nasa / bingo Goto Github PK

In implicit and explicit training data, the parameters of x and y (or dx_dt for implicit) are passed to the constructor to create a new instance. However, the constructor verifies that the data is shaped correctly. Because the member variables are exposed for both read and write access, this reshaping can be skipped and the data structure can be broken. Add read only properties to the values.

How/where in the codebase does this get implemented? I assume that this takes the form of artificially imposing certain equations in the initial population gene pool

Hi there I'm a new bingo user,
sometimes, especially when the value of the fitness function vastly varies, it useful to have relative errors. I saw there are different errors definition, so this functionality might just be added there. or simply when evaluating the fitness function.

Best regards and thanks for the great tool.

How do you assess whether a physics-regularized fitness function surpass the performance of non-SR state-of-the-art machine learning algorithms (accuracy, uncertainty, extrapolability, interpretability, etc.)?

Create a fitness function cpp class.

The Distance function compares row-wise for each element to see if they match. However, how does this reflect to AGraphs with the same equation tree, but with different command arrays. For example let,

command_array_1 = [[LOADX, 0, 0],
                   [LOADX, 1, 1],
                   [LOADC, 0, 0],
                   [MULTI, 0, 2]]

and

command_array_2 = [[LOADX, 0, 0],
                   [LOADC, 0, 0],
                   [MULTI, 0, 1],
                   [LOADX, 1, 1]]

And distance(command_array_1, command_array_2) = 8 since the first row is the same, and the 0 in the 1st column-index of the 2nd row-index is in both arrays. However they both describe an equation f(x) = c_0 * x_0. Should the distance reflect the AGraphs or the command arrays?

Hi,

I have installed Bingo with the following steps:

conda create -n bingo python=3.8
conda activate bingo
conda install --channel conda-forge --file requirements.txt
./.build_bingocpp.sh
pip install .

The installation was successful, but when I run pytest tests I get the error message:

ImportError: libmpi.so.12: cannot open shared object file: No such file or directory

This seems to be an issue with conda rather than Bingo itself, there is an easy fix by including in the bash:

export LD_LIBRARY_PATH=~/anaconda3/lib:$LD_LIBRARY_PATH

Maybe it would be good to put that in the README file for anybody else having this problem. Notice also that the mpi seems to work only with Python 3.8, otherwise I cannot even install this library.

The main one that comes to mind is gplearn, which I came across in the following article:

Weng, B.; Song, Z.; Zhu, R.; Yan, Q.; Sun, Q.; Grice, C. G.; Yan, Y.; Yin, W. J. Simple Descriptor Derived from Symbolic Regression Accelerating the Discovery of New Perovskite Catalysts. Nature Communications 2020, 11 (1), 1–8. https://doi.org/10.1038/s41467-020-17263-9.

And of course some others are out there.

Currently, the only place that this repository is described is in the README.md. All that is currently shown in the description field is No description, website, or topics provided. The first line of the README.md could probably work Bingo is an open source package for performing symbolic regression