Change G(n,p) to accept exact 0 or 1 probabilities #174
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When p=0, we'll have an empty graph with n nodes and zero edges. When p=1, we'll have a complete graph with n n(n-1) edges for directed graphs and n(n-1)/2 edges for undirected graphs. The time complexity stays the same. Let m be the max number of edges, then run time is O(n+p*m), which reduces to O(n) when p=0 and, when p=1 becomes O(n+n(n-1)) = O(n^2).
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In Qiskit#174 the gnp random functions were extended to support values of 0 and 1 for the probability representing either empty of full graphs. In that change comparisons between the value of probability and 1 were used to check if it's a full graph and we should just fast path adding an edge between every node. However, when running 'cargo clippy' on this it rightfully points out that we should probably check for equality to 1 within an error margin (given that floating point is never exact). Clippy suggested replacing using std::f64::EPSILON as the error value, which is ~2.2e-16 [1] and replacing the comparison with: (probability - 1.0).abs() < error This commit just makes that change. While this is unlikely to cause any issues in practice, because probability is a parameter and if someone is going to want a full graph they'll likely call the function with probability=1 in python. It's better to be safe just in case someone is computing the probability value and could have some error on the value. [1] https://doc.rust-lang.org/std/f64/constant.EPSILON.html
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In #174 the gnp random functions were extended to support values of 0 and 1 for the probability representing either empty of full graphs. In that change comparisons between the value of probability and 1 were used to check if it's a full graph and we should just fast path adding an edge between every node. However, when running 'cargo clippy' on this it rightfully points out that we should probably check for equality to 1 within an error margin (given that floating point is never exact). Clippy suggested replacing using std::f64::EPSILON as the error value, which is ~2.2e-16 [1] and replacing the comparison with: (probability - 1.0).abs() < error This commit just makes that change. While this is unlikely to cause any issues in practice, because probability is a parameter and if someone is going to want a full graph they'll likely call the function with probability=1 in python. It's better to be safe just in case someone is computing the probability value and could have some error on the value. [1] https://doc.rust-lang.org/std/f64/constant.EPSILON.html
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This adds two G(n,m) graph generators, one directed and one undirected. The generated graph will be a random graph out of all the possible graphs that are n nodes and m edges with max n*(n-1) edges for directed graphs and n*(n-1)/2 for undirected graphs, avoiding self-loops and multigraphs. The run time is O(n+m) Fixes Qiskit#174
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* Add G(n,m) random graph generator This adds two G(n,m) graph generators, one directed and one undirected. The generated graph will be a random graph out of all the possible graphs that are n nodes and m edges with max n*(n-1) edges for directed graphs and n*(n-1)/2 for undirected graphs, avoiding self-loops and multigraphs. The run time is O(n+m) Fixes #174 * Change !.is_some() to is_none() Apply suggestions from code review
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When p=0, we'll have an empty graph with n nodes and zero edges.
When p=1, we'll have a complete graph with n(n-1) edges
for directed graphs and n(n-1)/2 edges for undirected graphs.
The time complexity stays the same. Let m be the max number of edges,
then run time is O(n+p*m), which reduces to O(n) when p=0 and, when
p=1 becomes O(n+n(n-1)) = O(n^2).
Fixes #172