Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Chart | 1,057 | 3 years ago | 5 | mit | ||||||
Create the most popular types of charts by real or random data | ||||||||||
Littleballoffur | 621 | 4 months ago | 25 | January 22, 2022 | 1 | gpl-3.0 | Python | |||
Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020) | ||||||||||
Gnn Model Explainer | 491 | 2 years ago | 21 | apache-2.0 | Python | |||||
gnn explainer | ||||||||||
Jngen | 189 | 6 months ago | 4 | mit | C++ | |||||
Library for generating tests for olympiad problems | ||||||||||
Pyrgg | 174 | 1 | a month ago | 13 | November 30, 2022 | 6 | mit | Python | ||
🔧 Python Random Graph Generator | ||||||||||
Role2vec | 157 | 7 months ago | gpl-3.0 | Python | ||||||
A scalable Gensim implementation of "Learning Role-based Graph Embeddings" (IJCAI 2018). | ||||||||||
Netgan | 142 | 2 years ago | 1 | mit | Jupyter Notebook | |||||
Implementation of the paper "NetGAN: Generating Graphs via Random Walks". | ||||||||||
Dnw | 118 | 3 years ago | 4 | other | Python | |||||
Discovering Neural Wirings (https://arxiv.org/abs/1906.00586) | ||||||||||
Graph_sampling | 89 | 2 years ago | mit | Python | ||||||
Graph Sampling is a python package containing various approaches which samples the original graph according to different sample sizes. | ||||||||||
Molrep | 88 | 2 months ago | 5 | mit | Python | |||||
MolRep: A Deep Representation Learning Library for Molecular Property Prediction |
Jngen is a C++ library for generating testss for various competitive programming problems. It is written in pure C++ (compliant with C++11 standard) and distributed as a single header.
Among its features there are:
Check out the larger overview to see more capabilities and examples or see Getting started section to learn the simplest usecases immediately.
Find some real-world examples here.
Generate a random tree on n vertices with a 3-letter string assigned to each edge:
Tree t = Tree::random(5);
t.setEdgeWeights(TArray<string>::random(t.m(), "[a-z]{%d}", 3));
cout << t.add1().printN() << endl;
---
5
1 2 rqi
1 3 slv
1 4 foi
4 5 eju
Output a random permutation and its inverse:
setMod().add1();
auto a = Array::id(10).shuffled();
cout << a.size() << endl;
cout << a << endl;
cout << a.inverse() << endl;
---
10
3 8 5 7 2 4 1 10 9 6
7 5 1 6 3 10 4 2 9 8
Output a bamboo on n vertices connected to a star on m vertices, probably shuffled:
int main(int argc, char *argv[]) {
parseArgs(argc, argv);
int n, m;
getPositional(n, m);
Tree t = Tree::bamboo(n).link(n - 1, Tree::star(m), 0);
if (getOpt("shuffled", false)) {
t.shuffle();
}
cout << t.printN().add1() << endl;
}
Generate a connected graph with multi-edges:
cout << Graph::random(n, m).connected().allowMulti() << endl;
testlib.h is a wonderful library which has already saved hundreds of hours for contest writers. However, there are reasons why I did not build Jngen on top of existing testlib.h code.
I don't know.
It sounds similar to Jinotega, my ACM-ICPC team, maybe that's the way how it (unconsciously) came to my mind. Also it is similar to Jungen – "Young" in German. Or "Just 'Nother GENerator library". Well, who the hell cares.