From 5040c75893cb864f5e780b6644b8097f7beeb3a6 Mon Sep 17 00:00:00 2001
From: yuichi-ike <yuichi.ike.1990@gmail.com>
Date: Mon, 11 May 2020 10:45:02 +0900
Subject: document and comments added, weights modified

---
 src/python/doc/rips_complex_ref.rst       | 51 +++++++++++++++++++++++++++++++
 src/python/gudhi/weighted_rips_complex.py | 18 ++++++-----
 src/python/test/test_weighted_rips.py     |  2 +-
 3 files changed, 63 insertions(+), 8 deletions(-)

(limited to 'src')

diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst
index 22b5616c..8fc7e1b0 100644
--- a/src/python/doc/rips_complex_ref.rst
+++ b/src/python/doc/rips_complex_ref.rst
@@ -12,3 +12,54 @@ Rips complex reference manual
    :show-inheritance:
 
    .. automethod:: gudhi.RipsComplex.__init__
+
+======================================
+Weighted Rips complex reference manual
+======================================
+
+.. autoclass:: gudhi.WeightedRipsComplex
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+   .. automethod:: gudhi.WeightedRipsComplex.__init__
+
+Basic examples
+-------------
+
+The following example computes the weighted Rips filtration associated with a distance matrix and weights on vertices.
+
+.. testcode::
+
+    from gudhi.weighted_rips_complex import WeightedRipsComplex
+    dist = [[], [1]]
+    weights = [1, 100]
+    w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights)
+    st = w_rips.create_simplex_tree(max_dimension=2)
+    print(st.get_filtration())
+
+The output is:
+
+.. testoutput::
+
+    [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)]
+
+Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. 
+
+.. testcode::
+
+    import numpy as np
+    from scipy.spatial.distance import cdist
+    from gudhi.point_cloud.dtm import DistanceToMeasure
+    from gudhi.weighted_rips_complex import WeightedRipsComplex
+    pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
+    dist = cdist(pts,pts)
+    dtm = DistanceToMeasure(2, q=2, metric="precomputed")
+    r = dtm.fit_transform(dist)
+    w_rips = WeightedRipsComplex(distance_matrix=dist, weights=r)
+    st = w_rips.create_simplex_tree(max_dimension=2)
+    print(st.persistence())
+
+.. testoutput::
+
+    [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))]
diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py
index 83fa82c5..7401c428 100644
--- a/src/python/gudhi/weighted_rips_complex.py
+++ b/src/python/gudhi/weighted_rips_complex.py
@@ -11,23 +11,26 @@ from gudhi import SimplexTree
 
 class WeightedRipsComplex:
     """
-    Class to generate a weighted Rips complex from a distance matrix and weights on vertices.
+    Class to generate a weighted Rips complex from a distance matrix and weights on vertices, 
+    in the way described in the paper 'DTM-based filtrations' https://arxiv.org/abs/1811.04757.
+    Remark that the filtration value of a vertex is twice of its weight for the consistency with 
+    RipsComplex, which is different from the definition in the paper.
     """
     def __init__(self, 
                 distance_matrix, 
-                weights="diagonal",
+                weights=None,
                 max_filtration=float('inf')):
         """
         Args:
-            distance_matrix (list of list of float): distance matrix (full square or lower triangular).
-            weights (list of float): (one half of) weight for each vertex.
+            distance_matrix (Sequence[Sequence[float]]): distance matrix (full square or lower triangular).
+            weights (Sequence[float]): (one half of) weight for each vertex.
             max_filtration (float): specifies the maximal filtration value to be considered.      
         """
         self.distance_matrix = distance_matrix
-        if weights == "diagonal":
-            self.weights = [distance_matrix[i][i] for i in range(len(distance_matrix))]
-        else:
+        if weights is not None:
             self.weights = weights
+        else:
+            self.weights = [0] * len(distance_matrix)
         self.max_filtration = max_filtration
             
     def create_simplex_tree(self, max_dimension):
@@ -47,6 +50,7 @@ class WeightedRipsComplex:
         for i in range(num_pts):
             for j in range(i):
                 value = max(2*F[i], 2*F[j], dist[i][j] + F[i] + F[j])
+                # max is needed when F is not 1-Lipschitz
                 if value <= self.max_filtration:
                     st.insert([i,j], filtration=value)
                     
diff --git a/src/python/test/test_weighted_rips.py b/src/python/test/test_weighted_rips.py
index d3721115..59ec022a 100644
--- a/src/python/test/test_weighted_rips.py
+++ b/src/python/test/test_weighted_rips.py
@@ -51,7 +51,7 @@ def test_compatibility_with_filtered_rips():
     assert st.num_vertices() == 4
 
 def test_dtm_rips_complex():
-    pts = np.array([[2.0, 2], [0, 1], [3, 4]])
+    pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]])
     dist = cdist(pts,pts)
     dtm = DistanceToMeasure(2, q=2, metric="precomputed")
     r = dtm.fit_transform(dist)
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