afdee1c/symbolic-regression
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Update README.md

Browse source
This commit is contained in:
Silviu Marian Udrescu 2020-04-25 17:57:10 -04:00 • committed by GitHub
parent 1b8643dd9d
commit c832bb8b5e
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
1 changed files with 2 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

4
README.md
View file

@ -1,12 +1,12 @@
# AI-Feynman # AI-Feynman
This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression: https://arxiv.org/abs/1905.11481 This code is an improved implementation of AI Feynman: a Physics-Inspired Method for Symbolic Regression, Silviu-Marian Udrescu and Max Tegmark (2019) [[arXiv](https://arxiv.org/abs/1905.11481)][[Science Advances](https://advances.sciencemag.org/content/6/16/eaay2631/tab-pdf)].
Differently from the original paper (described in the mentioned paper), the new code doesn't output just one possible equation to describe the data, but a Pareto frontier of possible equations. Among other advantages, this approach allows the code to be more robust against noise and give good approximations to the actual equations, in case that one can't be found. Differently from the original paper (described in the mentioned paper), the new code doesn't output just one possible equation to describe the data, but a Pareto frontier of possible equations. Among other advantages, this approach allows the code to be more robust against noise and give good approximations to the actual equations, in case that one can't be found.
In order to get started, run compile.sh to compile the fortran files used for the brute force code. In order to get started, run compile.sh to compile the fortran files used for the brute force code.
ai_feynman_example.py contains an example of running a code on some examples (found in the example_data directory). More data files on which the code can be tested on can be found in the Feynman Symbolic Regression Database: https://space.mit.edu/home/tegmark/aifeynman.html. ai_feynman_example.py contains an example of running a code on some examples (found in the example_data directory). More data files on which the code can be tested on can be found in the [Feynman Symbolic Regression Database](https://space.mit.edu/home/tegmark/aifeynman.html).
The main function of the code, called by the user, has the following parameters: The main function of the code, called by the user, has the following parameters: