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Diffstat (limited to 'src/python/doc/rips_complex_user.rst')
| -rw-r--r-- | src/python/doc/rips_complex_user.rst | 8 |
1 files changed, 5 insertions, 3 deletions
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 <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_. +Remark that DTMRipsComplex class provides exactly this function. .. testcode:: @@ -402,14 +403,15 @@ The output is: DTM Rips Complex ---------------- -`DtmdRipsComplex <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ 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 <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ 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 <https://github.com/GUDHI/TDA-tutorial/blob/master/Tuto-GUDHI-DTM-filtrations.ipynb>`_ 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()) |