changed dimensions into homology_dimensions

7 files changed, 16 insertions, 16 deletions

diff --git a/src/python/doc/cubical_complex_tflow_itf_ref.rst b/src/python/doc/cubical_complex_tflow_itf_ref.rst
index 18b97adf..b32f5e47 100644
--- a/src/python/doc/cubical_complex_tflow_itf_ref.rst
+++ b/src/python/doc/cubical_complex_tflow_itf_ref.rst
@@ -16,7 +16,7 @@ Example of gradient computed from cubical persistence
     import tensorflow as tf
 
     X = tf.Variable([[0.,2.,2.],[2.,2.,2.],[2.,2.,1.]], dtype=tf.float32, trainable=True)
-    cl = CubicalLayer(dimensions=[0])
+    cl = CubicalLayer(homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = cl.call(X)[0][0]
diff --git a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
index b8518cdb..9d7d633f 100644
--- a/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
+++ b/src/python/doc/ls_simplex_tree_tflow_itf_ref.rst
@@ -29,7 +29,7 @@ Example of gradient computed from lower-star filtration of a simplex tree
     st.insert([9, 10]) 
 
     F = tf.Variable([6.,4.,3.,4.,5.,4.,3.,2.,3.,4.,5.], dtype=tf.float32, trainable=True)
-    sl = LowerStarSimplexTreeLayer(simplextree=st, dimensions=[0])
+    sl = LowerStarSimplexTreeLayer(simplextree=st, homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = sl.call(F)[0][0]
diff --git a/src/python/doc/rips_complex_tflow_itf_ref.rst b/src/python/doc/rips_complex_tflow_itf_ref.rst
index 6c65c562..3ce75868 100644
--- a/src/python/doc/rips_complex_tflow_itf_ref.rst
+++ b/src/python/doc/rips_complex_tflow_itf_ref.rst
@@ -21,7 +21,7 @@ Example of gradient computed from Vietoris-Rips persistence
     import tensorflow as tf
 
     X = tf.Variable([[1.,1.],[2.,2.]], dtype=tf.float32, trainable=True)
-    rl = RipsLayer(maximum_edge_length=2., dimensions=[0])
+    rl = RipsLayer(maximum_edge_length=2., homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = rl.call(X)[0][0]
diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py
index d68c7556..3304e719 100644
--- a/src/python/gudhi/tensorflow/cubical_layer.py
+++ b/src/python/gudhi/tensorflow/cubical_layer.py
@@ -37,17 +37,17 @@ class CubicalLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing the persistent homology of a cubical complex
     """
-    def __init__(self, dimensions, min_persistence=None, homology_coeff_field=11, **kwargs):
+    def __init__(self, homology_dimensions, min_persistence=None, homology_coeff_field=11, **kwargs):
         """
         Constructor for the CubicalLayer class
 
         Parameters:
-            dimensions (List[int]): homology dimensions
+            homology_dimensions (List[int]): list of homology dimensions
             min_persistence (List[float]): minimum distance-to-diagonal of the points in the output persistence diagrams (default None, in which case 0. is used for all dimensions)
             homology_coeff_field (int): homology field coefficient. Must be a prime number. Default value is 11. Max is 46337.
         """
         super().__init__(dynamic=True, **kwargs)
-        self.dimensions = dimensions
+        self.dimensions = homology_dimensions
         self.min_persistence = min_persistence if min_persistence != None else [0.] * len(self.dimensions)
         self.hcf = homology_coeff_field
         assert len(self.min_persistence) == len(self.dimensions)
diff --git a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
index 4ec3f7c7..5a8e5b75 100644
--- a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
+++ b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
@@ -43,18 +43,18 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing lower-star persistence out of a simplex tree
     """
-    def __init__(self, simplextree, dimensions, min_persistence=None, homology_coeff_field=11, **kwargs):
+    def __init__(self, simplextree, homology_dimensions, min_persistence=None, homology_coeff_field=11, **kwargs):
         """
         Constructor for the LowerStarSimplexTreeLayer class
   
         Parameters:
             simplextree (gudhi.SimplexTree): underlying simplex tree. Its vertices MUST be named with integers from 0 to n-1, where n is its number of vertices. Note that its filtration values are modified in each call of the class.
-            dimensions (List[int]): homology dimensions
+            homology_dimensions (List[int]): list of homology dimensions
             min_persistence (List[float]): minimum distance-to-diagonal of the points in the output persistence diagrams (default None, in which case 0. is used for all dimensions)
             homology_coeff_field (int): homology field coefficient. Must be a prime number. Default value is 11. Max is 46337.
         """
         super().__init__(dynamic=True, **kwargs)
-        self.dimensions  = dimensions
+        self.dimensions  = homology_dimensions
         self.simplextree = simplextree
         self.min_persistence = min_persistence if min_persistence != None else [0. for _ in range(len(self.dimensions))]
         self.hcf = homology_coeff_field
diff --git a/src/python/gudhi/tensorflow/rips_layer.py b/src/python/gudhi/tensorflow/rips_layer.py
index fca336f3..2a73472c 100644
--- a/src/python/gudhi/tensorflow/rips_layer.py
+++ b/src/python/gudhi/tensorflow/rips_layer.py
@@ -40,19 +40,19 @@ class RipsLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing Rips persistence out of a point cloud
     """
-    def __init__(self, dimensions, maximum_edge_length=np.inf, min_persistence=None, homology_coeff_field=11, **kwargs):
+    def __init__(self, homology_dimensions, maximum_edge_length=np.inf, min_persistence=None, homology_coeff_field=11, **kwargs):
         """
         Constructor for the RipsLayer class
 
         Parameters:
             maximum_edge_length (float): maximum edge length for the Rips complex 
-            dimensions (List[int]): homology dimensions
+            homology_dimensions (List[int]): list of homology dimensions
             min_persistence (List[float]): minimum distance-to-diagonal of the points in the output persistence diagrams (default None, in which case 0. is used for all dimensions)
             homology_coeff_field (int): homology field coefficient. Must be a prime number. Default value is 11. Max is 46337.
         """
         super().__init__(dynamic=True, **kwargs)
         self.max_edge = maximum_edge_length
-        self.dimensions = dimensions
+        self.dimensions = homology_dimensions
         self.min_persistence = min_persistence if min_persistence != None else [0. for _ in range(len(self.dimensions))]
         self.hcf = homology_coeff_field
         assert len(self.min_persistence) == len(self.dimensions)
diff --git a/src/python/test/test_diff.py b/src/python/test/test_diff.py
index e0a4717c..dca001a9 100644
--- a/src/python/test/test_diff.py
+++ b/src/python/test/test_diff.py
@@ -7,7 +7,7 @@ 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., dimensions=[0])
+    rl = RipsLayer(maximum_edge_length=2., homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = rl.call(X)[0][0]
@@ -19,7 +19,7 @@ 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(dimensions=[0])
+    cl = CubicalLayer(homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = cl.call(X)[0][0]
@@ -31,7 +31,7 @@ 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(dimensions=[0])
+    cl = CubicalLayer(homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = cl.call(X)[0][0]
@@ -66,7 +66,7 @@ def test_st_diff():
 
     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, dimensions=[0])
+    sl = LowerStarSimplexTreeLayer(simplextree=st, homology_dimensions=[0])
 
     with tf.GradientTape() as tape:
         dgm = sl.call(F)[0][0]