import numpy as np
import tensorflow as tf
from ..rips_complex import RipsComplex
############################
# Vietoris-Rips filtration #
############################
# The parameters of the model are the point coordinates.
def _Rips(DX, max_edge, dimensions, homology_coeff_field):
# Parameters: DX (distance matrix),
# max_edge (maximum edge length for Rips filtration),
# dimensions (homology 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(homology_coeff_field=homology_coeff_field)
pairs = st.flag_persistence_generators()
L_indices = []
for dimension in dimensions:
if dimension == 0:
finite_pairs = pairs[0]
essential_pairs = pairs[2]
else:
finite_pairs = pairs[1][dimension-1] if len(pairs[1]) >= dimension else np.empty(shape=[0,4])
essential_pairs = pairs[3][dimension-1] if len(pairs[3]) >= dimension else np.empty(shape=[0,2])
finite_indices = np.array(finite_pairs.flatten(), dtype=np.int32)
essential_indices = np.array(essential_pairs.flatten(), dtype=np.int32)
L_indices.append((finite_indices, essential_indices))
return L_indices
[docs]
class RipsLayer(tf.keras.layers.Layer):
"""
TensorFlow layer for computing Rips persistence out of a point cloud
"""
[docs]
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
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__(**kwargs)
self.max_edge = maximum_edge_length
self.dimensions = homology_dimensions
self.min_persistence = min_persistence if min_persistence is not None else [0. for _ in range(len(self.dimensions))]
self.hcf = homology_coeff_field
assert len(self.min_persistence) == len(self.dimensions)
[docs]
def call(self, X):
"""
Compute Rips persistence diagram associated to a point cloud
Parameters:
X (TensorFlow variable): point cloud of shape [number of points, number of dimensions]
Returns:
List[Tuple[tf.Tensor,tf.Tensor]]: List of Rips persistence diagrams. The length of this list is the same than that of dimensions, i.e., there is one persistence diagram per homology dimension provided in the input list dimensions. Moreover, the finite and essential parts of the persistence diagrams are provided separately: each element of this list is a tuple of size two that contains the finite and essential parts of the corresponding persistence diagram, of shapes [num_finite_points, 2] and [num_essential_points, 1] respectively
"""
# Compute distance matrix
DX = tf.norm(tf.expand_dims(X, 1)-tf.expand_dims(X, 0), axis=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, self.hcf)
# Get persistence diagrams by simply picking the corresponding entries in the distance matrix
self.dgms = []
for idx_dim, dimension in enumerate(self.dimensions):
cur_idx = indices[idx_dim]
if dimension > 0:
finite_dgm = tf.reshape(tf.gather_nd(DX, tf.reshape(cur_idx[0], [-1,2])), [-1,2])
essential_dgm = tf.reshape(tf.gather_nd(DX, tf.reshape(cur_idx[1], [-1,2])), [-1,1])
else:
reshaped_cur_idx = tf.reshape(cur_idx[0], [-1,3])
finite_dgm = tf.concat([tf.zeros([reshaped_cur_idx.shape[0],1]), tf.reshape(tf.gather_nd(DX, reshaped_cur_idx[:,1:]), [-1,1])], axis=1)
essential_dgm = tf.zeros([cur_idx[1].shape[0],1])
min_pers = self.min_persistence[idx_dim]
if min_pers >= 0:
persistent_indices = tf.where(tf.math.abs(finite_dgm[:,1]-finite_dgm[:,0]) > min_pers)
self.dgms.append((tf.reshape(tf.gather(finite_dgm, indices=persistent_indices),[-1,2]), essential_dgm))
else:
self.dgms.append((finite_dgm, essential_dgm))
return self.dgms
