From 370c09100d94dc73f582ebbabb994bcd2a3820eb Mon Sep 17 00:00:00 2001 From: MathieuCarriere Date: Fri, 24 Jun 2022 16:19:34 +0200 Subject: changed dimensions into homology_dimensions --- src/python/doc/cubical_complex_tflow_itf_ref.rst | 2 +- src/python/doc/ls_simplex_tree_tflow_itf_ref.rst | 2 +- src/python/doc/rips_complex_tflow_itf_ref.rst | 2 +- src/python/gudhi/tensorflow/cubical_layer.py | 6 +++--- src/python/gudhi/tensorflow/lower_star_simplex_tree_layer.py | 6 +++--- src/python/gudhi/tensorflow/rips_layer.py | 6 +++--- src/python/test/test_diff.py | 8 ++++---- 7 files changed, 16 insertions(+), 16 deletions(-) (limited to 'src') 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] -- cgit v1.2.3