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author | Marc Glisse <marc.glisse@inria.fr> | 2020-05-29 17:58:18 +0200 |
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committer | Marc Glisse <marc.glisse@inria.fr> | 2020-05-29 17:58:18 +0200 |
commit | 02baef8170649f917882db727156636315bae8cf (patch) | |
tree | dd685c0ae0d6778c3fef95d87225449e78671204 /src/python/doc/rips_complex_user.rst | |
parent | 1218f4540d51859b9527d2dd436ea8c50c429d68 (diff) | |
parent | c53567c85f936f78000471fcee6234e75f7742ca (diff) |
Merge remote-tracking branch 'origin/master' into tomato2
Diffstat (limited to 'src/python/doc/rips_complex_user.rst')
-rw-r--r-- | src/python/doc/rips_complex_user.rst | 24 |
1 files changed, 24 insertions, 0 deletions
diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index 819568be..dd2f2cc0 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 <rips_complex_user.html#dtm-rips-complex>`_ class provides exactly this function. .. testcode:: @@ -398,3 +399,26 @@ The output is: .. testoutput:: [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] + +.. _dtm-rips-complex: + +DTM Rips Complex +---------------- + +`DTMRipsComplex <rips_complex_ref.html#dtm-rips-complex-reference-manual>`_ builds a simplicial complex from a point set or a full distance 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 + pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) + dtm_rips = DTMRipsComplex(points=pts, k=2) + st = dtm_rips.create_simplex_tree(max_dimension=2) + print(st.persistence()) + +The output is: + +.. testoutput:: + + [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] |