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author | Théo Lacombe <lacombe1993@gmail.com> | 2020-06-29 10:24:44 +0200 |
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committer | GitHub <noreply@github.com> | 2020-06-29 10:24:44 +0200 |
commit | 0b4de61a18bc30f66a7fb45cc246cff2f55ba1a1 (patch) | |
tree | 47527fd4d63632f3c39a6f2660ec141417f093b6 /src/python/doc/rips_complex_user.rst | |
parent | 6c65d29acc3b03d21beca653834340787bf0c65e (diff) | |
parent | cec4a5d7df6d5ed43511e94f9db580489979105a (diff) |
Merge branch 'master' into fix342
Diffstat (limited to 'src/python/doc/rips_complex_user.rst')
-rw-r--r-- | src/python/doc/rips_complex_user.rst | 22 |
1 files changed, 22 insertions, 0 deletions
diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index 819568be..6048cc4e 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 +399,24 @@ The output is: .. testoutput:: [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] + +DTM Rips Complex +---------------- + +:class:`~gudhi.dtm_rips_complex.DTMRipsComplex` 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))] |