Exhaustive Symbolic Regression Function Sets

<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> <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> <ul> <li>"core_maths":&nbsp;<span>\(\{x, a, {\rm inv}, +, -, \times, \div, {\rm pow} \}\)</span></li> <li>"ext_maths":&nbsp;<span>\(\{x, a, {\rm inv}, \sqrt{\cdot}, {\rm square}, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> <li><span>"base_e_maths": \(\{x, a, {\rm inv}, \exp, \log, \exp, +, -, \times, \div, {\rm pow} \}\)</span></li> </ul> <p>where <span>\(x\)</span>&nbsp;is the input variable and <span>\(a\)</span>&nbsp;denotes a constant.</p> <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>