- Notifications
You must be signed in to change notification settings - Fork0
MultiVariate Empirical Quantile Function (grid-based)
License
poluyan/mveqf
Folders and files
| Name | Name | Last commit message | Last commit date | |
|---|---|---|---|---|
Repository files navigation
Multivariate empirical continuous quantile function (grid-based). There are two approaches to quantile function evaluation depending on the type of sample storage. In the first case, the sample is presented in the explicit (real-valued) form and stored in the matrix. In the second case, the sample is presented in the implicit form and the trie-based structure. Here presented header-only library that allows you to perform quantile transforms based on given sample points. For more info and examples see:poluyan.github.io/mveqf
To compile from source, you need C++ 17 compiler and CMake for building examples.
To use this library and perform quantile tranforms only header files frommveqf are needed.
$ git clone https://github.com/poluyan/mveqf$cd mveqf$ cmake.$ make
Clone the entire repository and build it locally.
Some examples of usingmveqf to perform quantile transform presented indemos directory. Follow these steps to build and run the examples. After these steps all the binaries should be generated and presented in thebin directory.
#include<mveqf/implicit.h>intmain(){using gt = std::uint8_t;// integer type to store grid node components: char, unsigned char, int, ... std::size_t d =2;// dimension std::vector<std::size_t> grid = {9,10};// regular grid sizes// data structure for sample storage - modified Trie with NodeCount nodesusing sample_type = mveqf::TrieBased<mveqf::NodeCount<gt>, gt>;// pointer to the sample which will be moved to quantile object std::shared_ptr<sample_type> sample = std::make_shared<sample_type>(); sample->set_dimension(d);// setting dimension sample->insert(std::vector<gt>{2,6});// adding grid node to sample sample->insert(std::vector<gt>{5,7});// first component from [0;8] range, second from [0;9] std::vector<float>lb(d, -3.0f);// lower bound for each component std::vector<float>ub(d,3.0f);// upper bound for each component mveqf::ImplicitQuantile<gt,float>mveqfunc(lb, ub, grid);// object to perform quantile transofrm mveqfunc.set_sample_shared_and_fill_count(sample);// moving sample to quantile object std::vector<float> values01 = {0.427f,0.791f};// values to transform std::vector<float>sampled(d);// vector to store values after transform mveqfunc.transform(values01, sampled);// performing transform and saving values to sampled}
S. V. Poluyan, N. M. Ershov, Quantile transform in structural bioinformatics problems // Computational nanotechnology, 2019, Vol. 6, no. 4, P. 29–43 DOI:10.33693/2313-223X-2019-6-4-29-43
Themveqf library is distributed under Apache License 2.0 and it is open-source software. Feel free to make a copy and modify the source code, but keep the copyright notice and license intact.