Modern sparse linear regression

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  • License: GNU Lesser General Public License v3 (LGPLv3) (MIT)
  • Author: Markus Quade
  • Requires: Python >=3.6
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Project description

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sparsereg is a collection of modern sparse (regularized) regression algorithms.

Installation

pip install sparsereg

Citation

If you use sparsereg please consider a citation:

@misc{markus_quade_sparsereg,
  author       = {Markus Quade},
  title        = {sparsereg - collection of modern sparse regression algorithms},
  month        = feb,
  year         = 2018,
  doi          = {10.5281/zenodo.1173754},
  url          = {https://github.com/ohjeah/sparsereg}
}

Implemented algorithms

  • Mcconaghy, T. (2011). FFX: Fast, Scalable, Deterministic Symbolic Regression Technology. Genetic Programming Theory and Practice IX, 235-260. DOI: 10.1007/978-1-4614-1770-5_13

  • Brunton, Steven L., Joshua L. Proctor, and J. Nathan Kutz. “Discovering governing equations from data by sparse identification of nonlinear dynamical systems.” Proceedings of the National Academy of Sciences 113.15 (2016): 3932-3937. DOI: 10.1073/pnas.1517384113

  • Bouchard, Kristofer E. “Bootstrapped Adaptive Threshold Selection for Statistical Model Selection and Estimation.” arXiv preprint arXiv:1505.03511 (2015).

  • Ignacio Arnaldo, Una-May O’Reilly, and Kalyan Veeramachaneni. “Building Predictive Models via Feature Synthesis.” In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO ’15), Sara Silva (Ed.). ACM, New York, NY, USA, 983-990. DOI: 10.1145/2739480.2754693

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