call(X)
         loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
-        grads = tape.gradient(loss, [X])
-        assert np.abs(grads[0].numpy()-np.array([[0.,0.,0.],[0.,.5,0.],[0.,0.,-.5]])).sum() <= 1e-6
+    grads = tape.gradient(loss, [X])
+    assert np.abs(grads[0].numpy()-np.array([[0.,0.,0.],[0.,.5,0.],[0.,0.,-.5]])).sum() <= 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)
-    model = LowerStarSimplexTreeModel(F, stbase="../../../data/filtered_simplicial_complex/simplextree.txt", dim=0, card=10)
+    sl = LowerStarSimplexTreeLayer(simplextree=st, dimension=0)
 
     with tf.GradientTape() as tape:
-        dgm = model.call()
+        dgm = sl.call(F)
         loss = tf.math.reduce_sum(tf.square(.5*(dgm[:,1]-dgm[:,0])))
-        grads = tape.gradient(loss, [F])
-        assert np.array_equal(np.array(grads[0].indices), np.array([2,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0])) 
-        assert np.array_equal(np.array(grads[0].values), np.array([-1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]))
+    grads = tape.gradient(loss, [F])
+
+    assert np.array_equal(np.array(grads[0].indices), np.array([2,4])) 
+    assert np.array_equal(np.array(grads[0].values), np.array([-1,1]))
+