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Dataset Open Access

# Exhaustive Symbolic Regression Function Sets Bartlett, Deaglan J.; Desmond, Harry; Ferreira, Pedro G.

### Citation Style Language JSON Export

{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.7339113",
"title": "Exhaustive Symbolic Regression Function Sets",
"issued": {
"date-parts": [
[
2022,
11,
20
]
]
},
"abstract": "<p>ESR (Exhaustive Symbolic Regression) is a symbolic regression algorithm which efficiently and systematically finds all possible equations at fixed complexity (defined to be the number of nodes in its tree representation) given a set of basis functions.&nbsp;This is achieved by identifying the unique equations, so that one minimises the number of equations which one would have to fit to data.</p>\n\n<p>Here we provide the functions generated, the unique equations, and the mappings between all equations and unique ones&nbsp;using different sets of basis functions. These are:</p>\n\n<ul>\n\t<li>&quot;core_maths&quot;:&nbsp;<span class=\"math-tex\">\$$\\{x, a, {\\rm inv}, +, -, \\times, \\div, {\\rm pow} \\}\$$</span></li>\n\t<li>&quot;ext_maths&quot;:&nbsp;<span class=\"math-tex\">\$$\\{x, a, {\\rm inv}, \\sqrt{\\cdot}, {\\rm square}, \\exp, +, -, \\times, \\div, {\\rm pow} \\}\$$</span></li>\n</ul>\n\n<p>where <span class=\"math-tex\">\$$x\$$</span>&nbsp;is the input variable and <span class=\"math-tex\">\$$a\$$</span>&nbsp;denotes a constant.</p>\n\n<p>One can fit these functions to a data set of interest by using the <a href=\"https://esr.readthedocs.io\">ESR package</a>.</p>",
"author": [
{
"family": "Bartlett, Deaglan J."
},
{
"family": "Desmond, Harry"
},
{
"family": "Ferreira, Pedro G."
}
],
"note": "DJB is supported by the Simons Collaboration on Learning the Universe'' and was supported by STFC and Oriel College, Oxford. HD is supported by a Royal Society University Research Fellowship (grant no. 211046). PGF acknowledges support from European Research Council Grant No: 693024 and the Beecroft Trust.",
"type": "dataset",
"id": "7339113"
}
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