diff options
| author | Marc Glisse <marc.glisse@inria.fr> | 2022-12-17 20:28:10 +0100 |
|---|---|---|
| committer | Marc Glisse <marc.glisse@inria.fr> | 2022-12-17 20:28:10 +0100 |
| commit | 7fdc81289f63d43aab884d3de3b4f0242fd984ff (patch) | |
| tree | 12515ffc7d40ae902066244615caa15568f2a3b2 | |
| parent | b8d6f1f5969de7edbb66f285ac0a9ccffa8878fb (diff) | |
Advertise edge collapses more.
| -rw-r--r-- | src/python/doc/rips_complex_user.rst | 9 |
1 files changed, 8 insertions, 1 deletions
diff --git a/src/python/doc/rips_complex_user.rst b/src/python/doc/rips_complex_user.rst index 489fbb4a..a4e83462 100644 --- a/src/python/doc/rips_complex_user.rst +++ b/src/python/doc/rips_complex_user.rst @@ -52,6 +52,13 @@ construction of a :class:`~gudhi.RipsComplex` object asks it to build a sparse R parameter :math:`\varepsilon=0.3`, while the default `sparse=None` builds the regular Rips complex. +Another option which is especially useful if you want to compute persistent homology in "high" dimension (2 or more, +sometimes even 1), is to build the Rips complex only up to dimension 1 (a graph), then use +:func:`~gudhi.SimplexTree.collapse_edges` to reduce the size of this graph, and finally call +:func:`~gudhi.SimplexTree.expansion` to get a simplicial complex of a suitable dimension to compute its homology. This +trick gives the same persistence diagram as one would get with a plain use of `RipsComplex`, with a complex that is +often significantly smaller and thus faster to process. + Point cloud ----------- @@ -210,7 +217,7 @@ until dimension 1 - one skeleton graph in other words), the output is: [4, 6] -> 9.49 [3, 6] -> 11.00 -In case this lower triangular matrix is stored in a CSV file, like data/distance_matrix/full_square_distance_matrix.csv in the Gudhi distribution, you can read it with :func:`~gudhi.read_lower_triangular_matrix_from_csv_file`. +In case this lower triangular matrix is stored in a CSV file, like `data/distance_matrix/full_square_distance_matrix.csv` in the Gudhi distribution, you can read it with :func:`~gudhi.read_lower_triangular_matrix_from_csv_file`. Correlation matrix ------------------ |