November 20, 2022 Dataset Open Access

Bartlett, Deaglan J.; Desmond, Harry; Ferreira, Pedro G.

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{
  "description": "<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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "CNRS & Sorbonne Universit\u00e9, Institut d'Astrophysique de Paris and Astrophysics, University of Oxford", 
      "@id": "https://orcid.org/0000-0001-9426-7723", 
      "@type": "Person", 
      "name": "Bartlett, Deaglan J."
    }, 
    {
      "affiliation": "Institute of Cosmology & Gravitation, University of Portsmouth", 
      "@id": "https://orcid.org/0000-0003-0685-9791", 
      "@type": "Person", 
      "name": "Desmond, Harry"
    }, 
    {
      "affiliation": "Astrophysics, University of Oxford", 
      "@id": "https://orcid.org/0000-0002-3021-2851", 
      "@type": "Person", 
      "name": "Ferreira, Pedro G."
    }
  ], 
  "url": "https://zenodo.org/record/7339113", 
  "datePublished": "2022-11-20", 
  "keywords": [
    "Symbolic Regression"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/5c5294e6-6476-4f7e-b4cd-ebfc77602d96/core_maths.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }, 
    {
      "contentUrl": "https://zenodo.org/api/files/5c5294e6-6476-4f7e-b4cd-ebfc77602d96/ext_maths.zip", 
      "encodingFormat": "zip", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.7339113", 
  "@id": "https://doi.org/10.5281/zenodo.7339113", 
  "@type": "Dataset", 
  "name": "Exhaustive Symbolic Regression Function Sets"
}