From 2ccc5ea97a5979f80fec93863da5549e4e6f2eea Mon Sep 17 00:00:00 2001 From: yuichi-ike Date: Fri, 22 May 2020 10:22:31 +0900 Subject: class name changed, documents modified --- src/python/doc/rips_complex_user.rst | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) (limited to 'src/python/doc/rips_complex_user.rst') diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index ac11a4b6..450e6c1a 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -378,6 +378,7 @@ Example from a point cloud combined with DistanceToMeasure ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Combining with DistanceToMeasure, one can compute the DTM-filtration of a point set, as in `this notebook `_. +Remark that DTMRipsComplex class provides exactly this function. .. testcode:: @@ -402,14 +403,15 @@ The output is: DTM Rips Complex ---------------- -`DtmdRipsComplex `_ builds a simplicial complex from a point set or a full distence matrix (in the form of ndarray), as described in the above example. +`DTMRipsComplex `_ builds a simplicial complex from a point set or a full distence matrix (in the form of ndarray), as described in the above example. +This class constructs a weighted Rips complex giving larger weights to outliers, which reduces their impact on the persistence diagram. See `this notebook `_ for some experiments. .. testcode:: import numpy as np - from gudhi.dtm_rips_complex import DtmRipsComplex + from gudhi.dtm_rips_complex import DTMRipsComplex pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) - dtm_rips = DtmRipsComplex(points=pts, k=2) + dtm_rips = DTMRipsComplex(points=pts, k=2) st = dtm_rips.create_simplex_tree(max_dimension=2) print(st.persistence()) -- cgit v1.2.3