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author | yuichi-ike <yuichi.ike.1990@gmail.com> | 2020-05-11 10:45:02 +0900 |
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committer | yuichi-ike <yuichi.ike.1990@gmail.com> | 2020-05-11 10:45:02 +0900 |
commit | 5040c75893cb864f5e780b6644b8097f7beeb3a6 (patch) | |
tree | 66e9d7635f909c13a11935299ae2d03d5e845fe1 /src/python/doc | |
parent | 3685c604e78b1aa8b472a7a96716f7c126497a70 (diff) |
document and comments added, weights modified
Diffstat (limited to 'src/python/doc')
-rw-r--r-- | src/python/doc/rips_complex_ref.rst | 51 |
1 files changed, 51 insertions, 0 deletions
diff --git a/src/python/doc/rips_complex_ref.rst b/src/python/doc/rips_complex_ref.rst index 22b5616c..8fc7e1b0 100644 --- a/src/python/doc/rips_complex_ref.rst +++ b/src/python/doc/rips_complex_ref.rst @@ -12,3 +12,54 @@ Rips complex reference manual :show-inheritance: .. automethod:: gudhi.RipsComplex.__init__ + +====================================== +Weighted Rips complex reference manual +====================================== + +.. autoclass:: gudhi.WeightedRipsComplex + :members: + :undoc-members: + :show-inheritance: + + .. automethod:: gudhi.WeightedRipsComplex.__init__ + +Basic examples +------------- + +The following example computes the weighted Rips filtration associated with a distance matrix and weights on vertices. + +.. testcode:: + + from gudhi.weighted_rips_complex import WeightedRipsComplex + dist = [[], [1]] + weights = [1, 100] + w_rips = WeightedRipsComplex(distance_matrix=dist, weights=weights) + st = w_rips.create_simplex_tree(max_dimension=2) + print(st.get_filtration()) + +The output is: + +.. testoutput:: + + [([0], 2.0), ([1], 200.0), ([0, 1], 200.0)] + +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>`_. + +.. testcode:: + + import numpy as np + from scipy.spatial.distance import cdist + from gudhi.point_cloud.dtm import DistanceToMeasure + from gudhi.weighted_rips_complex import WeightedRipsComplex + pts = np.array([[2.0, 2.0], [0.0, 1.0], [3.0, 4.0]]) + 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) + print(st.persistence()) + +.. testoutput:: + + [(0, (3.1622776601683795, inf)), (0, (3.1622776601683795, 5.39834563766817)), (0, (3.1622776601683795, 5.39834563766817))] |