filtration value doubled, comments modified

3 files changed, 123 insertions, 0 deletions

diff --git a/src/python/CMakeLists.txt b/src/python/CMakeLists.txt
index 055d5b23..adf4923b 100644
--- a/src/python/CMakeLists.txt
+++ b/src/python/CMakeLists.txt
@@ -57,6 +57,7 @@ if(PYTHONINTERP_FOUND)
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'representations', ")
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'wasserstein', ")
     set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'point_cloud', ")
+    set(GUDHI_PYTHON_MODULES_EXTRA "${GUDHI_PYTHON_MODULES_EXTRA}'weighted_rips_complex', ")
 
     add_gudhi_debug_info("Python version ${PYTHON_VERSION_STRING}")
     add_gudhi_debug_info("Cython version ${CYTHON_VERSION}")
@@ -231,6 +232,7 @@ if(PYTHONINTERP_FOUND)
     file(COPY "gudhi/representations" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi/")
     file(COPY "gudhi/wasserstein" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")
     file(COPY "gudhi/point_cloud" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")
+    file(COPY "gudhi/weighted_rips_complex.py" DESTINATION "${CMAKE_CURRENT_BINARY_DIR}/gudhi")
 
     add_custom_command(
         OUTPUT gudhi.so
@@ -484,6 +486,9 @@ if(PYTHONINTERP_FOUND)
       add_gudhi_py_test(test_dtm)
     endif()
 
+    # Weighted Rips
+    add_gudhi_py_test(test_weighted_rips)
+
     # Set missing or not modules
     set(GUDHI_MODULES ${GUDHI_MODULES} "python" CACHE INTERNAL "GUDHI_MODULES")
   else(CYTHON_FOUND)
diff --git a/src/python/gudhi/weighted_rips_complex.py b/src/python/gudhi/weighted_rips_complex.py
new file mode 100644
index 00000000..83fa82c5
--- /dev/null
+++ b/src/python/gudhi/weighted_rips_complex.py
@@ -0,0 +1,55 @@
+# 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):       Raphaël Tinarrage, Yuichi Ike, Masatoshi Takenouchi
+#
+# Copyright (C) 2020 Inria, Copyright (C) 2020 FUjitsu Laboratories Ltd.
+#
+# Modification(s):
+#   - YYYY/MM Author: Description of the modification
+
+from gudhi import SimplexTree
+
+class WeightedRipsComplex:
+    """
+    Class to generate a weighted Rips complex from a distance matrix and weights on vertices.
+    """
+    def __init__(self, 
+                distance_matrix, 
+                weights="diagonal",
+                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.
+            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:
+            self.weights = weights
+        self.max_filtration = max_filtration
+            
+    def create_simplex_tree(self, max_dimension):
+        """
+        Args:
+            max_dimension (int): graph expansion until this given dimension.
+        """
+        dist = self.distance_matrix
+        F = self.weights
+        num_pts = len(dist)
+        
+        st = SimplexTree()
+        
+        for i in range(num_pts):
+            if 2*F[i] <= self.max_filtration:
+                st.insert([i], 2*F[i])
+        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])
+                if value <= self.max_filtration:
+                    st.insert([i,j], filtration=value)
+                    
+        st.expansion(max_dimension) 
+        return st
+        
diff --git a/src/python/test/test_weighted_rips.py b/src/python/test/test_weighted_rips.py
new file mode 100644
index 00000000..d3721115
--- /dev/null
+++ b/src/python/test/test_weighted_rips.py
@@ -0,0 +1,63 @@
+""" 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 and Masatoshi Takenouchi
+
+    Copyright (C) 2020 Inria
+
+    Modification(s):
+      - YYYY/MM Author: Description of the modification
+"""
+
+from gudhi.weighted_rips_complex import WeightedRipsComplex
+from gudhi.point_cloud.dtm import DistanceToMeasure
+import numpy as np
+from math import sqrt
+from scipy.spatial.distance import cdist
+import pytest
+
+def test_non_dtm_rips_complex():
+    dist = [[], [1]]
+    weights = [1, 100]
+    w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights)
+    st = w_rips.create_simplex_tree(max_dimension=2)
+    assert st.filtration([0,1]) == pytest.approx(200.0)
+
+def test_compatibility_with_rips():
+    distance_matrix = [[0], [1, 0], [1, sqrt(2), 0], [sqrt(2), 1, 1, 0]]
+    w_rips = WeightedRipsComplex(distance_matrix=distance_matrix,max_filtration=42)
+    st = w_rips.create_simplex_tree(max_dimension=1)
+    assert list(st.get_filtration()) == [
+        ([0], 0.0),
+        ([1], 0.0),
+        ([2], 0.0),
+        ([3], 0.0),
+        ([0, 1], 1.0),
+        ([0, 2], 1.0),
+        ([1, 3], 1.0),
+        ([2, 3], 1.0),
+        ([1, 2], 1.4142135623730951),
+        ([0, 3], 1.4142135623730951),
+    ]
+
+def test_compatibility_with_filtered_rips():
+    distance_matrix = [[0], [1, 0], [1, sqrt(2), 0], [sqrt(2), 1, 1, 0]]
+    w_rips = WeightedRipsComplex(distance_matrix=distance_matrix,max_filtration=1.0)
+    st = w_rips.create_simplex_tree(max_dimension=1)
+    
+    assert st.__is_defined() == True
+    assert st.__is_persistence_defined() == False
+
+    assert st.num_simplices() == 8
+    assert st.num_vertices() == 4
+
+def test_dtm_rips_complex():
+    pts = np.array([[2.0, 2], [0, 1], [3, 4]])
+    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)
+    st.persistence()
+    persistence_intervals0 = st.persistence_intervals_in_dimension(0)
+    assert persistence_intervals0 == pytest.approx(np.array([[3.16227766, 5.39834564],[3.16227766, 5.39834564], [3.16227766, float("inf")]]))
+