added min persistence

3 files changed, 16 insertions, 22 deletions

diff --git a/src/python/gudhi/tensorflow/cubical_layer.py b/src/python/gudhi/tensorflow/cubical_layer.py
index d07a4cd8..8fe9cff0 100644
--- a/src/python/gudhi/tensorflow/cubical_layer.py
+++ b/src/python/gudhi/tensorflow/cubical_layer.py
@@ -8,7 +8,7 @@ from ..cubical_complex  import CubicalComplex
 
 # The parameters of the model are the pixel values.
 
-def _Cubical(Xflat, Xdim, dimensions):
+def _Cubical(Xflat, Xdim, dimensions, min_persistence):
     # Parameters: Xflat (flattened image),
     #             Xdim (shape of non-flattened image)
     #             dimensions (homology dimensions)
@@ -16,7 +16,7 @@ def _Cubical(Xflat, Xdim, dimensions):
     # Compute the persistence pairs with Gudhi
     # We reverse the dimensions because CubicalComplex uses Fortran ordering
     cc = CubicalComplex(dimensions=Xdim[::-1], top_dimensional_cells=Xflat)
-    cc.compute_persistence()
+    cc.compute_persistence(min_persistence=min_persistence)
 
     # Retrieve and ouput image indices/pixels corresponding to positive and negative simplices    
     cof_pp = cc.cofaces_of_persistence_pairs()
@@ -37,7 +37,7 @@ class CubicalLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing cubical persistence out of a cubical complex
     """
-    def __init__(self, dimensions, **kwargs):
+    def __init__(self, dimensions, min_persistence=0., **kwargs):
         """
         Constructor for the CubicalLayer class
 
@@ -46,9 +46,7 @@ class CubicalLayer(tf.keras.layers.Layer):
         """
         super().__init__(dynamic=True, **kwargs)
         self.dimensions = dimensions
-
-    def build(self):
-        super.build()
+        self.min_persistence = min_persistence
         
     def call(self, X):
         """
@@ -64,7 +62,7 @@ class CubicalLayer(tf.keras.layers.Layer):
         # Don't compute gradient for this operation
         Xflat = tf.reshape(X, [-1])
         Xdim = X.shape
-        indices = _Cubical(Xflat.numpy(), Xdim, self.dimensions)
+        indices = _Cubical(Xflat.numpy(), Xdim, self.dimensions, self.min_persistence)
         # Get persistence diagram by simply picking the corresponding entries in the image
         self.dgms = [tf.reshape(tf.gather(Xflat, indice), [-1,2]) for indice in indices]
         return self.dgms
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 aa55604a..5902e4a1 100644
--- a/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
+++ b/src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py
@@ -7,7 +7,7 @@ import tensorflow          as tf
 
 # The parameters of the model are the vertex function values of the simplex tree.
 
-def _LowerStarSimplexTree(simplextree, filtration, dimensions):
+def _LowerStarSimplexTree(simplextree, filtration, dimensions, min_persistence):
     # Parameters: simplextree (simplex tree on which to compute persistence)
     #             filtration (function values on the vertices of st),
     #             dimensions (homology dimensions),
@@ -21,7 +21,7 @@ def _LowerStarSimplexTree(simplextree, filtration, dimensions):
     simplextree.make_filtration_non_decreasing()
     
     # Compute persistence diagram
-    simplextree.compute_persistence()
+    simplextree.compute_persistence(min_persistence=min_persistence)
     
     # Get vertex pairs for optimization. First, get all simplex pairs
     pairs = simplextree.lower_star_persistence_generators()
@@ -43,7 +43,7 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing lower-star persistence out of a simplex tree
     """
-    def __init__(self, simplextree, dimensions, **kwargs):
+    def __init__(self, simplextree, dimensions, min_persistence=0., **kwargs):
         """
         Constructor for the LowerStarSimplexTreeLayer class
   
@@ -54,10 +54,8 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer):
         super().__init__(dynamic=True, **kwargs)
         self.dimensions  = dimensions
         self.simplextree = simplextree
-    
-    def build(self):
-        super.build()
-    
+        self.min_persistence = min_persistence
+        
     def call(self, filtration):
         """
         Compute lower-star persistence diagram associated to a function defined on the vertices of the simplex tree
@@ -69,7 +67,7 @@ class LowerStarSimplexTreeLayer(tf.keras.layers.Layer):
             dgms (list of tuple of TensorFlow variables): list of lower-star persistence diagrams of length self.dimensions, where each element of the list is a tuple that contains the finite and essential persistence diagrams of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
         """
         # Don't try to compute gradients for the vertex pairs
-        indices = _LowerStarSimplexTree(self.simplextree, filtration.numpy(), self.dimensions)
+        indices = _LowerStarSimplexTree(self.simplextree, filtration.numpy(), self.dimensions, self.min_persistence)
         # Get persistence diagrams
         self.dgms = []
         for idx_dim, dimension in enumerate(self.dimensions):
diff --git a/src/python/gudhi/tensorflow/rips_layer.py b/src/python/gudhi/tensorflow/rips_layer.py
index 472a418b..97f28d74 100644
--- a/src/python/gudhi/tensorflow/rips_layer.py
+++ b/src/python/gudhi/tensorflow/rips_layer.py
@@ -8,7 +8,7 @@ from ..rips_complex     import RipsComplex
 
 # The parameters of the model are the point coordinates.
 
-def _Rips(DX, max_edge, dimensions):
+def _Rips(DX, max_edge, dimensions, min_persistence):
     # Parameters: DX (distance matrix), 
     #             max_edge (maximum edge length for Rips filtration), 
     #             dimensions (homology dimensions)
@@ -16,7 +16,7 @@ def _Rips(DX, max_edge, dimensions):
     # Compute the persistence pairs with Gudhi
     rc = RipsComplex(distance_matrix=DX, max_edge_length=max_edge)
     st = rc.create_simplex_tree(max_dimension=max(dimensions)+1)
-    st.compute_persistence()
+    st.compute_persistence(min_persistence=min_persistence)
     pairs = st.flag_persistence_generators()
 
     L_indices = []
@@ -40,7 +40,7 @@ class RipsLayer(tf.keras.layers.Layer):
     """
     TensorFlow layer for computing Rips persistence out of a point cloud
     """
-    def __init__(self, dimensions, maximum_edge_length=12, **kwargs):
+    def __init__(self, dimensions, maximum_edge_length=12, min_persistence=0., **kwargs):
         """
         Constructor for the RipsLayer class
 
@@ -51,9 +51,7 @@ class RipsLayer(tf.keras.layers.Layer):
         super().__init__(dynamic=True, **kwargs)
         self.max_edge = maximum_edge_length
         self.dimensions = dimensions
-
-    def build(self):
-        super.build()
+        self.min_persistence = min_persistence
         
     def call(self, X):
         """
@@ -69,7 +67,7 @@ class RipsLayer(tf.keras.layers.Layer):
         DX = tf.math.sqrt(tf.reduce_sum((tf.expand_dims(X, 1)-tf.expand_dims(X, 0))**2, 2))
         # Compute vertices associated to positive and negative simplices 
         # Don't compute gradient for this operation
-        indices = _Rips(DX.numpy(), self.max_edge, self.dimensions)
+        indices = _Rips(DX.numpy(), self.max_edge, self.dimensions, self.min_persistence)
         # Get persistence diagrams by simply picking the corresponding entries in the distance matrix
         self.dgms = []
         for idx_dim, dimension in enumerate(self.dimensions):