Random graph methods #1039
Random graph methods #1039
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| * - YYYY/MM Author: Description of the modification | ||
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| #ifndef RANDOM__H_ |
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Shouldn't we put GUDHI in the name of the macro, to reduce the risk of conflicts?
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| private: | ||
| std::mt19937 gen_; |
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If we pick one, mt19937_64 may be better, the platforms we target are all 64 bits (and then the seed is 64 bits as well).
| #include <cstddef> // for std::size_t | ||
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| namespace Gudhi { | ||
| std::random_device rd; |
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As a global object, it may cause trouble if you try to link 2 .o that both use gudhi.
You could mark it inline (and then also hide it as a static member of the class or in some subnamespace), or create a temporary one just where it is used.
| #include <iostream> | ||
| #endif // DEBUG_TRACES | ||
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| std::random_device rd; |
| std::fill(to_permute.begin(), to_permute.begin() + 2, true); | ||
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| std::size_t nb_permutations = (nb_vertices * (nb_vertices - 1)) / 2; | ||
| auto random_values = Gudhi::Random().get_range<double>(nb_permutations); |
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If you are going to create Random objects left and right, I don't see how this will help determinism. There is no way for the user to specify a seed...
| std::vector<Vertex_handle> vertices(nb_vertices); | ||
| std::iota(vertices.begin(), vertices.end(), 0); // vertices is { 0, 1, 2, ..., 99 } when nb_vertices is 100 | ||
| st.insert_batch_vertices(vertices); |
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| std::vector<Vertex_handle> vertices(nb_vertices); | |
| std::iota(vertices.begin(), vertices.end(), 0); // vertices is { 0, 1, 2, ..., 99 } when nb_vertices is 100 | |
| st.insert_batch_vertices(vertices); | |
| st.insert_batch_vertices(boost::irange(nb_vertices)); |
https://www.boost.org/doc/libs/1_85_0/libs/range/doc/html/range/reference/ranges/irange.html (or counting_range)
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| template<typename Simplex_tree> | ||
| void simplex_tree_random_flag_complex( |
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It doesn't produce a flag complex, it produces a graph.
| namespace Gudhi { | ||
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| template <typename Vertex_handle> | ||
| std::vector<std::array<Vertex_handle, 2>> random_edges(Vertex_handle nb_vertices, double density = 0.15) { |
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The algorithm in there seems very complicated. What do you want exactly?
- For each pair of vertices, add the edge with proba 15% (independently)
- Get exactly 15% * n(n-1)/2 edges, chosen randomly
- Other
A new helper class
Gudhi::Randomthat can be constructed with a user given seed, or from a random seed if not given.This could help for #795 resolution.
The use of
Gudhi::Randomeverywherestd::mt19937is used in legacy code is not part of this PR, but can be done after on a different PR.The purpose of this PR is to provide 2 new methods to construct random graph:
random_edgesthat returns X% (cf.density argument) of permuted vertices (cf. nb_vertices argument), seen as a list of edgessimplex_tree_random_flag_complexthat fills a Simplex_tree withrandom_edges, but also with random filtration valuesMaybe
simplex_tree_random_flag_complexis a bit slow:But this is not what we want to benchmark.