docs/operators.md

Operators

Pre-defined

All Base julia operators that take 1 or 2 scalars as input, and output a scalar as output, are available. A selection of these and other valid operators are stated below.

Binary

+, -, *, /, ^, greater, mod, logical_or, logical_and

Unary

neg, square, cube, exp, abs, log, log10, log2, log1p, sqrt, sin, cos, tan, sinh, cosh, tanh, atan, asinh, acosh, atanh_clip (=atanh((x+1)%2 - 1)), erf, erfc, gamma, relu, round, floor, ceil, round, sign.

Custom

Instead of passing a predefined operator as a string, you can define with by passing it to the pysr function, with, e.g.,

    PySRRegressor(
        ...,
        unary_operators=["myfunction(x) = x^2"],
        binary_operators=["myotherfunction(x, y) = x^2*y"]
    )

Make sure that it works with Float32 as a datatype (for default precision, or Float64 if you set precision=64). That means you need to write 1.5f3 instead of 1.5e3, if you write any constant numbers, or simply convert a result to Float64(...).

PySR expects that operators not throw an error for any input value over the entire real line from -3.4e38 to +3.4e38. Thus, for "invalid" inputs, such as negative numbers to a sqrt function, you may simply return a NaN of the same type as the input. For example,

my_sqrt(x) = x >= 0 ? sqrt(x) : convert(typeof(x), NaN)

would be a valid operator. The genetic algorithm will preferentially selection expressions which avoid any invalid values over the training dataset.