1 files changed, 51 insertions, 0 deletions
diff --git a/src/python/gudhi/dtm_rips_complex.py b/src/python/gudhi/dtm_rips_complex.py
new file mode 100644
index 00000000..63c9b138
--- /dev/null
+++ b/src/python/gudhi/dtm_rips_complex.py
@@ -0,0 +1,51 @@
+# This file is part of the Gudhi Library - https://gudhi.inria.fr/ - which is released under MIT.
+# See file LICENSE or go to https://gudhi.inria.fr/licensing/ for full license details.
+# Author(s):       Yuichi Ike, Raphaël Tinarrage
+#
+# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd.
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+
+from gudhi.weighted_rips_complex import WeightedRipsComplex
+from gudhi.point_cloud.dtm import DistanceToMeasure
+from scipy.spatial.distance import cdist
+
+class DTMRipsComplex(WeightedRipsComplex):
+    """
+    Class to generate a DTM Rips complex from a distance matrix or a point set, 
+    in the way described in :cite:`dtmfiltrations`.
+    Remark that all the filtration values are doubled compared to the definition in the paper 
+    for the consistency with RipsComplex.
+    :Requires: `SciPy <installation.html#scipy>`_
+    """
+    def __init__(self, 
+                 points=None, 
+                 distance_matrix=None, 
+                 k=1, 
+                 q=2,
+                 max_filtration=float('inf')):
+        """
+        Args:
+            points (numpy.ndarray): array of points.
+            distance_matrix (numpy.ndarray): full distance matrix.
+            k (int): number of neighbors for the computation of DTM. Defaults to 1, which is equivalent to the usual Rips complex.
+            q (float): order used to compute the distance to measure. Defaults to 2.
+            max_filtration (float): specifies the maximal filtration value to be considered.      
+        """
+        if distance_matrix is None:
+            if points is None:
+                # Empty Rips construction
+                points=[]
+            distance_matrix = cdist(points,points)
+        self.distance_matrix = distance_matrix
+
+        # TODO: address the error when k is too large 
+        if k <= 1:
+            self.weights = [0] * len(distance_matrix)
+        else:
+            dtm = DistanceToMeasure(k, q=q, metric="precomputed")        
+            self.weights = dtm.fit_transform(distance_matrix)
+        self.max_filtration = max_filtration
+