Keplearn is named after Johannes Kepler, who deduced his laws of planetary motion by studying tables of astronomical observations taken by Tycho Brahe. That is what this program attempts to do: given columns of measurements, find the compact law that relates them.
More formally, it's a deterministic symbolic regression engine (written in Rust) that recovers closed-form equations from tabular data by best-first recursive reduction.
Name the column to predict with --target (the default is a column called target) and pipe in a TSV or CSV with a header row. The delimiter comes from the header: tab if present, else comma.
x1..xN are the feature columns in file order, target excluded; here x1=m1, x2=m2, x3=r, so the recovered law reads m1*m2/r**2. Candidates arrive best-first: results[0].model is the answer and its r2 is scored on the supplied data. Diagnostics print to stderr, so stdout is always one JSON object.
